productivity differences in hungary and mechanisms of tfp
TRANSCRIPT
Written by Balaacutezs Murakoumlzy Maacuterta Bisztray and Balaacutezs Reizer (CERS HAS) 2018
Productivity differences in
Hungary and mechanisms of
TFP growth slowdown
EUROPEAN COMMISSION
Directorate-General for Internal Market Industry Entrepreneurship and SMEs
Directorate A mdash Competitiveness and European Semester Unit A2 mdash European Semester and Member Statesrsquo Competitiveness
Contact Tomas Braumlnnstroumlm
E-mail GROW-A2eceuropaeu
European Commission B-1049 Brussels
EUROPEAN COMMISSION
Directorate-General for Internal Market Industry Entrepreneurship and SMEs
2018
PRODUCTIVITY DIFFERENCES
IN HUNGARY AND
MECHANISMS OF TFP GROWTH
SLOWDOWN
LEGAL NOTICE
This document has been prepared for the European Commission however it reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein
More information on the European Union is available on the Internet (httpwwweuropaeu)
Luxembourg Publications Office of the European Union 2018
ISBN 978-92-79-73462-5 doi 10287333213
copy European Union 2018
Reproduction is authorised provided the source is acknowledged
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
Table of contents
EXECUTIVE SUMMARY I
1 INTRODUCTION 1
2 DATA SOURCES 4
21 Cleaning the data and defining industry categories 5
22 Productivity estimation 6
23 Estimation sample 10
24 Firm-level variables 13
25 Industry categorization 16
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON 18
31 Convergence 18
32 Within-industry heterogeneity 24
33 Firm dynamics 28
34 Conclusions 31
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION 32
41 Context 32
42 Aggregate productivity and the self-employed 33
43 The evolution of productivity distribution in Hungary 36
44 Duality in productivity and productivity growth 47
45 Conclusions 56
5 ALLOCATIVE EFFICIENCY 58
51 Olley-Pakes efficiency 58
52 Product market and capital market distortions 62
53 Conclusions 70
6 REALLOCATION 73
61 Reallocation across industries 73
62 Reallocation across firms 76
63 Failure of reallocation Zombie firms 82
64 Conclusions 86
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS 89
71 Productivity growth 89
72 Employment growth 95
73 Entry and exit 99
74 Conclusions 103
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE 104
81 Context 104
82 Data 108
83 General trends 110
84 Allocative efficiency and reallocation 118
85 Trade 123
86 Policies Crisis taxes 131
87 Policies Mandatory Sunday closing 133
88 Conclusions 141
9 CONCLUSIONS 142
REFERENCES 144
APPENDIX 152
A3 Chapter 3 Internationally comparable data sources and methodology 152
A31 EU KLEMS amp OECD STAN 152
A32 OECD Structural and Demographic Business Statistics 152
A33 OECD Productivity Frontier 153
A4 Chapter 4 Evolution of the Productivity Distribution 154
A5 Chapter 5 Allocative Efficiency 160
A6 Chapter 6 Reallocation 171
A7 Chapter 7 Firm-level productivity growth and dynamics 175
A71 Productivity growth 175
A72 Employment growth 179
A73 Entry and exit 184
A8 Chapter 8 Retail 187
Productivity differences in Hungary and mechanisms of TFP growth slowdown
i
EXECUTIVE SUMMARY
Slow post-crisis total factor productivity (hereafter TFP) growth is a significant policy
challenge for many European countries in general and for Hungary in particular This
report aims at providing a comprehensive analysis of the processes behind productivity
growth slowdown in Hungary based on micro-data from administrative sources between
2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions A focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact detail of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Executive Summary
ii
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides an
exhaustive picture of Hungarian businesses It is important to keep in mind though that it
omits two important parts of the economy the overwhelming majority of the non-market
sector (including public works) and the self-employed Given the number of people
employed in these two sectors their performance has a strong effect on macro numbers
The available albeit scarce data for the self-employed qualify the findings by suggesting
that the measured productivity level and growth of this group is considerably below than
that of double-entry bookkeeping firms ndash implying that within-industry productivity
dispersion may be even larger than what is indicated by the balance sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
of capital increase on average its rise was disproportionally larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
Productivity differences in Hungary and mechanisms of TFP growth slowdown
iii
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly adding little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterized by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers have increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
The results of this report confirm that Hungary is atypical because of the relatively poor
productivity performance of frontier firms Importantly contrary to a strong version of the
duality concept this is not a result of Hungarian frontier firms being on the global frontier
typically they are quite far away from it This robust pattern underlines that besides
helping non-frontier firms policy may also have to focus on the performance of the
frontier group A transparent environment with a strong rule of law complemented by a
well-educated workforce and a robust innovation system is key for providing incentives to
invest into the most advanced technologies
Executive Summary
iv
The analysis in this report reinforces the impression that there is a large productivity gap
between globally engaged or owned and other firms the gap being about 35 percent in
manufacturing and above 60 percent in services This gap seems to be roughly constant in
the period under study The firm-level analysis in Chapter 7 also reveals that one of the
mechanisms which conserves the gap is that foreign frontier firms are able to increase
their productivity more than their domestic counterparts even from frontier levels These
findings reinforce the importance of well-designed policies that are able to help domestic
firms to catch up with foreign firms A key precondition for domestic firms to build linkages
with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A
more specific conclusion is the importance of enabling high-productivity domestic firms to
improve their productivity levels even further
The large within-industry productivity dispersion the relatively low (though not extreme
in international comparison) allocative efficiency documented in some of the industries the
strong positive contribution of reallocation to total TFP growth before the crisis and the
relatively low entry rate imply that policies promoting reallocation have a potential to
increase aggregate productivity levels significantly These policies can include improving
the general framework conditions by cutting administrative costs reducing entry and exit
barriers and using a neutral regulation The fact that capital market distortions still appear
to be significantly above their pre-crisis levels impliesthat policies that reduce financial
frictions may help the reallocation process The fact that exporters tend to expand faster
relative to non-exporters suggests that access to EU and global markets generate a strong
and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general
traded and more knowledge-intensive sectors fared better both in terms of productivity
growth and allocative efficiency The difference between traded and non-traded sectors
points once again to the importance of global competition in promoting higher productivity
and more efficient allocation of resources This also implies that adopting policies that
focus on innovation or reallocation in services may be especially important given the large
number of people working in those sectors The better performance of and reallocation into
more knowledge-intensive sectors underline the importance of education policies aimed at
developing up-to-date and flexible skills and the significance of innovation policies that
help to improve the knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people
working at firms with at least a few employees and those working in very small firms or as
self-employed The latter category represents 30-50 percent of the people engaged in
some important industries Inclusive policies may attempt to generate supportive
conditions for these people by providing knowledge and training as well as helping them
find jobs with wider perspectives or set up a well-operating firm The large share of these
unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a marked difference between the pre-
crisis period characterised by strong reallocation mainly via the expansion of large
foreign-owned chains and the post-crisis period with a stagnating share of large chains
This break is likely to be linked to post-crisis policies favouring smaller firms While halting
further concentration in a country with already one of the highest share of multinationals
in this sector can have a number of benefits in the long run it is likely to lead to higher
prices and lower industry-level productivity growth Policies should balance carefully
between these trade-offs Another key pattern identified is the increasing role of retailers
(and wholesalers) in trade intermediation both on the import and export side
Policymakers should encourage these trends and design policies which provide capabilities
for such firms to enter international markets probably via e-commerce
Productivity differences in Hungary and mechanisms of TFP growth slowdown
1
1 INTRODUCTION
Slow post-crisis TFP growth is a significant policy challenge for many European countries in
general and for Hungary in particular This report aims at providing a comprehensive
analysis of the processes behind productivity growth slowdown in Hungary based on
micro-data from administrative sources between 2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions The focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact details of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Introduction
2
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides a very
detailed and comprehensive picture of the Hungarian business economy It is important to
keep in mind though that it omits two important parts of the economy the overwhelming
majority of the non-market sector (including public works) and the self-employed Given
the number of people employed in these two sectors their performance has a strong effect
on macro numbers The available albeit scarce data for the self-employed qualify the
findings by suggesting that the measured productivity levels and growth of this group are
considerably below those of double-entry bookkeeping firms ndash implying that within-
industry productivity dispersion may even be larger than what is indicated by the balance
sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries the frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
Productivity differences in Hungary and mechanisms of TFP growth slowdown
3
of capital increase on average its rise was disproportionately larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly contributing little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterised by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
Data Sources
4
BOX 21 AMADEUS and the NAV balance sheet data
An alternative and frequently used source of balance sheet data is the AMADEUS dataset
In this box we compare the data about Hungary with the dataset used in this report
namely the administrative NAV panel
AMADEUS is a firm-level dataset collected and issued by Bureau Van Dijk a Moodyrsquos
Analytics Company It contains comprehensive financial information on around 21 million
companies across Europe with a focus on private company information It includes
information about company financials in a standard format (which makes it comparable
across countries) directors stock prices and detailed corporate ownership structures
(Global Ultimate Owners and subsidiaries) Financial information on firms consists of data
from balance sheets profit and loss statements and standard ratios Non-mandatory cells
are however often missing (eg employment) Therefore the drawbacks of this
database are that it is not representative and that not all firms provide enough
information to analyse issues such as productivity or TFP
Table B21 shows the coverage of AMADEUS (the number of firms as a share of the firms
in the administrative NAV data) by year and size category In earlier years the AMADEUS
sample consisted of mostly large firms but even the coverage of larger firms was
relatively low Recently the expanding coverage has made the AMADEUS sample more
representative While the smallest firms are still undersampled the coverage of firms with
more than 5 employees has reached nearly 100 (In some cases it is even above 100
because of slight differences in the number of employees reported)
The two databases also differ in terms of the variables they include The NAV data are
more detailed in terms of assets and liabilities AMADEUS in contrast provides more
information on ownership It defines the Global Ultimate Owner (GUO) for each company
and analyses their shareholding structure Ownership share is given in percentages and
in addition the degree of independence is also given
Our main aim in this report is to estimate productivity and its change reliably and
representatively for different types of firms small and large This requires a decent
coverage of all types of firms and reliable information on their finances for a number of
periods Because of this we prefer to use the NAV database with its large and universal
coverage and the rich information on firm inputs and outputs
2 DATA SOURCES
The main database we use in this project is the balance sheet panel of Hungarian firms
between 2000-2016 The balance sheet dataset is an administrative panel collected by the
National Tax Authority (NAV formerly APEH) from corporate tax declarations The
database includes the balance sheet and profit amp loss statements of all double-entry
bookkeeping Hungarian enterprises between 2000 and 2016 (see Section 42 for a brief
discussion of the size and the performance of the not double-entry bookkeeping sector of
the Hungarian economy) Besides key financial variables the database includes the
industry code of the firm the number of its employees its date of foundation the location
of its headquarters and whether it is domestically- or foreign-owned for each year
Productivity differences in Hungary and mechanisms of TFP growth slowdown
5
21 Cleaning the data and defining industry categories
We have taken a number of steps to clean the key variables in the balance sheet panel
First we impute missing observations for firms with more than 10 employees in the
preceding and following years For continuous variables we use the average of the
previous and following year values For categorical variables we use the value from the
previous year Similarly we impute missing data using lagged values for two of the largest
firms in year 2016
Then a baseline cleaning is applied to the values of all the financial variables to correct for
possible mistakes of reporting in HUF rather than 1000 HUF or for extremely small or big
values in the data Employment and sales are cleaned of extreme values and outliers
Suspiciously large jumps followed by another jump into the opposite direction are
smoothed by the average of the previous and following years Regarding capital stocks we
use the sum of tangible and intangible assets Whenever intangible assets are missing we
input a zero
We deflate the different variables with the appropriate price indices from the OECD STAN
which includes value added capital intermediate input and output price deflators at 2-
digit industry level1
Regarding industry codes the database in general includes the 2-digit industry code of a
firm in each year based on the actual industry classification system 4-digit industry codes
are only available between 2000 and 20052 We harmonize to NACE Rev 2 codes by using
1 A few industries are merged in the EU-KLEMS We will call this 64 category classification ldquo2-digitrdquo
industry in what follows
2 The database available in the CSO which we will use for Task 3 includes 4-digit codes for all years
BOX 21 Amadeus and the NAV database (cont)
Table B21 Coverage by employment categories (AmadeusNAV)
Year 1 emp 2-5 emp
6-10 emp
11-20 emp
21-49 emp
50-249 emp
250 lt emp
Total
2004 005 028 092 105 160 312 642 043
2005 010 050 169 288 483 1066 2227 108
2006 017 087 315 553 966 1935 3632 192
2007 2209 3006 4384 5249 5743 6082 7412 3135
2008 098 324 951 1692 2840 4868 7827 576
2009 5962 6070 7217 7428 7831 7798 9336 6301
2010 2142 4685 7034 7540 8424 8228 9634 4175
2011 2277 4736 7064 7753 8521 8657 9681 4220
2012 9397 8298 9305 9484 9507 9159 10121 8990
2013 7274 8140 9423 9981 9747 9445 10312 8044
Notes This table shows the number of observations in AMADEUS as a percentage of observations in the
NAV data for each year-size category cell
Data Sources
6
concordances from Eurostat3 We use these harmonized codes whenever we define deciles
and the frontier or within-industry variables so that NACE revisions should not affect the
results Finally we split those firms which switch from manufacturing to services or vice
versa adding separate firm identifiers for the two periods4
22 Productivity estimation
From many perspectives the most robust and convenient measure of productivity is
labour productivity We calculate this variable simply as the log of value added per
employee At the same time the key shortcoming of labour productivity is that it does not
reflect the differences in capital intensity across firms Total Factor Productivity (TFP) aims
to control for this issue We estimate TFP with the method of Ackerberg et al (2015) ndash we
refer to it as ACF ndash which can be regarded as the state of the art In the Appendix we
also provide robustness checks using different productivity measures
Technically firm-level TFP estimation involves estimating a production function
119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)
where i indexes firms t indexes years 119871119894119905 is the number of employees and 119870119894119905 is the capital
stock of firm i in year t In this specification the residual of the equation 휀119894119905 is the
estimated TFP for firm i in year t 120573119897 and 120573119896 are the output elasticities in the production
function reflecting the marginal product of labour and capital and the optimal capital
intensity
Estimating firm-level production functions involves several choices First it is usually
important to include year fixed effects in order to control for macro- or industry level
shocks Second industries may differ in their optimal capital intensity ie the coefficients
of the two variables To handle this we estimate the production function separately for
each 2-digit NACE industry Third financial data reported by small firms may not be very
accurate Including them into the sample on which the production function is estimated
may introduce bias into that regression Hence we estimate the production functions only
on the sample of firms with at least 5 employees but also predict the TFP for smaller firms
Fourth the Cobb-Douglas production function may be too restrictive in some cases but it is
possible to estimate more flexible functions (eg translog)
A key problem with firm-level TFP estimation is that input use (119871119894119905 and 119870119894119905) can be
correlated with the residual TFP Consequently OLS estimation may yield biased
coefficients The bias arises from attributing part of the productivity advantage to the
higher input use of more productive firms A simple and robust solution for this issue is to
estimate the production function with a fixed effects estimator This method controls for
endogeneity resulting from unobserved time-invariant firm characteristics
3 Because of the changes in the Hungarian industry classification in 2003 and 2008 industry code harmonization is required The Hungarian industry classification system (TEAOR) corresponds to NACE Rev 1 between 1998 and 2002 to NACE Rev11 from 2003 to 2007 and to NACE Rev 2 from 2008 onwards The conversion of industry codes in 2000-2002 to NACE Rev 11 is relatively straightforward and efficient thanks to the 4-digit codes The conversion from NACE Rev 11 to
NACE Rev 2 is less so as 4-digit codes are only available until 2005 Hence for each firm we assume that its 4-digit industry remained the same in the period of 2005-2007 and use this 4-digit industry for the conversion After these conversions we clean industry codes ignoring those changes when firms switch industries for 1-3 years and then switch back This process leads to a harmonized 2-digit NACE Rev 2 code for each year
4 After industry cleaning this can only happen either at the beginning or the end of the period when the firm is observed or if the firm switches industry for a period longer than 3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
7
A second and related problem is that input use can also be correlated with time-variant
productivity shocks This type of endogeneity is not corrected by the fixed effects
estimator More specifically managers (unlike economists analysing the balance sheet)
may observe productivity shocks at the beginning of the year and adjust the flexible inputs
(labour in our case) accordingly As a result we may falsely ascribe a productivity
improvement to the increase in labour input The recent best practice of handling this
issue is the control function approach in which one controls for the productivity shock by
using a proxy for it in an initial step The proxy is another flexible input usually materials
or energy use As we have reliable data on materials we will use that variable
In this report we rely on the method of Ackerberg et al (2015)5 Importantly with this
method the production function coefficient estimates are close to what is expected6 and
the returns to scale are slightly above one (typically between 1 and 12 see Figure 21)7 8
After estimating the coefficients we simply calculate the estimated TFP for firm i in year t
by subtracting the product of inputs and the estimated elasticities
119879119865119875119894119905 = 119871119899 119881119860119894119905 minus 120573 lowast 119897119899 119871119894119905 minus 120573 lowast 119897119899 119870119894119905 (22)
In this way we calculate a TFP level (rather than its value relative to year and industry
fixed effects) which is important when calculating productivity changes Note that the
calculated productivity changes are very similar to the logic of the Solow residual
When interpreting productivity estimates it is important to remember that both the labour
productivity and TFP estimates are value added-based measures In other words in cross-
sectional comparisons they show how many forints or euros (rather than cars or apples)
are produced with a given amount of inputs Therefore value added based productivity
reflects both physical productivity and markups9
5 We have estimated all of these with the prodest (Rovigatti and Mollisi 2016) command in Stata
6 Reassuringly Ackerberg et al (2015) themselves report some production function estimates using data from Chile and their estimated coefficients are similar to what we get 08-09 for labour and about 02 for capital
7 We also control for attrition of firms from the sample but this does not affect the estimates significantly
8 The Levinsohn-Petrin (2003) and Wooldridge (2009) production function estimates are less attractive Most importantly the estimated returns to scale are well below 1 typically between 07 and 08 These implausibly low returns to scale imply an implausibly high TFP for larger firms with their TFP advantage being many times their labour productivity advantage even though they employ much more capital per worker The implausibly low returns to scale strongly affect our calculations In such a framework if a firm doubles all of its inputs and outputs its estimated TFP increases by about 30 percent even though it transforms inputs into outputs in the same way In
productivity decompositions for example size and growth are mechanically related to TFP leading to overestimating allocative efficiency
9 Recent literature has emphasized the difference between value added-based (revenue) and physical productivity and has also proposed a number of methods to distinguish between the two (see Foster et al 2008 Hsieh and Klenow 2009 Syverson 2011 Bellone et al 2014 De Loecker and Goldberg 2014) Hornok and Murakoumlzy (2018) also apply such methods to investigate the markup differences of Hungarian importers and exporters
Data Sources
8
Figure 21 ACF production function coefficients
A) Manufacturing
B) Services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
9
We take some additional steps to clean our raw productivity estimates First we winsorize
productivity at the lowest and highest percentile of the 2-digit industry-year-specific
distribution of firms with at least 5 employees We fill out gaps of 1 or 2 years in the
productivity variable by using linear approximation Finally we clean the productivity of
firms with at least 5 employees based on changes We smooth large 1-year jumps10 and
disregard productivity values if there is a large jump after entry or before exit11
Table 21 presents the average labour productivity and TFP by 1-digit NACE categories in
2004 and 2016
Table 21 Average productivity measures by 1-digit industry in 2004 and 2016
unweighted
Labour productivity Total factor productivity
2004 2016 2004 2016
NACE Description Mean Stdev Mean Stdev Mean Stdev Mean Stdev
B Mining 797 088 867 086 408 087 441 065
C Manufacturing 777 087 806 079 581 079 598 077
D Electricity gas steam 929 106 953 138 629 091 634 132
E Water supply sewerage waste
812 085 830 089 604 091 593 094
F Construction 773 080 803 072 620 071 646 066
G Wholesale and retail trade 804 102 825 090 652 093 678 081
H Transportation and storage 841 071 837 072 625 067 623 072
I Accommodation 710 075 752 080 594 068 640 071
J ICT 834 094 862 090 631 101 669 098
M Professional scientific and technical activities
815 087 844 088 636 087 673 088
N Administrative and support services
763 098 792 094 640 107 662 113
Total 789 095 815 087 620 090 647 087
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
10 We replace 119910119905 with 119910119905minus1+119910119905+1
2 if abs(119910119905 minus 119910119905minus1)gt1 abs(119910119905+1 minus 119910119905minus1)le 05 abs(119910119905minus1 minus 119910119905minus2)le 03 timesabs(119910119905 minus
119910119905minus1) abs(119910119905+2 minus 119910119905+1) le 03 timesabs(119910119905 minus 119910119905minus1) where 119910119905 denotes a productivity measure in logs of year
t Corresponding conditions are modified to abs(119910119905+1 minus 119910119905minus1) le 1 abs(119910119905+1 minus 119910119905minus1) le 03 times abs(119910119905 minus119910119905minus1) in the second observed year and in the year before the last observed one
11 abs(119910119905 minus 119910119905minus1)gt15
Data Sources
10
23 Estimation sample
Next we introduce some restrictions to define our baseline sample As our aim is to focus
on the market economy we constrain our sample based on industry and legal form We
keep only the market economy according to the OECD definition dropping observations in
agriculture and in non-market services (NACE Rev 2 categories 53 84-94 and 96-99)
We also drop financial and insurance activities12 as well as observations for which industry
is missing even after cleaning
We also drop firms which functioned as non-profit budgetary institutions or institutions
with technical codes at any time during the observed period We also drop firms which
never reported positive employment We refer to the remaining sample as the baseline
sample
Our main sample used for most of the calculations and for the estimations consists of
observations with at least 5 employees a non-missing total factor productivity value and
no remaining large productivity jumps13 We refer to the resulting sample as our main
sample Excluding the smallest firms has multiple advantages First exclusion of small
firms reduces measurement error as the smallest firms are the most likely to misreport
Additionally one-employee firms cannot be told apart from the self-employed who create
a firm for administrative reasons but clearly do not operate as an ordinary firm The
existence of such firms as well as their financial variables are likely to be strongly
determined by the differential in the tax treatment of personal versus corporate incomes
Because of these reasons both productivity levels and productivity changes may be
measured with an excessive amount of noise for the very small firms and therefore we
exclude them from our main analysis
Table 22 shows the distribution of firms by size category in our baseline sample Clearly
our sample expands strongly between 2000 and 2004 which is mainly a result of legal
changes requiring a larger group of firms to use double-entry bookkeeping While this
expansion is the strongest for the smallest firms it also affects a large number of firms
with up to 20 employees This artificial `entryrsquo of firms can bias estimates of productivity
growth (yielding a negative composition effect) and its decomposition (a negative entry
effect) For this reason in many cases we will start our analysis in 2004
Figure 22 investigates how much the exclusion of very small firms matters It shows that
while the share of 0 and 1 employee firms was between 50 and 60 percent their share in
terms of employment and sales was only around 5-6 percent hence even after their
exclusion our sample captures much of the national output We however report
robustness checks for our main results with all firms with a positive number of employees
in the Appendix
12 We decide to drop the financial sector because of conceptual and measurement problems of defining the productivity of financial firms especially during the crisis It might also distort the aggregate results Dropping these firms also corresponds to the usual practice (eg McGowan et al 2017) However including financial firms does not have a significant impact on our main results
13 We exclude firms that had a log productivity change higher than 15 in absolute value at any one time We also exclude firms switching between manufacturing and services more than twice
Productivity differences in Hungary and mechanisms of TFP growth slowdown
11
Table 22 Distribution of firm size by employment categories
Year 0 emp 1 emp 2-4 emp 5-9 emp 10-19 emp 20-49 emp 50-99 emp 100 lt emp Total
2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739
2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987
2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568
2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376
2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880
2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844
2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671
2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709
2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318
2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113
2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104
2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509
2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335
2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665
2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678
2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519
2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727
Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742
Notes The sample is our baseline sample (see Section 23) also including observations without an
estimated TFP
Figure 22 The share of 0 and 1 employee firms in the number of firms employees and
sales
Data Sources
12
Table 23 shows the number of observations lost because of missing values cleaning and
sample restrictions compared to the original data Dropping firms based on industry and
legal form as well as firms which never report positive number of employees does not
reduce the sample considerably The baseline data contains about 23 of the firms in the
original data The coverage in terms of total employment or value added is even higher
While the reduced sample of firms with at least 5 employees contains only about 20 of
the original number of firms the coverage of total employment and value added is still
above 70 We lose an additional 4 of firms which have no estimated TFP (negative
value added or missing capital) or which have large TFP jumps over time The
corresponding reduction in employment and value added coverage is about 20 and 15
percentage points respectively14 In the main sample we capture almost 23 of the total
employment and value added which we have in the original data
Table 23 Change in sample size and coverage after introducing restrictions
Number of
firms
Total
employment
Total value
added
Original data (after imputing observations) 1000 1000 1000
Drop agriculture and missing industry 952 954 984
Drop non-market services 845 895 948
Drop based on legal form 844 885 946
Drop firms which never had positive
employment
708 885 935
Keep only market economy according to OECD 667 859 912
Drop financial and insurance activities 652 830 790
Baseline sample 652 830 790
Keep observations with at least 5 employees 196 726 723
Keep firms which have no big TFP jump and
observations with non-missing TFP
157 600 647
Main sample 157 600 647
Table 24 shows the share of observations in the main sample by 1-digit NACE industry
The industry composition is quite stable over time Wholesale and retail trade has the
largest share close to 13 followed by manufacturing (21-31) construction (13-14)
and professional scientific and technical activities (7-9) The largest decline over time
was in manufacturing (from 31 to 21) Construction transport and storage
accommodation professional scientific and technical activities and administrative and
support services increased their share by more than one percentage point
14 While this cleaning certainly drops a large number of firms this is standard practice when the aim is to capture and decompose aggregate dynamics
Productivity differences in Hungary and mechanisms of TFP growth slowdown
13
Table 24 The share of observations by industry
NACE Description 2000 2004 2008 2012 2016
B Mining 029 025 024 021 018
C Manufacturing 3085 2636 2352 2280 2122
D Electricity gas steam 021 027 026 025 024
E Water supply sewerage waste
103 112 114 127 098
F Construction 1263 1439 1447 1263 1375
G Wholesale and retail trade 3207 3026 2954 3005 3034
H Transportation and storage 479 551 606 642 683
I Accommodation 477 617 650 719 783
J ICT 351 328 396 403 400
M Professional scientific and
technical activities 688 691 859 915 891
N Administrative and support services
297 547 572 601 572
Total 100 100 100 100 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
24 Firm-level variables
For the present analysis we create firm groups based on different firm characteristics In
this subsection we explain these groupings and provide descriptive statistics
The database includes information on direct ownership Based on this one can identify
firms which are domestically-owned15 foreign-owned or state-owned (including municipal
ownership) We identify a firm as foreign-owned if the foreign share is above 10 percent
Similarly we classify a firm as state-owned if the state-owned share is above 50
percent16 Based on these definitions in 2016 nearly 10 percent of firms were foreign-
owned while the share of state-owned firms was about 1 percent (Table 25) Both foreign
and state ownership is more frequent in larger firms therefore foreign and state share is
higher in terms of employment 373 percent of employees work in foreign-owned firms
and 66 percent in state-owned ones Foreign ownership was concentrated in mining and
manufacturing electricity generation and distribution trade and ICT State ownership was
high in electricity generation and distribution and in utilities The fact that state-owned
firms are concentrated in these two industries limits the possibilities of how the effects of
state ownership and the effect of the peculiarities of these highly regulated industries can
be distinguished from each other Therefore in most cases we will not present results
separately for state-owned firms (except for Section 44)
15 For brevity we will mainly refer to domestically-owned private firms simply as domestically-owned
16 Only 15 of firms with more than 10 percent foreign share report a foreign share between 10 and 51 percent Re-classifing them as domestic does not affect our main results
Data Sources
14
Table 25 Share of state- and foreign-owned firms with at least 5 employees 2016
A) Number of firms
NACE Sector Domestic Foreign State Total
B Mining 8228 1772 000 100
C Manufacturing 8432 1522 046 100
D Electricity gas steam 5631 1942 2427 100
E Water supply sewerage waste 6351 450 3199 100
F Construction 9746 192 062 100
G Wholesale and retail 8957 1006 037 100
H Transportation 9005 890 105 100
I Accommodation 9411 467 121 100
J ICT 8314 1541 145 100
M Professional scientific and technical activities
8982 915 102 100
N Administrative and support services
8991 798 211 100
Total 8937 953 110 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
B) Employment
NACE Sector Domestic Foreign State Total
B Mining 725 275 00 100
C Manufacturing 437 552 12 100
D Electricity gas steam 674 234 91 100
E Water supply sewerage waste 189 32 780 100
F Construction 899 74 28 100
G Wholesale and retail 660 334 06 100
H Transportation 474 199 327 100
I Accommodation 867 111 22 100
J ICT 424 546 30 100
M Professional scientific and technical activities
650 331 20 100
N Administrative and support services
681 258 61 100
Total 560 373 66 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
The data include direct information on export sales and we classify a firm as an exporter
in a given year if its export sales are positive Table 26 shows the share of observations
both by ownership (foreign or private domestic) and exporter status The distribution of
firms across the four groups is stable over time Overall 65-75 of the firms are owned
domestically and supply only the domestic market The share of foreign firms decreased
from 143 in 2000 to 96 in 2016 After an initial decline the share of exporters
increased from 26 in 2000 to 315 by 2016 More than 23 of the foreign firms export
while the same ratio for domestic firms is less than 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
15
Table 26 Yearly share of observations by ownership and exporter status
Year Foreign
exporter
Foreign
non-
exporter
Domestic
exporter
Domestic
non-
exporter
2000 92 51 168 690
2001 89 46 172 693
2002 84 43 173 701
2003 79 40 164 717
2004 71 36 157 736
2005 70 34 160 736
2006 69 34 163 734
2007 73 32 180 715
2008 74 34 186 706
2009 78 36 195 692
2010 77 34 202 687
2011 78 32 215 675
2012 81 31 229 659
2013 79 30 236 655
2014 74 33 233 660
2015 71 31 238 659
2016 70 26 245 658
Total 76 35 196 692
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded
Table 27 presents some baseline descriptive statistics for the four firm groups created by
ownership and exporter status We define age using the year of foundation of the firm On
average foreign exporter firms are the largest and the most productive Within both
categories exporter firms are older larger and more productive in line with similar
patterns in other countries17 We will analyse differences further in Section 44
17 See for example Bernard-Jensen (1999)
Data Sources
16
Table 27 Average characteristics by ownership and exporter status in year 2004 and
2016
Foreign exporter
Foreign non-exporter
Domestic exporter
Domestic non-exporter
Year 2004
N of employees 1385 511 451 165
(5689) (2410) (1396) (404)
Labour productivity 877 825 830 769
(101) (120) (087) (086)
TFP ACF 666 660 634 611
(111) (112) (091) (084)
Age
101 85 99 85
(42) (46) (43) (43)
Year 2016
N of employees 1619 338 344 151
(6246) (1257) (1290) (405)
Labour productivity 906 839 844 793
(088) (115) (075) (080)
TFP ACF 696 684 651 639
(113) (109) (086) (080)
Age 160 105 149 124
(82) (75) (76) (75)
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded Standard deviations are in
parentheses
25 Industry categorization
As we have mentioned already the main industry identifier is the 2-digit NACE Rev 2
industry classification These are hierarchically ordered into 1-digit categories
These categories however do not always lend themselves to easy interpretation On the
one hand one may want to distinguish between different types of manufacturing activities
Here a key question concerns the knowledge intensity or the high-techness of the activity
On the other hand sometimes it is useful to aggregate some of the service activities to
obtain more easily interpretable results
In order to do this we use Eurostatrsquos high-tech aggregation of manufacturing and services
by NACE Rev 2 which we will call industry type18 Note that these sets of industries
include only activities carried out in market industries (ie 10 to 82 NACE Rev 2 industry
codes) When using these categories we do not include firms in non-market sectors like
education (85) or arts entertainment and recreation (90 to 93) (See Table 28)
We would like to point out that while the Eurostat categories clearly reflect the global
technology and knowledge intensity of each industry the actual activity conducted in a
given country may differ from the technology category of the industry This issue is highly
relevant in Hungary where MNEs in high-tech industries operate affiliates conducting
assembly activities in Hungary without much RampD or innovation Still we find this
categorization a good way of aggregating data but still preserving some heterogeneity
18 Retrieved from httpeceuropaeueurostatcachemetadataAnnexeshtec_esms_an3pdf
Productivity differences in Hungary and mechanisms of TFP growth slowdown
17
Table 28 Industry categorization
Manufacturing NACE Rev 2 codes
High-technology manuf 21 26
Medium-high technology manuf 20 27 to 30
Medium-low technology manuf 19 22 to 25 33
Low technology manuf 10 to 18 31 to 32
Services
Knowledge-intensive services (KIS) 50 to 51 58 to 63 64 to 66 69 to 75 78 80
Less knowledge-intensive services (LKIS) 45 to 47 49 52 55 to 56 77 79 81 82
Utilities 35 to 39
Construction 41 to 43
Productivity Trends Hungary in International Comparison
18
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON
The main aim of this chapter is to summarize existing evidence on Hungarian productivity
trends based on internationally comparable databases which include either industry-level
or micro-aggregated information The specificities and similarities of Hungary to
comparable countries will both guide and frame our analysis in the remaining chapters
which use Hungarian micro-data
31 Convergence
The fundamental question regarding the productivity evolution of Hungary or other less
developed EU member countries is whether productivity catches up with the most
developed countries at least in the medium or long run We investigate such medium- or
long-run trends in this subsection by analysing the evolution of relative productivity which
is defined as the level of labour productivity compared to one of the key economies of the
EU Germany (at ppp exchange rates) Figure 31 presents such a comparison of the
labour productivity levels of Hungary the Czech Republic Poland and Slovakia We use the
OECD STAN database for this exercise and present trends for as many years as possible to
reflect long-run developments
Figure 31 Relative labour productivity level (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN and GGDC Productivity Level Database The
market economy excludes real estate For more details see Appendix A3
Let us start with the evolution of aggregate labour productivity According to Figure 31 all
of these countries seemed to be on the road to convergence to frontier countries in terms
of labour productivity before the financial crisis In particular labour productivity in
Hungary increased from 50 percent of the German level in 1998 to 65 percent in 2008 A
similar pre-crisis convergence can be observed in all three comparator countries19
19 Note that TFP is not available for Hungary in the EU KLEMS after 2008 Therefore we restrict this
international comparison to labour productivity
Productivity differences in Hungary and mechanisms of TFP growth slowdown
19
Note that the labour productivity decline during the crisis does not show up in the above
figure because it also affected the baseline country Post-crisis Hungarian labour
productivity (relative to Germany) remained flat stabilizing at around 65 percent While
this is similar to the productivity evolution of the Czech Republic it differs remarkably from
Poland and Slovakia which were able to close their productivity gap relative to Germany
by about 5 percentage points between 2009 and 2015 This slowdown of aggregate
productivity growth and the lack of further convergence from previous levels is actually
the main motivation for this study
A key question is whether the slowdown characterises the whole economy or it is
constrained to some of the sectors or types of enterprises The first dimension is to
distinguish between the state sector and the market economy According to OECD STAN
non-market sectors accounted for about 27 percent of all employment in 201520 The
second panel of Figure 31 restricts the sample to the lsquomarket economyrsquo21 Interestingly
productivity differences relative to Germany are larger in the market economy compared
to the whole economy suggesting that the productivity levels of the public sector in the
two countries appear to be closer to each other In Hungary the relative productivity of
the market economy follows a very similar trend to the whole economy with about 10 pp
relative productivity increase between 1998 and 2005 and stagnation post-crisis With the
exception of Slovakia post-crisis productivity growth is also flat in the comparator
countries
Figure 32 Relative labour productivity in manufacturing and business services
Germany=100
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN Business services excludes real estate For
more details see Appendix A3
20 According to the EU KLEMS this share has remained more or less constant since 2003
21 This includes NACE Rev 2 Codes 5-82 except real estate (68)
Productivity Trends Hungary in International Comparison
20
The market economy can be further disaggregated into manufacturing and business
services (Figure 32) There is strong evidence of catching up in manufacturing between
1995 and 2008 when relative productivity increased by more than 10 percentage points
Relative productivity fell immediately after the crisis with positive growth after 2011
reaching pre-crisis (relative) levels by 2015 Comparator countries which started from
much lower levels caught up faster pre-crisis and faced a much smaller fall around the
crisis years In other words comparator countries have caught up with Hungary in terms
of manufacturing productivity but there is no evidence for a sharp break in the trend post-
crisis
This contrasts sharply with business services where a period of catch-up until 2005 was
followed by a substantial decline in relative labour productivity This is also in strong
contrast with the comparator countries where relative productivity of business services
either increased (Czech Republic and Poland) or stagnated (in Slovakia) Business services
appear to be a key source of aggregate productivity slowdown
Figure 33 presents productivity dynamics in four specific industries to substantiate the
more aggregated picture with some more concrete examples The first two examples are
manufacturing industries namely the textiles and the automotive industry The relative
productivity level of textiles stagnated during the crisis at quite low levels fell during the
crisis followed by some growth from 2012 In motor vehicles relative productivity
increased by nearly 10 percentage points relative to Germany between 2001 and 2009
followed by a significant fall around the crisis and a strong recovery from 2012 The
picture is also varied in services In retail and wholesale there had been some productivity
improvement before the crisis followed by a declining trend post-crisis Both the level and
dynamics of relative productivity compares unfavourably to the comparator countries In
professional services relative labour productivity had grown quickly until 2011 followed
by a declining trend
Figure 33 Relative labour productivity evolution (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
21
Similar observations can be made when analysing the relative productivity of all types of
industries (Figure 34) The difference in productivity levels relative to Germany tends to
be larger in manufacturing than in services Light industries have especially low relative
productivity levels In terms of productivity growth we see mostly positive trends in most
manufacturing industries and a less clear picture in services with a decline or stagnation
in many service industries
Figure 34 Labour productivity of different industries relative to Germany 2005 and 2015
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix A3
Even in countries and industries with a relatively low level of average productivity it is
possible that a segment of the economy operates at world-class levels or shows fast
convergence to that This possibility may be especially relevant in economies where a
number of large and probably foreign-owned firms operate together with many smaller
domestically-owned firms which is certainly the case in Hungary One approach to
investigate this possibility was suggested and implemented by the OECD (Andrews et al
2017) This approach builds on cross-country micro-data to calculate the productivity of
the most productive firms in the world (global frontier) and compare it with the
productivity of the most productive firms in a country (national frontier)
Figure 35 shows these comparisons based on the OECDrsquos calculations22 In particular the
horizontal axis shows how productive Hungarian frontier firms are relative to the global
22 We would like to thank Peter Gal and his colleagues in the OECD for sharing these data with us In
this version global frontier is defined as the top 10 percent most productive firms worldwide
while the national frontier is the top 10 percent within the country according to ORBIS See
Appendix 3 and Box 41 for details on these data
Productivity Trends Hungary in International Comparison
22
frontier (100 is the global frontier) while the vertical axis compares Hungarian and global
non-frontier firms The figures suggest a number of conclusions To start with the frontier
productivity gap is strongly associated with the non-frontier productivity gap showing that
in industries where the typical firms are of relatively low productivity so are the frontier
firms Importantly the slope of the fitted line (06) is well below 1 suggesting that on
average there is a smaller gap between a top global and a top Hungarian firm than
between a typical (non-frontier) global firm and a typical Hungarian firm This is in line
with the duality hypothesis
That said one has to emphasise that the picture does not support a ldquostrong versionrdquo of the
duality hypothesis ie that the best Hungarian firms operate at world-class productivity
levels Even in manufacturing Hungarian frontier firms typically produce 40-60 percent
less value added per employee compared to the global frontier (good examples are
machinery (28) and motor vehicles (29)) The smallest gaps appear in a few relatively
low-tech service industries (trade and repair of vehicles (45) or warehousing (52)) where
frontier productivity is actually above the global frontier23
The observation that such large productivity differences exist between global frontier and
Hungarian frontier firms even within relatively narrowly defined industries suggests that
the low relative productivity of the Hungarian market economy is not a consequence of
industry composition ndash it mainly results from within-industry gaps Importantly these
main patterns are very similar and independent of how productivity is measured (labour
productivity or TFP) namely they are not a consequence of capital intensity differences
Finally by and large there is no evidence for convergence of frontier firms to the global
frontier between 2009 and 201324 If anything the gap between the global and the
Hungarian frontier widened in this period while the difference between the global and the
Hungarian frontier was 34 percent in the median industry in 2009 it widened to 38 by
2013
23 Naturally this is likely to be the case in other similar countries Still in different discussions it is often supposed implicitly that the best Hungarian firms are indistinguishable from the global frontier
24 Prior to 20082009 the coverage of ORBIS the source for the OECD calculations is fairly limited for
Hungary hence those calculations are less reliable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
23
Figure 35 Productivity of Hungarian frontier and non-frontier firms relative to firms in
other countries (2013)
A) Labour productivity
B) TFP
Notes The industry codes are 2-digit NACE Rev 2 codes We have omitted industries with only few
observations (less than 5 Hungarian frontier firms) in the case of labour productivity outliers we
ignored those where the HU frontier was measured to be more productive than 125 percent of the
global frontier (ICT real estate and office administration services) Note that there are fewer
observations regarding TFP than labour productivity Source Data provided by the OECD calculated
from Andrews et al (2017) For more information see Appendix 3
Productivity Trends Hungary in International Comparison
24
We can draw a number of conclusions from these calculations First while Hungaryrsquos
labour productivity had been catching up similarly to other CEE countries to more
advanced economies before the crisis there was a trend break after the crisis especially
compared to Poland and Slovakia Only part of the productivity slowdown could be
explained by a slowdown in non-market sectors but there is also a pronounced slowdown
in the market economy This is not the result of having a combination of a few firms with
world-class productivity and many less efficient SMEs ndash actually the productivity of
frontier firms is only about 40-50 percent of global leaders even in industries where the
Hungarian frontier consists of many multinational firms There is no evidence that
Hungarian frontier firms were catching up with global leaders between 2009 and 2015
32 Within-industry heterogeneity
Since the beginning of the 2000s with the availability of detailed micro-data sets at the
firm-level it has become clear that within-industry heterogeneity in terms of productivity
is significantly larger than heterogeneity differences across industries (Bernard et al
2003 Bernard et al 2007 Bernard et al 2012 OECD 2017) Many factors have been
proposed which may generate and sustain the observed large productivity differences
including managerial practices different quality of labour capital and knowledge as well as
a number of external factors The exact role of different factors is an active area of
research (Syverson 2011) Recent research also hints at increasing dispersion within
sectors (Berlingieri et al 2017b)
In 2011 the level of the p90p10 ratio (90th and 10th percentile of productivity
distribution) was high in Hungary relative to other OECD countries taking a value of 279
in manufacturing and 329 in services (Table 31) These numbers are in logs representing
about 20-fold differences These numbers are similar to Chile and Indonesia A similar
pattern emerges with respect to TFP
Table 31 Productivity p90p10 ratio by country (2011)
Country
Year 2011
Log LP 90-10 ratio Log MFP 90-10 ratio
Manufacturing Services Manufacturing Services
Australia 187 205 190 212
Austria 196 242 - -
Belgium 160 174 180 178
Chile 300 353 307 387
Denmark 146 196 132 180
Finland 117 138 119 134
France 135 181 140 178
Hungary 279 329 254 286
Indonesia 311 - 341 -
Italy 166 201 160 188
Japan 126 138 117 138
Netherlands 200 298 227 227
New Zealand 184 209 192 208
sNorway 173 217 187 208
Portugal 188 265 188 275
Sweden 145 186 159 245
Notes This is a reproduction of Table 6 from Berlingieri et al (2017a) Note that the OECD uses the
term lsquoMFPrsquo (Multi-factor productivity) in the same sense as we use TFP in this report
Second as seen in Table 32 similarly to other OECD countries the overwhelming
majority of productivity differences results from within- rather than across-sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
25
differences The share of within-sector differences is 79 in manufacturing and 99 in
services The manufacturing share is close to the average of the countries in the sample
while the services share is at the high end
Table 32 Share of within-sector variance in total LP dispersion by country (2011)
Country
Year 2011
LP Dispersion
Manufacturing Services
Australia 98 99
Austria 86 90
Belgium 76 88
Chile 90 97
Denmark 84 63
Finland 65 76
France 63 85
Hungary 79 99
Indonesia 79 -
Italy 82 65
Japan 75 89
Netherlands 80 71
Norway 83 73
Portugal 62 76
Sweden 53 74
Notes This is a reproduction of Table 7 from Berlingieri et al (2017a)
These figures suggest that within-industry productivity dispersion is relatively high in
Hungary but it is not out of the range of countries at a similar level of development Still
these overall dispersion measures may not capture the duality between firms of different
sizes and ownership Internationally comparable data regarding productivity of firms in
different size classes is available from the OECD Structural and Demographic Business
Statistics (Figure 36) Size is strongly associated with productivity large firms are 45
times and 18 times as productive as very small firms in manufacturing and services
respectively However large these premia are not out of the range of similar countries in
services it is very similar to other CEE countries while in manufacturing it is at the high
end of the distribution but not extreme
Another relevant pattern in Figure 36 is that productivity differences by size are very
different between CEE countries and Western European countries This observation may
partly reflect the importance of large and productive multinational firms in CEE countries
but can also be a more or less automatic consequence of the fact that firm size distribution
significantly differs between the two groups of countries (Figure 37) Typically the share
of very small firms is larger in less developed economies leading to a more skewed firm
size distribution Such a distribution which is associated with a larger number of small
firms within size classes (the majority of firms with 1-9 employees in CEE employs only 1-
2 employees) leads to larger differences across size classes and larger within-industry
productivity dispersion The massive share of very small firms in these countries also
reflects that many of the lsquomicro-enterprisesrsquo (with only 1-2 employees) do not operate as
proper firms they behave more like individual entrepreneurs
Productivity Trends Hungary in International Comparison
26
Figure 36 Value added per person employed by size class (1-9 persons employed=100)
A) Manufacturing
B) Services of the business economy
Notes Value added per person employed defined as value added at factor costs divided by the
number of persons engaged in the reference period Economic sector lsquoManufacturingrsquo comprises
Divisions 10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business
economyrsquo comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities
of holding companies Source OECD SDBS For more details see Appendix 3 Main sample for 2015
Productivity differences in Hungary and mechanisms of TFP growth slowdown
27
Figure 37 Firm distribution by size class (2015)
A) Manufacturing
B) Services of the business economy
Notes Only enterprises with at least one employee are included lsquoManufacturingrsquo comprises Divisions
10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business economyrsquo
comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities of holding
companies Source OECD SDBS For more details see Appendix 3 Main sample
Productivity Trends Hungary in International Comparison
28
The main conclusion from investigating within-industry differences across firms is that both
the productivity dispersion and the productivity advantage of large firms is indeed
relatively large in international comparison but these numbers are not radically different
from similar countries Nevertheless differences in firm size distribution between more
and less developed countries go a long way towards explaining the differences between
Western European and CEE countries
33 Firm dynamics
A potential reason for declining productivity growth may be weak dynamics including low
entry and exit rates as well as slower reallocation The OECD Structural and Demographic
Business Statistics database provides international comparisons of entry and exit rates and
their changes across countries (Figure 38 and Figure 39)
In general both exit and entry rates are higher in CEE countries relative to Western
European economies25 This stronger dynamism may reflect stronger growth but it is also
affected (in a mechanistic way) by the differences in firm size distribution Importantly in
a cross-section entry and exit rates are strongly correlated suggesting that they capture
the same general aspect of firm dynamics Services are more dynamic than
manufacturing once again partly because of the different size distributions
Within CEE countries entry and exit rates seem to be associated with productivity growth
(and level) Countries with stronger post-crisis productivity growth (Poland Slovakia and
Romania) exhibit significantly higher entry and exit rates while those with less dynamic
productivity growth (Hungary and the Czech Republic) have lower churning This provides
some evidence that lower entry and exit rates may be systematically related to the weaker
productivity performance of these countries We will take a more detailed look at the
relationship between entry and exit and productivity growth in Chapters 6 and 7
When comparing 2012 and 2015 the pictures provide evidence for increased entry and
decreased exit in parallel with recovery and better growth prospects Still entry rates
remain one of the lowest in CEE indicating that entry and dynamic young firms may
contribute less to productivity growth in Hungary compared to other CEE countries
25 Note that these OECD statistics include all enterprises (even those with no employees) hence
changes in the tax treatment of firms relative to individual entrepreneurs may affect measured
dynamics Also firm death is defined based on the rsquodeathrsquo of the legal entity which may happen
many years after stopping production For more information see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
29
Figure 38 Birth rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Birth rate is defined as the number of enterprise births divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers The
economic sector lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo
comprises Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix A3
Productivity Trends Hungary in International Comparison
30
Figure 39 Death rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Death rate is defined as the number of enterprise deaths divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers
Poland has no available data for 2015 so the 2014 value is reported The economic sector
lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo comprises
Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
31
34 Conclusions
In international comparison productivity slowdown after the crisis was especially severe in
Hungary both in manufacturing and services There are large productivity differences
within industries and also between small and large firms While these are at the high end
in international comparison they are not extreme compared to similar countries A
comparison to the global frontier suggests that even top Hungarian firms are significantly
behind top global firms in terms of productivity These facts provide a motivation for our
analysis of the evolution of the shape of the productivity distribution in Chapter 4
International comparison of firm dynamics suggests that ndash similarly to other CEE countries
ndash Hungarian industries are more dynamic than their Western European counterparts but
entry and exit rates in Hungary and the Czech Republic are below the average of CEE
countries This motivates our investigation of the contribution of entry and exit to
productivity growth in Chapters 6 and 7
Evolution of the Productivity Distribution
32
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION
41 Context
The study of within-industry productivity differences is motivated by two concepts First
the OECD (2016) argues that one of the key issues of recent developments in productivity
growth is that there is a strong divergence between the productivity evolution of frontier
firms and other firms However this same publication reports that Hungary seems to be
an exception to this trend with slow productivity growth at the frontier and faster
productivity growth of less productive firms suggesting some within-industry catch-up
(Figure 41) We look into the particulars behind this phenomenon by following the
evolution of the average productivity of different deciles in the productivity distribution
Second as we have already mentioned a key concept of the Hungarian (and CEE) policy
debate is the lsquodualityrsquo of smalldomestically-owned and largeforeign-owned firms The
large gap between the two types of firms presents a challenge for policy but it also
indicates an opportunity for domestic firms to catch up with foreign firms which may use
more productive technology (still far in terms of productivity from the global frontier see
Chapter 31) The evolution of the productivity gap (or premium) between small and large
firms as well as domestic and foreign firms informs us about whether firms on the lsquowrong
sidersquo of the duality are able to catch up with the firms at the national frontier
The duality debate frames productivity differences partly as a consequence of the lsquomissingrsquo
medium-sized (domestic) firms Hsieh and Olken (2014) argue that in less productive
economies the full firm size distribution is shifted to the left because of the constraints on
the growth of small firms Thus according to this view the productivity difference is not a
result of too few medium sized firms but of too few firms which are not small
Figure 41 Divergence in labour productivity performance
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
33
B) Non-financial Services
Notes This is a reproduction of Figure 16 from OECD (2016)
In this chapter we investigate how the shape of the productivity distribution evolved over
the years Section 42 contrasts the development of firms with other types of economic
entities Section 43 analyses how average productivity and productivity deciles evolved
while 44 investigates the duality based on size and ownership
42 Aggregate productivity and the self-employed
Before turning to the productivity distribution of firms it is worthwhile to describe how the
productivity level and evolution of firms ndash and in particular double-entry bookkeeping
enterprises ndash differ from other entities in particular the self-employed Given the large
number of people employed by those entities this exercise can reveal a lot both about
productivity dispersion and the drivers of aggregate productivity growth
Let us motivate this investigation by comparing aggregate statistics (derived from data
applicable to all people engaged in an industry) with patterns calculated from our NAV data
(which includes only double-entry bookkeeping firms) Figure 42 shows the labour
productivity growth reported by OECD STAN and the evolution of the average labour
productivity as calculated from the NAV data weighted by sales and employment (Figure
42) According to the Figure while these series co-move they do so with some
discrepancies While productivity dynamics in Manufacturing are very similar across all
samples the relationship is looser for services and for the market economy with the NAV
series notably exhibiting less pronounced post-crisis slowdown than the OECD STAN data
Evolution of the Productivity Distribution
34
Figure 42 Cumulative labour productivity growth according to OECD STAN and the NAV
sample
There can be many reasons behind the differences between these series (see Biesebroeck
2008) but arguably one of the main factors is the discrepancy in the number of
employees in the two databases Firms in the full NAV database employed 24 million
people in 2015 compared with 286 million employed and 325 million lsquoengagedrsquo in the
market economy according to the OECD STAN One source of this difference may be that
while some unofficially employed workers report their true employment status in LFS
(Labour Force Survey) ndash which serves as the basis for our aggregate data ndash they do not
appear in any official registers and such the NAV data Benedek et al (2013) reaffirming
the statement compare LFS employment data with tax registers and show that 16-18
percent of jobs are not declared to the tax authorities
Even more importantly from our perspective the NAV data by definition includes no
information on the self-employed and typically small non-double-entry bookkeeping firms
operating under special taxation The distinct productivity dynamics of these two groups
along with changes in undeclared employment may explain another part of the difference
Obtaining direct information on this issue would be of great interest but acquiring it is far
from straightforward Some information on these entities is available from the Register of
Economic Organizations (Gazdasaacutegi Szervezetek Regisztere GSZR) which is available
between 2012 and 2015 Most importantly this database provides us with information on
the number of employees and sales updated annually This in and of itself does not allow
us to estimate productivity properly but with its help we can calculate a crude proxy
sales per employee for illustration
Table 41 reports26 the number of employees and the average sales per worker values for
three groups The first is the group of double-entry bookkeeping firms (ie the firms who
26 These tables were calculated as follows First we combined the GSZR and NAV databases for years 2012 and 2015 Observing that about 80 percent of the firms present in the NAV sample are also present in the GSZR register we restricted our sample to the entities who are listed in the GSZR so that our variables would be commensurable From this collection we selected those who
Productivity differences in Hungary and mechanisms of TFP growth slowdown
35
are present in the NAV data) the second is the category of the self-employed (ie those
who are registered as individual entrepreneurs) and the third category is that of lsquoother
firmsrsquo (ie entities who are registered as firms in the GFO (Gazdaacutelkodaacutesi Forma) coding
system but are not categorised as self-employed and are not following a double-entry
bookkeeping method) We distinguish between manufacturing and other industries of the
market economy27 We supply figures for the earliest and latest years for which data are
available The tables reveal two important observations
First according to the GSZR about 30 percent of reported employees in Manufacturing
and 50 percent of reported employees in other industries work outside the double-entry
bookkeeping group Importantly these numbers may be overestimates because the GSZR
may report the same person in multiple entities for example when they work part-time or
switch jobs within the year That said both the EU KLEMS and the GSZR suggest that a
large share of people work outside the double-entry bookkeeping group in the market
economy
Second while sales per worker is not drastically different between double-entry
bookkeeping firms and other firms the difference between firms and the self-employed is
between 6-10-fold This difference in sales per employee may represent 2-3-fold labour
productivity differences between people employed by firms and the self-employed on
average28
Third the dynamics of sales per worker differ markedly between double-entry
bookkeeping firms and other entities while it increased by 40 percent in the NAV sample
between 2012 and 2015 it stagnated for the self-employed This may results from a
number of factors ranging from composition effects changes in tax regulations or low
productivity growth Still the low measured productivity growth of this sector of the
economy may be an important factor behind the slower post-crisis aggregate productivity
growth in services compared to the NAV sample Table 41 illustrates this for the sales per
worker measure While it grew by 40 percent in the lsquoOtherrsquo category between 2012 and
2015 based on the NAV sample its lsquoaggregatersquo growth was only 6 percent during the same
period
Obviously one cannot draw far reaching conclusions from such statistics given the
immense measurement problems Still these patterns suggest that in a sense the duality
between firms and the self-employed may constitute a similarly deep divide to the one
belong to the lsquomarket economyrsquo (as defined in Chapter 2) and are registered as lsquofirmsrsquo according to GFO coding system (ie have 1-digit GFO codes 1 or 2) We tagged the firms present in the NAV sample as lsquodouble-entry bookkeeping firmsrsquo and marked those who have 2-digit GFO codes equalling to 23 as lsquoself-employedrsquo We categorised the rest of our sample as lsquoother firmsrsquo Further we distinguished between manufacturing and other market economy firms based on their NACE codes and then calculated for sales per worker measures on the level of each observation finally to compute for yearly aggregates for each group as indicated above
27 Notably in line with the definition in Chapter 2 these lsquoother industriesrsquo do not include agriculture
28 Needless to say this cannot be easily mapped into productivity differences given that firms using more intermediate inputs are more likely to choose double-entry bookkeping (and hence pay
taxes based on profits) rather than simplified taxes (and pay taxes based on sales) Still one can do the following back of the envelope calculation In the NAV sample the average ratio of material expenditure over sales was 066 both in 2012 and 2015 Therefore value added per employee (or labour productivity) could be about a third of the sales per employee variable If one conservativelly assumes that the self-employed have zero material costs their labour productivity is the same as their sales per employee index Based on this simple calculation the 6-10-fold difference in sales per employee map to at least 2-3-fold differences in labour productivity
Evolution of the Productivity Distribution
36
that exist between globally integrated and domestic-oriented firms Consequently policies
can be formulated with an explicit focus on this group
Table 41 Number of employees and sales per employee for different entities
Number of employees
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 621229 627391 1325299 1196332
Other firm 289636 296921 698326 771930
Self-employed 72674 74325 620699 638001
Total 983539 998637 2644324 2606263
Average sales per employee (HUF million)
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 140 199 196 278
Other firm 151 146 196 201
Self-employed 25 25 29 28
Total 92 99 105 111
43 The evolution of productivity distribution in Hungary
Average productivity
Let us continue by investigating the evolution of average productivity Table 42 presents
the average labour productivity and TFP growth rates for the market economy
manufacturing and services as defined in Chapter 2 We report both unweighted and
labour-weighted productivity growth for each year
Let us start with the whole market economy Between 2004 and 2007 both labour
productivity and TFP was growing strongly by 7-8 percent on average as expected in a
catching up economy (as we have seen in Chapter 3) Importantly the weighted growth
rate was higher than the unweighted one suggesting that reallocation played a positive
role in aggregate productivity growth (see Section 62 for more details)
During the crisis we see a slight productivity decline in 2008 a sharp fall of about 8
percent in 2009 followed by a strong recovery in 2010 The 2010 productivity recovery
resulted from the productivity growth of large firms unweighted average productivity
growth was very slow This suggests an asymmetry in recovering from the crisis-related
productivity decline
Post-crisis all measures document a slowdown in productivity growth with typical growth
rates between 25-35 percent Notably weighted productivity growth measures were
similar to unweighted ones in the wake of the crisis suggesting deterioration in the
efficiency of the reallocation process The 2010-2013 and 2013-2016 periods seem to be
quite similar to each other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
37
Importantly while labour productivity and TFP dynamics differ to some extent the overall
picture is very similar for the two productivity measures This is in line with the hypothesis
that any productivity slowdown is not merely a consequence of lower capital stock growth
The results are similar when using alternative TFP estimators (see Table A41 in the
Appendix)
Table 42 Labour productivity and (ACF) TFP growth in the sample
A) Market economy
Year LP TFP
unweighted emp w unweighted emp w
2005 20 58 19 74
2006 92 91 93 119
2007 53 60 39 56
2008 -10 -08 -10 -04
2009 -70 -81 -69 -82
2010 -05 44 11 80
2011 25 45 34 40
2012 25 22 21 01
2013 19 25 30 22
2014 39 45 40 59
2015 51 50 52 49
2016 36 19 20 03
Average
2004-2007 55 70 50 83
2007-2010 -28 -15 -23 -02
2010-2013 23 34 33 29
2013-2016 36 35 35 33
B) Manufacturing
Year LP TFP
unweighted emp w unweighted emp w
2005 37 148 20 114
2006 124 163 114 149
2007 100 114 78 71
2008 25 -03 17 -17
2009 -115 -94 -133 -117
2010 82 161 80 173
2011 -02 34 04 18
2012 05 -46 -02 -58
2013 -14 31 -12 05
2014 11 48 -01 27
2015 38 37 30 14
2016 26 01 04 -23
Average
2004-2007 87 141 71 111
2007-2010 -02 22 -12 13
2010-2013 -04 17 04 -03
2013-2016 15 29 05 06
Evolution of the Productivity Distribution
38
C) Market services
Year
LP TFP
unweighted emp w unweighted emp w
2005 12 -04 10 32
2006 80 47 79 90
2007 39 25 24 48
2008 -22 -06 -21 -03
2009 -57 -68 -52 -71
2010 -29 -17 -11 26
2011 33 49 43 57
2012 31 60 30 48
2013 29 21 39 29
2014 46 45 46 78
2015 54 58 54 72
2016 39 30 25 20
Average
2004-2007 43 23 38 57
2007-2010 -36 -31 -28 -16
2010-2013 31 44 39 51
2013-2016 42 39 41 50
Notes This figure presents growth rates of labour productivity and aggregate TFP for lsquomarket
industriesrsquo (see section 25) The sample does not include agriculture mining and financial services
Services include construction and utilities Only firms with at least 5 employees
Comparing manufacturing and services shows a key dichotomy between the two large
sectors In Manufacturing productivity growth was strong before the crisis with above 10
percent average weighted growth rates This fell to very low levels after 2010 Similarly to
the whole market economy reallocation processes had been more efficient before 2008 In
contrast for services no clear structural break appears around the time of the crisis either
in terms of pre- and post-crisis growth rates or reallocation efficiency
Table 43 looks into industry differences in more detail The picture is similar for
manufacturing industries in the various technology categories with a very substantial
slowdown in productivity growth Productivity growth was fastest in high-tech both before
and after the crisis Services are a bit more heterogeneous High-tech services behaved
similarly to high-tech manufacturing with strong pre-crisis growth (around 10 percent on
average) followed by a slowdown to growth rates around 5 percent per year In less
knowledge-intensive services which represent the majority of business service
employment growth rates were similar before and after the crisis (around 5 percent)29
Lastly we see moderate growth rates and then some slowdown in construction and
utilities
29 Note however that this may not be the case for the self-employed as has been discussed in the previous chapter
Productivity differences in Hungary and mechanisms of TFP growth slowdown
39
Table 43 TFP growth by type of industry (employment-weighted ACF TFP)
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 124 19 66 274 2006 240 137 39 33
2007 74 02 41 221
2008 -45 23 -15 59
2009 05 -191 -218 48
2010 135 111 264 168
2011 -45 18 34 100
2012 -15 -24 -83 -181
2013 -41 37 -22 125
2014 06 07 27 86
2015 65 01 -54 80
2016 -02 04 -27 -91
Average 2005-2007 146 53 49 176
2007-2010 32 -19 10 92
2010-2013 -34 07 -21 20
2013-2016 07 12 -19 50
B) Services
Year KIS LKIS Construction Utilities
2005 127 16 34 -48
2006 166 75 30 67
2007 13 58 42 29
2008 -16 14 -72 -26
2009 -63 -94 -04 25
2010 54 12 09 05
2011 97 46 65 29
2012 12 74 13 -22
2013 12 30 63 -07
2014 78 89 65 -81
2015 106 70 14 54
2016 16 31 -47 39
Average
2005-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the sales-weighted average ACF TFP growth rate by technology category (see
Section 25) Only firms with at least 5 employees The sample does not include agriculture mining
and financial services
In general patterns are similar for the unweighted measures (See Table A42 in the
Appendix) with weaker pre-crisis growth in manufacturing where reallocation seems to
have mattered most Labour productivity behaved similarly to TFP (See Table A43 in the
Appendix)
Evolution of the Productivity Distribution
40
Frontier firms
The key motivation for this investigation is to understand better how productivity dynamics
of lsquofrontierrsquo firms differ from firms in other parts of the productivity distribution Defining
frontier firms is not a straightforward task (Andrews et al 2017) Inevitably all such
attempts have to face the trade-off between a narrow definition which may to a large
extent capture the behaviour of outliers and a broader definition which may include
many firms which are very far from the actual frontier
One can find a sensible compromise between the too narrow and the too broad definitions
by following the OECD practice (Andrews et al 2017) This solves the problem of
including small firms with potentially large noise by restricting the sample to firms with at
least 20 employees on average in the sample period Frontier is defined as the top 5
percent of such firms for each industry-year combination An additional issue is that the
number of observations may change across years This is solved by calculating the top 5
based on the median number of observations per year We will call these firms frontier
firms
An alternative definition is simply to define the top decile within the productivity
distribution in industry-year combination as frontier based on our main sample We will
employ this strategy as well for the sake of comparison
Table 44 investigates the prevalence of frontier firms in different groups30 The probability
of being frontier is not related strongly to size A foreign-owned firm is 3-4 times more
likely to be frontier than a domestically-owned private firm State-owned firms are similar
to privately owned domestic firms in this respect As a result about half of the frontier
firms are foreign-owned Finally frontier firms are substantially more prevalent in the
more developed regions of the country especially in Central Hungary These patterns are
quite stable throughout the years and they prevail in a multiple regression analysis The
top decile of the productivity distribution has a similar composition (see Table A44 in the
Appendix)31
Table 44 The share of frontier firms () among firms with at least 20 employees
A) By size
2004 2007 2010 2013 2016
20-49 emp 357 327 34 362 329
50-99 emp 401 468 542 486 555
100- emp 293 358 414 42 462
B) By ownership
2004 2007 2010 2013 2016
Domestic 213 194 236 272 289
Foreign 873 955 896 82 821
State 181 211 166 167 263
30 Note that we restrict the sample to firms with at least 20 employees because the definition of frontier requires to have at least 20 employees on average
31 When the definition is based on labour productivity the share of frontier firms increases with size The foreign advantage is also larger
Productivity differences in Hungary and mechanisms of TFP growth slowdown
41
C) By region
2004 2007 2010 2013 2016
Central HU 596 621 652 552 579
Northern Hungary 174 104 176 237 168
Northern Great Plain 152 195 199 38 268
Southern Great Plain 128 127 18 277 224
Central Transdanubia 296 27 32 359 322
Western Transdanubia 408 313 305 433 395
Southern
Transdanubia 131 081 188 159 211
Another key question is the extent to which frontier status is persistent Figure 43 shows
a transition matrix ie it considers frontier firms in year t and reports their status in t+3
Do they remain frontier or become a non-frontier firms or exit the market altogether
Overall the 3-year persistence of the frontier status is around 45 percent ndash nearly half of
frontier firms will also be frontier 3 years later This is a bit higher than what is found in
other countries Antildeoacuten Higoacuten et al (2017) for example report that about half of all
national frontier firms remain on the frontier for a year but only about 20 percent for 5
years The persistence of frontier status remained largely unchanged across the years
Frontier status is more persistent for foreign and exporter firms The transition matrix of
top decile firms is similar with slightly weaker persistence (Figure A41 in the Appendix)
Figure 43 Transition matrix for frontier firms
Notes This figure shows how many of the frontier firms in year 2010 were still frontier in 2013 how
many exited and how many continued as non-frontier Only firms with at least 20 employees The first
panel shows this transition matrix for various 3-year periods
Evolution of the Productivity Distribution
42
Productivity evolution across deciles
The figures in this section compare the average productivity of frontier firms of the top
decile of the productivity distribution lsquohigh productivity firmsrsquo (8th and 9th deciles) lsquotypical
firmsrsquo (4th to 6th deciles) and lsquolow productivityrsquo firms (2nd and 3rd deciles) all of these
defined at the year-NACE 2 level This approach follows closely that of the OECD (2016)
Also we use the 8 lsquotechnologicalrsquo industry categories introduced in Section 25 to condense
information but still allow for heterogeneity across industries
Let us start with comparing TFP levels (Figure 44) and their cumulative changes (Figure
45) at the different parts of the productivity distribution (note that the vertical axes differ
across sectors) TFP levels are measured in natural logarithms For example in low-tech
manufacturing the difference between low-productivity firms and the frontier is about 2 log
points or more than 7-fold32 Within-industry productivity differentials are much larger
than across-industry differences or changes From a methodological point of view in most
industries frontier firms co-move with the top percentiles but there are a few exceptions
most prominently high-tech manufacturing
The overall productivity evolution is much in line with the averages reported in Table 42
There is strong pre-crisis growth in Manufacturing followed by a fall in 2009 and sluggish
growth afterwards High-tech manufacturing is a partial exception from this trend
Productivity growth actually accelerated after the crisis in services
Figure 44 TFP levels in various types of industries
A) Manufacturing
32 1198902 asymp 74
Productivity differences in Hungary and mechanisms of TFP growth slowdown
43
B) Services
Notes This figure shows the evolution of the (unweighted) average ACF TFP level of the different
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles with at least 20 employees on average lsquotop decilersquo is the 10th decile lsquohighrsquo
is the 8-9th decile typical is the 4-6th deciles while `lowrsquo is 2-3rd deciles Main sample The industry
categories are described in Section 25 The sample includes the sectors of the market economy
except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less knowledge-
intensive services
Most importantly we do not find evidence for an increasing gap between frontier and other
firms (in line with OECD 2016) in any of the industries Within manufacturing there is
convergence between frontier and non-frontier firms in medium-low and high-tech
industries However this is not robust for the alternative definition of frontier (top decile)
which moves strongly together with other deciles Based on this one may say that there is
no robust evidence either for convergence or divergence in manufacturing There are some
signs of convergence pre-crisis in knowledge-intensive and less knowledge-intensive
services as well as in construction followed by stronger productivity growth in the highest
quartiles post-crisis Importantly any convergence or divergence appears to be small
relative to already existing differences
Evolution of the Productivity Distribution
44
Figure 45 Cumulative TFP growth since 2004
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is the 10th
decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main sample The
industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less
knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
45
The picture is somewhat different when labour productivity is considered (Figure 46) In
this case the difference in growth rates between frontier and other firms is more
pronounced than in the case of TFP One can plausibly claim that less productive deciles of
the distribution caught up somewhat with the most productive firms in high-tech
manufacturing in the two service sectors and also in construction This suggests that
capital deepening by less productive firms (or low investment by frontier firms) may lead
to some convergence in terms of labour productivity but less so in terms of TFP33
Figure 46 Cumulative labour productivity growth since 2004 for labour productivity
deciles
A) Manufacturing
33 Note that these figures are the most directly comparable ones to Figure 41 which also presents results for labour productivity In line with that figure we find evidence for convergence between the median firm and frontier firms We also find that low-productivity firms converge The most important reason for this is that we exclude firms with less than 5 employees from our sample
Evolution of the Productivity Distribution
46
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average labour productivity level
for various deciles of the productivity distribution within each 2-digit industry-year combination
lsquoFrontier firmsrsquo are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo
is the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample The industry categories are described in Section 25 The sample includes the sectors of the
market economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo
Less knowledge-intensive services
Figure 47 zooms in to a few industries of interest which both confirm and qualify the
overall picture In textiles (a low-tech industry) frontier firms did not increase their
productivity in the period under study while lower productivity deciles experienced a
cumulative 40-50 percent productivity growth leading to an overall positive growth As
Section 61 discusses employment decline and firm exit were high in this industry
therefore the improvement of lower deciles may partly result from the exit of the lowest
productivity firms In machinery (a medium-high tech industry) all productivity deciles
had experienced strong TFP growth before the crisis and a significant fall during the crisis
followed by slow growth In this industry the full distribution has moved together
In retail (which is a member of the less knowledge-intensive services) TFP had grown to
some extent prior to the crisis followed by a large fall around the crisis and some growth
since 2012 Interestingly the fall was much larger and persistent for the most productive
firms while typical and low-productivity firms were able to maintain their pre-crisis
productivity levels The weak productivity performance of the top decile may have partly
resulted from regulatory changes and could have had large aggregate consequences given
the large employment share of retail (see Chapter 8) In lsquoComputer programming
consultancy and related activitiesrsquo there was a cumulative TFP increase of about 30 percent
since 2004 for all deciles without signs of convergence or divergence
Productivity differences in Hungary and mechanisms of TFP growth slowdown
47
Figure 47 Cumulative TFP growth since 2004 selected industries
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination in four industries
lsquoFrontier firmsrsquo are in the 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is
the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample
44 Duality in productivity and productivity growth
Besides the evolution of the overall shape of productivity distribution it is important to
understand the lsquodualityrsquo of productivity with respect to ownership
As a starting point Figure 48 shows the distribution of TFP and the natural logarithm of
the average wage for our main sample34 We filter out 2-digit industry fixed effects from
the two variables to control for industry-level differences
Comparing private domestic and foreign-owned firms one can make a number of
observations The foreign-owned distribution clearly stochastically dominates the
productivity and wage distribution of domestically-owned firms On average foreign firms
have 40 percent higher TFP and pay 75 percent higher wages than domestically-owned
firms in the same industry That said the within-group heterogeneity is larger than the
across-group heterogeneity generating a substantial overlap between the two
distributions For example 30 percent of domestically-owned firms are more productive
than the median foreign firm The averages between the two groups differ substantially
but there are many productive domestically-owned firms and unproductive foreign ones
34 Result for other TFP measures are very similar
Evolution of the Productivity Distribution
48
Another interesting difference between the distributions is that the foreign-owned
distribution is substantially more dispersed than the domestically-owned one (its standard
deviation is 23 percent larger) suggesting more technological heterogeneity within the
foreign-owned group This may suggest that this group includes both firms with world-
class technology and plants utilizing low-cost labour in a relatively unproductive way That
said the distribution is clearly not bi-modal there are no clearly distinguishable clusters of
high-tech and low-tech firms They operate along a continuum
Comparing state-owned firms to the other two groups shows that they are more similar to
the domestically-owned private firms with two interesting twists35 First the low-
productivity left tail of state-owned firms is much thicker than that of the privately owned
domestic firms Many state-owned firms operate with very low productivity levels (see also
Section 63) As a result the average productivity of these firms is 25 percent lower
compared to privately-owned domestic firms in the same industry
The second twist is that even though state-owned firms tend to be substantially less
productive than privately owned domestic firms they pay on average 25 percent higher
wages This may be a consequence of differences in worker composition but may also
suggest that these firms face soft budget constraints and their employees are able to
capture a larger slice from a smaller pie
Figure 48 Distribution of TFP and average wage by ownership (cleaned from industry-
year effects) 2016
Notes This figure shows the distribution of productivity and ln average wage after filtering out
industry-year fixed effects from it Domestically-owned is domestic privately-owned Main sample
35 Note that the sample of state owned firms is much smaller than the other two groups and operates in very specific indutries This may affect the distribution
Productivity differences in Hungary and mechanisms of TFP growth slowdown
49
Figure 49 shows the evolution of the productivity distributions across years Note that in
order to illustrate shifts in time industry-year fixed effects are not filtered out from this
figure Therefore comparing the distributions with Figure 47 shows how much industry
composition matters
Panel A) illustrates the productivity evolution of domestic private firms The shape of this
distribution remained remarkably similar across years There are clear rightward shifts
between 2004-2008 and 2012-2016 while the distribution did not change during the crisis
period Similar patterns can be observed regarding foreign-owned firms This distribution
was always more dispersed than the domestic one with little changes in its standard
deviation across years
The shape of the state-owned productivity distribution is more peculiar Most visibly it had
been bi-modal before the crisis This is mainly a consequence of industry composition the
low productivity part representing some utilities While the bi-modality disappeared post-
crisis the low-productivity tail of the distribution became thicker Finally we do not see
any rightward shift in this distribution there was little productivity improvement in this
small segment of the economy
Figure 49 Evolution of the distribution of TFP by ownership
A) Domestic private
Evolution of the Productivity Distribution
50
B) Foreign
C) State
Notes This figure shows the distribution of TFP Domestically-owned is domestic privately-owned
Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
51
BOX 41 Duality between domestic and foreign-owned firms in an international context
We are not the first to document the substantial wage and productivity advantage of foreign firms Earle
and Telegdy (2008) by using NAV data between 1986-2003 show that foreign-owned firms were almost
twice as productive as domestic private firms (measured in terms of labour productivity) and also paid
40 higher wages when controlling for employee characteristics A substantial part of this premium
results from foreign owners acquiring more productive firms (mostly during the privatisation process)
but even after controlling for this selection process the foreign wage premium remains 14 Similar
results are found by Telegdy et al (2012) when using the longer period between 1986 and 2008
Foreign-owned firms tend to have positive productivity and wage premia in most countries developed or
emerging Among others Aitken et al (1996) show that foreign-owned firms have higher productivity
and wages in Mexico and Venezuela even after controlling for firm size skill mix and capital intensity
Conyon et al (2002) use acquisitions in the UK in 1989-1994 to find that foreign firms pay 34 higher
wages which can be fully attributed to their 13 higher productivity Girma et al (2002) have a similar
result showing that foreign firms in the UK have 8-15 higher productivity which leads to 4-5 higher
wages Using UK data from 1981-1994 Girma and Goumlrg (2007) find wage differentials of a similar
magnitude but heterogeneous with regard to the source country of the foreign investor Huttunen
(2007) looks at Finland and finds 26-37 wage premium of firms 3 years after being acquired by
foreign investors In the Central-Eastern-European region Djankov and Hoekman (2000) show that
foreign investment in the 90s increased the productivity of recipient firms in the Czech Republic
Governments aim to attract foreign direct investment (FDI) as it is assumed to have a positive impact
on the domestic economy From an economic point of view it is justifiable to provide incentives to
foreign investors if their investments have positive spillovers to domestic firms increasing their
productivity The higher productivity of foreign-owned firms which is documented in the previously
mentioned studies is a necessary condition for that At the same time if foreign firms establish no links
with domestic firms there is only limited opportunity for knowledge spillovers In this case the inflow of
foreign investments results in a dual structure of the economy
Evidence is rather mixed on FDI spillovers to domestic firms in the same industry because a negative
competition effect might dominate the positive technology or knowledge effect Haskel et al (2007) find
that a 10-percentage-point increase in the share of foreign ownership increases the TFP of domestic
firms in the same industry by 05 in the UK Konings (2001) finds negative spillovers for Bulgaria and
Romania and no spillovers for Poland Positive spillovers in vertically related industries are much more
general Using Lithuanian data Javorcik (2004) shows that one standard deviation increase in the foreign
share of an industry is associated with 15 increase in the output of domestic firms operating in the
supplier industry Similarly Kugler (2006) finds no within-industry spillovers but positive spillovers in
vertically related industries in Colombia
Evolution of the Productivity Distribution
52
Let us turn to industry differences in duality The substantial difference between the average TFP of
domestic and foreign-owned firms is present in all kinds of industries (Figure 410 and 411) In
manufacturing the percentage difference is about 34 percent (a log difference of 03) while it is
around 65-100 percent in services Significantly the cumulative TFP growth of the two types of firms
was very similar by the end of the period There is no evidence for the catching-up of domestic firms
with foreign ones The duality in this respect does not seem to diminish substantially
The TFP gap between foreign and domestic firms is amplified by the much higher capital intensity of
foreign firms (Figure 412) In manufacturing foreign firms employed more than twice as much capital
per employee than domestic firms While the capital intensity of both domestic and foreign-owned
firms increased steadily during the period in that sector the gap remained constant showing little
catching-up of domestic firms in terms of capital deepening In a sharp contrast there was a decrease
in the capitallabour ratio in services and this phenomenon took place quicker in the case of foreign
firms
This picture is reinforced at the industry level (Figure 413) In textiles foreign firms invested more
than domestic ones leading to significant capital deepening for that group of firms In machinery both
groups of firms increased their capital intensity to a similar extent In retail foreign firms had invested
much before the crisis but cut their investments deeply after that while the capital intensity of
domestic firms remained mostly flat In programming capital intensity declined slightly following the
crisis
BOX 41 Duality between domestic and foreign-owned firms in an international context
(cont)
Looking at Hungarian data several papers show the existence of positive FDI spillovers to domestic
firms Halpern and Murakoumlzy (2007) find significantly positive spillovers in the supplier industry but
no evidence for within-industry spillovers Beacutekeacutes et al (2009) find a negative effect on low-
productivity firms in the same industry while the spillover effect is positive for high-productivity
firms Iwasaki et al (2012) find positive spillovers even within the same industry conditional on the
proximity in product and technological space At the same time Bisztray (2016) shows that the
large-scale foreign direct investment of Audi did not increase the productivity of domestic firms in
the supplier industry
We know from the literature that the effect of FDI on domestic firms is highly heterogeneous even in
the supplier industry (see Smeets 2008 for a review) A crucial precondition of positive spillovers is
the absorptive capacity of the domestic firms (Crespo-Fontoura 2007) Using data from Bulgaria
Poland and Romania Nicolini and Resmini (2010) show that firm size matters as well Additionally
they find within-industry spillovers in labour-intensive sectors and cross-industry spillovers in high-
tech sectors Also the characteristics of the foreign investment play an important role in the
magnitude of the spillover effect Javorcik (2004) estimates a positive effect on the productivity of
domestic firms only in the case of shared foreign and domestic ownership but not for fully foreign-
owned firms Javorcik and Spatareanu (2011) show that the distance of the investorrsquos country is
also important as investors from far-away countries establish more links with local suppliers In line
with that they estimate positive vertical spillovers from US investors but not from European
investors in Romania Lin et al (2009) show that vertical FDI spillovers in China are weaker for
export-oriented FDI compared to domestic-oriented
Productivity differences in Hungary and mechanisms of TFP growth slowdown
53
Figure 410 TFP levels of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the (unweighted) average ACF TFP level of foreign and domestically-owned firms Main
sample The industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge- intensive services lsquoLKISrsquo Less knowledge-
intensive services
Evolution of the Productivity Distribution
54
Figure 411 Cumulated TFP growth of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level of foreign and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
55
Figure 412 Capital intensity of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the average capital intensity (log tangible and intangible assetsemployee) of foreign- and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Evolution of the Productivity Distribution
56
Figure 413 Cumulative change in capital intensity of foreign and domestic firms selected industries
Notes This figure shows the (unweighted) average capital intensity (log tangible and intangible assetsemployee)
of foreign and domestically-owned firms since 2004 in four industries Main sample
45 Conclusions
Our investigation of the evolution of productivity distribution has yielded a number of relevant
conclusions which will inform the work conducted in the remaining sections In line with international
evidence we have found that productivity dispersion within industries is many times larger than the
differences between industries Importantly Hungary seems to be an exception to the international
trend of frontier firms diverging from the rest of the economy ndash if anything there is evidence for the
low productivity growth of frontier firms and for some catching-up by others
OECD (2016 Figure 16) has found such a pattern only in Hungary and Italy with divergence in all the
other countries under study (Austria Belgium Canada Chile Denmark Finland France Japan
Norway and Sweden) We find two kinds of explanations plausible First in Hungary (unlike most other
countries in that sample) national frontier firms are quite far away from the global frontier As
Andrews et al (2015) argue the productivity divergence mainly arises between global frontier firms
and the rest If national frontier firms are far away from the global frontier they may find themselves
on the wrong side of global divergence Second it is possible that the policies and institutional
environment for national firms in Hungary is less conducive to adopt local frontier technologies A way
to learn more about the background of this result would be to use cross-country micro-data to study
the behaviour of frontier firms in even more countries including other CEE countries
The low productivity growth of Hungarian national frontier firms constrains productivity growth
directly Furthermore if national frontier firms do not adopt the most developed technology potential
spillovers to other firms will also remain limited Andrews et al (2015) have shown that good
Productivity differences in Hungary and mechanisms of TFP growth slowdown
57
framework conditions (most importantly good regulatory practices in upstream sectors) and innovation
related policies such as providing incentives for RampD and building a more robust national innovation
system are associated with a stronger catch-up of national frontier firms to the global frontier
The results reveal that duality especially between foreign and domestic firms is substantial and there
is no evidence for catching-up by domestic firms The gap is especially large in the service industries
That said the gap between the two groups can be bridged indeed the productivity differences
between the two groups are smaller than within them Duality while a sign of inefficiency also
provides an opportunity for domestic firms to tap into the knowledge base possessed by their foreign-
owned counterparts and to integrate into global value chains by relying on the links of foreign firms
While efficient strategies aiming at maximizing the benefits from FDI and global value chains may
differ across countries there are a few policy options which unambiguously help countries in benefiting
from the presence of multinational firms A robust result of the recent spillover literature is that
domestic firms need strong absorptive capacity including technological knowledge and a skilled
workforce to be able to benefit from the presence of foreign-owned firms (Girma 2005 Crespo and
Fontoura 2007 Zhang et al 2010) One dimension of absorptive capacity building is creating an
effective innovation system with a strong knowledge base and easy access to that knowledge Another
dimension is developing technological and management capabilities which enable firms to understand
and apply advanced knowledge Such capabilities are essential both for technological upgrading and for
integrating into global value chains (Taglioni and Winkler 2016)
An important caveat regarding these results is that they are limited to double-entry bookkeeping firms
We have emphasised that a large share of people work outside the double-entry bookkeeping entities
included in our sample While data are scarce about the productivity of these entities available
information suggests that both the levels and dynamics of productivity may differ radically between
double-entry bookkeeping firms and other entities If so inclusive policies could focus on providing
skills and opportunities for the self-employed
State-owned firms constitute a small part of the Hungarian market economy but such firms are
prevalent in some industries including utilities The productivity of some of these firms is very low
when compared to the productivity of privately-owned firms while they pay higher wages Both of
these phenomena hint at soft budget constraints and other inefficiencies Policies aiming at providing
better incentives either by improving corporate governance of state-owned firms (Arrobio et al 2014)
or by creating framework conditions more conducive to competition may help in in promoting
productivity growth in these important industries
Allocative efficiency
58
5 ALLOCATIVE EFFICIENCY
A key insight of recent productivity research is that differences in productivity levels across countries
largely result from the inefficient allocation of resources across firms rather than from differences in
the productivity of lsquotypical firmsrsquo both in cross-section (Hsieh and Klenow 2009 Restrucca and
Rogerson 2017) and in time-series (Gopintah et al 2017) Inefficient allocation refers to the
phenomenon that low-productivity firms possess a large amount of capital and labour (rather than
shrinking or exiting) or when firms with similar marginal products use a different amount or
composition of inputs
In this chapter we employ two strategies to quantify the extent of such distortions The first strategy
proposed by Olley and Pakes (1996) simply asks whether more productive firms are larger A more
positive covariance between productivity and employment suggests a better allocation of resources
across firms and higher industry level (labour-weighted) productivity (even when holding the
unweighted productivity level unchanged) The Olley-Pakes method is generally agnostic about the
specific nature of distortions but measures their results in an intuitive and robust way at the industry-
year level
Hsieh and Klenow (2009) attempt to identify the sources of distortions36 In particular they argue that
firms can face two main distortions product market distortion (modelled as an implicit sales tax and
identified from the wedge between labour costs and value added) and capital market distortion
(modelled as an implicit capital tax and identified from differences in the cost share of capital) These
variables can be measured at the firm-level Industry-level distortions can be quantified both as the
average of firm-level distortions and also as the dispersion of firm-level measures
This chapter describes these measures at the industry-year level Section 51 presents the Olley-Pakes
covariance terms while Section 52 implements the Hsieh-Klenow method
51 Olley-Pakes efficiency
The Olley-Pakes (also called static) approach of productivity decomposition consists of decomposing
the aggregated (industry-region-level) productivity which is the weighted average of firm-level
productivity levels into the unweighted average firm-level productivity and the covariance between
productivity and firm size (Olley and Pakes 1996) The latter term reflects how efficiently resources in
this case labour are allocated across firms A more positive covariance between size and productivity
reflects stronger allocative efficiency
Let us start with cross-country evidence from the OECD (Andrews and Criscuolo 2013) According to
this source in 2005 static allocative efficiency in Hungarian manufacturing (the covariance term) was
positive but slightly below the average of OECD countries similar to Portugal and Italy (Figure 51)37
Allocative efficiency in services was negative one of the lowest of the countries in the sample
(Andrews and Cingano 2014 Figure 10) showing that less productive firms tended to be larger in the
service sector Andrews and Cingano (2014) also show that the relatively low allocative efficiency in
Hungary is partly explained by policies including product market regulation and creditor protection
36 For an overview of the reallocation literature see Hoppenhayn (2014)
37 Note that these calculations use the ORBISAMADEUS database covering a relatively small fraction of larger
Hungarian firms in 2005 (about 3300 firms) see Box 21
Productivity differences in Hungary and mechanisms of TFP growth slowdown
59
Figure 51 Static allocative efficiency in Hungarian Manufacturing (2005)
Notes This figure is a reproduction of Figure 7 from Andrews and Criscuolo (2013)
Let us turn to our data The logic of the static decomposition is presented in Figure 52 for our main
sample by 2-digit industry38 The horizontal axis shows the unweighted average log labour productivity
of each industry while the vertical axis shows the productivity weighted by employment If all firms
were of equal size (or at least firm size was independent of productivity) weighted and unweighted
productivity would be equal ie all industries would be on the 45-degree line If size and productivity
were positively correlated the weighted productivity would be larger than the unweighted one The
difference between the weighted and unweighted average is the covariance between size and
productivity This measure of allocative efficiency is equal to the vertical distance between each point
and the 45-degree line Allocative efficiency contributes positively to industry productivity in industries
above the 45-degree line while it has a negative contribution for industries below the line
For example in the manufacture of machineries (28) the unweighted average productivity is 646
while the weighted average productivity is 665 Allocative efficiency resulting from more productive
machine manufacturers being larger contributes with 019 to the aggregate productivity of this
industry An industry with negative allocative efficiency is warehousing (52) where the lower
productivity of larger firms contributes negatively to aggregate productivity (the unweighted
productivity being 681 and the weighted only 585)
38 Appendix Table A51-Table A56 summarise the Olley-Pakes (1996) measures by industry
Allocative efficiency
60
Importantly allocative efficiency is positive in most industries It is especially high in the most
knowledge-intensive services (scientific research (72) employment activities (78)) in service
industries with a few large firms (broadcasting (60) telecom (61)) and in key manufacturing
industries beverages (11) chemicals (20) machinery production (28) and vehicle production (29) In
a few industries low-productivity firms tend to be larger Prominent examples are professional
services advertising (69) and legal and accounting activities (73) services with many state-owned
firms transportation (39) waste management (49) and logistics (52) In line with OECD evidence
allocative efficiency tends to be more positive in manufacturing compared to services
Finally Figure A51 in the Appendix shows that allocative efficiency is significantly higher when labour
productivity is considered rather than TFP almost every industry has larger weighted labour
productivity than unweighted labour productivity This difference simply results from the positive
association between productivity and capital intensity
Figure 52 Weighted and unweighted TFP by 2-digit industry 2015 main sample
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted TFP while the vertical axis
shows its weighted TFP in the same year We have omitted industries with less than 1000 observations TFP is
estimated using the method of Ackerberg et al (2015)
Another conclusion that can be drawn from Figure 52 is that allocative efficiency is higher in sectors
with higher unweighted productivity represented by the fitted line in the figure In other words high
firm-level efficiency seems to move together with higher allocative efficiency in the industry One
mechanism behind this relationship may be that incentives for technology upgrading are stronger
when the reallocation process is more effective (Restruccia and Rogerson 2017) but stronger
international competition can also affect positively both within-firm productivity dynamics and
reallocation across firms In Figure 53 we investigate whether this relationship changed between
years The figure shows that the positive relationship between unweighted productivity and allocative
Productivity differences in Hungary and mechanisms of TFP growth slowdown
61
efficiency did not change substantially over time This relationship is similar when labour productivity is
considered (see Figure A52 in the Appendix)
Figure 53 The relationship between weighted and unweighted TFP by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted TFP levels run at the
2-digit industry level separately for 2005 2010 and 2016 TFP is estimated using the method of Ackerberg et al
(2015)
From the perspective of productivity slowdown a key question is whether allocative efficiency
deteriorated in some industries following the crisis Figure 54 shows the allocative efficiency of each
2-digit industry in 2010 and 2016 The axes here represent the distances from the 45-degree line in
Figure 52 If an industry is on the 45-degree line of this figure its allocative efficiency remained
unchanged in the period if an industry is above the line its allocative efficiency was better in 2016
compared to 2010 The first conclusion that can be drawn is that levels of allocative efficiency are
persistent industries cluster around the 45-degree line Also the fitted line shows that allocative
efficiency grew somewhat faster in industries where allocative efficiency was worse and this
relationship is statistically significant Therefore productivity growth decline is unlikely to be the result
of rapidly worsening allocative efficiency
One can however identify a couple of industries where substantial changes took place The machinery
industry (28) for example became more efficient partly because of the entry of new large foreign-
owned firms Office administration (82) and management activities (70) also increased their allocative
efficiency This is most likely due to the entry of large shared service providers Allocative efficiency
decreased in land transportation (39) waste management (49) and warehousing (52)
The evaluation of allocative efficiency in labour productivity shows similar patterns (Table A53 in the
Appendix)
Allocative efficiency
62
Figure 54 The change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences
between the weighted and unweighted TFP) in 2010 while the vertical axis shows the same quantity in 2016 TFP is
estimated using the method of Ackerberg et al (2015)
52 Product market and capital market distortions
The Olley-Pakes static decomposition framework can quantify the overall allocative efficiency of sectors
but it is incapable of informing us about the nature of distortions In this section we implement the
methodology of Hsieh and Klenow (2009)39 to distinguish between product and capital market
distortions This distinction is of much interest given that the crisis and its aftermath ran parallel with
both financial market frictions and changes in product market regulation
The logic of the Hsieh and Klenow (2009) method is the following Under the assumptions of
monopolistic competition on product markets (similarly to Melitz 2003) and frictionless labour
markets the marginal product of labour and capital should be equalized across firms in the absence of
market distortions In turn if the production function is Cobb-Douglas the equality of marginal
products implies that the share of labour costs in value added and capital intensity (capitallabour)
should be equalized across firms Under product market distortions (modelled with a firm-specific
implicit lsquosales taxrsquo or a negative rent) the wedge between labour costs and value added will differ
across firms because firms facing lower implicit taxes charge higher markups The more heterogeneous
the lsquosales taxrsquo is the larger the dispersion of the wedge Capital market distortions are modelled as
39 The Hsieh-Klenow approach has been criticized recently by Haltiwanger et al (2018)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
63
implicit firm-specific capital tax rates Firms facing different capital tax rates choose different capital
intensity levels and hence different capitallabour cost ratios Therefore the dispersion of capital
intensity (or more precisely the cost share of capital) reflects the dispersion of capital tax rates
Note that the implicit taxes proxy multiple sources of distortions from which differences in explicit
taxes represent only a small part The implicit lsquosales taxrsquo includes the cost of complying with different
types of regulations size-dependent regulation the effect of fixed costs and market power The
implicit lsquocapital taxrsquo includes for instance the full cost of accessing financing possible subsidies for
investment or differences in tax incentives to invest These implicit taxes provide a convenient way of
summarizing markup differences and differences in access to capital
As a result the dispersion of the wedge and capital intensity reflect how heterogeneous the two
implicit tax rates are More heterogeneity in implicit tax rates in turn implies more disperse total factor
productivity within industry40 and a less efficient allocation of resources In other words similarly
productive firms (having also similar marginal products of inputs) choose very different input quantities
and combinations
Product market distortions
We start our empirical investigation by calculating the rents (1-implicit sales tax rate) for every firm by
a proxy for markups41
1 minus 120591119884119904119894 =120590
120590minus1
120573119871119904+120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119881119860119904119894 (51)
where 120591119884119904119894 shows the size of the implicit lsquosales taxrsquo (or product market distortion) for firm i in sector s
120590 denotes the elasticity of substitution between firms by consumers and 120573119871119904 and 120573119870119904 are the
coefficients of labour and capital in the production function We follow the calibration of Hsieh and
Klenow (2009)42 and set 120590 = 3 while we plug in 120573119871119904 and 120573119870119904 using our production function estimation of
Section 22 119881119860119904119894 represents the real value added of the firm i in sector s while 119871119886119887119888119900119904119905119904119894 is labour
related expenses for firm i in sector s
The equation reflects the intuition that firms facing a lower implicit sales tax can charge higher
markups and as a result will pay a lower share of their value added to their employees Note that the
level of 120591119884119904119894 depends on a number of parameters and may be driven by differences in for example the
elasticity of substitution Therefore we will normalize the values of this estimate when comparing
typical distortions across industries
Figure 55 summarizes the implicit sales taxes by industry (120591119884119904119894) We standardise the values of 120591119884119904119894 by
subtracting the market level median from the firm-level implicit sales taxes and plot the median of
40 Appendix Table A57 summarises the dispersion of TFP within industry Note that dispersion in labour productivity
(log-value added per worker) is not necessarily related to product market distortions as firms with various
labour productivity may have the same TFP if the production function does not have the property of constant
return to scale
41 Hsieh and Klenow (2009) Equation 18
42 The predicted value of product market distortions crucially depends on the elasticity of substitution However the differences in 120591119884119904119894 across industries and years measures the changes in product market distortions even if the
elasticity of substitution is miscalibrated
Allocative efficiency
64
these standardised values by industry If the standardised bar is positive (negative) than the median
firm in the industry faces a higher (lower) implicit sales tax than the median firm in the economy We
find that product market distortions tend to be larger in highly regulated industries (energy
transportation ICT) while they tend to be lower in less regulated ones with strong competition
including manufacturing accommodation and administrative services The difference between
industries is non-trivial the difference between highly regulated sectors and manufacturing is
equivalent to an extra 10-20 percentage `sales tax ratersquo
The ranking of the industries (with the exception of energy) remained similar between 2006 and 2016
but differences became somewhat larger with a relative decrease in implicit taxes in manufacturing
and administrative services and an increase in transportation and ICT43
Figure 55 Implicit sales taxes (120591119884119904119894) by industry
Notes The figure above shows the median size of product market rents in 2006 and 2016 Industries with positive
tax measures can achieve rents below the market average due to product market distortions
The previous exercise has investigated across-industry differences A further question is whether firms
face different tax rates even within industries because of for example size-dependent taxes This is a
key measure to examine whether resources are misallocated across firms within industries Our
measure for this is the standard deviation of ln(1 minus 120591119884119904119894) (Figure 56)44 This dispersion is substantial
43 We report these measures in more detail in Table A55 of the Appendix
44 Also note that this measure of dispersion is independent of the elasticity of substitution and the production function parameters
Productivity differences in Hungary and mechanisms of TFP growth slowdown
65
with the standard deviation equivalent to a 100 percent sales tax45 Within-industry differences in this
variable are similar across industries with a relatively small dispersion only in mining and energy
Figure 56 Standard deviation of implicit sales tax rates (ln(1 minus 120591119884119904119894)) by industry
Notes The figure shows the within industry product market distortions in 2006 and 2016 Resources are less
effectively distributed in industries with larger distortion measures
Capital market distortions
Distortions on the capital market are identified from how the ratio of expenses on labour and capital
(capital intensity in cost terms) differ from what is predicted by the production function with no capital
tax46
119877(1 + 120591119870119904119894) =120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119870119904119894 (52)
The left hand side of this equation represents the implicit cost of capital for firm i in sector s backed
out from the capital intensity of the firm If it is 01 the firm faces an implicit lsquointerest ratersquo of 10
percent if it is 02 the lsquointerest ratersquo is 20 This can be decomposed into 119877 the frictionless user
45 Similar differences have been found in other countries as well and they are in line with the vast degree of heterogeneity in terms of size and productivity within industries
46 Hsieh and Klenow (2009) Equation 19
Allocative efficiency
66
costs47 of capital (having the same unit of measurement) multiplied by 1 plus the implicit lsquocapital tax
ratersquo 120591119870119904119894 which is firm-specific48
Similarly to the product market equation 120573119871119904 denotes the labour elasticity of the production function
120573119870119904 is the capital elasticity of the production function and 119871119886119887119888119900119904119905119904119894 is the total labour cost for firm i in
sector s The denominator consists the capital stock of the firm (119870119904119894)
It is not common in the literature to report 120591119870119904119894 because its absolute value depends crucially on the
calibration of the rental rate of capital This is an issue because it is hard to obtain reliable information
on the frictionless rate of capital which most likely changed substantially between the pre-crisis
disinflationary period and the wake of the crisis Besides 120591119870119904119894 takes extremely large values for firms
with low level of capital (eg if the firm rents its capital instead of owning it) Note that the levels of
this variable are identified from the difference between the observed capital intensity (in cost terms)
and the optimal one implied from the production function Therefore we prefer to report the more
easily interpretable implicit median cost of capital 119877(1 + 120591119870119904119894) by industry49
While we find differences and changes in the implicit cost of capital informative it is not a direct
measure of capital market distortions because it can also reflect differences in the user cost of capital
across industries and years However the ratio (or log difference) of the implicit cost of capital
between two firms measures the difference between their respective implicit capital tax rates (or more
precisely between their 1 + 120591119870119904119894) As a result the standard deviation of the log implicit cost of capital
provides a pure measure of the dispersion of implicit capital taxes independently from the exact value
of 119877 Its interpretation is the relative standard deviation of the user cost of capital which is identified
from the dispersion of capital intensities
Figure 57 summarizes the median size of implicit cost of capital across industries50 Administrative and
professional services and ICT seem to pay the highest implicit cost for capital it is above 40 percent in
these industries As opposed to these utilities accommodation and food services face implicit costs of
capital below 20 percent The large differences in access to capital across industries are likely to result
mainly from differences in the size and age distribution of firms as well as from the different share of
tangible capital in different industries Moreover the median implicit cost of capital rose practically in
all service industries but decreased slightly in manufacturing
47 The rental price of capital covers the interest rate and the depreciation of capital stock
48 If one is willing to assume a specific value for the frictionless user cost of capital it is easy to back out 120591119870119904119894 For
example if the implicit cost of capital for firm 119894 (the left hand side) is 02 and (following Hsieh and Klenow 2009) one sets R = 01 then 120591119870119904119894 = 1 meaning that firm 119894 can obtain capital at a 10 percentage points higher interest rate
relative to the frictionless rate
49 The median of 119877(1 minus 120591119870119904119894) is less dependent on the extreme values of the distribution than the average so it is a
more precise measure of capital market distortions a typical firm faces than the average of it
50 We can validate our implicit capital cost measure by comparing our results to Kaacutetay and Wolf (2004) According to their estimates (using a different methodology) the median user cost of capital was 189 percent between 1993 and 2002 Our results have similar magnitude as the median implicit cost of capital was 255 percent in 2006 and 287 percent in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
67
Figure 57 Median implicit cost of capital by industry
Notes The figure shows the average size of capital market distortions in 2006 and 2016 Industries with larger
distortion measures are more constrained in accessing capital due to capital market distortions
Again the differences in typical capital costs across industries are much smaller than differences across
firms within an industry (see Figure 58) In industries where median implicit capital costs are lower
the dispersion of those costs also tends to be smaller the estimated cost of accessing capital is
significantly more unequal in the retail sector and administrative services relative to manufacturing
The notable exemption is the energy sector which has the lowest median and the largest dispersion in
the implicit cost of capital reflecting a relatively low level of capital costs resulting from predictable
tangible capital intensive activities
Allocative efficiency
68
Figure 58 The standard deviation of the estimated implicit cost of capital by industry
Notes The figure shows the standard deviations of capital market distortions log (119877(1 + 120591119870119904119894)) in 2006 and 2016
Most importantly capital market distortions increased within nearly all industries both in terms of
their levels and dispersion Hungary is not an exception in this respect This trend has been
documented in other countries where FDI played important role in economic growth A key study on
this topic is Gopinath et al (2017) who show that large capital inflows and credit market constraints
of small firms jointly increased capital market distortions in Spain This evidence suggests that the
crisis led to similar developments in Hungary making capital costs more unequal by generating
financial frictions This inefficiency seems to have resulted in the misallocation of capital in all types of
industries
A key question from a policy perspective is whether one can identify types of firms which faced a
systematically large increase in their cost of capital We follow the approach of Gorodnichenko et al
(2018) who quantified the misallocation of capital at the firm-level and found that small and young
firms faced an exceptionally high cost of capital We follow this strategy to identify observables which
are likely to be related to the level and change of capital costs
Figure 59 plots the relationship between firm age firm size and the estimated implicit cost of capital
119877(1 + 120591119870119904119894) The figure sorts the firms into twenty equally-sized bins by age and size and plots the
median implicit cost of capital separately for 2006 and 2016 Panel (a) highlights that the implicit cost
of capital was decreasing with firm age even before the crisis with young firms facing about 25
percentage points higher capital costs compared to firms older than 10 years This function became
dramatically steeper by 2016 when the median `oldrsquo firm (more than 10 years old) faced an implicit
capital cost of 25 percent a median 5-year old firm paid 50 percent and a very young firm faced more
than a 100 percent implicit cost of capital This figure suggests that capital market frictions generate
important constraints for entry and the growth of small firms hindering reallocation and innovation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
69
Panel (b) of Figure 59 visualizes the relationship between employment and the implicit cost of capital
We find that firms with more than 20 employees faced an implicit cost of capital below the median of
the whole sample both in 2006 and 2016 As opposed to this small firms faced above the median
implicit cost of capital in 2006 and suffered from a disproportionally large increase in the next decade
This again constrains the growth of small firms relative to their larger peers
Figure 59 The evolution of the implicit cost of capital by age and firm size
A) Age of firms
B) Size of firms
Notes The figure shows the median implicit cost of capital 119877(1 + 120591119870119904119894) by age and size categories
Allocative efficiency
70
The results presented above have shown two patterns an increasing dispersion of the implicit cost of
capital on the one hand and a steeper gradient between observables (age and size) and capital taxes
on the other A natural question is whether increased financial friction led to larger differences in
access to capital along observables One explanation for this could be that banks have become more
wary about allocating capital to say firms operating in industries with much intangible capital The
alternative is that the increased variance in capital access reflects mainly differences along unobserved
dimensions by for example more scrutiny of managers when deciding about firm loans These two
possibilities can have different policy implications In the former case for example policymakers may
promote access to capital for specific groups of firms
Table 51 presents regressions with the implicit capital cost as a dependent variable and key firm-level
characteristics as explanatory variables Our first conclusion is that the regressions explain only a
relatively small part (less than 20 percent) of the variation in the implicit cost of capital the
overwhelming majority of the variation arises from unobservables In this sense policies targeting
specific types of firms may have a limited effect
That said the explanatory power of observables increased by around a third between 2006 and 2016
While the explanatory power of industry dummies slightly decreased that of age increased
substantially from 2 percent to 57 percent The explanatory power of size was much smaller in both
periods suggesting that its correlation with the implicit cost of capital may be confounded by its
correlation with age and industry This evidence together with Figure 59 suggests that indeed
capital access by young firms deteriorated substantially after the crisis
Table 51 Variance decomposition of implicit cost of capital
Variance in 2006 Variance in 2016
Variance
component
Share
of total
Variance
component
Share
of total
Total Variance of log-implicit
cost of capital
2126 100 2443 100
Components of Variance
Variance of age 0042 20 0140 57
Variance of size 0006 03 0002 01
Variance of ownership 0012 06 0022 09
Variance of region 0012 06 0025 10
Variance of industry 0202 95 0180 74
Residual 1830 861 1995 817
Notes Control variables are dummies for age ownership (private foreign or state-owned) region (7 NUTS2
region) and 2 digit industry
53 Conclusions
This section summarises the static measures of allocative efficiency by industry types (Table 52) A
key pattern that emerges is that resources are allocated more efficiently in the manufacturing sectors
First on average the OP covariance is strongly positive within manufacturing while it is very close to
zero in less knowledge-intensive services The Hsieh-Klenow (2009) efficiency measures suggest that
product market distortions are similar across sectors but capital market distortions are significantly
lower in manufacturing These findings are in line with the disciplinary effect of international
competition in the traded sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
71
Table 52 Allocative efficiency within industries sectors (2016)
Industry type TFP
level
in
2016
TFP
growth
between
2011 and
2016
Olley-
Pakes
allocative
efficiency
Dispersion
of implicit
sales taxes
Dispersion
of implicit
cost of
capital
Low-tech mfg 5694 0027 0197 111 146
Medium-low tech mfg 6081 0027 0017 102 142
Medium-high tech mfg 6129 -0093 0119 111 131
High-tech mfg 6708 0276 0072 107 145
Total manufacturing 5942 0021 0242 107 143
Knowledge-intensive serv 6706 0225 0403 106 166
Less knowledge-intensive serv 6566 021 -0081 108 159
Construction 6411 0082 0023 109 148
Utilities 5949 -0138 0801 093 155
Total services 6598 0212 0055 108 160
Notes The table summarises the allocative efficiency measures by broad industry categories The dispersion of
implicit sales taxes is measured by the standard deviation of 119897119899(1 minus 120591119884119904119894) while the dispersion of the implicit cost of
capital is measured by the standard deviation of ln (119877(1 + 120591119870119904119894))
Capital market distortions became more severe in the wake of the financial crisis while there was no
such trend in terms of product market distortions This finding is in line with results for Southern
Europe (Gamberoni et al 2016a Gopinath et al 2017) and CEE countries in general (Gamberoni et
al 2016b) (see Figure 510) This suggests that the financial intermediation system is still less
effective relative to its pre-crisis performance
Investigating at the firm-level we found that the deterioration of the financial conditions did not hit all
firms equally In particular young firms were hit especially hard by ever increasing capital costs even
though many policy tools were introduced to help such firms including the subsidized access to capital
by the Central Bank (eg the NHP program) Deteriorating access to capital by young firms can be
especially harmful for reallocation often driven by dynamic young firms Policies aimed at promoting
equal and efficient access to capital especially for young firms may help to reduce these inefficiencies
Given the magnitude of the still existing allocative inefficiency policies which support reallocation could
have a significant positive effect on aggregate productivity A key conclusion of recent research is that
firm-specific distortions which may result from discretionary policies or non-transparent regulations
have a quantitatively significant effect on aggregate productivity (Hsieh and Klenow 2009 Bartelsman
et al 2013 Restuccia and Rogerson 2017) In particular size-dependent taxes and regulations
(Garicano et al 2016) ineffective labour and product market regulations and FDI barriers (Andrews
and Cingano 2014) have been shown to be negatively associated with allocative efficiency and its
improvement Gamberoni et al (2016b) also demonstrate that higher corruption levels slow down the
improvement of allocative efficiency Chapter 8 will investigate the effects of such policies in more
detail using the example of the retail industry
The specific pattern showing that capital distortions are relatively high and have increased in Hungary
(similarly to other CEE and Southern European countries) suggests that policies which facilitate the
reduction of financial frictions and provide symmetric access to capital for all firms could improve
allocative efficiency to a large degree Specifically policies should attempt to facilitate capital flows to
Allocative efficiency
72
more efficient firms even if young rather than to firms with a higher net worth or more tangible
assets (Gopinath et al 2017)
Figure 510 Capital and labour misallocation in CEE countries country-specific weighted average
across sectors
Notes This is a reproduction of Figure 1 from Gamberoni et al (2016b)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
73
6 REALLOCATION
After investigating the level of allocative efficiency in Chapter 5 namely a lsquostaticrsquo approach we now
turn to a dynamic view focusing on how much reallocation across industries (Section 61) and firms
(Section 62) contributed to aggregate and sectoral productivity growth
61 Reallocation across industries
An important channel behind the relationship between economic development and productivity is the
structural change of the economy first from agriculture to manufacturing and then from
manufacturing to services (Herrendorf et al 2014 McMillan et al 2017) But at higher levels of
development economic growth is also associated with reallocation across industries within these broad
sectors primarily from more traditional to more knowledge-intensive ones (Hausmann and Rodrik
2003 Hausmann et al 2007) Kuunk et al (2017) demonstrate that in terms of its contribution to
productivity growth across-industry reallocation within sectors dominated reallocation across sectors
in CEE countries In this subsection we take a brief look at the importance of this process in Hungary
by quantifying the reallocation of employees across and within 2-digit industries
Table 61 shows how the employment share of different industries in our main sample changed over
time51 The most important pattern is a pronounced shift from manufacturing to services until 2010
and near-constant sectoral shares after that In particular the share of manufacturing decreased by
nearly a quarter from 38 percent to 32 percent between 2004 and 2010 but this number remained
unchanged in the years following the crisis The crisis seems to have constituted a structural break in
this process
A more detailed look at the composition of industries shows that ndash in net terms ndash this structural
change was driven by a transition of employment from low-tech manufacturing52 to both knowledge-
intensive and less knowledge-intensive services while the employment share of the more high-tech
manufacturing industries remained practically unchanged After 2010 the structure of manufacturing
remained mainly unchanged in this aspect with no further shift away from low-tech manufacturing
activities Within services we see a continuous increase in the share of knowledge-intensive services
both before and after the crisis In the 12 years under study the employment share of knowledge-
intensive services increased by 6 percentage points or nearly 60 percent
51 Note that these calculations in line with other parts of this report apply to the firm sector of the Hungarian
economy ie ignore the self-employed (see Section 42) When taking into account the self-employed the share of
services and sectoral share follow somewhat different dynamics
52 One factor behind this process might have been the almost doubling of the minimum wage in 2000 and 2001
(Koumlllő 2010 Harasztosi and Lindner 2017) and a growing import competition in the light industries (David et al
2013)
Reallocation
74
Table 61 Employment in different sectors (main sample)
2004 2007 2010 2013 2016
Low-tech mfg 152 117 107 105 100
Medium-low tech mfg 89 92 91 96 98
Medium-high tech mfg 94 96 82 89 93
High-tech mfg 49 49 44 39 35
Total manufacturing 384 355 324 329 327
Knowledge-intensive serv 107 128 149 158 167
Less knowledge-intensive serv 382 395 410 405 397
Construction 86 88 83 75 75
Utilities 40 34 34 33 34
Total services 616 645 676 671 673
Notes This table shows employment shares by industry type (see Section 25) for the full sample
To provide a more detailed picture Figure 61 illustrates how employment growth in different 2-digit
industries is associated with their initial productivity level (Figure 61) In particular if more productive
sectors increase their employment share faster aggregate productivity should grow
Figure 61 Employment change as a function of initial TFP
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
75
B) Services
Notes Industries are 2-digit NACE Rev 2 industries The fitted line is weighted with initial employment Main
sample
To quantify whether across-industry reallocation matters we decompose the aggregate productivity
growth observed in our sample into the contributions of cross-industry reallocation and within-industry
productivity growth We divide our sample into three-year periods and calculate the average yearly
productivity growth by periods
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119901119894119905minus3)119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+ sum 119905119891119901119894119905minus3 lowast (119904ℎ119886119903119890119894119905 minus 119904ℎ119886119903119890119894119905minus3)119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
(61)
where the left hand side variable is the change in aggregate TFP between years 119905 minus 3 and 119905 119904ℎ119886119903119890119894119905 is
the share of the (2-digit) industry i in year t in the total employment and 119905119891119901119894119905 is average TFP of the
industry The first term on the right side is the within-industry TFP growth weighted by initial market
shares and the second term is the between effect capturing whether more productive industries have
increased their employment shares53
The decomposition in Figure 62 presents the result of this reallocation exercise for annualized growth
rates Its interpretation is the following between 2004 and 2007 average annual productivity growth
was nearly 8 percent in the total economy Around 7 percentage points from it is explained by within-
industry developments and only about 1 percentage point by reallocation across industries
53 This decomposition gives a comprehensive measure of the reallocation between industries but it is unable to
show the importance of firm exits and entries We investigate this in the next section
Reallocation
76
In general the figure shows that within-industry reallocation rather than cross-industry
developments played the key role in aggregate productivity growth Furthermore in line with Table
61 the contribution of between-industry reallocation was effectively zero post-crisis During the crisis
cross-industry productivity growth contributed positively to aggregate productivity growth while within
industry reallocation dramatically lowered aggregate productivity
This overall picture suggests that the flow of resources from light industries to other manufacturing
the growing share of services and especially knowledge-intensive services were a detectable though
not dominant driver of productivity growth only before 2010 Within-industry developments were
quantitatively more important throughout the whole period under study
This latter finding hints at a deterioration in the environment determining the reallocation process
post-crisis This seems to be the case for the whole economy but the negative contribution of
reallocation is more pronounced in manufacturing
Figure 62 Across and within industry productivity growth annualized log
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth main
sample
62 Reallocation across firms
In this subsection we take a look at the role of reallocation from a different perspective Rather than
focusing on whether the resources flow across industries we take a firm-level focus and decompose
TFP growth to within and across firm components The usefulness of this approach lies in the fact that
it sheds more light on the flexibility and efficiency of the process determining resource flows across
firms and also allows us to distinguish between resource flows across continuing firms on the one hand
and entry and exit on the other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
77
There are two general methods of measuring the reallocation of resources from less efficient to more
efficient firms The first method quantifies the labour and capital gains of more efficient firms directly
(Harasztosi 2011 Petrin et al 2011 Petrin and Levinson 2012) The second method is based on
product-market developments allocation of resources improves if the market share of high
productivity firms increases (Baily et al 1992 Griliches and Regev 1995 Brown and Earle 2008)
We adopt this second method as it can quantify directly the TFP contribution of firm entries and exits
To begin with we decompose the aggregate TFP growth between years t and t-3 based on the method
of Foster et al (2001) and Foster et al (2008)
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905minus3 lowast ∆119905119891119901119894119905minus3119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
+ sum (119905119891119901119894119905minus3 minus 119905119891119905minus3 + ∆119905119891119901119894119905) lowast ∆119904ℎ119886119903119890119894119905minus3119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+
sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119905minus3)119894isin119873⏟ 119890119899119905119903119910 119890119891119891119890119888119905
+sum 119904ℎ119886119903119890119894119905minus3 lowast (119905119891119901119894119905minus3 minus 119905119891119905minus3)119894isin119873⏟ 119890119909119894119905 119890119891119891119890119888119905
where the left hand side variable is the average annual aggregate TFP growth between years t-3 and t
and 119905119891119905 is the employment weighted average aggregate TFP while 119905119891119901119894119905 is the TFP of firm i in year t
119904ℎ119886119903119890119894119905 denotes the employment share of firm i in year t The first and second sum contain every firm
while the third sum consists of only firms which enter between years t-3 and t and the fourth sum
consists firms which leave the market between years t-3 and t
Each element of this decomposition has an intuitive economic interpretation In order of inclusion
these are i) within-firm TFP growth weighted by initial market shares ii) between effect capturing
whether initially more productive firms have raised their market shares and whether firms with
increasing productivity also expand (cross effect) and the iii) entry effect and iv) exit effect We pull
the last two terms together and interpret it as net entry effect which captures whether more
productive firms entered than exited54
Figure 63 summarizes the results for the market economy Before the crisis all three components
contributed positively to aggregate productivity growth Reallocation both across continuing firms and
on the margin of entry and exit was an important driver of productivity growth Productivity growth
was negative during the crisis as we have seen in Section 43 This was a result of strong negative
within-firm growth partly counterbalanced by positive reallocation Within-firm growth was still
sluggish immediately after the crisis but reallocation was relatively intensive and efficient Within-firm
growth recovered after 2013 and the importance of reallocation decreased Still the contribution of all
three components is substantially smaller relative to pre-crisis suggesting that the productivity
slowdown results from a combination of low within-firm growth and less effective reallocation
54 Note that these quantities cannot be easily linked to the withinacross industry decomposition of the previous
section Across firm reallocation and the entry effect can take place both across and within sectors
(62)
Reallocation
78
Figure 63 Dynamic decomposition annualized log main sample
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth by 3-
year periods main sample
Figure 64 repeats the decomposition exercise for each industry type For ease of interpretation (and
to get more stable results) we aggregate the three non-high-tech manufacturing sectors for these
calculations
As we have seen in Section 43 productivity dynamics differed markedly across these sectors Still
there are some common patterns First the strong pre-crisis productivity growth resulted from a
combination of strong within-firm productivity growth and efficient reallocation The sectors differ in
terms of the weights of these forces reallocation (especially entry) was most important in non-high-
tech manufacturing while within-firm growth dominated in high-tech manufacturing In services the
two components were of roughly equal importance
As we have seen productivity increased even during the crisis in high-tech manufacturing as a
combination of within and across productivity growth In other industries productivity growth was
negative during the crisis In non-high-tech manufacturing a strongly negative within growth was
somewhat counterbalanced by positive reallocation In contrast we find evidence for a negative
reallocation effect in services during the crisis
Immediately following the crisis (2010-2013) within growth remained sluggish but reallocation
resulting from firm entry and exit intensified especially in non-high-tech manufacturing and high-tech
services By 2013-2016 within growth recovered and the effect of reallocation became smaller
Productivity differences in Hungary and mechanisms of TFP growth slowdown
79
Figure 64 Dynamic decomposition by sector
A) High-tech Manufacturing
B) Non-high-tech Manufacturing
Reallocation
80
C) Knowledge-intensive services (KIS)
D) Not knowledge-intensive services (NKIS)
Notes This figure shows the Foster et al (2008) type dynamic decomposition of the productivity growth in our
sample for 3 periods by broad sectors as defined by the EurostatOECD (httpeceuropaeueurostatstatistics-
explainedindexphpGlossaryHigh-tech)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
81
One of the main messages of our analysis in Section 44 has been the large and persistent duality
between globally oriented and other firms This motivates our investigation of the extent to which
exporters and foreign-owned firms contributed to productivity growth and also whether reallocation
via the expansion of the more productive group contributed to aggregate productivity growth In order
to investigate these questions we decompose aggregate productivity growth into three parts the
within contribution of exporters (in the starting period) the within-contribution of non-exporters and
the reallocation between the two groups (which mainly reflects the change in the market share of
exporters) We conduct a similar analysis between foreign and domestically-owned firms
Table 62 shows the decomposition by export status Pre-crisis exporters contributed substantially
more to productivity growth than non-exporters both in manufacturing and services The reallocation
of resources to exporters mattered little Exporters were still capable of improving their productivity
levels during the crisis though it was not enough at the aggregate to counterbalance the falling
productivity of non-exporters Post-crisis the productivity growth of exporters slowed down and
aggregate growth was mainly driven by productivity changes within the non-exporter group
Productivity growth became much less exporter-driven post-crisis
Table 62 TFP growth decomposition by exporter status annualized log
2004-2007
Total Exporter Non-exporter Across
Market economy 793 577 226 -009
Manufacturing 1053 877 164 011
Market services 606 387 222 -003
2007-2010
Total Exporter Non-exporter Across
Market economy -045 091 -136 000
Manufacturing 054 021 007 027
Market services -178 129 -305 -002
2010-2013
Total Exporter Non-exporter Across
Market economy 206 033 144 029
Manufacturing -122 -129 -012 019
Market services 471 077 289 106
2013-2016
Total Exporter Non-exporter Across
Market economy 362 155 206 001
Manufacturing 057 049 011 -003
Market services 650 243 382 025
Notes This table decomposes the sales-weighted productivity growth into within-exporter within-non-exporter
contributions and the contribution of the reallocation between the two groups main sample
Table 63 decomposes productivity growth by ownership The picture is similar to the exporter
decomposition with a key contribution of foreign-owned firms to productivity growth pre-crisis and a
much smaller contribution after that Again reallocation of resources to foreign-owned firms played a
limited role in productivity growth
Reallocation
82
Table 63 TFP growth decomposition by ownership status annualized log
2004-2007
Total Foreign Domestic Across
Market economy 793 255 516 022
Manufacturing 1053 375 619 059
Market services 606 148 383 075
2007-2010
Total Foreign Domestic Across
Market economy -045 018 -076 013
Manufacturing 054 081 -034 007
Market services -178 -131 -120 073
2010-2013
Total Foreign Domestic Across
Market economy 206 003 199 005
Manufacturing -122 -152 027 003
Market services 471 138 336 -003
2010-2016
Total Foreign Domestic Across
Market economy 362 106 264 -008
Manufacturing 057 011 040 005
Market services 650 234 452 -037
Notes This table decomposes the sales-weighted productivity growth into within-foreign within-domestic
contributions and the contribution of the reallocation between the two groups Main sample
63 Failure of reallocation Zombie firms
Following the crisis it was suggested that weak productivity performance could be linked to the
survival of unprofitable and ineffective firms The presence of many such firms limits the access of
better-managed firms to capital and generates congestion in the product markets which limits entry
(Caballero et al 2008) McGowan et al (2017) have argued and provided evidence that the share of
such ldquozombie firmsrdquo has risen since the middle of the 2000s and that the higher share of such firms is
associated with lower productivity growth and investment at the industry level
Given the productivity slowdown in Hungary and the extent to which the financial crisis has affected
bank lending it is of interest to see whether the prevalence of ldquozombie firmsrdquo increased
disproportionately after the crisis
Figure 65 shows the share of ldquozombie firmsrdquo in 9 OECD countries from McGowan et al (2017) The
share of such firms in the full sample increased from just below 3 percent in 2003 to 5 percent in
2013 The rise was especially noticeable in Spain and Italy where in 2013 the share of firms reached
11 and 5 percent respectively Even more importantly the employment share of ldquozombie firmsrdquo rose
above 15 percent in both countries by 2013 possibly generating significant effects for other firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
83
Figure 65 Share of ldquozombie firmsrdquo in some OECD countries
Notes This is a reproduction of Figure 5A from McGowan et al (2017) Country codes should be interpreted as
follows BEL ndash Belgium ESP ndash Spain FIN ndash Finland FRA ndash France GBR ndash Great Britain ITA ndash Italy KOR ndash South
Korea SWE ndash Sweden SVN ndash Slovenia
Our basic definition of ldquozombie firmsrdquo follows McGowan et al (2017) for comparability We define a
firm as a zombie if it is at least 10 years old and its interest coverage ratio (the ratio of operating
income to interest expenses) has been below one for the last three years A limitation of this definition
is that interest expenses are not reported (or missing) for many smaller firms which only submit a
less detailed financial statement (or have no bank financing) To overcome this problem we also
categorize firms as zombies if their operating profit is negative for three subsequent years In such
cases the coverage ratio is not defined but the firmrsquos income is clearly not enough to cover its interest
expenses Note that this is a very conservative definition ndash one could also input interest expenses for
external capital for firms with missing interest expenditures (Figure 66)55
55 In actual fact 95 percent of zombies defined in this manner have negative profits
Reallocation
84
Figure 66 Share of ldquozombie firmsrdquo in Hungary
Notes Main sample
The patterns are the following First the share of ldquozombie firmsrdquo among firms with at least 5
employees was relatively high even before the crisis reaching about 8 percent by 2006 This increased
slightly in the wake of the crisis but started to decline after that falling to 3 percent in 2016 ldquoZombie
employmentrdquo fluctuated around 12-15 percent in most years with a steep decline after 2014
Put in an international context it is clear that the existence of ldquozombie firmsrdquo is a relatively big issue in
Hungary with their employment share at the highest end of the distribution of the OECD countries
examined by McGowan et al (2017) The prevalence of such firms however had been relatively high
even before the crisis with a relatively moderate growth between 2009 and 2011 followed by a
significant fall of the share of these firms Therefore ldquozombie firmsrdquo may have constrained productivity
growth in Hungary in the whole period but it is unlikely that an increase in zombie share is a key
explanation for productivity slowdown following the crisis
Table 64 shows the employment share of zombie firms in different dimensions One can see a U-
shaped relationship in terms of size with the largest zombie share among the smallest and the largest
firms The somewhat larger share of zombies among small firms may be explained by the tendency of
such firms to report losses in order to evade business taxes Large firms may be able to operate
persistently under losses either because of their accumulated savings or even more likely because of
the deep pockets of their owners This is also suggested by part B) which shows that a firm is more
Productivity differences in Hungary and mechanisms of TFP growth slowdown
85
likely to be a zombie if owned by the state56 or by foreigners In the latter case profit-shifting motives
may also play a role in reporting losses for sustained periods in Hungary Finally zombies are more
prevalent in services compared to manufacturing and in low-tech industries compared to high-tech
Table 64 Zombie employment by size ownership and industry
A) By size
2004 2007 2010 2013 2016
5-9 emp 62 751 862 802 411
10-19 emp 618 626 679 652 297
20-49 emp 607 554 698 648 296
50-99 emp 711 685 792 829 438
100- emp 2005 1839 1559 1489 456
Total 1548 1367 1229 1194 417
B) By ownership
2004 2007 2010 2013 2016
Domestic 66 671 769 614 253
Foreign 989 1011 1139 1577 585
State 6289 5954 4127 2792 7
Total 155 1369 1228 1194 417
C) By type of industry
2004 2007 2010 2013 2016
Low-tech mfg 1236 1255 1149 906 371
Medium-low tech mfg 897 515 846 974 634
Medium-high tech mfg 542 407 936 486 269
High-tech mfg 427 1392 429 268 1
KIS 2098 737 875 1408 592
LKIS 282 2327 187 1867 365
Construction 258 394 339 515 352
Utilities 297 1031 705 593 756
Total 1553 1372 1229 1194 418
Notes Main sample
Importantly all these patterns persist in multiple regressions when one includes all these variables at
the same time together with other controls (ie larger firms are more likely to be zombies even when
controlling for ownership) In such regressions (lag) productivity is the strongest predictor of not
56 Obviously the extreme employment share of state-owned zombie firms partly results from the massive size of some large utilities including the national railways and the Hungarian Post
Reallocation
86
becoming a zombie firm later one standard deviation higher productivity is associated with a 5
percentage point lower probability of becoming a zombie in the next period Note however that
productivity is actually a close measure of profitability hence this finding mostly reflects a mechanical
relationship of high profitability firms being less likely to become low profitability firms in the future
Figure 67 shows a 3-year transition matrix for zombie firms ie the share of year t zombie firms
which ldquorecoverrdquo remain zombies or exit from the market by year t+3 One cannot see radical changes
across years with somewhat more firms recovering and less exiting in later periods In line with the
argument about deeper pockets larger firms are more likely to remain zombies and less likely to exit
than smaller ones This is related to ownership foreign (and to a smaller extent state-owned) firms
are more likely to remain zombies There also seems to be a characteristic difference between
manufacturing and services manufacturing firms seem to be less likely to lsquorecoverrsquo and more likely to
exit suggesting more persistence of low performance in that sector
Figure 67 What happens to zombie firms within 3 years (2010)
Notes Main sample
64 Conclusions
In line with the immense within-industry productivity heterogeneity documented in Chapter 4 and 5
we find that while there was some reallocation across sectors in the economy the overwhelming
majority of productivity growth took place within industries This emphasizes the usefulness of policies
which promote productivity growth in a sector-neutral way rather than prioritizing some sectors of the
economy
In line with the lower efficiency of capital allocation post-crisis we have found that by and large both
within-firm productivity growth and reallocation across firms and industries became less efficient post-
crisis relative to its pre-crisis level This may reflect the presence of policies which promote specific
sectors or inhibit the growth and entry of more productive firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
87
In terms of the participation of global networks we have found that at least pre-crisis exporters and
foreign-owned firms contributed significantly to productivity growth Post-crisis the productivity
contribution of internationalized firms became much less substantial Adopting policies that create an
environment which is favourable for innovative investments and does not hamper the expansion of
globally oriented firms may contribute substantially to strengthening productivity growth
The presence of firms which are loss making for an extended period of time suggests a serious failure
of the reallocation process The share of such firms was relatively high in Hungary employing well
above 10 percent of the employees in our sample in most years This level was already high pre-crisis
and increased further during the crisis but there has been substantial improvement in recent years
The problem is more severe for larger firms and state owned firms Improving the corporate
governance of these firms and the effectiveness of the banking system may help in further alleviating
the problem
Andrews et al (2017) argue that the presence of zombie firms ndash and other barriers to firm dynamics ndash
is heavily related to the efficiency of insolvency regimes and the effectiveness of the banking system
Figure 68 shows an insolvency regime index developed by the OECD (the higher the index value the
slower the restructuring) Hungary is one of the countries with the weakest insolvency systems with
all sub-measures taking high values This coupled with the presence of weak banks can be one of the
reasons for the permanently high zombie firm share as well as the increasingly inefficient capital
allocation across firms Therefore insolvency reform complemented with policies aimed at improving
bank forbearance can help to reduce the presence of zombie firms The presence of zombie firms may
also be related to the large share of bank financing Promoting market-based financing including bond
and venture capital markets may also help to diminish the problem
Figure 68 Insolvency regimes across countries
Notes This chart is a reproduction of Andrews et al (2017)rsquos original except for being restricted to European
states only The stacked bars represent the 3 main components of a countrys insolvency index for the year 2016
while the diamond figure indicates these measures aggregate for the year 2010 The authors constructed these
figures with the help of an OECD questionnaire Each measure is associated with a factor that in the long term is
thought to reduce a countrys business dynamism and consequently hamper its proclivity for productivity growth
The first one Personal costs of insolvency stands for environmental factors which could curb a failed
entrepreneurs ability to start new businesses in the future The second measure Lack of prevention and
streamlining indicates whether there are sufficient practices in place for the early detection and resolution of
Reallocation
88
financial distress Thirdly Barriers to restructuring shows how easy it is for a firm suffering from short-term
financial troubles to restructure its debt Country codes should be interpreted as follows GBR ndash Great Britian FRA
ndash France DNK ndash Denmark DEU ndash Germany ESP ndash Spain FIN ndash Finland IRL ndash Ireland SVN ndash Slovenia PRT ndash
Portugal AUT ndash Austria GRC ndash Greece SVK ndash Slovakia ITA ndash Italy LVA ndash Latvia POL ndash Poland NOR ndash Norway
SWE ndash Sweden LTU ndash Lithuania BEL ndash Belgium CZE ndash Czech Republic MLD ndash Moldova HUN ndash Hungary EST ndash
Estonia
Productivity differences in Hungary and mechanisms of TFP growth slowdown
89
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS
The main aim of this section is to investigate the micro-level processes which underlie the patterns
documented at the industry level in the previous chapters (especially in Chapter 6) by presenting a few
descriptive relationships between firm characteristics and firm dynamics More specifically we would
like to understand how various firm characteristics are associated with the observed patterns of
productivity and employment growth to illustrate the micro-level processes behind within-firm
productivity growth and reallocation Additionally we look at which types of firms enter and exit in
order to shed light on how they contribute to changes in the average productivity level
We seek to answer three main questions First was there a structural break either in the productivity
growth or in the reallocation process after the crisis which may have contributed to the productivity
growth slowdown Second do we see a structural difference in these processes along the main
dimensions of the lsquodualityrsquo of the Hungarian economy eg the characteristic differences between
globally involved large firms and their domestic market oriented peers Third can we find peculiar
patterns which may explain the unusual evolution of productivity quintiles namely the slow
productivity growth of frontier firms relative to less productive firms (as documented in detail in
Section 44)
In terms of firm characteristics we focus on variables which are likely to be related to the duality (see
Section 44) ownership size age and exporter status We do firm-level regression analyses which
allows us to use a rich set of controls and fixed effects Additionally we look at the interaction of the
different characteristics to get an even more precise picture about the main factors driving productivity
growth and reallocation
The structure of this chapter follows closely the logic of the dynamic productivity decomposition
exercise in Chapter 6 In Section 71 we investigate the determinants of within-firm productivity
growth In Section 72 we explore how firm characteristics are related to future employment growth ndash
ie to between firm reallocation ndash followed by the analysis of entry and exit in Section 73
71 Productivity growth
Questions and descriptive patterns
A key relationship of interest is how future productivity growth is related to current productivity levels
Our main motivation to study this question is that it can shed light on the extent of convergence to
more productive firms within the industry If there is a tendency for low-productivity firms to catch up
the productivity growth of such firms will be higher We analyse this relationship for the whole
economy and will also split the sample along different dimensions We are particularly interested in
three questions First is there a difference between the productivity growth rates of firms along some
dimensions even when controlling for productivity We think that this question is highly relevant but
will also qualify the findings of for example Section 32 where we compared firms with different
ownership structures and of different sizes with each other unconditionally which may mask the
different composition of the two groups in terms of initial productivity levels Second we are interested
in whether the slope of the relationship between the initial productivity level and subsequent
productivity growth differ along observable dimensions Is it the case for example that domestically-
owned firms face a productivity ceiling beyond which they cannot improve their efficiency any further
while foreign firms are better able to push forward even starting from very high productivity levels
Third we would like to find out whether there are structural changes in this relationship which may be
associated with the productivity slowdown following the crisis
Firm-level Productivity growth and dynamics
90
While the main mechanism behind this relationship is likely to result from a process of convergence
between firms the measured relationship can also partly arise from a mechanical negative relationship
coming from regression to the mean A large positive measurement error in productivity in year t
automatically generates a large negative growth rate from t As we are interested in the convergence
process rather than the mechanics of the regression to the mean we look at the relationship between
lagged productivity levels and 3-year productivity growth We assume that regression to the mean
resulting from measurement errors is less likely to show up when the productivity level is lagged An
additional limitation of this exercise is survivorship bias because lower productivity firms are more
likely to exit if they are unable to improve their productivity level We will analyse exit and entry
separately in Section 73
First to see the overall patterns we present the relationship between initial productivity levels and
productivity growth in the following 3 years in a non-parametric way (see Figure 71) To do so we
classify firms within each industry into 20 quantiles based on productivity in the previous year For
example we show how productivity growth between 2012 and 2015 is related to productivity levels in
2011 For each quantile we calculate average growth after partialling out 2-digit industry fixed
effects We show this relationship for different years to see whether there is a structural change in the
within-firm productivity growth process57 We demean lagged productivity levels by 2-digit industry
and year so zero on the horizontal axis corresponds to the mean productivity level We take four
periods pre-crisis (2003-2006) crisis (2006-2009) post-crisis (2009-2012) and recent (2012-
2015)58
Figure 71 shows that the relationship between previous productivity levels (on the horizontal axis) and
subsequent 3-year growth (on the vertical axis) can be well approximated with a linear relationship
We see a pronounced negative relationship in all periods reflecting that (surviving) lower productivity
firms increase their productivity faster than more productive firms generating some within-firm
convergence in the sample of continuing firms The slope of the relationship ie the productivity
growth premium of less productive firms is quite stable across non-crisis years but differs markedly in
the crisis showing that the crisis-related productivity decline was more severe for more productive
firms probably because these firms had been hit the hardest by the collapse of global trade59 Note
that this is much in line with the slow productivity growth of frontier firms in the same period
documented in Section 43 Figure 44 In normal times macro conditions seem to shift the whole line
up or down rather than rotate it The average 3-year productivity growth rate is the lowest during the
crisis and is still low in the post-crisis period but there is no difference between the pre-crisis and the
recent periods60
57 As in the previous chapters we use our main sample (see Chapter 2) in which we only consider firms with at
least 5 employees and measure productivity with the method of Ackerberg Caves and Frazer (2015)
58 Note that to measure subsequent growth we need three years following the base year when the level of
productivity is measured Consequently the last year we include is 2012 ndash and follow what happens to firms
between 2012 and 2015
59 More exit of low-productivity firms during the crisis may have also introduced a survivorship bias but as the
patterns in Figure A71 of the Appendix show this seems not to be the case
60 Table A71 of the Appendix shows the same patterns from a regression
Productivity differences in Hungary and mechanisms of TFP growth slowdown
91
Figure 71 The relationship between lagged productivity levels and subsequent productivity growth
over time
Notes This figure shows how the log of productivity in t-1 (on the horizontal axis demeaned by 2-digit industry
and year) is related to productivity growth between t and t+3 Each dot represents one of 20 quantiles of the
productivity level distribution and the average 3-year growth rate of firms within that quantile including 2-digit
industry fixed effects
Estimation
After establishing a linear relationship between lagged productivity level and subsequent growth we
look at the role of firm characteristics in productivity growth We do it in two steps First we look at
cross-sectional patterns taking the most recent period (2012-2015) We ask if there is a difference
between firm groups in productivity growth for the average firm (ie a firm having industry-average
productivity) and if there is a difference in the convergence pattern These two aspects correspond to
differences in the level and the slope of the line We estimate the following regressions
1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905
119896
119870
119896=1
+ 1205732(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1) +sum1205733119896(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1)119866119894119905
119896
119870
119896=1
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
We denote productivity of firm i in year t with 119905119891119901119894119905 1198893_119905119891119901119894119905 stands for 3-year productivity growth
119905119891119901 119895(119894)119905minus1 is the year-specific average lagged productivity in industry j of firm i G is a firm characteristic
(eg ownership or size) which contains K categories (eg one ownership group foreign or three size
categories) 119883119894119905 is a set of additional firm-level controls (these can be size age ownership or exporter
status) 120572119895(119894) is industry or industry-region fixed effects and 휀119894119905 is the error term Then 1205731119896 measures the
productivity-growth difference for average-productivity firms in firm group 119866119896 (eg foreign) compared
to average-productivity firms in the baseline category (eg domestic) 1205733119896 measures the difference in
the convergence patterns between firm group 119866119896 and the baseline category
(71)
Firm-level Productivity growth and dynamics
92
Second we also check dynamic patterns to see how the role of these firm characteristics changed over
time taking the same periods as in Figure 71 The baseline regression for comparing productivity
dynamics across years is as follows
1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905
119897
119897=200320062009
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
As before 1198893_119905119891119901119894119905 is the 3-year productivity growth of firm i from year t to t+3 and 119905119891119901119894119905minus1 denotes the
productivity level of firm i in t-1 119905119891119901 119895(119894)119905minus1119897 denotes the year-specific average lagged productivity in
industry j which firm i belongs to 119863119905119897 is an indicator for year l 119883119894119905 is a set of firm-specific time-variant
controls and 120572119895(119894) is industry or industry-region fixed effects as in the previous specification 1205731119897
measures the difference between the productivity growth of firms with industry-average productivity in
year l and in year 2012 The difference comes from two sources industry-level average productivity
levels could change over time and productivity growth for firms with the same productivity level could
also vary As we are interested in how the productivity growth of the average firm changed over time
we will not separate these two effects 1205732119897 measures the slope of the relationship between lagged
productivity levels and subsequent productivity growth in year l Comparing the different 1205732119897 coefficients
shows how the process of convergence between low- and high-productivity firms changed over time
We take a similar approach when we compare group-specific productivity dynamics over time We
interact group indicators demeaned productivity levels and the interaction of the two from the static
regression with a full set of year dummies and include year dummies separately as well
1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905
119896119863119905119897
119870
119896=1119897=2003200620092012
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ sum sum1205733119896119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119866119894119905119896
119870
119896=1
119863119905119897
119897=2003200620092012
+ sum 1205734119897119863119905
119897
119897=200320062009
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
Comparing 1205731119896119897 coefficients for different l-s shows how the difference between average-productivity
firms in the baseline category and in group k changed over time Similarly comparing 1205733119896119897 coefficients
with different l-s shows how convergence differences between the baseline category and group k firms
evolved over time These specifications allow us to add industry-year fixed effects so we can also
control for industry-specific trends
Results
Figure 72 shows the non-parametric relationships by firm characteristics creating scatter plots which
show productivity quantiles separately by firm groups These figures hint at the fact that on average
foreign-owned and exporter firms experience higher productivity growth conditional on initial
productivity levels In addition the relationship between the initial productivity level and subsequent
growth is weaker for foreign-owned firms suggesting that even highly productive foreign firms are
able to raise their productivity further while similar domestic firms have a harder time doing so Size
groups and age groups are similar to each other though the smallest firms have stronger convergence
patterns than the largest
We can discover the same scenarios using regression analysis in which we can control for the
abovementioned firm characteristics and fixed effects (Table 71) The most important conclusion is
that average-productivity foreign-owned firms raise their productivity faster relative to similar
domestic firms by about 10 percentage points Average exporters also have a TFP growth advantage
(72)
(73)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
93
relative to non-exporters but this premium disappears when we control for ownership We find some
evidence for a positive interaction between productivity levels and foreign ownership in line with lower
constraints for further TFP growth in the case of foreign frontier firms The same pattern applies to
exporter firms
Figure 72 The relationship between lagged productivity levels and subsequent productivity growth by
firm group
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to log productivity growth
between t and t+3 Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-
year growth rate of firms within that quantile including 2-digit industry fixed effects
The similar results for exporters and foreign-owned firms ndash and the strong correlation between foreign
ownership and exporter status ndash raise the question does this difference arise from foreign ownership
or exporting or do both variables have an independent effect Table A73 in the Appendix examines
this question only to find that the foreign premium in average productivity growth unconditional on
the productivity level is there both for exporters and non-exporters and is higher for younger and
smaller firms When we look at how the relationship between lagged productivity level and subsequent
growth differs by both characteristics at the same time we find that both foreign ownership and
exporter status matter but for different aspects of the relationship The difference in the slope of the
relationship comes both from the foreign-owned and from exporters compared to low-productivity
firms of the same category high-productivity firms grow relatively faster if they are foreign-owned The
same is true for comparing exporters and non-exporters At the same time the average difference in
Firm-level Productivity growth and dynamics
94
productivity growth comes from foreign ownership firms with industry-average productivity levels
have a higher productivity growth if they are foreign There is no significant additional effect for foreign
exporters on top of adding up foreign and exporter premia either in average productivity growth or in
convergence61
The TFP growth advantage of foreign-owned firms even when compared to domestically-owned firms
with the same productivity level points at a mechanism that reinforces the already existing duality
when domestic firms reach frontier productivity levels their TFP growth slows down much more than
that of foreign firms This self-reinforcing mechanism may be behind the non-convergence between
foreign and domestic firms (Section 44) With regard to size and age we find that high-productivity
firms have a relatively greater chance to increase their productivity if they are larger or older
compared to their smaller and younger counterparts (see Table A72 in the Appendix)
Table 71 The relationship between lagged productivity levels and subsequent productivity growth by
ownership and exporter status
Dep var TFP growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190
(000556) (000568) (000622) (000629) (000656) (000663)
TFP in t-1 Foreign 00475 00418 00433 00423
(00140) (00142) (00252) (00253)
TFP in t-1 Exporter 00477 00287 00216 00224
(00104) (00106) (00125) (00126)
TFP in t-1 Foreign exporter -000697 -00151
(00312) (00314)
Foreign 0117 0109 0120 0104
(00121) (00132) (00223) (00226)
Exporter 00240 000260 000401 0000919
(000791) (000845) (000851) (000881)
Foreign exporter -000670 000871
(00267) (00272)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 29717 29717 30135 30062 29717 29717
R-squared 0060 0072 0056 0073 0060 0072
61 Looking at the same patterns over time (Table A74 in the Appendix) suggests that higher average productivity
growth is a rather stable feature of foreign firms The only exception was the crisis period when it disappeared
Splitting the sample by broad sectors shows that foreign firms have higher average productivity growth both in
manufacturing and services The difference in within-group convergence patterns stayed the same for the
foreign The same is true for exporters except for the pre-crisis period when the coefficient is not significant
Productivity differences in Hungary and mechanisms of TFP growth slowdown
95
72 Employment growth
Question and descriptive patterns
The relationship between initial productivity levels and subsequent employment growth shows the
reallocation of continuing firms Between-firm reallocation results from more productive firms growing
faster In this subsection we ask how between-firm reallocation changed over time and how
reallocation patterns vary by different firm characteristics
To measure reallocation we use a similar approach to that in the previous subsection but the
lsquodependentrsquo variable will be 3-year employment growth in log terms rather than productivity growth
The slope of the estimated relationship reflects the employment growth advantage of more productive
firms or the strength of ldquocreative destructionrdquo among surviving firms Shifts in the level show changes
in the average growth rate
We calculate the 3-year employment growth using the formula119871119905+3minus119871119905
(119871119905+3+119871119905)2 where 119871119905 is the number of
employees in year t This formula shows the percentage increase in employment from year t to t+3
compared to the average size in year t and t+3 This measure performs better for smaller firms than a
simple log difference in employment as it does not result in extremely high numbers with a low initial
employment level62 In all the regressions of this subsection we control for exact firm size using the
logarithm of the number of employees
Figure 73 Reallocation by year
Notes The figure shows how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
62 Additionally while the baseline estimates are only for continuing firms this measure allows us to include firms
exiting in the period (t+1t+3) as well in some robustness checks In these cases we take Lt+3 = 0
Firm-level Productivity growth and dynamics
96
Figure 73 illustrates the patterns in the data non-parametrically The relationship between previous-
year productivity levels and subsequent employment growth is positive in all years This shows that in
line with the creative destruction hypothesis more productive firms are more likely to grow in the
subsequent three years The figure doesnrsquot show characteristic changes in the reallocation process
across years the slope of the curves being similar to each other Our regression estimates presented
in the Appendix (Tables A75 and A76) support that reallocation patterns are stable over time63 The
average growth rate of typical firms naturally follows the macro cycle strongly ndash aggregate changes
seem to shift the line up or down but do not seem to rotate it In other words with this approach we
do not find evidence for a structural change in the reallocation process therefore it is unlikely that
such a change should explain satisfactorily the productivity slowdown
We create similar figures for the most recent period (2012-2015) by different firm characteristics
(Figure 74) The most important result is that exporters grow significantly faster than non-exporters
when controlling for their initial productivity This leads to reallocation from non-exporters to
exporters Given that the productivity advantage of exporters is in the order of 30-100 percent in the
different industries (see Section 43) this reallocation process can yield enormous productivity gains
The slope of the curve is also less steep for exporters suggesting that their expansion is less
dependent on their productivity level relative to domestic firms in other words reallocation within the
exporter group is weaker relative to non-exporters
63 As before the relationship between lagged productivity levels and subsequent employment growth can be
properly approximated by a linear function
Productivity differences in Hungary and mechanisms of TFP growth slowdown
97
Figure 74 Reallocation by firm groups
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
Firm-level Productivity growth and dynamics
98
Estimation results
Table 72 Reallocation by ownership and exporter status
Dep var employment growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 0105 0102 0105 0107 0107 0108
(000484) (000493) (000539) (000546) (000570) (000575)
TFP in t-1 Foreign
-00369 -00328 -00252 -00224
(00123) (00124) (00217) (00217)
TFP in t-1 Exporter
-00344 -00298 -00250 -00249
(000913) (000932) (00109) (00110)
TFP in t-1 Foreign exporter
-0000806 000194
(00271) (00272)
Foreign 000105 -000863 -000786 -0000106
(00112) (00116) (00194) (00196)
Exporter 00586 00635 00653 00672
(000738) (000754) (000777) (000786)
Foreign exporter
-000647 -00123
(00234) (00238)
Industry FE YES YES YES
Industry-region FE
YES YES YES
Firm-level controls
YES YES YES
Log of employees
YES YES YES YES YES YES
Observations 31662 31662 32124 32043 31662 31662
R-squared 0035 0049 0038 0051 0037 0049
Looking at the regression results (Table 72) confirms our previous findings even after controlling for
fixed effects Exporters with an average productivity level grow about 6 percentage points faster than
non-exporters hinting at strong positive reallocation between the two groups with slightly weaker
reallocation within the exporter group64 At the same time average-productivity foreign-owned firms
do not have higher employment growth than domestic ones Similarly to productivity growth we find
no extra premium for foreign exporters65 66 Overall these results emphasise that participation in
64 We define exporters based on their export activity in year t so the group of exporters also includes those firms
which export in t but not any more afterwards This means that a worse subsequent performance ndash lower
growth and exiting from exporting ndash has no effect on our exporter classification
65 The main patterns concerning employment growth of average-productivity firms are robust to modifying the
employment growth measure in such a way that it includes exits as a full employment decline (See Table A77
in the Appendix) In this version employment growth of foreign firms is significantly lower overall but this is
counterbalanced by the significantly positive coefficient of the foreign exporter indicator
66 We show in Table A78 of the Appendix that the higher average growth of exporters is present in all size (except
for the largest) age and ownership groups Dynamic patterns suggest (in Table A79 of the Appendix) that the
higher growth rate of average-productivity exporters is robust over time This result is also robust to splitting
the sample into manufacturing and services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
99
international markets is an important driver of industry and aggregate productivity growth in Hungary
by providing opportunities for exporters to expand as Section 62 has documented
As Table 73 shows competitive pressure also seems to affect more the growth prospects of smaller
firms the relationship between initial TFP levels and employment growth is significantly stronger for
smaller firms Between-firm reallocation appears to be much stronger for smaller firms while less
productive large firms are unlikely to contract even if they are inefficient conditional on survival
There are no clear patterns by age groups
Table 73 Reallocation by size and age group
Dep var employment growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 0112 0110 00875 00863
(000486) (000502) (00133) (00133)
TFP in t-1 Group 2 -00455 -00431 00445 00384
(00128) (00129) (00182) (00183)
TFP in t-1 Group 3 -00745 -00748 000733 000822
(00194) (00196) (00141) (00142)
TFP in t-1 Group 4 -00953 -00957
(00218) (00220)
Group 2 -000596 -000810 -00188 -00212
(00122) (00123) (00141) (00141)
Group 3 -000928 -00157 -00115 -00169
(00199) (00201) (00114) (00115)
Group 4 00328 00280
(00276) (00278)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Log of employees YES YES YES YES
Observations 32124 32043 32124 32043
R-squared 0038 0052 0037 0051
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees and size group 4 is 100+
employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5 years age group 3 is
firms older than 5 The baseline category is firms of 2-3 years
73 Entry and exit
Questions
This subsection aims at investigating which firms enter and exit and in particular how productive
those firms are relative to continuing firms This corresponds to the micro-level equivalent of the net
entry effect (see Chapter 6) The motivation for the micro-level investigation is that in this manner we
Firm-level Productivity growth and dynamics
100
can study which firm-level factors determine the type of firms that enter and exit and control for
industry heterogeneity
Our approach is similar to the previous section with the main difference being that this time the
dependent variable is productivity while the variables of interest are entry and exit dummies Their
coefficients show the productivity lsquopremiarsquo (often negative) of new entrants and exiting firms relative
to continuing firms These premia are especially useful to answer two kinds of questions First their
magnitude and size inform us about how entry and exit contribute to productivity growth Second
changes in these premia are also indicative of the changes in the costs of entry and the survival of
low-productivity firms
Estimation
To use a symmetric approach we define entrants and exiting firms using a 3-year interval An entrant
is a firm that has entered in the previous 3 years67 This means we look at the productivity of firms in
year t and compare it between incumbents (ie firms older than 4 years) and entrants (ie firms being
2-4 years old) In a similar way we compare the productivity in year t of firms exiting in the following
3 years (ie the last time the firm reports positive employment is in the period (t t+2) and non-
exiting firms (firms still reporting positive employment in year t+3)
As before we start with a static approach looking at the productivity premium of entrants and exiting
firms in the most recent period (taking year 2015 for 2012-2014 entrants and 2012 for firms exiting in
2013-2015) Then as a dynamic approach we take all four time periods as before and interact the
premia with year dummies The static regression we estimate is as follows
119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905
119896119873119864119894119905119870119896=1 + 1205732119866119894119905
0119864119894119905 + sum 1205733119896119866119894119905
119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)
119905119891119901119894119905 is the productivity of firm i in year t (measured in logarithm) 119866119894119905119896 is the k-th category (eg size
category 5 with more than 100 employees) in a grouping according to firm characteristics G (eg
size) and 1198661198941199050 is the baseline category (eg firms with 5-49 employees) 119864119894119905 stands for entrant or
exiting firm dummy in the different specifications and 119873119864119894119905 are incumbent or continuing firms
accordingly Then 1205732 measures the entry or exit premium for firms in the baseline category and 1205733119896
measures the same premium for firms in category k of grouping G Both premia are calculated
compared to incumbentscontinuing firms in the baseline category 1198661198941199050 1205731
119896 measures the productivity
advantage or disadvantage of incumbentcontinuing firms in category k of grouping G also compared
to the average productivity level in the baseline group As before 119883119894119905 includes additional firm-level
characteristics and 120572119895(119894) is industry or industry-region fixed effects In those versions where we include
industry fixed effects we identify from within-industry differences This means that 1205733119896 measures the
same entry or exit premium for firms in category k of grouping G compared to incumbentscontinuing
firms in the same category As before we create the dynamic version of the above regression by
interacting 119866119894119905119896119873119864119894119905 119866119894119905
0119864119894119905 and 119866119894119905119896119864119894119905 with year dummies and including year dummies separately as well
67 We consider firms changing industry from manufacturing to services or vice versa as exitors and new entrants at the same time (see Chapter 2)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
101
Results
Table 74 shows how the productivity premium of entrants and exiting firms changed over time In
these specifications we compare the yearly average productivity of incumbents and entering or exiting
firms separately in each year The point estimates suggest that entering firms were about 2-4 percent
more productive than incumbents except for the 5 percent productivity disadvantage in the pre-crisis
period while exiting firms were 10-20 percent less productive than the continuing firms The
productivity advantage of entrants and the disadvantage of exiting firms did not change radically
during our time period This difference constitutes a potential for positive net entry effects in terms of
reallocation The exact value of the net entry effect also depends on the share of employees affected
by entry and exit While the premia of entering and exiting firms remained roughly the same in the
different periods exit and entry rates changed (see Section 33) which results in positive net entry
effects before the crisis and negative effects after that (see Section 62)
Table 74 Productivity premium of entering and exiting firms over time
Dep var TFP in year t
Firm group Entry Exit
(1) (2) (3) (4) (5) (6)
EntrantExitorPeriod 2003-2006
-00532 -00535 -00465 -0214 -0199 -0200
(000906) (000873) (000879) (00102) (000987) (000989)
EntrantExitorPeriod 2006-2009
00269 00212 00232 -0135 -0117 -0119
(000967) (000931) (000936) (000897) (000865) (000865)
EntrantExitorPeriod 2009-2012
00369 00444 00360 -0133 -0114 -0121
(000960) (000926) (000931) (000920) (000889) (000889)
EntrantExitorPeriod
2012-2015
00374 00324 00252 -0171 -0150 -0157
(000971) (000936) (000944) (00107) (00103) (00103)
Period 2003-2006 -0115 -00927 -00711 -00445
(000519) (000501) (000556) (000537)
Period 2006-2009 -0175 -0169 -000972 00122
(000522) (000503) (000537) (000518)
Period 2009-2012 -0116 -0117 -00580 -00528
(000528) (000508) (000543) (000522)
Year FE YES YES YES YES
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES YES YES
Industry-year FE YES YES
Observations 166607 166168 166168 158143 157711 157711
R-squared 0327 0380 0380 0332 0385 0387
Firm-level Productivity growth and dynamics
102
Next we focus on the most recent period and look at the productivity differences of entrants and
exiting firms by different firm groups
Figure 75 Productivity premium for entering and exiting firms by ownership
Figure 75 presents the premia of domestic and foreign entering and exiting firms relative to domestic
incumbents As we saw in Section 44 foreign firms are on average more productive than domestic
ones68 foreign incumbents have on average a premium of 669 Compared to domestic incumbents
foreign entrants have 513 higher productivity There is also a positive productivity premium of 29
for exiting foreign firms Similarly the productivity of exiting exporters is 186 higher than that of
continuing non-exporters69 This means that domestic incumbent firms can survive longer even with a
lower level of productivity Consequently having many foreign entrants has a positive effect on
average productivity while on average foreign exits do not affect average productivity70 71
68 Table A711 shows that the productivity advantage of foreign-owned firms is present in all size and age groups as well as both within the exporter and non-exporter firm groups
69 Table A710 of the Appendix shows the estimation results with standard errors
70 As Table A712 of the Appendix shows foreign entrant premium and the premium of continuing or exiting
foreign and exporter firms seem fairly stable over time The positive premium of entering and exiting foreign
firms is also robust for splitting the sample into manufacturing and services
71 As Table A713 of the Appendix shows there is no considerable difference in the productivity disadvantage of
exiting firms by size or age group
Productivity differences in Hungary and mechanisms of TFP growth slowdown
103
74 Conclusions
One contribution of this chapter is that we have documented that one of the factors behind the
sustained duality in productivity between foreign and domestically-owned firms is that foreign-owned
firms tend to be more capable of upgrading their productivity even from already high productivity
levels Similar patterns apply to the more globally oriented exporters This mechanism underlines the
importance of policies that promote absorptive capacity-building (see Section 45) a strong knowledge
base easy access to external knowledge and flexible and advanced skills are especially important
when upgrading productivity beyond already high levels
We have also found strong reallocation from non-exporters to exporters Given the high productivity
premia of exporters in Hungary (Beacutekeacutes et al 2011) and in general (Wagner 2007) such a
reallocation can lead to substantial improvement in aggregate productivity (and as we have seen in
Section 62 it did to some extent before the crisis) These results emphasise that participation in
international markets is an important driver of industry and aggregate productivity growth in Hungary
because it provides valuable opportunities for exporters to expand Note that this reallocation effect of
international openness has been in the focus of the recent literature on international trade (Melitz
2003 Bernard et al 2006 Amiti and Konings 2007 Topalova and Khandelwal 2011 De Loecker
2011) Note also that the asymmetric expansion possibilities of exporters and domestic firms also
amplify the duality between the two groups
The analysis of entry and exit has revealed that entrants are somewhat more productive than
incumbents even a few years after entry Exiting firms are significantly less productive This on the
one hand implies that exit and entry is a substantial source of reallocation (as Section 62 has shown)
On the other hand the low productivity of exiting firms also suggests that domestic firms can survive
long even with relatively low productivity levels maybe because of inefficiencies in the capital
allocation process including the insolvency regime
Productivity evolution and reallocation in retail trade
104
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE
The previous chapters have presented a number of results on the productivity and growth in different
sectors of the economy The aim of this chapter is to look deeper into one of the key sectors of the
economy namely retail trade for more detailed insights
Two main reasons have motivated us to choose the retail sector First retail is a key sector of the
economy which provides jobs for a great many people and influences what consumers can buy and at
what prices Retail (and wholesale) does not only interact with consumers it is a key supplier of inputs
while beig a buyer of outputs for all other firms in the market economy72 The degree to which it is
capable of supplying a large variety of intermediate inputs at reasonable prices is an important
determinant of the productivity of firms relying on these sources Its market structure also affects
fundamentally the incentives that producers experience73
The second reason is that there have been a number of regulatory changes in the retail sector in
Hungary in recent years While these policies had multiple motivations one of their common features
is that they are size-dependent either explicitly or implicitly As such they have a potential to increase
the costs of larger firms and influence the reallocation process in favour of smaller mostly
domestically-owned firms This may matter as international evidence has shown that much of retail
productivity growth in recent decades has resulted from the expansion of large store chains (Foster et
al 2006) Exactly because of the strong links between retail and other industries regulatory
restrictions in retail represent nearly a third of all service-related restrictions which are carried over to
other sectors of the economy74
The structure of this chapter is the following Section 81 describes the policy context of Hungarian
retailing Section 82 introduces the available datasets Section 83 describes the major developments
in retail productivity Section 84 describes trends in reallocation The last three sections describe three
specific questions Section 85 analyses the role of retailers and wholesalers in importing and
exporting Section 86 provides a few illustrative statistics on how size-dependent taxes could have
affected reallocation and prices Finally Section 87 evaluates a specific policy namely the mandatory
Sunday closing of larger shops Section 88 concludes
81 Context
The retail industry is an important employer in all EU member states and Hungary is not an exception
Its employment share in our sample has been around 12 percent (Figure 81) Similarly to the EU as a
whole retail productivity is below the average of the market economy therefore its GDP share is
below its employment share Still it represented 6-7 percent of total value added in our sample
72 See EC (2018) for the importance of the retail industry in Europe
73 See Smith (2016) for a review of this literature
74 EC (2018) p 5
Productivity differences in Hungary and mechanisms of TFP growth slowdown
105
Figure 81 The share of retail and wholesale firms in market economy value added and employment
Notes Full sample with at least 1 employee in any of the years
The largest sub-industry within retail is groceries (NACE 4711) Its share of the total turnover around
40 percent is at the lower end of the EU distribution75 Given its importance (and the large sample size
within it) we will often study only groceries in our empirical analyses
Measuring the restrictiveness of different regulations in any sector of the economy is not an easy task
The European Commission has designed a ldquoRetail Restrictiveness Indicatorrdquo to quantify the potential
effect of these regulations in force at the end of 2017 (see Figure 82) The higher values of the
indicator indicate more restrictive regulations76 According to this indicator the restrictiveness of retail
regulation in Hungary is slightly below the EU average and similar to other CEE countries
The indicator distinguishes between regulations related to the establishment of shops on the one hand
and those related to their operation on the other In Hungary there are few operations restrictions
(mainly restrictions on distribution channels) while entry is regulated more heavily mostly by size-
related restrictions and requirements for economic data
75 EC (2018) p 4
76 There is ample empirical evidence that entry barriers planning regulations and operating restrictions are related to productivity and prices in retail Some examples are Bertrand and Kramarz (2002) Viviano (2008) Haskel and Sadun (2012) Sadun (2015) Daveri et al (2016)
Productivity evolution and reallocation in retail trade
106
Figure 82 Retail Restrictiveness Indicator
Notes This is a reproduction of Figure 8 from EC (2018)
While regulation in Hungary is not especially restrictive a number of new measures were introduced
following the crisis (see Box 81) While these have various motivations a common feature of most of
them is that they are size-dependent As such they may distort competition and constrain reallocation
to larger firms
One type of size-dependent policies is size-dependent taxes Crisis taxes introduced right after the
crisis (and phased out in 2013) were highly progressive in sales volume Local business taxes have
been similarly progressive in total sales at the firm-level since 2013 Other size-dependent policies are
restrictions on the establishment of shops or their operation The Plaza Stop law constrained the
establishment of malls larger than 300 m2 Another peculiar policy was requiring larger shops to close
on Sundays between March 2015 and April 2016
Quantifying the effect of such policies is not an easy task In some cases it is not possible with the
data at hand to identify the shops and firms which were affected by the different types of taxes For
example without knowing the exact location of the establishment it is not possible to identify which
firms operate in malls and hence could have been affected by the Plaza Stop law As we discuss in
Section 85 the highest bracket of the crisis tax only affected 6 firms and thus it is hard to run
statistical tests with an appropriate power In contrast some of the effects of the mandatory Sunday
closing policy can be very effectively estimated based on shop-level data
Therefore we will apply two complementary strategies The first is to investigate whether there are
trend breaks in the reallocation process following the crisis when many of the new policies were
Productivity differences in Hungary and mechanisms of TFP growth slowdown
107
introduced While we find suggestive changes around the crisis one cannot make casual statements
based on this strategy given the number of other changes in the economy The second strategy is to
examine specific policies where a credible differences-in-differences identification is possible
Unfortunately this strategy is basically limited to Sunday closing
BOX 81 Size-dependent taxes and regulations in the retail sector
This box describes a number of size-dependent taxes and regulations which could be linked to the retail data and investigated during this exercise The list is only indicative and will be appended by desk research and possibly interviews
2010-2013 crisis taxes
Crisis taxes were introduced in 2010 and were in force (mostly) until 2013 They affected the energy telecom and retail sector as their base was operating profits resulting from these activities The tax rate was strongly progressive for retail
Below 500m HUF 0
Between 500m and 30bn HUF 01
Between 30bn and 100bn HUF 04
Above 100bn HUF 25
Between March 15 2015 and April 23 2016 Sunday closing for larger and non-employee owned retail stores
The 2014 CII law which came into force on March 15 2015 banned shops with a retail space of more than 400 square meters to open on Sundays with some exceptions most notably the new tobacco shops Smaller shops could only open if their workers had at least a 20 stake in the
business or if they were close relatives of the owner The law was repealed in 2016
2013-today Progressive local business tax
The base of local business tax is the ldquoadjustedrdquo revenue of firms This usually means revenue minus material expenditures but regulation stipulating the exact method of calculation has changed a number of times since the introduction of this type of tax In 2013 a progressive
element was introduced by making the definition of the cost of purchased goods size-dependent In particular smaller firms can now deduct more of their expenditures than larger ones The deductible part is
Below 500m HUF of net sales 100
Between 500m and 20bn HUF 85
Between 20bn and 80bn HUF 75
Above 80bn HUF 70 of the cost of goods is eligible
Productivity evolution and reallocation in retail trade
108
82 Data
We rely on two main data sources in this chapter The first one is the NAV balance sheet data
described in detail in Chapter 2 Based on the industry code identifier we restrict the sample to firms
in industry 47 retail There are a few firms which switch to this category from other industries (mainly
wholesale of food manufacturing) We keep the whole history of these firms throughout the analysis
Second we use a retail-specific survey conducted by the Hungarian Central Statistical Office which
samples firms and collects data for all shops of the sampled firm77 Firms included in the sample are
compelled by law to submit monthly reports on their turnover and 4-digit industry-codes plus for all
of their stores information about these entitiesrsquo location (municipality) identification number 4-digit
77 httpswwwkshhudocshuninfo02osap2018kerdoivk181045pdf
BOX 81 Size-dependent taxes and regulations in the retail sector (cont)
2013-today Licensing of tobacco wholesale and retail
On 22 April 2013 in line with Act CXXXIV ldquoon reducing smoking prevalence among young people
and the retail of tobacco productsrdquo (adopted by the Hungarian Parliament on 11 September
2012) the National Tobacco Trading Non-profit Company (a 100 government-owned joint-stock
company controlled by the relevant minister under the mandate of this law) was established
From then on only special ldquonational tobacco shopsrdquo licensed by the state have been allowed to
sell tobacco products These shops enjoy a number of benefits compared to other shops
Exempted from the Sunday closing for retail shops
National tobacco shops are exempted from the ban on selling alcohol after 10pm rarr in effect
tobacco shops do not come under the ruling of the commercial law Local municipalities can
otherwise regulate shops based on that law
2011- today ldquoPlaza Stoprdquo Law
The so-called Plaza Stop Law (the 2011 CLXVI Law) came into force in January 2012 It
prohibits the construction of new retail facilities or the expansion of any already existing one with
a leasable area of more than 300 msup2 Exemptions could be granted to certain developments by a
committee of ministry officials and with the approval of the Minister of National Economy
In 2013 the law was extended to include building conversions In February 2015 a new
amendment was ratified which basically renewed the effect of the 2011 law and introduced some
modifications to it Now retail facilities with a floor space of less than 400m2 can be built without
any special procedure Furthermore the right to grant exemptions was given to a special
administrative department which is supplemented by a committee made up of delegated
members of different ministries
Productivity differences in Hungary and mechanisms of TFP growth slowdown
109
industry-code sales and the monthly number of days spent open The sample consists of all larger
retail firms78 and a representative sample of other firms re-sampled on an annual basis
An important consequence of this design is that we observe each of the shops of the sampled firms
This is valuable in two respects First with this information it is possible to calculate the number of
shops and average shop size at the firm-level Second one can identify new and exiting shops for
firms which are in the sample continuously ie larger firms Further with the help of the firmsrsquo
identification number we are also able to link this information to data from the NAV database for
qualified analysis
Two caveats may be mentioned here First the re-sampling of the representative part of the sample
prevents us from following small firms through the entire sampling frame Second in the beginning of
2012 there was a switchover in the coding of shop-level identification numbers which prevents us
from linking shops before and after
As mentioned above the database also includes information on the industry classification of the shop
In most of our exercises we restrict the sample to grocery stores more formally bdquoRetail sale in non-
specialised stores with food beverages or tobacco predominatingrdquo (NACE 4711) Table 81 shows the
sample size of the merged database for groceries We have classified firms according to the number of
shops they have and report their number and their storesrsquo number according to these categories
Table 81 The number of firms and the number of shops in different size categories in Groceries
1 shop 2-4 shops 5-9 shops 10-49 shops gt50 shops year firm shop firm shop firm shop firm shop firm shop
2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446
2006 573 573 51 131 59 430 111 2008 30 1548
2007 546 546 53 125 60 429 110 1987 33 1634
2008 628 628 45 102 50 350 104 1823 21 1574
2009 527 527 33 72 41 290 99 1879 24 1754
2010 472 472 22 49 32 238 94 1793 22 1968
2011 537 537 14 30 29 212 92 1758 22 2027
2012 374 374 30 68 49 335 88 1643 23 2107
2013 503 503 48 121 42 277 88 1622 25 2094
2014 410 410 106 239 48 320 81 1530 24 2054
2015 512 512 135 311 42 292 80 1544 30 2090
2016 518 518 120 292 37 271 77 1457 23 2022
A key distinction in this merged database is the one between shops and firms Sales employment
ownership is observable only at the firm-year level so these variables are the same for each of the
shops of a firm for a calendar year Shops are only observable for sampled firms but we observe
sales and the number of days they were open at a monthly regularity As a result even if one runs
regressions at the shop-month-level productivity and employment can only vary at the firm-year
level For this reason we always cluster the standard errors at the firm or firm-year level
78 Larger firms are defined as having more than 7 stores in operation or with a number of employees of more than 50 and at least 6 stores or with a significantly large store in a product category
Productivity evolution and reallocation in retail trade
110
While balance sheet data includes information on exports it does not inform us about imports In
Section 84 we use detailed trade data to analyse importing by wholesale and retail firms This is
reported at the importer firm-product (8 digit Harmonized System)-country of origin level Most
importantly we can link this information to the balance sheet of the firm This is collected by a survey
following the European Unionrsquos practice79 We aggregate these data to the firm-year level but
distinguish between consumer goods capital goods and intermediate inputs used in further production
by relying on the correspondence table of the Eurostat between the Harmonized System and Broad
Economic Category classifications
83 General trends
Let us start with describing the firm size distribution across years (see Table 82) for firms with at least
one employee Similarly to other EU countries the majority of firms in retail are very small in
different years between 70-75 percent of retail firms employed less than 5 people80 The share of firms
with more than 50 employees fluctuated at around 1 percent
As one would expect larger firms have a significantly larger weight in terms of employment and sales
The top 05 percent of firms employed more than 30 percent of all employees in each year The
employment-share of these top firms increased nearly monotonically from 33 percent in 2004 to more
than 38 percent in 2011 when it reached its peak This was followed by a slightly declining trend to
357 percent in 2016 This time path represents the gradual expansion of large chains both organically
and via the acquisition of stores81 up to the crisis when this trend seems to have ended
The market share of large firms is even larger reaching 45 percent in 2016 The difference between
the employment share and sales share shows that large retail firms are substantially more efficient ndash
at least in terms of sales over employees ndash than the average firm At the other extreme the smallest
retail firms generate only 126 percent of sales with 20 percent of employees suggesting that in 2016
each of their employees sold only about half of the average The patterns are similar in other years
Efficiency differences are large in this sector though not larger than in most other sectors of the
economy (see Chapter 4)
79 See httpeceuropaeueurostatstatistics-explainedindexphpInternational_trade_statistics_-_background An important limitation of these data is that firms only report transactions above a specific size This may bias estimates of firm-level importing downward for small firms
80 As we discussed in Section 42 the NAV sample includes only double-entry bookeeping firms while the unemployed and people working in firms with simplified accounting are omitted from these data These people are likely to work in small economic units with low productivity levels
81 The increasing share of large retailers is a general trend globally see Ellickson (2016)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
111
Table 82 Share of firms in different size categories (at least 1 employee)
A) Number of firms
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100
2006 7333 1613 687 252 065 049 100
2007 7369 1597 674 244 067 050 100
2008 7410 1571 667 236 067 048 100
2009 7522 1535 614 221 059 048 100
2010 7551 1508 640 201 057 044 100
2011 7591 1493 623 194 057 041 100
2012 7625 1506 568 204 055 042 100
2013 7526 1596 577 207 052 042 100
2014 7380 1684 628 210 056 042 100
2015 7239 1771 661 232 056 041 100
2016 7163 1804 676 252 063 043 100
B) Employment
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000
2006 2034 1475 1269 1025 679 3525 10000
2007 2026 1443 1241 984 669 3644 10000
2008 2020 1391 1138 910 579 3980 10000
2009 2002 1427 1244 870 570 3789 10000
2010 2099 1443 1243 862 577 3731 10000
2011 2144 1458 1119 895 555 3830 10000
2012 2144 1541 1131 906 541 3713 10000
2013 2168 1591 1204 896 555 3650 10000
2014 2104 1644 1236 970 548 3538 10000
2015 2064 1620 1216 1006 588 3570 10000
2016 1999 1620 1216 1006 588 3570 10000
C) Sales
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000
2006 1114 1278 1587 1025 776 4226 10000
2007 1110 1266 1543 956 855 4268 10000
2008 1112 1524 1045 891 531 4906 10000
2009 1104 1641 1073 856 718 4607 10000
2010 1104 1521 1103 930 693 4594 10000
2011 1160 1577 1004 934 624 4714 10000
2012 1146 1647 1003 913 629 4578 10000
2013 1229 1411 1090 945 709 4360 10000
2014 1486 1472 1093 1001 752 4389 10000
2015 1293 1458 1118 985 660 4512 10000
2016 1266 1458 1118 985 660 4512 10000
Productivity evolution and reallocation in retail trade
112
Figure 83 presents C5 concentration measures82 for the full retail sector and for some of its
subsectors83 The share of the top 5 retail firms was around 30 percent of total retail sales
Concentration was increasing pre-crisis from 30 percent in 2003 to 35 percent in 2009 Concentration
decreased and returned to its 2003 value by 2014 The latter trend as we will discuss in Section 85
may be associated with size-dependent policies
The various sub-industries exhibit different patterns in terms of concentration Let us start with
groceries Pre-crisis the dynamics in this subsector was driven mainly by the expansion of large
chains Consequently concentration was strongly increasing with C5 growing from 50 percent in 2003
to more than 60 percent by 2008 Concentration in this subsector was rising further post-crisis but at
a somewhat slower pace We observe a similar pattern of increasing concentration with a trend break
around the crisis in sales of books and clothes in specialized stores In these sub-industries
establishment regulations like the Plaza Stop law could have played a more important role in the trend
break than taxes Specialized cosmetics retailing was already very highly concentrated at the
beginning of the period and remained largely unchanged
Figure 83 Concentration in retail and various sub-industries
This observation motivates a more detailed look at different measures of efficiency and prices Panel A)
of Table 83 calculates the average TFP levels84 both for different size categories and for the
aggregate Note that TFP calculated from balance sheet data is revenue productivity measuring the
82 Calculated as the sales share of the 5 firms with the largest sales
83 We rely on a slighly different version of the NAV data for this exercise which includes 4-digit identifiers but only runs until 2014
84 See Section 22 on details of TFP estimation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
113
amount of revenue produced by an input bundle Consequently it does not only measure physical
productivity (units sold per unit of input) but also markups This distinction is especially important in
retail85
Let us start with the two aggregate series one unweighted and the other weighted by employment
The employment-weighted series has higher values because more productive firms tend to be larger
(see Section 51) The two series follow a parallel trend suggesting that the correlation between size
and productivity did not change radically TFP has increased by about 15 percent from 2004 to 2006
remained constant until 2008 fallen by around 10 percent in 2009 and then started to grow by 4-5
percent each year from 2011
Note that this productivity evolution is similar to what is reported by OECD STAN before and during the
crisis but the post-crisis recovery in our data is much more pronounced (Figure 84) As we have
discussed in detail in Section 42 this is most likely a result of the large number of self-employed and
the distinct productivity level and evolution of that group86 Productivity has been definitely increasing
since 2011 in our sample
Interestingly TFP is not increasing monotonically with firm size There is a clear 25-30 percentage
point difference between the smallest firms (1-4 employees) and firms in other size categories which
have a similar TFP to each other Besides differences in efficiency this may also be partly explained by
the tax avoiding behaviour of the smallest firms ie under-reporting sales or over-reporting costs
Panel B) of Table 83 investigates gross margins These are calculated as
119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
which is the margin that retailer 119894 realises in year 119905 on the cost it pays for the sold goods in
percentage A value of 20 shows that the price the consumer pays is 20 percent higher than what the
retailer paid for the goods87 Note that gross margins reflect a combination of two factors `physical
productivityrsquo (how much capital and labour is needed for a given amount of sales) and markups Still
gross margins are of interest because they are the closest proxy available in financial statements of
prices paid by customers
We can make two key observations First on average (weighted) margins increased from about 155
percent to 19 percent during the period under study with a fall during the crisis Second margins
were about 5 percentage points higher in the smallest retail firms compared to larger ones
Interestingly during and immediately after the crisis (between 2009 and 2012) the margins of the
largest firms were substantially lower than those of other firms This is because the margins of the
largest firms actually fell during this period while that of smaller firms remained roughly constant
85 Measurement of productivity in retail raises a number of conceptual and measurement issues (Ratchford 2016) Two main problems are the measurement of output (conceptually retail services) and of the inputs used (for example shop area) In practice however such detailed data are not available and it is standard to use TFP
86 The figures for retail are similar to those for the whole of the service industry About a third of all people engaged are self-employed operating at a significantly lower productivity level than retail firms The productivity of the self-employed did not grow between 2012 and 2015
87 We winsorise it at the 5th and 95th percentiles Note that the cost of goods sold would be preferable to material costs but that is often missing from the data especially for small firms
Productivity evolution and reallocation in retail trade
114
Most likely larger firms were able to cut markups while smaller firms with already lower markups
were not able to do so
As we have mentioned above margins reflect a combination of cost factors and market power The
gross operating rate88 attempts to control for labour costs and shows margins after personal cost
119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
with value added calculated as discussed in Chapter 2 In international comparison gross operating
rates are relatively low in Hungary89 These rates show a clear downward trend with time Gross
operating margins are clearly decreasing with firm size showing that larger firms operate with large
scale and low margins Similarly to the gross margin we see a fall during the crisis
Figure 84 Productivity evolution in the NAV sample and the OECD STAN
88 httpeceuropaeueurostatstatistics-explainedindexphpGlossaryGross_operating_rate_-_SBS
89 As shown by EC (2018) Figure 2 Our weighted estimates are similar to what is reported there based on Eurostat data
Productivity differences in Hungary and mechanisms of TFP growth slowdown
115
Table 83 Performance and margins (at least 1 employee)
A) TFP
B) Gross Margin
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 1769 1548 1793 1883 1584 1495 1735 1554
2005 1839 1571 1858 1878 1504 1666 1794 1584
2006 2040 1658 1842 1879 1599 1660 1956 1653
2007 2220 1720 1875 1940 1664 1702 2104 1712
2008 2192 1792 1872 1992 1522 1739 2096 1728
2009 2084 1719 1864 1967 1599 1527 2006 1630
2010 2096 1749 1867 2102 1664 1535 2024 1682
2011 2172 1765 1862 2003 1759 1558 2084 1728
2012 2203 1740 1828 1913 1969 1541 2102 1733
2013 2243 1734 1806 2018 1999 1794 2128 1790
2014 2363 1789 1817 2149 2022 1869 2223 1855
2015 2390 1803 1885 2165 2215 1987 2245 1928
2016 2379 1799 1911 2172 2403 2170 2237 1948
C) Gross operating rate
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
unweighted weighted
2004 651 701 663 677 479 480 658 611
2005 827 757 777 712 526 560 806 644
2006 908 773 777 753 579 797 870 706
2007 807 635 653 704 500 580 764 606
2008 665 588 577 610 422 478 643 525
2009 618 535 538 522 351 339 595 456
2010 669 587 629 626 421 341 650 510
2011 698 617 591 637 410 393 676 542
2012 770 643 599 624 485 404 735 577
2013 735 594 555 631 478 369 697 530
2014 745 578 568 612 446 398 700 531
2015 766 609 625 646 547 452 724 579
2016 759 597 621 632 609 487 715 588
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 599 633 637 628 612 623 610 626
2005 610 635 643 628 616 626 619 631
2006 623 646 651 640 630 642 631 643
2007 624 643 650 641 627 643 631 642
2008 625 645 647 640 630 640 631 642
2009 619 641 642 631 619 630 626 630
2010 617 641 643 634 622 631 625 631
2011 621 643 645 637 629 639 628 639
2012 624 646 644 637 629 639 631 644
2013 626 647 642 640 637 642 632 645
2014 628 648 650 645 640 648 634 648
2015 638 658 658 655 652 659 644 659
2016 643 661 665 659 655 661 649 665
Productivity evolution and reallocation in retail trade
116
As Section 44 has shown for the market economy in general the Hungarian economy can be
characterised by a strong duality between foreign and domestically-owned firms Retail is one of the
sectors where this is the most transparent with many small domestic firms operating alongside large
multinational super- and hypermarket chains90 Figure 83 shows the share of foreign-owned firms in
terms of number employment and market share Foreign-owned retail firms are substantially larger
than domestic ones between 5-7 percent of firms are foreign-owned but they employ around 30
percent of employees and realise around 40 percent of sales This also implies that the salesworker
share is also larger in foreign firms than in domestic ones This results from the larger typical size of
foreign firms when controlling for size salesworker is not higher for foreign firms The market share
of foreign-owned firms is at the top of the distribution in EU countries with a larger foreign share only
in Latvia and Poland91
Figure 85 shows an inverted U-shaped pattern with an increasing market share of foreign firms until
2009 followed by a fall of nearly 5 percentage points between 2013 and 2016 This fall in foreign share
ran parallel with the introduction of policies favouring smaller firms in various ways
Figure 85 Share of foreign firms with at least 1 employee
There is much variation behind the overall pattern as Figure 86 illustrates plotting the market share
of foreign firms across sub-industries In groceries foreign share fluctuated around 70 percent It was
90 There is limited literature on the spillover effects generated by multinational retailers See for example Atkin et al (2018)
91 See EC (2018) Figure 2
Productivity differences in Hungary and mechanisms of TFP growth slowdown
117
rising slightly pre-crisis in parallel with the increasing concentration of the industry The increase of
the market share of foreign firms was the strongest in clothes reflecting the expansion of different
multinational chains mainly in plazas The increasing trend observable for the category seems to have
broken around 2012 which coincides with the introduction of the Plaza Stop regulation Foreign
market share was always high in the highly concentrated cosmetics sector A few foreign chains were
dominant in this sector throughout the period Foreign share actually decreased sharply in books and
newspapers
Figure 86 Foreign share in sub-industries
A key question when evaluating the expansion of foreign firms is their performance Foreign retail
firms are substantially more productive than domestic ones (Figure 87) With the exception of the
crisis years labour productivity advantage was between 60-80 percent while the TFP advantage was
between 20-40 percent The TFP advantage is smaller because of the larger capital intensity of foreign
firms These productivity premia are not purely a consequence of the larger size of foreign firms this
pattern is robust to controlling for firm size There is no clear trend in the premia they were declining
before the crisis (suggesting that domestically-owned firms were catching up) and rising after it The
figure also shows a large decline in the premia in the crisis years This is likely to be a consequence of
more pro-cyclical margins of foreign firms which are captured by revenue productivity measures
Productivity evolution and reallocation in retail trade
118
Figure 87 Productivity premia of foreign firms labour weighted
The main message of this section is that similarly to other industries large productivity differences
persist in retail These differences are primarily associated with size larger firms are more productive
and charge lower margins The performance of very small shops and the self-employed looks
especially weak The pre-crisis period was characterised by an expansion of large and foreign firms
but this growth stopped after 2010
84 Allocative efficiency and reallocation
In this section we follow the approach of Chapters 5 and 6 in analysing allocative efficiency and
reallocation with a focus on the retail industry
Chapter 5 showed that an important metric of allocative efficiency at any point in time is the degree of
co-variance of productivity and size which is directly related to aggregate productivity Figure 88
shows the elasticity of the number of employees with respect to labour productivity and TFP A more
positive relationship represents a more efficient allocation of labour across firms92 The figure shows
these relationships both for the full sample (of firms with at least 1 employee) and the main sample
(firms with at least 5 employees)
The elasticity depends crucially both on the sample and the productivity measure We find that the
correlations are much stronger when the full sample is considered rather than the base sample This
reflects our findings in Table 83 namely that the smallest firms differ substantially from other firms
92 These are coefficients from separate yearly univariate regressions with ln number of employees on the left hand side and productivity as the explanatory variable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
119
while firms with at least 5 employees are quite similar to each other The labour productivity premium
of larger firms is greater than their TFP premium reflecting their higher capital intensity
The key insight from Table 83 is that most of the Olley-Pakes correlation or measured allocative
efficiency results from the fact that very small firms are of very low productivity Within the group of
firms with at least 5 employees the correlation between TFP and size is practically zero There is a
positive although small correlation within the group between employment and labour productivity93
There is also no key trend in this measure of allocative efficiency some measures show improvement
while others a deterioration94
Figure 88 The elasticity of employment with respect to productivity main sample
Figure 89 performs the dynamic (Foster-type) productivity decomposition for the retail industry The
picture is not very different from the patterns found for services in general (see Figure 64) Pre-crisis
parallel with the strong growth of large chains growth was mainly driven by reallocation primarily in
the form of firm entry The crisis was accompanied by an annual 5 percent fall in productivity driven
by within-firm productivity decline As we have seen in Table 83 this was most likely the results of
margin-cutting by large firms Between 2010-2013 within-firm productivity growth and net entry
contributed similarly to the (relatively low) productivity growth Productivity growth sped up between
2013-2016 mainly driven by the within-firm component with little reallocation The trend break in the
growth of large chains is clearly reflected in this decomposition
93 As we have discussed in Chaper 5 this is not exceptional ndash actually similar correlations are found in services in other European contries
94 These low levels of allocative efficiency are in line with international evidence In fact these correlations have been negative in the majority of EU member states (EC 2018 p 7)
Productivity evolution and reallocation in retail trade
120
Figure 89 Dynamic decomposition of productivity growth in retail
While these results are informative about reallocation at the firm-level the shop-level data enable us
to investigate reallocation at a more detailed level These data enable us to investigate whether key
firm or shop-level variables are related to opening new shops closing shops or the growth of the shops
of continuing firms We investigate these questions in the paragraphs that follow
The simplest way to explore the shop-extensive margin or the change in the number of shops is to
aggregate the shop-level data to the firm-level In particular we calculate the change in the number of
shops the number of new shops and the number of old shops for each firm 119894 and year 119905 Denoting
these variables which show changes between year 119905 and 119905 + 1 by 119910119894119905 we run the following firm-level
regressions
119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905
where 119883119894119905 is a vector of firm-level variables These proxy productivity (by ln labour productivity) and
size (by the number of shops of the firm and the average sales per shop) 120575119905 is a full set of year
dummies
When estimating these equations one has to make a number of compromises Most importantly one
can only observe the change in shop numbers when the firm is present in the sample both in year 119905
and 119905 + 1 Otherwise one cannot be sure whether all the shops were closed or simply not sampled in
119905 + 1 Unfortunately this is a serious restriction for two reasons First one cannot observe the exit or
Productivity differences in Hungary and mechanisms of TFP growth slowdown
121
entry only survival for single-shop firms95 Second we also miss when a multi-shop firm exits with all
its shops
One also has to make a number of further methodological choices We restrict our sample to groceries
which is a relatively homogeneous group with many observations Another choice is that even though
we observe shops on a monthly basis we consider only year-to-year changes between May and the
following May Running the regressions on the monthly data would inflate artificially the number of
observations and introduce important methodological problems including seasonality
Table 84 presents the results In column (1) the dependent variable is the (net) change in the
number of shops The results suggest that productivity is of limited importance as a determinant of
change in shop numbers but size matters Firms with more and larger shops were more likely to
expand in terms of opening new shops Foreign firms expand faster because they are larger
conditional on size ownership does not matter Size is correlated both with shop opening and closing
firms with a larger average shop size are more likely to open new shops while chains with more shops
are less likely to close existing ones
Table 84 Determinants of the change in the number of shops at the firm-level groceries
(1) (2) (3)
Dependent Change in
number of
shops
New
shops
Closed
shops
Labour productivity 0001 0025 -0011
(0024) (0014) (0018)
Foreign-owned -0087 -0042 0019
(0064) (0034) (0045)
ln( (average
salesshop)
0056 0034 -0017
(0016) (0010) (0014)
5-9 shops 0176 0001 -0126
(0070) (0036) (0054)
10-49 shops 0231 -0009 -0167
(0067) (0034) (0052)
more than 50 shops 0194 0002 -0109
(0071) (0038) (0055)
Year FE yes yes yes
Observations 815 815 815
R-squared 0105 0093 0084
Notes One observation is a firm-year Standard errors are clustered at the firm-level
One may get a more detailed picture by investigating at the shop-level Here we can straightforwardly
estimate both the exit part of the extensive margin (did a specific shop close) and the intensive
margin (did the shop extend its sales)
95 For this reason we drop single-shop firms altogether from the analysis
Productivity evolution and reallocation in retail trade
122
In particular we run regressions of the following form
119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905
where 119894 denotes firms 119895 shops and 119905 years The outcome variable 119910119894119895119905 is either a dummy showing
that the shop closed96 between 119905 and 119905 + 1 or represents the growth of (log) sales of the shop 119883119894119905 are
firm-level variables such as productivity while 119885119894119895119905 are shop-level variables such as shop-level sales
The same restrictions apply as in the previous case
Table 85 reports basic regressions We run both the exit and sales growth regressions for three
subperiods 2004-2007 2008-2010 and 2012-2015 Our main question is whether one can identify
any changes in the relocation process across these subperiods
Let us start with the exit regressions Similarly to the firm-level results we find that productivity and
ownership are not associated with the probability of exit Shop size is significantly related to closing a
shop twice as large sales are associated with 5 percentage points lower probability of the event
occurring This relationship became stronger by the third period The number of shops of the firm is
also negatively associated with the probability of closing the shops and this effect only became
significant post-crisis In addition the explanatory power of the regression is also higher by nearly 50
percent in this last period compared to the earlier ones To sum up we find that the size of the shop
and the turned out to be more important post-crisis making such shops less likely to close
In contrast to the exit equation we do not find significant effects in the growth regressions Neither
size nor productivity seem to be related to growth at the shop-level
To sum up the level of allocative efficiency in retail is relatively low ndash similarly to other European
countries ndash and one cannot see a significant change in this respect Pre-crisis when large chains
expanded rapidly reallocation played a significant role in aggregate productivity growth while within-
firm growth became dominant after the crisis Shop-level data suggest that the expansion in terms of
number of shops is mainly determined by firm size rather than productivity and ownership Sales
growth of existing shops does not seem to be related to size ownership or productivity The lack of
evidence for a relationship between opening new shops or the growth of existing shops is much in line
with the low measured allocative efficiency in the industry
96 We run linear probability models for shop exits Probit models yield similar results
Productivity differences in Hungary and mechanisms of TFP growth slowdown
123
Table 85 Probability of closing a shop and growth regression NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent Closing the shop Growth
Period 2004-
2007
2008-
2010
2012-
2015
2004-
2007
2008-
2010
2012-
2015
labour productivity 0005 -0021 0102 0034 -0012 0097
(0009) (0015) (0087) (0018) (0022) (0063)
foreign-owned -0005 0063 0056 0016 -0014 -0035
(0023) (0045) (0057) (0023) (0055) (0066)
ln sales -0026 -0029 -0057 -0018 -0010 0010
(0006) (0007) (0021) (0007) (0017) (0005)
5-9 shops -0014 -0066 -0111 0061 -0035 -0055
(0026) (0051) (0043) (0046) (0044) (0026)
10-49 shops -0045 -0114 -0149 0046 -0038 -0023
(0024) (0048) (0039) (0044) (0043) (0017)
more than 50
shops
0001 -0077 -0134 0061 -0027 -0016
(0029) (0048) (0040) (0045) (0044) (0022)
Observations 15374 10946 15038 14025 10120 13458
R-squared 0030 0053 0073 0023 0041 0121
Notes OLS regressions run at the shop-year level only for firms present both in t and t+1 In columns (1)-(3) the
dependent variable is a dummy indicating whether the shop closes between t and t+1 while in columns (4)-(6) it is
the growth rate of sales between t and t+1 The explanatory variables are measured at year t The number of
shops variables are dummies representing the number of shops of the firm County and year fixed effects are
included Period 1 2004-2007 Period 2 2008-2010 period 3 2012-2015 Standard errors are clustered at the
firm-level
85 Trade
In small open economies a very important function of the wholesale and retail sector is the
intermediation of international trade for consumers and firms The operation and efficiency of these
industries can have a strong impact on aggregate welfare and productivity by determining both the
cost and variety of imported goods available as well as the cost of exporting products (Raff and
Schmitt 2016)
Many interesting questions emerge in this framework One of the key issues is the problem of double
marginalisation In the case of consumers (and consumer goods) one dimension of this question is
whether retailers import products directly or via wholesalers If retailers find it very hard to import
directly (because of say large fixed costs) double marginalisation can raise prices for consumers
Through this channel lower trade cost of retailers can benefit consumers As a result the share of
consumer goods imported directly by retailers may be an important proxy for the lower prevalence of
double marginalisation
In the case of intermediate inputs manufacturing firms face the choice of importing the product
directly (and paying the fixed costs of doing so) or relying on an intermediary Again reduced fixed
cost may make imported goods cheaper contributing positively to productivity growth Access to
imported intermediate inputs has been shown to be strongly correlated with the productivity of
Hungarian manufacturing firms (Halpern et al 2015)
Productivity evolution and reallocation in retail trade
124
The question of duality is also highly relevant in this context Multinational retailers can easily rely on
producers abroad hence their expansion can have important effects on Hungarian producers
Domestic chains on the other hand may find it hard to import a large variety of foreign products
which may result in a reduced choice set for consumers
Ultimately it is the questions above that motivate our investigation of importing and exporting by
wholesalers and retailers Our data are exceptionally suitable for this exercise Given that firm balance
sheets can be linked to detailed export and import data one can quantify the amount of products
imported and exported from different product categories by wholesalers and retailers
An important methodological note is that we only observe direct imports in the trade data The most
important consequence of this limitation is that while in actual fact the share of imported goods on a
retailerrsquos shelf is a combination of goods imported directly by the retailer and those imported by a
wholesaler and sold to the retailer with the data we are only able to observe the former (Basker and
Van 2010) Also note that in contrast to imports exports are reported in the balance sheet
Therefore we will use this source of information when analysing exporting
Importing
To start with Figure 810 shows the share of retailers and wholesalers from the total Hungarian
imports of different types of goods In terms of all imports the share of these two groups of firms
fluctuated around 25 percent with a slightly decreasing trend The bulk of the imports were conducted
by manufacturing firms with an especially large share by multinational affiliates strongly integrated
into global value chains for example in the automotive industry Overall wholesalersrsquo imports were
about 5 times larger than those of retailers97
Naturally wholesalers and retailers dominate the importing of consumer goods by a share of around
70 percent A key trend here is the increasing share of retailers In 2004 21 percent of intermediated
trade (imports of wholesalers and retailers) were imported by retailers which increased gradually to
33 by 2015 This is a significant shift which reflects in part the expansion of multinational retail
chains but probably also easier access to imports by retailers
The share of intermediated trade was around 20 percent both for intermediate inputs and capital
goods dominated by wholesalers This reflects that in aggregate terms the overwhelming majority of
goods used by firms in production are imported directly The share of intermediated trade decreased
strongly following the crisis from 20 percent in 2010 to 13 percent in 2015 Given the skewed size
distribution of manufacturing firms this does not mean that most firms import their inputs directly
many smaller firms rely strongly on trade intermediaries when purchasing their inputs
Figure 811 looks into the trends behind consumer goods imports in more detail The left hand side
figure shows the share of imports compared to the total cost of goods sold (COGS) by wholesalers and
retailers98 We find that this ratio is roughly constant for wholesalers namely around 10 percent99
97 This can be compared to the results of Bernard et al (2010) who report that retailers and firms active both in retail and wholesale represent 14 percent of importing firms and 9 percent of imports in the US
98 In particular we calculate total consumer goods imports for wholesale and retail firms and divide it with the sum cost of goods sold across all retailers
99 Needless to say wholesalers also import other type of goods which are part of their cost of goods sold This ratio was 36 percent in 2015 showing that more than a third of their sales was imported
Productivity differences in Hungary and mechanisms of TFP growth slowdown
125
This contrasts sharply with retailers where the share of directly imported goods nearly doubled
between 2005 and 2015 from 6 percent to 11 percent100 This corresponds to a substantial increase in
the share of imported goods offered to consumers by retailers and an increasing share of this volume
is imported directly by the retailer presumably with a smaller degree of double marginalisation
One can also decompose the increasing direct import share of retailers to its different margins One
possibility is that - probably thanks to the declining fixed costs of importing - more and more retailers
started to import (an extensive margin effect) The right panel of Figure 811 shows that this is not the
case the share of directly importing retailers stagnated at about 8 percent of firms (with at least 5
employees) in the whole period Instead the rise of direct imports was driven by the intensive margin
or the average direct import per retailer Other regressions (not reported) suggest that this does result
mainly from the increased imports of large retailers
Figure 810 Share of wholesale retail and other firmsrsquo imports relative to total imports across
product categories
100 Again considering all goods the importcost of goods sold ratio increased from 11 to 18 percent for retailers
Productivity evolution and reallocation in retail trade
126
Figure 811 The share of consumer goods imports relative to the cost of goods sold and the share of
direct consumer goods importers by industry
Notes Firms with at least 5 employees
Figure 812 distinguishes between foreign and domestically-owned retail firms Both the share of
importers and their intensive margins are much higher for foreign-owned firms in the industry The
share of consumer goods imports in foreign firms in terms of cost nearly tripled between 2005 and
2015 from 7 to 21 percent101 compared to the 2-5 percent increase for domestically-owned firms
The increase in imports by retailers hence was mainly driven by multinationals
101 A similar increase from 18 percent in 2005 to 32 percent in 2015 can be observed when non- consumer goods are considered
Productivity differences in Hungary and mechanisms of TFP growth slowdown
127
Figure 812 The share of consumer goods imports relative to cost of goods sold and the share of
direct consumer goods importers by ownership
Notes Firms with at least 5 employees
Table 86 presents the cross-sectional linear regressions in order to investigate the premia of importers
among retailers along several dimensions In these regressions the dependent variable is a dummy
which shows whether a firm imports at least 1 percent of its cost of goods sold102 We find substantial
and highly significant premia in terms of size productivity and ownership 100 percent higher
productivity translates into about 5 percentage points higher probability of importing This premium
was increasing significantly between 2005 and 2015 showing a stronger self-selection of more
productive retailers into direct importing Foreign retailers are 20-25 percentage points more likely to
import on average A doubling of employees is associated with around 9 percentage points higher
probability of importing103
102 These are linear probability models but probit specifications yield similar marginal effects
103 Similar premia are found for importers in most industries and are mainly explained by the fixed costs of importing (Vogel and Wagner 2010)
Productivity evolution and reallocation in retail trade
128
Table 86 Determinants of importing linear probability models Retailers
(1) (2) (3) (4) (5)
Year 2005 2008 2010 2012 2015
Dependent Imports at least 1 percent of purchases
Labour productivity 0050 0054 0047 0059 0065
(0003) (0003) (0003) (0003) (0003)
Foreign-owned 0249 0196 0224 0238 0217
(0014) (0011) (0012) (0012) (0012)
Ln employees 0082 0082 0075 0073 0088
(0004) (0004) (0004) (0004) (0004)
Constant -0470 -0536 -0464 -0551 -0637
(0027) (0026) (0027) (0028) (0027)
Observations 7467 7977 7400 7122 8308
R-squared 0116 0130 0127 0140 0143
Notes Firms with at least 5 employees These are cross-sectional regressions where the dependent variable is
dummy representing whether the firm imports at least 1 percent of its cost of goods sold
Exporting
Wholesalers and retailers can also play a significant role as export intermediaries Extended export
activities of these firms can be an important source of growth for these firms but can also benefit
many smaller producers who would not find it profitable to export directly (Ahn et al 2011)
Figure 813 shows that 85-90 percent of exporting was conducted directly by producers rather than by
wholesalers or retailers The share of intermediated exports was constant pre-crisis but started to fall
after 2012
Productivity differences in Hungary and mechanisms of TFP growth slowdown
129
Figure 813 Share of wholesale retail and other firmsrsquo exports relative to total exports of firms
Many wholesalers and retailers started to export in the period under study (Figure 814) The share of
exporters in wholesale firms increased from 25 percent in 2005 to 35 percent in 2015 while the share
of exporting retailers doubled in this period The share of exports in the turnover of these firms also
increased substantially
Figure 814 Share of exports relative to turnover and share of exporters by industry
While foreign-owned firms are about 4 times more likely to export than domestic ones entry into
exporting was not limited to foreign-owned firms (Figure 815) the share of exporters among
domestically-owned firms doubled between 2005 and 2015 This was paralleled with an increase in the
share of exports relative to total turnover
Productivity evolution and reallocation in retail trade
130
Figure 815 Share of exports relative to turnover and share of exporters by ownership for the retail
sector
Table 87 reports linear probability models with export status as the dependent variable More
productive larger and foreign-owned firms are more likely to export In general both the size and
labour productivity premia increased between 2005 and 2015 once again suggesting stronger self-
selection based on these variables
Table 87 Determinants of exporting linear probability models retail
(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015
Dependent Exports at least 1 percent of total revenue
Labour
productivity
0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)
Foreign-owned 0083 0137 0141 0119 0107
(0009) (0011) (0013) (0013) (0012)
Ln employees 0019 0029 0028 0031 0034
(0003) (0004) (0004) (0004) (0004)
Constant -0159 -0277 -0271 -0321 -0317
(0018) (0026) (0027) (0029) (0028)
Observations 7622 7976 7663 7384 8730
R-squared 0028 0045 0041 0041 0036
This section has shown that the role of retailers in international trade is becoming more and more
important in Hungary This can have many benefits from providing a larger variety of potentially lower
priced goods to consumers to letting smaller producers reach foreign markets Increasing exports
mostly reflect opportunities provided by European integration and the internet but policies can also
help firms to become more adapt at utilising these opportunities
Productivity differences in Hungary and mechanisms of TFP growth slowdown
131
86 Policies Crisis taxes
As we have described briefly in Section 81 some of the new policies introduced after the crisis were
size-dependent either explicitly or implicitly The crisis taxes and the local business tax104 were based
on explicitly taxing large firms at higher rates Such policies can have substantial effects at the sectoral
level (Guner et al 2008)
Evaluating the effects of these taxes is not a straightforward task A possible approach was followed in
Section 84 where we have investigated the reallocation process in detail While such an approach is
not capable of identifying the causal effects of specific policies it may provide a broad picture The
results most importantly Figure 88 suggest that the importance of the reallocation process declined
relative to within-firm productivity growth Still this could have resulted from many reasons other than
policy changes
A more direct approach is to identify specific firms which were affected by a policy and to compare
their behaviour to similar firms not affected by the policy Such a diff-in-diff approach may be an
effective policy evaluation tool when there are sharp breakpoints in the tax schedule with enough
`treatedrsquo and control firms in the two groups
As for the crisis taxes the only sharp discontinuity was at the top rate when the tax rate increased
from 04 to 25 percent of profits The cutoff was at HUF 100bn and according to our data altogether
6 retail firms qualified for inclusion in this group This sample size does not allow for a statistically
powerful test
Still a few graphs may illustrate the processes First the market share of these large mainly
multinational firms were expanding quickly before 2010 and stagnated afterwards (Figure 816)
Second we can illustrate some of the key performance measures discussed in Section 83 Figure 817
compares the treated firms to a control group consisting of firms with at least 100 employees We find
that the premium of the treated group in terms of both productivity measures and margins were
higher between 2010 and 2013 than before or after105 As we have discussed earlier at least in the
short term these revenue-based measures are likely to reflect changes in prices Hence this figure
hints at increased prices in the treated group relative to the control group suggesting that treated
firms passed on the tax to consumers Note that these differences are not statistically significant and
to reiterate may have resulted from many other factors rather than just the effects of this specific
policy
104 The effect of the local business tax is much harder to test given its more continuous nature
105 Note that the margin premia are in fact negative in line with the lower margins charged by the largest firms
Productivity evolution and reallocation in retail trade
132
Figure 816 Sales and employment share of firms in the top bracket of the crisis tax
Notes Full sample
Figure 817 Margin TFP and labour productivity advantage of firms in the top bracket of the crisis tax
firms with more than 100 employees
Productivity differences in Hungary and mechanisms of TFP growth slowdown
133
87 Policies Mandatory Sunday closing
One of the most characteristic non-tax based size-dependent policies was mandatory Sunday closing of
larger shops introduced in March 2015 and reversed in April 2016 While the policy had multiple aims
it was partly motivated by supporting smaller and family-owned shops In this section we investigate
two outcomes related to this policy First we aim at understanding its reallocation effects ie the
extent to which the market share of treated shops lost market share Second we are interested in the
extent to which consumption was reallocated to other days of the week
The shop-level data is ideal to investigate the effects of this policy First the policy was defined at the
shop- rather than the firm-level We can identify the affected shops precisely based on the number of
days they were open Second many shops have been affected by this policy making the test
powerful Third the policy has a clearly defined beginning and end making a difference in differences
strategy feasible
Our empirical approach starts with restricting the sample to comparable firms First we investigate
mainly grocery shops where we have sizable treated and control groups106 In the sample we include
only shops which were continuously in the sample between January 2015 and October 2016 An issue
is that the treated and the control group may be very different We attempt to guarantee that the
common support condition is satisfied by excluding very small and very large shops107 For similar
reasons we also exclude shops which were not open even on Saturdays either before or during the
policy108
An important part of the analysis is the definition of the treated group As we do not observe directly
the area and the ownership of the shop we rely on the change in the number of days open We
consider a shop treated if it was open for at least 30 days per month before the policy (in median) and
it was open for less than 26 days after the policy was introduced (again in median)109 The control
group consists of other firms in the sample
Taking a look at the number of days open for the two groups reveals that compliance was very high
More than 95 percent of the shops that had been open on Sundays before the policy were closed on
Sundays during the whole policy period More than 95 percent of shops in the control group were
closed on Sundays both before and after the policy There are few firms which deviated from this
pattern by for example opening on Sundays when the policy started110
106 In other 4-digit sectors either there are too few firms or nearly all of them are treated (clothes shoes etc) or none of them (fuel)
107 Based on the 5th and 95th percentiles of the median sales distribution based on sales before the policy Unfortunately we do not have other measures of shop size
108 More precisely we exclude shops for which the median monthly days open was below 21 days either before or during the policy
109 A potential worry with this approach is that some shops may have closed voluntarily when the policy was introduced We cannot exclude this possibility but this may not be that important for the relatively large shops in the sample One can expect that voluntary Sunday closure would not start exactly at the beginning of the policy but rather after a period of gathering information about consumer demand on Sunday By checking the monthly distribution of the number of days open we find only few firms which changed their behaviour in this respect during the policy
110 Note that many small shops remained open on Sundays but most of them are missing from our restricted sample because of small median sales
Productivity evolution and reallocation in retail trade
134
Figure 815 reports descriptive statistics of the key variables Panel A) compares the evolution of
average sales of the treated and the control group before during and after the introduction of the
policy The dynamics of sales growth was remarkably similar before the policy was introduced
suggesting that the parallel trend assumption was satisfied Average sales in the control group are
somewhat higher during the policy suggesting some reallocation of market share to that group After
the policy the treated group seems to slightly overperform the control group
Part B) of Figure 818 shows the evolution of average sales per day open Again the pre-policy trends
are similar for the two groups Sales per day increases significantly for both groups during the policy
consumers did their Sunday shopping on other days The increase is substantially larger for the treated
group showing that most of the former Sunday shopping took place in the same shop but on other
days of the week The fact that there is an increase in the control group shows that part of the former
Sunday shopping was reallocated to these shops Interestingly the sales per day advantage of the
treated group remained even after the policy was abandoned As we will see the main reason for this
is that after abandoning the policy some of the shops remained closed
Figure 818 The evolution of key variables in the treated group and the control group groceries
A) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
135
B) Sales per day
While these patterns are suggestive the data allow us to conduct a more precise econometric event
study exercise We do so by creating a number of quarterly event study dummies to capture the
differential dynamics of the treated and control groups We define the variable lsquoevent timersquo which
shows the number of months since the policy started (it is zero in March 2015) This variable takes
negative values before that date We define quarterly dummies based on the event time variable For
example the first treatment quarter dummy is one when event time is 0 1 or 2 and the firm is in the
treated group The first pre-treatment dummy takes the value of 1 when event time is -1 -2 or -3 and
the firm is in the treated group
We run the following regression to estimate these trends
119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591
120591 + 120583119894119895 + 120575119905 + 휀119894119895119905
In this regression the dependent variable is days open ln(monthly sales) and ln(salesdays open) 119894
denotes firms 119895 shops and 119905 time measured in month while 120591 is event time in quarters The variables
of interest are the full set of event study dummies The base category will be the second pre-trend
dummy (event time -4 -5 or -6) The motivation for this choice is that the policy was announced in
this period (December 2014) hence the first pre-trend period the beginning of 2015 may include
preparation for the policy 120583119894119895 are shop fixed effects to control for shop heterogeneity 120575119905 are time
(monthly) fixed effects which control both for seasonality and macro shocks When we run the
regression by pooling different 4-digit industries we allow these dummies to vary across industries In
a more demanding specification we also include firm-time fixed effects and identify from the
differences across the treated and non-treated shops of the same firm in the same month We cluster
standard errors at the shop-level
Figure 819 summarizes the main results for the whole retail sector while the regressions are reported
in Table A71 in the Appendix Panel A) shows the results for days open with the right-hand panel
including firm-time fixed effects We see that on average treated firms cut the number of days open
by 2-3 days relative to the control group ndash the effect is more pronounced with firm fixed effects There
Productivity evolution and reallocation in retail trade
136
is practically no pre-trend and the timing of the reduction of days open is strongly in line with the
introduction of the policy The number of days open increases sharply after the end of the policy but
only to below pre-policy levels This suggests that some shops did not re-open on Sundays after the
policy probably because they learned that their sales did not suffer much
Panel B) shows the behaviour of average monthly sales Again there is no evidence for a pre-trend
During the policy treated firms experienced a 2-3 percent lower sales growth relative to the control
group This shows how much of sales was re-allocated to other shops Post-policy variables suggest
full recovery to pre-policy levels
Panel C) of the same figure shows the effect of the policy on sales per day open This variable
increased by 5-10 percent in the treated group relative to the control group The bulk of consumers
seem to have remained loyal to their familiar shops and simply made their shopping on other days
This may have also been helped by longer opening hours on other days of the week and further efforts
made by shops to retain their customers Sales per day remain higher even after the end of the policy
most likely because some shops did not re-open on Sundays but probably also because of
organizational changes during the policy
Figure 819 Event study results for the whole retail sector
A) Days
Productivity differences in Hungary and mechanisms of TFP growth slowdown
137
B) Sales
C) Sales per day
Notes This figure presents point estimates and 95 confidence intervals from the event study regression showing
the evolution of number of days open sales and sales per day of the treated group compared to the control group
as described in the text All specifications include shop fixed effects The left panel regressions also include 4-digit
industry-time fixed effects while the right side panels include firm-time dummies
Productivity evolution and reallocation in retail trade
138
Figure 820 re-estimates the same regressions for groceries where the policy was most relevant The
regression results are reported in Table A72 in the Appendix We find very similar results to the whole
retail sector The only exception is that the evolution of post-policy behaviour of sales is less clear
Figure 820 Event study results for NACE 4711
A) Days
B) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
139
C) Sales per day
Notes The figure above presents point estimates and 95 confidence intervals from the event study regression
showing the evolution of number of days open sales and sales per day of the treated group compared to the
control group as described in the text All specifications include shop fixed effects The left panel regressions also
include 4-digit industry-time fixed effects while the right side panels include firm-time dummies
A possible concern with these estimates is that the increase in sales per day may result from a simple
composition effect If sales are usually very small on Sundays anyway then closing on Sundays may
mechanically increase average daily sales We check for this possibility by estimating sales on different
days of the week from the pre-policy period While we do not observe the sales on each day of the
week we observe sales in different months with a different combination of days We rely on this
variation to estimate a regression of the following form
ln 119904119886119897119890119904119894119895119905 = 120573 lowast 119883119905 + 120574 lowast 119889119886119905119890119905 + 120583119894119895 + 휀119894119895119905
where 119883119905 is a vector of variables containing the number of Mondays Tuesdays etc in month 119905 We
also control for the number of holidays in the month We control for seasonality by including dummies
for December January and summer months The regression also includes firm fixed effects and is
estimated on the period 2009-2014 120574 lowast 119889119886119905119890119905 is a linear trend The estimated results are reported in
Table A73 in the Appendix
The regression shows that sales on Sundays were not that small namely similar to a typical Monday
or Wednesday Thus the composition effect is unlikely to affect the results much To check for the
relevance of these composition effects Figure 821 A) reports sales predicted from the above
regression for the treated group (by setting the number of Sundays to be zero during the policy)
Therefore the `predictedrsquo line shows what would have happened if sales had remained the same on
Productivity evolution and reallocation in retail trade
140
non-Sundays during the policy The actual line is clearly above the predicted one suggesting that sales
on other days have increased
Panel B) of Figure 821 shows how sales per day would have evolved based on a similar regression
Note that predicted sales per day are slightly larger during the policy than beforehand thanks to the
mechanical composition effect resulting from the slightly lower sales on Sundays Actual sales per day
however are substantially higher than this simple prediction showing again that sales per day
increased on other days of the week
Figure 821 The evolution of the variables versus prediction
A) Sales
B) Sales per day
Productivity differences in Hungary and mechanisms of TFP growth slowdown
141
All in all the mandatory Sunday closing of shops was effective in terms of compliance It did not have
strong reallocative effects with a 2-3 percent fall in sales in the treated group Consumers seem to
have remained mostly loyal to the shop they had frequented and made their shopping on other days
of the week at the same shop Interestingly some of the shops seem to have learned that it is optimal
to remain closed on Sundays even after the policy was cancelled
88 Conclusions
In line with the main message of other parts of this study there are huge productivity differences
across firms within the retail sector There is a strong duality between small and large firms both in
terms of productivity and margins Consumers are likely to pay significantly lower prices in the shops
of large firms Many of the large firms are multinationals which had expanded rapidly before the crisis
At the other end of the range the exceptionally low performance of very small firms seems to be a
significant issue Many technologies applied by the most productive retailers could be adapted
relatively easily by some of the less productive firms Increasing absorptive capacity and effective
financing could help in promoting this Still many of the low-productivity very small shops may not be
viable in the long run
A key pattern observed is the increasing concentration of the retail sector pre-crisis resulting from the
expansion of large chains and foreign firms These trends seem to have stopped or slowed down after
the crisis In line with this pattern the contribution of reallocation decreased post-crisis relative to
earlier periods While many factors can play a role in this pattern it may be related to the different
size-dependent policies introduced after 2010 While these developments may help smaller retail firms
consumers may face higher prices in the long run
Not all the policies introduced can be properly evaluated based on the data at hand especially because
multiple policies were introduced at the same time with some of them affecting only few firms We
were able to analyse precisely the effects of mandatory Sunday closing based on store level data We
found that a relatively small share of the demand was lost by the treated shops and the majority of
consumers simply switched to shopping at the same place on other days Interestingly some of the
treated shops found it optimal not to re-open on Sundays even when the policy was reversed
Additionally retailers and wholesalers also play a large and increasing role in mediating imports and
exports We found a large increase in goods imported directly by retailers rather than indirectly via
wholesalers This was mainly driven by large foreign firms and may have benefited their consumers
thanks to a lower degree of double marginalisation Both the number of exporting firms and the
amount exported by wholesalers and retailers increased most likely benefitting from easy access to
markets of other EU member states and probably from the opportunities provided by e-commerce
This can benefit both the exporting firms and the Hungarian producers who can more easily reach
foreign markets with the help of these intermediaries Policies may help retailers to internationalise by
making international sales especially on the internet even easier
Conclusions
142
9 CONCLUSIONS
The results of this report confirm that Hungary is atypical because of the relatively poor productivity
performance of frontier firms Importantly contrary to a strong version of the duality concept this is
not a result of Hungarian frontier firms being on the global frontier typically they are quite far away
from it This robust pattern underlines that besides helping non-frontier firms policies may also have
to focus on the performance of the frontier group A transparent environment with a strong rule of law
complemented by a well-educated workforce and a strong innovation system is key for providing
incentives to invest into the most advanced technologies
The analysis in this report reinforces the impression that there is a large productivity gap between
globally engaged or owned and other firms the gap being about 35 percent in manufacturing and
above 60 percent in services This gap seems to be roughly constant in the period under study The
firm-level analysis in Chapter 7 also reveals that one of the mechanisms which conserves the gap is
that foreign frontier firms are able to increase their productivity more than their domestic counterparts
even from frontier levels These findings reinforce the importance of well-designed policies that are
able to help domestic firms to catch up with foreign firms A key precondition for domestic firms to
build linkages with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A more specific
conclusion is the importance of enabling high-productivity domestic firms to improve their productivity
levels even further
The large within-industry productivity dispersion the relatively low (though not extreme in
international comparison) allocative efficiency documented in some of the industries the strong
positive contribution of reallocation to total TFP growth before the crisis and the relatively low entry
rate imply that policies promoting reallocation have a potential to increase aggregate productivity
levels significantly These policies can include improving general framework conditions by cutting
administrative costs reducing entry and exit barriers and using a neutral regulation The fact that
capital market distortions still appear to be significantly above their pre-crisis levels implies that
policies that reduce financial frictions may help the reallocation process The fact that exporters tend to
expand faster relative to non-exporters indicates that access to EU and global markets generates a
strong and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general traded and
more knowledge-intensive sectors fared better both in terms of productivity growth and allocative
efficiency The difference between traded and non-traded sectors points again to the importance of
global competition in promoting higher productivity and more efficient allocation of resources This also
implies that adopting policies that focus on innovation or reallocation in services may be especially
important given the large number of people working in those sectors The better performance of and
reallocation into more knowledge-intensive sectors underlines the importance of education policies
aimed at developing up-to-date and flexible skills and innovation policies that help improve the
knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people working at
firms with at least a few employees and those working in very small firms or self-employed The latter
category represents 30-50 percent of people engaged in some important industries Inclusive policies
may attempt to generate supportive conditions for these people by providing knowledge and training
as well as helping them to find jobs with wider perspectives or to set up well-operating firms The large
share of these unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a characteristic difference between the pre-crisis
period characterised by strong reallocation mainly via the expansion of large foreign-owned chains
Productivity differences in Hungary and mechanisms of TFP growth slowdown
143
and the post-crisis period with a stagnating share of large chains This break is likely to be linked to
post-crisis policies favouring smaller firms While halting further concentration in a country with
already one of the highest share of multinationals in this sector can have a number of benefits it is
likely to lead to higher prices and lower industry-level productivity growth in the long run Policies
should balance carefully between these trade-offs Another key pattern identified is the increasing role
of retailers (and wholesalers) in trade intermediation both on the import and export side Policymakers
should encourage these trends and design policies which provide capabilities for such firms to enter
international markets probably via e-commerce
References
144
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revivalrdquo (No 21) OECD Publishing Paris
Antildeoacuten Higoacuten D Mantildeez J A Rochina-Barrachina M E Sanchis A and Sanchis-Llopis J A (2017)
ldquoThe determinants of firmsrsquo convergence to the European TFP frontierrdquo (No 1707) Department of
Applied Economics II Universidad de Valencia
Arrobbio A Barros A C H Beauchard R F Berg A S Brumby J Fortin H Garrido J Kikeri
S Moreno-Dodson B Nunez A Robinett D Steinhilper I F Vani S N Verhoeven M and
Zoratto L D C (2014) ldquoCorporate governance of state-owned enterprises a toolkit (English)rdquo
Washington DC World Bank Group
httpdocumentsworldbankorgcurateden228331468169750340Corporate-governance-of-state-
owned-enterprises-a-toolkit
Atkin D Faber B and Gonzalez-Navarro M (2018) ldquoRetail globalization and household welfare
Evidence from Mexicordquo Journal of Political Economy 126(1) 1-73
Baily M N Hulten C Campbell D Bresnahan T and Caves R (1992) ldquoProductivity dynamics in
manufacturing plantsrdquo Brookings Papers on Economic Activity Microeconomics 1 187ndash267
Bartelsman E Haltiwanger J and Scarpetta S (2013) ldquoCross-country differences in productivity
The role of allocation and selectionrdquo American Economic Review 103(1) 305-34
Productivity differences in Hungary and mechanisms of TFP growth slowdown
145
Basker E and Van P H (2010) ldquoImports lsquoЯrsquo us Retail chains as platforms for developing country importsrdquo American Economic Review 100(2) 414-18
Beacutekeacutes G Kleinert J and Toubal F (2009) ldquoSpillovers from multinationals to heterogeneous
domestic firms Evidence from Hungaryrdquo The World Economy 32(10) 1408-1433
Beacutekeacutes G Murakoumlzy B and Harasztosi P (2011) ldquoFirms and products in international trade
Evidence from Hungaryrdquo Economic Systems 35(1) 4-24
Bellone F Musso P Nesta L and Warzynski F (2014) ldquoInternational trade and firm-level
markups when location and quality matterrdquo Journal of Economic Geography 16(1) 67-91
Benedek D Elek P and Koumlllő J (2013) ldquoTax avoidance tax evasion black and grey employmentrdquo
In Fazekas K Benczuacuter P and Telegdy A (Eds) The Hungarian labour market Review and analysis
Centre for Economic and Regional Studies Hungarian Academy of Sciences and National Employment
Non-profit Public Company Budapest 161-168
Berlingieri G Blanchenay P Calligaris S and Criscuolo C (2017) ldquoThe Multiprod project A
comprehensive overviewrdquo OECD Science Technology and Industry Working Papers 201704 OECD
Publishing Paris httpdxdoiorg1017872069b6a3-en
Berlingieri G Blanchenay P and Criscuolo C (2017) ldquoThe great divergence(s)rdquo OECD Science
Technology and Industry Policy Papers (No 39) OECD Publishing Paris Available at
httpdxdoiorg101787953f3853-en
Bernard A B and Jensen J B (1999) ldquoExceptional exporter performance cause effect or
bothrdquo Journal of International Economics 47(1) 1-25
Bernard A B Eaton J Jensen J B and Kortum S (2003) ldquoPlants and productivity in international
traderdquo American Economic Review 93(4) 1268-1290
Bernard A B Jensen J B and Schott P K (2006) ldquoTrade costs firms and productivityrdquo Journal of
Monetary Economics 53(5) 917-937
Bernard A B Jensen J B Redding S J and Schott P K (2007) ldquoFirms in international traderdquo
Journal of Economic Perspectives 21(3) 105-130
Bernard A B Jensen J B Redding S J and Schott P K (2010) ldquoWholesalers and retailers in US traderdquo American Economic Review 100(2) 408-13
Bernard A B Jensen J B Redding S J and Schott P K (2012) ldquoThe empirics of firm
heterogeneity and international traderdquo Annual Review of Economics 4(1) 283-313
Bertrand M and Kramarz F (2002) ldquoDoes entry regulation hinder job creation Evidence from the French retail industryrdquo The Quarterly Journal of Economics 117(4) 1369-1413
Biesebroeck J V (2008) ldquoAggregating and decomposing productivityrdquo Review of Business and
Economics 53(2) 112ndash146
Bisztray M (2016) ldquoThe effect of FDI on local suppliers Evidence from Audi in Hungaryrdquo IEHAS
Discussion Papers (No 1622) Institute of Economics Centre for Economic and Regional Studies
Hungarian Academy of Sciences
References
146
Brown J D and Earle J S (2008) ldquoUnderstanding the contributions of reallocation to productivity
growth Lessons from a comparative firm-level analysisrdquo IZA Discussion Papers (No 3683)
Caballero R Hoshi T and Kashyap AK (2008) ldquoZombie lending and depressed restructuring in
Japanrdquo American Economic Review 98(5) 1943-1977
Cingano F and Schivardi F (2004) ldquoIdentifying the sources of local productivity growthrdquo Journal of
the European Economic Association 2(4) 720-742
Conyon M J Girma S Thompson S and Wright P W (2002) ldquoThe productivity and wage effects
of foreign acquisition in the United Kingdomrdquo The Journal of Industrial Economics 50(1) 85-102
Crespo N and Fontoura M P (2007) ldquoDeterminant factors of FDI spillovers What do we really
knowrdquo World Development 35(3) 410-425
Daveri F Lecat R and Parisi M L (2016) ldquoService deregulation competition and the performance of French and Italian firmsrdquo Scottish Journal of Political Economy 63(3) 278-302
David H Dorn D and Hanson G H (2013) ldquoThe China syndrome Local labor market effects of
import competition in the United Statesrdquo American Economic Review 103(6) 2121-68
De Loecker J (2011) ldquoProduct differentiation multiproduct firms and estimating the impact of trade
liberalization on productivityrdquo Econometrica 79(5) 1407-1451
De Loecker J and Goldberg P K (2014) ldquoFirm performance in a global marketrdquo Annual Review of
Economics 6(1) 201-227
Djankov S and Hoekman B (2000) ldquoForeign investment and productivity growth in Czech
enterprisesrdquo The World Bank Economic Review 14(1) 49-64
Earle J S and Telegdy A (2008) ldquoOwnership and wages Estimating public-private and foreign-
domestic differentials with LEED from Hungary 1986 to 2003rdquo In S Bender J Lane K L Shaw F
Andersson and T Wachter (Eds) The analysis of firms and employees Quantitative and qualitative
approaches University of Chicago Press Chicago 229-252
Earle JS Telegdy A and Antal G (2013) ldquoFDI and wages Evidence from firm-level and linked
employer-employee data in Hungary 1986-2008rdquo IZA Discussion Papers (No 7095) Available at
SSRN httpsssrncomabstract=2196760
EC (2018) Commission Staff Working Document accompanying the document ldquoA European retail sector fit for the 21st centuryrdquo Communication from the Commission to the European Parliament the
Council the European Economic and Social Committee and the Committee of the Regions
Ellickson P B (2016) ldquoThe evolution of the supermarket industry from AampP to Walmartrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 368-391
EUROSTAT (2017) Job vacancy statistics Available at httpeceuropaeu eurostatstatistics-
explainedindexphpJob_vacancy_statistics
EUROSTAT (nd) Statistics explained Glossary ldquoHigh-techrdquo Accessed October 30 2017
httpeceuropaeueurostatstatistics-explainedindexphpGlossaryHigh-tech
Productivity differences in Hungary and mechanisms of TFP growth slowdown
147
Fazekas K (2017) ldquoHungarian labour marketrdquo Centre for Economic and Regional Studies Hungarian
Academy of Sciences
Foster L Haltiwanger J and Krizan C J (2006) ldquoMarket selection reallocation and restructuring
in the US retail trade sector in the 1990srdquo The Review of Economics and Statistics 88(4) 748-758
Foster L Haltiwanger J and Syverson C (2008) Reallocation firm turnover and efficiency
Selection on productivity or profitabilityrdquo American Economic Review 98(1) 394-425
Foster L Haltiwanger J C and Krizan C J (2001) ldquoAggregate productivity growth Lessons from
microeconomic evidencerdquo In C R Hulten E R Dean and M J Harper (Eds) New developments in
productivity analysis University of Chicago Press Chicago 303-372
Gamberoni E Gartner C Giordano C and Lopez-Garcia P (2016) ldquoIs corruption efficiency-
enhancing A case study of nine Central and Eastern European countriesrdquo ECB Working Papers (No
1950) Available at SSRN httpsssrncomabstract=2832009
Gamberoni E Giordano C and Lopez-Garcia P (2016) ldquoCapital and labour (mis)allocation in the
Euro area Some stylized facts and determinantsrdquo Bank of Italy Occasional Papers (No 349) Available
at SSRN httpsssrncomabstract=2910362 or httpdxdoiorg102139ssrn2910362
Garicano L Lelarge C and Van Reenen J (2016) ldquoFirm size distortions and the productivity
distribution Evidence from Francerdquo The American Economic Review 106(11) 3439-3479
Girma S (2005) ldquoAbsorptive capacity and productivity spillovers from FDI A threshold regression
analysisrdquo Oxford Bulletin of Economics and Statistics 67(3) 281-306
Girma S and Goumlrg H (2007) ldquoEvaluating the foreign ownership wage premium using a difference-
in-differences matching approachrdquo Journal of International Economics 72(1) 97-112
Girma S Thompson S and Wright P W (2002) ldquoWhy are productivity and wages higher in foreign
firmsrdquo Economic and Social Review 33(1) 93-100
Gopinath G Kalemli-Ozcan S Karabarbounis L and Villegas-Sanchez C (2017) ldquoCapital allocation
and productivity in South Europerdquo Quarterly Journal of Economics 132(4) 1915-1967
Gorodnichenko Y Revoltella D Svejnar J and Weiss C T (2018) ldquoResource misallocation in
European firms The role of constraints firm characteristics and managerial decisionsrdquo NBER Working
Papers (No w24444) National Bureau of Economic Research University of Chicago Press Chicago
Griliches Z and Regev H (1995) ldquoFirm productivity in Israeli industry 1979-1988rdquo Journal of
Econometrics 65(1) 175ndash203
Guner N Ventura G and Xu Y (2008) ldquoMacroeconomic implications of size-dependent policiesrdquo Review of Economic Dynamics 11(4) 721-744
Halpern L Koren M and Szeidl A (2015) ldquoImported inputs and productivityrdquo American Economic
Review 105(12) 3660-3703
Halpern L and Murakoumlzy B (2007) ldquoDoes distance matter in spilloverrdquo Economics of Transition
15(4) 781-805
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Haltiwanger J Kulick R and Syverson C (2018) ldquoMisallocation measures The distortion that ate
the residualrdquo NBER Working Papers (No w24199) National Bureau of Economic Research University
of Chicago Press Chicago
Harasztosi P (2011) ldquoGrowth in Hungary 1994-2008 The role of capital labour productivity and
reallocationrdquo MNB Working Papers 201112
Harasztosi P and Lindner A (2017) ldquoWho Pays for the Minimum Wagerdquo Mimeo
Haskel J and Sadun R (2012) ldquoRegulation and UK retailing productivity Evidence from microdatardquo Economica 79(315) 425-448
Haskel J E Pereira S C and Slaughter M J (2007) ldquoDoes inward foreign direct investment boost
the productivity of domestic firmsrdquo The Review of Economics and Statistics 89(3) 482-496
Hausmann R and Rodrik D (2003) ldquoEconomic development as self-discoveryrdquo Journal of
Development Economics 72(2) 603-633
Hausmann R Hwang J and Rodrik D (2007) ldquoWhat you export mattersrdquo Journal of Economic
Growth 12(1) 1-25
Herrendorf B Rogerson R and Valentinyi A (2014) ldquoGrowth and structural transformationrdquo
Handbook of Economic Growth (Vol 2) Elsevier 855-941
Hopenhayn H A (2014) ldquoFirms misallocation and aggregate productivity A reviewrdquo Annual Review
of Economics 6(1) 735-770
Hornok C and Murakoumlzy B (2018) ldquoMarkups of exporters and importers Evidence from Hungaryrdquo
The Scandinavian Journal of Economics forthcoming
Hsieh C T and Klenow P J (2009) Misallocation and manufacturing TFP in China and Indiardquo The
Quarterly Journal of Economics 124(4) 1403-1448
Hsieh C T and Olken B A (2014) ldquoThe missing missing middlerdquo Journal of Economic Perspectives 28(3) 89-108
Huttunen K (2007) ldquoThe effect of foreign acquisition on employment and wages Evidence from Finnish establishmentsrdquo The Review of Economics and Statistics 89(3) 497-509 Inklaar R and Timmer M P (2008) ldquoGGDC productivity level database International comparisons of output inputs and productivity at the industry levelrdquo Groningen Growth and Development Centre Research Memorandum GD-104 University of Groningen Groningen
Inklaar R and Timmer M P (2009) ldquoProductivity convergence across industries and countries The
importance of theory-based measurementrdquo Macroeconomic Dynamics 13(S2) 218-240
Iwasaki I Csizmadia P Illeacutessy M Makoacute C and Szanyi M (2012) ldquoThe nested variable model of
FDI spillover effects Estimation using Hungarian panel datardquo International Economic Journal 26(4)
673-709
Javorcik B S (2004) ldquoDoes foreign direct investment increase the productivity of domestic firms In
search of spillovers through backward linkagesrdquo American Economic Review 94(3) 605-627
Productivity differences in Hungary and mechanisms of TFP growth slowdown
149
Javorcik B S and Spatareanu M (2011) ldquoDoes it matter where you come from Vertical spillovers
from Foreign Direct Investment and the origin of investorsrdquo Journal of Development Economics 96(1)
126-138
Jaumlger K (2017) ldquoEU KLEMS growth and productivity accounts 2017 releaserdquo Statistical Module
Retrieved from httpwwweuklemsnettcb2017metholology_eu20klems_2017pdfKaacutetay G and
Wolf Z (2004) ldquoInvestment behavior user cost and monetary policy transmission The case of
Hungaryrdquo MNB Working Papers 200412
Kertesi G and Koumlllő J (2004) ldquoFighting low equilibriarsquo by doubling the minimum wage Hungarys
experimentrdquo IZA Discussion Papers (No 970)
Konings J (2001) ldquoThe effects of Foreign Direct Investment on domestic firmsrdquo Economics of
Transition 9(3) 619-633
Koumlllő J (2010) ldquoHungary The consequences of doubling the minimum wagerdquo In D Vaughan-
Whitehead (Ed) The Minimum Wage Revisited in the Enlarged EU Chapter 8 Edward Elgar
Publishing Cheltenham UK
Kugler M (2006) ldquoSpillovers from Foreign Direct Investment Within or between industriesrdquo Journal
of Development Economics 80(2) 444-477
Kuusk A Staehr K and Varblane U (2017) ldquoSectoral change and labour productivity growth
during boom bust and recovery in Central and Eastern Europerdquo Economic Change and Restructuring
50(1) 21-43
Levinsohn J and Petrin A (2003) ldquoEstimating production functions using inputs to control for
unobservablesrdquo The Review of Economic Studies 70(2) 317-341
Lin P Liu Z and Zhang Y (2009) ldquoDo Chinese domestic firms benefit from FDI inflow Evidence
of horizontal and vertical spilloversrdquo China Economic Review 20(4) 677-691
McGowan M A Andrews D and Millot V (2017) ldquoThe walking dead Zombie firms and productivity
performance in OECD countriesrdquo OECD Economics Department Working Papers (No 1372)
McMillan M Rodrik D and Sepulveda C (2017) ldquoStructural change fundamentals and growth A
framework and case studiesrdquo NBER Working Papers (No w23378) National Bureau of Economic
Research University of Chicago Press Chicago
Melitz J (2003) ldquoThe impact of trade on intra-industry reallocations and aggregate industry
productivityrdquo Econometrica 71(6) 1695-1725
Nicolini M and Resmini L (2010) ldquoFDI spillovers in new EU member statesrdquo Economics of
Transition 18(3) 487-511
OECD (2016) ldquoThe productivity-inclusiveness nexus Preliminary versionrdquo OECD Publishing Paris
httpdxdoiorg1017879789264258303-en
Olley G and Pakes A (1996) ldquoThe dynamics of productivity in the telecommunications equipment
industryrdquo Econometrica 64(6) 1263-1297
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Perani G and Cirillo V (2015) ldquoMatching industry classifications A method for converting NACE
Rev2 to NACE Rev1rdquo Working Papers (No 1502) University of Urbino Carlo Bo
Petrin A and Levinsohn J (2012) ldquoMeasuring aggregate productivity growth using plant‐level datardquo
The RAND Journal of Economics 43(4) 705-725
Petrin A Reiter J and White K (2011) ldquoThe impact of plant-level resource reallocations and
technical progress on US macroeconomic growthrdquo Review of Economic Dynamics 14(1) 3ndash26
Raff H and Schmitt N (2016) ldquoRetailing and international traderdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 157-179
Ratchford B T (2016) ldquoRetail productivityrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 54-72
Restuccia D and Rogerson R (2017) ldquoThe causes and costs of misallocationrdquo Journal of Economic
Perspectives 31(3) 151-74
Rovigatti G and Mollisi V (2016) ldquoPRODEST Stata module for production function estimation based
on the control function approachrdquo Statistical Software Components S458239 Boston College
Department of Economics Revised 12 Jun 2017 Accessed October 26 2017
httpsideasrepecorgcbocbocodes458239html
Sadun R (2015) ldquoDoes planning regulation protect independent retailersrdquo Review of Economics and Statistics 97(5) 983-1001
Scarpetta S Hemmings P Tressel T and Woo J (2002) ldquoThe role of policy and institutions for
productivity and firm dynamics Evidence from micro and industry datardquo OECD Economics Department
Working Papers (No 329) Available at SSRN httpsssrncomabstract=308680 or
httpdxdoiorg102139ssrn308680
Smith H (2016) ldquoThe economics of retailer-supplier pricing relationships Theory and evidencerdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 97-136
Smeets R (2008) ldquoCollecting the pieces of the FDI knowledge spillovers puzzlerdquo The World Bank
Research Observer 23(2) 107-138
Syverson C (2011) ldquoWhat determines productivityrdquo Journal of Economic Literature 49(2) 326-65
Taglioni D and Winkler D (2016) ldquoMaking global value chains work for developmentrdquo The World
Bank Issue 143 1-10
Topalova P and Khandelwal A (2011) ldquoTrade liberalization and firm productivity The case of
Indiardquo Review of Economics and Statistics 93(3) 995-1009
Viviano E (2008) ldquoEntry regulations and labour market outcomes Evidence from the Italian retail trade sectorrdquo Labour Economics 15(6) 1200-1222
Vogel A and Wagner J (2010) ldquoHigher productivity in importing German manufacturing firms Self-selection learning from importing or bothrdquo Review of World Economics 145(4) 641-665
Wagner J (2007) ldquoExports and productivity A survey of the evidence from firm‐level datardquo The
World Economy 30(1) 60-82
Productivity differences in Hungary and mechanisms of TFP growth slowdown
151
Wooldridge J M (2009) ldquoOn estimating firm-level production functions using proxy variables to
control for unobservablesrdquo Economics Letters 104(3) 112-114
Zhang Y Li H Li Y and Zhou L A (2010) ldquoFDI spillovers in an emerging market The role of
foreign firms country origin diversity and domestic firms absorptive capacityrdquo Strategic Management
Journal 31(9) 969-989
Appendix
152
APPENDIX
A3 Chapter 3 Internationally comparable data sources and methodology
A31 EU KLEMS amp OECD STAN
The EU KLEMS project aimed at creating a database on measures of economic growth productivity
employment creation capital formation and technological change at the industry level for all European
Union member states from 1970 onwards The database provides an important input to policy
evaluation in particular for the assessment of the goals concerning competitiveness and economic
growth potential as established by the Lisbon and Barcelona summit goals
The input measures include various categories of capital labour energy material and service inputs
Productivity measures have also been developed in particular with growth accounting techniques
Several measures on knowledge creation have also been constructed
The basic data of the EU KLEMS is also available in the OECD STAN database sometimes in a more up
to date version We have downloaded the following variables from there
- EMPE Number of employees
- EMPN Number of persons engaged ndash total employment
- SELF Number of self-employed
- VALU Value added current prices (millions of national currency)
- VALK Value added volumes (current price of the reference year 2010 millions)
- VALP Value added deflators (reference year 2010 = 100))
Labour productivity is defined as gross value added at constant prices divided by the number of
persons engaged In order to create comparative labour productivity levels we used the 2005
benchmark from the GGDC Productivity Level Database111 This project provides productivity levels
relative to the USA that can be used together with EU KLEMS growth accounts to create comparable
productivity level extrapolations (Inklaar and Timmer 2008 Inklaar and Timmer 2009)
A32 OECD Structural and Demographic Business Statistics
The OECD Structural and Demographic Business Statistics (SDBS) consists of two databases the
OECD Business Demography Indicators (BDI) and the OECD Structural Business Statistics (SBS)
The OECD Business Demography Indicators (BDI) database contains data on births and deaths of
enterprises their life expectancy and the important role they play in economic growth and
productivity The OECD Structural Business Statistics (SBS) database features the data collection
of the Statistics Directorate relating to a number of key variables such as for example value added
operating surplus employment and the number of business units broken down by ISIC Rev 4
industry groups referred to as the Structural Statistics on Industry and Services (SSIS) database and
by economic sector and enterprise size class referred to as the Business Statistics by Size Class (BSC)
database For most countries the main sources of information used in the compilation of structural
business statistics are business surveys economic censuses and business registers
111 More information can be found on the homepage of GGDC Production Level Database
httpswwwrugnlggdcproductivitypldearlier-release
Productivity differences in Hungary and mechanisms of TFP growth slowdown
153
The statistical population is composed of enterprises (or establishments when no data on enterprises
are available) In the case of BDI database the population contains all enterprises including non-
employers ie enterprises with no employees while the population of SBS contains only the employer
enterprises ie firms with at least one employee
Birth rate of all enterprises is the ratio of the number of enterprise births and the number of
enterprises active in the reference period Births do not include entries into the population due to
mergers break-ups the split-off or restructuring of a set of enterprises It does not include entries
into a sub-population resulting only from a change of activity (Source BDI)
Death rate of all enterprises is the ratio of the number of enterprise deaths and the number of
enterprises active in the reference period Deaths do not include exits from the population due to
mergers take-overs break-ups or the restructuring of a set of enterprises It does not include exits
from a sub-population resulting only from a change of activity An enterprise is included in the count of
deaths only if it is not reactivated within two years Equally a reactivation within two years is not
counted as a birth (Source BDI)
Number of enterprises is a count of the number of enterprises active during at least a part of the
reference period (Source SBS)
A33 OECD Productivity Frontier
The OECD productivity frontier dataset is based on AMADEUSORBIS and calculates comparable labour
productivity and TFP (MFP) measures across countries The project aims at defining the most
productive (frontier) enterprises both globally and for every country at the 2-digit industry level
(Andrews et al 2016)
Here we use data kindly provided by the OECD for the global and the Hungarian national productivity
frontier Two types of productivity measures are presented labour productivity and Wooldridge MFP
Both frontier series are defined as the average of log-productivity of the top 10 within each 2-digit
industry and year To make this measure less sensitive to expanding coverage over time the 10 is
chosen based on the median number of observations within a 2-digit industry The median for each 2
digit industry is calculated over all the years retained in the analysis
A key issue with AMADEUSORBIS with regard to Hungary is its changing coverage (see Box in Chapter
2) This makes these comparisons meaningful only from 20082009 onwards The underlying sample
includes all firms that over their observed lifespan had at least 20 employees on average
To arrive at internationally comparable real series 2-digit country specific industry value added and
investment deflators were used (2005 = 1) and the monetary values were converted to 2005 USDs
using industry level PPPs from the Groningen Growth and Development Centrersquos Productivity Level
Database112
112 For more information visit the Centrersquos homepage httpswwwrugnlggdcproductivitypld
Appendix
154
A4 Chapter 4 Evolution of the Productivity Distribution
Table A41 Average TFP growth with alternative TFP measures
A) Market economy
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 19 74 16 60
2006 93 119 95 97
2007 39 56 49 65
2008 -10 -04 -06 01
2009 -69 -82 -65 -63
2010 11 80 05 60
2011 34 40 31 45
2012 21 01 24 18
2013 30 22 22 22
2014 40 59 36 48
2015 52 49 50 43
2016 20 03 25 12
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Manufacturing
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 20 114 24 127
2006 114 149 118 137
2007 78 71 86 98
2008 17 -17 32 -11
2009 -133 -117 -120 -87
2010 80 173 85 178
2011 04 18 01 25
2012 -02 -58 07 -38
2013 -12 05 -15 16
2014 -01 27 01 34
2015 30 14 34 19
2016 04 -23 14 -05
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Productivity differences in Hungary and mechanisms of TFP growth slowdown
155
C) Market services
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 10 32 06 01
2006 79 90 82 64
2007 24 48 35 44
2008 -21 -03 -19 05
2009 -52 -71 -51 -54
2010 -11 26 -19 -05
2011 43 57 40 57
2012 30 48 31 57
2013 39 29 31 25
2014 46 78 39 55
2015 54 72 52 58
2016 25 20 29 23
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table presents growth rates of TFP estimated with the translog ACF estimator and the Fixed Effects
estimator for lsquomarket industriesrsquo (see Section 25) The sample does not include agriculture mining and financial
services Services include construction and utilities
Appendix
156
Table A42 Unweighted TFP growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 21 -02 -09 144
2006 118 143 58 47
2007 59 43 90 348
2008 -09 79 17 111
2009 -53 -191 -197 -139
2010 80 76 85 130
2011 -22 17 10 153
2012 01 14 -57 -06
2013 -38 20 -38 54
2014 -03 -05 08 33
2015 61 04 -19 132
2016 09 -10 12 91
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Market Services
Year KIS LKIS Construction Utilities
2005 127 16 -01 -46
2006 166 75 94 66
2007 13 58 60 16
2008 -16 14 -37 -28
2009 -63 -94 -15 44
2010 54 12 -08 23
2011 97 46 77 -29
2012 12 74 06 -57
2013 12 30 60 -71
2014 78 89 65 -31
2015 106 70 22 12
2016 16 31 -47 37
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the unweighted average ACF TFP growth rate by technology category (see Section 25)
Only firms with at least 5 employees The sample does not include agriculture and financial services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
157
Table A43 Employment-weighted labour productivity growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 172 32 73 300
2006 266 114 54 10
2007 121 52 69 243
2008 -25 -17 -03 126
2009 31 -151 -186 35
2010 135 114 199 207
2011 -33 -10 96 96
2012 03 -34 -32 -226
2013 -35 22 26 253
2014 33 19 53 94
2015 82 -04 -06 102
2016 34 18 08 -110
Average
2004-2007 186 66 65 184
2007-2010 47 -18 03 123
2010-2013 -21 02 24 35
2013-2016 28 14 20 85
B) Services
Year KIS LKIS Construction Utilities
2005 127 -05 41 -31
2006 166 75 21 54
2007 13 11 25 -36
2008 -16 -19 05 -02
2009 -63 -117 09 04
2010 54 -01 -05 13
2011 97 47 54 13
2012 12 62 19 -47
2013 12 21 62 -44
2014 78 55 64 -39
2015 106 54 07 65
2016 16 49 -60 43
Average
2004-2007 102 27 29 -04
2007-2010 -08 -46 03 05
2010-2013 40 48 24 -01
2013-2016 53 45 18 06
Notes This table shows the employment-weighted average LP growth rate by technology category (see Section
25) Only firms with at least 5 employees The sample does not include agriculture and financial services
Appendix
158
Table A44 The share of firms in the top decile ()
A) By size
2004 2007 2010 2013 2016
5-9 emp 1049 1051 1043 1096 1045
10-19 emp 954 962 92 904 92
20-49 emp 994 903 939 856 998
50-99 emp 896 1024 1188 1009 1096
100- emp 721 81 839 748 728
B) By ownership
2004 2007 2010 2013 2016
Domestic 833 818 814 824 837
Foreign 2344 2499 2422 2384 2488
State 554 728 81 575 695
C) By region
2004 2007 2010 2013 2016
Central HU 567 568 59 56 549
Northern
Hungary 195 116 19 208 224
Northern
Great Plain 161 178 239 23 249
Southern
Great Plain 137 118 17 258 179
Central
Transdanubia 276 33 332 369 332
Western
Transdanubia 311 283 244 361 444
Southern
Transdanubia 184 201 235 143 181
Notes Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
159
Figure A41 Persistence of top decile status
Notes This figure shows how many of top decile firms in year 2010 were frontier in 2013 how many exited and
how many continued as non-frontier The first panel shows this transition matrix for different 3-year periods
Appendix
160
A5 Chapter 5 Allocative Efficiency
Table A51 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168
C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209
D - Electricity gas
steam and AC
6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743
E - Water supply
sewerage waste
6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872
F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023
G - Wholesale and
retail trade
6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303
H - Transportation
and storage
6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985
I - Accommodation
food service
6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250
J - Information and
Communication
6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046
M - Professional
Scientific and Tech Act
6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075
N - Administrative and support service
6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
161
Table A52 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692
C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109
D - Electricity
gas steam and
AC
6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291
E - Water supply
sewerage waste
6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916
F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436
G - Wholesale and
retail trade
6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260
H - Transportation
and storage
6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776
I -
Accommodation
food service
6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899
J - Information
and
Communication
6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800
M - Professional
Scientific and
Tech Act
6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242
N - Administrative
and support service
6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
162
Table A53 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covar
iance
unweight
ed LP
weighted
LP
covar
iance
B - Mining and quarrying 7509 8072 0564 8038 8583 0546 8378 9440 1063 8609 9028 0419
C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796
D - Electricity gas steam and
AC
9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197
E - Water supply sewerage waste
8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703
F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096
G - Wholesale and retail trade 7955 8252 0297 8036 8452 0415 7955 8307 0352 8197 8589 0392
H - Transportation and
storage
8364 8475 0110 8300 8525 0224 8194 7698 -
0496
8292 7289 -
1003
I - Accommodation food
service
7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651
J - Information and Communication
8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988
M - Professional Scientific and Tech Act
8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406
N - Administrative and
support service
7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238
Productivity differences in Hungary and mechanisms of TFP growth slowdown
163
Table A54 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance
B - Mining and
quarrying
7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358
C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960
D - Electricity gas
steam and AC
9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289
E - Water supply
sewerage waste
8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064
F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159
G - Wholesale and
retail trade
7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037
H - Transportation
and storage
8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022
I - Accommodation
food service
7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363
J - Information and
Communication
7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263
M - Professional
Scientific and Tech
Act
7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431
N - Administrative
and support service
7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216
Appendix
164
Table A55 Allocative efficiency based on Hsieh-Klenow (2009) ndash 1 digit industries
Distortions in 2001 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4802 1540 0299 0803 19127 1152
C - Manufacturing 5620 1300 0425 0818 12807 1008
D - Electricity gas steam and AC 6760 0503 0591 0456 6171 0784
E - Water supply sewerage waste 6629 0599 0103 1127 6245 1248
F - Construction 6706 0818 0280 0954 21186 1227
G - Wholesale and retail trade 7225 1088 0395 1007 21997 1211
H - Transportation and storage 6073 0984 -0154 1647 15193 1144
I - Accommodation food service 6201 0684 -0025 0919 7951 1263
J - Information and Communication 5499 1273 0549 0603 5387 1265
M - Professional Scientific and Tech Act 6961 0920 0253 1062 45052 1293
N - Administrative and support service 6778 1237 0084 1020 42372 1546
Productivity differences in Hungary and mechanisms of TFP growth slowdown
165
Table A55- continuedhellip
Distortions in 2005 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4211 1121 0269 0669 12217 0953
C - Manufacturing 5919 1173 0497 0890 13439 0998
D - Electricity gas steam and AC 6569 0880 0596 0553 6400 1181
E - Water supply sewerage waste 6433 0722 0091 1277 9084 1126
F - Construction 6794 0744 0155 0947 20440 1099
G - Wholesale and retail trade 7497 1199 0392 0771 20492 1543
H - Transportation and storage 6305 1063 0017 1205 11362 1232
I - Accommodation food service 6085 0660 0098 1287 5680 1239
J - Information and Communication 5867 1337 0608 0637 6375 1481
M - Professional Scientific and Tech Act 6926 0951 0129 1118 50400 1474
N - Administrative and support service 6904 1206 -0004 1055 47387 1649
Appendix
166
Table A55- continuedhellip
Distortions in 2010 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales
taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4219 0669 -0104 0759 11170 1012
C - Manufacturing 6024 1201 0523 0740 12732 1001
D - Electricity gas steam and AC 7260 1273 0813 0433 12091 1565
E - Water supply sewerage waste 6474 0700 0123 0965 13717 1279
F - Construction 6621 0775 0200 1075 30395 1437
G - Wholesale and retail trade 7471 1230 0310 0842 22833 1527
H - Transportation and storage 6517 1250 0123 1030 9632 1459
I - Accommodation food service 6080 0704 0001 1060 5570 1341
J - Information and Communication 5989 1245 0581 0870 11895 1572
M - Professional Scientific and Tech Act 7076 1042 0130 1077 78642 1486
Productivity differences in Hungary and mechanisms of TFP growth slowdown
167
Table A55- continuedhellip
Distortions in 2016 Productivity Productivity dispersion
Median implicit sales
taxes
Dispersion of implicit sales
taxes
Median implicit cost
of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4484 0705 0264 0601 13655 0812
C - Manufacturing 6022 1110 0514 0971 11130 1074
D - Electricity gas steam and AC 7341 0966 0724 0307 36231 2054
E - Water supply sewerage waste 6363 0763 0015 1134 15926 1399
F - Construction 6938 0809 0298 0868 28761 1453
G - Wholesale and retail trade 7511 1005 0312 0959 26886 1576
H - Transportation and storage 6656 0972 0104 1078 16755 1745
I - Accommodation food service 6492 0672 0163 0943 6439 1443
J - Information and Communication 6211 1165 0422 0747 23648 1609
M - Professional Scientific and Tech Act 7188 0956 0148 1223 72383 1567
N - Administrative and support service 7112 1219 -0081 1109 98641 1801
Notes Total factor productivity is measured by the method of Ackerberg et al (2015) See Chapter 52 for details
Appendix
168
Appendix Figure 51 Weighted and unweighted labour productivity by 2-digit industry 2016 firms with at least 5 employees
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted log labour productivity in 2016 while the horizontal axis shows its
weighted log labour productivity in the same year We have omitted industries with less than 1000 observations
Productivity differences in Hungary and mechanisms of TFP growth slowdown
169
Appendix Figure 52 The relationship between weighted and unweighted labour productivity by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted labour productivity levels run at the 2-digit industry level
separately for 2005 2010 and 2016
Appendix
170
Appendix Figure 53 the change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences between the weighted and unweighted
labour productivity) in 2010 while the vertical axis shows the same quantity in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
171
A6 Chapter 6 Reallocation
Table A61 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -93 -38 10 -65 -02 -10 50 -43
C Manufacturing 108 23 48 36 -02 -11 03 05
D Electricity gas 08 07 05 -04 26 -06 22 10
E Water supply sewerage 17 -17 31 03 08 -09 09 09
F Construction 26 04 08 13 -14 -02 -19 07
G Wholesale and retail trade 30 03 11 16 -55 -08 -65 18
H Transportation and storage -21 14 -43 08 -39 10 -57 08
I Accommodation 68 -07 53 22 -44 00 -37 -07
J ICT 96 10 63 23 29 -24 35 18
M Professional scientific 39 -13 35 17 -38 -04 -26 -08
N Administrative and support 104 11 37 56 -49 -02 -04 -43
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying 08 11 09 -12 41 60 -30 10
C Manufacturing -18 07 -30 05 10 -08 22 -04
D Electricity gas -26 26 -70 18 74 07 31 36
E Water supply sewerage -08 15 -05 -18 -04 05 00 -09
F Construction 42 03 26 13 01 06 -16 11
G Wholesale and retail trade 54 01 32 21 68 04 56 07
H Transportation and storage 89 14 51 25 21 -30 06 45
I Accommodation 85 -05 59 32 51 -03 46 08
J ICT 19 -02 11 10 47 -02 38 11
M Professional scientific 69 05 12 53 30 -04 18 16
N Administrative and support 50 00 36 14 106 01 87 18
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
172
Table A62 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -253 -59 -19 -175 73 -02 39 36
C Manufacturing 105 20 51 34 06 -14 03 16
D Electricity gas 06 09 03 -06 14 -14 23 05
E Water supply sewerage 21 -12 32 02 -06 -09 00 03
F Construction 35 07 12 16 -23 -03 -24 04
G Wholesale and retail trade 27 06 06 16 -39 03 -59 17
H Transportation and storage -34 17 -58 06 -33 14 -58 11
I Accommodation 67 -09 50 26 -42 03 -39 -05
J ICT 85 14 39 32 27 -14 21 19
M Professional scientific 46 -07 28 25 -28 -03 -22 -03
N Administrative and support 122 29 28 65 -49 00 -09 -40
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -08 11 07 -26 24 -03 06 22
C Manufacturing -12 06 -25 07 06 -04 11 -01
D Electricity gas 16 16 -15 15 30 00 25 06
E Water supply sewerage -07 15 -02 -20 -14 -01 02 -15
F Construction 45 03 22 20 10 02 -05 12
G Wholesale and retail trade 45 02 21 22 68 04 53 10
H Transportation and storage 85 13 45 27 75 00 08 66
I Accommodation 81 -04 54 30 51 -02 42 11
J ICT 13 00 00 13 49 10 33 06
M Professional scientific 64 08 11 45 32 00 14 18
N Administrative and support 50 08 15 27 80 19 49 12
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
173
Table A63 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 93 24 44 26 105 12 59 34
C Manufacturing 132 34 54 44 08 19 -12 01
D Electricity gas 13 -04 09 08 41 02 25 14
E Water supply sewerage 45 -02 37 09 -08 -09 04 -03
F Construction 24 07 10 07 -01 07 -09 01
G Wholesale and retail trade 38 08 12 18 -67 -04 -73 10
H Transportation and storage -25 06 -28 -04 -47 03 -56 06
I Accommodation 59 -03 56 07 -74 -12 -41 -21
J ICT 58 19 80 -40 20 -21 40 00
M Professional scientific 61 11 34 16 -67 02 -26 -43
N Administrative and support 61 -20 38 43 -63 -24 -11 -29
2010-2013 2013-2016
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 26 04 -01 24 -29 17 -28 -18
C Manufacturing 00 14 -21 07 33 16 19 -01
D Electricity gas -43 26 -85 16 90 25 13 52
E Water supply sewerage -20 -07 -08 -05 04 -03 06 01
F Construction 40 05 26 10 -03 05 -05 -03
G Wholesale and retail trade 49 05 31 13 68 13 57 -01
H Transportation and storage 59 12 46 01 09 -27 15 20
I Accommodation 74 -07 55 26 47 -08 54 02
J ICT 16 -07 11 12 22 -25 42 05
M Professional scientific 70 20 16 33 45 13 25 06
N Administrative and support 61 08 31 21 81 -11 76 15
Appendix
174
Table A64 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 48 15 -01 34 70 17 53 00
C Manufacturing 132 32 56 45 16 14 -03 05
D Electricity gas 14 -03 05 11 35 -07 30 12
E Water supply sewerage 48 00 39 09 -10 -06 03 -07
F Construction 28 10 14 04 03 06 -14 11
G Wholesale and retail trade 38 10 07 21 -47 07 -61 07
H Transportation and storage -35 09 -40 -04 -41 06 -57 10
I Accommodation 62 -03 52 12 -65 -09 -43 -13
J ICT 00 -15 49 -34 03 -22 14 12
M Professional scientific 75 20 27 28 -46 02 -27 -22
N Administrative and support 91 -05 25 71 -60 -11 -07 -42
2010-2013 2013-2016
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 33 -11 04 40 50 28 05 17
C Manufacturing 06 13 -15 07 28 12 16 00
D Electricity gas 16 18 -26 25 23 17 02 05
E Water supply sewerage -17 -06 -05 -05 04 -04 08 00
F Construction 44 05 26 14 03 02 07 -07
G Wholesale and retail trade 37 05 17 15 65 12 54 -01
H Transportation and storage 56 11 42 04 46 -07 16 36
I Accommodation 70 -07 52 25 44 -07 51 01
J ICT 26 07 04 16 17 -20 37 00
M Professional scientific 56 17 11 28 52 16 23 13
N Administrative and support 65 17 27 22 59 06 41 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
175
A7 Chapter 7 Firm-level productivity growth and dynamics
A71 Productivity growth
Table A71 Relationship between lagged productivity level and subsequent productivity
growth over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 -0188 -0203 -0203
(000550) (000558) (000551)
TFP in t-1 Year 2006 -0222 -0238 -0235
(000518) (000525) (000519)
TFP in t-1 Year 2009 -0143 -0159 -0155
(000570) (000579) (000572)
TFP in t-1 Year 2012 -0156 -0172 -0171
(000516) (000524) (000517)
Year 2003 -00313 -00297
(000507) (000510)
Year 2006 -0184 -0183
(000489) (000491)
Year 2009 -00766 -00762
(000492) (000493)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 114200 113900 113900
R-squared 0061 0067 0084
Appendix
176
Table A72 Relationship between lagged productivity levels and subsequent productivity
growth by size and age
Dep var TFP growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 -0170 -0186 -0213 -0223
(000561) (000578) (00155) (00155)
TFP in t-1 Group 2 00397 00243 -000502 -000776
(00146) (00147) (00213) (00213)
TFP in t-1 Group 3 00793 00652 00725 00600
(00221) (00222) (00164) (00165)
TFP in t-1 Group 4 00753 00666
(00244) (00247)
Group 2 00227 000593 -0000410 0000118
(000940) (000963) (00162) (00162)
Group 3 00216 -000934 00235 00220
(00150) (00154) (00131) (00132)
Group 4 00235 -00351
(00157) (00169)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Observations 30135 30062 30135 30062
R-squared 0056 0073 0056 0073
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees size group 4 is
100+ employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5
years age group 3 is firms older than 5 The baseline category is firms of 2-3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
177
Table A73 Differences in productivity growth by ownership group within different firm
groups
Dep var TFP growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 00476
(00114)
Foreign Non-exporter 00573
(00213)
Foreign Exporter 00610
(00139)
Foreign Size group 1 00295
(00162)
Foreign Size group 2 00849
(00243)
Foreign Size group 3 000361
(00340)
Foreign Size group 4 00662
(00318)
Foreign Age group 1 0119
(00381)
Foreign Age group 2 -00117
(00363)
Foreign Age group 3 00467
(00124)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 31642 31642 31642 31274
R-squared 0032 0033 0033 0033
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column 2 and size and age group dummies in columns 3 and 4 respectively
Appendix
178
Table A74 Relationship between lagged productivity levels and subsequent productivity
growth by ownership and exporter status over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
Firm categories by
foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003
00577 00607 00141 00214
(00151) (00151) (00124) (00124)
TFP in t-1 Firm group Year 2006
00703 0101 00361 00558
(00152) (00152) (00118) (00118)
TFP in t-1 Firm group Year 2009
00338 00306 00450 00406
(00153) (00153) (00122) (00121)
TFP in t-1 Firm group Year 2012
00758 00436 00474 00321
(00146) (00146) (00109) (00109)
Firm group Year 2003
00978 00756 00286 000961
(00128) (00130) (000912) (000977)
Firm group Year 2006
-00290 -00145 -00592 -00411
(00133) (00135) (000871) (000932)
Firm group Year 2009
0114 0116 00502 00457
(00124) (00127) (000824) (000881)
Firm group Year 2012
0120 0120 00234 00155
(00126) (00129) (000782) (000835)
Year FE YES YES
Industry FE YES YES
Firm-level controls
YES YES YES YES
Region FE YES YES
Industry-year FE
YES YES
Observations 112374 112374 113900 113900
R-squared 0066 0085 0065 0085
Notes Firm group refers to foreign ownership in columns (1) and (2) and to exporter status in
columns (3) and (4)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
179
A72 Employment growth
Table A75 Relationship between lagged productivity levels and subsequent employment
growth over time
Dep var employment growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0113 0113 0113
(000472) (000478) (000475)
TFP in t-1 Year 2006 0120 0120 0119
(000434) (000439) (000437)
TFP in t-1 Year 2009 0109 0109 0107
(000479) (000485) (000482)
TFP in t-1 Year 2012 00982 00958 00956
(000442) (000448) (000445)
Year 2003 -00171 -00125
(000441) (000444)
Year 2006 -0134 -0128
(000422) (000423)
Year 2009 -00899 -00873
(000425) (000426)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 123900 123574 123574
R-squared 0042 0049 0054
Appendix
180
Table A76 Relationship between lagged productivity levels and subsequent employment
growth over time with alternative employment growth measures including exiting firms
Dep var employment growth from t to t+3 (including exiting firms (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0156 0147 0148
(000641) (000647) (000644)
TFP in t-1 Year 2006 0134 0127 0128
(000581) (000587) (000584)
TFP in t-1 Year 2009 0139 0132 0134
(000648) (000655) (000651)
TFP in t-1 Year 2012 0132 0126 0127
(000618) (000624) (000621)
Year 2003 -00765 -00618
(000617) (000619)
Year 2006 -0220 -0211
(000586) (000587)
Year 2009 -0177 -0173
(000591) (000590)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 143011 142638 142638
R-squared 0037 0047 0051
Productivity differences in Hungary and mechanisms of TFP growth slowdown
181
Table A77 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status with alternative employment growth measures
including exiting firms
Dep var employment growth from t to t+3 (including exiting firms t=2012)
VARIABLES (1) (2) (3) (4) (5) (6)
TFP in t-1 0134 0130 0134 0137 0134 0136
(000651) (000660) (000722) (000729) (000764) (000767)
TFP in t-1 Foreign -00109 -00138 00116 000347
(00166) (00167) (00289) (00289)
TFP in t-1 Exporter -00371 -00256 -00304 -00226
(00124) (00126) (00148) (00148)
TFP in t-1 Foreign exporter -00222 -00165
(00364) (00365)
Foreign -00254 -00351 -0102 -00739
(00151) (00156) (00254) (00256)
Exporter 00998 00982 00940 00889
(00100) (00102) (00106) (00107)
Foreign exporter 00855 00605
(00312) (00315)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 34980 34980 35564 35473 34980 34980
R-squared 0031 0051 0037 0054 0034 0052
Appendix
182
Table A78 Differences in employment growth by exporter status within different firm
groups
Dep var employment growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Exporter 00876
(000741)
Exporter Domestic 00893
(000788)
Exporter Foreign 00703
(00207)
Exporter Size group 1 00858
(000850)
Exporter Size group 2 00872
(00159)
Exporter Size group 3 0154
(00276)
Exporter Size group 4 00345
(00329)
Exporter Age group 1 00968
(00230)
Exporter Age group 2 0139
(00212)
Exporter Age group 3 00810
(000801)
industry-region FE YES YES YES YES
Firm-group indicators YES YES YES
Observations 34418 33909 34418 33989
R-squared 0034 0034 0034 0036
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
183
Table A79 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status over time
Dep var Employment growth from t to t+3 (t=2003200620092012)
Firm categories by foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003 000927 00131 00178 00190
(00129) (00130) (00107) (00107)
TFP in t-1 Firm group Year 2006 00137 00103 00130 000821
(00126) (00127) (00101) (00101)
TFP in t-1 Firm group Year 2009 -00778 -00676 -00498 -00426
(00129) (00130) (00104) (00104)
TFP in t-1 Firm group Year 2012 -00389 -00321 -00350 -00306
(00126) (00126) (000942) (000942)
Firm group Year 2003 -00601 -00332 000244 00299
(00110) (00113) (000795) (000856)
Firm group Year 2006 -00159 -000559 00640 00786
(00112) (00115) (000752) (000807)
Firm group Year 2009 00404 00249 0111 00882
(00106) (00109) (000714) (000767)
Firm group Year 2012 -00102 -00116 00747 00607
(00110) (00112) (000684) (000735)
Year FE YES YES
Industry FE YES YES
Firm-level controls YES YES YES YES
Region FE YES YES
Industry-year FE YES YES
Observations 121954 121954 123574 123574
R-squared 0046 0055 0045 0055
Notes Firm group refers to foreign ownership in columns (1) and (2) and exporter status in columns
(3) and (4)
Appendix
184
A73 Entry and exit
Table A710 Entry and exit premium by ownership and exporter status
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
VARIABLES (1) (2) (3) (4) (5) (6)
Entry Domestic 00363 00433 Exit Domestic -0165 -0161 Exit Non-exporter
-0172 -0186
(00103) (00102) (00112) (00112) (00122) (00121)
Entry Foreign 0414 0354 Exit Foreign 0255 0203 Exit Exporter
0171 0126
(00284) (00281) (00311) (00309) (00213) (00211)
Incumbent Foreign
0512 0461 Continuing Foreign
0465 0411 Continuing Exporter
0279 0232
(00122) (00129) (00123) (00131) (000887) (000926)
Industry FE YES Industry FE YES Industry FE YES
Industry-region FE YES Industry-region FE
YES Industry-region FE
YES
Firm-level controls YES Firm-level controls
YES Firm-level controls
YES
Observations 44231 44231 Observations 38367 38367 Observations 39020 38916
R-squared 0355 0383 R-squared 0339 0369 R-squared 0331 0370
Table A711 Differences in productivity levels by ownership group within different firm
groups
Depvar TFP in year t (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 0429
(00118)
Foreign Non-exporter 0278
(00206)
Foreign Exporter 0397
(00146)
Foreign Size group 1 0523
(00162)
Foreign Size group 2 0472
(00254)
Foreign Size group 3 0416
(00363)
Foreign Size group 4 0235
(00341)
Foreign Age group 1 0258
(00352)
Foreign Age group 2 0381
(00356)
Foreign Age group 3 0460
(00131)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 38367 38367 38367 37822
R-squared 0350 0361 0353 0356
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
185
Table A712 Entry and exit premium by ownership and exporter status over time
Depvar TFP in year t (t=2006200920122015 for entry and t=2003200620092012 for exit)
VARIABLES (1) (2) VARIABLES (3) (4) VARIABLES (5) (6)
Entry Domestic Year 2006
-00510 -00403 Exit Domestic 2003 -0187 -0188 Exit Non-exporter 2003 -0197 -0198
(000924) (000923) (00107) (00106) (00114) (00113)
Entry Domestic Year 2009
00244 00230 Exit Domestic 2006 -00996 -0101 Exit Non-exporter 2006 -0114 -0118
(000999) (000996) (000917) (000911) (000977) (000971)
Entry Domestic Year 2012
00594 00515 Exit Domestic 2009 -0105 -0113 Exit Non-exporter 2009 -0116 -0123
(000985) (000983) (000942) (000937) (00101) (00101)
Entry Domestic Year 2015
00475 00392 Exit Domestic 2012 -0140 -0150 Exit Non-exporter 2012 -0167 -0174
(000998) (000999) (00111) (00110) (00119) (00119)
Entry Foreign Year 2006
0374 0313 Exit Foreign 2003 0116 00940 Exit Exporter 2003 00659 00517
(00265) (00264) (00264) (00263) (00196) (00197)
Entry Foreign Year 2009
0423 0410 Exit Foreign 2006 0199 0153 Exit Exporter 2006 0194 0165
(00257) (00257) (00267) (00265) (00183) (00183)
Entry Foreign Year 2012
0342 0334 Exit Foreign 2009 0197 0184 Exit Exporter 2009 00720 00760
(00279) (00278) (00278) (00277) (00185) (00185)
Entry Foreign Year 2015
0382 0365 Exit Foreign 2012 0217 0223 Exit Exporter 2012 0114 0137
(00276) (00275) (00307) (00305) (00208) (00208)
Incumbent Foreign Year 2006
0485 0428 Continuing Foreign 2003 0416 0386 Continuing Exporter 2003 0278 0257
(00122) (00124) (00124) (00126) (000943) (000994)
Incumbent Foreign Year 2009
0410 0391 Continuing Foreign 2006 0498 0446 Continuing Exporter 2006 0317 0280
(00120) (00122) (00122) (00124) (000895) (000943)
Incumbent Foreign Year 2012
0436 0439 Continuing Foreign 2009 0414 0404 Continuing Exporter 2009 0194 0201
(00122) (00124) (00119) (00122) (000867) (000915)
Incumbent Foreign Year 2015
0471 0476 Continuing Foreign 2012 0412 0422 Continuing Exporter 2012 0211 0239
(00118) (00120) (00120) (00122) (000827) (000876)
Year FE YES Year FE YES Year FE YES
Industry FE YES Industry FE YES Industry FE YES
Firm-level controls YES YES Firm-level controls YES YES Firm-level controls YES YES
Industry-year FE YES Industry-year FE YES Industry-year FE YES
Region FE YES Region FE YES Region FE YES
Observations 164136 164136 Observations 155657 155657 Observations 157711 157711
R-squared 0369 0380 R-squared 0373 0386 R-squared 0374 0387
Table A713 Entry and exit premium by size and age
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
Firm categories by size age
VARIABLES (1) (2) VARIABLES (3) (4) (5) (6)
Entry Group 1 00233 00151 Exit Group 1 -0170 -0171 -0214 -0210
(00108) (00105) (00121) (00118) (00250) (00241)
Entry Group 2 0106 000987 Exit Group 2 -0201 -0260 -0286 -0260
(00298) (00289) (00280) (00272) (00271) (00261)
Entry Group 3 0124 00204 Exit Group 3 -0152 -0245 -0219 -0207
(00574) (00556) (00479) (00464) (00179) (00173)
Entry Group 4 0123 -00552 Exit Group 4 -0291 -0453
(00720) (00697) (00532) (00517)
Incumbent Group 2 00137 -00620 Continuing Group 2 -00108 -00902 -00277 -00256
(00104) (00101) (00111) (00109) (00170) (00164)
Incumbent Group 3 00163 -0130 Continuing Group 3 000582 -0148 -00759 -00758
(00170) (00168) (00179) (00176) (00131) (00127)
Incumbent Group 4 00150 -0268 Continuing Group 4 -00159 -0293
(00181) (00185) (00188) (00192)
Industry FE YES Industry FE YES YES
Industry-region FE YES Industry-region FE YES YES
Firm-level controls YES Firm-level controls YES YES
Observations 46160 46034 39020 38916 38459 38357
R-squared 0296 0355 0311 0369 0315 0374
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is 50-99
employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is firms of 4-5
years age group 3 is firms older than 5 years
Figure A71 Share of exiting firms in the subsequent 3 years by lagged productivity levels in
different periods
A8 Chapter 8 Retail
Appendix Table A81 Event study regression for the whole retail industry
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 0005 0014 0012 0030 -0230 -0511 (0006) (0005) (0005) (0005) (0030) (0049)
pre_trend_treated3 -0010 -0003 -0008 -0003 -0037 -0031 (0006) (0008) (0006) (0008) (0030) (0056)
pre_trend_treated4 -0020 -0010 -0014 0000 -0209 -0299 (0006) (0007) (0006) (0007) (0029) (0051)
pre_trend_treated5 0004 0011 0006 0017 -0128 -0258 (0007) (0008) (0007) (0008) (0034) (0064)
pre_trend_treated6 -0008 0001 -0004 0008 -0129 -0222 (0007) (0008) (0006) (0008) (0035) (0065)
pre_trend_treated7 0001 -0001 0016 0017 -0365 -0496 (0010) (0013) (0010) (0012) (0053) (0072)
trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)
trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)
trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)
trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)
post_trend_treated1 -0039 -0006 -0007 0044 -0885 -1394 (0012) (0012) (0012) (0011) (0061) (0096)
post_trend_treated2 0022 0003 0044 0048 -0665 -1273 (0012) (0012) (0011) (0011) (0068) (0100)
post_trend_treated3 -0001 0004 0035 0058 -0993 -1531 (0012) (0012) (0012) (0012) (0058) (0092)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 225866 209604 225860 209598 225908 209647
R-squared 0958 0978 0961 0980 0684 0809
Appendix
188
Appendix Table A82 Event study regression for NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 -0008 -0004 -0002 0016 -0189 -0576 (0004) (0005) (0004) (0005) (0033) (0055)
pre_trend_treated3 -0016 -0018 -0013 -0014 -0057 -0064 (0006) (0011) (0006) (0010) (0034) (0059)
pre_trend_treated4 -0010 -0005 -0002 0008 -0236 -0351 (0004) (0007) (0004) (0007) (0034) (0060)
pre_trend_treated5 -0004 0002 -0001 0010 -0129 -0304 (0006) (0008) (0006) (0008) (0037) (0068)
pre_trend_treated6 0011 0000 0018 0011 -0173 -0307 (0007) (0009) (0007) (0009) (0045) (0085)
pre_trend_treated7 -0016 -0032 0002 -0007 -0433 -0640 (0010) (0016) (0009) (0015) (0068) (0091)
trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)
trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)
trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)
trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)
post_trend_treated1 -0009 -0032 0033 0036 -1163 -1875 (0008) (0012) (0008) (0011) (0084) (0118)
post_trend_treated2 0057 -0024 0087 0041 -0888 -1810 (0014) (0013) (0012) (0012) (0097) (0121)
post_trend_treated3 0014 -0031 0060 0044 -1255 -2040 (0011) (0013) (0010) (0012) (0079) (0108)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 94740 87533 94737 87530 94740 87533
R-squared 0968 0982 0973 0985 0642 0809
Appendix Table A83 Sales and the number of different days in a month
(1)
Dependent ln sales
Sunday 0049 (0001)
Saturday 0059 (0001)
Friday 0054 (0001)
Thursday 0050 (0001)
Wednesday 0053 (0001)
Tuesday 0060 (0001)
Monday 0048 (0001)
holiday 0008 (0000)
Jan -0169 (0002)
Dec 0138 (0003)
summer 0032 (0002)
date -0000 (0000)
Observations 463345
R-squared 0970
Appendix
190
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doi 10287333213
ET-0
4-1
7-8
33-E
N-N
EUROPEAN COMMISSION
Directorate-General for Internal Market Industry Entrepreneurship and SMEs
Directorate A mdash Competitiveness and European Semester Unit A2 mdash European Semester and Member Statesrsquo Competitiveness
Contact Tomas Braumlnnstroumlm
E-mail GROW-A2eceuropaeu
European Commission B-1049 Brussels
EUROPEAN COMMISSION
Directorate-General for Internal Market Industry Entrepreneurship and SMEs
2018
PRODUCTIVITY DIFFERENCES
IN HUNGARY AND
MECHANISMS OF TFP GROWTH
SLOWDOWN
LEGAL NOTICE
This document has been prepared for the European Commission however it reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein
More information on the European Union is available on the Internet (httpwwweuropaeu)
Luxembourg Publications Office of the European Union 2018
ISBN 978-92-79-73462-5 doi 10287333213
copy European Union 2018
Reproduction is authorised provided the source is acknowledged
Europe Direct is a service to help you find answers
to your questions about the European Union
Freephone number ()
00 800 6 7 8 9 10 11
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
Table of contents
EXECUTIVE SUMMARY I
1 INTRODUCTION 1
2 DATA SOURCES 4
21 Cleaning the data and defining industry categories 5
22 Productivity estimation 6
23 Estimation sample 10
24 Firm-level variables 13
25 Industry categorization 16
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON 18
31 Convergence 18
32 Within-industry heterogeneity 24
33 Firm dynamics 28
34 Conclusions 31
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION 32
41 Context 32
42 Aggregate productivity and the self-employed 33
43 The evolution of productivity distribution in Hungary 36
44 Duality in productivity and productivity growth 47
45 Conclusions 56
5 ALLOCATIVE EFFICIENCY 58
51 Olley-Pakes efficiency 58
52 Product market and capital market distortions 62
53 Conclusions 70
6 REALLOCATION 73
61 Reallocation across industries 73
62 Reallocation across firms 76
63 Failure of reallocation Zombie firms 82
64 Conclusions 86
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS 89
71 Productivity growth 89
72 Employment growth 95
73 Entry and exit 99
74 Conclusions 103
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE 104
81 Context 104
82 Data 108
83 General trends 110
84 Allocative efficiency and reallocation 118
85 Trade 123
86 Policies Crisis taxes 131
87 Policies Mandatory Sunday closing 133
88 Conclusions 141
9 CONCLUSIONS 142
REFERENCES 144
APPENDIX 152
A3 Chapter 3 Internationally comparable data sources and methodology 152
A31 EU KLEMS amp OECD STAN 152
A32 OECD Structural and Demographic Business Statistics 152
A33 OECD Productivity Frontier 153
A4 Chapter 4 Evolution of the Productivity Distribution 154
A5 Chapter 5 Allocative Efficiency 160
A6 Chapter 6 Reallocation 171
A7 Chapter 7 Firm-level productivity growth and dynamics 175
A71 Productivity growth 175
A72 Employment growth 179
A73 Entry and exit 184
A8 Chapter 8 Retail 187
Productivity differences in Hungary and mechanisms of TFP growth slowdown
i
EXECUTIVE SUMMARY
Slow post-crisis total factor productivity (hereafter TFP) growth is a significant policy
challenge for many European countries in general and for Hungary in particular This
report aims at providing a comprehensive analysis of the processes behind productivity
growth slowdown in Hungary based on micro-data from administrative sources between
2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions A focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact detail of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Executive Summary
ii
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides an
exhaustive picture of Hungarian businesses It is important to keep in mind though that it
omits two important parts of the economy the overwhelming majority of the non-market
sector (including public works) and the self-employed Given the number of people
employed in these two sectors their performance has a strong effect on macro numbers
The available albeit scarce data for the self-employed qualify the findings by suggesting
that the measured productivity level and growth of this group is considerably below than
that of double-entry bookkeeping firms ndash implying that within-industry productivity
dispersion may be even larger than what is indicated by the balance sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
of capital increase on average its rise was disproportionally larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
Productivity differences in Hungary and mechanisms of TFP growth slowdown
iii
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly adding little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterized by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers have increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
The results of this report confirm that Hungary is atypical because of the relatively poor
productivity performance of frontier firms Importantly contrary to a strong version of the
duality concept this is not a result of Hungarian frontier firms being on the global frontier
typically they are quite far away from it This robust pattern underlines that besides
helping non-frontier firms policy may also have to focus on the performance of the
frontier group A transparent environment with a strong rule of law complemented by a
well-educated workforce and a robust innovation system is key for providing incentives to
invest into the most advanced technologies
Executive Summary
iv
The analysis in this report reinforces the impression that there is a large productivity gap
between globally engaged or owned and other firms the gap being about 35 percent in
manufacturing and above 60 percent in services This gap seems to be roughly constant in
the period under study The firm-level analysis in Chapter 7 also reveals that one of the
mechanisms which conserves the gap is that foreign frontier firms are able to increase
their productivity more than their domestic counterparts even from frontier levels These
findings reinforce the importance of well-designed policies that are able to help domestic
firms to catch up with foreign firms A key precondition for domestic firms to build linkages
with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A
more specific conclusion is the importance of enabling high-productivity domestic firms to
improve their productivity levels even further
The large within-industry productivity dispersion the relatively low (though not extreme
in international comparison) allocative efficiency documented in some of the industries the
strong positive contribution of reallocation to total TFP growth before the crisis and the
relatively low entry rate imply that policies promoting reallocation have a potential to
increase aggregate productivity levels significantly These policies can include improving
the general framework conditions by cutting administrative costs reducing entry and exit
barriers and using a neutral regulation The fact that capital market distortions still appear
to be significantly above their pre-crisis levels impliesthat policies that reduce financial
frictions may help the reallocation process The fact that exporters tend to expand faster
relative to non-exporters suggests that access to EU and global markets generate a strong
and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general
traded and more knowledge-intensive sectors fared better both in terms of productivity
growth and allocative efficiency The difference between traded and non-traded sectors
points once again to the importance of global competition in promoting higher productivity
and more efficient allocation of resources This also implies that adopting policies that
focus on innovation or reallocation in services may be especially important given the large
number of people working in those sectors The better performance of and reallocation into
more knowledge-intensive sectors underline the importance of education policies aimed at
developing up-to-date and flexible skills and the significance of innovation policies that
help to improve the knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people
working at firms with at least a few employees and those working in very small firms or as
self-employed The latter category represents 30-50 percent of the people engaged in
some important industries Inclusive policies may attempt to generate supportive
conditions for these people by providing knowledge and training as well as helping them
find jobs with wider perspectives or set up a well-operating firm The large share of these
unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a marked difference between the pre-
crisis period characterised by strong reallocation mainly via the expansion of large
foreign-owned chains and the post-crisis period with a stagnating share of large chains
This break is likely to be linked to post-crisis policies favouring smaller firms While halting
further concentration in a country with already one of the highest share of multinationals
in this sector can have a number of benefits in the long run it is likely to lead to higher
prices and lower industry-level productivity growth Policies should balance carefully
between these trade-offs Another key pattern identified is the increasing role of retailers
(and wholesalers) in trade intermediation both on the import and export side
Policymakers should encourage these trends and design policies which provide capabilities
for such firms to enter international markets probably via e-commerce
Productivity differences in Hungary and mechanisms of TFP growth slowdown
1
1 INTRODUCTION
Slow post-crisis TFP growth is a significant policy challenge for many European countries in
general and for Hungary in particular This report aims at providing a comprehensive
analysis of the processes behind productivity growth slowdown in Hungary based on
micro-data from administrative sources between 2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions The focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact details of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Introduction
2
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides a very
detailed and comprehensive picture of the Hungarian business economy It is important to
keep in mind though that it omits two important parts of the economy the overwhelming
majority of the non-market sector (including public works) and the self-employed Given
the number of people employed in these two sectors their performance has a strong effect
on macro numbers The available albeit scarce data for the self-employed qualify the
findings by suggesting that the measured productivity levels and growth of this group are
considerably below those of double-entry bookkeeping firms ndash implying that within-
industry productivity dispersion may even be larger than what is indicated by the balance
sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries the frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
Productivity differences in Hungary and mechanisms of TFP growth slowdown
3
of capital increase on average its rise was disproportionately larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly contributing little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterised by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
Data Sources
4
BOX 21 AMADEUS and the NAV balance sheet data
An alternative and frequently used source of balance sheet data is the AMADEUS dataset
In this box we compare the data about Hungary with the dataset used in this report
namely the administrative NAV panel
AMADEUS is a firm-level dataset collected and issued by Bureau Van Dijk a Moodyrsquos
Analytics Company It contains comprehensive financial information on around 21 million
companies across Europe with a focus on private company information It includes
information about company financials in a standard format (which makes it comparable
across countries) directors stock prices and detailed corporate ownership structures
(Global Ultimate Owners and subsidiaries) Financial information on firms consists of data
from balance sheets profit and loss statements and standard ratios Non-mandatory cells
are however often missing (eg employment) Therefore the drawbacks of this
database are that it is not representative and that not all firms provide enough
information to analyse issues such as productivity or TFP
Table B21 shows the coverage of AMADEUS (the number of firms as a share of the firms
in the administrative NAV data) by year and size category In earlier years the AMADEUS
sample consisted of mostly large firms but even the coverage of larger firms was
relatively low Recently the expanding coverage has made the AMADEUS sample more
representative While the smallest firms are still undersampled the coverage of firms with
more than 5 employees has reached nearly 100 (In some cases it is even above 100
because of slight differences in the number of employees reported)
The two databases also differ in terms of the variables they include The NAV data are
more detailed in terms of assets and liabilities AMADEUS in contrast provides more
information on ownership It defines the Global Ultimate Owner (GUO) for each company
and analyses their shareholding structure Ownership share is given in percentages and
in addition the degree of independence is also given
Our main aim in this report is to estimate productivity and its change reliably and
representatively for different types of firms small and large This requires a decent
coverage of all types of firms and reliable information on their finances for a number of
periods Because of this we prefer to use the NAV database with its large and universal
coverage and the rich information on firm inputs and outputs
2 DATA SOURCES
The main database we use in this project is the balance sheet panel of Hungarian firms
between 2000-2016 The balance sheet dataset is an administrative panel collected by the
National Tax Authority (NAV formerly APEH) from corporate tax declarations The
database includes the balance sheet and profit amp loss statements of all double-entry
bookkeeping Hungarian enterprises between 2000 and 2016 (see Section 42 for a brief
discussion of the size and the performance of the not double-entry bookkeeping sector of
the Hungarian economy) Besides key financial variables the database includes the
industry code of the firm the number of its employees its date of foundation the location
of its headquarters and whether it is domestically- or foreign-owned for each year
Productivity differences in Hungary and mechanisms of TFP growth slowdown
5
21 Cleaning the data and defining industry categories
We have taken a number of steps to clean the key variables in the balance sheet panel
First we impute missing observations for firms with more than 10 employees in the
preceding and following years For continuous variables we use the average of the
previous and following year values For categorical variables we use the value from the
previous year Similarly we impute missing data using lagged values for two of the largest
firms in year 2016
Then a baseline cleaning is applied to the values of all the financial variables to correct for
possible mistakes of reporting in HUF rather than 1000 HUF or for extremely small or big
values in the data Employment and sales are cleaned of extreme values and outliers
Suspiciously large jumps followed by another jump into the opposite direction are
smoothed by the average of the previous and following years Regarding capital stocks we
use the sum of tangible and intangible assets Whenever intangible assets are missing we
input a zero
We deflate the different variables with the appropriate price indices from the OECD STAN
which includes value added capital intermediate input and output price deflators at 2-
digit industry level1
Regarding industry codes the database in general includes the 2-digit industry code of a
firm in each year based on the actual industry classification system 4-digit industry codes
are only available between 2000 and 20052 We harmonize to NACE Rev 2 codes by using
1 A few industries are merged in the EU-KLEMS We will call this 64 category classification ldquo2-digitrdquo
industry in what follows
2 The database available in the CSO which we will use for Task 3 includes 4-digit codes for all years
BOX 21 Amadeus and the NAV database (cont)
Table B21 Coverage by employment categories (AmadeusNAV)
Year 1 emp 2-5 emp
6-10 emp
11-20 emp
21-49 emp
50-249 emp
250 lt emp
Total
2004 005 028 092 105 160 312 642 043
2005 010 050 169 288 483 1066 2227 108
2006 017 087 315 553 966 1935 3632 192
2007 2209 3006 4384 5249 5743 6082 7412 3135
2008 098 324 951 1692 2840 4868 7827 576
2009 5962 6070 7217 7428 7831 7798 9336 6301
2010 2142 4685 7034 7540 8424 8228 9634 4175
2011 2277 4736 7064 7753 8521 8657 9681 4220
2012 9397 8298 9305 9484 9507 9159 10121 8990
2013 7274 8140 9423 9981 9747 9445 10312 8044
Notes This table shows the number of observations in AMADEUS as a percentage of observations in the
NAV data for each year-size category cell
Data Sources
6
concordances from Eurostat3 We use these harmonized codes whenever we define deciles
and the frontier or within-industry variables so that NACE revisions should not affect the
results Finally we split those firms which switch from manufacturing to services or vice
versa adding separate firm identifiers for the two periods4
22 Productivity estimation
From many perspectives the most robust and convenient measure of productivity is
labour productivity We calculate this variable simply as the log of value added per
employee At the same time the key shortcoming of labour productivity is that it does not
reflect the differences in capital intensity across firms Total Factor Productivity (TFP) aims
to control for this issue We estimate TFP with the method of Ackerberg et al (2015) ndash we
refer to it as ACF ndash which can be regarded as the state of the art In the Appendix we
also provide robustness checks using different productivity measures
Technically firm-level TFP estimation involves estimating a production function
119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)
where i indexes firms t indexes years 119871119894119905 is the number of employees and 119870119894119905 is the capital
stock of firm i in year t In this specification the residual of the equation 휀119894119905 is the
estimated TFP for firm i in year t 120573119897 and 120573119896 are the output elasticities in the production
function reflecting the marginal product of labour and capital and the optimal capital
intensity
Estimating firm-level production functions involves several choices First it is usually
important to include year fixed effects in order to control for macro- or industry level
shocks Second industries may differ in their optimal capital intensity ie the coefficients
of the two variables To handle this we estimate the production function separately for
each 2-digit NACE industry Third financial data reported by small firms may not be very
accurate Including them into the sample on which the production function is estimated
may introduce bias into that regression Hence we estimate the production functions only
on the sample of firms with at least 5 employees but also predict the TFP for smaller firms
Fourth the Cobb-Douglas production function may be too restrictive in some cases but it is
possible to estimate more flexible functions (eg translog)
A key problem with firm-level TFP estimation is that input use (119871119894119905 and 119870119894119905) can be
correlated with the residual TFP Consequently OLS estimation may yield biased
coefficients The bias arises from attributing part of the productivity advantage to the
higher input use of more productive firms A simple and robust solution for this issue is to
estimate the production function with a fixed effects estimator This method controls for
endogeneity resulting from unobserved time-invariant firm characteristics
3 Because of the changes in the Hungarian industry classification in 2003 and 2008 industry code harmonization is required The Hungarian industry classification system (TEAOR) corresponds to NACE Rev 1 between 1998 and 2002 to NACE Rev11 from 2003 to 2007 and to NACE Rev 2 from 2008 onwards The conversion of industry codes in 2000-2002 to NACE Rev 11 is relatively straightforward and efficient thanks to the 4-digit codes The conversion from NACE Rev 11 to
NACE Rev 2 is less so as 4-digit codes are only available until 2005 Hence for each firm we assume that its 4-digit industry remained the same in the period of 2005-2007 and use this 4-digit industry for the conversion After these conversions we clean industry codes ignoring those changes when firms switch industries for 1-3 years and then switch back This process leads to a harmonized 2-digit NACE Rev 2 code for each year
4 After industry cleaning this can only happen either at the beginning or the end of the period when the firm is observed or if the firm switches industry for a period longer than 3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
7
A second and related problem is that input use can also be correlated with time-variant
productivity shocks This type of endogeneity is not corrected by the fixed effects
estimator More specifically managers (unlike economists analysing the balance sheet)
may observe productivity shocks at the beginning of the year and adjust the flexible inputs
(labour in our case) accordingly As a result we may falsely ascribe a productivity
improvement to the increase in labour input The recent best practice of handling this
issue is the control function approach in which one controls for the productivity shock by
using a proxy for it in an initial step The proxy is another flexible input usually materials
or energy use As we have reliable data on materials we will use that variable
In this report we rely on the method of Ackerberg et al (2015)5 Importantly with this
method the production function coefficient estimates are close to what is expected6 and
the returns to scale are slightly above one (typically between 1 and 12 see Figure 21)7 8
After estimating the coefficients we simply calculate the estimated TFP for firm i in year t
by subtracting the product of inputs and the estimated elasticities
119879119865119875119894119905 = 119871119899 119881119860119894119905 minus 120573 lowast 119897119899 119871119894119905 minus 120573 lowast 119897119899 119870119894119905 (22)
In this way we calculate a TFP level (rather than its value relative to year and industry
fixed effects) which is important when calculating productivity changes Note that the
calculated productivity changes are very similar to the logic of the Solow residual
When interpreting productivity estimates it is important to remember that both the labour
productivity and TFP estimates are value added-based measures In other words in cross-
sectional comparisons they show how many forints or euros (rather than cars or apples)
are produced with a given amount of inputs Therefore value added based productivity
reflects both physical productivity and markups9
5 We have estimated all of these with the prodest (Rovigatti and Mollisi 2016) command in Stata
6 Reassuringly Ackerberg et al (2015) themselves report some production function estimates using data from Chile and their estimated coefficients are similar to what we get 08-09 for labour and about 02 for capital
7 We also control for attrition of firms from the sample but this does not affect the estimates significantly
8 The Levinsohn-Petrin (2003) and Wooldridge (2009) production function estimates are less attractive Most importantly the estimated returns to scale are well below 1 typically between 07 and 08 These implausibly low returns to scale imply an implausibly high TFP for larger firms with their TFP advantage being many times their labour productivity advantage even though they employ much more capital per worker The implausibly low returns to scale strongly affect our calculations In such a framework if a firm doubles all of its inputs and outputs its estimated TFP increases by about 30 percent even though it transforms inputs into outputs in the same way In
productivity decompositions for example size and growth are mechanically related to TFP leading to overestimating allocative efficiency
9 Recent literature has emphasized the difference between value added-based (revenue) and physical productivity and has also proposed a number of methods to distinguish between the two (see Foster et al 2008 Hsieh and Klenow 2009 Syverson 2011 Bellone et al 2014 De Loecker and Goldberg 2014) Hornok and Murakoumlzy (2018) also apply such methods to investigate the markup differences of Hungarian importers and exporters
Data Sources
8
Figure 21 ACF production function coefficients
A) Manufacturing
B) Services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
9
We take some additional steps to clean our raw productivity estimates First we winsorize
productivity at the lowest and highest percentile of the 2-digit industry-year-specific
distribution of firms with at least 5 employees We fill out gaps of 1 or 2 years in the
productivity variable by using linear approximation Finally we clean the productivity of
firms with at least 5 employees based on changes We smooth large 1-year jumps10 and
disregard productivity values if there is a large jump after entry or before exit11
Table 21 presents the average labour productivity and TFP by 1-digit NACE categories in
2004 and 2016
Table 21 Average productivity measures by 1-digit industry in 2004 and 2016
unweighted
Labour productivity Total factor productivity
2004 2016 2004 2016
NACE Description Mean Stdev Mean Stdev Mean Stdev Mean Stdev
B Mining 797 088 867 086 408 087 441 065
C Manufacturing 777 087 806 079 581 079 598 077
D Electricity gas steam 929 106 953 138 629 091 634 132
E Water supply sewerage waste
812 085 830 089 604 091 593 094
F Construction 773 080 803 072 620 071 646 066
G Wholesale and retail trade 804 102 825 090 652 093 678 081
H Transportation and storage 841 071 837 072 625 067 623 072
I Accommodation 710 075 752 080 594 068 640 071
J ICT 834 094 862 090 631 101 669 098
M Professional scientific and technical activities
815 087 844 088 636 087 673 088
N Administrative and support services
763 098 792 094 640 107 662 113
Total 789 095 815 087 620 090 647 087
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
10 We replace 119910119905 with 119910119905minus1+119910119905+1
2 if abs(119910119905 minus 119910119905minus1)gt1 abs(119910119905+1 minus 119910119905minus1)le 05 abs(119910119905minus1 minus 119910119905minus2)le 03 timesabs(119910119905 minus
119910119905minus1) abs(119910119905+2 minus 119910119905+1) le 03 timesabs(119910119905 minus 119910119905minus1) where 119910119905 denotes a productivity measure in logs of year
t Corresponding conditions are modified to abs(119910119905+1 minus 119910119905minus1) le 1 abs(119910119905+1 minus 119910119905minus1) le 03 times abs(119910119905 minus119910119905minus1) in the second observed year and in the year before the last observed one
11 abs(119910119905 minus 119910119905minus1)gt15
Data Sources
10
23 Estimation sample
Next we introduce some restrictions to define our baseline sample As our aim is to focus
on the market economy we constrain our sample based on industry and legal form We
keep only the market economy according to the OECD definition dropping observations in
agriculture and in non-market services (NACE Rev 2 categories 53 84-94 and 96-99)
We also drop financial and insurance activities12 as well as observations for which industry
is missing even after cleaning
We also drop firms which functioned as non-profit budgetary institutions or institutions
with technical codes at any time during the observed period We also drop firms which
never reported positive employment We refer to the remaining sample as the baseline
sample
Our main sample used for most of the calculations and for the estimations consists of
observations with at least 5 employees a non-missing total factor productivity value and
no remaining large productivity jumps13 We refer to the resulting sample as our main
sample Excluding the smallest firms has multiple advantages First exclusion of small
firms reduces measurement error as the smallest firms are the most likely to misreport
Additionally one-employee firms cannot be told apart from the self-employed who create
a firm for administrative reasons but clearly do not operate as an ordinary firm The
existence of such firms as well as their financial variables are likely to be strongly
determined by the differential in the tax treatment of personal versus corporate incomes
Because of these reasons both productivity levels and productivity changes may be
measured with an excessive amount of noise for the very small firms and therefore we
exclude them from our main analysis
Table 22 shows the distribution of firms by size category in our baseline sample Clearly
our sample expands strongly between 2000 and 2004 which is mainly a result of legal
changes requiring a larger group of firms to use double-entry bookkeeping While this
expansion is the strongest for the smallest firms it also affects a large number of firms
with up to 20 employees This artificial `entryrsquo of firms can bias estimates of productivity
growth (yielding a negative composition effect) and its decomposition (a negative entry
effect) For this reason in many cases we will start our analysis in 2004
Figure 22 investigates how much the exclusion of very small firms matters It shows that
while the share of 0 and 1 employee firms was between 50 and 60 percent their share in
terms of employment and sales was only around 5-6 percent hence even after their
exclusion our sample captures much of the national output We however report
robustness checks for our main results with all firms with a positive number of employees
in the Appendix
12 We decide to drop the financial sector because of conceptual and measurement problems of defining the productivity of financial firms especially during the crisis It might also distort the aggregate results Dropping these firms also corresponds to the usual practice (eg McGowan et al 2017) However including financial firms does not have a significant impact on our main results
13 We exclude firms that had a log productivity change higher than 15 in absolute value at any one time We also exclude firms switching between manufacturing and services more than twice
Productivity differences in Hungary and mechanisms of TFP growth slowdown
11
Table 22 Distribution of firm size by employment categories
Year 0 emp 1 emp 2-4 emp 5-9 emp 10-19 emp 20-49 emp 50-99 emp 100 lt emp Total
2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739
2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987
2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568
2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376
2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880
2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844
2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671
2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709
2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318
2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113
2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104
2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509
2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335
2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665
2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678
2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519
2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727
Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742
Notes The sample is our baseline sample (see Section 23) also including observations without an
estimated TFP
Figure 22 The share of 0 and 1 employee firms in the number of firms employees and
sales
Data Sources
12
Table 23 shows the number of observations lost because of missing values cleaning and
sample restrictions compared to the original data Dropping firms based on industry and
legal form as well as firms which never report positive number of employees does not
reduce the sample considerably The baseline data contains about 23 of the firms in the
original data The coverage in terms of total employment or value added is even higher
While the reduced sample of firms with at least 5 employees contains only about 20 of
the original number of firms the coverage of total employment and value added is still
above 70 We lose an additional 4 of firms which have no estimated TFP (negative
value added or missing capital) or which have large TFP jumps over time The
corresponding reduction in employment and value added coverage is about 20 and 15
percentage points respectively14 In the main sample we capture almost 23 of the total
employment and value added which we have in the original data
Table 23 Change in sample size and coverage after introducing restrictions
Number of
firms
Total
employment
Total value
added
Original data (after imputing observations) 1000 1000 1000
Drop agriculture and missing industry 952 954 984
Drop non-market services 845 895 948
Drop based on legal form 844 885 946
Drop firms which never had positive
employment
708 885 935
Keep only market economy according to OECD 667 859 912
Drop financial and insurance activities 652 830 790
Baseline sample 652 830 790
Keep observations with at least 5 employees 196 726 723
Keep firms which have no big TFP jump and
observations with non-missing TFP
157 600 647
Main sample 157 600 647
Table 24 shows the share of observations in the main sample by 1-digit NACE industry
The industry composition is quite stable over time Wholesale and retail trade has the
largest share close to 13 followed by manufacturing (21-31) construction (13-14)
and professional scientific and technical activities (7-9) The largest decline over time
was in manufacturing (from 31 to 21) Construction transport and storage
accommodation professional scientific and technical activities and administrative and
support services increased their share by more than one percentage point
14 While this cleaning certainly drops a large number of firms this is standard practice when the aim is to capture and decompose aggregate dynamics
Productivity differences in Hungary and mechanisms of TFP growth slowdown
13
Table 24 The share of observations by industry
NACE Description 2000 2004 2008 2012 2016
B Mining 029 025 024 021 018
C Manufacturing 3085 2636 2352 2280 2122
D Electricity gas steam 021 027 026 025 024
E Water supply sewerage waste
103 112 114 127 098
F Construction 1263 1439 1447 1263 1375
G Wholesale and retail trade 3207 3026 2954 3005 3034
H Transportation and storage 479 551 606 642 683
I Accommodation 477 617 650 719 783
J ICT 351 328 396 403 400
M Professional scientific and
technical activities 688 691 859 915 891
N Administrative and support services
297 547 572 601 572
Total 100 100 100 100 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
24 Firm-level variables
For the present analysis we create firm groups based on different firm characteristics In
this subsection we explain these groupings and provide descriptive statistics
The database includes information on direct ownership Based on this one can identify
firms which are domestically-owned15 foreign-owned or state-owned (including municipal
ownership) We identify a firm as foreign-owned if the foreign share is above 10 percent
Similarly we classify a firm as state-owned if the state-owned share is above 50
percent16 Based on these definitions in 2016 nearly 10 percent of firms were foreign-
owned while the share of state-owned firms was about 1 percent (Table 25) Both foreign
and state ownership is more frequent in larger firms therefore foreign and state share is
higher in terms of employment 373 percent of employees work in foreign-owned firms
and 66 percent in state-owned ones Foreign ownership was concentrated in mining and
manufacturing electricity generation and distribution trade and ICT State ownership was
high in electricity generation and distribution and in utilities The fact that state-owned
firms are concentrated in these two industries limits the possibilities of how the effects of
state ownership and the effect of the peculiarities of these highly regulated industries can
be distinguished from each other Therefore in most cases we will not present results
separately for state-owned firms (except for Section 44)
15 For brevity we will mainly refer to domestically-owned private firms simply as domestically-owned
16 Only 15 of firms with more than 10 percent foreign share report a foreign share between 10 and 51 percent Re-classifing them as domestic does not affect our main results
Data Sources
14
Table 25 Share of state- and foreign-owned firms with at least 5 employees 2016
A) Number of firms
NACE Sector Domestic Foreign State Total
B Mining 8228 1772 000 100
C Manufacturing 8432 1522 046 100
D Electricity gas steam 5631 1942 2427 100
E Water supply sewerage waste 6351 450 3199 100
F Construction 9746 192 062 100
G Wholesale and retail 8957 1006 037 100
H Transportation 9005 890 105 100
I Accommodation 9411 467 121 100
J ICT 8314 1541 145 100
M Professional scientific and technical activities
8982 915 102 100
N Administrative and support services
8991 798 211 100
Total 8937 953 110 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
B) Employment
NACE Sector Domestic Foreign State Total
B Mining 725 275 00 100
C Manufacturing 437 552 12 100
D Electricity gas steam 674 234 91 100
E Water supply sewerage waste 189 32 780 100
F Construction 899 74 28 100
G Wholesale and retail 660 334 06 100
H Transportation 474 199 327 100
I Accommodation 867 111 22 100
J ICT 424 546 30 100
M Professional scientific and technical activities
650 331 20 100
N Administrative and support services
681 258 61 100
Total 560 373 66 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
The data include direct information on export sales and we classify a firm as an exporter
in a given year if its export sales are positive Table 26 shows the share of observations
both by ownership (foreign or private domestic) and exporter status The distribution of
firms across the four groups is stable over time Overall 65-75 of the firms are owned
domestically and supply only the domestic market The share of foreign firms decreased
from 143 in 2000 to 96 in 2016 After an initial decline the share of exporters
increased from 26 in 2000 to 315 by 2016 More than 23 of the foreign firms export
while the same ratio for domestic firms is less than 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
15
Table 26 Yearly share of observations by ownership and exporter status
Year Foreign
exporter
Foreign
non-
exporter
Domestic
exporter
Domestic
non-
exporter
2000 92 51 168 690
2001 89 46 172 693
2002 84 43 173 701
2003 79 40 164 717
2004 71 36 157 736
2005 70 34 160 736
2006 69 34 163 734
2007 73 32 180 715
2008 74 34 186 706
2009 78 36 195 692
2010 77 34 202 687
2011 78 32 215 675
2012 81 31 229 659
2013 79 30 236 655
2014 74 33 233 660
2015 71 31 238 659
2016 70 26 245 658
Total 76 35 196 692
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded
Table 27 presents some baseline descriptive statistics for the four firm groups created by
ownership and exporter status We define age using the year of foundation of the firm On
average foreign exporter firms are the largest and the most productive Within both
categories exporter firms are older larger and more productive in line with similar
patterns in other countries17 We will analyse differences further in Section 44
17 See for example Bernard-Jensen (1999)
Data Sources
16
Table 27 Average characteristics by ownership and exporter status in year 2004 and
2016
Foreign exporter
Foreign non-exporter
Domestic exporter
Domestic non-exporter
Year 2004
N of employees 1385 511 451 165
(5689) (2410) (1396) (404)
Labour productivity 877 825 830 769
(101) (120) (087) (086)
TFP ACF 666 660 634 611
(111) (112) (091) (084)
Age
101 85 99 85
(42) (46) (43) (43)
Year 2016
N of employees 1619 338 344 151
(6246) (1257) (1290) (405)
Labour productivity 906 839 844 793
(088) (115) (075) (080)
TFP ACF 696 684 651 639
(113) (109) (086) (080)
Age 160 105 149 124
(82) (75) (76) (75)
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded Standard deviations are in
parentheses
25 Industry categorization
As we have mentioned already the main industry identifier is the 2-digit NACE Rev 2
industry classification These are hierarchically ordered into 1-digit categories
These categories however do not always lend themselves to easy interpretation On the
one hand one may want to distinguish between different types of manufacturing activities
Here a key question concerns the knowledge intensity or the high-techness of the activity
On the other hand sometimes it is useful to aggregate some of the service activities to
obtain more easily interpretable results
In order to do this we use Eurostatrsquos high-tech aggregation of manufacturing and services
by NACE Rev 2 which we will call industry type18 Note that these sets of industries
include only activities carried out in market industries (ie 10 to 82 NACE Rev 2 industry
codes) When using these categories we do not include firms in non-market sectors like
education (85) or arts entertainment and recreation (90 to 93) (See Table 28)
We would like to point out that while the Eurostat categories clearly reflect the global
technology and knowledge intensity of each industry the actual activity conducted in a
given country may differ from the technology category of the industry This issue is highly
relevant in Hungary where MNEs in high-tech industries operate affiliates conducting
assembly activities in Hungary without much RampD or innovation Still we find this
categorization a good way of aggregating data but still preserving some heterogeneity
18 Retrieved from httpeceuropaeueurostatcachemetadataAnnexeshtec_esms_an3pdf
Productivity differences in Hungary and mechanisms of TFP growth slowdown
17
Table 28 Industry categorization
Manufacturing NACE Rev 2 codes
High-technology manuf 21 26
Medium-high technology manuf 20 27 to 30
Medium-low technology manuf 19 22 to 25 33
Low technology manuf 10 to 18 31 to 32
Services
Knowledge-intensive services (KIS) 50 to 51 58 to 63 64 to 66 69 to 75 78 80
Less knowledge-intensive services (LKIS) 45 to 47 49 52 55 to 56 77 79 81 82
Utilities 35 to 39
Construction 41 to 43
Productivity Trends Hungary in International Comparison
18
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON
The main aim of this chapter is to summarize existing evidence on Hungarian productivity
trends based on internationally comparable databases which include either industry-level
or micro-aggregated information The specificities and similarities of Hungary to
comparable countries will both guide and frame our analysis in the remaining chapters
which use Hungarian micro-data
31 Convergence
The fundamental question regarding the productivity evolution of Hungary or other less
developed EU member countries is whether productivity catches up with the most
developed countries at least in the medium or long run We investigate such medium- or
long-run trends in this subsection by analysing the evolution of relative productivity which
is defined as the level of labour productivity compared to one of the key economies of the
EU Germany (at ppp exchange rates) Figure 31 presents such a comparison of the
labour productivity levels of Hungary the Czech Republic Poland and Slovakia We use the
OECD STAN database for this exercise and present trends for as many years as possible to
reflect long-run developments
Figure 31 Relative labour productivity level (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN and GGDC Productivity Level Database The
market economy excludes real estate For more details see Appendix A3
Let us start with the evolution of aggregate labour productivity According to Figure 31 all
of these countries seemed to be on the road to convergence to frontier countries in terms
of labour productivity before the financial crisis In particular labour productivity in
Hungary increased from 50 percent of the German level in 1998 to 65 percent in 2008 A
similar pre-crisis convergence can be observed in all three comparator countries19
19 Note that TFP is not available for Hungary in the EU KLEMS after 2008 Therefore we restrict this
international comparison to labour productivity
Productivity differences in Hungary and mechanisms of TFP growth slowdown
19
Note that the labour productivity decline during the crisis does not show up in the above
figure because it also affected the baseline country Post-crisis Hungarian labour
productivity (relative to Germany) remained flat stabilizing at around 65 percent While
this is similar to the productivity evolution of the Czech Republic it differs remarkably from
Poland and Slovakia which were able to close their productivity gap relative to Germany
by about 5 percentage points between 2009 and 2015 This slowdown of aggregate
productivity growth and the lack of further convergence from previous levels is actually
the main motivation for this study
A key question is whether the slowdown characterises the whole economy or it is
constrained to some of the sectors or types of enterprises The first dimension is to
distinguish between the state sector and the market economy According to OECD STAN
non-market sectors accounted for about 27 percent of all employment in 201520 The
second panel of Figure 31 restricts the sample to the lsquomarket economyrsquo21 Interestingly
productivity differences relative to Germany are larger in the market economy compared
to the whole economy suggesting that the productivity levels of the public sector in the
two countries appear to be closer to each other In Hungary the relative productivity of
the market economy follows a very similar trend to the whole economy with about 10 pp
relative productivity increase between 1998 and 2005 and stagnation post-crisis With the
exception of Slovakia post-crisis productivity growth is also flat in the comparator
countries
Figure 32 Relative labour productivity in manufacturing and business services
Germany=100
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN Business services excludes real estate For
more details see Appendix A3
20 According to the EU KLEMS this share has remained more or less constant since 2003
21 This includes NACE Rev 2 Codes 5-82 except real estate (68)
Productivity Trends Hungary in International Comparison
20
The market economy can be further disaggregated into manufacturing and business
services (Figure 32) There is strong evidence of catching up in manufacturing between
1995 and 2008 when relative productivity increased by more than 10 percentage points
Relative productivity fell immediately after the crisis with positive growth after 2011
reaching pre-crisis (relative) levels by 2015 Comparator countries which started from
much lower levels caught up faster pre-crisis and faced a much smaller fall around the
crisis years In other words comparator countries have caught up with Hungary in terms
of manufacturing productivity but there is no evidence for a sharp break in the trend post-
crisis
This contrasts sharply with business services where a period of catch-up until 2005 was
followed by a substantial decline in relative labour productivity This is also in strong
contrast with the comparator countries where relative productivity of business services
either increased (Czech Republic and Poland) or stagnated (in Slovakia) Business services
appear to be a key source of aggregate productivity slowdown
Figure 33 presents productivity dynamics in four specific industries to substantiate the
more aggregated picture with some more concrete examples The first two examples are
manufacturing industries namely the textiles and the automotive industry The relative
productivity level of textiles stagnated during the crisis at quite low levels fell during the
crisis followed by some growth from 2012 In motor vehicles relative productivity
increased by nearly 10 percentage points relative to Germany between 2001 and 2009
followed by a significant fall around the crisis and a strong recovery from 2012 The
picture is also varied in services In retail and wholesale there had been some productivity
improvement before the crisis followed by a declining trend post-crisis Both the level and
dynamics of relative productivity compares unfavourably to the comparator countries In
professional services relative labour productivity had grown quickly until 2011 followed
by a declining trend
Figure 33 Relative labour productivity evolution (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
21
Similar observations can be made when analysing the relative productivity of all types of
industries (Figure 34) The difference in productivity levels relative to Germany tends to
be larger in manufacturing than in services Light industries have especially low relative
productivity levels In terms of productivity growth we see mostly positive trends in most
manufacturing industries and a less clear picture in services with a decline or stagnation
in many service industries
Figure 34 Labour productivity of different industries relative to Germany 2005 and 2015
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix A3
Even in countries and industries with a relatively low level of average productivity it is
possible that a segment of the economy operates at world-class levels or shows fast
convergence to that This possibility may be especially relevant in economies where a
number of large and probably foreign-owned firms operate together with many smaller
domestically-owned firms which is certainly the case in Hungary One approach to
investigate this possibility was suggested and implemented by the OECD (Andrews et al
2017) This approach builds on cross-country micro-data to calculate the productivity of
the most productive firms in the world (global frontier) and compare it with the
productivity of the most productive firms in a country (national frontier)
Figure 35 shows these comparisons based on the OECDrsquos calculations22 In particular the
horizontal axis shows how productive Hungarian frontier firms are relative to the global
22 We would like to thank Peter Gal and his colleagues in the OECD for sharing these data with us In
this version global frontier is defined as the top 10 percent most productive firms worldwide
while the national frontier is the top 10 percent within the country according to ORBIS See
Appendix 3 and Box 41 for details on these data
Productivity Trends Hungary in International Comparison
22
frontier (100 is the global frontier) while the vertical axis compares Hungarian and global
non-frontier firms The figures suggest a number of conclusions To start with the frontier
productivity gap is strongly associated with the non-frontier productivity gap showing that
in industries where the typical firms are of relatively low productivity so are the frontier
firms Importantly the slope of the fitted line (06) is well below 1 suggesting that on
average there is a smaller gap between a top global and a top Hungarian firm than
between a typical (non-frontier) global firm and a typical Hungarian firm This is in line
with the duality hypothesis
That said one has to emphasise that the picture does not support a ldquostrong versionrdquo of the
duality hypothesis ie that the best Hungarian firms operate at world-class productivity
levels Even in manufacturing Hungarian frontier firms typically produce 40-60 percent
less value added per employee compared to the global frontier (good examples are
machinery (28) and motor vehicles (29)) The smallest gaps appear in a few relatively
low-tech service industries (trade and repair of vehicles (45) or warehousing (52)) where
frontier productivity is actually above the global frontier23
The observation that such large productivity differences exist between global frontier and
Hungarian frontier firms even within relatively narrowly defined industries suggests that
the low relative productivity of the Hungarian market economy is not a consequence of
industry composition ndash it mainly results from within-industry gaps Importantly these
main patterns are very similar and independent of how productivity is measured (labour
productivity or TFP) namely they are not a consequence of capital intensity differences
Finally by and large there is no evidence for convergence of frontier firms to the global
frontier between 2009 and 201324 If anything the gap between the global and the
Hungarian frontier widened in this period while the difference between the global and the
Hungarian frontier was 34 percent in the median industry in 2009 it widened to 38 by
2013
23 Naturally this is likely to be the case in other similar countries Still in different discussions it is often supposed implicitly that the best Hungarian firms are indistinguishable from the global frontier
24 Prior to 20082009 the coverage of ORBIS the source for the OECD calculations is fairly limited for
Hungary hence those calculations are less reliable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
23
Figure 35 Productivity of Hungarian frontier and non-frontier firms relative to firms in
other countries (2013)
A) Labour productivity
B) TFP
Notes The industry codes are 2-digit NACE Rev 2 codes We have omitted industries with only few
observations (less than 5 Hungarian frontier firms) in the case of labour productivity outliers we
ignored those where the HU frontier was measured to be more productive than 125 percent of the
global frontier (ICT real estate and office administration services) Note that there are fewer
observations regarding TFP than labour productivity Source Data provided by the OECD calculated
from Andrews et al (2017) For more information see Appendix 3
Productivity Trends Hungary in International Comparison
24
We can draw a number of conclusions from these calculations First while Hungaryrsquos
labour productivity had been catching up similarly to other CEE countries to more
advanced economies before the crisis there was a trend break after the crisis especially
compared to Poland and Slovakia Only part of the productivity slowdown could be
explained by a slowdown in non-market sectors but there is also a pronounced slowdown
in the market economy This is not the result of having a combination of a few firms with
world-class productivity and many less efficient SMEs ndash actually the productivity of
frontier firms is only about 40-50 percent of global leaders even in industries where the
Hungarian frontier consists of many multinational firms There is no evidence that
Hungarian frontier firms were catching up with global leaders between 2009 and 2015
32 Within-industry heterogeneity
Since the beginning of the 2000s with the availability of detailed micro-data sets at the
firm-level it has become clear that within-industry heterogeneity in terms of productivity
is significantly larger than heterogeneity differences across industries (Bernard et al
2003 Bernard et al 2007 Bernard et al 2012 OECD 2017) Many factors have been
proposed which may generate and sustain the observed large productivity differences
including managerial practices different quality of labour capital and knowledge as well as
a number of external factors The exact role of different factors is an active area of
research (Syverson 2011) Recent research also hints at increasing dispersion within
sectors (Berlingieri et al 2017b)
In 2011 the level of the p90p10 ratio (90th and 10th percentile of productivity
distribution) was high in Hungary relative to other OECD countries taking a value of 279
in manufacturing and 329 in services (Table 31) These numbers are in logs representing
about 20-fold differences These numbers are similar to Chile and Indonesia A similar
pattern emerges with respect to TFP
Table 31 Productivity p90p10 ratio by country (2011)
Country
Year 2011
Log LP 90-10 ratio Log MFP 90-10 ratio
Manufacturing Services Manufacturing Services
Australia 187 205 190 212
Austria 196 242 - -
Belgium 160 174 180 178
Chile 300 353 307 387
Denmark 146 196 132 180
Finland 117 138 119 134
France 135 181 140 178
Hungary 279 329 254 286
Indonesia 311 - 341 -
Italy 166 201 160 188
Japan 126 138 117 138
Netherlands 200 298 227 227
New Zealand 184 209 192 208
sNorway 173 217 187 208
Portugal 188 265 188 275
Sweden 145 186 159 245
Notes This is a reproduction of Table 6 from Berlingieri et al (2017a) Note that the OECD uses the
term lsquoMFPrsquo (Multi-factor productivity) in the same sense as we use TFP in this report
Second as seen in Table 32 similarly to other OECD countries the overwhelming
majority of productivity differences results from within- rather than across-sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
25
differences The share of within-sector differences is 79 in manufacturing and 99 in
services The manufacturing share is close to the average of the countries in the sample
while the services share is at the high end
Table 32 Share of within-sector variance in total LP dispersion by country (2011)
Country
Year 2011
LP Dispersion
Manufacturing Services
Australia 98 99
Austria 86 90
Belgium 76 88
Chile 90 97
Denmark 84 63
Finland 65 76
France 63 85
Hungary 79 99
Indonesia 79 -
Italy 82 65
Japan 75 89
Netherlands 80 71
Norway 83 73
Portugal 62 76
Sweden 53 74
Notes This is a reproduction of Table 7 from Berlingieri et al (2017a)
These figures suggest that within-industry productivity dispersion is relatively high in
Hungary but it is not out of the range of countries at a similar level of development Still
these overall dispersion measures may not capture the duality between firms of different
sizes and ownership Internationally comparable data regarding productivity of firms in
different size classes is available from the OECD Structural and Demographic Business
Statistics (Figure 36) Size is strongly associated with productivity large firms are 45
times and 18 times as productive as very small firms in manufacturing and services
respectively However large these premia are not out of the range of similar countries in
services it is very similar to other CEE countries while in manufacturing it is at the high
end of the distribution but not extreme
Another relevant pattern in Figure 36 is that productivity differences by size are very
different between CEE countries and Western European countries This observation may
partly reflect the importance of large and productive multinational firms in CEE countries
but can also be a more or less automatic consequence of the fact that firm size distribution
significantly differs between the two groups of countries (Figure 37) Typically the share
of very small firms is larger in less developed economies leading to a more skewed firm
size distribution Such a distribution which is associated with a larger number of small
firms within size classes (the majority of firms with 1-9 employees in CEE employs only 1-
2 employees) leads to larger differences across size classes and larger within-industry
productivity dispersion The massive share of very small firms in these countries also
reflects that many of the lsquomicro-enterprisesrsquo (with only 1-2 employees) do not operate as
proper firms they behave more like individual entrepreneurs
Productivity Trends Hungary in International Comparison
26
Figure 36 Value added per person employed by size class (1-9 persons employed=100)
A) Manufacturing
B) Services of the business economy
Notes Value added per person employed defined as value added at factor costs divided by the
number of persons engaged in the reference period Economic sector lsquoManufacturingrsquo comprises
Divisions 10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business
economyrsquo comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities
of holding companies Source OECD SDBS For more details see Appendix 3 Main sample for 2015
Productivity differences in Hungary and mechanisms of TFP growth slowdown
27
Figure 37 Firm distribution by size class (2015)
A) Manufacturing
B) Services of the business economy
Notes Only enterprises with at least one employee are included lsquoManufacturingrsquo comprises Divisions
10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business economyrsquo
comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities of holding
companies Source OECD SDBS For more details see Appendix 3 Main sample
Productivity Trends Hungary in International Comparison
28
The main conclusion from investigating within-industry differences across firms is that both
the productivity dispersion and the productivity advantage of large firms is indeed
relatively large in international comparison but these numbers are not radically different
from similar countries Nevertheless differences in firm size distribution between more
and less developed countries go a long way towards explaining the differences between
Western European and CEE countries
33 Firm dynamics
A potential reason for declining productivity growth may be weak dynamics including low
entry and exit rates as well as slower reallocation The OECD Structural and Demographic
Business Statistics database provides international comparisons of entry and exit rates and
their changes across countries (Figure 38 and Figure 39)
In general both exit and entry rates are higher in CEE countries relative to Western
European economies25 This stronger dynamism may reflect stronger growth but it is also
affected (in a mechanistic way) by the differences in firm size distribution Importantly in
a cross-section entry and exit rates are strongly correlated suggesting that they capture
the same general aspect of firm dynamics Services are more dynamic than
manufacturing once again partly because of the different size distributions
Within CEE countries entry and exit rates seem to be associated with productivity growth
(and level) Countries with stronger post-crisis productivity growth (Poland Slovakia and
Romania) exhibit significantly higher entry and exit rates while those with less dynamic
productivity growth (Hungary and the Czech Republic) have lower churning This provides
some evidence that lower entry and exit rates may be systematically related to the weaker
productivity performance of these countries We will take a more detailed look at the
relationship between entry and exit and productivity growth in Chapters 6 and 7
When comparing 2012 and 2015 the pictures provide evidence for increased entry and
decreased exit in parallel with recovery and better growth prospects Still entry rates
remain one of the lowest in CEE indicating that entry and dynamic young firms may
contribute less to productivity growth in Hungary compared to other CEE countries
25 Note that these OECD statistics include all enterprises (even those with no employees) hence
changes in the tax treatment of firms relative to individual entrepreneurs may affect measured
dynamics Also firm death is defined based on the rsquodeathrsquo of the legal entity which may happen
many years after stopping production For more information see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
29
Figure 38 Birth rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Birth rate is defined as the number of enterprise births divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers The
economic sector lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo
comprises Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix A3
Productivity Trends Hungary in International Comparison
30
Figure 39 Death rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Death rate is defined as the number of enterprise deaths divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers
Poland has no available data for 2015 so the 2014 value is reported The economic sector
lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo comprises
Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
31
34 Conclusions
In international comparison productivity slowdown after the crisis was especially severe in
Hungary both in manufacturing and services There are large productivity differences
within industries and also between small and large firms While these are at the high end
in international comparison they are not extreme compared to similar countries A
comparison to the global frontier suggests that even top Hungarian firms are significantly
behind top global firms in terms of productivity These facts provide a motivation for our
analysis of the evolution of the shape of the productivity distribution in Chapter 4
International comparison of firm dynamics suggests that ndash similarly to other CEE countries
ndash Hungarian industries are more dynamic than their Western European counterparts but
entry and exit rates in Hungary and the Czech Republic are below the average of CEE
countries This motivates our investigation of the contribution of entry and exit to
productivity growth in Chapters 6 and 7
Evolution of the Productivity Distribution
32
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION
41 Context
The study of within-industry productivity differences is motivated by two concepts First
the OECD (2016) argues that one of the key issues of recent developments in productivity
growth is that there is a strong divergence between the productivity evolution of frontier
firms and other firms However this same publication reports that Hungary seems to be
an exception to this trend with slow productivity growth at the frontier and faster
productivity growth of less productive firms suggesting some within-industry catch-up
(Figure 41) We look into the particulars behind this phenomenon by following the
evolution of the average productivity of different deciles in the productivity distribution
Second as we have already mentioned a key concept of the Hungarian (and CEE) policy
debate is the lsquodualityrsquo of smalldomestically-owned and largeforeign-owned firms The
large gap between the two types of firms presents a challenge for policy but it also
indicates an opportunity for domestic firms to catch up with foreign firms which may use
more productive technology (still far in terms of productivity from the global frontier see
Chapter 31) The evolution of the productivity gap (or premium) between small and large
firms as well as domestic and foreign firms informs us about whether firms on the lsquowrong
sidersquo of the duality are able to catch up with the firms at the national frontier
The duality debate frames productivity differences partly as a consequence of the lsquomissingrsquo
medium-sized (domestic) firms Hsieh and Olken (2014) argue that in less productive
economies the full firm size distribution is shifted to the left because of the constraints on
the growth of small firms Thus according to this view the productivity difference is not a
result of too few medium sized firms but of too few firms which are not small
Figure 41 Divergence in labour productivity performance
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
33
B) Non-financial Services
Notes This is a reproduction of Figure 16 from OECD (2016)
In this chapter we investigate how the shape of the productivity distribution evolved over
the years Section 42 contrasts the development of firms with other types of economic
entities Section 43 analyses how average productivity and productivity deciles evolved
while 44 investigates the duality based on size and ownership
42 Aggregate productivity and the self-employed
Before turning to the productivity distribution of firms it is worthwhile to describe how the
productivity level and evolution of firms ndash and in particular double-entry bookkeeping
enterprises ndash differ from other entities in particular the self-employed Given the large
number of people employed by those entities this exercise can reveal a lot both about
productivity dispersion and the drivers of aggregate productivity growth
Let us motivate this investigation by comparing aggregate statistics (derived from data
applicable to all people engaged in an industry) with patterns calculated from our NAV data
(which includes only double-entry bookkeeping firms) Figure 42 shows the labour
productivity growth reported by OECD STAN and the evolution of the average labour
productivity as calculated from the NAV data weighted by sales and employment (Figure
42) According to the Figure while these series co-move they do so with some
discrepancies While productivity dynamics in Manufacturing are very similar across all
samples the relationship is looser for services and for the market economy with the NAV
series notably exhibiting less pronounced post-crisis slowdown than the OECD STAN data
Evolution of the Productivity Distribution
34
Figure 42 Cumulative labour productivity growth according to OECD STAN and the NAV
sample
There can be many reasons behind the differences between these series (see Biesebroeck
2008) but arguably one of the main factors is the discrepancy in the number of
employees in the two databases Firms in the full NAV database employed 24 million
people in 2015 compared with 286 million employed and 325 million lsquoengagedrsquo in the
market economy according to the OECD STAN One source of this difference may be that
while some unofficially employed workers report their true employment status in LFS
(Labour Force Survey) ndash which serves as the basis for our aggregate data ndash they do not
appear in any official registers and such the NAV data Benedek et al (2013) reaffirming
the statement compare LFS employment data with tax registers and show that 16-18
percent of jobs are not declared to the tax authorities
Even more importantly from our perspective the NAV data by definition includes no
information on the self-employed and typically small non-double-entry bookkeeping firms
operating under special taxation The distinct productivity dynamics of these two groups
along with changes in undeclared employment may explain another part of the difference
Obtaining direct information on this issue would be of great interest but acquiring it is far
from straightforward Some information on these entities is available from the Register of
Economic Organizations (Gazdasaacutegi Szervezetek Regisztere GSZR) which is available
between 2012 and 2015 Most importantly this database provides us with information on
the number of employees and sales updated annually This in and of itself does not allow
us to estimate productivity properly but with its help we can calculate a crude proxy
sales per employee for illustration
Table 41 reports26 the number of employees and the average sales per worker values for
three groups The first is the group of double-entry bookkeeping firms (ie the firms who
26 These tables were calculated as follows First we combined the GSZR and NAV databases for years 2012 and 2015 Observing that about 80 percent of the firms present in the NAV sample are also present in the GSZR register we restricted our sample to the entities who are listed in the GSZR so that our variables would be commensurable From this collection we selected those who
Productivity differences in Hungary and mechanisms of TFP growth slowdown
35
are present in the NAV data) the second is the category of the self-employed (ie those
who are registered as individual entrepreneurs) and the third category is that of lsquoother
firmsrsquo (ie entities who are registered as firms in the GFO (Gazdaacutelkodaacutesi Forma) coding
system but are not categorised as self-employed and are not following a double-entry
bookkeeping method) We distinguish between manufacturing and other industries of the
market economy27 We supply figures for the earliest and latest years for which data are
available The tables reveal two important observations
First according to the GSZR about 30 percent of reported employees in Manufacturing
and 50 percent of reported employees in other industries work outside the double-entry
bookkeeping group Importantly these numbers may be overestimates because the GSZR
may report the same person in multiple entities for example when they work part-time or
switch jobs within the year That said both the EU KLEMS and the GSZR suggest that a
large share of people work outside the double-entry bookkeeping group in the market
economy
Second while sales per worker is not drastically different between double-entry
bookkeeping firms and other firms the difference between firms and the self-employed is
between 6-10-fold This difference in sales per employee may represent 2-3-fold labour
productivity differences between people employed by firms and the self-employed on
average28
Third the dynamics of sales per worker differ markedly between double-entry
bookkeeping firms and other entities while it increased by 40 percent in the NAV sample
between 2012 and 2015 it stagnated for the self-employed This may results from a
number of factors ranging from composition effects changes in tax regulations or low
productivity growth Still the low measured productivity growth of this sector of the
economy may be an important factor behind the slower post-crisis aggregate productivity
growth in services compared to the NAV sample Table 41 illustrates this for the sales per
worker measure While it grew by 40 percent in the lsquoOtherrsquo category between 2012 and
2015 based on the NAV sample its lsquoaggregatersquo growth was only 6 percent during the same
period
Obviously one cannot draw far reaching conclusions from such statistics given the
immense measurement problems Still these patterns suggest that in a sense the duality
between firms and the self-employed may constitute a similarly deep divide to the one
belong to the lsquomarket economyrsquo (as defined in Chapter 2) and are registered as lsquofirmsrsquo according to GFO coding system (ie have 1-digit GFO codes 1 or 2) We tagged the firms present in the NAV sample as lsquodouble-entry bookkeeping firmsrsquo and marked those who have 2-digit GFO codes equalling to 23 as lsquoself-employedrsquo We categorised the rest of our sample as lsquoother firmsrsquo Further we distinguished between manufacturing and other market economy firms based on their NACE codes and then calculated for sales per worker measures on the level of each observation finally to compute for yearly aggregates for each group as indicated above
27 Notably in line with the definition in Chapter 2 these lsquoother industriesrsquo do not include agriculture
28 Needless to say this cannot be easily mapped into productivity differences given that firms using more intermediate inputs are more likely to choose double-entry bookkeping (and hence pay
taxes based on profits) rather than simplified taxes (and pay taxes based on sales) Still one can do the following back of the envelope calculation In the NAV sample the average ratio of material expenditure over sales was 066 both in 2012 and 2015 Therefore value added per employee (or labour productivity) could be about a third of the sales per employee variable If one conservativelly assumes that the self-employed have zero material costs their labour productivity is the same as their sales per employee index Based on this simple calculation the 6-10-fold difference in sales per employee map to at least 2-3-fold differences in labour productivity
Evolution of the Productivity Distribution
36
that exist between globally integrated and domestic-oriented firms Consequently policies
can be formulated with an explicit focus on this group
Table 41 Number of employees and sales per employee for different entities
Number of employees
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 621229 627391 1325299 1196332
Other firm 289636 296921 698326 771930
Self-employed 72674 74325 620699 638001
Total 983539 998637 2644324 2606263
Average sales per employee (HUF million)
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 140 199 196 278
Other firm 151 146 196 201
Self-employed 25 25 29 28
Total 92 99 105 111
43 The evolution of productivity distribution in Hungary
Average productivity
Let us continue by investigating the evolution of average productivity Table 42 presents
the average labour productivity and TFP growth rates for the market economy
manufacturing and services as defined in Chapter 2 We report both unweighted and
labour-weighted productivity growth for each year
Let us start with the whole market economy Between 2004 and 2007 both labour
productivity and TFP was growing strongly by 7-8 percent on average as expected in a
catching up economy (as we have seen in Chapter 3) Importantly the weighted growth
rate was higher than the unweighted one suggesting that reallocation played a positive
role in aggregate productivity growth (see Section 62 for more details)
During the crisis we see a slight productivity decline in 2008 a sharp fall of about 8
percent in 2009 followed by a strong recovery in 2010 The 2010 productivity recovery
resulted from the productivity growth of large firms unweighted average productivity
growth was very slow This suggests an asymmetry in recovering from the crisis-related
productivity decline
Post-crisis all measures document a slowdown in productivity growth with typical growth
rates between 25-35 percent Notably weighted productivity growth measures were
similar to unweighted ones in the wake of the crisis suggesting deterioration in the
efficiency of the reallocation process The 2010-2013 and 2013-2016 periods seem to be
quite similar to each other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
37
Importantly while labour productivity and TFP dynamics differ to some extent the overall
picture is very similar for the two productivity measures This is in line with the hypothesis
that any productivity slowdown is not merely a consequence of lower capital stock growth
The results are similar when using alternative TFP estimators (see Table A41 in the
Appendix)
Table 42 Labour productivity and (ACF) TFP growth in the sample
A) Market economy
Year LP TFP
unweighted emp w unweighted emp w
2005 20 58 19 74
2006 92 91 93 119
2007 53 60 39 56
2008 -10 -08 -10 -04
2009 -70 -81 -69 -82
2010 -05 44 11 80
2011 25 45 34 40
2012 25 22 21 01
2013 19 25 30 22
2014 39 45 40 59
2015 51 50 52 49
2016 36 19 20 03
Average
2004-2007 55 70 50 83
2007-2010 -28 -15 -23 -02
2010-2013 23 34 33 29
2013-2016 36 35 35 33
B) Manufacturing
Year LP TFP
unweighted emp w unweighted emp w
2005 37 148 20 114
2006 124 163 114 149
2007 100 114 78 71
2008 25 -03 17 -17
2009 -115 -94 -133 -117
2010 82 161 80 173
2011 -02 34 04 18
2012 05 -46 -02 -58
2013 -14 31 -12 05
2014 11 48 -01 27
2015 38 37 30 14
2016 26 01 04 -23
Average
2004-2007 87 141 71 111
2007-2010 -02 22 -12 13
2010-2013 -04 17 04 -03
2013-2016 15 29 05 06
Evolution of the Productivity Distribution
38
C) Market services
Year
LP TFP
unweighted emp w unweighted emp w
2005 12 -04 10 32
2006 80 47 79 90
2007 39 25 24 48
2008 -22 -06 -21 -03
2009 -57 -68 -52 -71
2010 -29 -17 -11 26
2011 33 49 43 57
2012 31 60 30 48
2013 29 21 39 29
2014 46 45 46 78
2015 54 58 54 72
2016 39 30 25 20
Average
2004-2007 43 23 38 57
2007-2010 -36 -31 -28 -16
2010-2013 31 44 39 51
2013-2016 42 39 41 50
Notes This figure presents growth rates of labour productivity and aggregate TFP for lsquomarket
industriesrsquo (see section 25) The sample does not include agriculture mining and financial services
Services include construction and utilities Only firms with at least 5 employees
Comparing manufacturing and services shows a key dichotomy between the two large
sectors In Manufacturing productivity growth was strong before the crisis with above 10
percent average weighted growth rates This fell to very low levels after 2010 Similarly to
the whole market economy reallocation processes had been more efficient before 2008 In
contrast for services no clear structural break appears around the time of the crisis either
in terms of pre- and post-crisis growth rates or reallocation efficiency
Table 43 looks into industry differences in more detail The picture is similar for
manufacturing industries in the various technology categories with a very substantial
slowdown in productivity growth Productivity growth was fastest in high-tech both before
and after the crisis Services are a bit more heterogeneous High-tech services behaved
similarly to high-tech manufacturing with strong pre-crisis growth (around 10 percent on
average) followed by a slowdown to growth rates around 5 percent per year In less
knowledge-intensive services which represent the majority of business service
employment growth rates were similar before and after the crisis (around 5 percent)29
Lastly we see moderate growth rates and then some slowdown in construction and
utilities
29 Note however that this may not be the case for the self-employed as has been discussed in the previous chapter
Productivity differences in Hungary and mechanisms of TFP growth slowdown
39
Table 43 TFP growth by type of industry (employment-weighted ACF TFP)
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 124 19 66 274 2006 240 137 39 33
2007 74 02 41 221
2008 -45 23 -15 59
2009 05 -191 -218 48
2010 135 111 264 168
2011 -45 18 34 100
2012 -15 -24 -83 -181
2013 -41 37 -22 125
2014 06 07 27 86
2015 65 01 -54 80
2016 -02 04 -27 -91
Average 2005-2007 146 53 49 176
2007-2010 32 -19 10 92
2010-2013 -34 07 -21 20
2013-2016 07 12 -19 50
B) Services
Year KIS LKIS Construction Utilities
2005 127 16 34 -48
2006 166 75 30 67
2007 13 58 42 29
2008 -16 14 -72 -26
2009 -63 -94 -04 25
2010 54 12 09 05
2011 97 46 65 29
2012 12 74 13 -22
2013 12 30 63 -07
2014 78 89 65 -81
2015 106 70 14 54
2016 16 31 -47 39
Average
2005-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the sales-weighted average ACF TFP growth rate by technology category (see
Section 25) Only firms with at least 5 employees The sample does not include agriculture mining
and financial services
In general patterns are similar for the unweighted measures (See Table A42 in the
Appendix) with weaker pre-crisis growth in manufacturing where reallocation seems to
have mattered most Labour productivity behaved similarly to TFP (See Table A43 in the
Appendix)
Evolution of the Productivity Distribution
40
Frontier firms
The key motivation for this investigation is to understand better how productivity dynamics
of lsquofrontierrsquo firms differ from firms in other parts of the productivity distribution Defining
frontier firms is not a straightforward task (Andrews et al 2017) Inevitably all such
attempts have to face the trade-off between a narrow definition which may to a large
extent capture the behaviour of outliers and a broader definition which may include
many firms which are very far from the actual frontier
One can find a sensible compromise between the too narrow and the too broad definitions
by following the OECD practice (Andrews et al 2017) This solves the problem of
including small firms with potentially large noise by restricting the sample to firms with at
least 20 employees on average in the sample period Frontier is defined as the top 5
percent of such firms for each industry-year combination An additional issue is that the
number of observations may change across years This is solved by calculating the top 5
based on the median number of observations per year We will call these firms frontier
firms
An alternative definition is simply to define the top decile within the productivity
distribution in industry-year combination as frontier based on our main sample We will
employ this strategy as well for the sake of comparison
Table 44 investigates the prevalence of frontier firms in different groups30 The probability
of being frontier is not related strongly to size A foreign-owned firm is 3-4 times more
likely to be frontier than a domestically-owned private firm State-owned firms are similar
to privately owned domestic firms in this respect As a result about half of the frontier
firms are foreign-owned Finally frontier firms are substantially more prevalent in the
more developed regions of the country especially in Central Hungary These patterns are
quite stable throughout the years and they prevail in a multiple regression analysis The
top decile of the productivity distribution has a similar composition (see Table A44 in the
Appendix)31
Table 44 The share of frontier firms () among firms with at least 20 employees
A) By size
2004 2007 2010 2013 2016
20-49 emp 357 327 34 362 329
50-99 emp 401 468 542 486 555
100- emp 293 358 414 42 462
B) By ownership
2004 2007 2010 2013 2016
Domestic 213 194 236 272 289
Foreign 873 955 896 82 821
State 181 211 166 167 263
30 Note that we restrict the sample to firms with at least 20 employees because the definition of frontier requires to have at least 20 employees on average
31 When the definition is based on labour productivity the share of frontier firms increases with size The foreign advantage is also larger
Productivity differences in Hungary and mechanisms of TFP growth slowdown
41
C) By region
2004 2007 2010 2013 2016
Central HU 596 621 652 552 579
Northern Hungary 174 104 176 237 168
Northern Great Plain 152 195 199 38 268
Southern Great Plain 128 127 18 277 224
Central Transdanubia 296 27 32 359 322
Western Transdanubia 408 313 305 433 395
Southern
Transdanubia 131 081 188 159 211
Another key question is the extent to which frontier status is persistent Figure 43 shows
a transition matrix ie it considers frontier firms in year t and reports their status in t+3
Do they remain frontier or become a non-frontier firms or exit the market altogether
Overall the 3-year persistence of the frontier status is around 45 percent ndash nearly half of
frontier firms will also be frontier 3 years later This is a bit higher than what is found in
other countries Antildeoacuten Higoacuten et al (2017) for example report that about half of all
national frontier firms remain on the frontier for a year but only about 20 percent for 5
years The persistence of frontier status remained largely unchanged across the years
Frontier status is more persistent for foreign and exporter firms The transition matrix of
top decile firms is similar with slightly weaker persistence (Figure A41 in the Appendix)
Figure 43 Transition matrix for frontier firms
Notes This figure shows how many of the frontier firms in year 2010 were still frontier in 2013 how
many exited and how many continued as non-frontier Only firms with at least 20 employees The first
panel shows this transition matrix for various 3-year periods
Evolution of the Productivity Distribution
42
Productivity evolution across deciles
The figures in this section compare the average productivity of frontier firms of the top
decile of the productivity distribution lsquohigh productivity firmsrsquo (8th and 9th deciles) lsquotypical
firmsrsquo (4th to 6th deciles) and lsquolow productivityrsquo firms (2nd and 3rd deciles) all of these
defined at the year-NACE 2 level This approach follows closely that of the OECD (2016)
Also we use the 8 lsquotechnologicalrsquo industry categories introduced in Section 25 to condense
information but still allow for heterogeneity across industries
Let us start with comparing TFP levels (Figure 44) and their cumulative changes (Figure
45) at the different parts of the productivity distribution (note that the vertical axes differ
across sectors) TFP levels are measured in natural logarithms For example in low-tech
manufacturing the difference between low-productivity firms and the frontier is about 2 log
points or more than 7-fold32 Within-industry productivity differentials are much larger
than across-industry differences or changes From a methodological point of view in most
industries frontier firms co-move with the top percentiles but there are a few exceptions
most prominently high-tech manufacturing
The overall productivity evolution is much in line with the averages reported in Table 42
There is strong pre-crisis growth in Manufacturing followed by a fall in 2009 and sluggish
growth afterwards High-tech manufacturing is a partial exception from this trend
Productivity growth actually accelerated after the crisis in services
Figure 44 TFP levels in various types of industries
A) Manufacturing
32 1198902 asymp 74
Productivity differences in Hungary and mechanisms of TFP growth slowdown
43
B) Services
Notes This figure shows the evolution of the (unweighted) average ACF TFP level of the different
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles with at least 20 employees on average lsquotop decilersquo is the 10th decile lsquohighrsquo
is the 8-9th decile typical is the 4-6th deciles while `lowrsquo is 2-3rd deciles Main sample The industry
categories are described in Section 25 The sample includes the sectors of the market economy
except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less knowledge-
intensive services
Most importantly we do not find evidence for an increasing gap between frontier and other
firms (in line with OECD 2016) in any of the industries Within manufacturing there is
convergence between frontier and non-frontier firms in medium-low and high-tech
industries However this is not robust for the alternative definition of frontier (top decile)
which moves strongly together with other deciles Based on this one may say that there is
no robust evidence either for convergence or divergence in manufacturing There are some
signs of convergence pre-crisis in knowledge-intensive and less knowledge-intensive
services as well as in construction followed by stronger productivity growth in the highest
quartiles post-crisis Importantly any convergence or divergence appears to be small
relative to already existing differences
Evolution of the Productivity Distribution
44
Figure 45 Cumulative TFP growth since 2004
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is the 10th
decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main sample The
industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less
knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
45
The picture is somewhat different when labour productivity is considered (Figure 46) In
this case the difference in growth rates between frontier and other firms is more
pronounced than in the case of TFP One can plausibly claim that less productive deciles of
the distribution caught up somewhat with the most productive firms in high-tech
manufacturing in the two service sectors and also in construction This suggests that
capital deepening by less productive firms (or low investment by frontier firms) may lead
to some convergence in terms of labour productivity but less so in terms of TFP33
Figure 46 Cumulative labour productivity growth since 2004 for labour productivity
deciles
A) Manufacturing
33 Note that these figures are the most directly comparable ones to Figure 41 which also presents results for labour productivity In line with that figure we find evidence for convergence between the median firm and frontier firms We also find that low-productivity firms converge The most important reason for this is that we exclude firms with less than 5 employees from our sample
Evolution of the Productivity Distribution
46
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average labour productivity level
for various deciles of the productivity distribution within each 2-digit industry-year combination
lsquoFrontier firmsrsquo are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo
is the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample The industry categories are described in Section 25 The sample includes the sectors of the
market economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo
Less knowledge-intensive services
Figure 47 zooms in to a few industries of interest which both confirm and qualify the
overall picture In textiles (a low-tech industry) frontier firms did not increase their
productivity in the period under study while lower productivity deciles experienced a
cumulative 40-50 percent productivity growth leading to an overall positive growth As
Section 61 discusses employment decline and firm exit were high in this industry
therefore the improvement of lower deciles may partly result from the exit of the lowest
productivity firms In machinery (a medium-high tech industry) all productivity deciles
had experienced strong TFP growth before the crisis and a significant fall during the crisis
followed by slow growth In this industry the full distribution has moved together
In retail (which is a member of the less knowledge-intensive services) TFP had grown to
some extent prior to the crisis followed by a large fall around the crisis and some growth
since 2012 Interestingly the fall was much larger and persistent for the most productive
firms while typical and low-productivity firms were able to maintain their pre-crisis
productivity levels The weak productivity performance of the top decile may have partly
resulted from regulatory changes and could have had large aggregate consequences given
the large employment share of retail (see Chapter 8) In lsquoComputer programming
consultancy and related activitiesrsquo there was a cumulative TFP increase of about 30 percent
since 2004 for all deciles without signs of convergence or divergence
Productivity differences in Hungary and mechanisms of TFP growth slowdown
47
Figure 47 Cumulative TFP growth since 2004 selected industries
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination in four industries
lsquoFrontier firmsrsquo are in the 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is
the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample
44 Duality in productivity and productivity growth
Besides the evolution of the overall shape of productivity distribution it is important to
understand the lsquodualityrsquo of productivity with respect to ownership
As a starting point Figure 48 shows the distribution of TFP and the natural logarithm of
the average wage for our main sample34 We filter out 2-digit industry fixed effects from
the two variables to control for industry-level differences
Comparing private domestic and foreign-owned firms one can make a number of
observations The foreign-owned distribution clearly stochastically dominates the
productivity and wage distribution of domestically-owned firms On average foreign firms
have 40 percent higher TFP and pay 75 percent higher wages than domestically-owned
firms in the same industry That said the within-group heterogeneity is larger than the
across-group heterogeneity generating a substantial overlap between the two
distributions For example 30 percent of domestically-owned firms are more productive
than the median foreign firm The averages between the two groups differ substantially
but there are many productive domestically-owned firms and unproductive foreign ones
34 Result for other TFP measures are very similar
Evolution of the Productivity Distribution
48
Another interesting difference between the distributions is that the foreign-owned
distribution is substantially more dispersed than the domestically-owned one (its standard
deviation is 23 percent larger) suggesting more technological heterogeneity within the
foreign-owned group This may suggest that this group includes both firms with world-
class technology and plants utilizing low-cost labour in a relatively unproductive way That
said the distribution is clearly not bi-modal there are no clearly distinguishable clusters of
high-tech and low-tech firms They operate along a continuum
Comparing state-owned firms to the other two groups shows that they are more similar to
the domestically-owned private firms with two interesting twists35 First the low-
productivity left tail of state-owned firms is much thicker than that of the privately owned
domestic firms Many state-owned firms operate with very low productivity levels (see also
Section 63) As a result the average productivity of these firms is 25 percent lower
compared to privately-owned domestic firms in the same industry
The second twist is that even though state-owned firms tend to be substantially less
productive than privately owned domestic firms they pay on average 25 percent higher
wages This may be a consequence of differences in worker composition but may also
suggest that these firms face soft budget constraints and their employees are able to
capture a larger slice from a smaller pie
Figure 48 Distribution of TFP and average wage by ownership (cleaned from industry-
year effects) 2016
Notes This figure shows the distribution of productivity and ln average wage after filtering out
industry-year fixed effects from it Domestically-owned is domestic privately-owned Main sample
35 Note that the sample of state owned firms is much smaller than the other two groups and operates in very specific indutries This may affect the distribution
Productivity differences in Hungary and mechanisms of TFP growth slowdown
49
Figure 49 shows the evolution of the productivity distributions across years Note that in
order to illustrate shifts in time industry-year fixed effects are not filtered out from this
figure Therefore comparing the distributions with Figure 47 shows how much industry
composition matters
Panel A) illustrates the productivity evolution of domestic private firms The shape of this
distribution remained remarkably similar across years There are clear rightward shifts
between 2004-2008 and 2012-2016 while the distribution did not change during the crisis
period Similar patterns can be observed regarding foreign-owned firms This distribution
was always more dispersed than the domestic one with little changes in its standard
deviation across years
The shape of the state-owned productivity distribution is more peculiar Most visibly it had
been bi-modal before the crisis This is mainly a consequence of industry composition the
low productivity part representing some utilities While the bi-modality disappeared post-
crisis the low-productivity tail of the distribution became thicker Finally we do not see
any rightward shift in this distribution there was little productivity improvement in this
small segment of the economy
Figure 49 Evolution of the distribution of TFP by ownership
A) Domestic private
Evolution of the Productivity Distribution
50
B) Foreign
C) State
Notes This figure shows the distribution of TFP Domestically-owned is domestic privately-owned
Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
51
BOX 41 Duality between domestic and foreign-owned firms in an international context
We are not the first to document the substantial wage and productivity advantage of foreign firms Earle
and Telegdy (2008) by using NAV data between 1986-2003 show that foreign-owned firms were almost
twice as productive as domestic private firms (measured in terms of labour productivity) and also paid
40 higher wages when controlling for employee characteristics A substantial part of this premium
results from foreign owners acquiring more productive firms (mostly during the privatisation process)
but even after controlling for this selection process the foreign wage premium remains 14 Similar
results are found by Telegdy et al (2012) when using the longer period between 1986 and 2008
Foreign-owned firms tend to have positive productivity and wage premia in most countries developed or
emerging Among others Aitken et al (1996) show that foreign-owned firms have higher productivity
and wages in Mexico and Venezuela even after controlling for firm size skill mix and capital intensity
Conyon et al (2002) use acquisitions in the UK in 1989-1994 to find that foreign firms pay 34 higher
wages which can be fully attributed to their 13 higher productivity Girma et al (2002) have a similar
result showing that foreign firms in the UK have 8-15 higher productivity which leads to 4-5 higher
wages Using UK data from 1981-1994 Girma and Goumlrg (2007) find wage differentials of a similar
magnitude but heterogeneous with regard to the source country of the foreign investor Huttunen
(2007) looks at Finland and finds 26-37 wage premium of firms 3 years after being acquired by
foreign investors In the Central-Eastern-European region Djankov and Hoekman (2000) show that
foreign investment in the 90s increased the productivity of recipient firms in the Czech Republic
Governments aim to attract foreign direct investment (FDI) as it is assumed to have a positive impact
on the domestic economy From an economic point of view it is justifiable to provide incentives to
foreign investors if their investments have positive spillovers to domestic firms increasing their
productivity The higher productivity of foreign-owned firms which is documented in the previously
mentioned studies is a necessary condition for that At the same time if foreign firms establish no links
with domestic firms there is only limited opportunity for knowledge spillovers In this case the inflow of
foreign investments results in a dual structure of the economy
Evidence is rather mixed on FDI spillovers to domestic firms in the same industry because a negative
competition effect might dominate the positive technology or knowledge effect Haskel et al (2007) find
that a 10-percentage-point increase in the share of foreign ownership increases the TFP of domestic
firms in the same industry by 05 in the UK Konings (2001) finds negative spillovers for Bulgaria and
Romania and no spillovers for Poland Positive spillovers in vertically related industries are much more
general Using Lithuanian data Javorcik (2004) shows that one standard deviation increase in the foreign
share of an industry is associated with 15 increase in the output of domestic firms operating in the
supplier industry Similarly Kugler (2006) finds no within-industry spillovers but positive spillovers in
vertically related industries in Colombia
Evolution of the Productivity Distribution
52
Let us turn to industry differences in duality The substantial difference between the average TFP of
domestic and foreign-owned firms is present in all kinds of industries (Figure 410 and 411) In
manufacturing the percentage difference is about 34 percent (a log difference of 03) while it is
around 65-100 percent in services Significantly the cumulative TFP growth of the two types of firms
was very similar by the end of the period There is no evidence for the catching-up of domestic firms
with foreign ones The duality in this respect does not seem to diminish substantially
The TFP gap between foreign and domestic firms is amplified by the much higher capital intensity of
foreign firms (Figure 412) In manufacturing foreign firms employed more than twice as much capital
per employee than domestic firms While the capital intensity of both domestic and foreign-owned
firms increased steadily during the period in that sector the gap remained constant showing little
catching-up of domestic firms in terms of capital deepening In a sharp contrast there was a decrease
in the capitallabour ratio in services and this phenomenon took place quicker in the case of foreign
firms
This picture is reinforced at the industry level (Figure 413) In textiles foreign firms invested more
than domestic ones leading to significant capital deepening for that group of firms In machinery both
groups of firms increased their capital intensity to a similar extent In retail foreign firms had invested
much before the crisis but cut their investments deeply after that while the capital intensity of
domestic firms remained mostly flat In programming capital intensity declined slightly following the
crisis
BOX 41 Duality between domestic and foreign-owned firms in an international context
(cont)
Looking at Hungarian data several papers show the existence of positive FDI spillovers to domestic
firms Halpern and Murakoumlzy (2007) find significantly positive spillovers in the supplier industry but
no evidence for within-industry spillovers Beacutekeacutes et al (2009) find a negative effect on low-
productivity firms in the same industry while the spillover effect is positive for high-productivity
firms Iwasaki et al (2012) find positive spillovers even within the same industry conditional on the
proximity in product and technological space At the same time Bisztray (2016) shows that the
large-scale foreign direct investment of Audi did not increase the productivity of domestic firms in
the supplier industry
We know from the literature that the effect of FDI on domestic firms is highly heterogeneous even in
the supplier industry (see Smeets 2008 for a review) A crucial precondition of positive spillovers is
the absorptive capacity of the domestic firms (Crespo-Fontoura 2007) Using data from Bulgaria
Poland and Romania Nicolini and Resmini (2010) show that firm size matters as well Additionally
they find within-industry spillovers in labour-intensive sectors and cross-industry spillovers in high-
tech sectors Also the characteristics of the foreign investment play an important role in the
magnitude of the spillover effect Javorcik (2004) estimates a positive effect on the productivity of
domestic firms only in the case of shared foreign and domestic ownership but not for fully foreign-
owned firms Javorcik and Spatareanu (2011) show that the distance of the investorrsquos country is
also important as investors from far-away countries establish more links with local suppliers In line
with that they estimate positive vertical spillovers from US investors but not from European
investors in Romania Lin et al (2009) show that vertical FDI spillovers in China are weaker for
export-oriented FDI compared to domestic-oriented
Productivity differences in Hungary and mechanisms of TFP growth slowdown
53
Figure 410 TFP levels of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the (unweighted) average ACF TFP level of foreign and domestically-owned firms Main
sample The industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge- intensive services lsquoLKISrsquo Less knowledge-
intensive services
Evolution of the Productivity Distribution
54
Figure 411 Cumulated TFP growth of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level of foreign and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
55
Figure 412 Capital intensity of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the average capital intensity (log tangible and intangible assetsemployee) of foreign- and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Evolution of the Productivity Distribution
56
Figure 413 Cumulative change in capital intensity of foreign and domestic firms selected industries
Notes This figure shows the (unweighted) average capital intensity (log tangible and intangible assetsemployee)
of foreign and domestically-owned firms since 2004 in four industries Main sample
45 Conclusions
Our investigation of the evolution of productivity distribution has yielded a number of relevant
conclusions which will inform the work conducted in the remaining sections In line with international
evidence we have found that productivity dispersion within industries is many times larger than the
differences between industries Importantly Hungary seems to be an exception to the international
trend of frontier firms diverging from the rest of the economy ndash if anything there is evidence for the
low productivity growth of frontier firms and for some catching-up by others
OECD (2016 Figure 16) has found such a pattern only in Hungary and Italy with divergence in all the
other countries under study (Austria Belgium Canada Chile Denmark Finland France Japan
Norway and Sweden) We find two kinds of explanations plausible First in Hungary (unlike most other
countries in that sample) national frontier firms are quite far away from the global frontier As
Andrews et al (2015) argue the productivity divergence mainly arises between global frontier firms
and the rest If national frontier firms are far away from the global frontier they may find themselves
on the wrong side of global divergence Second it is possible that the policies and institutional
environment for national firms in Hungary is less conducive to adopt local frontier technologies A way
to learn more about the background of this result would be to use cross-country micro-data to study
the behaviour of frontier firms in even more countries including other CEE countries
The low productivity growth of Hungarian national frontier firms constrains productivity growth
directly Furthermore if national frontier firms do not adopt the most developed technology potential
spillovers to other firms will also remain limited Andrews et al (2015) have shown that good
Productivity differences in Hungary and mechanisms of TFP growth slowdown
57
framework conditions (most importantly good regulatory practices in upstream sectors) and innovation
related policies such as providing incentives for RampD and building a more robust national innovation
system are associated with a stronger catch-up of national frontier firms to the global frontier
The results reveal that duality especially between foreign and domestic firms is substantial and there
is no evidence for catching-up by domestic firms The gap is especially large in the service industries
That said the gap between the two groups can be bridged indeed the productivity differences
between the two groups are smaller than within them Duality while a sign of inefficiency also
provides an opportunity for domestic firms to tap into the knowledge base possessed by their foreign-
owned counterparts and to integrate into global value chains by relying on the links of foreign firms
While efficient strategies aiming at maximizing the benefits from FDI and global value chains may
differ across countries there are a few policy options which unambiguously help countries in benefiting
from the presence of multinational firms A robust result of the recent spillover literature is that
domestic firms need strong absorptive capacity including technological knowledge and a skilled
workforce to be able to benefit from the presence of foreign-owned firms (Girma 2005 Crespo and
Fontoura 2007 Zhang et al 2010) One dimension of absorptive capacity building is creating an
effective innovation system with a strong knowledge base and easy access to that knowledge Another
dimension is developing technological and management capabilities which enable firms to understand
and apply advanced knowledge Such capabilities are essential both for technological upgrading and for
integrating into global value chains (Taglioni and Winkler 2016)
An important caveat regarding these results is that they are limited to double-entry bookkeeping firms
We have emphasised that a large share of people work outside the double-entry bookkeeping entities
included in our sample While data are scarce about the productivity of these entities available
information suggests that both the levels and dynamics of productivity may differ radically between
double-entry bookkeeping firms and other entities If so inclusive policies could focus on providing
skills and opportunities for the self-employed
State-owned firms constitute a small part of the Hungarian market economy but such firms are
prevalent in some industries including utilities The productivity of some of these firms is very low
when compared to the productivity of privately-owned firms while they pay higher wages Both of
these phenomena hint at soft budget constraints and other inefficiencies Policies aiming at providing
better incentives either by improving corporate governance of state-owned firms (Arrobio et al 2014)
or by creating framework conditions more conducive to competition may help in in promoting
productivity growth in these important industries
Allocative efficiency
58
5 ALLOCATIVE EFFICIENCY
A key insight of recent productivity research is that differences in productivity levels across countries
largely result from the inefficient allocation of resources across firms rather than from differences in
the productivity of lsquotypical firmsrsquo both in cross-section (Hsieh and Klenow 2009 Restrucca and
Rogerson 2017) and in time-series (Gopintah et al 2017) Inefficient allocation refers to the
phenomenon that low-productivity firms possess a large amount of capital and labour (rather than
shrinking or exiting) or when firms with similar marginal products use a different amount or
composition of inputs
In this chapter we employ two strategies to quantify the extent of such distortions The first strategy
proposed by Olley and Pakes (1996) simply asks whether more productive firms are larger A more
positive covariance between productivity and employment suggests a better allocation of resources
across firms and higher industry level (labour-weighted) productivity (even when holding the
unweighted productivity level unchanged) The Olley-Pakes method is generally agnostic about the
specific nature of distortions but measures their results in an intuitive and robust way at the industry-
year level
Hsieh and Klenow (2009) attempt to identify the sources of distortions36 In particular they argue that
firms can face two main distortions product market distortion (modelled as an implicit sales tax and
identified from the wedge between labour costs and value added) and capital market distortion
(modelled as an implicit capital tax and identified from differences in the cost share of capital) These
variables can be measured at the firm-level Industry-level distortions can be quantified both as the
average of firm-level distortions and also as the dispersion of firm-level measures
This chapter describes these measures at the industry-year level Section 51 presents the Olley-Pakes
covariance terms while Section 52 implements the Hsieh-Klenow method
51 Olley-Pakes efficiency
The Olley-Pakes (also called static) approach of productivity decomposition consists of decomposing
the aggregated (industry-region-level) productivity which is the weighted average of firm-level
productivity levels into the unweighted average firm-level productivity and the covariance between
productivity and firm size (Olley and Pakes 1996) The latter term reflects how efficiently resources in
this case labour are allocated across firms A more positive covariance between size and productivity
reflects stronger allocative efficiency
Let us start with cross-country evidence from the OECD (Andrews and Criscuolo 2013) According to
this source in 2005 static allocative efficiency in Hungarian manufacturing (the covariance term) was
positive but slightly below the average of OECD countries similar to Portugal and Italy (Figure 51)37
Allocative efficiency in services was negative one of the lowest of the countries in the sample
(Andrews and Cingano 2014 Figure 10) showing that less productive firms tended to be larger in the
service sector Andrews and Cingano (2014) also show that the relatively low allocative efficiency in
Hungary is partly explained by policies including product market regulation and creditor protection
36 For an overview of the reallocation literature see Hoppenhayn (2014)
37 Note that these calculations use the ORBISAMADEUS database covering a relatively small fraction of larger
Hungarian firms in 2005 (about 3300 firms) see Box 21
Productivity differences in Hungary and mechanisms of TFP growth slowdown
59
Figure 51 Static allocative efficiency in Hungarian Manufacturing (2005)
Notes This figure is a reproduction of Figure 7 from Andrews and Criscuolo (2013)
Let us turn to our data The logic of the static decomposition is presented in Figure 52 for our main
sample by 2-digit industry38 The horizontal axis shows the unweighted average log labour productivity
of each industry while the vertical axis shows the productivity weighted by employment If all firms
were of equal size (or at least firm size was independent of productivity) weighted and unweighted
productivity would be equal ie all industries would be on the 45-degree line If size and productivity
were positively correlated the weighted productivity would be larger than the unweighted one The
difference between the weighted and unweighted average is the covariance between size and
productivity This measure of allocative efficiency is equal to the vertical distance between each point
and the 45-degree line Allocative efficiency contributes positively to industry productivity in industries
above the 45-degree line while it has a negative contribution for industries below the line
For example in the manufacture of machineries (28) the unweighted average productivity is 646
while the weighted average productivity is 665 Allocative efficiency resulting from more productive
machine manufacturers being larger contributes with 019 to the aggregate productivity of this
industry An industry with negative allocative efficiency is warehousing (52) where the lower
productivity of larger firms contributes negatively to aggregate productivity (the unweighted
productivity being 681 and the weighted only 585)
38 Appendix Table A51-Table A56 summarise the Olley-Pakes (1996) measures by industry
Allocative efficiency
60
Importantly allocative efficiency is positive in most industries It is especially high in the most
knowledge-intensive services (scientific research (72) employment activities (78)) in service
industries with a few large firms (broadcasting (60) telecom (61)) and in key manufacturing
industries beverages (11) chemicals (20) machinery production (28) and vehicle production (29) In
a few industries low-productivity firms tend to be larger Prominent examples are professional
services advertising (69) and legal and accounting activities (73) services with many state-owned
firms transportation (39) waste management (49) and logistics (52) In line with OECD evidence
allocative efficiency tends to be more positive in manufacturing compared to services
Finally Figure A51 in the Appendix shows that allocative efficiency is significantly higher when labour
productivity is considered rather than TFP almost every industry has larger weighted labour
productivity than unweighted labour productivity This difference simply results from the positive
association between productivity and capital intensity
Figure 52 Weighted and unweighted TFP by 2-digit industry 2015 main sample
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted TFP while the vertical axis
shows its weighted TFP in the same year We have omitted industries with less than 1000 observations TFP is
estimated using the method of Ackerberg et al (2015)
Another conclusion that can be drawn from Figure 52 is that allocative efficiency is higher in sectors
with higher unweighted productivity represented by the fitted line in the figure In other words high
firm-level efficiency seems to move together with higher allocative efficiency in the industry One
mechanism behind this relationship may be that incentives for technology upgrading are stronger
when the reallocation process is more effective (Restruccia and Rogerson 2017) but stronger
international competition can also affect positively both within-firm productivity dynamics and
reallocation across firms In Figure 53 we investigate whether this relationship changed between
years The figure shows that the positive relationship between unweighted productivity and allocative
Productivity differences in Hungary and mechanisms of TFP growth slowdown
61
efficiency did not change substantially over time This relationship is similar when labour productivity is
considered (see Figure A52 in the Appendix)
Figure 53 The relationship between weighted and unweighted TFP by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted TFP levels run at the
2-digit industry level separately for 2005 2010 and 2016 TFP is estimated using the method of Ackerberg et al
(2015)
From the perspective of productivity slowdown a key question is whether allocative efficiency
deteriorated in some industries following the crisis Figure 54 shows the allocative efficiency of each
2-digit industry in 2010 and 2016 The axes here represent the distances from the 45-degree line in
Figure 52 If an industry is on the 45-degree line of this figure its allocative efficiency remained
unchanged in the period if an industry is above the line its allocative efficiency was better in 2016
compared to 2010 The first conclusion that can be drawn is that levels of allocative efficiency are
persistent industries cluster around the 45-degree line Also the fitted line shows that allocative
efficiency grew somewhat faster in industries where allocative efficiency was worse and this
relationship is statistically significant Therefore productivity growth decline is unlikely to be the result
of rapidly worsening allocative efficiency
One can however identify a couple of industries where substantial changes took place The machinery
industry (28) for example became more efficient partly because of the entry of new large foreign-
owned firms Office administration (82) and management activities (70) also increased their allocative
efficiency This is most likely due to the entry of large shared service providers Allocative efficiency
decreased in land transportation (39) waste management (49) and warehousing (52)
The evaluation of allocative efficiency in labour productivity shows similar patterns (Table A53 in the
Appendix)
Allocative efficiency
62
Figure 54 The change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences
between the weighted and unweighted TFP) in 2010 while the vertical axis shows the same quantity in 2016 TFP is
estimated using the method of Ackerberg et al (2015)
52 Product market and capital market distortions
The Olley-Pakes static decomposition framework can quantify the overall allocative efficiency of sectors
but it is incapable of informing us about the nature of distortions In this section we implement the
methodology of Hsieh and Klenow (2009)39 to distinguish between product and capital market
distortions This distinction is of much interest given that the crisis and its aftermath ran parallel with
both financial market frictions and changes in product market regulation
The logic of the Hsieh and Klenow (2009) method is the following Under the assumptions of
monopolistic competition on product markets (similarly to Melitz 2003) and frictionless labour
markets the marginal product of labour and capital should be equalized across firms in the absence of
market distortions In turn if the production function is Cobb-Douglas the equality of marginal
products implies that the share of labour costs in value added and capital intensity (capitallabour)
should be equalized across firms Under product market distortions (modelled with a firm-specific
implicit lsquosales taxrsquo or a negative rent) the wedge between labour costs and value added will differ
across firms because firms facing lower implicit taxes charge higher markups The more heterogeneous
the lsquosales taxrsquo is the larger the dispersion of the wedge Capital market distortions are modelled as
39 The Hsieh-Klenow approach has been criticized recently by Haltiwanger et al (2018)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
63
implicit firm-specific capital tax rates Firms facing different capital tax rates choose different capital
intensity levels and hence different capitallabour cost ratios Therefore the dispersion of capital
intensity (or more precisely the cost share of capital) reflects the dispersion of capital tax rates
Note that the implicit taxes proxy multiple sources of distortions from which differences in explicit
taxes represent only a small part The implicit lsquosales taxrsquo includes the cost of complying with different
types of regulations size-dependent regulation the effect of fixed costs and market power The
implicit lsquocapital taxrsquo includes for instance the full cost of accessing financing possible subsidies for
investment or differences in tax incentives to invest These implicit taxes provide a convenient way of
summarizing markup differences and differences in access to capital
As a result the dispersion of the wedge and capital intensity reflect how heterogeneous the two
implicit tax rates are More heterogeneity in implicit tax rates in turn implies more disperse total factor
productivity within industry40 and a less efficient allocation of resources In other words similarly
productive firms (having also similar marginal products of inputs) choose very different input quantities
and combinations
Product market distortions
We start our empirical investigation by calculating the rents (1-implicit sales tax rate) for every firm by
a proxy for markups41
1 minus 120591119884119904119894 =120590
120590minus1
120573119871119904+120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119881119860119904119894 (51)
where 120591119884119904119894 shows the size of the implicit lsquosales taxrsquo (or product market distortion) for firm i in sector s
120590 denotes the elasticity of substitution between firms by consumers and 120573119871119904 and 120573119870119904 are the
coefficients of labour and capital in the production function We follow the calibration of Hsieh and
Klenow (2009)42 and set 120590 = 3 while we plug in 120573119871119904 and 120573119870119904 using our production function estimation of
Section 22 119881119860119904119894 represents the real value added of the firm i in sector s while 119871119886119887119888119900119904119905119904119894 is labour
related expenses for firm i in sector s
The equation reflects the intuition that firms facing a lower implicit sales tax can charge higher
markups and as a result will pay a lower share of their value added to their employees Note that the
level of 120591119884119904119894 depends on a number of parameters and may be driven by differences in for example the
elasticity of substitution Therefore we will normalize the values of this estimate when comparing
typical distortions across industries
Figure 55 summarizes the implicit sales taxes by industry (120591119884119904119894) We standardise the values of 120591119884119904119894 by
subtracting the market level median from the firm-level implicit sales taxes and plot the median of
40 Appendix Table A57 summarises the dispersion of TFP within industry Note that dispersion in labour productivity
(log-value added per worker) is not necessarily related to product market distortions as firms with various
labour productivity may have the same TFP if the production function does not have the property of constant
return to scale
41 Hsieh and Klenow (2009) Equation 18
42 The predicted value of product market distortions crucially depends on the elasticity of substitution However the differences in 120591119884119904119894 across industries and years measures the changes in product market distortions even if the
elasticity of substitution is miscalibrated
Allocative efficiency
64
these standardised values by industry If the standardised bar is positive (negative) than the median
firm in the industry faces a higher (lower) implicit sales tax than the median firm in the economy We
find that product market distortions tend to be larger in highly regulated industries (energy
transportation ICT) while they tend to be lower in less regulated ones with strong competition
including manufacturing accommodation and administrative services The difference between
industries is non-trivial the difference between highly regulated sectors and manufacturing is
equivalent to an extra 10-20 percentage `sales tax ratersquo
The ranking of the industries (with the exception of energy) remained similar between 2006 and 2016
but differences became somewhat larger with a relative decrease in implicit taxes in manufacturing
and administrative services and an increase in transportation and ICT43
Figure 55 Implicit sales taxes (120591119884119904119894) by industry
Notes The figure above shows the median size of product market rents in 2006 and 2016 Industries with positive
tax measures can achieve rents below the market average due to product market distortions
The previous exercise has investigated across-industry differences A further question is whether firms
face different tax rates even within industries because of for example size-dependent taxes This is a
key measure to examine whether resources are misallocated across firms within industries Our
measure for this is the standard deviation of ln(1 minus 120591119884119904119894) (Figure 56)44 This dispersion is substantial
43 We report these measures in more detail in Table A55 of the Appendix
44 Also note that this measure of dispersion is independent of the elasticity of substitution and the production function parameters
Productivity differences in Hungary and mechanisms of TFP growth slowdown
65
with the standard deviation equivalent to a 100 percent sales tax45 Within-industry differences in this
variable are similar across industries with a relatively small dispersion only in mining and energy
Figure 56 Standard deviation of implicit sales tax rates (ln(1 minus 120591119884119904119894)) by industry
Notes The figure shows the within industry product market distortions in 2006 and 2016 Resources are less
effectively distributed in industries with larger distortion measures
Capital market distortions
Distortions on the capital market are identified from how the ratio of expenses on labour and capital
(capital intensity in cost terms) differ from what is predicted by the production function with no capital
tax46
119877(1 + 120591119870119904119894) =120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119870119904119894 (52)
The left hand side of this equation represents the implicit cost of capital for firm i in sector s backed
out from the capital intensity of the firm If it is 01 the firm faces an implicit lsquointerest ratersquo of 10
percent if it is 02 the lsquointerest ratersquo is 20 This can be decomposed into 119877 the frictionless user
45 Similar differences have been found in other countries as well and they are in line with the vast degree of heterogeneity in terms of size and productivity within industries
46 Hsieh and Klenow (2009) Equation 19
Allocative efficiency
66
costs47 of capital (having the same unit of measurement) multiplied by 1 plus the implicit lsquocapital tax
ratersquo 120591119870119904119894 which is firm-specific48
Similarly to the product market equation 120573119871119904 denotes the labour elasticity of the production function
120573119870119904 is the capital elasticity of the production function and 119871119886119887119888119900119904119905119904119894 is the total labour cost for firm i in
sector s The denominator consists the capital stock of the firm (119870119904119894)
It is not common in the literature to report 120591119870119904119894 because its absolute value depends crucially on the
calibration of the rental rate of capital This is an issue because it is hard to obtain reliable information
on the frictionless rate of capital which most likely changed substantially between the pre-crisis
disinflationary period and the wake of the crisis Besides 120591119870119904119894 takes extremely large values for firms
with low level of capital (eg if the firm rents its capital instead of owning it) Note that the levels of
this variable are identified from the difference between the observed capital intensity (in cost terms)
and the optimal one implied from the production function Therefore we prefer to report the more
easily interpretable implicit median cost of capital 119877(1 + 120591119870119904119894) by industry49
While we find differences and changes in the implicit cost of capital informative it is not a direct
measure of capital market distortions because it can also reflect differences in the user cost of capital
across industries and years However the ratio (or log difference) of the implicit cost of capital
between two firms measures the difference between their respective implicit capital tax rates (or more
precisely between their 1 + 120591119870119904119894) As a result the standard deviation of the log implicit cost of capital
provides a pure measure of the dispersion of implicit capital taxes independently from the exact value
of 119877 Its interpretation is the relative standard deviation of the user cost of capital which is identified
from the dispersion of capital intensities
Figure 57 summarizes the median size of implicit cost of capital across industries50 Administrative and
professional services and ICT seem to pay the highest implicit cost for capital it is above 40 percent in
these industries As opposed to these utilities accommodation and food services face implicit costs of
capital below 20 percent The large differences in access to capital across industries are likely to result
mainly from differences in the size and age distribution of firms as well as from the different share of
tangible capital in different industries Moreover the median implicit cost of capital rose practically in
all service industries but decreased slightly in manufacturing
47 The rental price of capital covers the interest rate and the depreciation of capital stock
48 If one is willing to assume a specific value for the frictionless user cost of capital it is easy to back out 120591119870119904119894 For
example if the implicit cost of capital for firm 119894 (the left hand side) is 02 and (following Hsieh and Klenow 2009) one sets R = 01 then 120591119870119904119894 = 1 meaning that firm 119894 can obtain capital at a 10 percentage points higher interest rate
relative to the frictionless rate
49 The median of 119877(1 minus 120591119870119904119894) is less dependent on the extreme values of the distribution than the average so it is a
more precise measure of capital market distortions a typical firm faces than the average of it
50 We can validate our implicit capital cost measure by comparing our results to Kaacutetay and Wolf (2004) According to their estimates (using a different methodology) the median user cost of capital was 189 percent between 1993 and 2002 Our results have similar magnitude as the median implicit cost of capital was 255 percent in 2006 and 287 percent in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
67
Figure 57 Median implicit cost of capital by industry
Notes The figure shows the average size of capital market distortions in 2006 and 2016 Industries with larger
distortion measures are more constrained in accessing capital due to capital market distortions
Again the differences in typical capital costs across industries are much smaller than differences across
firms within an industry (see Figure 58) In industries where median implicit capital costs are lower
the dispersion of those costs also tends to be smaller the estimated cost of accessing capital is
significantly more unequal in the retail sector and administrative services relative to manufacturing
The notable exemption is the energy sector which has the lowest median and the largest dispersion in
the implicit cost of capital reflecting a relatively low level of capital costs resulting from predictable
tangible capital intensive activities
Allocative efficiency
68
Figure 58 The standard deviation of the estimated implicit cost of capital by industry
Notes The figure shows the standard deviations of capital market distortions log (119877(1 + 120591119870119904119894)) in 2006 and 2016
Most importantly capital market distortions increased within nearly all industries both in terms of
their levels and dispersion Hungary is not an exception in this respect This trend has been
documented in other countries where FDI played important role in economic growth A key study on
this topic is Gopinath et al (2017) who show that large capital inflows and credit market constraints
of small firms jointly increased capital market distortions in Spain This evidence suggests that the
crisis led to similar developments in Hungary making capital costs more unequal by generating
financial frictions This inefficiency seems to have resulted in the misallocation of capital in all types of
industries
A key question from a policy perspective is whether one can identify types of firms which faced a
systematically large increase in their cost of capital We follow the approach of Gorodnichenko et al
(2018) who quantified the misallocation of capital at the firm-level and found that small and young
firms faced an exceptionally high cost of capital We follow this strategy to identify observables which
are likely to be related to the level and change of capital costs
Figure 59 plots the relationship between firm age firm size and the estimated implicit cost of capital
119877(1 + 120591119870119904119894) The figure sorts the firms into twenty equally-sized bins by age and size and plots the
median implicit cost of capital separately for 2006 and 2016 Panel (a) highlights that the implicit cost
of capital was decreasing with firm age even before the crisis with young firms facing about 25
percentage points higher capital costs compared to firms older than 10 years This function became
dramatically steeper by 2016 when the median `oldrsquo firm (more than 10 years old) faced an implicit
capital cost of 25 percent a median 5-year old firm paid 50 percent and a very young firm faced more
than a 100 percent implicit cost of capital This figure suggests that capital market frictions generate
important constraints for entry and the growth of small firms hindering reallocation and innovation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
69
Panel (b) of Figure 59 visualizes the relationship between employment and the implicit cost of capital
We find that firms with more than 20 employees faced an implicit cost of capital below the median of
the whole sample both in 2006 and 2016 As opposed to this small firms faced above the median
implicit cost of capital in 2006 and suffered from a disproportionally large increase in the next decade
This again constrains the growth of small firms relative to their larger peers
Figure 59 The evolution of the implicit cost of capital by age and firm size
A) Age of firms
B) Size of firms
Notes The figure shows the median implicit cost of capital 119877(1 + 120591119870119904119894) by age and size categories
Allocative efficiency
70
The results presented above have shown two patterns an increasing dispersion of the implicit cost of
capital on the one hand and a steeper gradient between observables (age and size) and capital taxes
on the other A natural question is whether increased financial friction led to larger differences in
access to capital along observables One explanation for this could be that banks have become more
wary about allocating capital to say firms operating in industries with much intangible capital The
alternative is that the increased variance in capital access reflects mainly differences along unobserved
dimensions by for example more scrutiny of managers when deciding about firm loans These two
possibilities can have different policy implications In the former case for example policymakers may
promote access to capital for specific groups of firms
Table 51 presents regressions with the implicit capital cost as a dependent variable and key firm-level
characteristics as explanatory variables Our first conclusion is that the regressions explain only a
relatively small part (less than 20 percent) of the variation in the implicit cost of capital the
overwhelming majority of the variation arises from unobservables In this sense policies targeting
specific types of firms may have a limited effect
That said the explanatory power of observables increased by around a third between 2006 and 2016
While the explanatory power of industry dummies slightly decreased that of age increased
substantially from 2 percent to 57 percent The explanatory power of size was much smaller in both
periods suggesting that its correlation with the implicit cost of capital may be confounded by its
correlation with age and industry This evidence together with Figure 59 suggests that indeed
capital access by young firms deteriorated substantially after the crisis
Table 51 Variance decomposition of implicit cost of capital
Variance in 2006 Variance in 2016
Variance
component
Share
of total
Variance
component
Share
of total
Total Variance of log-implicit
cost of capital
2126 100 2443 100
Components of Variance
Variance of age 0042 20 0140 57
Variance of size 0006 03 0002 01
Variance of ownership 0012 06 0022 09
Variance of region 0012 06 0025 10
Variance of industry 0202 95 0180 74
Residual 1830 861 1995 817
Notes Control variables are dummies for age ownership (private foreign or state-owned) region (7 NUTS2
region) and 2 digit industry
53 Conclusions
This section summarises the static measures of allocative efficiency by industry types (Table 52) A
key pattern that emerges is that resources are allocated more efficiently in the manufacturing sectors
First on average the OP covariance is strongly positive within manufacturing while it is very close to
zero in less knowledge-intensive services The Hsieh-Klenow (2009) efficiency measures suggest that
product market distortions are similar across sectors but capital market distortions are significantly
lower in manufacturing These findings are in line with the disciplinary effect of international
competition in the traded sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
71
Table 52 Allocative efficiency within industries sectors (2016)
Industry type TFP
level
in
2016
TFP
growth
between
2011 and
2016
Olley-
Pakes
allocative
efficiency
Dispersion
of implicit
sales taxes
Dispersion
of implicit
cost of
capital
Low-tech mfg 5694 0027 0197 111 146
Medium-low tech mfg 6081 0027 0017 102 142
Medium-high tech mfg 6129 -0093 0119 111 131
High-tech mfg 6708 0276 0072 107 145
Total manufacturing 5942 0021 0242 107 143
Knowledge-intensive serv 6706 0225 0403 106 166
Less knowledge-intensive serv 6566 021 -0081 108 159
Construction 6411 0082 0023 109 148
Utilities 5949 -0138 0801 093 155
Total services 6598 0212 0055 108 160
Notes The table summarises the allocative efficiency measures by broad industry categories The dispersion of
implicit sales taxes is measured by the standard deviation of 119897119899(1 minus 120591119884119904119894) while the dispersion of the implicit cost of
capital is measured by the standard deviation of ln (119877(1 + 120591119870119904119894))
Capital market distortions became more severe in the wake of the financial crisis while there was no
such trend in terms of product market distortions This finding is in line with results for Southern
Europe (Gamberoni et al 2016a Gopinath et al 2017) and CEE countries in general (Gamberoni et
al 2016b) (see Figure 510) This suggests that the financial intermediation system is still less
effective relative to its pre-crisis performance
Investigating at the firm-level we found that the deterioration of the financial conditions did not hit all
firms equally In particular young firms were hit especially hard by ever increasing capital costs even
though many policy tools were introduced to help such firms including the subsidized access to capital
by the Central Bank (eg the NHP program) Deteriorating access to capital by young firms can be
especially harmful for reallocation often driven by dynamic young firms Policies aimed at promoting
equal and efficient access to capital especially for young firms may help to reduce these inefficiencies
Given the magnitude of the still existing allocative inefficiency policies which support reallocation could
have a significant positive effect on aggregate productivity A key conclusion of recent research is that
firm-specific distortions which may result from discretionary policies or non-transparent regulations
have a quantitatively significant effect on aggregate productivity (Hsieh and Klenow 2009 Bartelsman
et al 2013 Restuccia and Rogerson 2017) In particular size-dependent taxes and regulations
(Garicano et al 2016) ineffective labour and product market regulations and FDI barriers (Andrews
and Cingano 2014) have been shown to be negatively associated with allocative efficiency and its
improvement Gamberoni et al (2016b) also demonstrate that higher corruption levels slow down the
improvement of allocative efficiency Chapter 8 will investigate the effects of such policies in more
detail using the example of the retail industry
The specific pattern showing that capital distortions are relatively high and have increased in Hungary
(similarly to other CEE and Southern European countries) suggests that policies which facilitate the
reduction of financial frictions and provide symmetric access to capital for all firms could improve
allocative efficiency to a large degree Specifically policies should attempt to facilitate capital flows to
Allocative efficiency
72
more efficient firms even if young rather than to firms with a higher net worth or more tangible
assets (Gopinath et al 2017)
Figure 510 Capital and labour misallocation in CEE countries country-specific weighted average
across sectors
Notes This is a reproduction of Figure 1 from Gamberoni et al (2016b)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
73
6 REALLOCATION
After investigating the level of allocative efficiency in Chapter 5 namely a lsquostaticrsquo approach we now
turn to a dynamic view focusing on how much reallocation across industries (Section 61) and firms
(Section 62) contributed to aggregate and sectoral productivity growth
61 Reallocation across industries
An important channel behind the relationship between economic development and productivity is the
structural change of the economy first from agriculture to manufacturing and then from
manufacturing to services (Herrendorf et al 2014 McMillan et al 2017) But at higher levels of
development economic growth is also associated with reallocation across industries within these broad
sectors primarily from more traditional to more knowledge-intensive ones (Hausmann and Rodrik
2003 Hausmann et al 2007) Kuunk et al (2017) demonstrate that in terms of its contribution to
productivity growth across-industry reallocation within sectors dominated reallocation across sectors
in CEE countries In this subsection we take a brief look at the importance of this process in Hungary
by quantifying the reallocation of employees across and within 2-digit industries
Table 61 shows how the employment share of different industries in our main sample changed over
time51 The most important pattern is a pronounced shift from manufacturing to services until 2010
and near-constant sectoral shares after that In particular the share of manufacturing decreased by
nearly a quarter from 38 percent to 32 percent between 2004 and 2010 but this number remained
unchanged in the years following the crisis The crisis seems to have constituted a structural break in
this process
A more detailed look at the composition of industries shows that ndash in net terms ndash this structural
change was driven by a transition of employment from low-tech manufacturing52 to both knowledge-
intensive and less knowledge-intensive services while the employment share of the more high-tech
manufacturing industries remained practically unchanged After 2010 the structure of manufacturing
remained mainly unchanged in this aspect with no further shift away from low-tech manufacturing
activities Within services we see a continuous increase in the share of knowledge-intensive services
both before and after the crisis In the 12 years under study the employment share of knowledge-
intensive services increased by 6 percentage points or nearly 60 percent
51 Note that these calculations in line with other parts of this report apply to the firm sector of the Hungarian
economy ie ignore the self-employed (see Section 42) When taking into account the self-employed the share of
services and sectoral share follow somewhat different dynamics
52 One factor behind this process might have been the almost doubling of the minimum wage in 2000 and 2001
(Koumlllő 2010 Harasztosi and Lindner 2017) and a growing import competition in the light industries (David et al
2013)
Reallocation
74
Table 61 Employment in different sectors (main sample)
2004 2007 2010 2013 2016
Low-tech mfg 152 117 107 105 100
Medium-low tech mfg 89 92 91 96 98
Medium-high tech mfg 94 96 82 89 93
High-tech mfg 49 49 44 39 35
Total manufacturing 384 355 324 329 327
Knowledge-intensive serv 107 128 149 158 167
Less knowledge-intensive serv 382 395 410 405 397
Construction 86 88 83 75 75
Utilities 40 34 34 33 34
Total services 616 645 676 671 673
Notes This table shows employment shares by industry type (see Section 25) for the full sample
To provide a more detailed picture Figure 61 illustrates how employment growth in different 2-digit
industries is associated with their initial productivity level (Figure 61) In particular if more productive
sectors increase their employment share faster aggregate productivity should grow
Figure 61 Employment change as a function of initial TFP
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
75
B) Services
Notes Industries are 2-digit NACE Rev 2 industries The fitted line is weighted with initial employment Main
sample
To quantify whether across-industry reallocation matters we decompose the aggregate productivity
growth observed in our sample into the contributions of cross-industry reallocation and within-industry
productivity growth We divide our sample into three-year periods and calculate the average yearly
productivity growth by periods
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119901119894119905minus3)119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+ sum 119905119891119901119894119905minus3 lowast (119904ℎ119886119903119890119894119905 minus 119904ℎ119886119903119890119894119905minus3)119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
(61)
where the left hand side variable is the change in aggregate TFP between years 119905 minus 3 and 119905 119904ℎ119886119903119890119894119905 is
the share of the (2-digit) industry i in year t in the total employment and 119905119891119901119894119905 is average TFP of the
industry The first term on the right side is the within-industry TFP growth weighted by initial market
shares and the second term is the between effect capturing whether more productive industries have
increased their employment shares53
The decomposition in Figure 62 presents the result of this reallocation exercise for annualized growth
rates Its interpretation is the following between 2004 and 2007 average annual productivity growth
was nearly 8 percent in the total economy Around 7 percentage points from it is explained by within-
industry developments and only about 1 percentage point by reallocation across industries
53 This decomposition gives a comprehensive measure of the reallocation between industries but it is unable to
show the importance of firm exits and entries We investigate this in the next section
Reallocation
76
In general the figure shows that within-industry reallocation rather than cross-industry
developments played the key role in aggregate productivity growth Furthermore in line with Table
61 the contribution of between-industry reallocation was effectively zero post-crisis During the crisis
cross-industry productivity growth contributed positively to aggregate productivity growth while within
industry reallocation dramatically lowered aggregate productivity
This overall picture suggests that the flow of resources from light industries to other manufacturing
the growing share of services and especially knowledge-intensive services were a detectable though
not dominant driver of productivity growth only before 2010 Within-industry developments were
quantitatively more important throughout the whole period under study
This latter finding hints at a deterioration in the environment determining the reallocation process
post-crisis This seems to be the case for the whole economy but the negative contribution of
reallocation is more pronounced in manufacturing
Figure 62 Across and within industry productivity growth annualized log
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth main
sample
62 Reallocation across firms
In this subsection we take a look at the role of reallocation from a different perspective Rather than
focusing on whether the resources flow across industries we take a firm-level focus and decompose
TFP growth to within and across firm components The usefulness of this approach lies in the fact that
it sheds more light on the flexibility and efficiency of the process determining resource flows across
firms and also allows us to distinguish between resource flows across continuing firms on the one hand
and entry and exit on the other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
77
There are two general methods of measuring the reallocation of resources from less efficient to more
efficient firms The first method quantifies the labour and capital gains of more efficient firms directly
(Harasztosi 2011 Petrin et al 2011 Petrin and Levinson 2012) The second method is based on
product-market developments allocation of resources improves if the market share of high
productivity firms increases (Baily et al 1992 Griliches and Regev 1995 Brown and Earle 2008)
We adopt this second method as it can quantify directly the TFP contribution of firm entries and exits
To begin with we decompose the aggregate TFP growth between years t and t-3 based on the method
of Foster et al (2001) and Foster et al (2008)
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905minus3 lowast ∆119905119891119901119894119905minus3119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
+ sum (119905119891119901119894119905minus3 minus 119905119891119905minus3 + ∆119905119891119901119894119905) lowast ∆119904ℎ119886119903119890119894119905minus3119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+
sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119905minus3)119894isin119873⏟ 119890119899119905119903119910 119890119891119891119890119888119905
+sum 119904ℎ119886119903119890119894119905minus3 lowast (119905119891119901119894119905minus3 minus 119905119891119905minus3)119894isin119873⏟ 119890119909119894119905 119890119891119891119890119888119905
where the left hand side variable is the average annual aggregate TFP growth between years t-3 and t
and 119905119891119905 is the employment weighted average aggregate TFP while 119905119891119901119894119905 is the TFP of firm i in year t
119904ℎ119886119903119890119894119905 denotes the employment share of firm i in year t The first and second sum contain every firm
while the third sum consists of only firms which enter between years t-3 and t and the fourth sum
consists firms which leave the market between years t-3 and t
Each element of this decomposition has an intuitive economic interpretation In order of inclusion
these are i) within-firm TFP growth weighted by initial market shares ii) between effect capturing
whether initially more productive firms have raised their market shares and whether firms with
increasing productivity also expand (cross effect) and the iii) entry effect and iv) exit effect We pull
the last two terms together and interpret it as net entry effect which captures whether more
productive firms entered than exited54
Figure 63 summarizes the results for the market economy Before the crisis all three components
contributed positively to aggregate productivity growth Reallocation both across continuing firms and
on the margin of entry and exit was an important driver of productivity growth Productivity growth
was negative during the crisis as we have seen in Section 43 This was a result of strong negative
within-firm growth partly counterbalanced by positive reallocation Within-firm growth was still
sluggish immediately after the crisis but reallocation was relatively intensive and efficient Within-firm
growth recovered after 2013 and the importance of reallocation decreased Still the contribution of all
three components is substantially smaller relative to pre-crisis suggesting that the productivity
slowdown results from a combination of low within-firm growth and less effective reallocation
54 Note that these quantities cannot be easily linked to the withinacross industry decomposition of the previous
section Across firm reallocation and the entry effect can take place both across and within sectors
(62)
Reallocation
78
Figure 63 Dynamic decomposition annualized log main sample
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth by 3-
year periods main sample
Figure 64 repeats the decomposition exercise for each industry type For ease of interpretation (and
to get more stable results) we aggregate the three non-high-tech manufacturing sectors for these
calculations
As we have seen in Section 43 productivity dynamics differed markedly across these sectors Still
there are some common patterns First the strong pre-crisis productivity growth resulted from a
combination of strong within-firm productivity growth and efficient reallocation The sectors differ in
terms of the weights of these forces reallocation (especially entry) was most important in non-high-
tech manufacturing while within-firm growth dominated in high-tech manufacturing In services the
two components were of roughly equal importance
As we have seen productivity increased even during the crisis in high-tech manufacturing as a
combination of within and across productivity growth In other industries productivity growth was
negative during the crisis In non-high-tech manufacturing a strongly negative within growth was
somewhat counterbalanced by positive reallocation In contrast we find evidence for a negative
reallocation effect in services during the crisis
Immediately following the crisis (2010-2013) within growth remained sluggish but reallocation
resulting from firm entry and exit intensified especially in non-high-tech manufacturing and high-tech
services By 2013-2016 within growth recovered and the effect of reallocation became smaller
Productivity differences in Hungary and mechanisms of TFP growth slowdown
79
Figure 64 Dynamic decomposition by sector
A) High-tech Manufacturing
B) Non-high-tech Manufacturing
Reallocation
80
C) Knowledge-intensive services (KIS)
D) Not knowledge-intensive services (NKIS)
Notes This figure shows the Foster et al (2008) type dynamic decomposition of the productivity growth in our
sample for 3 periods by broad sectors as defined by the EurostatOECD (httpeceuropaeueurostatstatistics-
explainedindexphpGlossaryHigh-tech)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
81
One of the main messages of our analysis in Section 44 has been the large and persistent duality
between globally oriented and other firms This motivates our investigation of the extent to which
exporters and foreign-owned firms contributed to productivity growth and also whether reallocation
via the expansion of the more productive group contributed to aggregate productivity growth In order
to investigate these questions we decompose aggregate productivity growth into three parts the
within contribution of exporters (in the starting period) the within-contribution of non-exporters and
the reallocation between the two groups (which mainly reflects the change in the market share of
exporters) We conduct a similar analysis between foreign and domestically-owned firms
Table 62 shows the decomposition by export status Pre-crisis exporters contributed substantially
more to productivity growth than non-exporters both in manufacturing and services The reallocation
of resources to exporters mattered little Exporters were still capable of improving their productivity
levels during the crisis though it was not enough at the aggregate to counterbalance the falling
productivity of non-exporters Post-crisis the productivity growth of exporters slowed down and
aggregate growth was mainly driven by productivity changes within the non-exporter group
Productivity growth became much less exporter-driven post-crisis
Table 62 TFP growth decomposition by exporter status annualized log
2004-2007
Total Exporter Non-exporter Across
Market economy 793 577 226 -009
Manufacturing 1053 877 164 011
Market services 606 387 222 -003
2007-2010
Total Exporter Non-exporter Across
Market economy -045 091 -136 000
Manufacturing 054 021 007 027
Market services -178 129 -305 -002
2010-2013
Total Exporter Non-exporter Across
Market economy 206 033 144 029
Manufacturing -122 -129 -012 019
Market services 471 077 289 106
2013-2016
Total Exporter Non-exporter Across
Market economy 362 155 206 001
Manufacturing 057 049 011 -003
Market services 650 243 382 025
Notes This table decomposes the sales-weighted productivity growth into within-exporter within-non-exporter
contributions and the contribution of the reallocation between the two groups main sample
Table 63 decomposes productivity growth by ownership The picture is similar to the exporter
decomposition with a key contribution of foreign-owned firms to productivity growth pre-crisis and a
much smaller contribution after that Again reallocation of resources to foreign-owned firms played a
limited role in productivity growth
Reallocation
82
Table 63 TFP growth decomposition by ownership status annualized log
2004-2007
Total Foreign Domestic Across
Market economy 793 255 516 022
Manufacturing 1053 375 619 059
Market services 606 148 383 075
2007-2010
Total Foreign Domestic Across
Market economy -045 018 -076 013
Manufacturing 054 081 -034 007
Market services -178 -131 -120 073
2010-2013
Total Foreign Domestic Across
Market economy 206 003 199 005
Manufacturing -122 -152 027 003
Market services 471 138 336 -003
2010-2016
Total Foreign Domestic Across
Market economy 362 106 264 -008
Manufacturing 057 011 040 005
Market services 650 234 452 -037
Notes This table decomposes the sales-weighted productivity growth into within-foreign within-domestic
contributions and the contribution of the reallocation between the two groups Main sample
63 Failure of reallocation Zombie firms
Following the crisis it was suggested that weak productivity performance could be linked to the
survival of unprofitable and ineffective firms The presence of many such firms limits the access of
better-managed firms to capital and generates congestion in the product markets which limits entry
(Caballero et al 2008) McGowan et al (2017) have argued and provided evidence that the share of
such ldquozombie firmsrdquo has risen since the middle of the 2000s and that the higher share of such firms is
associated with lower productivity growth and investment at the industry level
Given the productivity slowdown in Hungary and the extent to which the financial crisis has affected
bank lending it is of interest to see whether the prevalence of ldquozombie firmsrdquo increased
disproportionately after the crisis
Figure 65 shows the share of ldquozombie firmsrdquo in 9 OECD countries from McGowan et al (2017) The
share of such firms in the full sample increased from just below 3 percent in 2003 to 5 percent in
2013 The rise was especially noticeable in Spain and Italy where in 2013 the share of firms reached
11 and 5 percent respectively Even more importantly the employment share of ldquozombie firmsrdquo rose
above 15 percent in both countries by 2013 possibly generating significant effects for other firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
83
Figure 65 Share of ldquozombie firmsrdquo in some OECD countries
Notes This is a reproduction of Figure 5A from McGowan et al (2017) Country codes should be interpreted as
follows BEL ndash Belgium ESP ndash Spain FIN ndash Finland FRA ndash France GBR ndash Great Britain ITA ndash Italy KOR ndash South
Korea SWE ndash Sweden SVN ndash Slovenia
Our basic definition of ldquozombie firmsrdquo follows McGowan et al (2017) for comparability We define a
firm as a zombie if it is at least 10 years old and its interest coverage ratio (the ratio of operating
income to interest expenses) has been below one for the last three years A limitation of this definition
is that interest expenses are not reported (or missing) for many smaller firms which only submit a
less detailed financial statement (or have no bank financing) To overcome this problem we also
categorize firms as zombies if their operating profit is negative for three subsequent years In such
cases the coverage ratio is not defined but the firmrsquos income is clearly not enough to cover its interest
expenses Note that this is a very conservative definition ndash one could also input interest expenses for
external capital for firms with missing interest expenditures (Figure 66)55
55 In actual fact 95 percent of zombies defined in this manner have negative profits
Reallocation
84
Figure 66 Share of ldquozombie firmsrdquo in Hungary
Notes Main sample
The patterns are the following First the share of ldquozombie firmsrdquo among firms with at least 5
employees was relatively high even before the crisis reaching about 8 percent by 2006 This increased
slightly in the wake of the crisis but started to decline after that falling to 3 percent in 2016 ldquoZombie
employmentrdquo fluctuated around 12-15 percent in most years with a steep decline after 2014
Put in an international context it is clear that the existence of ldquozombie firmsrdquo is a relatively big issue in
Hungary with their employment share at the highest end of the distribution of the OECD countries
examined by McGowan et al (2017) The prevalence of such firms however had been relatively high
even before the crisis with a relatively moderate growth between 2009 and 2011 followed by a
significant fall of the share of these firms Therefore ldquozombie firmsrdquo may have constrained productivity
growth in Hungary in the whole period but it is unlikely that an increase in zombie share is a key
explanation for productivity slowdown following the crisis
Table 64 shows the employment share of zombie firms in different dimensions One can see a U-
shaped relationship in terms of size with the largest zombie share among the smallest and the largest
firms The somewhat larger share of zombies among small firms may be explained by the tendency of
such firms to report losses in order to evade business taxes Large firms may be able to operate
persistently under losses either because of their accumulated savings or even more likely because of
the deep pockets of their owners This is also suggested by part B) which shows that a firm is more
Productivity differences in Hungary and mechanisms of TFP growth slowdown
85
likely to be a zombie if owned by the state56 or by foreigners In the latter case profit-shifting motives
may also play a role in reporting losses for sustained periods in Hungary Finally zombies are more
prevalent in services compared to manufacturing and in low-tech industries compared to high-tech
Table 64 Zombie employment by size ownership and industry
A) By size
2004 2007 2010 2013 2016
5-9 emp 62 751 862 802 411
10-19 emp 618 626 679 652 297
20-49 emp 607 554 698 648 296
50-99 emp 711 685 792 829 438
100- emp 2005 1839 1559 1489 456
Total 1548 1367 1229 1194 417
B) By ownership
2004 2007 2010 2013 2016
Domestic 66 671 769 614 253
Foreign 989 1011 1139 1577 585
State 6289 5954 4127 2792 7
Total 155 1369 1228 1194 417
C) By type of industry
2004 2007 2010 2013 2016
Low-tech mfg 1236 1255 1149 906 371
Medium-low tech mfg 897 515 846 974 634
Medium-high tech mfg 542 407 936 486 269
High-tech mfg 427 1392 429 268 1
KIS 2098 737 875 1408 592
LKIS 282 2327 187 1867 365
Construction 258 394 339 515 352
Utilities 297 1031 705 593 756
Total 1553 1372 1229 1194 418
Notes Main sample
Importantly all these patterns persist in multiple regressions when one includes all these variables at
the same time together with other controls (ie larger firms are more likely to be zombies even when
controlling for ownership) In such regressions (lag) productivity is the strongest predictor of not
56 Obviously the extreme employment share of state-owned zombie firms partly results from the massive size of some large utilities including the national railways and the Hungarian Post
Reallocation
86
becoming a zombie firm later one standard deviation higher productivity is associated with a 5
percentage point lower probability of becoming a zombie in the next period Note however that
productivity is actually a close measure of profitability hence this finding mostly reflects a mechanical
relationship of high profitability firms being less likely to become low profitability firms in the future
Figure 67 shows a 3-year transition matrix for zombie firms ie the share of year t zombie firms
which ldquorecoverrdquo remain zombies or exit from the market by year t+3 One cannot see radical changes
across years with somewhat more firms recovering and less exiting in later periods In line with the
argument about deeper pockets larger firms are more likely to remain zombies and less likely to exit
than smaller ones This is related to ownership foreign (and to a smaller extent state-owned) firms
are more likely to remain zombies There also seems to be a characteristic difference between
manufacturing and services manufacturing firms seem to be less likely to lsquorecoverrsquo and more likely to
exit suggesting more persistence of low performance in that sector
Figure 67 What happens to zombie firms within 3 years (2010)
Notes Main sample
64 Conclusions
In line with the immense within-industry productivity heterogeneity documented in Chapter 4 and 5
we find that while there was some reallocation across sectors in the economy the overwhelming
majority of productivity growth took place within industries This emphasizes the usefulness of policies
which promote productivity growth in a sector-neutral way rather than prioritizing some sectors of the
economy
In line with the lower efficiency of capital allocation post-crisis we have found that by and large both
within-firm productivity growth and reallocation across firms and industries became less efficient post-
crisis relative to its pre-crisis level This may reflect the presence of policies which promote specific
sectors or inhibit the growth and entry of more productive firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
87
In terms of the participation of global networks we have found that at least pre-crisis exporters and
foreign-owned firms contributed significantly to productivity growth Post-crisis the productivity
contribution of internationalized firms became much less substantial Adopting policies that create an
environment which is favourable for innovative investments and does not hamper the expansion of
globally oriented firms may contribute substantially to strengthening productivity growth
The presence of firms which are loss making for an extended period of time suggests a serious failure
of the reallocation process The share of such firms was relatively high in Hungary employing well
above 10 percent of the employees in our sample in most years This level was already high pre-crisis
and increased further during the crisis but there has been substantial improvement in recent years
The problem is more severe for larger firms and state owned firms Improving the corporate
governance of these firms and the effectiveness of the banking system may help in further alleviating
the problem
Andrews et al (2017) argue that the presence of zombie firms ndash and other barriers to firm dynamics ndash
is heavily related to the efficiency of insolvency regimes and the effectiveness of the banking system
Figure 68 shows an insolvency regime index developed by the OECD (the higher the index value the
slower the restructuring) Hungary is one of the countries with the weakest insolvency systems with
all sub-measures taking high values This coupled with the presence of weak banks can be one of the
reasons for the permanently high zombie firm share as well as the increasingly inefficient capital
allocation across firms Therefore insolvency reform complemented with policies aimed at improving
bank forbearance can help to reduce the presence of zombie firms The presence of zombie firms may
also be related to the large share of bank financing Promoting market-based financing including bond
and venture capital markets may also help to diminish the problem
Figure 68 Insolvency regimes across countries
Notes This chart is a reproduction of Andrews et al (2017)rsquos original except for being restricted to European
states only The stacked bars represent the 3 main components of a countrys insolvency index for the year 2016
while the diamond figure indicates these measures aggregate for the year 2010 The authors constructed these
figures with the help of an OECD questionnaire Each measure is associated with a factor that in the long term is
thought to reduce a countrys business dynamism and consequently hamper its proclivity for productivity growth
The first one Personal costs of insolvency stands for environmental factors which could curb a failed
entrepreneurs ability to start new businesses in the future The second measure Lack of prevention and
streamlining indicates whether there are sufficient practices in place for the early detection and resolution of
Reallocation
88
financial distress Thirdly Barriers to restructuring shows how easy it is for a firm suffering from short-term
financial troubles to restructure its debt Country codes should be interpreted as follows GBR ndash Great Britian FRA
ndash France DNK ndash Denmark DEU ndash Germany ESP ndash Spain FIN ndash Finland IRL ndash Ireland SVN ndash Slovenia PRT ndash
Portugal AUT ndash Austria GRC ndash Greece SVK ndash Slovakia ITA ndash Italy LVA ndash Latvia POL ndash Poland NOR ndash Norway
SWE ndash Sweden LTU ndash Lithuania BEL ndash Belgium CZE ndash Czech Republic MLD ndash Moldova HUN ndash Hungary EST ndash
Estonia
Productivity differences in Hungary and mechanisms of TFP growth slowdown
89
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS
The main aim of this section is to investigate the micro-level processes which underlie the patterns
documented at the industry level in the previous chapters (especially in Chapter 6) by presenting a few
descriptive relationships between firm characteristics and firm dynamics More specifically we would
like to understand how various firm characteristics are associated with the observed patterns of
productivity and employment growth to illustrate the micro-level processes behind within-firm
productivity growth and reallocation Additionally we look at which types of firms enter and exit in
order to shed light on how they contribute to changes in the average productivity level
We seek to answer three main questions First was there a structural break either in the productivity
growth or in the reallocation process after the crisis which may have contributed to the productivity
growth slowdown Second do we see a structural difference in these processes along the main
dimensions of the lsquodualityrsquo of the Hungarian economy eg the characteristic differences between
globally involved large firms and their domestic market oriented peers Third can we find peculiar
patterns which may explain the unusual evolution of productivity quintiles namely the slow
productivity growth of frontier firms relative to less productive firms (as documented in detail in
Section 44)
In terms of firm characteristics we focus on variables which are likely to be related to the duality (see
Section 44) ownership size age and exporter status We do firm-level regression analyses which
allows us to use a rich set of controls and fixed effects Additionally we look at the interaction of the
different characteristics to get an even more precise picture about the main factors driving productivity
growth and reallocation
The structure of this chapter follows closely the logic of the dynamic productivity decomposition
exercise in Chapter 6 In Section 71 we investigate the determinants of within-firm productivity
growth In Section 72 we explore how firm characteristics are related to future employment growth ndash
ie to between firm reallocation ndash followed by the analysis of entry and exit in Section 73
71 Productivity growth
Questions and descriptive patterns
A key relationship of interest is how future productivity growth is related to current productivity levels
Our main motivation to study this question is that it can shed light on the extent of convergence to
more productive firms within the industry If there is a tendency for low-productivity firms to catch up
the productivity growth of such firms will be higher We analyse this relationship for the whole
economy and will also split the sample along different dimensions We are particularly interested in
three questions First is there a difference between the productivity growth rates of firms along some
dimensions even when controlling for productivity We think that this question is highly relevant but
will also qualify the findings of for example Section 32 where we compared firms with different
ownership structures and of different sizes with each other unconditionally which may mask the
different composition of the two groups in terms of initial productivity levels Second we are interested
in whether the slope of the relationship between the initial productivity level and subsequent
productivity growth differ along observable dimensions Is it the case for example that domestically-
owned firms face a productivity ceiling beyond which they cannot improve their efficiency any further
while foreign firms are better able to push forward even starting from very high productivity levels
Third we would like to find out whether there are structural changes in this relationship which may be
associated with the productivity slowdown following the crisis
Firm-level Productivity growth and dynamics
90
While the main mechanism behind this relationship is likely to result from a process of convergence
between firms the measured relationship can also partly arise from a mechanical negative relationship
coming from regression to the mean A large positive measurement error in productivity in year t
automatically generates a large negative growth rate from t As we are interested in the convergence
process rather than the mechanics of the regression to the mean we look at the relationship between
lagged productivity levels and 3-year productivity growth We assume that regression to the mean
resulting from measurement errors is less likely to show up when the productivity level is lagged An
additional limitation of this exercise is survivorship bias because lower productivity firms are more
likely to exit if they are unable to improve their productivity level We will analyse exit and entry
separately in Section 73
First to see the overall patterns we present the relationship between initial productivity levels and
productivity growth in the following 3 years in a non-parametric way (see Figure 71) To do so we
classify firms within each industry into 20 quantiles based on productivity in the previous year For
example we show how productivity growth between 2012 and 2015 is related to productivity levels in
2011 For each quantile we calculate average growth after partialling out 2-digit industry fixed
effects We show this relationship for different years to see whether there is a structural change in the
within-firm productivity growth process57 We demean lagged productivity levels by 2-digit industry
and year so zero on the horizontal axis corresponds to the mean productivity level We take four
periods pre-crisis (2003-2006) crisis (2006-2009) post-crisis (2009-2012) and recent (2012-
2015)58
Figure 71 shows that the relationship between previous productivity levels (on the horizontal axis) and
subsequent 3-year growth (on the vertical axis) can be well approximated with a linear relationship
We see a pronounced negative relationship in all periods reflecting that (surviving) lower productivity
firms increase their productivity faster than more productive firms generating some within-firm
convergence in the sample of continuing firms The slope of the relationship ie the productivity
growth premium of less productive firms is quite stable across non-crisis years but differs markedly in
the crisis showing that the crisis-related productivity decline was more severe for more productive
firms probably because these firms had been hit the hardest by the collapse of global trade59 Note
that this is much in line with the slow productivity growth of frontier firms in the same period
documented in Section 43 Figure 44 In normal times macro conditions seem to shift the whole line
up or down rather than rotate it The average 3-year productivity growth rate is the lowest during the
crisis and is still low in the post-crisis period but there is no difference between the pre-crisis and the
recent periods60
57 As in the previous chapters we use our main sample (see Chapter 2) in which we only consider firms with at
least 5 employees and measure productivity with the method of Ackerberg Caves and Frazer (2015)
58 Note that to measure subsequent growth we need three years following the base year when the level of
productivity is measured Consequently the last year we include is 2012 ndash and follow what happens to firms
between 2012 and 2015
59 More exit of low-productivity firms during the crisis may have also introduced a survivorship bias but as the
patterns in Figure A71 of the Appendix show this seems not to be the case
60 Table A71 of the Appendix shows the same patterns from a regression
Productivity differences in Hungary and mechanisms of TFP growth slowdown
91
Figure 71 The relationship between lagged productivity levels and subsequent productivity growth
over time
Notes This figure shows how the log of productivity in t-1 (on the horizontal axis demeaned by 2-digit industry
and year) is related to productivity growth between t and t+3 Each dot represents one of 20 quantiles of the
productivity level distribution and the average 3-year growth rate of firms within that quantile including 2-digit
industry fixed effects
Estimation
After establishing a linear relationship between lagged productivity level and subsequent growth we
look at the role of firm characteristics in productivity growth We do it in two steps First we look at
cross-sectional patterns taking the most recent period (2012-2015) We ask if there is a difference
between firm groups in productivity growth for the average firm (ie a firm having industry-average
productivity) and if there is a difference in the convergence pattern These two aspects correspond to
differences in the level and the slope of the line We estimate the following regressions
1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905
119896
119870
119896=1
+ 1205732(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1) +sum1205733119896(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1)119866119894119905
119896
119870
119896=1
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
We denote productivity of firm i in year t with 119905119891119901119894119905 1198893_119905119891119901119894119905 stands for 3-year productivity growth
119905119891119901 119895(119894)119905minus1 is the year-specific average lagged productivity in industry j of firm i G is a firm characteristic
(eg ownership or size) which contains K categories (eg one ownership group foreign or three size
categories) 119883119894119905 is a set of additional firm-level controls (these can be size age ownership or exporter
status) 120572119895(119894) is industry or industry-region fixed effects and 휀119894119905 is the error term Then 1205731119896 measures the
productivity-growth difference for average-productivity firms in firm group 119866119896 (eg foreign) compared
to average-productivity firms in the baseline category (eg domestic) 1205733119896 measures the difference in
the convergence patterns between firm group 119866119896 and the baseline category
(71)
Firm-level Productivity growth and dynamics
92
Second we also check dynamic patterns to see how the role of these firm characteristics changed over
time taking the same periods as in Figure 71 The baseline regression for comparing productivity
dynamics across years is as follows
1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905
119897
119897=200320062009
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
As before 1198893_119905119891119901119894119905 is the 3-year productivity growth of firm i from year t to t+3 and 119905119891119901119894119905minus1 denotes the
productivity level of firm i in t-1 119905119891119901 119895(119894)119905minus1119897 denotes the year-specific average lagged productivity in
industry j which firm i belongs to 119863119905119897 is an indicator for year l 119883119894119905 is a set of firm-specific time-variant
controls and 120572119895(119894) is industry or industry-region fixed effects as in the previous specification 1205731119897
measures the difference between the productivity growth of firms with industry-average productivity in
year l and in year 2012 The difference comes from two sources industry-level average productivity
levels could change over time and productivity growth for firms with the same productivity level could
also vary As we are interested in how the productivity growth of the average firm changed over time
we will not separate these two effects 1205732119897 measures the slope of the relationship between lagged
productivity levels and subsequent productivity growth in year l Comparing the different 1205732119897 coefficients
shows how the process of convergence between low- and high-productivity firms changed over time
We take a similar approach when we compare group-specific productivity dynamics over time We
interact group indicators demeaned productivity levels and the interaction of the two from the static
regression with a full set of year dummies and include year dummies separately as well
1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905
119896119863119905119897
119870
119896=1119897=2003200620092012
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ sum sum1205733119896119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119866119894119905119896
119870
119896=1
119863119905119897
119897=2003200620092012
+ sum 1205734119897119863119905
119897
119897=200320062009
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
Comparing 1205731119896119897 coefficients for different l-s shows how the difference between average-productivity
firms in the baseline category and in group k changed over time Similarly comparing 1205733119896119897 coefficients
with different l-s shows how convergence differences between the baseline category and group k firms
evolved over time These specifications allow us to add industry-year fixed effects so we can also
control for industry-specific trends
Results
Figure 72 shows the non-parametric relationships by firm characteristics creating scatter plots which
show productivity quantiles separately by firm groups These figures hint at the fact that on average
foreign-owned and exporter firms experience higher productivity growth conditional on initial
productivity levels In addition the relationship between the initial productivity level and subsequent
growth is weaker for foreign-owned firms suggesting that even highly productive foreign firms are
able to raise their productivity further while similar domestic firms have a harder time doing so Size
groups and age groups are similar to each other though the smallest firms have stronger convergence
patterns than the largest
We can discover the same scenarios using regression analysis in which we can control for the
abovementioned firm characteristics and fixed effects (Table 71) The most important conclusion is
that average-productivity foreign-owned firms raise their productivity faster relative to similar
domestic firms by about 10 percentage points Average exporters also have a TFP growth advantage
(72)
(73)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
93
relative to non-exporters but this premium disappears when we control for ownership We find some
evidence for a positive interaction between productivity levels and foreign ownership in line with lower
constraints for further TFP growth in the case of foreign frontier firms The same pattern applies to
exporter firms
Figure 72 The relationship between lagged productivity levels and subsequent productivity growth by
firm group
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to log productivity growth
between t and t+3 Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-
year growth rate of firms within that quantile including 2-digit industry fixed effects
The similar results for exporters and foreign-owned firms ndash and the strong correlation between foreign
ownership and exporter status ndash raise the question does this difference arise from foreign ownership
or exporting or do both variables have an independent effect Table A73 in the Appendix examines
this question only to find that the foreign premium in average productivity growth unconditional on
the productivity level is there both for exporters and non-exporters and is higher for younger and
smaller firms When we look at how the relationship between lagged productivity level and subsequent
growth differs by both characteristics at the same time we find that both foreign ownership and
exporter status matter but for different aspects of the relationship The difference in the slope of the
relationship comes both from the foreign-owned and from exporters compared to low-productivity
firms of the same category high-productivity firms grow relatively faster if they are foreign-owned The
same is true for comparing exporters and non-exporters At the same time the average difference in
Firm-level Productivity growth and dynamics
94
productivity growth comes from foreign ownership firms with industry-average productivity levels
have a higher productivity growth if they are foreign There is no significant additional effect for foreign
exporters on top of adding up foreign and exporter premia either in average productivity growth or in
convergence61
The TFP growth advantage of foreign-owned firms even when compared to domestically-owned firms
with the same productivity level points at a mechanism that reinforces the already existing duality
when domestic firms reach frontier productivity levels their TFP growth slows down much more than
that of foreign firms This self-reinforcing mechanism may be behind the non-convergence between
foreign and domestic firms (Section 44) With regard to size and age we find that high-productivity
firms have a relatively greater chance to increase their productivity if they are larger or older
compared to their smaller and younger counterparts (see Table A72 in the Appendix)
Table 71 The relationship between lagged productivity levels and subsequent productivity growth by
ownership and exporter status
Dep var TFP growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190
(000556) (000568) (000622) (000629) (000656) (000663)
TFP in t-1 Foreign 00475 00418 00433 00423
(00140) (00142) (00252) (00253)
TFP in t-1 Exporter 00477 00287 00216 00224
(00104) (00106) (00125) (00126)
TFP in t-1 Foreign exporter -000697 -00151
(00312) (00314)
Foreign 0117 0109 0120 0104
(00121) (00132) (00223) (00226)
Exporter 00240 000260 000401 0000919
(000791) (000845) (000851) (000881)
Foreign exporter -000670 000871
(00267) (00272)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 29717 29717 30135 30062 29717 29717
R-squared 0060 0072 0056 0073 0060 0072
61 Looking at the same patterns over time (Table A74 in the Appendix) suggests that higher average productivity
growth is a rather stable feature of foreign firms The only exception was the crisis period when it disappeared
Splitting the sample by broad sectors shows that foreign firms have higher average productivity growth both in
manufacturing and services The difference in within-group convergence patterns stayed the same for the
foreign The same is true for exporters except for the pre-crisis period when the coefficient is not significant
Productivity differences in Hungary and mechanisms of TFP growth slowdown
95
72 Employment growth
Question and descriptive patterns
The relationship between initial productivity levels and subsequent employment growth shows the
reallocation of continuing firms Between-firm reallocation results from more productive firms growing
faster In this subsection we ask how between-firm reallocation changed over time and how
reallocation patterns vary by different firm characteristics
To measure reallocation we use a similar approach to that in the previous subsection but the
lsquodependentrsquo variable will be 3-year employment growth in log terms rather than productivity growth
The slope of the estimated relationship reflects the employment growth advantage of more productive
firms or the strength of ldquocreative destructionrdquo among surviving firms Shifts in the level show changes
in the average growth rate
We calculate the 3-year employment growth using the formula119871119905+3minus119871119905
(119871119905+3+119871119905)2 where 119871119905 is the number of
employees in year t This formula shows the percentage increase in employment from year t to t+3
compared to the average size in year t and t+3 This measure performs better for smaller firms than a
simple log difference in employment as it does not result in extremely high numbers with a low initial
employment level62 In all the regressions of this subsection we control for exact firm size using the
logarithm of the number of employees
Figure 73 Reallocation by year
Notes The figure shows how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
62 Additionally while the baseline estimates are only for continuing firms this measure allows us to include firms
exiting in the period (t+1t+3) as well in some robustness checks In these cases we take Lt+3 = 0
Firm-level Productivity growth and dynamics
96
Figure 73 illustrates the patterns in the data non-parametrically The relationship between previous-
year productivity levels and subsequent employment growth is positive in all years This shows that in
line with the creative destruction hypothesis more productive firms are more likely to grow in the
subsequent three years The figure doesnrsquot show characteristic changes in the reallocation process
across years the slope of the curves being similar to each other Our regression estimates presented
in the Appendix (Tables A75 and A76) support that reallocation patterns are stable over time63 The
average growth rate of typical firms naturally follows the macro cycle strongly ndash aggregate changes
seem to shift the line up or down but do not seem to rotate it In other words with this approach we
do not find evidence for a structural change in the reallocation process therefore it is unlikely that
such a change should explain satisfactorily the productivity slowdown
We create similar figures for the most recent period (2012-2015) by different firm characteristics
(Figure 74) The most important result is that exporters grow significantly faster than non-exporters
when controlling for their initial productivity This leads to reallocation from non-exporters to
exporters Given that the productivity advantage of exporters is in the order of 30-100 percent in the
different industries (see Section 43) this reallocation process can yield enormous productivity gains
The slope of the curve is also less steep for exporters suggesting that their expansion is less
dependent on their productivity level relative to domestic firms in other words reallocation within the
exporter group is weaker relative to non-exporters
63 As before the relationship between lagged productivity levels and subsequent employment growth can be
properly approximated by a linear function
Productivity differences in Hungary and mechanisms of TFP growth slowdown
97
Figure 74 Reallocation by firm groups
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
Firm-level Productivity growth and dynamics
98
Estimation results
Table 72 Reallocation by ownership and exporter status
Dep var employment growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 0105 0102 0105 0107 0107 0108
(000484) (000493) (000539) (000546) (000570) (000575)
TFP in t-1 Foreign
-00369 -00328 -00252 -00224
(00123) (00124) (00217) (00217)
TFP in t-1 Exporter
-00344 -00298 -00250 -00249
(000913) (000932) (00109) (00110)
TFP in t-1 Foreign exporter
-0000806 000194
(00271) (00272)
Foreign 000105 -000863 -000786 -0000106
(00112) (00116) (00194) (00196)
Exporter 00586 00635 00653 00672
(000738) (000754) (000777) (000786)
Foreign exporter
-000647 -00123
(00234) (00238)
Industry FE YES YES YES
Industry-region FE
YES YES YES
Firm-level controls
YES YES YES
Log of employees
YES YES YES YES YES YES
Observations 31662 31662 32124 32043 31662 31662
R-squared 0035 0049 0038 0051 0037 0049
Looking at the regression results (Table 72) confirms our previous findings even after controlling for
fixed effects Exporters with an average productivity level grow about 6 percentage points faster than
non-exporters hinting at strong positive reallocation between the two groups with slightly weaker
reallocation within the exporter group64 At the same time average-productivity foreign-owned firms
do not have higher employment growth than domestic ones Similarly to productivity growth we find
no extra premium for foreign exporters65 66 Overall these results emphasise that participation in
64 We define exporters based on their export activity in year t so the group of exporters also includes those firms
which export in t but not any more afterwards This means that a worse subsequent performance ndash lower
growth and exiting from exporting ndash has no effect on our exporter classification
65 The main patterns concerning employment growth of average-productivity firms are robust to modifying the
employment growth measure in such a way that it includes exits as a full employment decline (See Table A77
in the Appendix) In this version employment growth of foreign firms is significantly lower overall but this is
counterbalanced by the significantly positive coefficient of the foreign exporter indicator
66 We show in Table A78 of the Appendix that the higher average growth of exporters is present in all size (except
for the largest) age and ownership groups Dynamic patterns suggest (in Table A79 of the Appendix) that the
higher growth rate of average-productivity exporters is robust over time This result is also robust to splitting
the sample into manufacturing and services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
99
international markets is an important driver of industry and aggregate productivity growth in Hungary
by providing opportunities for exporters to expand as Section 62 has documented
As Table 73 shows competitive pressure also seems to affect more the growth prospects of smaller
firms the relationship between initial TFP levels and employment growth is significantly stronger for
smaller firms Between-firm reallocation appears to be much stronger for smaller firms while less
productive large firms are unlikely to contract even if they are inefficient conditional on survival
There are no clear patterns by age groups
Table 73 Reallocation by size and age group
Dep var employment growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 0112 0110 00875 00863
(000486) (000502) (00133) (00133)
TFP in t-1 Group 2 -00455 -00431 00445 00384
(00128) (00129) (00182) (00183)
TFP in t-1 Group 3 -00745 -00748 000733 000822
(00194) (00196) (00141) (00142)
TFP in t-1 Group 4 -00953 -00957
(00218) (00220)
Group 2 -000596 -000810 -00188 -00212
(00122) (00123) (00141) (00141)
Group 3 -000928 -00157 -00115 -00169
(00199) (00201) (00114) (00115)
Group 4 00328 00280
(00276) (00278)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Log of employees YES YES YES YES
Observations 32124 32043 32124 32043
R-squared 0038 0052 0037 0051
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees and size group 4 is 100+
employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5 years age group 3 is
firms older than 5 The baseline category is firms of 2-3 years
73 Entry and exit
Questions
This subsection aims at investigating which firms enter and exit and in particular how productive
those firms are relative to continuing firms This corresponds to the micro-level equivalent of the net
entry effect (see Chapter 6) The motivation for the micro-level investigation is that in this manner we
Firm-level Productivity growth and dynamics
100
can study which firm-level factors determine the type of firms that enter and exit and control for
industry heterogeneity
Our approach is similar to the previous section with the main difference being that this time the
dependent variable is productivity while the variables of interest are entry and exit dummies Their
coefficients show the productivity lsquopremiarsquo (often negative) of new entrants and exiting firms relative
to continuing firms These premia are especially useful to answer two kinds of questions First their
magnitude and size inform us about how entry and exit contribute to productivity growth Second
changes in these premia are also indicative of the changes in the costs of entry and the survival of
low-productivity firms
Estimation
To use a symmetric approach we define entrants and exiting firms using a 3-year interval An entrant
is a firm that has entered in the previous 3 years67 This means we look at the productivity of firms in
year t and compare it between incumbents (ie firms older than 4 years) and entrants (ie firms being
2-4 years old) In a similar way we compare the productivity in year t of firms exiting in the following
3 years (ie the last time the firm reports positive employment is in the period (t t+2) and non-
exiting firms (firms still reporting positive employment in year t+3)
As before we start with a static approach looking at the productivity premium of entrants and exiting
firms in the most recent period (taking year 2015 for 2012-2014 entrants and 2012 for firms exiting in
2013-2015) Then as a dynamic approach we take all four time periods as before and interact the
premia with year dummies The static regression we estimate is as follows
119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905
119896119873119864119894119905119870119896=1 + 1205732119866119894119905
0119864119894119905 + sum 1205733119896119866119894119905
119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)
119905119891119901119894119905 is the productivity of firm i in year t (measured in logarithm) 119866119894119905119896 is the k-th category (eg size
category 5 with more than 100 employees) in a grouping according to firm characteristics G (eg
size) and 1198661198941199050 is the baseline category (eg firms with 5-49 employees) 119864119894119905 stands for entrant or
exiting firm dummy in the different specifications and 119873119864119894119905 are incumbent or continuing firms
accordingly Then 1205732 measures the entry or exit premium for firms in the baseline category and 1205733119896
measures the same premium for firms in category k of grouping G Both premia are calculated
compared to incumbentscontinuing firms in the baseline category 1198661198941199050 1205731
119896 measures the productivity
advantage or disadvantage of incumbentcontinuing firms in category k of grouping G also compared
to the average productivity level in the baseline group As before 119883119894119905 includes additional firm-level
characteristics and 120572119895(119894) is industry or industry-region fixed effects In those versions where we include
industry fixed effects we identify from within-industry differences This means that 1205733119896 measures the
same entry or exit premium for firms in category k of grouping G compared to incumbentscontinuing
firms in the same category As before we create the dynamic version of the above regression by
interacting 119866119894119905119896119873119864119894119905 119866119894119905
0119864119894119905 and 119866119894119905119896119864119894119905 with year dummies and including year dummies separately as well
67 We consider firms changing industry from manufacturing to services or vice versa as exitors and new entrants at the same time (see Chapter 2)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
101
Results
Table 74 shows how the productivity premium of entrants and exiting firms changed over time In
these specifications we compare the yearly average productivity of incumbents and entering or exiting
firms separately in each year The point estimates suggest that entering firms were about 2-4 percent
more productive than incumbents except for the 5 percent productivity disadvantage in the pre-crisis
period while exiting firms were 10-20 percent less productive than the continuing firms The
productivity advantage of entrants and the disadvantage of exiting firms did not change radically
during our time period This difference constitutes a potential for positive net entry effects in terms of
reallocation The exact value of the net entry effect also depends on the share of employees affected
by entry and exit While the premia of entering and exiting firms remained roughly the same in the
different periods exit and entry rates changed (see Section 33) which results in positive net entry
effects before the crisis and negative effects after that (see Section 62)
Table 74 Productivity premium of entering and exiting firms over time
Dep var TFP in year t
Firm group Entry Exit
(1) (2) (3) (4) (5) (6)
EntrantExitorPeriod 2003-2006
-00532 -00535 -00465 -0214 -0199 -0200
(000906) (000873) (000879) (00102) (000987) (000989)
EntrantExitorPeriod 2006-2009
00269 00212 00232 -0135 -0117 -0119
(000967) (000931) (000936) (000897) (000865) (000865)
EntrantExitorPeriod 2009-2012
00369 00444 00360 -0133 -0114 -0121
(000960) (000926) (000931) (000920) (000889) (000889)
EntrantExitorPeriod
2012-2015
00374 00324 00252 -0171 -0150 -0157
(000971) (000936) (000944) (00107) (00103) (00103)
Period 2003-2006 -0115 -00927 -00711 -00445
(000519) (000501) (000556) (000537)
Period 2006-2009 -0175 -0169 -000972 00122
(000522) (000503) (000537) (000518)
Period 2009-2012 -0116 -0117 -00580 -00528
(000528) (000508) (000543) (000522)
Year FE YES YES YES YES
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES YES YES
Industry-year FE YES YES
Observations 166607 166168 166168 158143 157711 157711
R-squared 0327 0380 0380 0332 0385 0387
Firm-level Productivity growth and dynamics
102
Next we focus on the most recent period and look at the productivity differences of entrants and
exiting firms by different firm groups
Figure 75 Productivity premium for entering and exiting firms by ownership
Figure 75 presents the premia of domestic and foreign entering and exiting firms relative to domestic
incumbents As we saw in Section 44 foreign firms are on average more productive than domestic
ones68 foreign incumbents have on average a premium of 669 Compared to domestic incumbents
foreign entrants have 513 higher productivity There is also a positive productivity premium of 29
for exiting foreign firms Similarly the productivity of exiting exporters is 186 higher than that of
continuing non-exporters69 This means that domestic incumbent firms can survive longer even with a
lower level of productivity Consequently having many foreign entrants has a positive effect on
average productivity while on average foreign exits do not affect average productivity70 71
68 Table A711 shows that the productivity advantage of foreign-owned firms is present in all size and age groups as well as both within the exporter and non-exporter firm groups
69 Table A710 of the Appendix shows the estimation results with standard errors
70 As Table A712 of the Appendix shows foreign entrant premium and the premium of continuing or exiting
foreign and exporter firms seem fairly stable over time The positive premium of entering and exiting foreign
firms is also robust for splitting the sample into manufacturing and services
71 As Table A713 of the Appendix shows there is no considerable difference in the productivity disadvantage of
exiting firms by size or age group
Productivity differences in Hungary and mechanisms of TFP growth slowdown
103
74 Conclusions
One contribution of this chapter is that we have documented that one of the factors behind the
sustained duality in productivity between foreign and domestically-owned firms is that foreign-owned
firms tend to be more capable of upgrading their productivity even from already high productivity
levels Similar patterns apply to the more globally oriented exporters This mechanism underlines the
importance of policies that promote absorptive capacity-building (see Section 45) a strong knowledge
base easy access to external knowledge and flexible and advanced skills are especially important
when upgrading productivity beyond already high levels
We have also found strong reallocation from non-exporters to exporters Given the high productivity
premia of exporters in Hungary (Beacutekeacutes et al 2011) and in general (Wagner 2007) such a
reallocation can lead to substantial improvement in aggregate productivity (and as we have seen in
Section 62 it did to some extent before the crisis) These results emphasise that participation in
international markets is an important driver of industry and aggregate productivity growth in Hungary
because it provides valuable opportunities for exporters to expand Note that this reallocation effect of
international openness has been in the focus of the recent literature on international trade (Melitz
2003 Bernard et al 2006 Amiti and Konings 2007 Topalova and Khandelwal 2011 De Loecker
2011) Note also that the asymmetric expansion possibilities of exporters and domestic firms also
amplify the duality between the two groups
The analysis of entry and exit has revealed that entrants are somewhat more productive than
incumbents even a few years after entry Exiting firms are significantly less productive This on the
one hand implies that exit and entry is a substantial source of reallocation (as Section 62 has shown)
On the other hand the low productivity of exiting firms also suggests that domestic firms can survive
long even with relatively low productivity levels maybe because of inefficiencies in the capital
allocation process including the insolvency regime
Productivity evolution and reallocation in retail trade
104
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE
The previous chapters have presented a number of results on the productivity and growth in different
sectors of the economy The aim of this chapter is to look deeper into one of the key sectors of the
economy namely retail trade for more detailed insights
Two main reasons have motivated us to choose the retail sector First retail is a key sector of the
economy which provides jobs for a great many people and influences what consumers can buy and at
what prices Retail (and wholesale) does not only interact with consumers it is a key supplier of inputs
while beig a buyer of outputs for all other firms in the market economy72 The degree to which it is
capable of supplying a large variety of intermediate inputs at reasonable prices is an important
determinant of the productivity of firms relying on these sources Its market structure also affects
fundamentally the incentives that producers experience73
The second reason is that there have been a number of regulatory changes in the retail sector in
Hungary in recent years While these policies had multiple motivations one of their common features
is that they are size-dependent either explicitly or implicitly As such they have a potential to increase
the costs of larger firms and influence the reallocation process in favour of smaller mostly
domestically-owned firms This may matter as international evidence has shown that much of retail
productivity growth in recent decades has resulted from the expansion of large store chains (Foster et
al 2006) Exactly because of the strong links between retail and other industries regulatory
restrictions in retail represent nearly a third of all service-related restrictions which are carried over to
other sectors of the economy74
The structure of this chapter is the following Section 81 describes the policy context of Hungarian
retailing Section 82 introduces the available datasets Section 83 describes the major developments
in retail productivity Section 84 describes trends in reallocation The last three sections describe three
specific questions Section 85 analyses the role of retailers and wholesalers in importing and
exporting Section 86 provides a few illustrative statistics on how size-dependent taxes could have
affected reallocation and prices Finally Section 87 evaluates a specific policy namely the mandatory
Sunday closing of larger shops Section 88 concludes
81 Context
The retail industry is an important employer in all EU member states and Hungary is not an exception
Its employment share in our sample has been around 12 percent (Figure 81) Similarly to the EU as a
whole retail productivity is below the average of the market economy therefore its GDP share is
below its employment share Still it represented 6-7 percent of total value added in our sample
72 See EC (2018) for the importance of the retail industry in Europe
73 See Smith (2016) for a review of this literature
74 EC (2018) p 5
Productivity differences in Hungary and mechanisms of TFP growth slowdown
105
Figure 81 The share of retail and wholesale firms in market economy value added and employment
Notes Full sample with at least 1 employee in any of the years
The largest sub-industry within retail is groceries (NACE 4711) Its share of the total turnover around
40 percent is at the lower end of the EU distribution75 Given its importance (and the large sample size
within it) we will often study only groceries in our empirical analyses
Measuring the restrictiveness of different regulations in any sector of the economy is not an easy task
The European Commission has designed a ldquoRetail Restrictiveness Indicatorrdquo to quantify the potential
effect of these regulations in force at the end of 2017 (see Figure 82) The higher values of the
indicator indicate more restrictive regulations76 According to this indicator the restrictiveness of retail
regulation in Hungary is slightly below the EU average and similar to other CEE countries
The indicator distinguishes between regulations related to the establishment of shops on the one hand
and those related to their operation on the other In Hungary there are few operations restrictions
(mainly restrictions on distribution channels) while entry is regulated more heavily mostly by size-
related restrictions and requirements for economic data
75 EC (2018) p 4
76 There is ample empirical evidence that entry barriers planning regulations and operating restrictions are related to productivity and prices in retail Some examples are Bertrand and Kramarz (2002) Viviano (2008) Haskel and Sadun (2012) Sadun (2015) Daveri et al (2016)
Productivity evolution and reallocation in retail trade
106
Figure 82 Retail Restrictiveness Indicator
Notes This is a reproduction of Figure 8 from EC (2018)
While regulation in Hungary is not especially restrictive a number of new measures were introduced
following the crisis (see Box 81) While these have various motivations a common feature of most of
them is that they are size-dependent As such they may distort competition and constrain reallocation
to larger firms
One type of size-dependent policies is size-dependent taxes Crisis taxes introduced right after the
crisis (and phased out in 2013) were highly progressive in sales volume Local business taxes have
been similarly progressive in total sales at the firm-level since 2013 Other size-dependent policies are
restrictions on the establishment of shops or their operation The Plaza Stop law constrained the
establishment of malls larger than 300 m2 Another peculiar policy was requiring larger shops to close
on Sundays between March 2015 and April 2016
Quantifying the effect of such policies is not an easy task In some cases it is not possible with the
data at hand to identify the shops and firms which were affected by the different types of taxes For
example without knowing the exact location of the establishment it is not possible to identify which
firms operate in malls and hence could have been affected by the Plaza Stop law As we discuss in
Section 85 the highest bracket of the crisis tax only affected 6 firms and thus it is hard to run
statistical tests with an appropriate power In contrast some of the effects of the mandatory Sunday
closing policy can be very effectively estimated based on shop-level data
Therefore we will apply two complementary strategies The first is to investigate whether there are
trend breaks in the reallocation process following the crisis when many of the new policies were
Productivity differences in Hungary and mechanisms of TFP growth slowdown
107
introduced While we find suggestive changes around the crisis one cannot make casual statements
based on this strategy given the number of other changes in the economy The second strategy is to
examine specific policies where a credible differences-in-differences identification is possible
Unfortunately this strategy is basically limited to Sunday closing
BOX 81 Size-dependent taxes and regulations in the retail sector
This box describes a number of size-dependent taxes and regulations which could be linked to the retail data and investigated during this exercise The list is only indicative and will be appended by desk research and possibly interviews
2010-2013 crisis taxes
Crisis taxes were introduced in 2010 and were in force (mostly) until 2013 They affected the energy telecom and retail sector as their base was operating profits resulting from these activities The tax rate was strongly progressive for retail
Below 500m HUF 0
Between 500m and 30bn HUF 01
Between 30bn and 100bn HUF 04
Above 100bn HUF 25
Between March 15 2015 and April 23 2016 Sunday closing for larger and non-employee owned retail stores
The 2014 CII law which came into force on March 15 2015 banned shops with a retail space of more than 400 square meters to open on Sundays with some exceptions most notably the new tobacco shops Smaller shops could only open if their workers had at least a 20 stake in the
business or if they were close relatives of the owner The law was repealed in 2016
2013-today Progressive local business tax
The base of local business tax is the ldquoadjustedrdquo revenue of firms This usually means revenue minus material expenditures but regulation stipulating the exact method of calculation has changed a number of times since the introduction of this type of tax In 2013 a progressive
element was introduced by making the definition of the cost of purchased goods size-dependent In particular smaller firms can now deduct more of their expenditures than larger ones The deductible part is
Below 500m HUF of net sales 100
Between 500m and 20bn HUF 85
Between 20bn and 80bn HUF 75
Above 80bn HUF 70 of the cost of goods is eligible
Productivity evolution and reallocation in retail trade
108
82 Data
We rely on two main data sources in this chapter The first one is the NAV balance sheet data
described in detail in Chapter 2 Based on the industry code identifier we restrict the sample to firms
in industry 47 retail There are a few firms which switch to this category from other industries (mainly
wholesale of food manufacturing) We keep the whole history of these firms throughout the analysis
Second we use a retail-specific survey conducted by the Hungarian Central Statistical Office which
samples firms and collects data for all shops of the sampled firm77 Firms included in the sample are
compelled by law to submit monthly reports on their turnover and 4-digit industry-codes plus for all
of their stores information about these entitiesrsquo location (municipality) identification number 4-digit
77 httpswwwkshhudocshuninfo02osap2018kerdoivk181045pdf
BOX 81 Size-dependent taxes and regulations in the retail sector (cont)
2013-today Licensing of tobacco wholesale and retail
On 22 April 2013 in line with Act CXXXIV ldquoon reducing smoking prevalence among young people
and the retail of tobacco productsrdquo (adopted by the Hungarian Parliament on 11 September
2012) the National Tobacco Trading Non-profit Company (a 100 government-owned joint-stock
company controlled by the relevant minister under the mandate of this law) was established
From then on only special ldquonational tobacco shopsrdquo licensed by the state have been allowed to
sell tobacco products These shops enjoy a number of benefits compared to other shops
Exempted from the Sunday closing for retail shops
National tobacco shops are exempted from the ban on selling alcohol after 10pm rarr in effect
tobacco shops do not come under the ruling of the commercial law Local municipalities can
otherwise regulate shops based on that law
2011- today ldquoPlaza Stoprdquo Law
The so-called Plaza Stop Law (the 2011 CLXVI Law) came into force in January 2012 It
prohibits the construction of new retail facilities or the expansion of any already existing one with
a leasable area of more than 300 msup2 Exemptions could be granted to certain developments by a
committee of ministry officials and with the approval of the Minister of National Economy
In 2013 the law was extended to include building conversions In February 2015 a new
amendment was ratified which basically renewed the effect of the 2011 law and introduced some
modifications to it Now retail facilities with a floor space of less than 400m2 can be built without
any special procedure Furthermore the right to grant exemptions was given to a special
administrative department which is supplemented by a committee made up of delegated
members of different ministries
Productivity differences in Hungary and mechanisms of TFP growth slowdown
109
industry-code sales and the monthly number of days spent open The sample consists of all larger
retail firms78 and a representative sample of other firms re-sampled on an annual basis
An important consequence of this design is that we observe each of the shops of the sampled firms
This is valuable in two respects First with this information it is possible to calculate the number of
shops and average shop size at the firm-level Second one can identify new and exiting shops for
firms which are in the sample continuously ie larger firms Further with the help of the firmsrsquo
identification number we are also able to link this information to data from the NAV database for
qualified analysis
Two caveats may be mentioned here First the re-sampling of the representative part of the sample
prevents us from following small firms through the entire sampling frame Second in the beginning of
2012 there was a switchover in the coding of shop-level identification numbers which prevents us
from linking shops before and after
As mentioned above the database also includes information on the industry classification of the shop
In most of our exercises we restrict the sample to grocery stores more formally bdquoRetail sale in non-
specialised stores with food beverages or tobacco predominatingrdquo (NACE 4711) Table 81 shows the
sample size of the merged database for groceries We have classified firms according to the number of
shops they have and report their number and their storesrsquo number according to these categories
Table 81 The number of firms and the number of shops in different size categories in Groceries
1 shop 2-4 shops 5-9 shops 10-49 shops gt50 shops year firm shop firm shop firm shop firm shop firm shop
2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446
2006 573 573 51 131 59 430 111 2008 30 1548
2007 546 546 53 125 60 429 110 1987 33 1634
2008 628 628 45 102 50 350 104 1823 21 1574
2009 527 527 33 72 41 290 99 1879 24 1754
2010 472 472 22 49 32 238 94 1793 22 1968
2011 537 537 14 30 29 212 92 1758 22 2027
2012 374 374 30 68 49 335 88 1643 23 2107
2013 503 503 48 121 42 277 88 1622 25 2094
2014 410 410 106 239 48 320 81 1530 24 2054
2015 512 512 135 311 42 292 80 1544 30 2090
2016 518 518 120 292 37 271 77 1457 23 2022
A key distinction in this merged database is the one between shops and firms Sales employment
ownership is observable only at the firm-year level so these variables are the same for each of the
shops of a firm for a calendar year Shops are only observable for sampled firms but we observe
sales and the number of days they were open at a monthly regularity As a result even if one runs
regressions at the shop-month-level productivity and employment can only vary at the firm-year
level For this reason we always cluster the standard errors at the firm or firm-year level
78 Larger firms are defined as having more than 7 stores in operation or with a number of employees of more than 50 and at least 6 stores or with a significantly large store in a product category
Productivity evolution and reallocation in retail trade
110
While balance sheet data includes information on exports it does not inform us about imports In
Section 84 we use detailed trade data to analyse importing by wholesale and retail firms This is
reported at the importer firm-product (8 digit Harmonized System)-country of origin level Most
importantly we can link this information to the balance sheet of the firm This is collected by a survey
following the European Unionrsquos practice79 We aggregate these data to the firm-year level but
distinguish between consumer goods capital goods and intermediate inputs used in further production
by relying on the correspondence table of the Eurostat between the Harmonized System and Broad
Economic Category classifications
83 General trends
Let us start with describing the firm size distribution across years (see Table 82) for firms with at least
one employee Similarly to other EU countries the majority of firms in retail are very small in
different years between 70-75 percent of retail firms employed less than 5 people80 The share of firms
with more than 50 employees fluctuated at around 1 percent
As one would expect larger firms have a significantly larger weight in terms of employment and sales
The top 05 percent of firms employed more than 30 percent of all employees in each year The
employment-share of these top firms increased nearly monotonically from 33 percent in 2004 to more
than 38 percent in 2011 when it reached its peak This was followed by a slightly declining trend to
357 percent in 2016 This time path represents the gradual expansion of large chains both organically
and via the acquisition of stores81 up to the crisis when this trend seems to have ended
The market share of large firms is even larger reaching 45 percent in 2016 The difference between
the employment share and sales share shows that large retail firms are substantially more efficient ndash
at least in terms of sales over employees ndash than the average firm At the other extreme the smallest
retail firms generate only 126 percent of sales with 20 percent of employees suggesting that in 2016
each of their employees sold only about half of the average The patterns are similar in other years
Efficiency differences are large in this sector though not larger than in most other sectors of the
economy (see Chapter 4)
79 See httpeceuropaeueurostatstatistics-explainedindexphpInternational_trade_statistics_-_background An important limitation of these data is that firms only report transactions above a specific size This may bias estimates of firm-level importing downward for small firms
80 As we discussed in Section 42 the NAV sample includes only double-entry bookeeping firms while the unemployed and people working in firms with simplified accounting are omitted from these data These people are likely to work in small economic units with low productivity levels
81 The increasing share of large retailers is a general trend globally see Ellickson (2016)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
111
Table 82 Share of firms in different size categories (at least 1 employee)
A) Number of firms
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100
2006 7333 1613 687 252 065 049 100
2007 7369 1597 674 244 067 050 100
2008 7410 1571 667 236 067 048 100
2009 7522 1535 614 221 059 048 100
2010 7551 1508 640 201 057 044 100
2011 7591 1493 623 194 057 041 100
2012 7625 1506 568 204 055 042 100
2013 7526 1596 577 207 052 042 100
2014 7380 1684 628 210 056 042 100
2015 7239 1771 661 232 056 041 100
2016 7163 1804 676 252 063 043 100
B) Employment
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000
2006 2034 1475 1269 1025 679 3525 10000
2007 2026 1443 1241 984 669 3644 10000
2008 2020 1391 1138 910 579 3980 10000
2009 2002 1427 1244 870 570 3789 10000
2010 2099 1443 1243 862 577 3731 10000
2011 2144 1458 1119 895 555 3830 10000
2012 2144 1541 1131 906 541 3713 10000
2013 2168 1591 1204 896 555 3650 10000
2014 2104 1644 1236 970 548 3538 10000
2015 2064 1620 1216 1006 588 3570 10000
2016 1999 1620 1216 1006 588 3570 10000
C) Sales
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000
2006 1114 1278 1587 1025 776 4226 10000
2007 1110 1266 1543 956 855 4268 10000
2008 1112 1524 1045 891 531 4906 10000
2009 1104 1641 1073 856 718 4607 10000
2010 1104 1521 1103 930 693 4594 10000
2011 1160 1577 1004 934 624 4714 10000
2012 1146 1647 1003 913 629 4578 10000
2013 1229 1411 1090 945 709 4360 10000
2014 1486 1472 1093 1001 752 4389 10000
2015 1293 1458 1118 985 660 4512 10000
2016 1266 1458 1118 985 660 4512 10000
Productivity evolution and reallocation in retail trade
112
Figure 83 presents C5 concentration measures82 for the full retail sector and for some of its
subsectors83 The share of the top 5 retail firms was around 30 percent of total retail sales
Concentration was increasing pre-crisis from 30 percent in 2003 to 35 percent in 2009 Concentration
decreased and returned to its 2003 value by 2014 The latter trend as we will discuss in Section 85
may be associated with size-dependent policies
The various sub-industries exhibit different patterns in terms of concentration Let us start with
groceries Pre-crisis the dynamics in this subsector was driven mainly by the expansion of large
chains Consequently concentration was strongly increasing with C5 growing from 50 percent in 2003
to more than 60 percent by 2008 Concentration in this subsector was rising further post-crisis but at
a somewhat slower pace We observe a similar pattern of increasing concentration with a trend break
around the crisis in sales of books and clothes in specialized stores In these sub-industries
establishment regulations like the Plaza Stop law could have played a more important role in the trend
break than taxes Specialized cosmetics retailing was already very highly concentrated at the
beginning of the period and remained largely unchanged
Figure 83 Concentration in retail and various sub-industries
This observation motivates a more detailed look at different measures of efficiency and prices Panel A)
of Table 83 calculates the average TFP levels84 both for different size categories and for the
aggregate Note that TFP calculated from balance sheet data is revenue productivity measuring the
82 Calculated as the sales share of the 5 firms with the largest sales
83 We rely on a slighly different version of the NAV data for this exercise which includes 4-digit identifiers but only runs until 2014
84 See Section 22 on details of TFP estimation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
113
amount of revenue produced by an input bundle Consequently it does not only measure physical
productivity (units sold per unit of input) but also markups This distinction is especially important in
retail85
Let us start with the two aggregate series one unweighted and the other weighted by employment
The employment-weighted series has higher values because more productive firms tend to be larger
(see Section 51) The two series follow a parallel trend suggesting that the correlation between size
and productivity did not change radically TFP has increased by about 15 percent from 2004 to 2006
remained constant until 2008 fallen by around 10 percent in 2009 and then started to grow by 4-5
percent each year from 2011
Note that this productivity evolution is similar to what is reported by OECD STAN before and during the
crisis but the post-crisis recovery in our data is much more pronounced (Figure 84) As we have
discussed in detail in Section 42 this is most likely a result of the large number of self-employed and
the distinct productivity level and evolution of that group86 Productivity has been definitely increasing
since 2011 in our sample
Interestingly TFP is not increasing monotonically with firm size There is a clear 25-30 percentage
point difference between the smallest firms (1-4 employees) and firms in other size categories which
have a similar TFP to each other Besides differences in efficiency this may also be partly explained by
the tax avoiding behaviour of the smallest firms ie under-reporting sales or over-reporting costs
Panel B) of Table 83 investigates gross margins These are calculated as
119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
which is the margin that retailer 119894 realises in year 119905 on the cost it pays for the sold goods in
percentage A value of 20 shows that the price the consumer pays is 20 percent higher than what the
retailer paid for the goods87 Note that gross margins reflect a combination of two factors `physical
productivityrsquo (how much capital and labour is needed for a given amount of sales) and markups Still
gross margins are of interest because they are the closest proxy available in financial statements of
prices paid by customers
We can make two key observations First on average (weighted) margins increased from about 155
percent to 19 percent during the period under study with a fall during the crisis Second margins
were about 5 percentage points higher in the smallest retail firms compared to larger ones
Interestingly during and immediately after the crisis (between 2009 and 2012) the margins of the
largest firms were substantially lower than those of other firms This is because the margins of the
largest firms actually fell during this period while that of smaller firms remained roughly constant
85 Measurement of productivity in retail raises a number of conceptual and measurement issues (Ratchford 2016) Two main problems are the measurement of output (conceptually retail services) and of the inputs used (for example shop area) In practice however such detailed data are not available and it is standard to use TFP
86 The figures for retail are similar to those for the whole of the service industry About a third of all people engaged are self-employed operating at a significantly lower productivity level than retail firms The productivity of the self-employed did not grow between 2012 and 2015
87 We winsorise it at the 5th and 95th percentiles Note that the cost of goods sold would be preferable to material costs but that is often missing from the data especially for small firms
Productivity evolution and reallocation in retail trade
114
Most likely larger firms were able to cut markups while smaller firms with already lower markups
were not able to do so
As we have mentioned above margins reflect a combination of cost factors and market power The
gross operating rate88 attempts to control for labour costs and shows margins after personal cost
119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
with value added calculated as discussed in Chapter 2 In international comparison gross operating
rates are relatively low in Hungary89 These rates show a clear downward trend with time Gross
operating margins are clearly decreasing with firm size showing that larger firms operate with large
scale and low margins Similarly to the gross margin we see a fall during the crisis
Figure 84 Productivity evolution in the NAV sample and the OECD STAN
88 httpeceuropaeueurostatstatistics-explainedindexphpGlossaryGross_operating_rate_-_SBS
89 As shown by EC (2018) Figure 2 Our weighted estimates are similar to what is reported there based on Eurostat data
Productivity differences in Hungary and mechanisms of TFP growth slowdown
115
Table 83 Performance and margins (at least 1 employee)
A) TFP
B) Gross Margin
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 1769 1548 1793 1883 1584 1495 1735 1554
2005 1839 1571 1858 1878 1504 1666 1794 1584
2006 2040 1658 1842 1879 1599 1660 1956 1653
2007 2220 1720 1875 1940 1664 1702 2104 1712
2008 2192 1792 1872 1992 1522 1739 2096 1728
2009 2084 1719 1864 1967 1599 1527 2006 1630
2010 2096 1749 1867 2102 1664 1535 2024 1682
2011 2172 1765 1862 2003 1759 1558 2084 1728
2012 2203 1740 1828 1913 1969 1541 2102 1733
2013 2243 1734 1806 2018 1999 1794 2128 1790
2014 2363 1789 1817 2149 2022 1869 2223 1855
2015 2390 1803 1885 2165 2215 1987 2245 1928
2016 2379 1799 1911 2172 2403 2170 2237 1948
C) Gross operating rate
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
unweighted weighted
2004 651 701 663 677 479 480 658 611
2005 827 757 777 712 526 560 806 644
2006 908 773 777 753 579 797 870 706
2007 807 635 653 704 500 580 764 606
2008 665 588 577 610 422 478 643 525
2009 618 535 538 522 351 339 595 456
2010 669 587 629 626 421 341 650 510
2011 698 617 591 637 410 393 676 542
2012 770 643 599 624 485 404 735 577
2013 735 594 555 631 478 369 697 530
2014 745 578 568 612 446 398 700 531
2015 766 609 625 646 547 452 724 579
2016 759 597 621 632 609 487 715 588
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 599 633 637 628 612 623 610 626
2005 610 635 643 628 616 626 619 631
2006 623 646 651 640 630 642 631 643
2007 624 643 650 641 627 643 631 642
2008 625 645 647 640 630 640 631 642
2009 619 641 642 631 619 630 626 630
2010 617 641 643 634 622 631 625 631
2011 621 643 645 637 629 639 628 639
2012 624 646 644 637 629 639 631 644
2013 626 647 642 640 637 642 632 645
2014 628 648 650 645 640 648 634 648
2015 638 658 658 655 652 659 644 659
2016 643 661 665 659 655 661 649 665
Productivity evolution and reallocation in retail trade
116
As Section 44 has shown for the market economy in general the Hungarian economy can be
characterised by a strong duality between foreign and domestically-owned firms Retail is one of the
sectors where this is the most transparent with many small domestic firms operating alongside large
multinational super- and hypermarket chains90 Figure 83 shows the share of foreign-owned firms in
terms of number employment and market share Foreign-owned retail firms are substantially larger
than domestic ones between 5-7 percent of firms are foreign-owned but they employ around 30
percent of employees and realise around 40 percent of sales This also implies that the salesworker
share is also larger in foreign firms than in domestic ones This results from the larger typical size of
foreign firms when controlling for size salesworker is not higher for foreign firms The market share
of foreign-owned firms is at the top of the distribution in EU countries with a larger foreign share only
in Latvia and Poland91
Figure 85 shows an inverted U-shaped pattern with an increasing market share of foreign firms until
2009 followed by a fall of nearly 5 percentage points between 2013 and 2016 This fall in foreign share
ran parallel with the introduction of policies favouring smaller firms in various ways
Figure 85 Share of foreign firms with at least 1 employee
There is much variation behind the overall pattern as Figure 86 illustrates plotting the market share
of foreign firms across sub-industries In groceries foreign share fluctuated around 70 percent It was
90 There is limited literature on the spillover effects generated by multinational retailers See for example Atkin et al (2018)
91 See EC (2018) Figure 2
Productivity differences in Hungary and mechanisms of TFP growth slowdown
117
rising slightly pre-crisis in parallel with the increasing concentration of the industry The increase of
the market share of foreign firms was the strongest in clothes reflecting the expansion of different
multinational chains mainly in plazas The increasing trend observable for the category seems to have
broken around 2012 which coincides with the introduction of the Plaza Stop regulation Foreign
market share was always high in the highly concentrated cosmetics sector A few foreign chains were
dominant in this sector throughout the period Foreign share actually decreased sharply in books and
newspapers
Figure 86 Foreign share in sub-industries
A key question when evaluating the expansion of foreign firms is their performance Foreign retail
firms are substantially more productive than domestic ones (Figure 87) With the exception of the
crisis years labour productivity advantage was between 60-80 percent while the TFP advantage was
between 20-40 percent The TFP advantage is smaller because of the larger capital intensity of foreign
firms These productivity premia are not purely a consequence of the larger size of foreign firms this
pattern is robust to controlling for firm size There is no clear trend in the premia they were declining
before the crisis (suggesting that domestically-owned firms were catching up) and rising after it The
figure also shows a large decline in the premia in the crisis years This is likely to be a consequence of
more pro-cyclical margins of foreign firms which are captured by revenue productivity measures
Productivity evolution and reallocation in retail trade
118
Figure 87 Productivity premia of foreign firms labour weighted
The main message of this section is that similarly to other industries large productivity differences
persist in retail These differences are primarily associated with size larger firms are more productive
and charge lower margins The performance of very small shops and the self-employed looks
especially weak The pre-crisis period was characterised by an expansion of large and foreign firms
but this growth stopped after 2010
84 Allocative efficiency and reallocation
In this section we follow the approach of Chapters 5 and 6 in analysing allocative efficiency and
reallocation with a focus on the retail industry
Chapter 5 showed that an important metric of allocative efficiency at any point in time is the degree of
co-variance of productivity and size which is directly related to aggregate productivity Figure 88
shows the elasticity of the number of employees with respect to labour productivity and TFP A more
positive relationship represents a more efficient allocation of labour across firms92 The figure shows
these relationships both for the full sample (of firms with at least 1 employee) and the main sample
(firms with at least 5 employees)
The elasticity depends crucially both on the sample and the productivity measure We find that the
correlations are much stronger when the full sample is considered rather than the base sample This
reflects our findings in Table 83 namely that the smallest firms differ substantially from other firms
92 These are coefficients from separate yearly univariate regressions with ln number of employees on the left hand side and productivity as the explanatory variable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
119
while firms with at least 5 employees are quite similar to each other The labour productivity premium
of larger firms is greater than their TFP premium reflecting their higher capital intensity
The key insight from Table 83 is that most of the Olley-Pakes correlation or measured allocative
efficiency results from the fact that very small firms are of very low productivity Within the group of
firms with at least 5 employees the correlation between TFP and size is practically zero There is a
positive although small correlation within the group between employment and labour productivity93
There is also no key trend in this measure of allocative efficiency some measures show improvement
while others a deterioration94
Figure 88 The elasticity of employment with respect to productivity main sample
Figure 89 performs the dynamic (Foster-type) productivity decomposition for the retail industry The
picture is not very different from the patterns found for services in general (see Figure 64) Pre-crisis
parallel with the strong growth of large chains growth was mainly driven by reallocation primarily in
the form of firm entry The crisis was accompanied by an annual 5 percent fall in productivity driven
by within-firm productivity decline As we have seen in Table 83 this was most likely the results of
margin-cutting by large firms Between 2010-2013 within-firm productivity growth and net entry
contributed similarly to the (relatively low) productivity growth Productivity growth sped up between
2013-2016 mainly driven by the within-firm component with little reallocation The trend break in the
growth of large chains is clearly reflected in this decomposition
93 As we have discussed in Chaper 5 this is not exceptional ndash actually similar correlations are found in services in other European contries
94 These low levels of allocative efficiency are in line with international evidence In fact these correlations have been negative in the majority of EU member states (EC 2018 p 7)
Productivity evolution and reallocation in retail trade
120
Figure 89 Dynamic decomposition of productivity growth in retail
While these results are informative about reallocation at the firm-level the shop-level data enable us
to investigate reallocation at a more detailed level These data enable us to investigate whether key
firm or shop-level variables are related to opening new shops closing shops or the growth of the shops
of continuing firms We investigate these questions in the paragraphs that follow
The simplest way to explore the shop-extensive margin or the change in the number of shops is to
aggregate the shop-level data to the firm-level In particular we calculate the change in the number of
shops the number of new shops and the number of old shops for each firm 119894 and year 119905 Denoting
these variables which show changes between year 119905 and 119905 + 1 by 119910119894119905 we run the following firm-level
regressions
119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905
where 119883119894119905 is a vector of firm-level variables These proxy productivity (by ln labour productivity) and
size (by the number of shops of the firm and the average sales per shop) 120575119905 is a full set of year
dummies
When estimating these equations one has to make a number of compromises Most importantly one
can only observe the change in shop numbers when the firm is present in the sample both in year 119905
and 119905 + 1 Otherwise one cannot be sure whether all the shops were closed or simply not sampled in
119905 + 1 Unfortunately this is a serious restriction for two reasons First one cannot observe the exit or
Productivity differences in Hungary and mechanisms of TFP growth slowdown
121
entry only survival for single-shop firms95 Second we also miss when a multi-shop firm exits with all
its shops
One also has to make a number of further methodological choices We restrict our sample to groceries
which is a relatively homogeneous group with many observations Another choice is that even though
we observe shops on a monthly basis we consider only year-to-year changes between May and the
following May Running the regressions on the monthly data would inflate artificially the number of
observations and introduce important methodological problems including seasonality
Table 84 presents the results In column (1) the dependent variable is the (net) change in the
number of shops The results suggest that productivity is of limited importance as a determinant of
change in shop numbers but size matters Firms with more and larger shops were more likely to
expand in terms of opening new shops Foreign firms expand faster because they are larger
conditional on size ownership does not matter Size is correlated both with shop opening and closing
firms with a larger average shop size are more likely to open new shops while chains with more shops
are less likely to close existing ones
Table 84 Determinants of the change in the number of shops at the firm-level groceries
(1) (2) (3)
Dependent Change in
number of
shops
New
shops
Closed
shops
Labour productivity 0001 0025 -0011
(0024) (0014) (0018)
Foreign-owned -0087 -0042 0019
(0064) (0034) (0045)
ln( (average
salesshop)
0056 0034 -0017
(0016) (0010) (0014)
5-9 shops 0176 0001 -0126
(0070) (0036) (0054)
10-49 shops 0231 -0009 -0167
(0067) (0034) (0052)
more than 50 shops 0194 0002 -0109
(0071) (0038) (0055)
Year FE yes yes yes
Observations 815 815 815
R-squared 0105 0093 0084
Notes One observation is a firm-year Standard errors are clustered at the firm-level
One may get a more detailed picture by investigating at the shop-level Here we can straightforwardly
estimate both the exit part of the extensive margin (did a specific shop close) and the intensive
margin (did the shop extend its sales)
95 For this reason we drop single-shop firms altogether from the analysis
Productivity evolution and reallocation in retail trade
122
In particular we run regressions of the following form
119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905
where 119894 denotes firms 119895 shops and 119905 years The outcome variable 119910119894119895119905 is either a dummy showing
that the shop closed96 between 119905 and 119905 + 1 or represents the growth of (log) sales of the shop 119883119894119905 are
firm-level variables such as productivity while 119885119894119895119905 are shop-level variables such as shop-level sales
The same restrictions apply as in the previous case
Table 85 reports basic regressions We run both the exit and sales growth regressions for three
subperiods 2004-2007 2008-2010 and 2012-2015 Our main question is whether one can identify
any changes in the relocation process across these subperiods
Let us start with the exit regressions Similarly to the firm-level results we find that productivity and
ownership are not associated with the probability of exit Shop size is significantly related to closing a
shop twice as large sales are associated with 5 percentage points lower probability of the event
occurring This relationship became stronger by the third period The number of shops of the firm is
also negatively associated with the probability of closing the shops and this effect only became
significant post-crisis In addition the explanatory power of the regression is also higher by nearly 50
percent in this last period compared to the earlier ones To sum up we find that the size of the shop
and the turned out to be more important post-crisis making such shops less likely to close
In contrast to the exit equation we do not find significant effects in the growth regressions Neither
size nor productivity seem to be related to growth at the shop-level
To sum up the level of allocative efficiency in retail is relatively low ndash similarly to other European
countries ndash and one cannot see a significant change in this respect Pre-crisis when large chains
expanded rapidly reallocation played a significant role in aggregate productivity growth while within-
firm growth became dominant after the crisis Shop-level data suggest that the expansion in terms of
number of shops is mainly determined by firm size rather than productivity and ownership Sales
growth of existing shops does not seem to be related to size ownership or productivity The lack of
evidence for a relationship between opening new shops or the growth of existing shops is much in line
with the low measured allocative efficiency in the industry
96 We run linear probability models for shop exits Probit models yield similar results
Productivity differences in Hungary and mechanisms of TFP growth slowdown
123
Table 85 Probability of closing a shop and growth regression NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent Closing the shop Growth
Period 2004-
2007
2008-
2010
2012-
2015
2004-
2007
2008-
2010
2012-
2015
labour productivity 0005 -0021 0102 0034 -0012 0097
(0009) (0015) (0087) (0018) (0022) (0063)
foreign-owned -0005 0063 0056 0016 -0014 -0035
(0023) (0045) (0057) (0023) (0055) (0066)
ln sales -0026 -0029 -0057 -0018 -0010 0010
(0006) (0007) (0021) (0007) (0017) (0005)
5-9 shops -0014 -0066 -0111 0061 -0035 -0055
(0026) (0051) (0043) (0046) (0044) (0026)
10-49 shops -0045 -0114 -0149 0046 -0038 -0023
(0024) (0048) (0039) (0044) (0043) (0017)
more than 50
shops
0001 -0077 -0134 0061 -0027 -0016
(0029) (0048) (0040) (0045) (0044) (0022)
Observations 15374 10946 15038 14025 10120 13458
R-squared 0030 0053 0073 0023 0041 0121
Notes OLS regressions run at the shop-year level only for firms present both in t and t+1 In columns (1)-(3) the
dependent variable is a dummy indicating whether the shop closes between t and t+1 while in columns (4)-(6) it is
the growth rate of sales between t and t+1 The explanatory variables are measured at year t The number of
shops variables are dummies representing the number of shops of the firm County and year fixed effects are
included Period 1 2004-2007 Period 2 2008-2010 period 3 2012-2015 Standard errors are clustered at the
firm-level
85 Trade
In small open economies a very important function of the wholesale and retail sector is the
intermediation of international trade for consumers and firms The operation and efficiency of these
industries can have a strong impact on aggregate welfare and productivity by determining both the
cost and variety of imported goods available as well as the cost of exporting products (Raff and
Schmitt 2016)
Many interesting questions emerge in this framework One of the key issues is the problem of double
marginalisation In the case of consumers (and consumer goods) one dimension of this question is
whether retailers import products directly or via wholesalers If retailers find it very hard to import
directly (because of say large fixed costs) double marginalisation can raise prices for consumers
Through this channel lower trade cost of retailers can benefit consumers As a result the share of
consumer goods imported directly by retailers may be an important proxy for the lower prevalence of
double marginalisation
In the case of intermediate inputs manufacturing firms face the choice of importing the product
directly (and paying the fixed costs of doing so) or relying on an intermediary Again reduced fixed
cost may make imported goods cheaper contributing positively to productivity growth Access to
imported intermediate inputs has been shown to be strongly correlated with the productivity of
Hungarian manufacturing firms (Halpern et al 2015)
Productivity evolution and reallocation in retail trade
124
The question of duality is also highly relevant in this context Multinational retailers can easily rely on
producers abroad hence their expansion can have important effects on Hungarian producers
Domestic chains on the other hand may find it hard to import a large variety of foreign products
which may result in a reduced choice set for consumers
Ultimately it is the questions above that motivate our investigation of importing and exporting by
wholesalers and retailers Our data are exceptionally suitable for this exercise Given that firm balance
sheets can be linked to detailed export and import data one can quantify the amount of products
imported and exported from different product categories by wholesalers and retailers
An important methodological note is that we only observe direct imports in the trade data The most
important consequence of this limitation is that while in actual fact the share of imported goods on a
retailerrsquos shelf is a combination of goods imported directly by the retailer and those imported by a
wholesaler and sold to the retailer with the data we are only able to observe the former (Basker and
Van 2010) Also note that in contrast to imports exports are reported in the balance sheet
Therefore we will use this source of information when analysing exporting
Importing
To start with Figure 810 shows the share of retailers and wholesalers from the total Hungarian
imports of different types of goods In terms of all imports the share of these two groups of firms
fluctuated around 25 percent with a slightly decreasing trend The bulk of the imports were conducted
by manufacturing firms with an especially large share by multinational affiliates strongly integrated
into global value chains for example in the automotive industry Overall wholesalersrsquo imports were
about 5 times larger than those of retailers97
Naturally wholesalers and retailers dominate the importing of consumer goods by a share of around
70 percent A key trend here is the increasing share of retailers In 2004 21 percent of intermediated
trade (imports of wholesalers and retailers) were imported by retailers which increased gradually to
33 by 2015 This is a significant shift which reflects in part the expansion of multinational retail
chains but probably also easier access to imports by retailers
The share of intermediated trade was around 20 percent both for intermediate inputs and capital
goods dominated by wholesalers This reflects that in aggregate terms the overwhelming majority of
goods used by firms in production are imported directly The share of intermediated trade decreased
strongly following the crisis from 20 percent in 2010 to 13 percent in 2015 Given the skewed size
distribution of manufacturing firms this does not mean that most firms import their inputs directly
many smaller firms rely strongly on trade intermediaries when purchasing their inputs
Figure 811 looks into the trends behind consumer goods imports in more detail The left hand side
figure shows the share of imports compared to the total cost of goods sold (COGS) by wholesalers and
retailers98 We find that this ratio is roughly constant for wholesalers namely around 10 percent99
97 This can be compared to the results of Bernard et al (2010) who report that retailers and firms active both in retail and wholesale represent 14 percent of importing firms and 9 percent of imports in the US
98 In particular we calculate total consumer goods imports for wholesale and retail firms and divide it with the sum cost of goods sold across all retailers
99 Needless to say wholesalers also import other type of goods which are part of their cost of goods sold This ratio was 36 percent in 2015 showing that more than a third of their sales was imported
Productivity differences in Hungary and mechanisms of TFP growth slowdown
125
This contrasts sharply with retailers where the share of directly imported goods nearly doubled
between 2005 and 2015 from 6 percent to 11 percent100 This corresponds to a substantial increase in
the share of imported goods offered to consumers by retailers and an increasing share of this volume
is imported directly by the retailer presumably with a smaller degree of double marginalisation
One can also decompose the increasing direct import share of retailers to its different margins One
possibility is that - probably thanks to the declining fixed costs of importing - more and more retailers
started to import (an extensive margin effect) The right panel of Figure 811 shows that this is not the
case the share of directly importing retailers stagnated at about 8 percent of firms (with at least 5
employees) in the whole period Instead the rise of direct imports was driven by the intensive margin
or the average direct import per retailer Other regressions (not reported) suggest that this does result
mainly from the increased imports of large retailers
Figure 810 Share of wholesale retail and other firmsrsquo imports relative to total imports across
product categories
100 Again considering all goods the importcost of goods sold ratio increased from 11 to 18 percent for retailers
Productivity evolution and reallocation in retail trade
126
Figure 811 The share of consumer goods imports relative to the cost of goods sold and the share of
direct consumer goods importers by industry
Notes Firms with at least 5 employees
Figure 812 distinguishes between foreign and domestically-owned retail firms Both the share of
importers and their intensive margins are much higher for foreign-owned firms in the industry The
share of consumer goods imports in foreign firms in terms of cost nearly tripled between 2005 and
2015 from 7 to 21 percent101 compared to the 2-5 percent increase for domestically-owned firms
The increase in imports by retailers hence was mainly driven by multinationals
101 A similar increase from 18 percent in 2005 to 32 percent in 2015 can be observed when non- consumer goods are considered
Productivity differences in Hungary and mechanisms of TFP growth slowdown
127
Figure 812 The share of consumer goods imports relative to cost of goods sold and the share of
direct consumer goods importers by ownership
Notes Firms with at least 5 employees
Table 86 presents the cross-sectional linear regressions in order to investigate the premia of importers
among retailers along several dimensions In these regressions the dependent variable is a dummy
which shows whether a firm imports at least 1 percent of its cost of goods sold102 We find substantial
and highly significant premia in terms of size productivity and ownership 100 percent higher
productivity translates into about 5 percentage points higher probability of importing This premium
was increasing significantly between 2005 and 2015 showing a stronger self-selection of more
productive retailers into direct importing Foreign retailers are 20-25 percentage points more likely to
import on average A doubling of employees is associated with around 9 percentage points higher
probability of importing103
102 These are linear probability models but probit specifications yield similar marginal effects
103 Similar premia are found for importers in most industries and are mainly explained by the fixed costs of importing (Vogel and Wagner 2010)
Productivity evolution and reallocation in retail trade
128
Table 86 Determinants of importing linear probability models Retailers
(1) (2) (3) (4) (5)
Year 2005 2008 2010 2012 2015
Dependent Imports at least 1 percent of purchases
Labour productivity 0050 0054 0047 0059 0065
(0003) (0003) (0003) (0003) (0003)
Foreign-owned 0249 0196 0224 0238 0217
(0014) (0011) (0012) (0012) (0012)
Ln employees 0082 0082 0075 0073 0088
(0004) (0004) (0004) (0004) (0004)
Constant -0470 -0536 -0464 -0551 -0637
(0027) (0026) (0027) (0028) (0027)
Observations 7467 7977 7400 7122 8308
R-squared 0116 0130 0127 0140 0143
Notes Firms with at least 5 employees These are cross-sectional regressions where the dependent variable is
dummy representing whether the firm imports at least 1 percent of its cost of goods sold
Exporting
Wholesalers and retailers can also play a significant role as export intermediaries Extended export
activities of these firms can be an important source of growth for these firms but can also benefit
many smaller producers who would not find it profitable to export directly (Ahn et al 2011)
Figure 813 shows that 85-90 percent of exporting was conducted directly by producers rather than by
wholesalers or retailers The share of intermediated exports was constant pre-crisis but started to fall
after 2012
Productivity differences in Hungary and mechanisms of TFP growth slowdown
129
Figure 813 Share of wholesale retail and other firmsrsquo exports relative to total exports of firms
Many wholesalers and retailers started to export in the period under study (Figure 814) The share of
exporters in wholesale firms increased from 25 percent in 2005 to 35 percent in 2015 while the share
of exporting retailers doubled in this period The share of exports in the turnover of these firms also
increased substantially
Figure 814 Share of exports relative to turnover and share of exporters by industry
While foreign-owned firms are about 4 times more likely to export than domestic ones entry into
exporting was not limited to foreign-owned firms (Figure 815) the share of exporters among
domestically-owned firms doubled between 2005 and 2015 This was paralleled with an increase in the
share of exports relative to total turnover
Productivity evolution and reallocation in retail trade
130
Figure 815 Share of exports relative to turnover and share of exporters by ownership for the retail
sector
Table 87 reports linear probability models with export status as the dependent variable More
productive larger and foreign-owned firms are more likely to export In general both the size and
labour productivity premia increased between 2005 and 2015 once again suggesting stronger self-
selection based on these variables
Table 87 Determinants of exporting linear probability models retail
(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015
Dependent Exports at least 1 percent of total revenue
Labour
productivity
0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)
Foreign-owned 0083 0137 0141 0119 0107
(0009) (0011) (0013) (0013) (0012)
Ln employees 0019 0029 0028 0031 0034
(0003) (0004) (0004) (0004) (0004)
Constant -0159 -0277 -0271 -0321 -0317
(0018) (0026) (0027) (0029) (0028)
Observations 7622 7976 7663 7384 8730
R-squared 0028 0045 0041 0041 0036
This section has shown that the role of retailers in international trade is becoming more and more
important in Hungary This can have many benefits from providing a larger variety of potentially lower
priced goods to consumers to letting smaller producers reach foreign markets Increasing exports
mostly reflect opportunities provided by European integration and the internet but policies can also
help firms to become more adapt at utilising these opportunities
Productivity differences in Hungary and mechanisms of TFP growth slowdown
131
86 Policies Crisis taxes
As we have described briefly in Section 81 some of the new policies introduced after the crisis were
size-dependent either explicitly or implicitly The crisis taxes and the local business tax104 were based
on explicitly taxing large firms at higher rates Such policies can have substantial effects at the sectoral
level (Guner et al 2008)
Evaluating the effects of these taxes is not a straightforward task A possible approach was followed in
Section 84 where we have investigated the reallocation process in detail While such an approach is
not capable of identifying the causal effects of specific policies it may provide a broad picture The
results most importantly Figure 88 suggest that the importance of the reallocation process declined
relative to within-firm productivity growth Still this could have resulted from many reasons other than
policy changes
A more direct approach is to identify specific firms which were affected by a policy and to compare
their behaviour to similar firms not affected by the policy Such a diff-in-diff approach may be an
effective policy evaluation tool when there are sharp breakpoints in the tax schedule with enough
`treatedrsquo and control firms in the two groups
As for the crisis taxes the only sharp discontinuity was at the top rate when the tax rate increased
from 04 to 25 percent of profits The cutoff was at HUF 100bn and according to our data altogether
6 retail firms qualified for inclusion in this group This sample size does not allow for a statistically
powerful test
Still a few graphs may illustrate the processes First the market share of these large mainly
multinational firms were expanding quickly before 2010 and stagnated afterwards (Figure 816)
Second we can illustrate some of the key performance measures discussed in Section 83 Figure 817
compares the treated firms to a control group consisting of firms with at least 100 employees We find
that the premium of the treated group in terms of both productivity measures and margins were
higher between 2010 and 2013 than before or after105 As we have discussed earlier at least in the
short term these revenue-based measures are likely to reflect changes in prices Hence this figure
hints at increased prices in the treated group relative to the control group suggesting that treated
firms passed on the tax to consumers Note that these differences are not statistically significant and
to reiterate may have resulted from many other factors rather than just the effects of this specific
policy
104 The effect of the local business tax is much harder to test given its more continuous nature
105 Note that the margin premia are in fact negative in line with the lower margins charged by the largest firms
Productivity evolution and reallocation in retail trade
132
Figure 816 Sales and employment share of firms in the top bracket of the crisis tax
Notes Full sample
Figure 817 Margin TFP and labour productivity advantage of firms in the top bracket of the crisis tax
firms with more than 100 employees
Productivity differences in Hungary and mechanisms of TFP growth slowdown
133
87 Policies Mandatory Sunday closing
One of the most characteristic non-tax based size-dependent policies was mandatory Sunday closing of
larger shops introduced in March 2015 and reversed in April 2016 While the policy had multiple aims
it was partly motivated by supporting smaller and family-owned shops In this section we investigate
two outcomes related to this policy First we aim at understanding its reallocation effects ie the
extent to which the market share of treated shops lost market share Second we are interested in the
extent to which consumption was reallocated to other days of the week
The shop-level data is ideal to investigate the effects of this policy First the policy was defined at the
shop- rather than the firm-level We can identify the affected shops precisely based on the number of
days they were open Second many shops have been affected by this policy making the test
powerful Third the policy has a clearly defined beginning and end making a difference in differences
strategy feasible
Our empirical approach starts with restricting the sample to comparable firms First we investigate
mainly grocery shops where we have sizable treated and control groups106 In the sample we include
only shops which were continuously in the sample between January 2015 and October 2016 An issue
is that the treated and the control group may be very different We attempt to guarantee that the
common support condition is satisfied by excluding very small and very large shops107 For similar
reasons we also exclude shops which were not open even on Saturdays either before or during the
policy108
An important part of the analysis is the definition of the treated group As we do not observe directly
the area and the ownership of the shop we rely on the change in the number of days open We
consider a shop treated if it was open for at least 30 days per month before the policy (in median) and
it was open for less than 26 days after the policy was introduced (again in median)109 The control
group consists of other firms in the sample
Taking a look at the number of days open for the two groups reveals that compliance was very high
More than 95 percent of the shops that had been open on Sundays before the policy were closed on
Sundays during the whole policy period More than 95 percent of shops in the control group were
closed on Sundays both before and after the policy There are few firms which deviated from this
pattern by for example opening on Sundays when the policy started110
106 In other 4-digit sectors either there are too few firms or nearly all of them are treated (clothes shoes etc) or none of them (fuel)
107 Based on the 5th and 95th percentiles of the median sales distribution based on sales before the policy Unfortunately we do not have other measures of shop size
108 More precisely we exclude shops for which the median monthly days open was below 21 days either before or during the policy
109 A potential worry with this approach is that some shops may have closed voluntarily when the policy was introduced We cannot exclude this possibility but this may not be that important for the relatively large shops in the sample One can expect that voluntary Sunday closure would not start exactly at the beginning of the policy but rather after a period of gathering information about consumer demand on Sunday By checking the monthly distribution of the number of days open we find only few firms which changed their behaviour in this respect during the policy
110 Note that many small shops remained open on Sundays but most of them are missing from our restricted sample because of small median sales
Productivity evolution and reallocation in retail trade
134
Figure 815 reports descriptive statistics of the key variables Panel A) compares the evolution of
average sales of the treated and the control group before during and after the introduction of the
policy The dynamics of sales growth was remarkably similar before the policy was introduced
suggesting that the parallel trend assumption was satisfied Average sales in the control group are
somewhat higher during the policy suggesting some reallocation of market share to that group After
the policy the treated group seems to slightly overperform the control group
Part B) of Figure 818 shows the evolution of average sales per day open Again the pre-policy trends
are similar for the two groups Sales per day increases significantly for both groups during the policy
consumers did their Sunday shopping on other days The increase is substantially larger for the treated
group showing that most of the former Sunday shopping took place in the same shop but on other
days of the week The fact that there is an increase in the control group shows that part of the former
Sunday shopping was reallocated to these shops Interestingly the sales per day advantage of the
treated group remained even after the policy was abandoned As we will see the main reason for this
is that after abandoning the policy some of the shops remained closed
Figure 818 The evolution of key variables in the treated group and the control group groceries
A) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
135
B) Sales per day
While these patterns are suggestive the data allow us to conduct a more precise econometric event
study exercise We do so by creating a number of quarterly event study dummies to capture the
differential dynamics of the treated and control groups We define the variable lsquoevent timersquo which
shows the number of months since the policy started (it is zero in March 2015) This variable takes
negative values before that date We define quarterly dummies based on the event time variable For
example the first treatment quarter dummy is one when event time is 0 1 or 2 and the firm is in the
treated group The first pre-treatment dummy takes the value of 1 when event time is -1 -2 or -3 and
the firm is in the treated group
We run the following regression to estimate these trends
119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591
120591 + 120583119894119895 + 120575119905 + 휀119894119895119905
In this regression the dependent variable is days open ln(monthly sales) and ln(salesdays open) 119894
denotes firms 119895 shops and 119905 time measured in month while 120591 is event time in quarters The variables
of interest are the full set of event study dummies The base category will be the second pre-trend
dummy (event time -4 -5 or -6) The motivation for this choice is that the policy was announced in
this period (December 2014) hence the first pre-trend period the beginning of 2015 may include
preparation for the policy 120583119894119895 are shop fixed effects to control for shop heterogeneity 120575119905 are time
(monthly) fixed effects which control both for seasonality and macro shocks When we run the
regression by pooling different 4-digit industries we allow these dummies to vary across industries In
a more demanding specification we also include firm-time fixed effects and identify from the
differences across the treated and non-treated shops of the same firm in the same month We cluster
standard errors at the shop-level
Figure 819 summarizes the main results for the whole retail sector while the regressions are reported
in Table A71 in the Appendix Panel A) shows the results for days open with the right-hand panel
including firm-time fixed effects We see that on average treated firms cut the number of days open
by 2-3 days relative to the control group ndash the effect is more pronounced with firm fixed effects There
Productivity evolution and reallocation in retail trade
136
is practically no pre-trend and the timing of the reduction of days open is strongly in line with the
introduction of the policy The number of days open increases sharply after the end of the policy but
only to below pre-policy levels This suggests that some shops did not re-open on Sundays after the
policy probably because they learned that their sales did not suffer much
Panel B) shows the behaviour of average monthly sales Again there is no evidence for a pre-trend
During the policy treated firms experienced a 2-3 percent lower sales growth relative to the control
group This shows how much of sales was re-allocated to other shops Post-policy variables suggest
full recovery to pre-policy levels
Panel C) of the same figure shows the effect of the policy on sales per day open This variable
increased by 5-10 percent in the treated group relative to the control group The bulk of consumers
seem to have remained loyal to their familiar shops and simply made their shopping on other days
This may have also been helped by longer opening hours on other days of the week and further efforts
made by shops to retain their customers Sales per day remain higher even after the end of the policy
most likely because some shops did not re-open on Sundays but probably also because of
organizational changes during the policy
Figure 819 Event study results for the whole retail sector
A) Days
Productivity differences in Hungary and mechanisms of TFP growth slowdown
137
B) Sales
C) Sales per day
Notes This figure presents point estimates and 95 confidence intervals from the event study regression showing
the evolution of number of days open sales and sales per day of the treated group compared to the control group
as described in the text All specifications include shop fixed effects The left panel regressions also include 4-digit
industry-time fixed effects while the right side panels include firm-time dummies
Productivity evolution and reallocation in retail trade
138
Figure 820 re-estimates the same regressions for groceries where the policy was most relevant The
regression results are reported in Table A72 in the Appendix We find very similar results to the whole
retail sector The only exception is that the evolution of post-policy behaviour of sales is less clear
Figure 820 Event study results for NACE 4711
A) Days
B) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
139
C) Sales per day
Notes The figure above presents point estimates and 95 confidence intervals from the event study regression
showing the evolution of number of days open sales and sales per day of the treated group compared to the
control group as described in the text All specifications include shop fixed effects The left panel regressions also
include 4-digit industry-time fixed effects while the right side panels include firm-time dummies
A possible concern with these estimates is that the increase in sales per day may result from a simple
composition effect If sales are usually very small on Sundays anyway then closing on Sundays may
mechanically increase average daily sales We check for this possibility by estimating sales on different
days of the week from the pre-policy period While we do not observe the sales on each day of the
week we observe sales in different months with a different combination of days We rely on this
variation to estimate a regression of the following form
ln 119904119886119897119890119904119894119895119905 = 120573 lowast 119883119905 + 120574 lowast 119889119886119905119890119905 + 120583119894119895 + 휀119894119895119905
where 119883119905 is a vector of variables containing the number of Mondays Tuesdays etc in month 119905 We
also control for the number of holidays in the month We control for seasonality by including dummies
for December January and summer months The regression also includes firm fixed effects and is
estimated on the period 2009-2014 120574 lowast 119889119886119905119890119905 is a linear trend The estimated results are reported in
Table A73 in the Appendix
The regression shows that sales on Sundays were not that small namely similar to a typical Monday
or Wednesday Thus the composition effect is unlikely to affect the results much To check for the
relevance of these composition effects Figure 821 A) reports sales predicted from the above
regression for the treated group (by setting the number of Sundays to be zero during the policy)
Therefore the `predictedrsquo line shows what would have happened if sales had remained the same on
Productivity evolution and reallocation in retail trade
140
non-Sundays during the policy The actual line is clearly above the predicted one suggesting that sales
on other days have increased
Panel B) of Figure 821 shows how sales per day would have evolved based on a similar regression
Note that predicted sales per day are slightly larger during the policy than beforehand thanks to the
mechanical composition effect resulting from the slightly lower sales on Sundays Actual sales per day
however are substantially higher than this simple prediction showing again that sales per day
increased on other days of the week
Figure 821 The evolution of the variables versus prediction
A) Sales
B) Sales per day
Productivity differences in Hungary and mechanisms of TFP growth slowdown
141
All in all the mandatory Sunday closing of shops was effective in terms of compliance It did not have
strong reallocative effects with a 2-3 percent fall in sales in the treated group Consumers seem to
have remained mostly loyal to the shop they had frequented and made their shopping on other days
of the week at the same shop Interestingly some of the shops seem to have learned that it is optimal
to remain closed on Sundays even after the policy was cancelled
88 Conclusions
In line with the main message of other parts of this study there are huge productivity differences
across firms within the retail sector There is a strong duality between small and large firms both in
terms of productivity and margins Consumers are likely to pay significantly lower prices in the shops
of large firms Many of the large firms are multinationals which had expanded rapidly before the crisis
At the other end of the range the exceptionally low performance of very small firms seems to be a
significant issue Many technologies applied by the most productive retailers could be adapted
relatively easily by some of the less productive firms Increasing absorptive capacity and effective
financing could help in promoting this Still many of the low-productivity very small shops may not be
viable in the long run
A key pattern observed is the increasing concentration of the retail sector pre-crisis resulting from the
expansion of large chains and foreign firms These trends seem to have stopped or slowed down after
the crisis In line with this pattern the contribution of reallocation decreased post-crisis relative to
earlier periods While many factors can play a role in this pattern it may be related to the different
size-dependent policies introduced after 2010 While these developments may help smaller retail firms
consumers may face higher prices in the long run
Not all the policies introduced can be properly evaluated based on the data at hand especially because
multiple policies were introduced at the same time with some of them affecting only few firms We
were able to analyse precisely the effects of mandatory Sunday closing based on store level data We
found that a relatively small share of the demand was lost by the treated shops and the majority of
consumers simply switched to shopping at the same place on other days Interestingly some of the
treated shops found it optimal not to re-open on Sundays even when the policy was reversed
Additionally retailers and wholesalers also play a large and increasing role in mediating imports and
exports We found a large increase in goods imported directly by retailers rather than indirectly via
wholesalers This was mainly driven by large foreign firms and may have benefited their consumers
thanks to a lower degree of double marginalisation Both the number of exporting firms and the
amount exported by wholesalers and retailers increased most likely benefitting from easy access to
markets of other EU member states and probably from the opportunities provided by e-commerce
This can benefit both the exporting firms and the Hungarian producers who can more easily reach
foreign markets with the help of these intermediaries Policies may help retailers to internationalise by
making international sales especially on the internet even easier
Conclusions
142
9 CONCLUSIONS
The results of this report confirm that Hungary is atypical because of the relatively poor productivity
performance of frontier firms Importantly contrary to a strong version of the duality concept this is
not a result of Hungarian frontier firms being on the global frontier typically they are quite far away
from it This robust pattern underlines that besides helping non-frontier firms policies may also have
to focus on the performance of the frontier group A transparent environment with a strong rule of law
complemented by a well-educated workforce and a strong innovation system is key for providing
incentives to invest into the most advanced technologies
The analysis in this report reinforces the impression that there is a large productivity gap between
globally engaged or owned and other firms the gap being about 35 percent in manufacturing and
above 60 percent in services This gap seems to be roughly constant in the period under study The
firm-level analysis in Chapter 7 also reveals that one of the mechanisms which conserves the gap is
that foreign frontier firms are able to increase their productivity more than their domestic counterparts
even from frontier levels These findings reinforce the importance of well-designed policies that are
able to help domestic firms to catch up with foreign firms A key precondition for domestic firms to
build linkages with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A more specific
conclusion is the importance of enabling high-productivity domestic firms to improve their productivity
levels even further
The large within-industry productivity dispersion the relatively low (though not extreme in
international comparison) allocative efficiency documented in some of the industries the strong
positive contribution of reallocation to total TFP growth before the crisis and the relatively low entry
rate imply that policies promoting reallocation have a potential to increase aggregate productivity
levels significantly These policies can include improving general framework conditions by cutting
administrative costs reducing entry and exit barriers and using a neutral regulation The fact that
capital market distortions still appear to be significantly above their pre-crisis levels implies that
policies that reduce financial frictions may help the reallocation process The fact that exporters tend to
expand faster relative to non-exporters indicates that access to EU and global markets generates a
strong and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general traded and
more knowledge-intensive sectors fared better both in terms of productivity growth and allocative
efficiency The difference between traded and non-traded sectors points again to the importance of
global competition in promoting higher productivity and more efficient allocation of resources This also
implies that adopting policies that focus on innovation or reallocation in services may be especially
important given the large number of people working in those sectors The better performance of and
reallocation into more knowledge-intensive sectors underlines the importance of education policies
aimed at developing up-to-date and flexible skills and innovation policies that help improve the
knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people working at
firms with at least a few employees and those working in very small firms or self-employed The latter
category represents 30-50 percent of people engaged in some important industries Inclusive policies
may attempt to generate supportive conditions for these people by providing knowledge and training
as well as helping them to find jobs with wider perspectives or to set up well-operating firms The large
share of these unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a characteristic difference between the pre-crisis
period characterised by strong reallocation mainly via the expansion of large foreign-owned chains
Productivity differences in Hungary and mechanisms of TFP growth slowdown
143
and the post-crisis period with a stagnating share of large chains This break is likely to be linked to
post-crisis policies favouring smaller firms While halting further concentration in a country with
already one of the highest share of multinationals in this sector can have a number of benefits it is
likely to lead to higher prices and lower industry-level productivity growth in the long run Policies
should balance carefully between these trade-offs Another key pattern identified is the increasing role
of retailers (and wholesalers) in trade intermediation both on the import and export side Policymakers
should encourage these trends and design policies which provide capabilities for such firms to enter
international markets probably via e-commerce
References
144
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
145
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Inklaar R and Timmer M P (2009) ldquoProductivity convergence across industries and countries The
importance of theory-based measurementrdquo Macroeconomic Dynamics 13(S2) 218-240
Iwasaki I Csizmadia P Illeacutessy M Makoacute C and Szanyi M (2012) ldquoThe nested variable model of
FDI spillover effects Estimation using Hungarian panel datardquo International Economic Journal 26(4)
673-709
Javorcik B S (2004) ldquoDoes foreign direct investment increase the productivity of domestic firms In
search of spillovers through backward linkagesrdquo American Economic Review 94(3) 605-627
Productivity differences in Hungary and mechanisms of TFP growth slowdown
149
Javorcik B S and Spatareanu M (2011) ldquoDoes it matter where you come from Vertical spillovers
from Foreign Direct Investment and the origin of investorsrdquo Journal of Development Economics 96(1)
126-138
Jaumlger K (2017) ldquoEU KLEMS growth and productivity accounts 2017 releaserdquo Statistical Module
Retrieved from httpwwweuklemsnettcb2017metholology_eu20klems_2017pdfKaacutetay G and
Wolf Z (2004) ldquoInvestment behavior user cost and monetary policy transmission The case of
Hungaryrdquo MNB Working Papers 200412
Kertesi G and Koumlllő J (2004) ldquoFighting low equilibriarsquo by doubling the minimum wage Hungarys
experimentrdquo IZA Discussion Papers (No 970)
Konings J (2001) ldquoThe effects of Foreign Direct Investment on domestic firmsrdquo Economics of
Transition 9(3) 619-633
Koumlllő J (2010) ldquoHungary The consequences of doubling the minimum wagerdquo In D Vaughan-
Whitehead (Ed) The Minimum Wage Revisited in the Enlarged EU Chapter 8 Edward Elgar
Publishing Cheltenham UK
Kugler M (2006) ldquoSpillovers from Foreign Direct Investment Within or between industriesrdquo Journal
of Development Economics 80(2) 444-477
Kuusk A Staehr K and Varblane U (2017) ldquoSectoral change and labour productivity growth
during boom bust and recovery in Central and Eastern Europerdquo Economic Change and Restructuring
50(1) 21-43
Levinsohn J and Petrin A (2003) ldquoEstimating production functions using inputs to control for
unobservablesrdquo The Review of Economic Studies 70(2) 317-341
Lin P Liu Z and Zhang Y (2009) ldquoDo Chinese domestic firms benefit from FDI inflow Evidence
of horizontal and vertical spilloversrdquo China Economic Review 20(4) 677-691
McGowan M A Andrews D and Millot V (2017) ldquoThe walking dead Zombie firms and productivity
performance in OECD countriesrdquo OECD Economics Department Working Papers (No 1372)
McMillan M Rodrik D and Sepulveda C (2017) ldquoStructural change fundamentals and growth A
framework and case studiesrdquo NBER Working Papers (No w23378) National Bureau of Economic
Research University of Chicago Press Chicago
Melitz J (2003) ldquoThe impact of trade on intra-industry reallocations and aggregate industry
productivityrdquo Econometrica 71(6) 1695-1725
Nicolini M and Resmini L (2010) ldquoFDI spillovers in new EU member statesrdquo Economics of
Transition 18(3) 487-511
OECD (2016) ldquoThe productivity-inclusiveness nexus Preliminary versionrdquo OECD Publishing Paris
httpdxdoiorg1017879789264258303-en
Olley G and Pakes A (1996) ldquoThe dynamics of productivity in the telecommunications equipment
industryrdquo Econometrica 64(6) 1263-1297
References
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Rev2 to NACE Rev1rdquo Working Papers (No 1502) University of Urbino Carlo Bo
Petrin A and Levinsohn J (2012) ldquoMeasuring aggregate productivity growth using plant‐level datardquo
The RAND Journal of Economics 43(4) 705-725
Petrin A Reiter J and White K (2011) ldquoThe impact of plant-level resource reallocations and
technical progress on US macroeconomic growthrdquo Review of Economic Dynamics 14(1) 3ndash26
Raff H and Schmitt N (2016) ldquoRetailing and international traderdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 157-179
Ratchford B T (2016) ldquoRetail productivityrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 54-72
Restuccia D and Rogerson R (2017) ldquoThe causes and costs of misallocationrdquo Journal of Economic
Perspectives 31(3) 151-74
Rovigatti G and Mollisi V (2016) ldquoPRODEST Stata module for production function estimation based
on the control function approachrdquo Statistical Software Components S458239 Boston College
Department of Economics Revised 12 Jun 2017 Accessed October 26 2017
httpsideasrepecorgcbocbocodes458239html
Sadun R (2015) ldquoDoes planning regulation protect independent retailersrdquo Review of Economics and Statistics 97(5) 983-1001
Scarpetta S Hemmings P Tressel T and Woo J (2002) ldquoThe role of policy and institutions for
productivity and firm dynamics Evidence from micro and industry datardquo OECD Economics Department
Working Papers (No 329) Available at SSRN httpsssrncomabstract=308680 or
httpdxdoiorg102139ssrn308680
Smith H (2016) ldquoThe economics of retailer-supplier pricing relationships Theory and evidencerdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 97-136
Smeets R (2008) ldquoCollecting the pieces of the FDI knowledge spillovers puzzlerdquo The World Bank
Research Observer 23(2) 107-138
Syverson C (2011) ldquoWhat determines productivityrdquo Journal of Economic Literature 49(2) 326-65
Taglioni D and Winkler D (2016) ldquoMaking global value chains work for developmentrdquo The World
Bank Issue 143 1-10
Topalova P and Khandelwal A (2011) ldquoTrade liberalization and firm productivity The case of
Indiardquo Review of Economics and Statistics 93(3) 995-1009
Viviano E (2008) ldquoEntry regulations and labour market outcomes Evidence from the Italian retail trade sectorrdquo Labour Economics 15(6) 1200-1222
Vogel A and Wagner J (2010) ldquoHigher productivity in importing German manufacturing firms Self-selection learning from importing or bothrdquo Review of World Economics 145(4) 641-665
Wagner J (2007) ldquoExports and productivity A survey of the evidence from firm‐level datardquo The
World Economy 30(1) 60-82
Productivity differences in Hungary and mechanisms of TFP growth slowdown
151
Wooldridge J M (2009) ldquoOn estimating firm-level production functions using proxy variables to
control for unobservablesrdquo Economics Letters 104(3) 112-114
Zhang Y Li H Li Y and Zhou L A (2010) ldquoFDI spillovers in an emerging market The role of
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Journal 31(9) 969-989
Appendix
152
APPENDIX
A3 Chapter 3 Internationally comparable data sources and methodology
A31 EU KLEMS amp OECD STAN
The EU KLEMS project aimed at creating a database on measures of economic growth productivity
employment creation capital formation and technological change at the industry level for all European
Union member states from 1970 onwards The database provides an important input to policy
evaluation in particular for the assessment of the goals concerning competitiveness and economic
growth potential as established by the Lisbon and Barcelona summit goals
The input measures include various categories of capital labour energy material and service inputs
Productivity measures have also been developed in particular with growth accounting techniques
Several measures on knowledge creation have also been constructed
The basic data of the EU KLEMS is also available in the OECD STAN database sometimes in a more up
to date version We have downloaded the following variables from there
- EMPE Number of employees
- EMPN Number of persons engaged ndash total employment
- SELF Number of self-employed
- VALU Value added current prices (millions of national currency)
- VALK Value added volumes (current price of the reference year 2010 millions)
- VALP Value added deflators (reference year 2010 = 100))
Labour productivity is defined as gross value added at constant prices divided by the number of
persons engaged In order to create comparative labour productivity levels we used the 2005
benchmark from the GGDC Productivity Level Database111 This project provides productivity levels
relative to the USA that can be used together with EU KLEMS growth accounts to create comparable
productivity level extrapolations (Inklaar and Timmer 2008 Inklaar and Timmer 2009)
A32 OECD Structural and Demographic Business Statistics
The OECD Structural and Demographic Business Statistics (SDBS) consists of two databases the
OECD Business Demography Indicators (BDI) and the OECD Structural Business Statistics (SBS)
The OECD Business Demography Indicators (BDI) database contains data on births and deaths of
enterprises their life expectancy and the important role they play in economic growth and
productivity The OECD Structural Business Statistics (SBS) database features the data collection
of the Statistics Directorate relating to a number of key variables such as for example value added
operating surplus employment and the number of business units broken down by ISIC Rev 4
industry groups referred to as the Structural Statistics on Industry and Services (SSIS) database and
by economic sector and enterprise size class referred to as the Business Statistics by Size Class (BSC)
database For most countries the main sources of information used in the compilation of structural
business statistics are business surveys economic censuses and business registers
111 More information can be found on the homepage of GGDC Production Level Database
httpswwwrugnlggdcproductivitypldearlier-release
Productivity differences in Hungary and mechanisms of TFP growth slowdown
153
The statistical population is composed of enterprises (or establishments when no data on enterprises
are available) In the case of BDI database the population contains all enterprises including non-
employers ie enterprises with no employees while the population of SBS contains only the employer
enterprises ie firms with at least one employee
Birth rate of all enterprises is the ratio of the number of enterprise births and the number of
enterprises active in the reference period Births do not include entries into the population due to
mergers break-ups the split-off or restructuring of a set of enterprises It does not include entries
into a sub-population resulting only from a change of activity (Source BDI)
Death rate of all enterprises is the ratio of the number of enterprise deaths and the number of
enterprises active in the reference period Deaths do not include exits from the population due to
mergers take-overs break-ups or the restructuring of a set of enterprises It does not include exits
from a sub-population resulting only from a change of activity An enterprise is included in the count of
deaths only if it is not reactivated within two years Equally a reactivation within two years is not
counted as a birth (Source BDI)
Number of enterprises is a count of the number of enterprises active during at least a part of the
reference period (Source SBS)
A33 OECD Productivity Frontier
The OECD productivity frontier dataset is based on AMADEUSORBIS and calculates comparable labour
productivity and TFP (MFP) measures across countries The project aims at defining the most
productive (frontier) enterprises both globally and for every country at the 2-digit industry level
(Andrews et al 2016)
Here we use data kindly provided by the OECD for the global and the Hungarian national productivity
frontier Two types of productivity measures are presented labour productivity and Wooldridge MFP
Both frontier series are defined as the average of log-productivity of the top 10 within each 2-digit
industry and year To make this measure less sensitive to expanding coverage over time the 10 is
chosen based on the median number of observations within a 2-digit industry The median for each 2
digit industry is calculated over all the years retained in the analysis
A key issue with AMADEUSORBIS with regard to Hungary is its changing coverage (see Box in Chapter
2) This makes these comparisons meaningful only from 20082009 onwards The underlying sample
includes all firms that over their observed lifespan had at least 20 employees on average
To arrive at internationally comparable real series 2-digit country specific industry value added and
investment deflators were used (2005 = 1) and the monetary values were converted to 2005 USDs
using industry level PPPs from the Groningen Growth and Development Centrersquos Productivity Level
Database112
112 For more information visit the Centrersquos homepage httpswwwrugnlggdcproductivitypld
Appendix
154
A4 Chapter 4 Evolution of the Productivity Distribution
Table A41 Average TFP growth with alternative TFP measures
A) Market economy
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 19 74 16 60
2006 93 119 95 97
2007 39 56 49 65
2008 -10 -04 -06 01
2009 -69 -82 -65 -63
2010 11 80 05 60
2011 34 40 31 45
2012 21 01 24 18
2013 30 22 22 22
2014 40 59 36 48
2015 52 49 50 43
2016 20 03 25 12
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Manufacturing
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 20 114 24 127
2006 114 149 118 137
2007 78 71 86 98
2008 17 -17 32 -11
2009 -133 -117 -120 -87
2010 80 173 85 178
2011 04 18 01 25
2012 -02 -58 07 -38
2013 -12 05 -15 16
2014 -01 27 01 34
2015 30 14 34 19
2016 04 -23 14 -05
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Productivity differences in Hungary and mechanisms of TFP growth slowdown
155
C) Market services
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 10 32 06 01
2006 79 90 82 64
2007 24 48 35 44
2008 -21 -03 -19 05
2009 -52 -71 -51 -54
2010 -11 26 -19 -05
2011 43 57 40 57
2012 30 48 31 57
2013 39 29 31 25
2014 46 78 39 55
2015 54 72 52 58
2016 25 20 29 23
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table presents growth rates of TFP estimated with the translog ACF estimator and the Fixed Effects
estimator for lsquomarket industriesrsquo (see Section 25) The sample does not include agriculture mining and financial
services Services include construction and utilities
Appendix
156
Table A42 Unweighted TFP growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 21 -02 -09 144
2006 118 143 58 47
2007 59 43 90 348
2008 -09 79 17 111
2009 -53 -191 -197 -139
2010 80 76 85 130
2011 -22 17 10 153
2012 01 14 -57 -06
2013 -38 20 -38 54
2014 -03 -05 08 33
2015 61 04 -19 132
2016 09 -10 12 91
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Market Services
Year KIS LKIS Construction Utilities
2005 127 16 -01 -46
2006 166 75 94 66
2007 13 58 60 16
2008 -16 14 -37 -28
2009 -63 -94 -15 44
2010 54 12 -08 23
2011 97 46 77 -29
2012 12 74 06 -57
2013 12 30 60 -71
2014 78 89 65 -31
2015 106 70 22 12
2016 16 31 -47 37
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the unweighted average ACF TFP growth rate by technology category (see Section 25)
Only firms with at least 5 employees The sample does not include agriculture and financial services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
157
Table A43 Employment-weighted labour productivity growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 172 32 73 300
2006 266 114 54 10
2007 121 52 69 243
2008 -25 -17 -03 126
2009 31 -151 -186 35
2010 135 114 199 207
2011 -33 -10 96 96
2012 03 -34 -32 -226
2013 -35 22 26 253
2014 33 19 53 94
2015 82 -04 -06 102
2016 34 18 08 -110
Average
2004-2007 186 66 65 184
2007-2010 47 -18 03 123
2010-2013 -21 02 24 35
2013-2016 28 14 20 85
B) Services
Year KIS LKIS Construction Utilities
2005 127 -05 41 -31
2006 166 75 21 54
2007 13 11 25 -36
2008 -16 -19 05 -02
2009 -63 -117 09 04
2010 54 -01 -05 13
2011 97 47 54 13
2012 12 62 19 -47
2013 12 21 62 -44
2014 78 55 64 -39
2015 106 54 07 65
2016 16 49 -60 43
Average
2004-2007 102 27 29 -04
2007-2010 -08 -46 03 05
2010-2013 40 48 24 -01
2013-2016 53 45 18 06
Notes This table shows the employment-weighted average LP growth rate by technology category (see Section
25) Only firms with at least 5 employees The sample does not include agriculture and financial services
Appendix
158
Table A44 The share of firms in the top decile ()
A) By size
2004 2007 2010 2013 2016
5-9 emp 1049 1051 1043 1096 1045
10-19 emp 954 962 92 904 92
20-49 emp 994 903 939 856 998
50-99 emp 896 1024 1188 1009 1096
100- emp 721 81 839 748 728
B) By ownership
2004 2007 2010 2013 2016
Domestic 833 818 814 824 837
Foreign 2344 2499 2422 2384 2488
State 554 728 81 575 695
C) By region
2004 2007 2010 2013 2016
Central HU 567 568 59 56 549
Northern
Hungary 195 116 19 208 224
Northern
Great Plain 161 178 239 23 249
Southern
Great Plain 137 118 17 258 179
Central
Transdanubia 276 33 332 369 332
Western
Transdanubia 311 283 244 361 444
Southern
Transdanubia 184 201 235 143 181
Notes Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
159
Figure A41 Persistence of top decile status
Notes This figure shows how many of top decile firms in year 2010 were frontier in 2013 how many exited and
how many continued as non-frontier The first panel shows this transition matrix for different 3-year periods
Appendix
160
A5 Chapter 5 Allocative Efficiency
Table A51 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168
C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209
D - Electricity gas
steam and AC
6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743
E - Water supply
sewerage waste
6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872
F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023
G - Wholesale and
retail trade
6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303
H - Transportation
and storage
6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985
I - Accommodation
food service
6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250
J - Information and
Communication
6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046
M - Professional
Scientific and Tech Act
6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075
N - Administrative and support service
6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
161
Table A52 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692
C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109
D - Electricity
gas steam and
AC
6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291
E - Water supply
sewerage waste
6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916
F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436
G - Wholesale and
retail trade
6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260
H - Transportation
and storage
6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776
I -
Accommodation
food service
6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899
J - Information
and
Communication
6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800
M - Professional
Scientific and
Tech Act
6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242
N - Administrative
and support service
6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
162
Table A53 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covar
iance
unweight
ed LP
weighted
LP
covar
iance
B - Mining and quarrying 7509 8072 0564 8038 8583 0546 8378 9440 1063 8609 9028 0419
C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796
D - Electricity gas steam and
AC
9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197
E - Water supply sewerage waste
8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703
F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096
G - Wholesale and retail trade 7955 8252 0297 8036 8452 0415 7955 8307 0352 8197 8589 0392
H - Transportation and
storage
8364 8475 0110 8300 8525 0224 8194 7698 -
0496
8292 7289 -
1003
I - Accommodation food
service
7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651
J - Information and Communication
8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988
M - Professional Scientific and Tech Act
8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406
N - Administrative and
support service
7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238
Productivity differences in Hungary and mechanisms of TFP growth slowdown
163
Table A54 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance
B - Mining and
quarrying
7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358
C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960
D - Electricity gas
steam and AC
9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289
E - Water supply
sewerage waste
8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064
F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159
G - Wholesale and
retail trade
7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037
H - Transportation
and storage
8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022
I - Accommodation
food service
7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363
J - Information and
Communication
7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263
M - Professional
Scientific and Tech
Act
7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431
N - Administrative
and support service
7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216
Appendix
164
Table A55 Allocative efficiency based on Hsieh-Klenow (2009) ndash 1 digit industries
Distortions in 2001 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4802 1540 0299 0803 19127 1152
C - Manufacturing 5620 1300 0425 0818 12807 1008
D - Electricity gas steam and AC 6760 0503 0591 0456 6171 0784
E - Water supply sewerage waste 6629 0599 0103 1127 6245 1248
F - Construction 6706 0818 0280 0954 21186 1227
G - Wholesale and retail trade 7225 1088 0395 1007 21997 1211
H - Transportation and storage 6073 0984 -0154 1647 15193 1144
I - Accommodation food service 6201 0684 -0025 0919 7951 1263
J - Information and Communication 5499 1273 0549 0603 5387 1265
M - Professional Scientific and Tech Act 6961 0920 0253 1062 45052 1293
N - Administrative and support service 6778 1237 0084 1020 42372 1546
Productivity differences in Hungary and mechanisms of TFP growth slowdown
165
Table A55- continuedhellip
Distortions in 2005 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4211 1121 0269 0669 12217 0953
C - Manufacturing 5919 1173 0497 0890 13439 0998
D - Electricity gas steam and AC 6569 0880 0596 0553 6400 1181
E - Water supply sewerage waste 6433 0722 0091 1277 9084 1126
F - Construction 6794 0744 0155 0947 20440 1099
G - Wholesale and retail trade 7497 1199 0392 0771 20492 1543
H - Transportation and storage 6305 1063 0017 1205 11362 1232
I - Accommodation food service 6085 0660 0098 1287 5680 1239
J - Information and Communication 5867 1337 0608 0637 6375 1481
M - Professional Scientific and Tech Act 6926 0951 0129 1118 50400 1474
N - Administrative and support service 6904 1206 -0004 1055 47387 1649
Appendix
166
Table A55- continuedhellip
Distortions in 2010 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales
taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4219 0669 -0104 0759 11170 1012
C - Manufacturing 6024 1201 0523 0740 12732 1001
D - Electricity gas steam and AC 7260 1273 0813 0433 12091 1565
E - Water supply sewerage waste 6474 0700 0123 0965 13717 1279
F - Construction 6621 0775 0200 1075 30395 1437
G - Wholesale and retail trade 7471 1230 0310 0842 22833 1527
H - Transportation and storage 6517 1250 0123 1030 9632 1459
I - Accommodation food service 6080 0704 0001 1060 5570 1341
J - Information and Communication 5989 1245 0581 0870 11895 1572
M - Professional Scientific and Tech Act 7076 1042 0130 1077 78642 1486
Productivity differences in Hungary and mechanisms of TFP growth slowdown
167
Table A55- continuedhellip
Distortions in 2016 Productivity Productivity dispersion
Median implicit sales
taxes
Dispersion of implicit sales
taxes
Median implicit cost
of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4484 0705 0264 0601 13655 0812
C - Manufacturing 6022 1110 0514 0971 11130 1074
D - Electricity gas steam and AC 7341 0966 0724 0307 36231 2054
E - Water supply sewerage waste 6363 0763 0015 1134 15926 1399
F - Construction 6938 0809 0298 0868 28761 1453
G - Wholesale and retail trade 7511 1005 0312 0959 26886 1576
H - Transportation and storage 6656 0972 0104 1078 16755 1745
I - Accommodation food service 6492 0672 0163 0943 6439 1443
J - Information and Communication 6211 1165 0422 0747 23648 1609
M - Professional Scientific and Tech Act 7188 0956 0148 1223 72383 1567
N - Administrative and support service 7112 1219 -0081 1109 98641 1801
Notes Total factor productivity is measured by the method of Ackerberg et al (2015) See Chapter 52 for details
Appendix
168
Appendix Figure 51 Weighted and unweighted labour productivity by 2-digit industry 2016 firms with at least 5 employees
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted log labour productivity in 2016 while the horizontal axis shows its
weighted log labour productivity in the same year We have omitted industries with less than 1000 observations
Productivity differences in Hungary and mechanisms of TFP growth slowdown
169
Appendix Figure 52 The relationship between weighted and unweighted labour productivity by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted labour productivity levels run at the 2-digit industry level
separately for 2005 2010 and 2016
Appendix
170
Appendix Figure 53 the change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences between the weighted and unweighted
labour productivity) in 2010 while the vertical axis shows the same quantity in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
171
A6 Chapter 6 Reallocation
Table A61 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -93 -38 10 -65 -02 -10 50 -43
C Manufacturing 108 23 48 36 -02 -11 03 05
D Electricity gas 08 07 05 -04 26 -06 22 10
E Water supply sewerage 17 -17 31 03 08 -09 09 09
F Construction 26 04 08 13 -14 -02 -19 07
G Wholesale and retail trade 30 03 11 16 -55 -08 -65 18
H Transportation and storage -21 14 -43 08 -39 10 -57 08
I Accommodation 68 -07 53 22 -44 00 -37 -07
J ICT 96 10 63 23 29 -24 35 18
M Professional scientific 39 -13 35 17 -38 -04 -26 -08
N Administrative and support 104 11 37 56 -49 -02 -04 -43
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying 08 11 09 -12 41 60 -30 10
C Manufacturing -18 07 -30 05 10 -08 22 -04
D Electricity gas -26 26 -70 18 74 07 31 36
E Water supply sewerage -08 15 -05 -18 -04 05 00 -09
F Construction 42 03 26 13 01 06 -16 11
G Wholesale and retail trade 54 01 32 21 68 04 56 07
H Transportation and storage 89 14 51 25 21 -30 06 45
I Accommodation 85 -05 59 32 51 -03 46 08
J ICT 19 -02 11 10 47 -02 38 11
M Professional scientific 69 05 12 53 30 -04 18 16
N Administrative and support 50 00 36 14 106 01 87 18
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
172
Table A62 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -253 -59 -19 -175 73 -02 39 36
C Manufacturing 105 20 51 34 06 -14 03 16
D Electricity gas 06 09 03 -06 14 -14 23 05
E Water supply sewerage 21 -12 32 02 -06 -09 00 03
F Construction 35 07 12 16 -23 -03 -24 04
G Wholesale and retail trade 27 06 06 16 -39 03 -59 17
H Transportation and storage -34 17 -58 06 -33 14 -58 11
I Accommodation 67 -09 50 26 -42 03 -39 -05
J ICT 85 14 39 32 27 -14 21 19
M Professional scientific 46 -07 28 25 -28 -03 -22 -03
N Administrative and support 122 29 28 65 -49 00 -09 -40
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -08 11 07 -26 24 -03 06 22
C Manufacturing -12 06 -25 07 06 -04 11 -01
D Electricity gas 16 16 -15 15 30 00 25 06
E Water supply sewerage -07 15 -02 -20 -14 -01 02 -15
F Construction 45 03 22 20 10 02 -05 12
G Wholesale and retail trade 45 02 21 22 68 04 53 10
H Transportation and storage 85 13 45 27 75 00 08 66
I Accommodation 81 -04 54 30 51 -02 42 11
J ICT 13 00 00 13 49 10 33 06
M Professional scientific 64 08 11 45 32 00 14 18
N Administrative and support 50 08 15 27 80 19 49 12
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
173
Table A63 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 93 24 44 26 105 12 59 34
C Manufacturing 132 34 54 44 08 19 -12 01
D Electricity gas 13 -04 09 08 41 02 25 14
E Water supply sewerage 45 -02 37 09 -08 -09 04 -03
F Construction 24 07 10 07 -01 07 -09 01
G Wholesale and retail trade 38 08 12 18 -67 -04 -73 10
H Transportation and storage -25 06 -28 -04 -47 03 -56 06
I Accommodation 59 -03 56 07 -74 -12 -41 -21
J ICT 58 19 80 -40 20 -21 40 00
M Professional scientific 61 11 34 16 -67 02 -26 -43
N Administrative and support 61 -20 38 43 -63 -24 -11 -29
2010-2013 2013-2016
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 26 04 -01 24 -29 17 -28 -18
C Manufacturing 00 14 -21 07 33 16 19 -01
D Electricity gas -43 26 -85 16 90 25 13 52
E Water supply sewerage -20 -07 -08 -05 04 -03 06 01
F Construction 40 05 26 10 -03 05 -05 -03
G Wholesale and retail trade 49 05 31 13 68 13 57 -01
H Transportation and storage 59 12 46 01 09 -27 15 20
I Accommodation 74 -07 55 26 47 -08 54 02
J ICT 16 -07 11 12 22 -25 42 05
M Professional scientific 70 20 16 33 45 13 25 06
N Administrative and support 61 08 31 21 81 -11 76 15
Appendix
174
Table A64 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 48 15 -01 34 70 17 53 00
C Manufacturing 132 32 56 45 16 14 -03 05
D Electricity gas 14 -03 05 11 35 -07 30 12
E Water supply sewerage 48 00 39 09 -10 -06 03 -07
F Construction 28 10 14 04 03 06 -14 11
G Wholesale and retail trade 38 10 07 21 -47 07 -61 07
H Transportation and storage -35 09 -40 -04 -41 06 -57 10
I Accommodation 62 -03 52 12 -65 -09 -43 -13
J ICT 00 -15 49 -34 03 -22 14 12
M Professional scientific 75 20 27 28 -46 02 -27 -22
N Administrative and support 91 -05 25 71 -60 -11 -07 -42
2010-2013 2013-2016
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 33 -11 04 40 50 28 05 17
C Manufacturing 06 13 -15 07 28 12 16 00
D Electricity gas 16 18 -26 25 23 17 02 05
E Water supply sewerage -17 -06 -05 -05 04 -04 08 00
F Construction 44 05 26 14 03 02 07 -07
G Wholesale and retail trade 37 05 17 15 65 12 54 -01
H Transportation and storage 56 11 42 04 46 -07 16 36
I Accommodation 70 -07 52 25 44 -07 51 01
J ICT 26 07 04 16 17 -20 37 00
M Professional scientific 56 17 11 28 52 16 23 13
N Administrative and support 65 17 27 22 59 06 41 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
175
A7 Chapter 7 Firm-level productivity growth and dynamics
A71 Productivity growth
Table A71 Relationship between lagged productivity level and subsequent productivity
growth over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 -0188 -0203 -0203
(000550) (000558) (000551)
TFP in t-1 Year 2006 -0222 -0238 -0235
(000518) (000525) (000519)
TFP in t-1 Year 2009 -0143 -0159 -0155
(000570) (000579) (000572)
TFP in t-1 Year 2012 -0156 -0172 -0171
(000516) (000524) (000517)
Year 2003 -00313 -00297
(000507) (000510)
Year 2006 -0184 -0183
(000489) (000491)
Year 2009 -00766 -00762
(000492) (000493)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 114200 113900 113900
R-squared 0061 0067 0084
Appendix
176
Table A72 Relationship between lagged productivity levels and subsequent productivity
growth by size and age
Dep var TFP growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 -0170 -0186 -0213 -0223
(000561) (000578) (00155) (00155)
TFP in t-1 Group 2 00397 00243 -000502 -000776
(00146) (00147) (00213) (00213)
TFP in t-1 Group 3 00793 00652 00725 00600
(00221) (00222) (00164) (00165)
TFP in t-1 Group 4 00753 00666
(00244) (00247)
Group 2 00227 000593 -0000410 0000118
(000940) (000963) (00162) (00162)
Group 3 00216 -000934 00235 00220
(00150) (00154) (00131) (00132)
Group 4 00235 -00351
(00157) (00169)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Observations 30135 30062 30135 30062
R-squared 0056 0073 0056 0073
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees size group 4 is
100+ employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5
years age group 3 is firms older than 5 The baseline category is firms of 2-3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
177
Table A73 Differences in productivity growth by ownership group within different firm
groups
Dep var TFP growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 00476
(00114)
Foreign Non-exporter 00573
(00213)
Foreign Exporter 00610
(00139)
Foreign Size group 1 00295
(00162)
Foreign Size group 2 00849
(00243)
Foreign Size group 3 000361
(00340)
Foreign Size group 4 00662
(00318)
Foreign Age group 1 0119
(00381)
Foreign Age group 2 -00117
(00363)
Foreign Age group 3 00467
(00124)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 31642 31642 31642 31274
R-squared 0032 0033 0033 0033
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column 2 and size and age group dummies in columns 3 and 4 respectively
Appendix
178
Table A74 Relationship between lagged productivity levels and subsequent productivity
growth by ownership and exporter status over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
Firm categories by
foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003
00577 00607 00141 00214
(00151) (00151) (00124) (00124)
TFP in t-1 Firm group Year 2006
00703 0101 00361 00558
(00152) (00152) (00118) (00118)
TFP in t-1 Firm group Year 2009
00338 00306 00450 00406
(00153) (00153) (00122) (00121)
TFP in t-1 Firm group Year 2012
00758 00436 00474 00321
(00146) (00146) (00109) (00109)
Firm group Year 2003
00978 00756 00286 000961
(00128) (00130) (000912) (000977)
Firm group Year 2006
-00290 -00145 -00592 -00411
(00133) (00135) (000871) (000932)
Firm group Year 2009
0114 0116 00502 00457
(00124) (00127) (000824) (000881)
Firm group Year 2012
0120 0120 00234 00155
(00126) (00129) (000782) (000835)
Year FE YES YES
Industry FE YES YES
Firm-level controls
YES YES YES YES
Region FE YES YES
Industry-year FE
YES YES
Observations 112374 112374 113900 113900
R-squared 0066 0085 0065 0085
Notes Firm group refers to foreign ownership in columns (1) and (2) and to exporter status in
columns (3) and (4)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
179
A72 Employment growth
Table A75 Relationship between lagged productivity levels and subsequent employment
growth over time
Dep var employment growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0113 0113 0113
(000472) (000478) (000475)
TFP in t-1 Year 2006 0120 0120 0119
(000434) (000439) (000437)
TFP in t-1 Year 2009 0109 0109 0107
(000479) (000485) (000482)
TFP in t-1 Year 2012 00982 00958 00956
(000442) (000448) (000445)
Year 2003 -00171 -00125
(000441) (000444)
Year 2006 -0134 -0128
(000422) (000423)
Year 2009 -00899 -00873
(000425) (000426)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 123900 123574 123574
R-squared 0042 0049 0054
Appendix
180
Table A76 Relationship between lagged productivity levels and subsequent employment
growth over time with alternative employment growth measures including exiting firms
Dep var employment growth from t to t+3 (including exiting firms (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0156 0147 0148
(000641) (000647) (000644)
TFP in t-1 Year 2006 0134 0127 0128
(000581) (000587) (000584)
TFP in t-1 Year 2009 0139 0132 0134
(000648) (000655) (000651)
TFP in t-1 Year 2012 0132 0126 0127
(000618) (000624) (000621)
Year 2003 -00765 -00618
(000617) (000619)
Year 2006 -0220 -0211
(000586) (000587)
Year 2009 -0177 -0173
(000591) (000590)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 143011 142638 142638
R-squared 0037 0047 0051
Productivity differences in Hungary and mechanisms of TFP growth slowdown
181
Table A77 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status with alternative employment growth measures
including exiting firms
Dep var employment growth from t to t+3 (including exiting firms t=2012)
VARIABLES (1) (2) (3) (4) (5) (6)
TFP in t-1 0134 0130 0134 0137 0134 0136
(000651) (000660) (000722) (000729) (000764) (000767)
TFP in t-1 Foreign -00109 -00138 00116 000347
(00166) (00167) (00289) (00289)
TFP in t-1 Exporter -00371 -00256 -00304 -00226
(00124) (00126) (00148) (00148)
TFP in t-1 Foreign exporter -00222 -00165
(00364) (00365)
Foreign -00254 -00351 -0102 -00739
(00151) (00156) (00254) (00256)
Exporter 00998 00982 00940 00889
(00100) (00102) (00106) (00107)
Foreign exporter 00855 00605
(00312) (00315)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 34980 34980 35564 35473 34980 34980
R-squared 0031 0051 0037 0054 0034 0052
Appendix
182
Table A78 Differences in employment growth by exporter status within different firm
groups
Dep var employment growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Exporter 00876
(000741)
Exporter Domestic 00893
(000788)
Exporter Foreign 00703
(00207)
Exporter Size group 1 00858
(000850)
Exporter Size group 2 00872
(00159)
Exporter Size group 3 0154
(00276)
Exporter Size group 4 00345
(00329)
Exporter Age group 1 00968
(00230)
Exporter Age group 2 0139
(00212)
Exporter Age group 3 00810
(000801)
industry-region FE YES YES YES YES
Firm-group indicators YES YES YES
Observations 34418 33909 34418 33989
R-squared 0034 0034 0034 0036
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
183
Table A79 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status over time
Dep var Employment growth from t to t+3 (t=2003200620092012)
Firm categories by foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003 000927 00131 00178 00190
(00129) (00130) (00107) (00107)
TFP in t-1 Firm group Year 2006 00137 00103 00130 000821
(00126) (00127) (00101) (00101)
TFP in t-1 Firm group Year 2009 -00778 -00676 -00498 -00426
(00129) (00130) (00104) (00104)
TFP in t-1 Firm group Year 2012 -00389 -00321 -00350 -00306
(00126) (00126) (000942) (000942)
Firm group Year 2003 -00601 -00332 000244 00299
(00110) (00113) (000795) (000856)
Firm group Year 2006 -00159 -000559 00640 00786
(00112) (00115) (000752) (000807)
Firm group Year 2009 00404 00249 0111 00882
(00106) (00109) (000714) (000767)
Firm group Year 2012 -00102 -00116 00747 00607
(00110) (00112) (000684) (000735)
Year FE YES YES
Industry FE YES YES
Firm-level controls YES YES YES YES
Region FE YES YES
Industry-year FE YES YES
Observations 121954 121954 123574 123574
R-squared 0046 0055 0045 0055
Notes Firm group refers to foreign ownership in columns (1) and (2) and exporter status in columns
(3) and (4)
Appendix
184
A73 Entry and exit
Table A710 Entry and exit premium by ownership and exporter status
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
VARIABLES (1) (2) (3) (4) (5) (6)
Entry Domestic 00363 00433 Exit Domestic -0165 -0161 Exit Non-exporter
-0172 -0186
(00103) (00102) (00112) (00112) (00122) (00121)
Entry Foreign 0414 0354 Exit Foreign 0255 0203 Exit Exporter
0171 0126
(00284) (00281) (00311) (00309) (00213) (00211)
Incumbent Foreign
0512 0461 Continuing Foreign
0465 0411 Continuing Exporter
0279 0232
(00122) (00129) (00123) (00131) (000887) (000926)
Industry FE YES Industry FE YES Industry FE YES
Industry-region FE YES Industry-region FE
YES Industry-region FE
YES
Firm-level controls YES Firm-level controls
YES Firm-level controls
YES
Observations 44231 44231 Observations 38367 38367 Observations 39020 38916
R-squared 0355 0383 R-squared 0339 0369 R-squared 0331 0370
Table A711 Differences in productivity levels by ownership group within different firm
groups
Depvar TFP in year t (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 0429
(00118)
Foreign Non-exporter 0278
(00206)
Foreign Exporter 0397
(00146)
Foreign Size group 1 0523
(00162)
Foreign Size group 2 0472
(00254)
Foreign Size group 3 0416
(00363)
Foreign Size group 4 0235
(00341)
Foreign Age group 1 0258
(00352)
Foreign Age group 2 0381
(00356)
Foreign Age group 3 0460
(00131)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 38367 38367 38367 37822
R-squared 0350 0361 0353 0356
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
185
Table A712 Entry and exit premium by ownership and exporter status over time
Depvar TFP in year t (t=2006200920122015 for entry and t=2003200620092012 for exit)
VARIABLES (1) (2) VARIABLES (3) (4) VARIABLES (5) (6)
Entry Domestic Year 2006
-00510 -00403 Exit Domestic 2003 -0187 -0188 Exit Non-exporter 2003 -0197 -0198
(000924) (000923) (00107) (00106) (00114) (00113)
Entry Domestic Year 2009
00244 00230 Exit Domestic 2006 -00996 -0101 Exit Non-exporter 2006 -0114 -0118
(000999) (000996) (000917) (000911) (000977) (000971)
Entry Domestic Year 2012
00594 00515 Exit Domestic 2009 -0105 -0113 Exit Non-exporter 2009 -0116 -0123
(000985) (000983) (000942) (000937) (00101) (00101)
Entry Domestic Year 2015
00475 00392 Exit Domestic 2012 -0140 -0150 Exit Non-exporter 2012 -0167 -0174
(000998) (000999) (00111) (00110) (00119) (00119)
Entry Foreign Year 2006
0374 0313 Exit Foreign 2003 0116 00940 Exit Exporter 2003 00659 00517
(00265) (00264) (00264) (00263) (00196) (00197)
Entry Foreign Year 2009
0423 0410 Exit Foreign 2006 0199 0153 Exit Exporter 2006 0194 0165
(00257) (00257) (00267) (00265) (00183) (00183)
Entry Foreign Year 2012
0342 0334 Exit Foreign 2009 0197 0184 Exit Exporter 2009 00720 00760
(00279) (00278) (00278) (00277) (00185) (00185)
Entry Foreign Year 2015
0382 0365 Exit Foreign 2012 0217 0223 Exit Exporter 2012 0114 0137
(00276) (00275) (00307) (00305) (00208) (00208)
Incumbent Foreign Year 2006
0485 0428 Continuing Foreign 2003 0416 0386 Continuing Exporter 2003 0278 0257
(00122) (00124) (00124) (00126) (000943) (000994)
Incumbent Foreign Year 2009
0410 0391 Continuing Foreign 2006 0498 0446 Continuing Exporter 2006 0317 0280
(00120) (00122) (00122) (00124) (000895) (000943)
Incumbent Foreign Year 2012
0436 0439 Continuing Foreign 2009 0414 0404 Continuing Exporter 2009 0194 0201
(00122) (00124) (00119) (00122) (000867) (000915)
Incumbent Foreign Year 2015
0471 0476 Continuing Foreign 2012 0412 0422 Continuing Exporter 2012 0211 0239
(00118) (00120) (00120) (00122) (000827) (000876)
Year FE YES Year FE YES Year FE YES
Industry FE YES Industry FE YES Industry FE YES
Firm-level controls YES YES Firm-level controls YES YES Firm-level controls YES YES
Industry-year FE YES Industry-year FE YES Industry-year FE YES
Region FE YES Region FE YES Region FE YES
Observations 164136 164136 Observations 155657 155657 Observations 157711 157711
R-squared 0369 0380 R-squared 0373 0386 R-squared 0374 0387
Table A713 Entry and exit premium by size and age
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
Firm categories by size age
VARIABLES (1) (2) VARIABLES (3) (4) (5) (6)
Entry Group 1 00233 00151 Exit Group 1 -0170 -0171 -0214 -0210
(00108) (00105) (00121) (00118) (00250) (00241)
Entry Group 2 0106 000987 Exit Group 2 -0201 -0260 -0286 -0260
(00298) (00289) (00280) (00272) (00271) (00261)
Entry Group 3 0124 00204 Exit Group 3 -0152 -0245 -0219 -0207
(00574) (00556) (00479) (00464) (00179) (00173)
Entry Group 4 0123 -00552 Exit Group 4 -0291 -0453
(00720) (00697) (00532) (00517)
Incumbent Group 2 00137 -00620 Continuing Group 2 -00108 -00902 -00277 -00256
(00104) (00101) (00111) (00109) (00170) (00164)
Incumbent Group 3 00163 -0130 Continuing Group 3 000582 -0148 -00759 -00758
(00170) (00168) (00179) (00176) (00131) (00127)
Incumbent Group 4 00150 -0268 Continuing Group 4 -00159 -0293
(00181) (00185) (00188) (00192)
Industry FE YES Industry FE YES YES
Industry-region FE YES Industry-region FE YES YES
Firm-level controls YES Firm-level controls YES YES
Observations 46160 46034 39020 38916 38459 38357
R-squared 0296 0355 0311 0369 0315 0374
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is 50-99
employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is firms of 4-5
years age group 3 is firms older than 5 years
Figure A71 Share of exiting firms in the subsequent 3 years by lagged productivity levels in
different periods
A8 Chapter 8 Retail
Appendix Table A81 Event study regression for the whole retail industry
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 0005 0014 0012 0030 -0230 -0511 (0006) (0005) (0005) (0005) (0030) (0049)
pre_trend_treated3 -0010 -0003 -0008 -0003 -0037 -0031 (0006) (0008) (0006) (0008) (0030) (0056)
pre_trend_treated4 -0020 -0010 -0014 0000 -0209 -0299 (0006) (0007) (0006) (0007) (0029) (0051)
pre_trend_treated5 0004 0011 0006 0017 -0128 -0258 (0007) (0008) (0007) (0008) (0034) (0064)
pre_trend_treated6 -0008 0001 -0004 0008 -0129 -0222 (0007) (0008) (0006) (0008) (0035) (0065)
pre_trend_treated7 0001 -0001 0016 0017 -0365 -0496 (0010) (0013) (0010) (0012) (0053) (0072)
trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)
trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)
trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)
trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)
post_trend_treated1 -0039 -0006 -0007 0044 -0885 -1394 (0012) (0012) (0012) (0011) (0061) (0096)
post_trend_treated2 0022 0003 0044 0048 -0665 -1273 (0012) (0012) (0011) (0011) (0068) (0100)
post_trend_treated3 -0001 0004 0035 0058 -0993 -1531 (0012) (0012) (0012) (0012) (0058) (0092)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 225866 209604 225860 209598 225908 209647
R-squared 0958 0978 0961 0980 0684 0809
Appendix
188
Appendix Table A82 Event study regression for NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 -0008 -0004 -0002 0016 -0189 -0576 (0004) (0005) (0004) (0005) (0033) (0055)
pre_trend_treated3 -0016 -0018 -0013 -0014 -0057 -0064 (0006) (0011) (0006) (0010) (0034) (0059)
pre_trend_treated4 -0010 -0005 -0002 0008 -0236 -0351 (0004) (0007) (0004) (0007) (0034) (0060)
pre_trend_treated5 -0004 0002 -0001 0010 -0129 -0304 (0006) (0008) (0006) (0008) (0037) (0068)
pre_trend_treated6 0011 0000 0018 0011 -0173 -0307 (0007) (0009) (0007) (0009) (0045) (0085)
pre_trend_treated7 -0016 -0032 0002 -0007 -0433 -0640 (0010) (0016) (0009) (0015) (0068) (0091)
trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)
trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)
trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)
trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)
post_trend_treated1 -0009 -0032 0033 0036 -1163 -1875 (0008) (0012) (0008) (0011) (0084) (0118)
post_trend_treated2 0057 -0024 0087 0041 -0888 -1810 (0014) (0013) (0012) (0012) (0097) (0121)
post_trend_treated3 0014 -0031 0060 0044 -1255 -2040 (0011) (0013) (0010) (0012) (0079) (0108)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 94740 87533 94737 87530 94740 87533
R-squared 0968 0982 0973 0985 0642 0809
Appendix Table A83 Sales and the number of different days in a month
(1)
Dependent ln sales
Sunday 0049 (0001)
Saturday 0059 (0001)
Friday 0054 (0001)
Thursday 0050 (0001)
Wednesday 0053 (0001)
Tuesday 0060 (0001)
Monday 0048 (0001)
holiday 0008 (0000)
Jan -0169 (0002)
Dec 0138 (0003)
summer 0032 (0002)
date -0000 (0000)
Observations 463345
R-squared 0970
Appendix
190
HOW TO OBTAIN EU PUBLICATIONS
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(httpeeaseuropaeudelegationsindex_enhtm)
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doi 10287333213
ET-0
4-1
7-8
33-E
N-N
EUROPEAN COMMISSION
Directorate-General for Internal Market Industry Entrepreneurship and SMEs
2018
PRODUCTIVITY DIFFERENCES
IN HUNGARY AND
MECHANISMS OF TFP GROWTH
SLOWDOWN
LEGAL NOTICE
This document has been prepared for the European Commission however it reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein
More information on the European Union is available on the Internet (httpwwweuropaeu)
Luxembourg Publications Office of the European Union 2018
ISBN 978-92-79-73462-5 doi 10287333213
copy European Union 2018
Reproduction is authorised provided the source is acknowledged
Europe Direct is a service to help you find answers
to your questions about the European Union
Freephone number ()
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
Table of contents
EXECUTIVE SUMMARY I
1 INTRODUCTION 1
2 DATA SOURCES 4
21 Cleaning the data and defining industry categories 5
22 Productivity estimation 6
23 Estimation sample 10
24 Firm-level variables 13
25 Industry categorization 16
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON 18
31 Convergence 18
32 Within-industry heterogeneity 24
33 Firm dynamics 28
34 Conclusions 31
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION 32
41 Context 32
42 Aggregate productivity and the self-employed 33
43 The evolution of productivity distribution in Hungary 36
44 Duality in productivity and productivity growth 47
45 Conclusions 56
5 ALLOCATIVE EFFICIENCY 58
51 Olley-Pakes efficiency 58
52 Product market and capital market distortions 62
53 Conclusions 70
6 REALLOCATION 73
61 Reallocation across industries 73
62 Reallocation across firms 76
63 Failure of reallocation Zombie firms 82
64 Conclusions 86
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS 89
71 Productivity growth 89
72 Employment growth 95
73 Entry and exit 99
74 Conclusions 103
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE 104
81 Context 104
82 Data 108
83 General trends 110
84 Allocative efficiency and reallocation 118
85 Trade 123
86 Policies Crisis taxes 131
87 Policies Mandatory Sunday closing 133
88 Conclusions 141
9 CONCLUSIONS 142
REFERENCES 144
APPENDIX 152
A3 Chapter 3 Internationally comparable data sources and methodology 152
A31 EU KLEMS amp OECD STAN 152
A32 OECD Structural and Demographic Business Statistics 152
A33 OECD Productivity Frontier 153
A4 Chapter 4 Evolution of the Productivity Distribution 154
A5 Chapter 5 Allocative Efficiency 160
A6 Chapter 6 Reallocation 171
A7 Chapter 7 Firm-level productivity growth and dynamics 175
A71 Productivity growth 175
A72 Employment growth 179
A73 Entry and exit 184
A8 Chapter 8 Retail 187
Productivity differences in Hungary and mechanisms of TFP growth slowdown
i
EXECUTIVE SUMMARY
Slow post-crisis total factor productivity (hereafter TFP) growth is a significant policy
challenge for many European countries in general and for Hungary in particular This
report aims at providing a comprehensive analysis of the processes behind productivity
growth slowdown in Hungary based on micro-data from administrative sources between
2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions A focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact detail of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Executive Summary
ii
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides an
exhaustive picture of Hungarian businesses It is important to keep in mind though that it
omits two important parts of the economy the overwhelming majority of the non-market
sector (including public works) and the self-employed Given the number of people
employed in these two sectors their performance has a strong effect on macro numbers
The available albeit scarce data for the self-employed qualify the findings by suggesting
that the measured productivity level and growth of this group is considerably below than
that of double-entry bookkeeping firms ndash implying that within-industry productivity
dispersion may be even larger than what is indicated by the balance sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
of capital increase on average its rise was disproportionally larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
Productivity differences in Hungary and mechanisms of TFP growth slowdown
iii
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly adding little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterized by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers have increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
The results of this report confirm that Hungary is atypical because of the relatively poor
productivity performance of frontier firms Importantly contrary to a strong version of the
duality concept this is not a result of Hungarian frontier firms being on the global frontier
typically they are quite far away from it This robust pattern underlines that besides
helping non-frontier firms policy may also have to focus on the performance of the
frontier group A transparent environment with a strong rule of law complemented by a
well-educated workforce and a robust innovation system is key for providing incentives to
invest into the most advanced technologies
Executive Summary
iv
The analysis in this report reinforces the impression that there is a large productivity gap
between globally engaged or owned and other firms the gap being about 35 percent in
manufacturing and above 60 percent in services This gap seems to be roughly constant in
the period under study The firm-level analysis in Chapter 7 also reveals that one of the
mechanisms which conserves the gap is that foreign frontier firms are able to increase
their productivity more than their domestic counterparts even from frontier levels These
findings reinforce the importance of well-designed policies that are able to help domestic
firms to catch up with foreign firms A key precondition for domestic firms to build linkages
with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A
more specific conclusion is the importance of enabling high-productivity domestic firms to
improve their productivity levels even further
The large within-industry productivity dispersion the relatively low (though not extreme
in international comparison) allocative efficiency documented in some of the industries the
strong positive contribution of reallocation to total TFP growth before the crisis and the
relatively low entry rate imply that policies promoting reallocation have a potential to
increase aggregate productivity levels significantly These policies can include improving
the general framework conditions by cutting administrative costs reducing entry and exit
barriers and using a neutral regulation The fact that capital market distortions still appear
to be significantly above their pre-crisis levels impliesthat policies that reduce financial
frictions may help the reallocation process The fact that exporters tend to expand faster
relative to non-exporters suggests that access to EU and global markets generate a strong
and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general
traded and more knowledge-intensive sectors fared better both in terms of productivity
growth and allocative efficiency The difference between traded and non-traded sectors
points once again to the importance of global competition in promoting higher productivity
and more efficient allocation of resources This also implies that adopting policies that
focus on innovation or reallocation in services may be especially important given the large
number of people working in those sectors The better performance of and reallocation into
more knowledge-intensive sectors underline the importance of education policies aimed at
developing up-to-date and flexible skills and the significance of innovation policies that
help to improve the knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people
working at firms with at least a few employees and those working in very small firms or as
self-employed The latter category represents 30-50 percent of the people engaged in
some important industries Inclusive policies may attempt to generate supportive
conditions for these people by providing knowledge and training as well as helping them
find jobs with wider perspectives or set up a well-operating firm The large share of these
unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a marked difference between the pre-
crisis period characterised by strong reallocation mainly via the expansion of large
foreign-owned chains and the post-crisis period with a stagnating share of large chains
This break is likely to be linked to post-crisis policies favouring smaller firms While halting
further concentration in a country with already one of the highest share of multinationals
in this sector can have a number of benefits in the long run it is likely to lead to higher
prices and lower industry-level productivity growth Policies should balance carefully
between these trade-offs Another key pattern identified is the increasing role of retailers
(and wholesalers) in trade intermediation both on the import and export side
Policymakers should encourage these trends and design policies which provide capabilities
for such firms to enter international markets probably via e-commerce
Productivity differences in Hungary and mechanisms of TFP growth slowdown
1
1 INTRODUCTION
Slow post-crisis TFP growth is a significant policy challenge for many European countries in
general and for Hungary in particular This report aims at providing a comprehensive
analysis of the processes behind productivity growth slowdown in Hungary based on
micro-data from administrative sources between 2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions The focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact details of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Introduction
2
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides a very
detailed and comprehensive picture of the Hungarian business economy It is important to
keep in mind though that it omits two important parts of the economy the overwhelming
majority of the non-market sector (including public works) and the self-employed Given
the number of people employed in these two sectors their performance has a strong effect
on macro numbers The available albeit scarce data for the self-employed qualify the
findings by suggesting that the measured productivity levels and growth of this group are
considerably below those of double-entry bookkeeping firms ndash implying that within-
industry productivity dispersion may even be larger than what is indicated by the balance
sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries the frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
Productivity differences in Hungary and mechanisms of TFP growth slowdown
3
of capital increase on average its rise was disproportionately larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly contributing little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterised by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
Data Sources
4
BOX 21 AMADEUS and the NAV balance sheet data
An alternative and frequently used source of balance sheet data is the AMADEUS dataset
In this box we compare the data about Hungary with the dataset used in this report
namely the administrative NAV panel
AMADEUS is a firm-level dataset collected and issued by Bureau Van Dijk a Moodyrsquos
Analytics Company It contains comprehensive financial information on around 21 million
companies across Europe with a focus on private company information It includes
information about company financials in a standard format (which makes it comparable
across countries) directors stock prices and detailed corporate ownership structures
(Global Ultimate Owners and subsidiaries) Financial information on firms consists of data
from balance sheets profit and loss statements and standard ratios Non-mandatory cells
are however often missing (eg employment) Therefore the drawbacks of this
database are that it is not representative and that not all firms provide enough
information to analyse issues such as productivity or TFP
Table B21 shows the coverage of AMADEUS (the number of firms as a share of the firms
in the administrative NAV data) by year and size category In earlier years the AMADEUS
sample consisted of mostly large firms but even the coverage of larger firms was
relatively low Recently the expanding coverage has made the AMADEUS sample more
representative While the smallest firms are still undersampled the coverage of firms with
more than 5 employees has reached nearly 100 (In some cases it is even above 100
because of slight differences in the number of employees reported)
The two databases also differ in terms of the variables they include The NAV data are
more detailed in terms of assets and liabilities AMADEUS in contrast provides more
information on ownership It defines the Global Ultimate Owner (GUO) for each company
and analyses their shareholding structure Ownership share is given in percentages and
in addition the degree of independence is also given
Our main aim in this report is to estimate productivity and its change reliably and
representatively for different types of firms small and large This requires a decent
coverage of all types of firms and reliable information on their finances for a number of
periods Because of this we prefer to use the NAV database with its large and universal
coverage and the rich information on firm inputs and outputs
2 DATA SOURCES
The main database we use in this project is the balance sheet panel of Hungarian firms
between 2000-2016 The balance sheet dataset is an administrative panel collected by the
National Tax Authority (NAV formerly APEH) from corporate tax declarations The
database includes the balance sheet and profit amp loss statements of all double-entry
bookkeeping Hungarian enterprises between 2000 and 2016 (see Section 42 for a brief
discussion of the size and the performance of the not double-entry bookkeeping sector of
the Hungarian economy) Besides key financial variables the database includes the
industry code of the firm the number of its employees its date of foundation the location
of its headquarters and whether it is domestically- or foreign-owned for each year
Productivity differences in Hungary and mechanisms of TFP growth slowdown
5
21 Cleaning the data and defining industry categories
We have taken a number of steps to clean the key variables in the balance sheet panel
First we impute missing observations for firms with more than 10 employees in the
preceding and following years For continuous variables we use the average of the
previous and following year values For categorical variables we use the value from the
previous year Similarly we impute missing data using lagged values for two of the largest
firms in year 2016
Then a baseline cleaning is applied to the values of all the financial variables to correct for
possible mistakes of reporting in HUF rather than 1000 HUF or for extremely small or big
values in the data Employment and sales are cleaned of extreme values and outliers
Suspiciously large jumps followed by another jump into the opposite direction are
smoothed by the average of the previous and following years Regarding capital stocks we
use the sum of tangible and intangible assets Whenever intangible assets are missing we
input a zero
We deflate the different variables with the appropriate price indices from the OECD STAN
which includes value added capital intermediate input and output price deflators at 2-
digit industry level1
Regarding industry codes the database in general includes the 2-digit industry code of a
firm in each year based on the actual industry classification system 4-digit industry codes
are only available between 2000 and 20052 We harmonize to NACE Rev 2 codes by using
1 A few industries are merged in the EU-KLEMS We will call this 64 category classification ldquo2-digitrdquo
industry in what follows
2 The database available in the CSO which we will use for Task 3 includes 4-digit codes for all years
BOX 21 Amadeus and the NAV database (cont)
Table B21 Coverage by employment categories (AmadeusNAV)
Year 1 emp 2-5 emp
6-10 emp
11-20 emp
21-49 emp
50-249 emp
250 lt emp
Total
2004 005 028 092 105 160 312 642 043
2005 010 050 169 288 483 1066 2227 108
2006 017 087 315 553 966 1935 3632 192
2007 2209 3006 4384 5249 5743 6082 7412 3135
2008 098 324 951 1692 2840 4868 7827 576
2009 5962 6070 7217 7428 7831 7798 9336 6301
2010 2142 4685 7034 7540 8424 8228 9634 4175
2011 2277 4736 7064 7753 8521 8657 9681 4220
2012 9397 8298 9305 9484 9507 9159 10121 8990
2013 7274 8140 9423 9981 9747 9445 10312 8044
Notes This table shows the number of observations in AMADEUS as a percentage of observations in the
NAV data for each year-size category cell
Data Sources
6
concordances from Eurostat3 We use these harmonized codes whenever we define deciles
and the frontier or within-industry variables so that NACE revisions should not affect the
results Finally we split those firms which switch from manufacturing to services or vice
versa adding separate firm identifiers for the two periods4
22 Productivity estimation
From many perspectives the most robust and convenient measure of productivity is
labour productivity We calculate this variable simply as the log of value added per
employee At the same time the key shortcoming of labour productivity is that it does not
reflect the differences in capital intensity across firms Total Factor Productivity (TFP) aims
to control for this issue We estimate TFP with the method of Ackerberg et al (2015) ndash we
refer to it as ACF ndash which can be regarded as the state of the art In the Appendix we
also provide robustness checks using different productivity measures
Technically firm-level TFP estimation involves estimating a production function
119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)
where i indexes firms t indexes years 119871119894119905 is the number of employees and 119870119894119905 is the capital
stock of firm i in year t In this specification the residual of the equation 휀119894119905 is the
estimated TFP for firm i in year t 120573119897 and 120573119896 are the output elasticities in the production
function reflecting the marginal product of labour and capital and the optimal capital
intensity
Estimating firm-level production functions involves several choices First it is usually
important to include year fixed effects in order to control for macro- or industry level
shocks Second industries may differ in their optimal capital intensity ie the coefficients
of the two variables To handle this we estimate the production function separately for
each 2-digit NACE industry Third financial data reported by small firms may not be very
accurate Including them into the sample on which the production function is estimated
may introduce bias into that regression Hence we estimate the production functions only
on the sample of firms with at least 5 employees but also predict the TFP for smaller firms
Fourth the Cobb-Douglas production function may be too restrictive in some cases but it is
possible to estimate more flexible functions (eg translog)
A key problem with firm-level TFP estimation is that input use (119871119894119905 and 119870119894119905) can be
correlated with the residual TFP Consequently OLS estimation may yield biased
coefficients The bias arises from attributing part of the productivity advantage to the
higher input use of more productive firms A simple and robust solution for this issue is to
estimate the production function with a fixed effects estimator This method controls for
endogeneity resulting from unobserved time-invariant firm characteristics
3 Because of the changes in the Hungarian industry classification in 2003 and 2008 industry code harmonization is required The Hungarian industry classification system (TEAOR) corresponds to NACE Rev 1 between 1998 and 2002 to NACE Rev11 from 2003 to 2007 and to NACE Rev 2 from 2008 onwards The conversion of industry codes in 2000-2002 to NACE Rev 11 is relatively straightforward and efficient thanks to the 4-digit codes The conversion from NACE Rev 11 to
NACE Rev 2 is less so as 4-digit codes are only available until 2005 Hence for each firm we assume that its 4-digit industry remained the same in the period of 2005-2007 and use this 4-digit industry for the conversion After these conversions we clean industry codes ignoring those changes when firms switch industries for 1-3 years and then switch back This process leads to a harmonized 2-digit NACE Rev 2 code for each year
4 After industry cleaning this can only happen either at the beginning or the end of the period when the firm is observed or if the firm switches industry for a period longer than 3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
7
A second and related problem is that input use can also be correlated with time-variant
productivity shocks This type of endogeneity is not corrected by the fixed effects
estimator More specifically managers (unlike economists analysing the balance sheet)
may observe productivity shocks at the beginning of the year and adjust the flexible inputs
(labour in our case) accordingly As a result we may falsely ascribe a productivity
improvement to the increase in labour input The recent best practice of handling this
issue is the control function approach in which one controls for the productivity shock by
using a proxy for it in an initial step The proxy is another flexible input usually materials
or energy use As we have reliable data on materials we will use that variable
In this report we rely on the method of Ackerberg et al (2015)5 Importantly with this
method the production function coefficient estimates are close to what is expected6 and
the returns to scale are slightly above one (typically between 1 and 12 see Figure 21)7 8
After estimating the coefficients we simply calculate the estimated TFP for firm i in year t
by subtracting the product of inputs and the estimated elasticities
119879119865119875119894119905 = 119871119899 119881119860119894119905 minus 120573 lowast 119897119899 119871119894119905 minus 120573 lowast 119897119899 119870119894119905 (22)
In this way we calculate a TFP level (rather than its value relative to year and industry
fixed effects) which is important when calculating productivity changes Note that the
calculated productivity changes are very similar to the logic of the Solow residual
When interpreting productivity estimates it is important to remember that both the labour
productivity and TFP estimates are value added-based measures In other words in cross-
sectional comparisons they show how many forints or euros (rather than cars or apples)
are produced with a given amount of inputs Therefore value added based productivity
reflects both physical productivity and markups9
5 We have estimated all of these with the prodest (Rovigatti and Mollisi 2016) command in Stata
6 Reassuringly Ackerberg et al (2015) themselves report some production function estimates using data from Chile and their estimated coefficients are similar to what we get 08-09 for labour and about 02 for capital
7 We also control for attrition of firms from the sample but this does not affect the estimates significantly
8 The Levinsohn-Petrin (2003) and Wooldridge (2009) production function estimates are less attractive Most importantly the estimated returns to scale are well below 1 typically between 07 and 08 These implausibly low returns to scale imply an implausibly high TFP for larger firms with their TFP advantage being many times their labour productivity advantage even though they employ much more capital per worker The implausibly low returns to scale strongly affect our calculations In such a framework if a firm doubles all of its inputs and outputs its estimated TFP increases by about 30 percent even though it transforms inputs into outputs in the same way In
productivity decompositions for example size and growth are mechanically related to TFP leading to overestimating allocative efficiency
9 Recent literature has emphasized the difference between value added-based (revenue) and physical productivity and has also proposed a number of methods to distinguish between the two (see Foster et al 2008 Hsieh and Klenow 2009 Syverson 2011 Bellone et al 2014 De Loecker and Goldberg 2014) Hornok and Murakoumlzy (2018) also apply such methods to investigate the markup differences of Hungarian importers and exporters
Data Sources
8
Figure 21 ACF production function coefficients
A) Manufacturing
B) Services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
9
We take some additional steps to clean our raw productivity estimates First we winsorize
productivity at the lowest and highest percentile of the 2-digit industry-year-specific
distribution of firms with at least 5 employees We fill out gaps of 1 or 2 years in the
productivity variable by using linear approximation Finally we clean the productivity of
firms with at least 5 employees based on changes We smooth large 1-year jumps10 and
disregard productivity values if there is a large jump after entry or before exit11
Table 21 presents the average labour productivity and TFP by 1-digit NACE categories in
2004 and 2016
Table 21 Average productivity measures by 1-digit industry in 2004 and 2016
unweighted
Labour productivity Total factor productivity
2004 2016 2004 2016
NACE Description Mean Stdev Mean Stdev Mean Stdev Mean Stdev
B Mining 797 088 867 086 408 087 441 065
C Manufacturing 777 087 806 079 581 079 598 077
D Electricity gas steam 929 106 953 138 629 091 634 132
E Water supply sewerage waste
812 085 830 089 604 091 593 094
F Construction 773 080 803 072 620 071 646 066
G Wholesale and retail trade 804 102 825 090 652 093 678 081
H Transportation and storage 841 071 837 072 625 067 623 072
I Accommodation 710 075 752 080 594 068 640 071
J ICT 834 094 862 090 631 101 669 098
M Professional scientific and technical activities
815 087 844 088 636 087 673 088
N Administrative and support services
763 098 792 094 640 107 662 113
Total 789 095 815 087 620 090 647 087
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
10 We replace 119910119905 with 119910119905minus1+119910119905+1
2 if abs(119910119905 minus 119910119905minus1)gt1 abs(119910119905+1 minus 119910119905minus1)le 05 abs(119910119905minus1 minus 119910119905minus2)le 03 timesabs(119910119905 minus
119910119905minus1) abs(119910119905+2 minus 119910119905+1) le 03 timesabs(119910119905 minus 119910119905minus1) where 119910119905 denotes a productivity measure in logs of year
t Corresponding conditions are modified to abs(119910119905+1 minus 119910119905minus1) le 1 abs(119910119905+1 minus 119910119905minus1) le 03 times abs(119910119905 minus119910119905minus1) in the second observed year and in the year before the last observed one
11 abs(119910119905 minus 119910119905minus1)gt15
Data Sources
10
23 Estimation sample
Next we introduce some restrictions to define our baseline sample As our aim is to focus
on the market economy we constrain our sample based on industry and legal form We
keep only the market economy according to the OECD definition dropping observations in
agriculture and in non-market services (NACE Rev 2 categories 53 84-94 and 96-99)
We also drop financial and insurance activities12 as well as observations for which industry
is missing even after cleaning
We also drop firms which functioned as non-profit budgetary institutions or institutions
with technical codes at any time during the observed period We also drop firms which
never reported positive employment We refer to the remaining sample as the baseline
sample
Our main sample used for most of the calculations and for the estimations consists of
observations with at least 5 employees a non-missing total factor productivity value and
no remaining large productivity jumps13 We refer to the resulting sample as our main
sample Excluding the smallest firms has multiple advantages First exclusion of small
firms reduces measurement error as the smallest firms are the most likely to misreport
Additionally one-employee firms cannot be told apart from the self-employed who create
a firm for administrative reasons but clearly do not operate as an ordinary firm The
existence of such firms as well as their financial variables are likely to be strongly
determined by the differential in the tax treatment of personal versus corporate incomes
Because of these reasons both productivity levels and productivity changes may be
measured with an excessive amount of noise for the very small firms and therefore we
exclude them from our main analysis
Table 22 shows the distribution of firms by size category in our baseline sample Clearly
our sample expands strongly between 2000 and 2004 which is mainly a result of legal
changes requiring a larger group of firms to use double-entry bookkeeping While this
expansion is the strongest for the smallest firms it also affects a large number of firms
with up to 20 employees This artificial `entryrsquo of firms can bias estimates of productivity
growth (yielding a negative composition effect) and its decomposition (a negative entry
effect) For this reason in many cases we will start our analysis in 2004
Figure 22 investigates how much the exclusion of very small firms matters It shows that
while the share of 0 and 1 employee firms was between 50 and 60 percent their share in
terms of employment and sales was only around 5-6 percent hence even after their
exclusion our sample captures much of the national output We however report
robustness checks for our main results with all firms with a positive number of employees
in the Appendix
12 We decide to drop the financial sector because of conceptual and measurement problems of defining the productivity of financial firms especially during the crisis It might also distort the aggregate results Dropping these firms also corresponds to the usual practice (eg McGowan et al 2017) However including financial firms does not have a significant impact on our main results
13 We exclude firms that had a log productivity change higher than 15 in absolute value at any one time We also exclude firms switching between manufacturing and services more than twice
Productivity differences in Hungary and mechanisms of TFP growth slowdown
11
Table 22 Distribution of firm size by employment categories
Year 0 emp 1 emp 2-4 emp 5-9 emp 10-19 emp 20-49 emp 50-99 emp 100 lt emp Total
2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739
2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987
2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568
2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376
2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880
2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844
2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671
2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709
2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318
2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113
2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104
2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509
2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335
2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665
2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678
2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519
2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727
Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742
Notes The sample is our baseline sample (see Section 23) also including observations without an
estimated TFP
Figure 22 The share of 0 and 1 employee firms in the number of firms employees and
sales
Data Sources
12
Table 23 shows the number of observations lost because of missing values cleaning and
sample restrictions compared to the original data Dropping firms based on industry and
legal form as well as firms which never report positive number of employees does not
reduce the sample considerably The baseline data contains about 23 of the firms in the
original data The coverage in terms of total employment or value added is even higher
While the reduced sample of firms with at least 5 employees contains only about 20 of
the original number of firms the coverage of total employment and value added is still
above 70 We lose an additional 4 of firms which have no estimated TFP (negative
value added or missing capital) or which have large TFP jumps over time The
corresponding reduction in employment and value added coverage is about 20 and 15
percentage points respectively14 In the main sample we capture almost 23 of the total
employment and value added which we have in the original data
Table 23 Change in sample size and coverage after introducing restrictions
Number of
firms
Total
employment
Total value
added
Original data (after imputing observations) 1000 1000 1000
Drop agriculture and missing industry 952 954 984
Drop non-market services 845 895 948
Drop based on legal form 844 885 946
Drop firms which never had positive
employment
708 885 935
Keep only market economy according to OECD 667 859 912
Drop financial and insurance activities 652 830 790
Baseline sample 652 830 790
Keep observations with at least 5 employees 196 726 723
Keep firms which have no big TFP jump and
observations with non-missing TFP
157 600 647
Main sample 157 600 647
Table 24 shows the share of observations in the main sample by 1-digit NACE industry
The industry composition is quite stable over time Wholesale and retail trade has the
largest share close to 13 followed by manufacturing (21-31) construction (13-14)
and professional scientific and technical activities (7-9) The largest decline over time
was in manufacturing (from 31 to 21) Construction transport and storage
accommodation professional scientific and technical activities and administrative and
support services increased their share by more than one percentage point
14 While this cleaning certainly drops a large number of firms this is standard practice when the aim is to capture and decompose aggregate dynamics
Productivity differences in Hungary and mechanisms of TFP growth slowdown
13
Table 24 The share of observations by industry
NACE Description 2000 2004 2008 2012 2016
B Mining 029 025 024 021 018
C Manufacturing 3085 2636 2352 2280 2122
D Electricity gas steam 021 027 026 025 024
E Water supply sewerage waste
103 112 114 127 098
F Construction 1263 1439 1447 1263 1375
G Wholesale and retail trade 3207 3026 2954 3005 3034
H Transportation and storage 479 551 606 642 683
I Accommodation 477 617 650 719 783
J ICT 351 328 396 403 400
M Professional scientific and
technical activities 688 691 859 915 891
N Administrative and support services
297 547 572 601 572
Total 100 100 100 100 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
24 Firm-level variables
For the present analysis we create firm groups based on different firm characteristics In
this subsection we explain these groupings and provide descriptive statistics
The database includes information on direct ownership Based on this one can identify
firms which are domestically-owned15 foreign-owned or state-owned (including municipal
ownership) We identify a firm as foreign-owned if the foreign share is above 10 percent
Similarly we classify a firm as state-owned if the state-owned share is above 50
percent16 Based on these definitions in 2016 nearly 10 percent of firms were foreign-
owned while the share of state-owned firms was about 1 percent (Table 25) Both foreign
and state ownership is more frequent in larger firms therefore foreign and state share is
higher in terms of employment 373 percent of employees work in foreign-owned firms
and 66 percent in state-owned ones Foreign ownership was concentrated in mining and
manufacturing electricity generation and distribution trade and ICT State ownership was
high in electricity generation and distribution and in utilities The fact that state-owned
firms are concentrated in these two industries limits the possibilities of how the effects of
state ownership and the effect of the peculiarities of these highly regulated industries can
be distinguished from each other Therefore in most cases we will not present results
separately for state-owned firms (except for Section 44)
15 For brevity we will mainly refer to domestically-owned private firms simply as domestically-owned
16 Only 15 of firms with more than 10 percent foreign share report a foreign share between 10 and 51 percent Re-classifing them as domestic does not affect our main results
Data Sources
14
Table 25 Share of state- and foreign-owned firms with at least 5 employees 2016
A) Number of firms
NACE Sector Domestic Foreign State Total
B Mining 8228 1772 000 100
C Manufacturing 8432 1522 046 100
D Electricity gas steam 5631 1942 2427 100
E Water supply sewerage waste 6351 450 3199 100
F Construction 9746 192 062 100
G Wholesale and retail 8957 1006 037 100
H Transportation 9005 890 105 100
I Accommodation 9411 467 121 100
J ICT 8314 1541 145 100
M Professional scientific and technical activities
8982 915 102 100
N Administrative and support services
8991 798 211 100
Total 8937 953 110 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
B) Employment
NACE Sector Domestic Foreign State Total
B Mining 725 275 00 100
C Manufacturing 437 552 12 100
D Electricity gas steam 674 234 91 100
E Water supply sewerage waste 189 32 780 100
F Construction 899 74 28 100
G Wholesale and retail 660 334 06 100
H Transportation 474 199 327 100
I Accommodation 867 111 22 100
J ICT 424 546 30 100
M Professional scientific and technical activities
650 331 20 100
N Administrative and support services
681 258 61 100
Total 560 373 66 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
The data include direct information on export sales and we classify a firm as an exporter
in a given year if its export sales are positive Table 26 shows the share of observations
both by ownership (foreign or private domestic) and exporter status The distribution of
firms across the four groups is stable over time Overall 65-75 of the firms are owned
domestically and supply only the domestic market The share of foreign firms decreased
from 143 in 2000 to 96 in 2016 After an initial decline the share of exporters
increased from 26 in 2000 to 315 by 2016 More than 23 of the foreign firms export
while the same ratio for domestic firms is less than 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
15
Table 26 Yearly share of observations by ownership and exporter status
Year Foreign
exporter
Foreign
non-
exporter
Domestic
exporter
Domestic
non-
exporter
2000 92 51 168 690
2001 89 46 172 693
2002 84 43 173 701
2003 79 40 164 717
2004 71 36 157 736
2005 70 34 160 736
2006 69 34 163 734
2007 73 32 180 715
2008 74 34 186 706
2009 78 36 195 692
2010 77 34 202 687
2011 78 32 215 675
2012 81 31 229 659
2013 79 30 236 655
2014 74 33 233 660
2015 71 31 238 659
2016 70 26 245 658
Total 76 35 196 692
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded
Table 27 presents some baseline descriptive statistics for the four firm groups created by
ownership and exporter status We define age using the year of foundation of the firm On
average foreign exporter firms are the largest and the most productive Within both
categories exporter firms are older larger and more productive in line with similar
patterns in other countries17 We will analyse differences further in Section 44
17 See for example Bernard-Jensen (1999)
Data Sources
16
Table 27 Average characteristics by ownership and exporter status in year 2004 and
2016
Foreign exporter
Foreign non-exporter
Domestic exporter
Domestic non-exporter
Year 2004
N of employees 1385 511 451 165
(5689) (2410) (1396) (404)
Labour productivity 877 825 830 769
(101) (120) (087) (086)
TFP ACF 666 660 634 611
(111) (112) (091) (084)
Age
101 85 99 85
(42) (46) (43) (43)
Year 2016
N of employees 1619 338 344 151
(6246) (1257) (1290) (405)
Labour productivity 906 839 844 793
(088) (115) (075) (080)
TFP ACF 696 684 651 639
(113) (109) (086) (080)
Age 160 105 149 124
(82) (75) (76) (75)
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded Standard deviations are in
parentheses
25 Industry categorization
As we have mentioned already the main industry identifier is the 2-digit NACE Rev 2
industry classification These are hierarchically ordered into 1-digit categories
These categories however do not always lend themselves to easy interpretation On the
one hand one may want to distinguish between different types of manufacturing activities
Here a key question concerns the knowledge intensity or the high-techness of the activity
On the other hand sometimes it is useful to aggregate some of the service activities to
obtain more easily interpretable results
In order to do this we use Eurostatrsquos high-tech aggregation of manufacturing and services
by NACE Rev 2 which we will call industry type18 Note that these sets of industries
include only activities carried out in market industries (ie 10 to 82 NACE Rev 2 industry
codes) When using these categories we do not include firms in non-market sectors like
education (85) or arts entertainment and recreation (90 to 93) (See Table 28)
We would like to point out that while the Eurostat categories clearly reflect the global
technology and knowledge intensity of each industry the actual activity conducted in a
given country may differ from the technology category of the industry This issue is highly
relevant in Hungary where MNEs in high-tech industries operate affiliates conducting
assembly activities in Hungary without much RampD or innovation Still we find this
categorization a good way of aggregating data but still preserving some heterogeneity
18 Retrieved from httpeceuropaeueurostatcachemetadataAnnexeshtec_esms_an3pdf
Productivity differences in Hungary and mechanisms of TFP growth slowdown
17
Table 28 Industry categorization
Manufacturing NACE Rev 2 codes
High-technology manuf 21 26
Medium-high technology manuf 20 27 to 30
Medium-low technology manuf 19 22 to 25 33
Low technology manuf 10 to 18 31 to 32
Services
Knowledge-intensive services (KIS) 50 to 51 58 to 63 64 to 66 69 to 75 78 80
Less knowledge-intensive services (LKIS) 45 to 47 49 52 55 to 56 77 79 81 82
Utilities 35 to 39
Construction 41 to 43
Productivity Trends Hungary in International Comparison
18
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON
The main aim of this chapter is to summarize existing evidence on Hungarian productivity
trends based on internationally comparable databases which include either industry-level
or micro-aggregated information The specificities and similarities of Hungary to
comparable countries will both guide and frame our analysis in the remaining chapters
which use Hungarian micro-data
31 Convergence
The fundamental question regarding the productivity evolution of Hungary or other less
developed EU member countries is whether productivity catches up with the most
developed countries at least in the medium or long run We investigate such medium- or
long-run trends in this subsection by analysing the evolution of relative productivity which
is defined as the level of labour productivity compared to one of the key economies of the
EU Germany (at ppp exchange rates) Figure 31 presents such a comparison of the
labour productivity levels of Hungary the Czech Republic Poland and Slovakia We use the
OECD STAN database for this exercise and present trends for as many years as possible to
reflect long-run developments
Figure 31 Relative labour productivity level (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN and GGDC Productivity Level Database The
market economy excludes real estate For more details see Appendix A3
Let us start with the evolution of aggregate labour productivity According to Figure 31 all
of these countries seemed to be on the road to convergence to frontier countries in terms
of labour productivity before the financial crisis In particular labour productivity in
Hungary increased from 50 percent of the German level in 1998 to 65 percent in 2008 A
similar pre-crisis convergence can be observed in all three comparator countries19
19 Note that TFP is not available for Hungary in the EU KLEMS after 2008 Therefore we restrict this
international comparison to labour productivity
Productivity differences in Hungary and mechanisms of TFP growth slowdown
19
Note that the labour productivity decline during the crisis does not show up in the above
figure because it also affected the baseline country Post-crisis Hungarian labour
productivity (relative to Germany) remained flat stabilizing at around 65 percent While
this is similar to the productivity evolution of the Czech Republic it differs remarkably from
Poland and Slovakia which were able to close their productivity gap relative to Germany
by about 5 percentage points between 2009 and 2015 This slowdown of aggregate
productivity growth and the lack of further convergence from previous levels is actually
the main motivation for this study
A key question is whether the slowdown characterises the whole economy or it is
constrained to some of the sectors or types of enterprises The first dimension is to
distinguish between the state sector and the market economy According to OECD STAN
non-market sectors accounted for about 27 percent of all employment in 201520 The
second panel of Figure 31 restricts the sample to the lsquomarket economyrsquo21 Interestingly
productivity differences relative to Germany are larger in the market economy compared
to the whole economy suggesting that the productivity levels of the public sector in the
two countries appear to be closer to each other In Hungary the relative productivity of
the market economy follows a very similar trend to the whole economy with about 10 pp
relative productivity increase between 1998 and 2005 and stagnation post-crisis With the
exception of Slovakia post-crisis productivity growth is also flat in the comparator
countries
Figure 32 Relative labour productivity in manufacturing and business services
Germany=100
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN Business services excludes real estate For
more details see Appendix A3
20 According to the EU KLEMS this share has remained more or less constant since 2003
21 This includes NACE Rev 2 Codes 5-82 except real estate (68)
Productivity Trends Hungary in International Comparison
20
The market economy can be further disaggregated into manufacturing and business
services (Figure 32) There is strong evidence of catching up in manufacturing between
1995 and 2008 when relative productivity increased by more than 10 percentage points
Relative productivity fell immediately after the crisis with positive growth after 2011
reaching pre-crisis (relative) levels by 2015 Comparator countries which started from
much lower levels caught up faster pre-crisis and faced a much smaller fall around the
crisis years In other words comparator countries have caught up with Hungary in terms
of manufacturing productivity but there is no evidence for a sharp break in the trend post-
crisis
This contrasts sharply with business services where a period of catch-up until 2005 was
followed by a substantial decline in relative labour productivity This is also in strong
contrast with the comparator countries where relative productivity of business services
either increased (Czech Republic and Poland) or stagnated (in Slovakia) Business services
appear to be a key source of aggregate productivity slowdown
Figure 33 presents productivity dynamics in four specific industries to substantiate the
more aggregated picture with some more concrete examples The first two examples are
manufacturing industries namely the textiles and the automotive industry The relative
productivity level of textiles stagnated during the crisis at quite low levels fell during the
crisis followed by some growth from 2012 In motor vehicles relative productivity
increased by nearly 10 percentage points relative to Germany between 2001 and 2009
followed by a significant fall around the crisis and a strong recovery from 2012 The
picture is also varied in services In retail and wholesale there had been some productivity
improvement before the crisis followed by a declining trend post-crisis Both the level and
dynamics of relative productivity compares unfavourably to the comparator countries In
professional services relative labour productivity had grown quickly until 2011 followed
by a declining trend
Figure 33 Relative labour productivity evolution (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
21
Similar observations can be made when analysing the relative productivity of all types of
industries (Figure 34) The difference in productivity levels relative to Germany tends to
be larger in manufacturing than in services Light industries have especially low relative
productivity levels In terms of productivity growth we see mostly positive trends in most
manufacturing industries and a less clear picture in services with a decline or stagnation
in many service industries
Figure 34 Labour productivity of different industries relative to Germany 2005 and 2015
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix A3
Even in countries and industries with a relatively low level of average productivity it is
possible that a segment of the economy operates at world-class levels or shows fast
convergence to that This possibility may be especially relevant in economies where a
number of large and probably foreign-owned firms operate together with many smaller
domestically-owned firms which is certainly the case in Hungary One approach to
investigate this possibility was suggested and implemented by the OECD (Andrews et al
2017) This approach builds on cross-country micro-data to calculate the productivity of
the most productive firms in the world (global frontier) and compare it with the
productivity of the most productive firms in a country (national frontier)
Figure 35 shows these comparisons based on the OECDrsquos calculations22 In particular the
horizontal axis shows how productive Hungarian frontier firms are relative to the global
22 We would like to thank Peter Gal and his colleagues in the OECD for sharing these data with us In
this version global frontier is defined as the top 10 percent most productive firms worldwide
while the national frontier is the top 10 percent within the country according to ORBIS See
Appendix 3 and Box 41 for details on these data
Productivity Trends Hungary in International Comparison
22
frontier (100 is the global frontier) while the vertical axis compares Hungarian and global
non-frontier firms The figures suggest a number of conclusions To start with the frontier
productivity gap is strongly associated with the non-frontier productivity gap showing that
in industries where the typical firms are of relatively low productivity so are the frontier
firms Importantly the slope of the fitted line (06) is well below 1 suggesting that on
average there is a smaller gap between a top global and a top Hungarian firm than
between a typical (non-frontier) global firm and a typical Hungarian firm This is in line
with the duality hypothesis
That said one has to emphasise that the picture does not support a ldquostrong versionrdquo of the
duality hypothesis ie that the best Hungarian firms operate at world-class productivity
levels Even in manufacturing Hungarian frontier firms typically produce 40-60 percent
less value added per employee compared to the global frontier (good examples are
machinery (28) and motor vehicles (29)) The smallest gaps appear in a few relatively
low-tech service industries (trade and repair of vehicles (45) or warehousing (52)) where
frontier productivity is actually above the global frontier23
The observation that such large productivity differences exist between global frontier and
Hungarian frontier firms even within relatively narrowly defined industries suggests that
the low relative productivity of the Hungarian market economy is not a consequence of
industry composition ndash it mainly results from within-industry gaps Importantly these
main patterns are very similar and independent of how productivity is measured (labour
productivity or TFP) namely they are not a consequence of capital intensity differences
Finally by and large there is no evidence for convergence of frontier firms to the global
frontier between 2009 and 201324 If anything the gap between the global and the
Hungarian frontier widened in this period while the difference between the global and the
Hungarian frontier was 34 percent in the median industry in 2009 it widened to 38 by
2013
23 Naturally this is likely to be the case in other similar countries Still in different discussions it is often supposed implicitly that the best Hungarian firms are indistinguishable from the global frontier
24 Prior to 20082009 the coverage of ORBIS the source for the OECD calculations is fairly limited for
Hungary hence those calculations are less reliable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
23
Figure 35 Productivity of Hungarian frontier and non-frontier firms relative to firms in
other countries (2013)
A) Labour productivity
B) TFP
Notes The industry codes are 2-digit NACE Rev 2 codes We have omitted industries with only few
observations (less than 5 Hungarian frontier firms) in the case of labour productivity outliers we
ignored those where the HU frontier was measured to be more productive than 125 percent of the
global frontier (ICT real estate and office administration services) Note that there are fewer
observations regarding TFP than labour productivity Source Data provided by the OECD calculated
from Andrews et al (2017) For more information see Appendix 3
Productivity Trends Hungary in International Comparison
24
We can draw a number of conclusions from these calculations First while Hungaryrsquos
labour productivity had been catching up similarly to other CEE countries to more
advanced economies before the crisis there was a trend break after the crisis especially
compared to Poland and Slovakia Only part of the productivity slowdown could be
explained by a slowdown in non-market sectors but there is also a pronounced slowdown
in the market economy This is not the result of having a combination of a few firms with
world-class productivity and many less efficient SMEs ndash actually the productivity of
frontier firms is only about 40-50 percent of global leaders even in industries where the
Hungarian frontier consists of many multinational firms There is no evidence that
Hungarian frontier firms were catching up with global leaders between 2009 and 2015
32 Within-industry heterogeneity
Since the beginning of the 2000s with the availability of detailed micro-data sets at the
firm-level it has become clear that within-industry heterogeneity in terms of productivity
is significantly larger than heterogeneity differences across industries (Bernard et al
2003 Bernard et al 2007 Bernard et al 2012 OECD 2017) Many factors have been
proposed which may generate and sustain the observed large productivity differences
including managerial practices different quality of labour capital and knowledge as well as
a number of external factors The exact role of different factors is an active area of
research (Syverson 2011) Recent research also hints at increasing dispersion within
sectors (Berlingieri et al 2017b)
In 2011 the level of the p90p10 ratio (90th and 10th percentile of productivity
distribution) was high in Hungary relative to other OECD countries taking a value of 279
in manufacturing and 329 in services (Table 31) These numbers are in logs representing
about 20-fold differences These numbers are similar to Chile and Indonesia A similar
pattern emerges with respect to TFP
Table 31 Productivity p90p10 ratio by country (2011)
Country
Year 2011
Log LP 90-10 ratio Log MFP 90-10 ratio
Manufacturing Services Manufacturing Services
Australia 187 205 190 212
Austria 196 242 - -
Belgium 160 174 180 178
Chile 300 353 307 387
Denmark 146 196 132 180
Finland 117 138 119 134
France 135 181 140 178
Hungary 279 329 254 286
Indonesia 311 - 341 -
Italy 166 201 160 188
Japan 126 138 117 138
Netherlands 200 298 227 227
New Zealand 184 209 192 208
sNorway 173 217 187 208
Portugal 188 265 188 275
Sweden 145 186 159 245
Notes This is a reproduction of Table 6 from Berlingieri et al (2017a) Note that the OECD uses the
term lsquoMFPrsquo (Multi-factor productivity) in the same sense as we use TFP in this report
Second as seen in Table 32 similarly to other OECD countries the overwhelming
majority of productivity differences results from within- rather than across-sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
25
differences The share of within-sector differences is 79 in manufacturing and 99 in
services The manufacturing share is close to the average of the countries in the sample
while the services share is at the high end
Table 32 Share of within-sector variance in total LP dispersion by country (2011)
Country
Year 2011
LP Dispersion
Manufacturing Services
Australia 98 99
Austria 86 90
Belgium 76 88
Chile 90 97
Denmark 84 63
Finland 65 76
France 63 85
Hungary 79 99
Indonesia 79 -
Italy 82 65
Japan 75 89
Netherlands 80 71
Norway 83 73
Portugal 62 76
Sweden 53 74
Notes This is a reproduction of Table 7 from Berlingieri et al (2017a)
These figures suggest that within-industry productivity dispersion is relatively high in
Hungary but it is not out of the range of countries at a similar level of development Still
these overall dispersion measures may not capture the duality between firms of different
sizes and ownership Internationally comparable data regarding productivity of firms in
different size classes is available from the OECD Structural and Demographic Business
Statistics (Figure 36) Size is strongly associated with productivity large firms are 45
times and 18 times as productive as very small firms in manufacturing and services
respectively However large these premia are not out of the range of similar countries in
services it is very similar to other CEE countries while in manufacturing it is at the high
end of the distribution but not extreme
Another relevant pattern in Figure 36 is that productivity differences by size are very
different between CEE countries and Western European countries This observation may
partly reflect the importance of large and productive multinational firms in CEE countries
but can also be a more or less automatic consequence of the fact that firm size distribution
significantly differs between the two groups of countries (Figure 37) Typically the share
of very small firms is larger in less developed economies leading to a more skewed firm
size distribution Such a distribution which is associated with a larger number of small
firms within size classes (the majority of firms with 1-9 employees in CEE employs only 1-
2 employees) leads to larger differences across size classes and larger within-industry
productivity dispersion The massive share of very small firms in these countries also
reflects that many of the lsquomicro-enterprisesrsquo (with only 1-2 employees) do not operate as
proper firms they behave more like individual entrepreneurs
Productivity Trends Hungary in International Comparison
26
Figure 36 Value added per person employed by size class (1-9 persons employed=100)
A) Manufacturing
B) Services of the business economy
Notes Value added per person employed defined as value added at factor costs divided by the
number of persons engaged in the reference period Economic sector lsquoManufacturingrsquo comprises
Divisions 10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business
economyrsquo comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities
of holding companies Source OECD SDBS For more details see Appendix 3 Main sample for 2015
Productivity differences in Hungary and mechanisms of TFP growth slowdown
27
Figure 37 Firm distribution by size class (2015)
A) Manufacturing
B) Services of the business economy
Notes Only enterprises with at least one employee are included lsquoManufacturingrsquo comprises Divisions
10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business economyrsquo
comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities of holding
companies Source OECD SDBS For more details see Appendix 3 Main sample
Productivity Trends Hungary in International Comparison
28
The main conclusion from investigating within-industry differences across firms is that both
the productivity dispersion and the productivity advantage of large firms is indeed
relatively large in international comparison but these numbers are not radically different
from similar countries Nevertheless differences in firm size distribution between more
and less developed countries go a long way towards explaining the differences between
Western European and CEE countries
33 Firm dynamics
A potential reason for declining productivity growth may be weak dynamics including low
entry and exit rates as well as slower reallocation The OECD Structural and Demographic
Business Statistics database provides international comparisons of entry and exit rates and
their changes across countries (Figure 38 and Figure 39)
In general both exit and entry rates are higher in CEE countries relative to Western
European economies25 This stronger dynamism may reflect stronger growth but it is also
affected (in a mechanistic way) by the differences in firm size distribution Importantly in
a cross-section entry and exit rates are strongly correlated suggesting that they capture
the same general aspect of firm dynamics Services are more dynamic than
manufacturing once again partly because of the different size distributions
Within CEE countries entry and exit rates seem to be associated with productivity growth
(and level) Countries with stronger post-crisis productivity growth (Poland Slovakia and
Romania) exhibit significantly higher entry and exit rates while those with less dynamic
productivity growth (Hungary and the Czech Republic) have lower churning This provides
some evidence that lower entry and exit rates may be systematically related to the weaker
productivity performance of these countries We will take a more detailed look at the
relationship between entry and exit and productivity growth in Chapters 6 and 7
When comparing 2012 and 2015 the pictures provide evidence for increased entry and
decreased exit in parallel with recovery and better growth prospects Still entry rates
remain one of the lowest in CEE indicating that entry and dynamic young firms may
contribute less to productivity growth in Hungary compared to other CEE countries
25 Note that these OECD statistics include all enterprises (even those with no employees) hence
changes in the tax treatment of firms relative to individual entrepreneurs may affect measured
dynamics Also firm death is defined based on the rsquodeathrsquo of the legal entity which may happen
many years after stopping production For more information see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
29
Figure 38 Birth rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Birth rate is defined as the number of enterprise births divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers The
economic sector lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo
comprises Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix A3
Productivity Trends Hungary in International Comparison
30
Figure 39 Death rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Death rate is defined as the number of enterprise deaths divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers
Poland has no available data for 2015 so the 2014 value is reported The economic sector
lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo comprises
Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
31
34 Conclusions
In international comparison productivity slowdown after the crisis was especially severe in
Hungary both in manufacturing and services There are large productivity differences
within industries and also between small and large firms While these are at the high end
in international comparison they are not extreme compared to similar countries A
comparison to the global frontier suggests that even top Hungarian firms are significantly
behind top global firms in terms of productivity These facts provide a motivation for our
analysis of the evolution of the shape of the productivity distribution in Chapter 4
International comparison of firm dynamics suggests that ndash similarly to other CEE countries
ndash Hungarian industries are more dynamic than their Western European counterparts but
entry and exit rates in Hungary and the Czech Republic are below the average of CEE
countries This motivates our investigation of the contribution of entry and exit to
productivity growth in Chapters 6 and 7
Evolution of the Productivity Distribution
32
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION
41 Context
The study of within-industry productivity differences is motivated by two concepts First
the OECD (2016) argues that one of the key issues of recent developments in productivity
growth is that there is a strong divergence between the productivity evolution of frontier
firms and other firms However this same publication reports that Hungary seems to be
an exception to this trend with slow productivity growth at the frontier and faster
productivity growth of less productive firms suggesting some within-industry catch-up
(Figure 41) We look into the particulars behind this phenomenon by following the
evolution of the average productivity of different deciles in the productivity distribution
Second as we have already mentioned a key concept of the Hungarian (and CEE) policy
debate is the lsquodualityrsquo of smalldomestically-owned and largeforeign-owned firms The
large gap between the two types of firms presents a challenge for policy but it also
indicates an opportunity for domestic firms to catch up with foreign firms which may use
more productive technology (still far in terms of productivity from the global frontier see
Chapter 31) The evolution of the productivity gap (or premium) between small and large
firms as well as domestic and foreign firms informs us about whether firms on the lsquowrong
sidersquo of the duality are able to catch up with the firms at the national frontier
The duality debate frames productivity differences partly as a consequence of the lsquomissingrsquo
medium-sized (domestic) firms Hsieh and Olken (2014) argue that in less productive
economies the full firm size distribution is shifted to the left because of the constraints on
the growth of small firms Thus according to this view the productivity difference is not a
result of too few medium sized firms but of too few firms which are not small
Figure 41 Divergence in labour productivity performance
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
33
B) Non-financial Services
Notes This is a reproduction of Figure 16 from OECD (2016)
In this chapter we investigate how the shape of the productivity distribution evolved over
the years Section 42 contrasts the development of firms with other types of economic
entities Section 43 analyses how average productivity and productivity deciles evolved
while 44 investigates the duality based on size and ownership
42 Aggregate productivity and the self-employed
Before turning to the productivity distribution of firms it is worthwhile to describe how the
productivity level and evolution of firms ndash and in particular double-entry bookkeeping
enterprises ndash differ from other entities in particular the self-employed Given the large
number of people employed by those entities this exercise can reveal a lot both about
productivity dispersion and the drivers of aggregate productivity growth
Let us motivate this investigation by comparing aggregate statistics (derived from data
applicable to all people engaged in an industry) with patterns calculated from our NAV data
(which includes only double-entry bookkeeping firms) Figure 42 shows the labour
productivity growth reported by OECD STAN and the evolution of the average labour
productivity as calculated from the NAV data weighted by sales and employment (Figure
42) According to the Figure while these series co-move they do so with some
discrepancies While productivity dynamics in Manufacturing are very similar across all
samples the relationship is looser for services and for the market economy with the NAV
series notably exhibiting less pronounced post-crisis slowdown than the OECD STAN data
Evolution of the Productivity Distribution
34
Figure 42 Cumulative labour productivity growth according to OECD STAN and the NAV
sample
There can be many reasons behind the differences between these series (see Biesebroeck
2008) but arguably one of the main factors is the discrepancy in the number of
employees in the two databases Firms in the full NAV database employed 24 million
people in 2015 compared with 286 million employed and 325 million lsquoengagedrsquo in the
market economy according to the OECD STAN One source of this difference may be that
while some unofficially employed workers report their true employment status in LFS
(Labour Force Survey) ndash which serves as the basis for our aggregate data ndash they do not
appear in any official registers and such the NAV data Benedek et al (2013) reaffirming
the statement compare LFS employment data with tax registers and show that 16-18
percent of jobs are not declared to the tax authorities
Even more importantly from our perspective the NAV data by definition includes no
information on the self-employed and typically small non-double-entry bookkeeping firms
operating under special taxation The distinct productivity dynamics of these two groups
along with changes in undeclared employment may explain another part of the difference
Obtaining direct information on this issue would be of great interest but acquiring it is far
from straightforward Some information on these entities is available from the Register of
Economic Organizations (Gazdasaacutegi Szervezetek Regisztere GSZR) which is available
between 2012 and 2015 Most importantly this database provides us with information on
the number of employees and sales updated annually This in and of itself does not allow
us to estimate productivity properly but with its help we can calculate a crude proxy
sales per employee for illustration
Table 41 reports26 the number of employees and the average sales per worker values for
three groups The first is the group of double-entry bookkeeping firms (ie the firms who
26 These tables were calculated as follows First we combined the GSZR and NAV databases for years 2012 and 2015 Observing that about 80 percent of the firms present in the NAV sample are also present in the GSZR register we restricted our sample to the entities who are listed in the GSZR so that our variables would be commensurable From this collection we selected those who
Productivity differences in Hungary and mechanisms of TFP growth slowdown
35
are present in the NAV data) the second is the category of the self-employed (ie those
who are registered as individual entrepreneurs) and the third category is that of lsquoother
firmsrsquo (ie entities who are registered as firms in the GFO (Gazdaacutelkodaacutesi Forma) coding
system but are not categorised as self-employed and are not following a double-entry
bookkeeping method) We distinguish between manufacturing and other industries of the
market economy27 We supply figures for the earliest and latest years for which data are
available The tables reveal two important observations
First according to the GSZR about 30 percent of reported employees in Manufacturing
and 50 percent of reported employees in other industries work outside the double-entry
bookkeeping group Importantly these numbers may be overestimates because the GSZR
may report the same person in multiple entities for example when they work part-time or
switch jobs within the year That said both the EU KLEMS and the GSZR suggest that a
large share of people work outside the double-entry bookkeeping group in the market
economy
Second while sales per worker is not drastically different between double-entry
bookkeeping firms and other firms the difference between firms and the self-employed is
between 6-10-fold This difference in sales per employee may represent 2-3-fold labour
productivity differences between people employed by firms and the self-employed on
average28
Third the dynamics of sales per worker differ markedly between double-entry
bookkeeping firms and other entities while it increased by 40 percent in the NAV sample
between 2012 and 2015 it stagnated for the self-employed This may results from a
number of factors ranging from composition effects changes in tax regulations or low
productivity growth Still the low measured productivity growth of this sector of the
economy may be an important factor behind the slower post-crisis aggregate productivity
growth in services compared to the NAV sample Table 41 illustrates this for the sales per
worker measure While it grew by 40 percent in the lsquoOtherrsquo category between 2012 and
2015 based on the NAV sample its lsquoaggregatersquo growth was only 6 percent during the same
period
Obviously one cannot draw far reaching conclusions from such statistics given the
immense measurement problems Still these patterns suggest that in a sense the duality
between firms and the self-employed may constitute a similarly deep divide to the one
belong to the lsquomarket economyrsquo (as defined in Chapter 2) and are registered as lsquofirmsrsquo according to GFO coding system (ie have 1-digit GFO codes 1 or 2) We tagged the firms present in the NAV sample as lsquodouble-entry bookkeeping firmsrsquo and marked those who have 2-digit GFO codes equalling to 23 as lsquoself-employedrsquo We categorised the rest of our sample as lsquoother firmsrsquo Further we distinguished between manufacturing and other market economy firms based on their NACE codes and then calculated for sales per worker measures on the level of each observation finally to compute for yearly aggregates for each group as indicated above
27 Notably in line with the definition in Chapter 2 these lsquoother industriesrsquo do not include agriculture
28 Needless to say this cannot be easily mapped into productivity differences given that firms using more intermediate inputs are more likely to choose double-entry bookkeping (and hence pay
taxes based on profits) rather than simplified taxes (and pay taxes based on sales) Still one can do the following back of the envelope calculation In the NAV sample the average ratio of material expenditure over sales was 066 both in 2012 and 2015 Therefore value added per employee (or labour productivity) could be about a third of the sales per employee variable If one conservativelly assumes that the self-employed have zero material costs their labour productivity is the same as their sales per employee index Based on this simple calculation the 6-10-fold difference in sales per employee map to at least 2-3-fold differences in labour productivity
Evolution of the Productivity Distribution
36
that exist between globally integrated and domestic-oriented firms Consequently policies
can be formulated with an explicit focus on this group
Table 41 Number of employees and sales per employee for different entities
Number of employees
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 621229 627391 1325299 1196332
Other firm 289636 296921 698326 771930
Self-employed 72674 74325 620699 638001
Total 983539 998637 2644324 2606263
Average sales per employee (HUF million)
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 140 199 196 278
Other firm 151 146 196 201
Self-employed 25 25 29 28
Total 92 99 105 111
43 The evolution of productivity distribution in Hungary
Average productivity
Let us continue by investigating the evolution of average productivity Table 42 presents
the average labour productivity and TFP growth rates for the market economy
manufacturing and services as defined in Chapter 2 We report both unweighted and
labour-weighted productivity growth for each year
Let us start with the whole market economy Between 2004 and 2007 both labour
productivity and TFP was growing strongly by 7-8 percent on average as expected in a
catching up economy (as we have seen in Chapter 3) Importantly the weighted growth
rate was higher than the unweighted one suggesting that reallocation played a positive
role in aggregate productivity growth (see Section 62 for more details)
During the crisis we see a slight productivity decline in 2008 a sharp fall of about 8
percent in 2009 followed by a strong recovery in 2010 The 2010 productivity recovery
resulted from the productivity growth of large firms unweighted average productivity
growth was very slow This suggests an asymmetry in recovering from the crisis-related
productivity decline
Post-crisis all measures document a slowdown in productivity growth with typical growth
rates between 25-35 percent Notably weighted productivity growth measures were
similar to unweighted ones in the wake of the crisis suggesting deterioration in the
efficiency of the reallocation process The 2010-2013 and 2013-2016 periods seem to be
quite similar to each other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
37
Importantly while labour productivity and TFP dynamics differ to some extent the overall
picture is very similar for the two productivity measures This is in line with the hypothesis
that any productivity slowdown is not merely a consequence of lower capital stock growth
The results are similar when using alternative TFP estimators (see Table A41 in the
Appendix)
Table 42 Labour productivity and (ACF) TFP growth in the sample
A) Market economy
Year LP TFP
unweighted emp w unweighted emp w
2005 20 58 19 74
2006 92 91 93 119
2007 53 60 39 56
2008 -10 -08 -10 -04
2009 -70 -81 -69 -82
2010 -05 44 11 80
2011 25 45 34 40
2012 25 22 21 01
2013 19 25 30 22
2014 39 45 40 59
2015 51 50 52 49
2016 36 19 20 03
Average
2004-2007 55 70 50 83
2007-2010 -28 -15 -23 -02
2010-2013 23 34 33 29
2013-2016 36 35 35 33
B) Manufacturing
Year LP TFP
unweighted emp w unweighted emp w
2005 37 148 20 114
2006 124 163 114 149
2007 100 114 78 71
2008 25 -03 17 -17
2009 -115 -94 -133 -117
2010 82 161 80 173
2011 -02 34 04 18
2012 05 -46 -02 -58
2013 -14 31 -12 05
2014 11 48 -01 27
2015 38 37 30 14
2016 26 01 04 -23
Average
2004-2007 87 141 71 111
2007-2010 -02 22 -12 13
2010-2013 -04 17 04 -03
2013-2016 15 29 05 06
Evolution of the Productivity Distribution
38
C) Market services
Year
LP TFP
unweighted emp w unweighted emp w
2005 12 -04 10 32
2006 80 47 79 90
2007 39 25 24 48
2008 -22 -06 -21 -03
2009 -57 -68 -52 -71
2010 -29 -17 -11 26
2011 33 49 43 57
2012 31 60 30 48
2013 29 21 39 29
2014 46 45 46 78
2015 54 58 54 72
2016 39 30 25 20
Average
2004-2007 43 23 38 57
2007-2010 -36 -31 -28 -16
2010-2013 31 44 39 51
2013-2016 42 39 41 50
Notes This figure presents growth rates of labour productivity and aggregate TFP for lsquomarket
industriesrsquo (see section 25) The sample does not include agriculture mining and financial services
Services include construction and utilities Only firms with at least 5 employees
Comparing manufacturing and services shows a key dichotomy between the two large
sectors In Manufacturing productivity growth was strong before the crisis with above 10
percent average weighted growth rates This fell to very low levels after 2010 Similarly to
the whole market economy reallocation processes had been more efficient before 2008 In
contrast for services no clear structural break appears around the time of the crisis either
in terms of pre- and post-crisis growth rates or reallocation efficiency
Table 43 looks into industry differences in more detail The picture is similar for
manufacturing industries in the various technology categories with a very substantial
slowdown in productivity growth Productivity growth was fastest in high-tech both before
and after the crisis Services are a bit more heterogeneous High-tech services behaved
similarly to high-tech manufacturing with strong pre-crisis growth (around 10 percent on
average) followed by a slowdown to growth rates around 5 percent per year In less
knowledge-intensive services which represent the majority of business service
employment growth rates were similar before and after the crisis (around 5 percent)29
Lastly we see moderate growth rates and then some slowdown in construction and
utilities
29 Note however that this may not be the case for the self-employed as has been discussed in the previous chapter
Productivity differences in Hungary and mechanisms of TFP growth slowdown
39
Table 43 TFP growth by type of industry (employment-weighted ACF TFP)
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 124 19 66 274 2006 240 137 39 33
2007 74 02 41 221
2008 -45 23 -15 59
2009 05 -191 -218 48
2010 135 111 264 168
2011 -45 18 34 100
2012 -15 -24 -83 -181
2013 -41 37 -22 125
2014 06 07 27 86
2015 65 01 -54 80
2016 -02 04 -27 -91
Average 2005-2007 146 53 49 176
2007-2010 32 -19 10 92
2010-2013 -34 07 -21 20
2013-2016 07 12 -19 50
B) Services
Year KIS LKIS Construction Utilities
2005 127 16 34 -48
2006 166 75 30 67
2007 13 58 42 29
2008 -16 14 -72 -26
2009 -63 -94 -04 25
2010 54 12 09 05
2011 97 46 65 29
2012 12 74 13 -22
2013 12 30 63 -07
2014 78 89 65 -81
2015 106 70 14 54
2016 16 31 -47 39
Average
2005-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the sales-weighted average ACF TFP growth rate by technology category (see
Section 25) Only firms with at least 5 employees The sample does not include agriculture mining
and financial services
In general patterns are similar for the unweighted measures (See Table A42 in the
Appendix) with weaker pre-crisis growth in manufacturing where reallocation seems to
have mattered most Labour productivity behaved similarly to TFP (See Table A43 in the
Appendix)
Evolution of the Productivity Distribution
40
Frontier firms
The key motivation for this investigation is to understand better how productivity dynamics
of lsquofrontierrsquo firms differ from firms in other parts of the productivity distribution Defining
frontier firms is not a straightforward task (Andrews et al 2017) Inevitably all such
attempts have to face the trade-off between a narrow definition which may to a large
extent capture the behaviour of outliers and a broader definition which may include
many firms which are very far from the actual frontier
One can find a sensible compromise between the too narrow and the too broad definitions
by following the OECD practice (Andrews et al 2017) This solves the problem of
including small firms with potentially large noise by restricting the sample to firms with at
least 20 employees on average in the sample period Frontier is defined as the top 5
percent of such firms for each industry-year combination An additional issue is that the
number of observations may change across years This is solved by calculating the top 5
based on the median number of observations per year We will call these firms frontier
firms
An alternative definition is simply to define the top decile within the productivity
distribution in industry-year combination as frontier based on our main sample We will
employ this strategy as well for the sake of comparison
Table 44 investigates the prevalence of frontier firms in different groups30 The probability
of being frontier is not related strongly to size A foreign-owned firm is 3-4 times more
likely to be frontier than a domestically-owned private firm State-owned firms are similar
to privately owned domestic firms in this respect As a result about half of the frontier
firms are foreign-owned Finally frontier firms are substantially more prevalent in the
more developed regions of the country especially in Central Hungary These patterns are
quite stable throughout the years and they prevail in a multiple regression analysis The
top decile of the productivity distribution has a similar composition (see Table A44 in the
Appendix)31
Table 44 The share of frontier firms () among firms with at least 20 employees
A) By size
2004 2007 2010 2013 2016
20-49 emp 357 327 34 362 329
50-99 emp 401 468 542 486 555
100- emp 293 358 414 42 462
B) By ownership
2004 2007 2010 2013 2016
Domestic 213 194 236 272 289
Foreign 873 955 896 82 821
State 181 211 166 167 263
30 Note that we restrict the sample to firms with at least 20 employees because the definition of frontier requires to have at least 20 employees on average
31 When the definition is based on labour productivity the share of frontier firms increases with size The foreign advantage is also larger
Productivity differences in Hungary and mechanisms of TFP growth slowdown
41
C) By region
2004 2007 2010 2013 2016
Central HU 596 621 652 552 579
Northern Hungary 174 104 176 237 168
Northern Great Plain 152 195 199 38 268
Southern Great Plain 128 127 18 277 224
Central Transdanubia 296 27 32 359 322
Western Transdanubia 408 313 305 433 395
Southern
Transdanubia 131 081 188 159 211
Another key question is the extent to which frontier status is persistent Figure 43 shows
a transition matrix ie it considers frontier firms in year t and reports their status in t+3
Do they remain frontier or become a non-frontier firms or exit the market altogether
Overall the 3-year persistence of the frontier status is around 45 percent ndash nearly half of
frontier firms will also be frontier 3 years later This is a bit higher than what is found in
other countries Antildeoacuten Higoacuten et al (2017) for example report that about half of all
national frontier firms remain on the frontier for a year but only about 20 percent for 5
years The persistence of frontier status remained largely unchanged across the years
Frontier status is more persistent for foreign and exporter firms The transition matrix of
top decile firms is similar with slightly weaker persistence (Figure A41 in the Appendix)
Figure 43 Transition matrix for frontier firms
Notes This figure shows how many of the frontier firms in year 2010 were still frontier in 2013 how
many exited and how many continued as non-frontier Only firms with at least 20 employees The first
panel shows this transition matrix for various 3-year periods
Evolution of the Productivity Distribution
42
Productivity evolution across deciles
The figures in this section compare the average productivity of frontier firms of the top
decile of the productivity distribution lsquohigh productivity firmsrsquo (8th and 9th deciles) lsquotypical
firmsrsquo (4th to 6th deciles) and lsquolow productivityrsquo firms (2nd and 3rd deciles) all of these
defined at the year-NACE 2 level This approach follows closely that of the OECD (2016)
Also we use the 8 lsquotechnologicalrsquo industry categories introduced in Section 25 to condense
information but still allow for heterogeneity across industries
Let us start with comparing TFP levels (Figure 44) and their cumulative changes (Figure
45) at the different parts of the productivity distribution (note that the vertical axes differ
across sectors) TFP levels are measured in natural logarithms For example in low-tech
manufacturing the difference between low-productivity firms and the frontier is about 2 log
points or more than 7-fold32 Within-industry productivity differentials are much larger
than across-industry differences or changes From a methodological point of view in most
industries frontier firms co-move with the top percentiles but there are a few exceptions
most prominently high-tech manufacturing
The overall productivity evolution is much in line with the averages reported in Table 42
There is strong pre-crisis growth in Manufacturing followed by a fall in 2009 and sluggish
growth afterwards High-tech manufacturing is a partial exception from this trend
Productivity growth actually accelerated after the crisis in services
Figure 44 TFP levels in various types of industries
A) Manufacturing
32 1198902 asymp 74
Productivity differences in Hungary and mechanisms of TFP growth slowdown
43
B) Services
Notes This figure shows the evolution of the (unweighted) average ACF TFP level of the different
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles with at least 20 employees on average lsquotop decilersquo is the 10th decile lsquohighrsquo
is the 8-9th decile typical is the 4-6th deciles while `lowrsquo is 2-3rd deciles Main sample The industry
categories are described in Section 25 The sample includes the sectors of the market economy
except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less knowledge-
intensive services
Most importantly we do not find evidence for an increasing gap between frontier and other
firms (in line with OECD 2016) in any of the industries Within manufacturing there is
convergence between frontier and non-frontier firms in medium-low and high-tech
industries However this is not robust for the alternative definition of frontier (top decile)
which moves strongly together with other deciles Based on this one may say that there is
no robust evidence either for convergence or divergence in manufacturing There are some
signs of convergence pre-crisis in knowledge-intensive and less knowledge-intensive
services as well as in construction followed by stronger productivity growth in the highest
quartiles post-crisis Importantly any convergence or divergence appears to be small
relative to already existing differences
Evolution of the Productivity Distribution
44
Figure 45 Cumulative TFP growth since 2004
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is the 10th
decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main sample The
industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less
knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
45
The picture is somewhat different when labour productivity is considered (Figure 46) In
this case the difference in growth rates between frontier and other firms is more
pronounced than in the case of TFP One can plausibly claim that less productive deciles of
the distribution caught up somewhat with the most productive firms in high-tech
manufacturing in the two service sectors and also in construction This suggests that
capital deepening by less productive firms (or low investment by frontier firms) may lead
to some convergence in terms of labour productivity but less so in terms of TFP33
Figure 46 Cumulative labour productivity growth since 2004 for labour productivity
deciles
A) Manufacturing
33 Note that these figures are the most directly comparable ones to Figure 41 which also presents results for labour productivity In line with that figure we find evidence for convergence between the median firm and frontier firms We also find that low-productivity firms converge The most important reason for this is that we exclude firms with less than 5 employees from our sample
Evolution of the Productivity Distribution
46
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average labour productivity level
for various deciles of the productivity distribution within each 2-digit industry-year combination
lsquoFrontier firmsrsquo are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo
is the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample The industry categories are described in Section 25 The sample includes the sectors of the
market economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo
Less knowledge-intensive services
Figure 47 zooms in to a few industries of interest which both confirm and qualify the
overall picture In textiles (a low-tech industry) frontier firms did not increase their
productivity in the period under study while lower productivity deciles experienced a
cumulative 40-50 percent productivity growth leading to an overall positive growth As
Section 61 discusses employment decline and firm exit were high in this industry
therefore the improvement of lower deciles may partly result from the exit of the lowest
productivity firms In machinery (a medium-high tech industry) all productivity deciles
had experienced strong TFP growth before the crisis and a significant fall during the crisis
followed by slow growth In this industry the full distribution has moved together
In retail (which is a member of the less knowledge-intensive services) TFP had grown to
some extent prior to the crisis followed by a large fall around the crisis and some growth
since 2012 Interestingly the fall was much larger and persistent for the most productive
firms while typical and low-productivity firms were able to maintain their pre-crisis
productivity levels The weak productivity performance of the top decile may have partly
resulted from regulatory changes and could have had large aggregate consequences given
the large employment share of retail (see Chapter 8) In lsquoComputer programming
consultancy and related activitiesrsquo there was a cumulative TFP increase of about 30 percent
since 2004 for all deciles without signs of convergence or divergence
Productivity differences in Hungary and mechanisms of TFP growth slowdown
47
Figure 47 Cumulative TFP growth since 2004 selected industries
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination in four industries
lsquoFrontier firmsrsquo are in the 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is
the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample
44 Duality in productivity and productivity growth
Besides the evolution of the overall shape of productivity distribution it is important to
understand the lsquodualityrsquo of productivity with respect to ownership
As a starting point Figure 48 shows the distribution of TFP and the natural logarithm of
the average wage for our main sample34 We filter out 2-digit industry fixed effects from
the two variables to control for industry-level differences
Comparing private domestic and foreign-owned firms one can make a number of
observations The foreign-owned distribution clearly stochastically dominates the
productivity and wage distribution of domestically-owned firms On average foreign firms
have 40 percent higher TFP and pay 75 percent higher wages than domestically-owned
firms in the same industry That said the within-group heterogeneity is larger than the
across-group heterogeneity generating a substantial overlap between the two
distributions For example 30 percent of domestically-owned firms are more productive
than the median foreign firm The averages between the two groups differ substantially
but there are many productive domestically-owned firms and unproductive foreign ones
34 Result for other TFP measures are very similar
Evolution of the Productivity Distribution
48
Another interesting difference between the distributions is that the foreign-owned
distribution is substantially more dispersed than the domestically-owned one (its standard
deviation is 23 percent larger) suggesting more technological heterogeneity within the
foreign-owned group This may suggest that this group includes both firms with world-
class technology and plants utilizing low-cost labour in a relatively unproductive way That
said the distribution is clearly not bi-modal there are no clearly distinguishable clusters of
high-tech and low-tech firms They operate along a continuum
Comparing state-owned firms to the other two groups shows that they are more similar to
the domestically-owned private firms with two interesting twists35 First the low-
productivity left tail of state-owned firms is much thicker than that of the privately owned
domestic firms Many state-owned firms operate with very low productivity levels (see also
Section 63) As a result the average productivity of these firms is 25 percent lower
compared to privately-owned domestic firms in the same industry
The second twist is that even though state-owned firms tend to be substantially less
productive than privately owned domestic firms they pay on average 25 percent higher
wages This may be a consequence of differences in worker composition but may also
suggest that these firms face soft budget constraints and their employees are able to
capture a larger slice from a smaller pie
Figure 48 Distribution of TFP and average wage by ownership (cleaned from industry-
year effects) 2016
Notes This figure shows the distribution of productivity and ln average wage after filtering out
industry-year fixed effects from it Domestically-owned is domestic privately-owned Main sample
35 Note that the sample of state owned firms is much smaller than the other two groups and operates in very specific indutries This may affect the distribution
Productivity differences in Hungary and mechanisms of TFP growth slowdown
49
Figure 49 shows the evolution of the productivity distributions across years Note that in
order to illustrate shifts in time industry-year fixed effects are not filtered out from this
figure Therefore comparing the distributions with Figure 47 shows how much industry
composition matters
Panel A) illustrates the productivity evolution of domestic private firms The shape of this
distribution remained remarkably similar across years There are clear rightward shifts
between 2004-2008 and 2012-2016 while the distribution did not change during the crisis
period Similar patterns can be observed regarding foreign-owned firms This distribution
was always more dispersed than the domestic one with little changes in its standard
deviation across years
The shape of the state-owned productivity distribution is more peculiar Most visibly it had
been bi-modal before the crisis This is mainly a consequence of industry composition the
low productivity part representing some utilities While the bi-modality disappeared post-
crisis the low-productivity tail of the distribution became thicker Finally we do not see
any rightward shift in this distribution there was little productivity improvement in this
small segment of the economy
Figure 49 Evolution of the distribution of TFP by ownership
A) Domestic private
Evolution of the Productivity Distribution
50
B) Foreign
C) State
Notes This figure shows the distribution of TFP Domestically-owned is domestic privately-owned
Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
51
BOX 41 Duality between domestic and foreign-owned firms in an international context
We are not the first to document the substantial wage and productivity advantage of foreign firms Earle
and Telegdy (2008) by using NAV data between 1986-2003 show that foreign-owned firms were almost
twice as productive as domestic private firms (measured in terms of labour productivity) and also paid
40 higher wages when controlling for employee characteristics A substantial part of this premium
results from foreign owners acquiring more productive firms (mostly during the privatisation process)
but even after controlling for this selection process the foreign wage premium remains 14 Similar
results are found by Telegdy et al (2012) when using the longer period between 1986 and 2008
Foreign-owned firms tend to have positive productivity and wage premia in most countries developed or
emerging Among others Aitken et al (1996) show that foreign-owned firms have higher productivity
and wages in Mexico and Venezuela even after controlling for firm size skill mix and capital intensity
Conyon et al (2002) use acquisitions in the UK in 1989-1994 to find that foreign firms pay 34 higher
wages which can be fully attributed to their 13 higher productivity Girma et al (2002) have a similar
result showing that foreign firms in the UK have 8-15 higher productivity which leads to 4-5 higher
wages Using UK data from 1981-1994 Girma and Goumlrg (2007) find wage differentials of a similar
magnitude but heterogeneous with regard to the source country of the foreign investor Huttunen
(2007) looks at Finland and finds 26-37 wage premium of firms 3 years after being acquired by
foreign investors In the Central-Eastern-European region Djankov and Hoekman (2000) show that
foreign investment in the 90s increased the productivity of recipient firms in the Czech Republic
Governments aim to attract foreign direct investment (FDI) as it is assumed to have a positive impact
on the domestic economy From an economic point of view it is justifiable to provide incentives to
foreign investors if their investments have positive spillovers to domestic firms increasing their
productivity The higher productivity of foreign-owned firms which is documented in the previously
mentioned studies is a necessary condition for that At the same time if foreign firms establish no links
with domestic firms there is only limited opportunity for knowledge spillovers In this case the inflow of
foreign investments results in a dual structure of the economy
Evidence is rather mixed on FDI spillovers to domestic firms in the same industry because a negative
competition effect might dominate the positive technology or knowledge effect Haskel et al (2007) find
that a 10-percentage-point increase in the share of foreign ownership increases the TFP of domestic
firms in the same industry by 05 in the UK Konings (2001) finds negative spillovers for Bulgaria and
Romania and no spillovers for Poland Positive spillovers in vertically related industries are much more
general Using Lithuanian data Javorcik (2004) shows that one standard deviation increase in the foreign
share of an industry is associated with 15 increase in the output of domestic firms operating in the
supplier industry Similarly Kugler (2006) finds no within-industry spillovers but positive spillovers in
vertically related industries in Colombia
Evolution of the Productivity Distribution
52
Let us turn to industry differences in duality The substantial difference between the average TFP of
domestic and foreign-owned firms is present in all kinds of industries (Figure 410 and 411) In
manufacturing the percentage difference is about 34 percent (a log difference of 03) while it is
around 65-100 percent in services Significantly the cumulative TFP growth of the two types of firms
was very similar by the end of the period There is no evidence for the catching-up of domestic firms
with foreign ones The duality in this respect does not seem to diminish substantially
The TFP gap between foreign and domestic firms is amplified by the much higher capital intensity of
foreign firms (Figure 412) In manufacturing foreign firms employed more than twice as much capital
per employee than domestic firms While the capital intensity of both domestic and foreign-owned
firms increased steadily during the period in that sector the gap remained constant showing little
catching-up of domestic firms in terms of capital deepening In a sharp contrast there was a decrease
in the capitallabour ratio in services and this phenomenon took place quicker in the case of foreign
firms
This picture is reinforced at the industry level (Figure 413) In textiles foreign firms invested more
than domestic ones leading to significant capital deepening for that group of firms In machinery both
groups of firms increased their capital intensity to a similar extent In retail foreign firms had invested
much before the crisis but cut their investments deeply after that while the capital intensity of
domestic firms remained mostly flat In programming capital intensity declined slightly following the
crisis
BOX 41 Duality between domestic and foreign-owned firms in an international context
(cont)
Looking at Hungarian data several papers show the existence of positive FDI spillovers to domestic
firms Halpern and Murakoumlzy (2007) find significantly positive spillovers in the supplier industry but
no evidence for within-industry spillovers Beacutekeacutes et al (2009) find a negative effect on low-
productivity firms in the same industry while the spillover effect is positive for high-productivity
firms Iwasaki et al (2012) find positive spillovers even within the same industry conditional on the
proximity in product and technological space At the same time Bisztray (2016) shows that the
large-scale foreign direct investment of Audi did not increase the productivity of domestic firms in
the supplier industry
We know from the literature that the effect of FDI on domestic firms is highly heterogeneous even in
the supplier industry (see Smeets 2008 for a review) A crucial precondition of positive spillovers is
the absorptive capacity of the domestic firms (Crespo-Fontoura 2007) Using data from Bulgaria
Poland and Romania Nicolini and Resmini (2010) show that firm size matters as well Additionally
they find within-industry spillovers in labour-intensive sectors and cross-industry spillovers in high-
tech sectors Also the characteristics of the foreign investment play an important role in the
magnitude of the spillover effect Javorcik (2004) estimates a positive effect on the productivity of
domestic firms only in the case of shared foreign and domestic ownership but not for fully foreign-
owned firms Javorcik and Spatareanu (2011) show that the distance of the investorrsquos country is
also important as investors from far-away countries establish more links with local suppliers In line
with that they estimate positive vertical spillovers from US investors but not from European
investors in Romania Lin et al (2009) show that vertical FDI spillovers in China are weaker for
export-oriented FDI compared to domestic-oriented
Productivity differences in Hungary and mechanisms of TFP growth slowdown
53
Figure 410 TFP levels of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the (unweighted) average ACF TFP level of foreign and domestically-owned firms Main
sample The industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge- intensive services lsquoLKISrsquo Less knowledge-
intensive services
Evolution of the Productivity Distribution
54
Figure 411 Cumulated TFP growth of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level of foreign and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
55
Figure 412 Capital intensity of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the average capital intensity (log tangible and intangible assetsemployee) of foreign- and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Evolution of the Productivity Distribution
56
Figure 413 Cumulative change in capital intensity of foreign and domestic firms selected industries
Notes This figure shows the (unweighted) average capital intensity (log tangible and intangible assetsemployee)
of foreign and domestically-owned firms since 2004 in four industries Main sample
45 Conclusions
Our investigation of the evolution of productivity distribution has yielded a number of relevant
conclusions which will inform the work conducted in the remaining sections In line with international
evidence we have found that productivity dispersion within industries is many times larger than the
differences between industries Importantly Hungary seems to be an exception to the international
trend of frontier firms diverging from the rest of the economy ndash if anything there is evidence for the
low productivity growth of frontier firms and for some catching-up by others
OECD (2016 Figure 16) has found such a pattern only in Hungary and Italy with divergence in all the
other countries under study (Austria Belgium Canada Chile Denmark Finland France Japan
Norway and Sweden) We find two kinds of explanations plausible First in Hungary (unlike most other
countries in that sample) national frontier firms are quite far away from the global frontier As
Andrews et al (2015) argue the productivity divergence mainly arises between global frontier firms
and the rest If national frontier firms are far away from the global frontier they may find themselves
on the wrong side of global divergence Second it is possible that the policies and institutional
environment for national firms in Hungary is less conducive to adopt local frontier technologies A way
to learn more about the background of this result would be to use cross-country micro-data to study
the behaviour of frontier firms in even more countries including other CEE countries
The low productivity growth of Hungarian national frontier firms constrains productivity growth
directly Furthermore if national frontier firms do not adopt the most developed technology potential
spillovers to other firms will also remain limited Andrews et al (2015) have shown that good
Productivity differences in Hungary and mechanisms of TFP growth slowdown
57
framework conditions (most importantly good regulatory practices in upstream sectors) and innovation
related policies such as providing incentives for RampD and building a more robust national innovation
system are associated with a stronger catch-up of national frontier firms to the global frontier
The results reveal that duality especially between foreign and domestic firms is substantial and there
is no evidence for catching-up by domestic firms The gap is especially large in the service industries
That said the gap between the two groups can be bridged indeed the productivity differences
between the two groups are smaller than within them Duality while a sign of inefficiency also
provides an opportunity for domestic firms to tap into the knowledge base possessed by their foreign-
owned counterparts and to integrate into global value chains by relying on the links of foreign firms
While efficient strategies aiming at maximizing the benefits from FDI and global value chains may
differ across countries there are a few policy options which unambiguously help countries in benefiting
from the presence of multinational firms A robust result of the recent spillover literature is that
domestic firms need strong absorptive capacity including technological knowledge and a skilled
workforce to be able to benefit from the presence of foreign-owned firms (Girma 2005 Crespo and
Fontoura 2007 Zhang et al 2010) One dimension of absorptive capacity building is creating an
effective innovation system with a strong knowledge base and easy access to that knowledge Another
dimension is developing technological and management capabilities which enable firms to understand
and apply advanced knowledge Such capabilities are essential both for technological upgrading and for
integrating into global value chains (Taglioni and Winkler 2016)
An important caveat regarding these results is that they are limited to double-entry bookkeeping firms
We have emphasised that a large share of people work outside the double-entry bookkeeping entities
included in our sample While data are scarce about the productivity of these entities available
information suggests that both the levels and dynamics of productivity may differ radically between
double-entry bookkeeping firms and other entities If so inclusive policies could focus on providing
skills and opportunities for the self-employed
State-owned firms constitute a small part of the Hungarian market economy but such firms are
prevalent in some industries including utilities The productivity of some of these firms is very low
when compared to the productivity of privately-owned firms while they pay higher wages Both of
these phenomena hint at soft budget constraints and other inefficiencies Policies aiming at providing
better incentives either by improving corporate governance of state-owned firms (Arrobio et al 2014)
or by creating framework conditions more conducive to competition may help in in promoting
productivity growth in these important industries
Allocative efficiency
58
5 ALLOCATIVE EFFICIENCY
A key insight of recent productivity research is that differences in productivity levels across countries
largely result from the inefficient allocation of resources across firms rather than from differences in
the productivity of lsquotypical firmsrsquo both in cross-section (Hsieh and Klenow 2009 Restrucca and
Rogerson 2017) and in time-series (Gopintah et al 2017) Inefficient allocation refers to the
phenomenon that low-productivity firms possess a large amount of capital and labour (rather than
shrinking or exiting) or when firms with similar marginal products use a different amount or
composition of inputs
In this chapter we employ two strategies to quantify the extent of such distortions The first strategy
proposed by Olley and Pakes (1996) simply asks whether more productive firms are larger A more
positive covariance between productivity and employment suggests a better allocation of resources
across firms and higher industry level (labour-weighted) productivity (even when holding the
unweighted productivity level unchanged) The Olley-Pakes method is generally agnostic about the
specific nature of distortions but measures their results in an intuitive and robust way at the industry-
year level
Hsieh and Klenow (2009) attempt to identify the sources of distortions36 In particular they argue that
firms can face two main distortions product market distortion (modelled as an implicit sales tax and
identified from the wedge between labour costs and value added) and capital market distortion
(modelled as an implicit capital tax and identified from differences in the cost share of capital) These
variables can be measured at the firm-level Industry-level distortions can be quantified both as the
average of firm-level distortions and also as the dispersion of firm-level measures
This chapter describes these measures at the industry-year level Section 51 presents the Olley-Pakes
covariance terms while Section 52 implements the Hsieh-Klenow method
51 Olley-Pakes efficiency
The Olley-Pakes (also called static) approach of productivity decomposition consists of decomposing
the aggregated (industry-region-level) productivity which is the weighted average of firm-level
productivity levels into the unweighted average firm-level productivity and the covariance between
productivity and firm size (Olley and Pakes 1996) The latter term reflects how efficiently resources in
this case labour are allocated across firms A more positive covariance between size and productivity
reflects stronger allocative efficiency
Let us start with cross-country evidence from the OECD (Andrews and Criscuolo 2013) According to
this source in 2005 static allocative efficiency in Hungarian manufacturing (the covariance term) was
positive but slightly below the average of OECD countries similar to Portugal and Italy (Figure 51)37
Allocative efficiency in services was negative one of the lowest of the countries in the sample
(Andrews and Cingano 2014 Figure 10) showing that less productive firms tended to be larger in the
service sector Andrews and Cingano (2014) also show that the relatively low allocative efficiency in
Hungary is partly explained by policies including product market regulation and creditor protection
36 For an overview of the reallocation literature see Hoppenhayn (2014)
37 Note that these calculations use the ORBISAMADEUS database covering a relatively small fraction of larger
Hungarian firms in 2005 (about 3300 firms) see Box 21
Productivity differences in Hungary and mechanisms of TFP growth slowdown
59
Figure 51 Static allocative efficiency in Hungarian Manufacturing (2005)
Notes This figure is a reproduction of Figure 7 from Andrews and Criscuolo (2013)
Let us turn to our data The logic of the static decomposition is presented in Figure 52 for our main
sample by 2-digit industry38 The horizontal axis shows the unweighted average log labour productivity
of each industry while the vertical axis shows the productivity weighted by employment If all firms
were of equal size (or at least firm size was independent of productivity) weighted and unweighted
productivity would be equal ie all industries would be on the 45-degree line If size and productivity
were positively correlated the weighted productivity would be larger than the unweighted one The
difference between the weighted and unweighted average is the covariance between size and
productivity This measure of allocative efficiency is equal to the vertical distance between each point
and the 45-degree line Allocative efficiency contributes positively to industry productivity in industries
above the 45-degree line while it has a negative contribution for industries below the line
For example in the manufacture of machineries (28) the unweighted average productivity is 646
while the weighted average productivity is 665 Allocative efficiency resulting from more productive
machine manufacturers being larger contributes with 019 to the aggregate productivity of this
industry An industry with negative allocative efficiency is warehousing (52) where the lower
productivity of larger firms contributes negatively to aggregate productivity (the unweighted
productivity being 681 and the weighted only 585)
38 Appendix Table A51-Table A56 summarise the Olley-Pakes (1996) measures by industry
Allocative efficiency
60
Importantly allocative efficiency is positive in most industries It is especially high in the most
knowledge-intensive services (scientific research (72) employment activities (78)) in service
industries with a few large firms (broadcasting (60) telecom (61)) and in key manufacturing
industries beverages (11) chemicals (20) machinery production (28) and vehicle production (29) In
a few industries low-productivity firms tend to be larger Prominent examples are professional
services advertising (69) and legal and accounting activities (73) services with many state-owned
firms transportation (39) waste management (49) and logistics (52) In line with OECD evidence
allocative efficiency tends to be more positive in manufacturing compared to services
Finally Figure A51 in the Appendix shows that allocative efficiency is significantly higher when labour
productivity is considered rather than TFP almost every industry has larger weighted labour
productivity than unweighted labour productivity This difference simply results from the positive
association between productivity and capital intensity
Figure 52 Weighted and unweighted TFP by 2-digit industry 2015 main sample
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted TFP while the vertical axis
shows its weighted TFP in the same year We have omitted industries with less than 1000 observations TFP is
estimated using the method of Ackerberg et al (2015)
Another conclusion that can be drawn from Figure 52 is that allocative efficiency is higher in sectors
with higher unweighted productivity represented by the fitted line in the figure In other words high
firm-level efficiency seems to move together with higher allocative efficiency in the industry One
mechanism behind this relationship may be that incentives for technology upgrading are stronger
when the reallocation process is more effective (Restruccia and Rogerson 2017) but stronger
international competition can also affect positively both within-firm productivity dynamics and
reallocation across firms In Figure 53 we investigate whether this relationship changed between
years The figure shows that the positive relationship between unweighted productivity and allocative
Productivity differences in Hungary and mechanisms of TFP growth slowdown
61
efficiency did not change substantially over time This relationship is similar when labour productivity is
considered (see Figure A52 in the Appendix)
Figure 53 The relationship between weighted and unweighted TFP by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted TFP levels run at the
2-digit industry level separately for 2005 2010 and 2016 TFP is estimated using the method of Ackerberg et al
(2015)
From the perspective of productivity slowdown a key question is whether allocative efficiency
deteriorated in some industries following the crisis Figure 54 shows the allocative efficiency of each
2-digit industry in 2010 and 2016 The axes here represent the distances from the 45-degree line in
Figure 52 If an industry is on the 45-degree line of this figure its allocative efficiency remained
unchanged in the period if an industry is above the line its allocative efficiency was better in 2016
compared to 2010 The first conclusion that can be drawn is that levels of allocative efficiency are
persistent industries cluster around the 45-degree line Also the fitted line shows that allocative
efficiency grew somewhat faster in industries where allocative efficiency was worse and this
relationship is statistically significant Therefore productivity growth decline is unlikely to be the result
of rapidly worsening allocative efficiency
One can however identify a couple of industries where substantial changes took place The machinery
industry (28) for example became more efficient partly because of the entry of new large foreign-
owned firms Office administration (82) and management activities (70) also increased their allocative
efficiency This is most likely due to the entry of large shared service providers Allocative efficiency
decreased in land transportation (39) waste management (49) and warehousing (52)
The evaluation of allocative efficiency in labour productivity shows similar patterns (Table A53 in the
Appendix)
Allocative efficiency
62
Figure 54 The change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences
between the weighted and unweighted TFP) in 2010 while the vertical axis shows the same quantity in 2016 TFP is
estimated using the method of Ackerberg et al (2015)
52 Product market and capital market distortions
The Olley-Pakes static decomposition framework can quantify the overall allocative efficiency of sectors
but it is incapable of informing us about the nature of distortions In this section we implement the
methodology of Hsieh and Klenow (2009)39 to distinguish between product and capital market
distortions This distinction is of much interest given that the crisis and its aftermath ran parallel with
both financial market frictions and changes in product market regulation
The logic of the Hsieh and Klenow (2009) method is the following Under the assumptions of
monopolistic competition on product markets (similarly to Melitz 2003) and frictionless labour
markets the marginal product of labour and capital should be equalized across firms in the absence of
market distortions In turn if the production function is Cobb-Douglas the equality of marginal
products implies that the share of labour costs in value added and capital intensity (capitallabour)
should be equalized across firms Under product market distortions (modelled with a firm-specific
implicit lsquosales taxrsquo or a negative rent) the wedge between labour costs and value added will differ
across firms because firms facing lower implicit taxes charge higher markups The more heterogeneous
the lsquosales taxrsquo is the larger the dispersion of the wedge Capital market distortions are modelled as
39 The Hsieh-Klenow approach has been criticized recently by Haltiwanger et al (2018)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
63
implicit firm-specific capital tax rates Firms facing different capital tax rates choose different capital
intensity levels and hence different capitallabour cost ratios Therefore the dispersion of capital
intensity (or more precisely the cost share of capital) reflects the dispersion of capital tax rates
Note that the implicit taxes proxy multiple sources of distortions from which differences in explicit
taxes represent only a small part The implicit lsquosales taxrsquo includes the cost of complying with different
types of regulations size-dependent regulation the effect of fixed costs and market power The
implicit lsquocapital taxrsquo includes for instance the full cost of accessing financing possible subsidies for
investment or differences in tax incentives to invest These implicit taxes provide a convenient way of
summarizing markup differences and differences in access to capital
As a result the dispersion of the wedge and capital intensity reflect how heterogeneous the two
implicit tax rates are More heterogeneity in implicit tax rates in turn implies more disperse total factor
productivity within industry40 and a less efficient allocation of resources In other words similarly
productive firms (having also similar marginal products of inputs) choose very different input quantities
and combinations
Product market distortions
We start our empirical investigation by calculating the rents (1-implicit sales tax rate) for every firm by
a proxy for markups41
1 minus 120591119884119904119894 =120590
120590minus1
120573119871119904+120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119881119860119904119894 (51)
where 120591119884119904119894 shows the size of the implicit lsquosales taxrsquo (or product market distortion) for firm i in sector s
120590 denotes the elasticity of substitution between firms by consumers and 120573119871119904 and 120573119870119904 are the
coefficients of labour and capital in the production function We follow the calibration of Hsieh and
Klenow (2009)42 and set 120590 = 3 while we plug in 120573119871119904 and 120573119870119904 using our production function estimation of
Section 22 119881119860119904119894 represents the real value added of the firm i in sector s while 119871119886119887119888119900119904119905119904119894 is labour
related expenses for firm i in sector s
The equation reflects the intuition that firms facing a lower implicit sales tax can charge higher
markups and as a result will pay a lower share of their value added to their employees Note that the
level of 120591119884119904119894 depends on a number of parameters and may be driven by differences in for example the
elasticity of substitution Therefore we will normalize the values of this estimate when comparing
typical distortions across industries
Figure 55 summarizes the implicit sales taxes by industry (120591119884119904119894) We standardise the values of 120591119884119904119894 by
subtracting the market level median from the firm-level implicit sales taxes and plot the median of
40 Appendix Table A57 summarises the dispersion of TFP within industry Note that dispersion in labour productivity
(log-value added per worker) is not necessarily related to product market distortions as firms with various
labour productivity may have the same TFP if the production function does not have the property of constant
return to scale
41 Hsieh and Klenow (2009) Equation 18
42 The predicted value of product market distortions crucially depends on the elasticity of substitution However the differences in 120591119884119904119894 across industries and years measures the changes in product market distortions even if the
elasticity of substitution is miscalibrated
Allocative efficiency
64
these standardised values by industry If the standardised bar is positive (negative) than the median
firm in the industry faces a higher (lower) implicit sales tax than the median firm in the economy We
find that product market distortions tend to be larger in highly regulated industries (energy
transportation ICT) while they tend to be lower in less regulated ones with strong competition
including manufacturing accommodation and administrative services The difference between
industries is non-trivial the difference between highly regulated sectors and manufacturing is
equivalent to an extra 10-20 percentage `sales tax ratersquo
The ranking of the industries (with the exception of energy) remained similar between 2006 and 2016
but differences became somewhat larger with a relative decrease in implicit taxes in manufacturing
and administrative services and an increase in transportation and ICT43
Figure 55 Implicit sales taxes (120591119884119904119894) by industry
Notes The figure above shows the median size of product market rents in 2006 and 2016 Industries with positive
tax measures can achieve rents below the market average due to product market distortions
The previous exercise has investigated across-industry differences A further question is whether firms
face different tax rates even within industries because of for example size-dependent taxes This is a
key measure to examine whether resources are misallocated across firms within industries Our
measure for this is the standard deviation of ln(1 minus 120591119884119904119894) (Figure 56)44 This dispersion is substantial
43 We report these measures in more detail in Table A55 of the Appendix
44 Also note that this measure of dispersion is independent of the elasticity of substitution and the production function parameters
Productivity differences in Hungary and mechanisms of TFP growth slowdown
65
with the standard deviation equivalent to a 100 percent sales tax45 Within-industry differences in this
variable are similar across industries with a relatively small dispersion only in mining and energy
Figure 56 Standard deviation of implicit sales tax rates (ln(1 minus 120591119884119904119894)) by industry
Notes The figure shows the within industry product market distortions in 2006 and 2016 Resources are less
effectively distributed in industries with larger distortion measures
Capital market distortions
Distortions on the capital market are identified from how the ratio of expenses on labour and capital
(capital intensity in cost terms) differ from what is predicted by the production function with no capital
tax46
119877(1 + 120591119870119904119894) =120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119870119904119894 (52)
The left hand side of this equation represents the implicit cost of capital for firm i in sector s backed
out from the capital intensity of the firm If it is 01 the firm faces an implicit lsquointerest ratersquo of 10
percent if it is 02 the lsquointerest ratersquo is 20 This can be decomposed into 119877 the frictionless user
45 Similar differences have been found in other countries as well and they are in line with the vast degree of heterogeneity in terms of size and productivity within industries
46 Hsieh and Klenow (2009) Equation 19
Allocative efficiency
66
costs47 of capital (having the same unit of measurement) multiplied by 1 plus the implicit lsquocapital tax
ratersquo 120591119870119904119894 which is firm-specific48
Similarly to the product market equation 120573119871119904 denotes the labour elasticity of the production function
120573119870119904 is the capital elasticity of the production function and 119871119886119887119888119900119904119905119904119894 is the total labour cost for firm i in
sector s The denominator consists the capital stock of the firm (119870119904119894)
It is not common in the literature to report 120591119870119904119894 because its absolute value depends crucially on the
calibration of the rental rate of capital This is an issue because it is hard to obtain reliable information
on the frictionless rate of capital which most likely changed substantially between the pre-crisis
disinflationary period and the wake of the crisis Besides 120591119870119904119894 takes extremely large values for firms
with low level of capital (eg if the firm rents its capital instead of owning it) Note that the levels of
this variable are identified from the difference between the observed capital intensity (in cost terms)
and the optimal one implied from the production function Therefore we prefer to report the more
easily interpretable implicit median cost of capital 119877(1 + 120591119870119904119894) by industry49
While we find differences and changes in the implicit cost of capital informative it is not a direct
measure of capital market distortions because it can also reflect differences in the user cost of capital
across industries and years However the ratio (or log difference) of the implicit cost of capital
between two firms measures the difference between their respective implicit capital tax rates (or more
precisely between their 1 + 120591119870119904119894) As a result the standard deviation of the log implicit cost of capital
provides a pure measure of the dispersion of implicit capital taxes independently from the exact value
of 119877 Its interpretation is the relative standard deviation of the user cost of capital which is identified
from the dispersion of capital intensities
Figure 57 summarizes the median size of implicit cost of capital across industries50 Administrative and
professional services and ICT seem to pay the highest implicit cost for capital it is above 40 percent in
these industries As opposed to these utilities accommodation and food services face implicit costs of
capital below 20 percent The large differences in access to capital across industries are likely to result
mainly from differences in the size and age distribution of firms as well as from the different share of
tangible capital in different industries Moreover the median implicit cost of capital rose practically in
all service industries but decreased slightly in manufacturing
47 The rental price of capital covers the interest rate and the depreciation of capital stock
48 If one is willing to assume a specific value for the frictionless user cost of capital it is easy to back out 120591119870119904119894 For
example if the implicit cost of capital for firm 119894 (the left hand side) is 02 and (following Hsieh and Klenow 2009) one sets R = 01 then 120591119870119904119894 = 1 meaning that firm 119894 can obtain capital at a 10 percentage points higher interest rate
relative to the frictionless rate
49 The median of 119877(1 minus 120591119870119904119894) is less dependent on the extreme values of the distribution than the average so it is a
more precise measure of capital market distortions a typical firm faces than the average of it
50 We can validate our implicit capital cost measure by comparing our results to Kaacutetay and Wolf (2004) According to their estimates (using a different methodology) the median user cost of capital was 189 percent between 1993 and 2002 Our results have similar magnitude as the median implicit cost of capital was 255 percent in 2006 and 287 percent in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
67
Figure 57 Median implicit cost of capital by industry
Notes The figure shows the average size of capital market distortions in 2006 and 2016 Industries with larger
distortion measures are more constrained in accessing capital due to capital market distortions
Again the differences in typical capital costs across industries are much smaller than differences across
firms within an industry (see Figure 58) In industries where median implicit capital costs are lower
the dispersion of those costs also tends to be smaller the estimated cost of accessing capital is
significantly more unequal in the retail sector and administrative services relative to manufacturing
The notable exemption is the energy sector which has the lowest median and the largest dispersion in
the implicit cost of capital reflecting a relatively low level of capital costs resulting from predictable
tangible capital intensive activities
Allocative efficiency
68
Figure 58 The standard deviation of the estimated implicit cost of capital by industry
Notes The figure shows the standard deviations of capital market distortions log (119877(1 + 120591119870119904119894)) in 2006 and 2016
Most importantly capital market distortions increased within nearly all industries both in terms of
their levels and dispersion Hungary is not an exception in this respect This trend has been
documented in other countries where FDI played important role in economic growth A key study on
this topic is Gopinath et al (2017) who show that large capital inflows and credit market constraints
of small firms jointly increased capital market distortions in Spain This evidence suggests that the
crisis led to similar developments in Hungary making capital costs more unequal by generating
financial frictions This inefficiency seems to have resulted in the misallocation of capital in all types of
industries
A key question from a policy perspective is whether one can identify types of firms which faced a
systematically large increase in their cost of capital We follow the approach of Gorodnichenko et al
(2018) who quantified the misallocation of capital at the firm-level and found that small and young
firms faced an exceptionally high cost of capital We follow this strategy to identify observables which
are likely to be related to the level and change of capital costs
Figure 59 plots the relationship between firm age firm size and the estimated implicit cost of capital
119877(1 + 120591119870119904119894) The figure sorts the firms into twenty equally-sized bins by age and size and plots the
median implicit cost of capital separately for 2006 and 2016 Panel (a) highlights that the implicit cost
of capital was decreasing with firm age even before the crisis with young firms facing about 25
percentage points higher capital costs compared to firms older than 10 years This function became
dramatically steeper by 2016 when the median `oldrsquo firm (more than 10 years old) faced an implicit
capital cost of 25 percent a median 5-year old firm paid 50 percent and a very young firm faced more
than a 100 percent implicit cost of capital This figure suggests that capital market frictions generate
important constraints for entry and the growth of small firms hindering reallocation and innovation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
69
Panel (b) of Figure 59 visualizes the relationship between employment and the implicit cost of capital
We find that firms with more than 20 employees faced an implicit cost of capital below the median of
the whole sample both in 2006 and 2016 As opposed to this small firms faced above the median
implicit cost of capital in 2006 and suffered from a disproportionally large increase in the next decade
This again constrains the growth of small firms relative to their larger peers
Figure 59 The evolution of the implicit cost of capital by age and firm size
A) Age of firms
B) Size of firms
Notes The figure shows the median implicit cost of capital 119877(1 + 120591119870119904119894) by age and size categories
Allocative efficiency
70
The results presented above have shown two patterns an increasing dispersion of the implicit cost of
capital on the one hand and a steeper gradient between observables (age and size) and capital taxes
on the other A natural question is whether increased financial friction led to larger differences in
access to capital along observables One explanation for this could be that banks have become more
wary about allocating capital to say firms operating in industries with much intangible capital The
alternative is that the increased variance in capital access reflects mainly differences along unobserved
dimensions by for example more scrutiny of managers when deciding about firm loans These two
possibilities can have different policy implications In the former case for example policymakers may
promote access to capital for specific groups of firms
Table 51 presents regressions with the implicit capital cost as a dependent variable and key firm-level
characteristics as explanatory variables Our first conclusion is that the regressions explain only a
relatively small part (less than 20 percent) of the variation in the implicit cost of capital the
overwhelming majority of the variation arises from unobservables In this sense policies targeting
specific types of firms may have a limited effect
That said the explanatory power of observables increased by around a third between 2006 and 2016
While the explanatory power of industry dummies slightly decreased that of age increased
substantially from 2 percent to 57 percent The explanatory power of size was much smaller in both
periods suggesting that its correlation with the implicit cost of capital may be confounded by its
correlation with age and industry This evidence together with Figure 59 suggests that indeed
capital access by young firms deteriorated substantially after the crisis
Table 51 Variance decomposition of implicit cost of capital
Variance in 2006 Variance in 2016
Variance
component
Share
of total
Variance
component
Share
of total
Total Variance of log-implicit
cost of capital
2126 100 2443 100
Components of Variance
Variance of age 0042 20 0140 57
Variance of size 0006 03 0002 01
Variance of ownership 0012 06 0022 09
Variance of region 0012 06 0025 10
Variance of industry 0202 95 0180 74
Residual 1830 861 1995 817
Notes Control variables are dummies for age ownership (private foreign or state-owned) region (7 NUTS2
region) and 2 digit industry
53 Conclusions
This section summarises the static measures of allocative efficiency by industry types (Table 52) A
key pattern that emerges is that resources are allocated more efficiently in the manufacturing sectors
First on average the OP covariance is strongly positive within manufacturing while it is very close to
zero in less knowledge-intensive services The Hsieh-Klenow (2009) efficiency measures suggest that
product market distortions are similar across sectors but capital market distortions are significantly
lower in manufacturing These findings are in line with the disciplinary effect of international
competition in the traded sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
71
Table 52 Allocative efficiency within industries sectors (2016)
Industry type TFP
level
in
2016
TFP
growth
between
2011 and
2016
Olley-
Pakes
allocative
efficiency
Dispersion
of implicit
sales taxes
Dispersion
of implicit
cost of
capital
Low-tech mfg 5694 0027 0197 111 146
Medium-low tech mfg 6081 0027 0017 102 142
Medium-high tech mfg 6129 -0093 0119 111 131
High-tech mfg 6708 0276 0072 107 145
Total manufacturing 5942 0021 0242 107 143
Knowledge-intensive serv 6706 0225 0403 106 166
Less knowledge-intensive serv 6566 021 -0081 108 159
Construction 6411 0082 0023 109 148
Utilities 5949 -0138 0801 093 155
Total services 6598 0212 0055 108 160
Notes The table summarises the allocative efficiency measures by broad industry categories The dispersion of
implicit sales taxes is measured by the standard deviation of 119897119899(1 minus 120591119884119904119894) while the dispersion of the implicit cost of
capital is measured by the standard deviation of ln (119877(1 + 120591119870119904119894))
Capital market distortions became more severe in the wake of the financial crisis while there was no
such trend in terms of product market distortions This finding is in line with results for Southern
Europe (Gamberoni et al 2016a Gopinath et al 2017) and CEE countries in general (Gamberoni et
al 2016b) (see Figure 510) This suggests that the financial intermediation system is still less
effective relative to its pre-crisis performance
Investigating at the firm-level we found that the deterioration of the financial conditions did not hit all
firms equally In particular young firms were hit especially hard by ever increasing capital costs even
though many policy tools were introduced to help such firms including the subsidized access to capital
by the Central Bank (eg the NHP program) Deteriorating access to capital by young firms can be
especially harmful for reallocation often driven by dynamic young firms Policies aimed at promoting
equal and efficient access to capital especially for young firms may help to reduce these inefficiencies
Given the magnitude of the still existing allocative inefficiency policies which support reallocation could
have a significant positive effect on aggregate productivity A key conclusion of recent research is that
firm-specific distortions which may result from discretionary policies or non-transparent regulations
have a quantitatively significant effect on aggregate productivity (Hsieh and Klenow 2009 Bartelsman
et al 2013 Restuccia and Rogerson 2017) In particular size-dependent taxes and regulations
(Garicano et al 2016) ineffective labour and product market regulations and FDI barriers (Andrews
and Cingano 2014) have been shown to be negatively associated with allocative efficiency and its
improvement Gamberoni et al (2016b) also demonstrate that higher corruption levels slow down the
improvement of allocative efficiency Chapter 8 will investigate the effects of such policies in more
detail using the example of the retail industry
The specific pattern showing that capital distortions are relatively high and have increased in Hungary
(similarly to other CEE and Southern European countries) suggests that policies which facilitate the
reduction of financial frictions and provide symmetric access to capital for all firms could improve
allocative efficiency to a large degree Specifically policies should attempt to facilitate capital flows to
Allocative efficiency
72
more efficient firms even if young rather than to firms with a higher net worth or more tangible
assets (Gopinath et al 2017)
Figure 510 Capital and labour misallocation in CEE countries country-specific weighted average
across sectors
Notes This is a reproduction of Figure 1 from Gamberoni et al (2016b)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
73
6 REALLOCATION
After investigating the level of allocative efficiency in Chapter 5 namely a lsquostaticrsquo approach we now
turn to a dynamic view focusing on how much reallocation across industries (Section 61) and firms
(Section 62) contributed to aggregate and sectoral productivity growth
61 Reallocation across industries
An important channel behind the relationship between economic development and productivity is the
structural change of the economy first from agriculture to manufacturing and then from
manufacturing to services (Herrendorf et al 2014 McMillan et al 2017) But at higher levels of
development economic growth is also associated with reallocation across industries within these broad
sectors primarily from more traditional to more knowledge-intensive ones (Hausmann and Rodrik
2003 Hausmann et al 2007) Kuunk et al (2017) demonstrate that in terms of its contribution to
productivity growth across-industry reallocation within sectors dominated reallocation across sectors
in CEE countries In this subsection we take a brief look at the importance of this process in Hungary
by quantifying the reallocation of employees across and within 2-digit industries
Table 61 shows how the employment share of different industries in our main sample changed over
time51 The most important pattern is a pronounced shift from manufacturing to services until 2010
and near-constant sectoral shares after that In particular the share of manufacturing decreased by
nearly a quarter from 38 percent to 32 percent between 2004 and 2010 but this number remained
unchanged in the years following the crisis The crisis seems to have constituted a structural break in
this process
A more detailed look at the composition of industries shows that ndash in net terms ndash this structural
change was driven by a transition of employment from low-tech manufacturing52 to both knowledge-
intensive and less knowledge-intensive services while the employment share of the more high-tech
manufacturing industries remained practically unchanged After 2010 the structure of manufacturing
remained mainly unchanged in this aspect with no further shift away from low-tech manufacturing
activities Within services we see a continuous increase in the share of knowledge-intensive services
both before and after the crisis In the 12 years under study the employment share of knowledge-
intensive services increased by 6 percentage points or nearly 60 percent
51 Note that these calculations in line with other parts of this report apply to the firm sector of the Hungarian
economy ie ignore the self-employed (see Section 42) When taking into account the self-employed the share of
services and sectoral share follow somewhat different dynamics
52 One factor behind this process might have been the almost doubling of the minimum wage in 2000 and 2001
(Koumlllő 2010 Harasztosi and Lindner 2017) and a growing import competition in the light industries (David et al
2013)
Reallocation
74
Table 61 Employment in different sectors (main sample)
2004 2007 2010 2013 2016
Low-tech mfg 152 117 107 105 100
Medium-low tech mfg 89 92 91 96 98
Medium-high tech mfg 94 96 82 89 93
High-tech mfg 49 49 44 39 35
Total manufacturing 384 355 324 329 327
Knowledge-intensive serv 107 128 149 158 167
Less knowledge-intensive serv 382 395 410 405 397
Construction 86 88 83 75 75
Utilities 40 34 34 33 34
Total services 616 645 676 671 673
Notes This table shows employment shares by industry type (see Section 25) for the full sample
To provide a more detailed picture Figure 61 illustrates how employment growth in different 2-digit
industries is associated with their initial productivity level (Figure 61) In particular if more productive
sectors increase their employment share faster aggregate productivity should grow
Figure 61 Employment change as a function of initial TFP
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
75
B) Services
Notes Industries are 2-digit NACE Rev 2 industries The fitted line is weighted with initial employment Main
sample
To quantify whether across-industry reallocation matters we decompose the aggregate productivity
growth observed in our sample into the contributions of cross-industry reallocation and within-industry
productivity growth We divide our sample into three-year periods and calculate the average yearly
productivity growth by periods
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119901119894119905minus3)119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+ sum 119905119891119901119894119905minus3 lowast (119904ℎ119886119903119890119894119905 minus 119904ℎ119886119903119890119894119905minus3)119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
(61)
where the left hand side variable is the change in aggregate TFP between years 119905 minus 3 and 119905 119904ℎ119886119903119890119894119905 is
the share of the (2-digit) industry i in year t in the total employment and 119905119891119901119894119905 is average TFP of the
industry The first term on the right side is the within-industry TFP growth weighted by initial market
shares and the second term is the between effect capturing whether more productive industries have
increased their employment shares53
The decomposition in Figure 62 presents the result of this reallocation exercise for annualized growth
rates Its interpretation is the following between 2004 and 2007 average annual productivity growth
was nearly 8 percent in the total economy Around 7 percentage points from it is explained by within-
industry developments and only about 1 percentage point by reallocation across industries
53 This decomposition gives a comprehensive measure of the reallocation between industries but it is unable to
show the importance of firm exits and entries We investigate this in the next section
Reallocation
76
In general the figure shows that within-industry reallocation rather than cross-industry
developments played the key role in aggregate productivity growth Furthermore in line with Table
61 the contribution of between-industry reallocation was effectively zero post-crisis During the crisis
cross-industry productivity growth contributed positively to aggregate productivity growth while within
industry reallocation dramatically lowered aggregate productivity
This overall picture suggests that the flow of resources from light industries to other manufacturing
the growing share of services and especially knowledge-intensive services were a detectable though
not dominant driver of productivity growth only before 2010 Within-industry developments were
quantitatively more important throughout the whole period under study
This latter finding hints at a deterioration in the environment determining the reallocation process
post-crisis This seems to be the case for the whole economy but the negative contribution of
reallocation is more pronounced in manufacturing
Figure 62 Across and within industry productivity growth annualized log
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth main
sample
62 Reallocation across firms
In this subsection we take a look at the role of reallocation from a different perspective Rather than
focusing on whether the resources flow across industries we take a firm-level focus and decompose
TFP growth to within and across firm components The usefulness of this approach lies in the fact that
it sheds more light on the flexibility and efficiency of the process determining resource flows across
firms and also allows us to distinguish between resource flows across continuing firms on the one hand
and entry and exit on the other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
77
There are two general methods of measuring the reallocation of resources from less efficient to more
efficient firms The first method quantifies the labour and capital gains of more efficient firms directly
(Harasztosi 2011 Petrin et al 2011 Petrin and Levinson 2012) The second method is based on
product-market developments allocation of resources improves if the market share of high
productivity firms increases (Baily et al 1992 Griliches and Regev 1995 Brown and Earle 2008)
We adopt this second method as it can quantify directly the TFP contribution of firm entries and exits
To begin with we decompose the aggregate TFP growth between years t and t-3 based on the method
of Foster et al (2001) and Foster et al (2008)
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905minus3 lowast ∆119905119891119901119894119905minus3119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
+ sum (119905119891119901119894119905minus3 minus 119905119891119905minus3 + ∆119905119891119901119894119905) lowast ∆119904ℎ119886119903119890119894119905minus3119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+
sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119905minus3)119894isin119873⏟ 119890119899119905119903119910 119890119891119891119890119888119905
+sum 119904ℎ119886119903119890119894119905minus3 lowast (119905119891119901119894119905minus3 minus 119905119891119905minus3)119894isin119873⏟ 119890119909119894119905 119890119891119891119890119888119905
where the left hand side variable is the average annual aggregate TFP growth between years t-3 and t
and 119905119891119905 is the employment weighted average aggregate TFP while 119905119891119901119894119905 is the TFP of firm i in year t
119904ℎ119886119903119890119894119905 denotes the employment share of firm i in year t The first and second sum contain every firm
while the third sum consists of only firms which enter between years t-3 and t and the fourth sum
consists firms which leave the market between years t-3 and t
Each element of this decomposition has an intuitive economic interpretation In order of inclusion
these are i) within-firm TFP growth weighted by initial market shares ii) between effect capturing
whether initially more productive firms have raised their market shares and whether firms with
increasing productivity also expand (cross effect) and the iii) entry effect and iv) exit effect We pull
the last two terms together and interpret it as net entry effect which captures whether more
productive firms entered than exited54
Figure 63 summarizes the results for the market economy Before the crisis all three components
contributed positively to aggregate productivity growth Reallocation both across continuing firms and
on the margin of entry and exit was an important driver of productivity growth Productivity growth
was negative during the crisis as we have seen in Section 43 This was a result of strong negative
within-firm growth partly counterbalanced by positive reallocation Within-firm growth was still
sluggish immediately after the crisis but reallocation was relatively intensive and efficient Within-firm
growth recovered after 2013 and the importance of reallocation decreased Still the contribution of all
three components is substantially smaller relative to pre-crisis suggesting that the productivity
slowdown results from a combination of low within-firm growth and less effective reallocation
54 Note that these quantities cannot be easily linked to the withinacross industry decomposition of the previous
section Across firm reallocation and the entry effect can take place both across and within sectors
(62)
Reallocation
78
Figure 63 Dynamic decomposition annualized log main sample
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth by 3-
year periods main sample
Figure 64 repeats the decomposition exercise for each industry type For ease of interpretation (and
to get more stable results) we aggregate the three non-high-tech manufacturing sectors for these
calculations
As we have seen in Section 43 productivity dynamics differed markedly across these sectors Still
there are some common patterns First the strong pre-crisis productivity growth resulted from a
combination of strong within-firm productivity growth and efficient reallocation The sectors differ in
terms of the weights of these forces reallocation (especially entry) was most important in non-high-
tech manufacturing while within-firm growth dominated in high-tech manufacturing In services the
two components were of roughly equal importance
As we have seen productivity increased even during the crisis in high-tech manufacturing as a
combination of within and across productivity growth In other industries productivity growth was
negative during the crisis In non-high-tech manufacturing a strongly negative within growth was
somewhat counterbalanced by positive reallocation In contrast we find evidence for a negative
reallocation effect in services during the crisis
Immediately following the crisis (2010-2013) within growth remained sluggish but reallocation
resulting from firm entry and exit intensified especially in non-high-tech manufacturing and high-tech
services By 2013-2016 within growth recovered and the effect of reallocation became smaller
Productivity differences in Hungary and mechanisms of TFP growth slowdown
79
Figure 64 Dynamic decomposition by sector
A) High-tech Manufacturing
B) Non-high-tech Manufacturing
Reallocation
80
C) Knowledge-intensive services (KIS)
D) Not knowledge-intensive services (NKIS)
Notes This figure shows the Foster et al (2008) type dynamic decomposition of the productivity growth in our
sample for 3 periods by broad sectors as defined by the EurostatOECD (httpeceuropaeueurostatstatistics-
explainedindexphpGlossaryHigh-tech)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
81
One of the main messages of our analysis in Section 44 has been the large and persistent duality
between globally oriented and other firms This motivates our investigation of the extent to which
exporters and foreign-owned firms contributed to productivity growth and also whether reallocation
via the expansion of the more productive group contributed to aggregate productivity growth In order
to investigate these questions we decompose aggregate productivity growth into three parts the
within contribution of exporters (in the starting period) the within-contribution of non-exporters and
the reallocation between the two groups (which mainly reflects the change in the market share of
exporters) We conduct a similar analysis between foreign and domestically-owned firms
Table 62 shows the decomposition by export status Pre-crisis exporters contributed substantially
more to productivity growth than non-exporters both in manufacturing and services The reallocation
of resources to exporters mattered little Exporters were still capable of improving their productivity
levels during the crisis though it was not enough at the aggregate to counterbalance the falling
productivity of non-exporters Post-crisis the productivity growth of exporters slowed down and
aggregate growth was mainly driven by productivity changes within the non-exporter group
Productivity growth became much less exporter-driven post-crisis
Table 62 TFP growth decomposition by exporter status annualized log
2004-2007
Total Exporter Non-exporter Across
Market economy 793 577 226 -009
Manufacturing 1053 877 164 011
Market services 606 387 222 -003
2007-2010
Total Exporter Non-exporter Across
Market economy -045 091 -136 000
Manufacturing 054 021 007 027
Market services -178 129 -305 -002
2010-2013
Total Exporter Non-exporter Across
Market economy 206 033 144 029
Manufacturing -122 -129 -012 019
Market services 471 077 289 106
2013-2016
Total Exporter Non-exporter Across
Market economy 362 155 206 001
Manufacturing 057 049 011 -003
Market services 650 243 382 025
Notes This table decomposes the sales-weighted productivity growth into within-exporter within-non-exporter
contributions and the contribution of the reallocation between the two groups main sample
Table 63 decomposes productivity growth by ownership The picture is similar to the exporter
decomposition with a key contribution of foreign-owned firms to productivity growth pre-crisis and a
much smaller contribution after that Again reallocation of resources to foreign-owned firms played a
limited role in productivity growth
Reallocation
82
Table 63 TFP growth decomposition by ownership status annualized log
2004-2007
Total Foreign Domestic Across
Market economy 793 255 516 022
Manufacturing 1053 375 619 059
Market services 606 148 383 075
2007-2010
Total Foreign Domestic Across
Market economy -045 018 -076 013
Manufacturing 054 081 -034 007
Market services -178 -131 -120 073
2010-2013
Total Foreign Domestic Across
Market economy 206 003 199 005
Manufacturing -122 -152 027 003
Market services 471 138 336 -003
2010-2016
Total Foreign Domestic Across
Market economy 362 106 264 -008
Manufacturing 057 011 040 005
Market services 650 234 452 -037
Notes This table decomposes the sales-weighted productivity growth into within-foreign within-domestic
contributions and the contribution of the reallocation between the two groups Main sample
63 Failure of reallocation Zombie firms
Following the crisis it was suggested that weak productivity performance could be linked to the
survival of unprofitable and ineffective firms The presence of many such firms limits the access of
better-managed firms to capital and generates congestion in the product markets which limits entry
(Caballero et al 2008) McGowan et al (2017) have argued and provided evidence that the share of
such ldquozombie firmsrdquo has risen since the middle of the 2000s and that the higher share of such firms is
associated with lower productivity growth and investment at the industry level
Given the productivity slowdown in Hungary and the extent to which the financial crisis has affected
bank lending it is of interest to see whether the prevalence of ldquozombie firmsrdquo increased
disproportionately after the crisis
Figure 65 shows the share of ldquozombie firmsrdquo in 9 OECD countries from McGowan et al (2017) The
share of such firms in the full sample increased from just below 3 percent in 2003 to 5 percent in
2013 The rise was especially noticeable in Spain and Italy where in 2013 the share of firms reached
11 and 5 percent respectively Even more importantly the employment share of ldquozombie firmsrdquo rose
above 15 percent in both countries by 2013 possibly generating significant effects for other firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
83
Figure 65 Share of ldquozombie firmsrdquo in some OECD countries
Notes This is a reproduction of Figure 5A from McGowan et al (2017) Country codes should be interpreted as
follows BEL ndash Belgium ESP ndash Spain FIN ndash Finland FRA ndash France GBR ndash Great Britain ITA ndash Italy KOR ndash South
Korea SWE ndash Sweden SVN ndash Slovenia
Our basic definition of ldquozombie firmsrdquo follows McGowan et al (2017) for comparability We define a
firm as a zombie if it is at least 10 years old and its interest coverage ratio (the ratio of operating
income to interest expenses) has been below one for the last three years A limitation of this definition
is that interest expenses are not reported (or missing) for many smaller firms which only submit a
less detailed financial statement (or have no bank financing) To overcome this problem we also
categorize firms as zombies if their operating profit is negative for three subsequent years In such
cases the coverage ratio is not defined but the firmrsquos income is clearly not enough to cover its interest
expenses Note that this is a very conservative definition ndash one could also input interest expenses for
external capital for firms with missing interest expenditures (Figure 66)55
55 In actual fact 95 percent of zombies defined in this manner have negative profits
Reallocation
84
Figure 66 Share of ldquozombie firmsrdquo in Hungary
Notes Main sample
The patterns are the following First the share of ldquozombie firmsrdquo among firms with at least 5
employees was relatively high even before the crisis reaching about 8 percent by 2006 This increased
slightly in the wake of the crisis but started to decline after that falling to 3 percent in 2016 ldquoZombie
employmentrdquo fluctuated around 12-15 percent in most years with a steep decline after 2014
Put in an international context it is clear that the existence of ldquozombie firmsrdquo is a relatively big issue in
Hungary with their employment share at the highest end of the distribution of the OECD countries
examined by McGowan et al (2017) The prevalence of such firms however had been relatively high
even before the crisis with a relatively moderate growth between 2009 and 2011 followed by a
significant fall of the share of these firms Therefore ldquozombie firmsrdquo may have constrained productivity
growth in Hungary in the whole period but it is unlikely that an increase in zombie share is a key
explanation for productivity slowdown following the crisis
Table 64 shows the employment share of zombie firms in different dimensions One can see a U-
shaped relationship in terms of size with the largest zombie share among the smallest and the largest
firms The somewhat larger share of zombies among small firms may be explained by the tendency of
such firms to report losses in order to evade business taxes Large firms may be able to operate
persistently under losses either because of their accumulated savings or even more likely because of
the deep pockets of their owners This is also suggested by part B) which shows that a firm is more
Productivity differences in Hungary and mechanisms of TFP growth slowdown
85
likely to be a zombie if owned by the state56 or by foreigners In the latter case profit-shifting motives
may also play a role in reporting losses for sustained periods in Hungary Finally zombies are more
prevalent in services compared to manufacturing and in low-tech industries compared to high-tech
Table 64 Zombie employment by size ownership and industry
A) By size
2004 2007 2010 2013 2016
5-9 emp 62 751 862 802 411
10-19 emp 618 626 679 652 297
20-49 emp 607 554 698 648 296
50-99 emp 711 685 792 829 438
100- emp 2005 1839 1559 1489 456
Total 1548 1367 1229 1194 417
B) By ownership
2004 2007 2010 2013 2016
Domestic 66 671 769 614 253
Foreign 989 1011 1139 1577 585
State 6289 5954 4127 2792 7
Total 155 1369 1228 1194 417
C) By type of industry
2004 2007 2010 2013 2016
Low-tech mfg 1236 1255 1149 906 371
Medium-low tech mfg 897 515 846 974 634
Medium-high tech mfg 542 407 936 486 269
High-tech mfg 427 1392 429 268 1
KIS 2098 737 875 1408 592
LKIS 282 2327 187 1867 365
Construction 258 394 339 515 352
Utilities 297 1031 705 593 756
Total 1553 1372 1229 1194 418
Notes Main sample
Importantly all these patterns persist in multiple regressions when one includes all these variables at
the same time together with other controls (ie larger firms are more likely to be zombies even when
controlling for ownership) In such regressions (lag) productivity is the strongest predictor of not
56 Obviously the extreme employment share of state-owned zombie firms partly results from the massive size of some large utilities including the national railways and the Hungarian Post
Reallocation
86
becoming a zombie firm later one standard deviation higher productivity is associated with a 5
percentage point lower probability of becoming a zombie in the next period Note however that
productivity is actually a close measure of profitability hence this finding mostly reflects a mechanical
relationship of high profitability firms being less likely to become low profitability firms in the future
Figure 67 shows a 3-year transition matrix for zombie firms ie the share of year t zombie firms
which ldquorecoverrdquo remain zombies or exit from the market by year t+3 One cannot see radical changes
across years with somewhat more firms recovering and less exiting in later periods In line with the
argument about deeper pockets larger firms are more likely to remain zombies and less likely to exit
than smaller ones This is related to ownership foreign (and to a smaller extent state-owned) firms
are more likely to remain zombies There also seems to be a characteristic difference between
manufacturing and services manufacturing firms seem to be less likely to lsquorecoverrsquo and more likely to
exit suggesting more persistence of low performance in that sector
Figure 67 What happens to zombie firms within 3 years (2010)
Notes Main sample
64 Conclusions
In line with the immense within-industry productivity heterogeneity documented in Chapter 4 and 5
we find that while there was some reallocation across sectors in the economy the overwhelming
majority of productivity growth took place within industries This emphasizes the usefulness of policies
which promote productivity growth in a sector-neutral way rather than prioritizing some sectors of the
economy
In line with the lower efficiency of capital allocation post-crisis we have found that by and large both
within-firm productivity growth and reallocation across firms and industries became less efficient post-
crisis relative to its pre-crisis level This may reflect the presence of policies which promote specific
sectors or inhibit the growth and entry of more productive firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
87
In terms of the participation of global networks we have found that at least pre-crisis exporters and
foreign-owned firms contributed significantly to productivity growth Post-crisis the productivity
contribution of internationalized firms became much less substantial Adopting policies that create an
environment which is favourable for innovative investments and does not hamper the expansion of
globally oriented firms may contribute substantially to strengthening productivity growth
The presence of firms which are loss making for an extended period of time suggests a serious failure
of the reallocation process The share of such firms was relatively high in Hungary employing well
above 10 percent of the employees in our sample in most years This level was already high pre-crisis
and increased further during the crisis but there has been substantial improvement in recent years
The problem is more severe for larger firms and state owned firms Improving the corporate
governance of these firms and the effectiveness of the banking system may help in further alleviating
the problem
Andrews et al (2017) argue that the presence of zombie firms ndash and other barriers to firm dynamics ndash
is heavily related to the efficiency of insolvency regimes and the effectiveness of the banking system
Figure 68 shows an insolvency regime index developed by the OECD (the higher the index value the
slower the restructuring) Hungary is one of the countries with the weakest insolvency systems with
all sub-measures taking high values This coupled with the presence of weak banks can be one of the
reasons for the permanently high zombie firm share as well as the increasingly inefficient capital
allocation across firms Therefore insolvency reform complemented with policies aimed at improving
bank forbearance can help to reduce the presence of zombie firms The presence of zombie firms may
also be related to the large share of bank financing Promoting market-based financing including bond
and venture capital markets may also help to diminish the problem
Figure 68 Insolvency regimes across countries
Notes This chart is a reproduction of Andrews et al (2017)rsquos original except for being restricted to European
states only The stacked bars represent the 3 main components of a countrys insolvency index for the year 2016
while the diamond figure indicates these measures aggregate for the year 2010 The authors constructed these
figures with the help of an OECD questionnaire Each measure is associated with a factor that in the long term is
thought to reduce a countrys business dynamism and consequently hamper its proclivity for productivity growth
The first one Personal costs of insolvency stands for environmental factors which could curb a failed
entrepreneurs ability to start new businesses in the future The second measure Lack of prevention and
streamlining indicates whether there are sufficient practices in place for the early detection and resolution of
Reallocation
88
financial distress Thirdly Barriers to restructuring shows how easy it is for a firm suffering from short-term
financial troubles to restructure its debt Country codes should be interpreted as follows GBR ndash Great Britian FRA
ndash France DNK ndash Denmark DEU ndash Germany ESP ndash Spain FIN ndash Finland IRL ndash Ireland SVN ndash Slovenia PRT ndash
Portugal AUT ndash Austria GRC ndash Greece SVK ndash Slovakia ITA ndash Italy LVA ndash Latvia POL ndash Poland NOR ndash Norway
SWE ndash Sweden LTU ndash Lithuania BEL ndash Belgium CZE ndash Czech Republic MLD ndash Moldova HUN ndash Hungary EST ndash
Estonia
Productivity differences in Hungary and mechanisms of TFP growth slowdown
89
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS
The main aim of this section is to investigate the micro-level processes which underlie the patterns
documented at the industry level in the previous chapters (especially in Chapter 6) by presenting a few
descriptive relationships between firm characteristics and firm dynamics More specifically we would
like to understand how various firm characteristics are associated with the observed patterns of
productivity and employment growth to illustrate the micro-level processes behind within-firm
productivity growth and reallocation Additionally we look at which types of firms enter and exit in
order to shed light on how they contribute to changes in the average productivity level
We seek to answer three main questions First was there a structural break either in the productivity
growth or in the reallocation process after the crisis which may have contributed to the productivity
growth slowdown Second do we see a structural difference in these processes along the main
dimensions of the lsquodualityrsquo of the Hungarian economy eg the characteristic differences between
globally involved large firms and their domestic market oriented peers Third can we find peculiar
patterns which may explain the unusual evolution of productivity quintiles namely the slow
productivity growth of frontier firms relative to less productive firms (as documented in detail in
Section 44)
In terms of firm characteristics we focus on variables which are likely to be related to the duality (see
Section 44) ownership size age and exporter status We do firm-level regression analyses which
allows us to use a rich set of controls and fixed effects Additionally we look at the interaction of the
different characteristics to get an even more precise picture about the main factors driving productivity
growth and reallocation
The structure of this chapter follows closely the logic of the dynamic productivity decomposition
exercise in Chapter 6 In Section 71 we investigate the determinants of within-firm productivity
growth In Section 72 we explore how firm characteristics are related to future employment growth ndash
ie to between firm reallocation ndash followed by the analysis of entry and exit in Section 73
71 Productivity growth
Questions and descriptive patterns
A key relationship of interest is how future productivity growth is related to current productivity levels
Our main motivation to study this question is that it can shed light on the extent of convergence to
more productive firms within the industry If there is a tendency for low-productivity firms to catch up
the productivity growth of such firms will be higher We analyse this relationship for the whole
economy and will also split the sample along different dimensions We are particularly interested in
three questions First is there a difference between the productivity growth rates of firms along some
dimensions even when controlling for productivity We think that this question is highly relevant but
will also qualify the findings of for example Section 32 where we compared firms with different
ownership structures and of different sizes with each other unconditionally which may mask the
different composition of the two groups in terms of initial productivity levels Second we are interested
in whether the slope of the relationship between the initial productivity level and subsequent
productivity growth differ along observable dimensions Is it the case for example that domestically-
owned firms face a productivity ceiling beyond which they cannot improve their efficiency any further
while foreign firms are better able to push forward even starting from very high productivity levels
Third we would like to find out whether there are structural changes in this relationship which may be
associated with the productivity slowdown following the crisis
Firm-level Productivity growth and dynamics
90
While the main mechanism behind this relationship is likely to result from a process of convergence
between firms the measured relationship can also partly arise from a mechanical negative relationship
coming from regression to the mean A large positive measurement error in productivity in year t
automatically generates a large negative growth rate from t As we are interested in the convergence
process rather than the mechanics of the regression to the mean we look at the relationship between
lagged productivity levels and 3-year productivity growth We assume that regression to the mean
resulting from measurement errors is less likely to show up when the productivity level is lagged An
additional limitation of this exercise is survivorship bias because lower productivity firms are more
likely to exit if they are unable to improve their productivity level We will analyse exit and entry
separately in Section 73
First to see the overall patterns we present the relationship between initial productivity levels and
productivity growth in the following 3 years in a non-parametric way (see Figure 71) To do so we
classify firms within each industry into 20 quantiles based on productivity in the previous year For
example we show how productivity growth between 2012 and 2015 is related to productivity levels in
2011 For each quantile we calculate average growth after partialling out 2-digit industry fixed
effects We show this relationship for different years to see whether there is a structural change in the
within-firm productivity growth process57 We demean lagged productivity levels by 2-digit industry
and year so zero on the horizontal axis corresponds to the mean productivity level We take four
periods pre-crisis (2003-2006) crisis (2006-2009) post-crisis (2009-2012) and recent (2012-
2015)58
Figure 71 shows that the relationship between previous productivity levels (on the horizontal axis) and
subsequent 3-year growth (on the vertical axis) can be well approximated with a linear relationship
We see a pronounced negative relationship in all periods reflecting that (surviving) lower productivity
firms increase their productivity faster than more productive firms generating some within-firm
convergence in the sample of continuing firms The slope of the relationship ie the productivity
growth premium of less productive firms is quite stable across non-crisis years but differs markedly in
the crisis showing that the crisis-related productivity decline was more severe for more productive
firms probably because these firms had been hit the hardest by the collapse of global trade59 Note
that this is much in line with the slow productivity growth of frontier firms in the same period
documented in Section 43 Figure 44 In normal times macro conditions seem to shift the whole line
up or down rather than rotate it The average 3-year productivity growth rate is the lowest during the
crisis and is still low in the post-crisis period but there is no difference between the pre-crisis and the
recent periods60
57 As in the previous chapters we use our main sample (see Chapter 2) in which we only consider firms with at
least 5 employees and measure productivity with the method of Ackerberg Caves and Frazer (2015)
58 Note that to measure subsequent growth we need three years following the base year when the level of
productivity is measured Consequently the last year we include is 2012 ndash and follow what happens to firms
between 2012 and 2015
59 More exit of low-productivity firms during the crisis may have also introduced a survivorship bias but as the
patterns in Figure A71 of the Appendix show this seems not to be the case
60 Table A71 of the Appendix shows the same patterns from a regression
Productivity differences in Hungary and mechanisms of TFP growth slowdown
91
Figure 71 The relationship between lagged productivity levels and subsequent productivity growth
over time
Notes This figure shows how the log of productivity in t-1 (on the horizontal axis demeaned by 2-digit industry
and year) is related to productivity growth between t and t+3 Each dot represents one of 20 quantiles of the
productivity level distribution and the average 3-year growth rate of firms within that quantile including 2-digit
industry fixed effects
Estimation
After establishing a linear relationship between lagged productivity level and subsequent growth we
look at the role of firm characteristics in productivity growth We do it in two steps First we look at
cross-sectional patterns taking the most recent period (2012-2015) We ask if there is a difference
between firm groups in productivity growth for the average firm (ie a firm having industry-average
productivity) and if there is a difference in the convergence pattern These two aspects correspond to
differences in the level and the slope of the line We estimate the following regressions
1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905
119896
119870
119896=1
+ 1205732(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1) +sum1205733119896(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1)119866119894119905
119896
119870
119896=1
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
We denote productivity of firm i in year t with 119905119891119901119894119905 1198893_119905119891119901119894119905 stands for 3-year productivity growth
119905119891119901 119895(119894)119905minus1 is the year-specific average lagged productivity in industry j of firm i G is a firm characteristic
(eg ownership or size) which contains K categories (eg one ownership group foreign or three size
categories) 119883119894119905 is a set of additional firm-level controls (these can be size age ownership or exporter
status) 120572119895(119894) is industry or industry-region fixed effects and 휀119894119905 is the error term Then 1205731119896 measures the
productivity-growth difference for average-productivity firms in firm group 119866119896 (eg foreign) compared
to average-productivity firms in the baseline category (eg domestic) 1205733119896 measures the difference in
the convergence patterns between firm group 119866119896 and the baseline category
(71)
Firm-level Productivity growth and dynamics
92
Second we also check dynamic patterns to see how the role of these firm characteristics changed over
time taking the same periods as in Figure 71 The baseline regression for comparing productivity
dynamics across years is as follows
1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905
119897
119897=200320062009
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
As before 1198893_119905119891119901119894119905 is the 3-year productivity growth of firm i from year t to t+3 and 119905119891119901119894119905minus1 denotes the
productivity level of firm i in t-1 119905119891119901 119895(119894)119905minus1119897 denotes the year-specific average lagged productivity in
industry j which firm i belongs to 119863119905119897 is an indicator for year l 119883119894119905 is a set of firm-specific time-variant
controls and 120572119895(119894) is industry or industry-region fixed effects as in the previous specification 1205731119897
measures the difference between the productivity growth of firms with industry-average productivity in
year l and in year 2012 The difference comes from two sources industry-level average productivity
levels could change over time and productivity growth for firms with the same productivity level could
also vary As we are interested in how the productivity growth of the average firm changed over time
we will not separate these two effects 1205732119897 measures the slope of the relationship between lagged
productivity levels and subsequent productivity growth in year l Comparing the different 1205732119897 coefficients
shows how the process of convergence between low- and high-productivity firms changed over time
We take a similar approach when we compare group-specific productivity dynamics over time We
interact group indicators demeaned productivity levels and the interaction of the two from the static
regression with a full set of year dummies and include year dummies separately as well
1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905
119896119863119905119897
119870
119896=1119897=2003200620092012
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ sum sum1205733119896119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119866119894119905119896
119870
119896=1
119863119905119897
119897=2003200620092012
+ sum 1205734119897119863119905
119897
119897=200320062009
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
Comparing 1205731119896119897 coefficients for different l-s shows how the difference between average-productivity
firms in the baseline category and in group k changed over time Similarly comparing 1205733119896119897 coefficients
with different l-s shows how convergence differences between the baseline category and group k firms
evolved over time These specifications allow us to add industry-year fixed effects so we can also
control for industry-specific trends
Results
Figure 72 shows the non-parametric relationships by firm characteristics creating scatter plots which
show productivity quantiles separately by firm groups These figures hint at the fact that on average
foreign-owned and exporter firms experience higher productivity growth conditional on initial
productivity levels In addition the relationship between the initial productivity level and subsequent
growth is weaker for foreign-owned firms suggesting that even highly productive foreign firms are
able to raise their productivity further while similar domestic firms have a harder time doing so Size
groups and age groups are similar to each other though the smallest firms have stronger convergence
patterns than the largest
We can discover the same scenarios using regression analysis in which we can control for the
abovementioned firm characteristics and fixed effects (Table 71) The most important conclusion is
that average-productivity foreign-owned firms raise their productivity faster relative to similar
domestic firms by about 10 percentage points Average exporters also have a TFP growth advantage
(72)
(73)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
93
relative to non-exporters but this premium disappears when we control for ownership We find some
evidence for a positive interaction between productivity levels and foreign ownership in line with lower
constraints for further TFP growth in the case of foreign frontier firms The same pattern applies to
exporter firms
Figure 72 The relationship between lagged productivity levels and subsequent productivity growth by
firm group
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to log productivity growth
between t and t+3 Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-
year growth rate of firms within that quantile including 2-digit industry fixed effects
The similar results for exporters and foreign-owned firms ndash and the strong correlation between foreign
ownership and exporter status ndash raise the question does this difference arise from foreign ownership
or exporting or do both variables have an independent effect Table A73 in the Appendix examines
this question only to find that the foreign premium in average productivity growth unconditional on
the productivity level is there both for exporters and non-exporters and is higher for younger and
smaller firms When we look at how the relationship between lagged productivity level and subsequent
growth differs by both characteristics at the same time we find that both foreign ownership and
exporter status matter but for different aspects of the relationship The difference in the slope of the
relationship comes both from the foreign-owned and from exporters compared to low-productivity
firms of the same category high-productivity firms grow relatively faster if they are foreign-owned The
same is true for comparing exporters and non-exporters At the same time the average difference in
Firm-level Productivity growth and dynamics
94
productivity growth comes from foreign ownership firms with industry-average productivity levels
have a higher productivity growth if they are foreign There is no significant additional effect for foreign
exporters on top of adding up foreign and exporter premia either in average productivity growth or in
convergence61
The TFP growth advantage of foreign-owned firms even when compared to domestically-owned firms
with the same productivity level points at a mechanism that reinforces the already existing duality
when domestic firms reach frontier productivity levels their TFP growth slows down much more than
that of foreign firms This self-reinforcing mechanism may be behind the non-convergence between
foreign and domestic firms (Section 44) With regard to size and age we find that high-productivity
firms have a relatively greater chance to increase their productivity if they are larger or older
compared to their smaller and younger counterparts (see Table A72 in the Appendix)
Table 71 The relationship between lagged productivity levels and subsequent productivity growth by
ownership and exporter status
Dep var TFP growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190
(000556) (000568) (000622) (000629) (000656) (000663)
TFP in t-1 Foreign 00475 00418 00433 00423
(00140) (00142) (00252) (00253)
TFP in t-1 Exporter 00477 00287 00216 00224
(00104) (00106) (00125) (00126)
TFP in t-1 Foreign exporter -000697 -00151
(00312) (00314)
Foreign 0117 0109 0120 0104
(00121) (00132) (00223) (00226)
Exporter 00240 000260 000401 0000919
(000791) (000845) (000851) (000881)
Foreign exporter -000670 000871
(00267) (00272)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 29717 29717 30135 30062 29717 29717
R-squared 0060 0072 0056 0073 0060 0072
61 Looking at the same patterns over time (Table A74 in the Appendix) suggests that higher average productivity
growth is a rather stable feature of foreign firms The only exception was the crisis period when it disappeared
Splitting the sample by broad sectors shows that foreign firms have higher average productivity growth both in
manufacturing and services The difference in within-group convergence patterns stayed the same for the
foreign The same is true for exporters except for the pre-crisis period when the coefficient is not significant
Productivity differences in Hungary and mechanisms of TFP growth slowdown
95
72 Employment growth
Question and descriptive patterns
The relationship between initial productivity levels and subsequent employment growth shows the
reallocation of continuing firms Between-firm reallocation results from more productive firms growing
faster In this subsection we ask how between-firm reallocation changed over time and how
reallocation patterns vary by different firm characteristics
To measure reallocation we use a similar approach to that in the previous subsection but the
lsquodependentrsquo variable will be 3-year employment growth in log terms rather than productivity growth
The slope of the estimated relationship reflects the employment growth advantage of more productive
firms or the strength of ldquocreative destructionrdquo among surviving firms Shifts in the level show changes
in the average growth rate
We calculate the 3-year employment growth using the formula119871119905+3minus119871119905
(119871119905+3+119871119905)2 where 119871119905 is the number of
employees in year t This formula shows the percentage increase in employment from year t to t+3
compared to the average size in year t and t+3 This measure performs better for smaller firms than a
simple log difference in employment as it does not result in extremely high numbers with a low initial
employment level62 In all the regressions of this subsection we control for exact firm size using the
logarithm of the number of employees
Figure 73 Reallocation by year
Notes The figure shows how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
62 Additionally while the baseline estimates are only for continuing firms this measure allows us to include firms
exiting in the period (t+1t+3) as well in some robustness checks In these cases we take Lt+3 = 0
Firm-level Productivity growth and dynamics
96
Figure 73 illustrates the patterns in the data non-parametrically The relationship between previous-
year productivity levels and subsequent employment growth is positive in all years This shows that in
line with the creative destruction hypothesis more productive firms are more likely to grow in the
subsequent three years The figure doesnrsquot show characteristic changes in the reallocation process
across years the slope of the curves being similar to each other Our regression estimates presented
in the Appendix (Tables A75 and A76) support that reallocation patterns are stable over time63 The
average growth rate of typical firms naturally follows the macro cycle strongly ndash aggregate changes
seem to shift the line up or down but do not seem to rotate it In other words with this approach we
do not find evidence for a structural change in the reallocation process therefore it is unlikely that
such a change should explain satisfactorily the productivity slowdown
We create similar figures for the most recent period (2012-2015) by different firm characteristics
(Figure 74) The most important result is that exporters grow significantly faster than non-exporters
when controlling for their initial productivity This leads to reallocation from non-exporters to
exporters Given that the productivity advantage of exporters is in the order of 30-100 percent in the
different industries (see Section 43) this reallocation process can yield enormous productivity gains
The slope of the curve is also less steep for exporters suggesting that their expansion is less
dependent on their productivity level relative to domestic firms in other words reallocation within the
exporter group is weaker relative to non-exporters
63 As before the relationship between lagged productivity levels and subsequent employment growth can be
properly approximated by a linear function
Productivity differences in Hungary and mechanisms of TFP growth slowdown
97
Figure 74 Reallocation by firm groups
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
Firm-level Productivity growth and dynamics
98
Estimation results
Table 72 Reallocation by ownership and exporter status
Dep var employment growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 0105 0102 0105 0107 0107 0108
(000484) (000493) (000539) (000546) (000570) (000575)
TFP in t-1 Foreign
-00369 -00328 -00252 -00224
(00123) (00124) (00217) (00217)
TFP in t-1 Exporter
-00344 -00298 -00250 -00249
(000913) (000932) (00109) (00110)
TFP in t-1 Foreign exporter
-0000806 000194
(00271) (00272)
Foreign 000105 -000863 -000786 -0000106
(00112) (00116) (00194) (00196)
Exporter 00586 00635 00653 00672
(000738) (000754) (000777) (000786)
Foreign exporter
-000647 -00123
(00234) (00238)
Industry FE YES YES YES
Industry-region FE
YES YES YES
Firm-level controls
YES YES YES
Log of employees
YES YES YES YES YES YES
Observations 31662 31662 32124 32043 31662 31662
R-squared 0035 0049 0038 0051 0037 0049
Looking at the regression results (Table 72) confirms our previous findings even after controlling for
fixed effects Exporters with an average productivity level grow about 6 percentage points faster than
non-exporters hinting at strong positive reallocation between the two groups with slightly weaker
reallocation within the exporter group64 At the same time average-productivity foreign-owned firms
do not have higher employment growth than domestic ones Similarly to productivity growth we find
no extra premium for foreign exporters65 66 Overall these results emphasise that participation in
64 We define exporters based on their export activity in year t so the group of exporters also includes those firms
which export in t but not any more afterwards This means that a worse subsequent performance ndash lower
growth and exiting from exporting ndash has no effect on our exporter classification
65 The main patterns concerning employment growth of average-productivity firms are robust to modifying the
employment growth measure in such a way that it includes exits as a full employment decline (See Table A77
in the Appendix) In this version employment growth of foreign firms is significantly lower overall but this is
counterbalanced by the significantly positive coefficient of the foreign exporter indicator
66 We show in Table A78 of the Appendix that the higher average growth of exporters is present in all size (except
for the largest) age and ownership groups Dynamic patterns suggest (in Table A79 of the Appendix) that the
higher growth rate of average-productivity exporters is robust over time This result is also robust to splitting
the sample into manufacturing and services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
99
international markets is an important driver of industry and aggregate productivity growth in Hungary
by providing opportunities for exporters to expand as Section 62 has documented
As Table 73 shows competitive pressure also seems to affect more the growth prospects of smaller
firms the relationship between initial TFP levels and employment growth is significantly stronger for
smaller firms Between-firm reallocation appears to be much stronger for smaller firms while less
productive large firms are unlikely to contract even if they are inefficient conditional on survival
There are no clear patterns by age groups
Table 73 Reallocation by size and age group
Dep var employment growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 0112 0110 00875 00863
(000486) (000502) (00133) (00133)
TFP in t-1 Group 2 -00455 -00431 00445 00384
(00128) (00129) (00182) (00183)
TFP in t-1 Group 3 -00745 -00748 000733 000822
(00194) (00196) (00141) (00142)
TFP in t-1 Group 4 -00953 -00957
(00218) (00220)
Group 2 -000596 -000810 -00188 -00212
(00122) (00123) (00141) (00141)
Group 3 -000928 -00157 -00115 -00169
(00199) (00201) (00114) (00115)
Group 4 00328 00280
(00276) (00278)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Log of employees YES YES YES YES
Observations 32124 32043 32124 32043
R-squared 0038 0052 0037 0051
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees and size group 4 is 100+
employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5 years age group 3 is
firms older than 5 The baseline category is firms of 2-3 years
73 Entry and exit
Questions
This subsection aims at investigating which firms enter and exit and in particular how productive
those firms are relative to continuing firms This corresponds to the micro-level equivalent of the net
entry effect (see Chapter 6) The motivation for the micro-level investigation is that in this manner we
Firm-level Productivity growth and dynamics
100
can study which firm-level factors determine the type of firms that enter and exit and control for
industry heterogeneity
Our approach is similar to the previous section with the main difference being that this time the
dependent variable is productivity while the variables of interest are entry and exit dummies Their
coefficients show the productivity lsquopremiarsquo (often negative) of new entrants and exiting firms relative
to continuing firms These premia are especially useful to answer two kinds of questions First their
magnitude and size inform us about how entry and exit contribute to productivity growth Second
changes in these premia are also indicative of the changes in the costs of entry and the survival of
low-productivity firms
Estimation
To use a symmetric approach we define entrants and exiting firms using a 3-year interval An entrant
is a firm that has entered in the previous 3 years67 This means we look at the productivity of firms in
year t and compare it between incumbents (ie firms older than 4 years) and entrants (ie firms being
2-4 years old) In a similar way we compare the productivity in year t of firms exiting in the following
3 years (ie the last time the firm reports positive employment is in the period (t t+2) and non-
exiting firms (firms still reporting positive employment in year t+3)
As before we start with a static approach looking at the productivity premium of entrants and exiting
firms in the most recent period (taking year 2015 for 2012-2014 entrants and 2012 for firms exiting in
2013-2015) Then as a dynamic approach we take all four time periods as before and interact the
premia with year dummies The static regression we estimate is as follows
119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905
119896119873119864119894119905119870119896=1 + 1205732119866119894119905
0119864119894119905 + sum 1205733119896119866119894119905
119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)
119905119891119901119894119905 is the productivity of firm i in year t (measured in logarithm) 119866119894119905119896 is the k-th category (eg size
category 5 with more than 100 employees) in a grouping according to firm characteristics G (eg
size) and 1198661198941199050 is the baseline category (eg firms with 5-49 employees) 119864119894119905 stands for entrant or
exiting firm dummy in the different specifications and 119873119864119894119905 are incumbent or continuing firms
accordingly Then 1205732 measures the entry or exit premium for firms in the baseline category and 1205733119896
measures the same premium for firms in category k of grouping G Both premia are calculated
compared to incumbentscontinuing firms in the baseline category 1198661198941199050 1205731
119896 measures the productivity
advantage or disadvantage of incumbentcontinuing firms in category k of grouping G also compared
to the average productivity level in the baseline group As before 119883119894119905 includes additional firm-level
characteristics and 120572119895(119894) is industry or industry-region fixed effects In those versions where we include
industry fixed effects we identify from within-industry differences This means that 1205733119896 measures the
same entry or exit premium for firms in category k of grouping G compared to incumbentscontinuing
firms in the same category As before we create the dynamic version of the above regression by
interacting 119866119894119905119896119873119864119894119905 119866119894119905
0119864119894119905 and 119866119894119905119896119864119894119905 with year dummies and including year dummies separately as well
67 We consider firms changing industry from manufacturing to services or vice versa as exitors and new entrants at the same time (see Chapter 2)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
101
Results
Table 74 shows how the productivity premium of entrants and exiting firms changed over time In
these specifications we compare the yearly average productivity of incumbents and entering or exiting
firms separately in each year The point estimates suggest that entering firms were about 2-4 percent
more productive than incumbents except for the 5 percent productivity disadvantage in the pre-crisis
period while exiting firms were 10-20 percent less productive than the continuing firms The
productivity advantage of entrants and the disadvantage of exiting firms did not change radically
during our time period This difference constitutes a potential for positive net entry effects in terms of
reallocation The exact value of the net entry effect also depends on the share of employees affected
by entry and exit While the premia of entering and exiting firms remained roughly the same in the
different periods exit and entry rates changed (see Section 33) which results in positive net entry
effects before the crisis and negative effects after that (see Section 62)
Table 74 Productivity premium of entering and exiting firms over time
Dep var TFP in year t
Firm group Entry Exit
(1) (2) (3) (4) (5) (6)
EntrantExitorPeriod 2003-2006
-00532 -00535 -00465 -0214 -0199 -0200
(000906) (000873) (000879) (00102) (000987) (000989)
EntrantExitorPeriod 2006-2009
00269 00212 00232 -0135 -0117 -0119
(000967) (000931) (000936) (000897) (000865) (000865)
EntrantExitorPeriod 2009-2012
00369 00444 00360 -0133 -0114 -0121
(000960) (000926) (000931) (000920) (000889) (000889)
EntrantExitorPeriod
2012-2015
00374 00324 00252 -0171 -0150 -0157
(000971) (000936) (000944) (00107) (00103) (00103)
Period 2003-2006 -0115 -00927 -00711 -00445
(000519) (000501) (000556) (000537)
Period 2006-2009 -0175 -0169 -000972 00122
(000522) (000503) (000537) (000518)
Period 2009-2012 -0116 -0117 -00580 -00528
(000528) (000508) (000543) (000522)
Year FE YES YES YES YES
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES YES YES
Industry-year FE YES YES
Observations 166607 166168 166168 158143 157711 157711
R-squared 0327 0380 0380 0332 0385 0387
Firm-level Productivity growth and dynamics
102
Next we focus on the most recent period and look at the productivity differences of entrants and
exiting firms by different firm groups
Figure 75 Productivity premium for entering and exiting firms by ownership
Figure 75 presents the premia of domestic and foreign entering and exiting firms relative to domestic
incumbents As we saw in Section 44 foreign firms are on average more productive than domestic
ones68 foreign incumbents have on average a premium of 669 Compared to domestic incumbents
foreign entrants have 513 higher productivity There is also a positive productivity premium of 29
for exiting foreign firms Similarly the productivity of exiting exporters is 186 higher than that of
continuing non-exporters69 This means that domestic incumbent firms can survive longer even with a
lower level of productivity Consequently having many foreign entrants has a positive effect on
average productivity while on average foreign exits do not affect average productivity70 71
68 Table A711 shows that the productivity advantage of foreign-owned firms is present in all size and age groups as well as both within the exporter and non-exporter firm groups
69 Table A710 of the Appendix shows the estimation results with standard errors
70 As Table A712 of the Appendix shows foreign entrant premium and the premium of continuing or exiting
foreign and exporter firms seem fairly stable over time The positive premium of entering and exiting foreign
firms is also robust for splitting the sample into manufacturing and services
71 As Table A713 of the Appendix shows there is no considerable difference in the productivity disadvantage of
exiting firms by size or age group
Productivity differences in Hungary and mechanisms of TFP growth slowdown
103
74 Conclusions
One contribution of this chapter is that we have documented that one of the factors behind the
sustained duality in productivity between foreign and domestically-owned firms is that foreign-owned
firms tend to be more capable of upgrading their productivity even from already high productivity
levels Similar patterns apply to the more globally oriented exporters This mechanism underlines the
importance of policies that promote absorptive capacity-building (see Section 45) a strong knowledge
base easy access to external knowledge and flexible and advanced skills are especially important
when upgrading productivity beyond already high levels
We have also found strong reallocation from non-exporters to exporters Given the high productivity
premia of exporters in Hungary (Beacutekeacutes et al 2011) and in general (Wagner 2007) such a
reallocation can lead to substantial improvement in aggregate productivity (and as we have seen in
Section 62 it did to some extent before the crisis) These results emphasise that participation in
international markets is an important driver of industry and aggregate productivity growth in Hungary
because it provides valuable opportunities for exporters to expand Note that this reallocation effect of
international openness has been in the focus of the recent literature on international trade (Melitz
2003 Bernard et al 2006 Amiti and Konings 2007 Topalova and Khandelwal 2011 De Loecker
2011) Note also that the asymmetric expansion possibilities of exporters and domestic firms also
amplify the duality between the two groups
The analysis of entry and exit has revealed that entrants are somewhat more productive than
incumbents even a few years after entry Exiting firms are significantly less productive This on the
one hand implies that exit and entry is a substantial source of reallocation (as Section 62 has shown)
On the other hand the low productivity of exiting firms also suggests that domestic firms can survive
long even with relatively low productivity levels maybe because of inefficiencies in the capital
allocation process including the insolvency regime
Productivity evolution and reallocation in retail trade
104
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE
The previous chapters have presented a number of results on the productivity and growth in different
sectors of the economy The aim of this chapter is to look deeper into one of the key sectors of the
economy namely retail trade for more detailed insights
Two main reasons have motivated us to choose the retail sector First retail is a key sector of the
economy which provides jobs for a great many people and influences what consumers can buy and at
what prices Retail (and wholesale) does not only interact with consumers it is a key supplier of inputs
while beig a buyer of outputs for all other firms in the market economy72 The degree to which it is
capable of supplying a large variety of intermediate inputs at reasonable prices is an important
determinant of the productivity of firms relying on these sources Its market structure also affects
fundamentally the incentives that producers experience73
The second reason is that there have been a number of regulatory changes in the retail sector in
Hungary in recent years While these policies had multiple motivations one of their common features
is that they are size-dependent either explicitly or implicitly As such they have a potential to increase
the costs of larger firms and influence the reallocation process in favour of smaller mostly
domestically-owned firms This may matter as international evidence has shown that much of retail
productivity growth in recent decades has resulted from the expansion of large store chains (Foster et
al 2006) Exactly because of the strong links between retail and other industries regulatory
restrictions in retail represent nearly a third of all service-related restrictions which are carried over to
other sectors of the economy74
The structure of this chapter is the following Section 81 describes the policy context of Hungarian
retailing Section 82 introduces the available datasets Section 83 describes the major developments
in retail productivity Section 84 describes trends in reallocation The last three sections describe three
specific questions Section 85 analyses the role of retailers and wholesalers in importing and
exporting Section 86 provides a few illustrative statistics on how size-dependent taxes could have
affected reallocation and prices Finally Section 87 evaluates a specific policy namely the mandatory
Sunday closing of larger shops Section 88 concludes
81 Context
The retail industry is an important employer in all EU member states and Hungary is not an exception
Its employment share in our sample has been around 12 percent (Figure 81) Similarly to the EU as a
whole retail productivity is below the average of the market economy therefore its GDP share is
below its employment share Still it represented 6-7 percent of total value added in our sample
72 See EC (2018) for the importance of the retail industry in Europe
73 See Smith (2016) for a review of this literature
74 EC (2018) p 5
Productivity differences in Hungary and mechanisms of TFP growth slowdown
105
Figure 81 The share of retail and wholesale firms in market economy value added and employment
Notes Full sample with at least 1 employee in any of the years
The largest sub-industry within retail is groceries (NACE 4711) Its share of the total turnover around
40 percent is at the lower end of the EU distribution75 Given its importance (and the large sample size
within it) we will often study only groceries in our empirical analyses
Measuring the restrictiveness of different regulations in any sector of the economy is not an easy task
The European Commission has designed a ldquoRetail Restrictiveness Indicatorrdquo to quantify the potential
effect of these regulations in force at the end of 2017 (see Figure 82) The higher values of the
indicator indicate more restrictive regulations76 According to this indicator the restrictiveness of retail
regulation in Hungary is slightly below the EU average and similar to other CEE countries
The indicator distinguishes between regulations related to the establishment of shops on the one hand
and those related to their operation on the other In Hungary there are few operations restrictions
(mainly restrictions on distribution channels) while entry is regulated more heavily mostly by size-
related restrictions and requirements for economic data
75 EC (2018) p 4
76 There is ample empirical evidence that entry barriers planning regulations and operating restrictions are related to productivity and prices in retail Some examples are Bertrand and Kramarz (2002) Viviano (2008) Haskel and Sadun (2012) Sadun (2015) Daveri et al (2016)
Productivity evolution and reallocation in retail trade
106
Figure 82 Retail Restrictiveness Indicator
Notes This is a reproduction of Figure 8 from EC (2018)
While regulation in Hungary is not especially restrictive a number of new measures were introduced
following the crisis (see Box 81) While these have various motivations a common feature of most of
them is that they are size-dependent As such they may distort competition and constrain reallocation
to larger firms
One type of size-dependent policies is size-dependent taxes Crisis taxes introduced right after the
crisis (and phased out in 2013) were highly progressive in sales volume Local business taxes have
been similarly progressive in total sales at the firm-level since 2013 Other size-dependent policies are
restrictions on the establishment of shops or their operation The Plaza Stop law constrained the
establishment of malls larger than 300 m2 Another peculiar policy was requiring larger shops to close
on Sundays between March 2015 and April 2016
Quantifying the effect of such policies is not an easy task In some cases it is not possible with the
data at hand to identify the shops and firms which were affected by the different types of taxes For
example without knowing the exact location of the establishment it is not possible to identify which
firms operate in malls and hence could have been affected by the Plaza Stop law As we discuss in
Section 85 the highest bracket of the crisis tax only affected 6 firms and thus it is hard to run
statistical tests with an appropriate power In contrast some of the effects of the mandatory Sunday
closing policy can be very effectively estimated based on shop-level data
Therefore we will apply two complementary strategies The first is to investigate whether there are
trend breaks in the reallocation process following the crisis when many of the new policies were
Productivity differences in Hungary and mechanisms of TFP growth slowdown
107
introduced While we find suggestive changes around the crisis one cannot make casual statements
based on this strategy given the number of other changes in the economy The second strategy is to
examine specific policies where a credible differences-in-differences identification is possible
Unfortunately this strategy is basically limited to Sunday closing
BOX 81 Size-dependent taxes and regulations in the retail sector
This box describes a number of size-dependent taxes and regulations which could be linked to the retail data and investigated during this exercise The list is only indicative and will be appended by desk research and possibly interviews
2010-2013 crisis taxes
Crisis taxes were introduced in 2010 and were in force (mostly) until 2013 They affected the energy telecom and retail sector as their base was operating profits resulting from these activities The tax rate was strongly progressive for retail
Below 500m HUF 0
Between 500m and 30bn HUF 01
Between 30bn and 100bn HUF 04
Above 100bn HUF 25
Between March 15 2015 and April 23 2016 Sunday closing for larger and non-employee owned retail stores
The 2014 CII law which came into force on March 15 2015 banned shops with a retail space of more than 400 square meters to open on Sundays with some exceptions most notably the new tobacco shops Smaller shops could only open if their workers had at least a 20 stake in the
business or if they were close relatives of the owner The law was repealed in 2016
2013-today Progressive local business tax
The base of local business tax is the ldquoadjustedrdquo revenue of firms This usually means revenue minus material expenditures but regulation stipulating the exact method of calculation has changed a number of times since the introduction of this type of tax In 2013 a progressive
element was introduced by making the definition of the cost of purchased goods size-dependent In particular smaller firms can now deduct more of their expenditures than larger ones The deductible part is
Below 500m HUF of net sales 100
Between 500m and 20bn HUF 85
Between 20bn and 80bn HUF 75
Above 80bn HUF 70 of the cost of goods is eligible
Productivity evolution and reallocation in retail trade
108
82 Data
We rely on two main data sources in this chapter The first one is the NAV balance sheet data
described in detail in Chapter 2 Based on the industry code identifier we restrict the sample to firms
in industry 47 retail There are a few firms which switch to this category from other industries (mainly
wholesale of food manufacturing) We keep the whole history of these firms throughout the analysis
Second we use a retail-specific survey conducted by the Hungarian Central Statistical Office which
samples firms and collects data for all shops of the sampled firm77 Firms included in the sample are
compelled by law to submit monthly reports on their turnover and 4-digit industry-codes plus for all
of their stores information about these entitiesrsquo location (municipality) identification number 4-digit
77 httpswwwkshhudocshuninfo02osap2018kerdoivk181045pdf
BOX 81 Size-dependent taxes and regulations in the retail sector (cont)
2013-today Licensing of tobacco wholesale and retail
On 22 April 2013 in line with Act CXXXIV ldquoon reducing smoking prevalence among young people
and the retail of tobacco productsrdquo (adopted by the Hungarian Parliament on 11 September
2012) the National Tobacco Trading Non-profit Company (a 100 government-owned joint-stock
company controlled by the relevant minister under the mandate of this law) was established
From then on only special ldquonational tobacco shopsrdquo licensed by the state have been allowed to
sell tobacco products These shops enjoy a number of benefits compared to other shops
Exempted from the Sunday closing for retail shops
National tobacco shops are exempted from the ban on selling alcohol after 10pm rarr in effect
tobacco shops do not come under the ruling of the commercial law Local municipalities can
otherwise regulate shops based on that law
2011- today ldquoPlaza Stoprdquo Law
The so-called Plaza Stop Law (the 2011 CLXVI Law) came into force in January 2012 It
prohibits the construction of new retail facilities or the expansion of any already existing one with
a leasable area of more than 300 msup2 Exemptions could be granted to certain developments by a
committee of ministry officials and with the approval of the Minister of National Economy
In 2013 the law was extended to include building conversions In February 2015 a new
amendment was ratified which basically renewed the effect of the 2011 law and introduced some
modifications to it Now retail facilities with a floor space of less than 400m2 can be built without
any special procedure Furthermore the right to grant exemptions was given to a special
administrative department which is supplemented by a committee made up of delegated
members of different ministries
Productivity differences in Hungary and mechanisms of TFP growth slowdown
109
industry-code sales and the monthly number of days spent open The sample consists of all larger
retail firms78 and a representative sample of other firms re-sampled on an annual basis
An important consequence of this design is that we observe each of the shops of the sampled firms
This is valuable in two respects First with this information it is possible to calculate the number of
shops and average shop size at the firm-level Second one can identify new and exiting shops for
firms which are in the sample continuously ie larger firms Further with the help of the firmsrsquo
identification number we are also able to link this information to data from the NAV database for
qualified analysis
Two caveats may be mentioned here First the re-sampling of the representative part of the sample
prevents us from following small firms through the entire sampling frame Second in the beginning of
2012 there was a switchover in the coding of shop-level identification numbers which prevents us
from linking shops before and after
As mentioned above the database also includes information on the industry classification of the shop
In most of our exercises we restrict the sample to grocery stores more formally bdquoRetail sale in non-
specialised stores with food beverages or tobacco predominatingrdquo (NACE 4711) Table 81 shows the
sample size of the merged database for groceries We have classified firms according to the number of
shops they have and report their number and their storesrsquo number according to these categories
Table 81 The number of firms and the number of shops in different size categories in Groceries
1 shop 2-4 shops 5-9 shops 10-49 shops gt50 shops year firm shop firm shop firm shop firm shop firm shop
2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446
2006 573 573 51 131 59 430 111 2008 30 1548
2007 546 546 53 125 60 429 110 1987 33 1634
2008 628 628 45 102 50 350 104 1823 21 1574
2009 527 527 33 72 41 290 99 1879 24 1754
2010 472 472 22 49 32 238 94 1793 22 1968
2011 537 537 14 30 29 212 92 1758 22 2027
2012 374 374 30 68 49 335 88 1643 23 2107
2013 503 503 48 121 42 277 88 1622 25 2094
2014 410 410 106 239 48 320 81 1530 24 2054
2015 512 512 135 311 42 292 80 1544 30 2090
2016 518 518 120 292 37 271 77 1457 23 2022
A key distinction in this merged database is the one between shops and firms Sales employment
ownership is observable only at the firm-year level so these variables are the same for each of the
shops of a firm for a calendar year Shops are only observable for sampled firms but we observe
sales and the number of days they were open at a monthly regularity As a result even if one runs
regressions at the shop-month-level productivity and employment can only vary at the firm-year
level For this reason we always cluster the standard errors at the firm or firm-year level
78 Larger firms are defined as having more than 7 stores in operation or with a number of employees of more than 50 and at least 6 stores or with a significantly large store in a product category
Productivity evolution and reallocation in retail trade
110
While balance sheet data includes information on exports it does not inform us about imports In
Section 84 we use detailed trade data to analyse importing by wholesale and retail firms This is
reported at the importer firm-product (8 digit Harmonized System)-country of origin level Most
importantly we can link this information to the balance sheet of the firm This is collected by a survey
following the European Unionrsquos practice79 We aggregate these data to the firm-year level but
distinguish between consumer goods capital goods and intermediate inputs used in further production
by relying on the correspondence table of the Eurostat between the Harmonized System and Broad
Economic Category classifications
83 General trends
Let us start with describing the firm size distribution across years (see Table 82) for firms with at least
one employee Similarly to other EU countries the majority of firms in retail are very small in
different years between 70-75 percent of retail firms employed less than 5 people80 The share of firms
with more than 50 employees fluctuated at around 1 percent
As one would expect larger firms have a significantly larger weight in terms of employment and sales
The top 05 percent of firms employed more than 30 percent of all employees in each year The
employment-share of these top firms increased nearly monotonically from 33 percent in 2004 to more
than 38 percent in 2011 when it reached its peak This was followed by a slightly declining trend to
357 percent in 2016 This time path represents the gradual expansion of large chains both organically
and via the acquisition of stores81 up to the crisis when this trend seems to have ended
The market share of large firms is even larger reaching 45 percent in 2016 The difference between
the employment share and sales share shows that large retail firms are substantially more efficient ndash
at least in terms of sales over employees ndash than the average firm At the other extreme the smallest
retail firms generate only 126 percent of sales with 20 percent of employees suggesting that in 2016
each of their employees sold only about half of the average The patterns are similar in other years
Efficiency differences are large in this sector though not larger than in most other sectors of the
economy (see Chapter 4)
79 See httpeceuropaeueurostatstatistics-explainedindexphpInternational_trade_statistics_-_background An important limitation of these data is that firms only report transactions above a specific size This may bias estimates of firm-level importing downward for small firms
80 As we discussed in Section 42 the NAV sample includes only double-entry bookeeping firms while the unemployed and people working in firms with simplified accounting are omitted from these data These people are likely to work in small economic units with low productivity levels
81 The increasing share of large retailers is a general trend globally see Ellickson (2016)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
111
Table 82 Share of firms in different size categories (at least 1 employee)
A) Number of firms
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100
2006 7333 1613 687 252 065 049 100
2007 7369 1597 674 244 067 050 100
2008 7410 1571 667 236 067 048 100
2009 7522 1535 614 221 059 048 100
2010 7551 1508 640 201 057 044 100
2011 7591 1493 623 194 057 041 100
2012 7625 1506 568 204 055 042 100
2013 7526 1596 577 207 052 042 100
2014 7380 1684 628 210 056 042 100
2015 7239 1771 661 232 056 041 100
2016 7163 1804 676 252 063 043 100
B) Employment
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000
2006 2034 1475 1269 1025 679 3525 10000
2007 2026 1443 1241 984 669 3644 10000
2008 2020 1391 1138 910 579 3980 10000
2009 2002 1427 1244 870 570 3789 10000
2010 2099 1443 1243 862 577 3731 10000
2011 2144 1458 1119 895 555 3830 10000
2012 2144 1541 1131 906 541 3713 10000
2013 2168 1591 1204 896 555 3650 10000
2014 2104 1644 1236 970 548 3538 10000
2015 2064 1620 1216 1006 588 3570 10000
2016 1999 1620 1216 1006 588 3570 10000
C) Sales
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000
2006 1114 1278 1587 1025 776 4226 10000
2007 1110 1266 1543 956 855 4268 10000
2008 1112 1524 1045 891 531 4906 10000
2009 1104 1641 1073 856 718 4607 10000
2010 1104 1521 1103 930 693 4594 10000
2011 1160 1577 1004 934 624 4714 10000
2012 1146 1647 1003 913 629 4578 10000
2013 1229 1411 1090 945 709 4360 10000
2014 1486 1472 1093 1001 752 4389 10000
2015 1293 1458 1118 985 660 4512 10000
2016 1266 1458 1118 985 660 4512 10000
Productivity evolution and reallocation in retail trade
112
Figure 83 presents C5 concentration measures82 for the full retail sector and for some of its
subsectors83 The share of the top 5 retail firms was around 30 percent of total retail sales
Concentration was increasing pre-crisis from 30 percent in 2003 to 35 percent in 2009 Concentration
decreased and returned to its 2003 value by 2014 The latter trend as we will discuss in Section 85
may be associated with size-dependent policies
The various sub-industries exhibit different patterns in terms of concentration Let us start with
groceries Pre-crisis the dynamics in this subsector was driven mainly by the expansion of large
chains Consequently concentration was strongly increasing with C5 growing from 50 percent in 2003
to more than 60 percent by 2008 Concentration in this subsector was rising further post-crisis but at
a somewhat slower pace We observe a similar pattern of increasing concentration with a trend break
around the crisis in sales of books and clothes in specialized stores In these sub-industries
establishment regulations like the Plaza Stop law could have played a more important role in the trend
break than taxes Specialized cosmetics retailing was already very highly concentrated at the
beginning of the period and remained largely unchanged
Figure 83 Concentration in retail and various sub-industries
This observation motivates a more detailed look at different measures of efficiency and prices Panel A)
of Table 83 calculates the average TFP levels84 both for different size categories and for the
aggregate Note that TFP calculated from balance sheet data is revenue productivity measuring the
82 Calculated as the sales share of the 5 firms with the largest sales
83 We rely on a slighly different version of the NAV data for this exercise which includes 4-digit identifiers but only runs until 2014
84 See Section 22 on details of TFP estimation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
113
amount of revenue produced by an input bundle Consequently it does not only measure physical
productivity (units sold per unit of input) but also markups This distinction is especially important in
retail85
Let us start with the two aggregate series one unweighted and the other weighted by employment
The employment-weighted series has higher values because more productive firms tend to be larger
(see Section 51) The two series follow a parallel trend suggesting that the correlation between size
and productivity did not change radically TFP has increased by about 15 percent from 2004 to 2006
remained constant until 2008 fallen by around 10 percent in 2009 and then started to grow by 4-5
percent each year from 2011
Note that this productivity evolution is similar to what is reported by OECD STAN before and during the
crisis but the post-crisis recovery in our data is much more pronounced (Figure 84) As we have
discussed in detail in Section 42 this is most likely a result of the large number of self-employed and
the distinct productivity level and evolution of that group86 Productivity has been definitely increasing
since 2011 in our sample
Interestingly TFP is not increasing monotonically with firm size There is a clear 25-30 percentage
point difference between the smallest firms (1-4 employees) and firms in other size categories which
have a similar TFP to each other Besides differences in efficiency this may also be partly explained by
the tax avoiding behaviour of the smallest firms ie under-reporting sales or over-reporting costs
Panel B) of Table 83 investigates gross margins These are calculated as
119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
which is the margin that retailer 119894 realises in year 119905 on the cost it pays for the sold goods in
percentage A value of 20 shows that the price the consumer pays is 20 percent higher than what the
retailer paid for the goods87 Note that gross margins reflect a combination of two factors `physical
productivityrsquo (how much capital and labour is needed for a given amount of sales) and markups Still
gross margins are of interest because they are the closest proxy available in financial statements of
prices paid by customers
We can make two key observations First on average (weighted) margins increased from about 155
percent to 19 percent during the period under study with a fall during the crisis Second margins
were about 5 percentage points higher in the smallest retail firms compared to larger ones
Interestingly during and immediately after the crisis (between 2009 and 2012) the margins of the
largest firms were substantially lower than those of other firms This is because the margins of the
largest firms actually fell during this period while that of smaller firms remained roughly constant
85 Measurement of productivity in retail raises a number of conceptual and measurement issues (Ratchford 2016) Two main problems are the measurement of output (conceptually retail services) and of the inputs used (for example shop area) In practice however such detailed data are not available and it is standard to use TFP
86 The figures for retail are similar to those for the whole of the service industry About a third of all people engaged are self-employed operating at a significantly lower productivity level than retail firms The productivity of the self-employed did not grow between 2012 and 2015
87 We winsorise it at the 5th and 95th percentiles Note that the cost of goods sold would be preferable to material costs but that is often missing from the data especially for small firms
Productivity evolution and reallocation in retail trade
114
Most likely larger firms were able to cut markups while smaller firms with already lower markups
were not able to do so
As we have mentioned above margins reflect a combination of cost factors and market power The
gross operating rate88 attempts to control for labour costs and shows margins after personal cost
119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
with value added calculated as discussed in Chapter 2 In international comparison gross operating
rates are relatively low in Hungary89 These rates show a clear downward trend with time Gross
operating margins are clearly decreasing with firm size showing that larger firms operate with large
scale and low margins Similarly to the gross margin we see a fall during the crisis
Figure 84 Productivity evolution in the NAV sample and the OECD STAN
88 httpeceuropaeueurostatstatistics-explainedindexphpGlossaryGross_operating_rate_-_SBS
89 As shown by EC (2018) Figure 2 Our weighted estimates are similar to what is reported there based on Eurostat data
Productivity differences in Hungary and mechanisms of TFP growth slowdown
115
Table 83 Performance and margins (at least 1 employee)
A) TFP
B) Gross Margin
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 1769 1548 1793 1883 1584 1495 1735 1554
2005 1839 1571 1858 1878 1504 1666 1794 1584
2006 2040 1658 1842 1879 1599 1660 1956 1653
2007 2220 1720 1875 1940 1664 1702 2104 1712
2008 2192 1792 1872 1992 1522 1739 2096 1728
2009 2084 1719 1864 1967 1599 1527 2006 1630
2010 2096 1749 1867 2102 1664 1535 2024 1682
2011 2172 1765 1862 2003 1759 1558 2084 1728
2012 2203 1740 1828 1913 1969 1541 2102 1733
2013 2243 1734 1806 2018 1999 1794 2128 1790
2014 2363 1789 1817 2149 2022 1869 2223 1855
2015 2390 1803 1885 2165 2215 1987 2245 1928
2016 2379 1799 1911 2172 2403 2170 2237 1948
C) Gross operating rate
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
unweighted weighted
2004 651 701 663 677 479 480 658 611
2005 827 757 777 712 526 560 806 644
2006 908 773 777 753 579 797 870 706
2007 807 635 653 704 500 580 764 606
2008 665 588 577 610 422 478 643 525
2009 618 535 538 522 351 339 595 456
2010 669 587 629 626 421 341 650 510
2011 698 617 591 637 410 393 676 542
2012 770 643 599 624 485 404 735 577
2013 735 594 555 631 478 369 697 530
2014 745 578 568 612 446 398 700 531
2015 766 609 625 646 547 452 724 579
2016 759 597 621 632 609 487 715 588
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 599 633 637 628 612 623 610 626
2005 610 635 643 628 616 626 619 631
2006 623 646 651 640 630 642 631 643
2007 624 643 650 641 627 643 631 642
2008 625 645 647 640 630 640 631 642
2009 619 641 642 631 619 630 626 630
2010 617 641 643 634 622 631 625 631
2011 621 643 645 637 629 639 628 639
2012 624 646 644 637 629 639 631 644
2013 626 647 642 640 637 642 632 645
2014 628 648 650 645 640 648 634 648
2015 638 658 658 655 652 659 644 659
2016 643 661 665 659 655 661 649 665
Productivity evolution and reallocation in retail trade
116
As Section 44 has shown for the market economy in general the Hungarian economy can be
characterised by a strong duality between foreign and domestically-owned firms Retail is one of the
sectors where this is the most transparent with many small domestic firms operating alongside large
multinational super- and hypermarket chains90 Figure 83 shows the share of foreign-owned firms in
terms of number employment and market share Foreign-owned retail firms are substantially larger
than domestic ones between 5-7 percent of firms are foreign-owned but they employ around 30
percent of employees and realise around 40 percent of sales This also implies that the salesworker
share is also larger in foreign firms than in domestic ones This results from the larger typical size of
foreign firms when controlling for size salesworker is not higher for foreign firms The market share
of foreign-owned firms is at the top of the distribution in EU countries with a larger foreign share only
in Latvia and Poland91
Figure 85 shows an inverted U-shaped pattern with an increasing market share of foreign firms until
2009 followed by a fall of nearly 5 percentage points between 2013 and 2016 This fall in foreign share
ran parallel with the introduction of policies favouring smaller firms in various ways
Figure 85 Share of foreign firms with at least 1 employee
There is much variation behind the overall pattern as Figure 86 illustrates plotting the market share
of foreign firms across sub-industries In groceries foreign share fluctuated around 70 percent It was
90 There is limited literature on the spillover effects generated by multinational retailers See for example Atkin et al (2018)
91 See EC (2018) Figure 2
Productivity differences in Hungary and mechanisms of TFP growth slowdown
117
rising slightly pre-crisis in parallel with the increasing concentration of the industry The increase of
the market share of foreign firms was the strongest in clothes reflecting the expansion of different
multinational chains mainly in plazas The increasing trend observable for the category seems to have
broken around 2012 which coincides with the introduction of the Plaza Stop regulation Foreign
market share was always high in the highly concentrated cosmetics sector A few foreign chains were
dominant in this sector throughout the period Foreign share actually decreased sharply in books and
newspapers
Figure 86 Foreign share in sub-industries
A key question when evaluating the expansion of foreign firms is their performance Foreign retail
firms are substantially more productive than domestic ones (Figure 87) With the exception of the
crisis years labour productivity advantage was between 60-80 percent while the TFP advantage was
between 20-40 percent The TFP advantage is smaller because of the larger capital intensity of foreign
firms These productivity premia are not purely a consequence of the larger size of foreign firms this
pattern is robust to controlling for firm size There is no clear trend in the premia they were declining
before the crisis (suggesting that domestically-owned firms were catching up) and rising after it The
figure also shows a large decline in the premia in the crisis years This is likely to be a consequence of
more pro-cyclical margins of foreign firms which are captured by revenue productivity measures
Productivity evolution and reallocation in retail trade
118
Figure 87 Productivity premia of foreign firms labour weighted
The main message of this section is that similarly to other industries large productivity differences
persist in retail These differences are primarily associated with size larger firms are more productive
and charge lower margins The performance of very small shops and the self-employed looks
especially weak The pre-crisis period was characterised by an expansion of large and foreign firms
but this growth stopped after 2010
84 Allocative efficiency and reallocation
In this section we follow the approach of Chapters 5 and 6 in analysing allocative efficiency and
reallocation with a focus on the retail industry
Chapter 5 showed that an important metric of allocative efficiency at any point in time is the degree of
co-variance of productivity and size which is directly related to aggregate productivity Figure 88
shows the elasticity of the number of employees with respect to labour productivity and TFP A more
positive relationship represents a more efficient allocation of labour across firms92 The figure shows
these relationships both for the full sample (of firms with at least 1 employee) and the main sample
(firms with at least 5 employees)
The elasticity depends crucially both on the sample and the productivity measure We find that the
correlations are much stronger when the full sample is considered rather than the base sample This
reflects our findings in Table 83 namely that the smallest firms differ substantially from other firms
92 These are coefficients from separate yearly univariate regressions with ln number of employees on the left hand side and productivity as the explanatory variable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
119
while firms with at least 5 employees are quite similar to each other The labour productivity premium
of larger firms is greater than their TFP premium reflecting their higher capital intensity
The key insight from Table 83 is that most of the Olley-Pakes correlation or measured allocative
efficiency results from the fact that very small firms are of very low productivity Within the group of
firms with at least 5 employees the correlation between TFP and size is practically zero There is a
positive although small correlation within the group between employment and labour productivity93
There is also no key trend in this measure of allocative efficiency some measures show improvement
while others a deterioration94
Figure 88 The elasticity of employment with respect to productivity main sample
Figure 89 performs the dynamic (Foster-type) productivity decomposition for the retail industry The
picture is not very different from the patterns found for services in general (see Figure 64) Pre-crisis
parallel with the strong growth of large chains growth was mainly driven by reallocation primarily in
the form of firm entry The crisis was accompanied by an annual 5 percent fall in productivity driven
by within-firm productivity decline As we have seen in Table 83 this was most likely the results of
margin-cutting by large firms Between 2010-2013 within-firm productivity growth and net entry
contributed similarly to the (relatively low) productivity growth Productivity growth sped up between
2013-2016 mainly driven by the within-firm component with little reallocation The trend break in the
growth of large chains is clearly reflected in this decomposition
93 As we have discussed in Chaper 5 this is not exceptional ndash actually similar correlations are found in services in other European contries
94 These low levels of allocative efficiency are in line with international evidence In fact these correlations have been negative in the majority of EU member states (EC 2018 p 7)
Productivity evolution and reallocation in retail trade
120
Figure 89 Dynamic decomposition of productivity growth in retail
While these results are informative about reallocation at the firm-level the shop-level data enable us
to investigate reallocation at a more detailed level These data enable us to investigate whether key
firm or shop-level variables are related to opening new shops closing shops or the growth of the shops
of continuing firms We investigate these questions in the paragraphs that follow
The simplest way to explore the shop-extensive margin or the change in the number of shops is to
aggregate the shop-level data to the firm-level In particular we calculate the change in the number of
shops the number of new shops and the number of old shops for each firm 119894 and year 119905 Denoting
these variables which show changes between year 119905 and 119905 + 1 by 119910119894119905 we run the following firm-level
regressions
119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905
where 119883119894119905 is a vector of firm-level variables These proxy productivity (by ln labour productivity) and
size (by the number of shops of the firm and the average sales per shop) 120575119905 is a full set of year
dummies
When estimating these equations one has to make a number of compromises Most importantly one
can only observe the change in shop numbers when the firm is present in the sample both in year 119905
and 119905 + 1 Otherwise one cannot be sure whether all the shops were closed or simply not sampled in
119905 + 1 Unfortunately this is a serious restriction for two reasons First one cannot observe the exit or
Productivity differences in Hungary and mechanisms of TFP growth slowdown
121
entry only survival for single-shop firms95 Second we also miss when a multi-shop firm exits with all
its shops
One also has to make a number of further methodological choices We restrict our sample to groceries
which is a relatively homogeneous group with many observations Another choice is that even though
we observe shops on a monthly basis we consider only year-to-year changes between May and the
following May Running the regressions on the monthly data would inflate artificially the number of
observations and introduce important methodological problems including seasonality
Table 84 presents the results In column (1) the dependent variable is the (net) change in the
number of shops The results suggest that productivity is of limited importance as a determinant of
change in shop numbers but size matters Firms with more and larger shops were more likely to
expand in terms of opening new shops Foreign firms expand faster because they are larger
conditional on size ownership does not matter Size is correlated both with shop opening and closing
firms with a larger average shop size are more likely to open new shops while chains with more shops
are less likely to close existing ones
Table 84 Determinants of the change in the number of shops at the firm-level groceries
(1) (2) (3)
Dependent Change in
number of
shops
New
shops
Closed
shops
Labour productivity 0001 0025 -0011
(0024) (0014) (0018)
Foreign-owned -0087 -0042 0019
(0064) (0034) (0045)
ln( (average
salesshop)
0056 0034 -0017
(0016) (0010) (0014)
5-9 shops 0176 0001 -0126
(0070) (0036) (0054)
10-49 shops 0231 -0009 -0167
(0067) (0034) (0052)
more than 50 shops 0194 0002 -0109
(0071) (0038) (0055)
Year FE yes yes yes
Observations 815 815 815
R-squared 0105 0093 0084
Notes One observation is a firm-year Standard errors are clustered at the firm-level
One may get a more detailed picture by investigating at the shop-level Here we can straightforwardly
estimate both the exit part of the extensive margin (did a specific shop close) and the intensive
margin (did the shop extend its sales)
95 For this reason we drop single-shop firms altogether from the analysis
Productivity evolution and reallocation in retail trade
122
In particular we run regressions of the following form
119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905
where 119894 denotes firms 119895 shops and 119905 years The outcome variable 119910119894119895119905 is either a dummy showing
that the shop closed96 between 119905 and 119905 + 1 or represents the growth of (log) sales of the shop 119883119894119905 are
firm-level variables such as productivity while 119885119894119895119905 are shop-level variables such as shop-level sales
The same restrictions apply as in the previous case
Table 85 reports basic regressions We run both the exit and sales growth regressions for three
subperiods 2004-2007 2008-2010 and 2012-2015 Our main question is whether one can identify
any changes in the relocation process across these subperiods
Let us start with the exit regressions Similarly to the firm-level results we find that productivity and
ownership are not associated with the probability of exit Shop size is significantly related to closing a
shop twice as large sales are associated with 5 percentage points lower probability of the event
occurring This relationship became stronger by the third period The number of shops of the firm is
also negatively associated with the probability of closing the shops and this effect only became
significant post-crisis In addition the explanatory power of the regression is also higher by nearly 50
percent in this last period compared to the earlier ones To sum up we find that the size of the shop
and the turned out to be more important post-crisis making such shops less likely to close
In contrast to the exit equation we do not find significant effects in the growth regressions Neither
size nor productivity seem to be related to growth at the shop-level
To sum up the level of allocative efficiency in retail is relatively low ndash similarly to other European
countries ndash and one cannot see a significant change in this respect Pre-crisis when large chains
expanded rapidly reallocation played a significant role in aggregate productivity growth while within-
firm growth became dominant after the crisis Shop-level data suggest that the expansion in terms of
number of shops is mainly determined by firm size rather than productivity and ownership Sales
growth of existing shops does not seem to be related to size ownership or productivity The lack of
evidence for a relationship between opening new shops or the growth of existing shops is much in line
with the low measured allocative efficiency in the industry
96 We run linear probability models for shop exits Probit models yield similar results
Productivity differences in Hungary and mechanisms of TFP growth slowdown
123
Table 85 Probability of closing a shop and growth regression NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent Closing the shop Growth
Period 2004-
2007
2008-
2010
2012-
2015
2004-
2007
2008-
2010
2012-
2015
labour productivity 0005 -0021 0102 0034 -0012 0097
(0009) (0015) (0087) (0018) (0022) (0063)
foreign-owned -0005 0063 0056 0016 -0014 -0035
(0023) (0045) (0057) (0023) (0055) (0066)
ln sales -0026 -0029 -0057 -0018 -0010 0010
(0006) (0007) (0021) (0007) (0017) (0005)
5-9 shops -0014 -0066 -0111 0061 -0035 -0055
(0026) (0051) (0043) (0046) (0044) (0026)
10-49 shops -0045 -0114 -0149 0046 -0038 -0023
(0024) (0048) (0039) (0044) (0043) (0017)
more than 50
shops
0001 -0077 -0134 0061 -0027 -0016
(0029) (0048) (0040) (0045) (0044) (0022)
Observations 15374 10946 15038 14025 10120 13458
R-squared 0030 0053 0073 0023 0041 0121
Notes OLS regressions run at the shop-year level only for firms present both in t and t+1 In columns (1)-(3) the
dependent variable is a dummy indicating whether the shop closes between t and t+1 while in columns (4)-(6) it is
the growth rate of sales between t and t+1 The explanatory variables are measured at year t The number of
shops variables are dummies representing the number of shops of the firm County and year fixed effects are
included Period 1 2004-2007 Period 2 2008-2010 period 3 2012-2015 Standard errors are clustered at the
firm-level
85 Trade
In small open economies a very important function of the wholesale and retail sector is the
intermediation of international trade for consumers and firms The operation and efficiency of these
industries can have a strong impact on aggregate welfare and productivity by determining both the
cost and variety of imported goods available as well as the cost of exporting products (Raff and
Schmitt 2016)
Many interesting questions emerge in this framework One of the key issues is the problem of double
marginalisation In the case of consumers (and consumer goods) one dimension of this question is
whether retailers import products directly or via wholesalers If retailers find it very hard to import
directly (because of say large fixed costs) double marginalisation can raise prices for consumers
Through this channel lower trade cost of retailers can benefit consumers As a result the share of
consumer goods imported directly by retailers may be an important proxy for the lower prevalence of
double marginalisation
In the case of intermediate inputs manufacturing firms face the choice of importing the product
directly (and paying the fixed costs of doing so) or relying on an intermediary Again reduced fixed
cost may make imported goods cheaper contributing positively to productivity growth Access to
imported intermediate inputs has been shown to be strongly correlated with the productivity of
Hungarian manufacturing firms (Halpern et al 2015)
Productivity evolution and reallocation in retail trade
124
The question of duality is also highly relevant in this context Multinational retailers can easily rely on
producers abroad hence their expansion can have important effects on Hungarian producers
Domestic chains on the other hand may find it hard to import a large variety of foreign products
which may result in a reduced choice set for consumers
Ultimately it is the questions above that motivate our investigation of importing and exporting by
wholesalers and retailers Our data are exceptionally suitable for this exercise Given that firm balance
sheets can be linked to detailed export and import data one can quantify the amount of products
imported and exported from different product categories by wholesalers and retailers
An important methodological note is that we only observe direct imports in the trade data The most
important consequence of this limitation is that while in actual fact the share of imported goods on a
retailerrsquos shelf is a combination of goods imported directly by the retailer and those imported by a
wholesaler and sold to the retailer with the data we are only able to observe the former (Basker and
Van 2010) Also note that in contrast to imports exports are reported in the balance sheet
Therefore we will use this source of information when analysing exporting
Importing
To start with Figure 810 shows the share of retailers and wholesalers from the total Hungarian
imports of different types of goods In terms of all imports the share of these two groups of firms
fluctuated around 25 percent with a slightly decreasing trend The bulk of the imports were conducted
by manufacturing firms with an especially large share by multinational affiliates strongly integrated
into global value chains for example in the automotive industry Overall wholesalersrsquo imports were
about 5 times larger than those of retailers97
Naturally wholesalers and retailers dominate the importing of consumer goods by a share of around
70 percent A key trend here is the increasing share of retailers In 2004 21 percent of intermediated
trade (imports of wholesalers and retailers) were imported by retailers which increased gradually to
33 by 2015 This is a significant shift which reflects in part the expansion of multinational retail
chains but probably also easier access to imports by retailers
The share of intermediated trade was around 20 percent both for intermediate inputs and capital
goods dominated by wholesalers This reflects that in aggregate terms the overwhelming majority of
goods used by firms in production are imported directly The share of intermediated trade decreased
strongly following the crisis from 20 percent in 2010 to 13 percent in 2015 Given the skewed size
distribution of manufacturing firms this does not mean that most firms import their inputs directly
many smaller firms rely strongly on trade intermediaries when purchasing their inputs
Figure 811 looks into the trends behind consumer goods imports in more detail The left hand side
figure shows the share of imports compared to the total cost of goods sold (COGS) by wholesalers and
retailers98 We find that this ratio is roughly constant for wholesalers namely around 10 percent99
97 This can be compared to the results of Bernard et al (2010) who report that retailers and firms active both in retail and wholesale represent 14 percent of importing firms and 9 percent of imports in the US
98 In particular we calculate total consumer goods imports for wholesale and retail firms and divide it with the sum cost of goods sold across all retailers
99 Needless to say wholesalers also import other type of goods which are part of their cost of goods sold This ratio was 36 percent in 2015 showing that more than a third of their sales was imported
Productivity differences in Hungary and mechanisms of TFP growth slowdown
125
This contrasts sharply with retailers where the share of directly imported goods nearly doubled
between 2005 and 2015 from 6 percent to 11 percent100 This corresponds to a substantial increase in
the share of imported goods offered to consumers by retailers and an increasing share of this volume
is imported directly by the retailer presumably with a smaller degree of double marginalisation
One can also decompose the increasing direct import share of retailers to its different margins One
possibility is that - probably thanks to the declining fixed costs of importing - more and more retailers
started to import (an extensive margin effect) The right panel of Figure 811 shows that this is not the
case the share of directly importing retailers stagnated at about 8 percent of firms (with at least 5
employees) in the whole period Instead the rise of direct imports was driven by the intensive margin
or the average direct import per retailer Other regressions (not reported) suggest that this does result
mainly from the increased imports of large retailers
Figure 810 Share of wholesale retail and other firmsrsquo imports relative to total imports across
product categories
100 Again considering all goods the importcost of goods sold ratio increased from 11 to 18 percent for retailers
Productivity evolution and reallocation in retail trade
126
Figure 811 The share of consumer goods imports relative to the cost of goods sold and the share of
direct consumer goods importers by industry
Notes Firms with at least 5 employees
Figure 812 distinguishes between foreign and domestically-owned retail firms Both the share of
importers and their intensive margins are much higher for foreign-owned firms in the industry The
share of consumer goods imports in foreign firms in terms of cost nearly tripled between 2005 and
2015 from 7 to 21 percent101 compared to the 2-5 percent increase for domestically-owned firms
The increase in imports by retailers hence was mainly driven by multinationals
101 A similar increase from 18 percent in 2005 to 32 percent in 2015 can be observed when non- consumer goods are considered
Productivity differences in Hungary and mechanisms of TFP growth slowdown
127
Figure 812 The share of consumer goods imports relative to cost of goods sold and the share of
direct consumer goods importers by ownership
Notes Firms with at least 5 employees
Table 86 presents the cross-sectional linear regressions in order to investigate the premia of importers
among retailers along several dimensions In these regressions the dependent variable is a dummy
which shows whether a firm imports at least 1 percent of its cost of goods sold102 We find substantial
and highly significant premia in terms of size productivity and ownership 100 percent higher
productivity translates into about 5 percentage points higher probability of importing This premium
was increasing significantly between 2005 and 2015 showing a stronger self-selection of more
productive retailers into direct importing Foreign retailers are 20-25 percentage points more likely to
import on average A doubling of employees is associated with around 9 percentage points higher
probability of importing103
102 These are linear probability models but probit specifications yield similar marginal effects
103 Similar premia are found for importers in most industries and are mainly explained by the fixed costs of importing (Vogel and Wagner 2010)
Productivity evolution and reallocation in retail trade
128
Table 86 Determinants of importing linear probability models Retailers
(1) (2) (3) (4) (5)
Year 2005 2008 2010 2012 2015
Dependent Imports at least 1 percent of purchases
Labour productivity 0050 0054 0047 0059 0065
(0003) (0003) (0003) (0003) (0003)
Foreign-owned 0249 0196 0224 0238 0217
(0014) (0011) (0012) (0012) (0012)
Ln employees 0082 0082 0075 0073 0088
(0004) (0004) (0004) (0004) (0004)
Constant -0470 -0536 -0464 -0551 -0637
(0027) (0026) (0027) (0028) (0027)
Observations 7467 7977 7400 7122 8308
R-squared 0116 0130 0127 0140 0143
Notes Firms with at least 5 employees These are cross-sectional regressions where the dependent variable is
dummy representing whether the firm imports at least 1 percent of its cost of goods sold
Exporting
Wholesalers and retailers can also play a significant role as export intermediaries Extended export
activities of these firms can be an important source of growth for these firms but can also benefit
many smaller producers who would not find it profitable to export directly (Ahn et al 2011)
Figure 813 shows that 85-90 percent of exporting was conducted directly by producers rather than by
wholesalers or retailers The share of intermediated exports was constant pre-crisis but started to fall
after 2012
Productivity differences in Hungary and mechanisms of TFP growth slowdown
129
Figure 813 Share of wholesale retail and other firmsrsquo exports relative to total exports of firms
Many wholesalers and retailers started to export in the period under study (Figure 814) The share of
exporters in wholesale firms increased from 25 percent in 2005 to 35 percent in 2015 while the share
of exporting retailers doubled in this period The share of exports in the turnover of these firms also
increased substantially
Figure 814 Share of exports relative to turnover and share of exporters by industry
While foreign-owned firms are about 4 times more likely to export than domestic ones entry into
exporting was not limited to foreign-owned firms (Figure 815) the share of exporters among
domestically-owned firms doubled between 2005 and 2015 This was paralleled with an increase in the
share of exports relative to total turnover
Productivity evolution and reallocation in retail trade
130
Figure 815 Share of exports relative to turnover and share of exporters by ownership for the retail
sector
Table 87 reports linear probability models with export status as the dependent variable More
productive larger and foreign-owned firms are more likely to export In general both the size and
labour productivity premia increased between 2005 and 2015 once again suggesting stronger self-
selection based on these variables
Table 87 Determinants of exporting linear probability models retail
(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015
Dependent Exports at least 1 percent of total revenue
Labour
productivity
0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)
Foreign-owned 0083 0137 0141 0119 0107
(0009) (0011) (0013) (0013) (0012)
Ln employees 0019 0029 0028 0031 0034
(0003) (0004) (0004) (0004) (0004)
Constant -0159 -0277 -0271 -0321 -0317
(0018) (0026) (0027) (0029) (0028)
Observations 7622 7976 7663 7384 8730
R-squared 0028 0045 0041 0041 0036
This section has shown that the role of retailers in international trade is becoming more and more
important in Hungary This can have many benefits from providing a larger variety of potentially lower
priced goods to consumers to letting smaller producers reach foreign markets Increasing exports
mostly reflect opportunities provided by European integration and the internet but policies can also
help firms to become more adapt at utilising these opportunities
Productivity differences in Hungary and mechanisms of TFP growth slowdown
131
86 Policies Crisis taxes
As we have described briefly in Section 81 some of the new policies introduced after the crisis were
size-dependent either explicitly or implicitly The crisis taxes and the local business tax104 were based
on explicitly taxing large firms at higher rates Such policies can have substantial effects at the sectoral
level (Guner et al 2008)
Evaluating the effects of these taxes is not a straightforward task A possible approach was followed in
Section 84 where we have investigated the reallocation process in detail While such an approach is
not capable of identifying the causal effects of specific policies it may provide a broad picture The
results most importantly Figure 88 suggest that the importance of the reallocation process declined
relative to within-firm productivity growth Still this could have resulted from many reasons other than
policy changes
A more direct approach is to identify specific firms which were affected by a policy and to compare
their behaviour to similar firms not affected by the policy Such a diff-in-diff approach may be an
effective policy evaluation tool when there are sharp breakpoints in the tax schedule with enough
`treatedrsquo and control firms in the two groups
As for the crisis taxes the only sharp discontinuity was at the top rate when the tax rate increased
from 04 to 25 percent of profits The cutoff was at HUF 100bn and according to our data altogether
6 retail firms qualified for inclusion in this group This sample size does not allow for a statistically
powerful test
Still a few graphs may illustrate the processes First the market share of these large mainly
multinational firms were expanding quickly before 2010 and stagnated afterwards (Figure 816)
Second we can illustrate some of the key performance measures discussed in Section 83 Figure 817
compares the treated firms to a control group consisting of firms with at least 100 employees We find
that the premium of the treated group in terms of both productivity measures and margins were
higher between 2010 and 2013 than before or after105 As we have discussed earlier at least in the
short term these revenue-based measures are likely to reflect changes in prices Hence this figure
hints at increased prices in the treated group relative to the control group suggesting that treated
firms passed on the tax to consumers Note that these differences are not statistically significant and
to reiterate may have resulted from many other factors rather than just the effects of this specific
policy
104 The effect of the local business tax is much harder to test given its more continuous nature
105 Note that the margin premia are in fact negative in line with the lower margins charged by the largest firms
Productivity evolution and reallocation in retail trade
132
Figure 816 Sales and employment share of firms in the top bracket of the crisis tax
Notes Full sample
Figure 817 Margin TFP and labour productivity advantage of firms in the top bracket of the crisis tax
firms with more than 100 employees
Productivity differences in Hungary and mechanisms of TFP growth slowdown
133
87 Policies Mandatory Sunday closing
One of the most characteristic non-tax based size-dependent policies was mandatory Sunday closing of
larger shops introduced in March 2015 and reversed in April 2016 While the policy had multiple aims
it was partly motivated by supporting smaller and family-owned shops In this section we investigate
two outcomes related to this policy First we aim at understanding its reallocation effects ie the
extent to which the market share of treated shops lost market share Second we are interested in the
extent to which consumption was reallocated to other days of the week
The shop-level data is ideal to investigate the effects of this policy First the policy was defined at the
shop- rather than the firm-level We can identify the affected shops precisely based on the number of
days they were open Second many shops have been affected by this policy making the test
powerful Third the policy has a clearly defined beginning and end making a difference in differences
strategy feasible
Our empirical approach starts with restricting the sample to comparable firms First we investigate
mainly grocery shops where we have sizable treated and control groups106 In the sample we include
only shops which were continuously in the sample between January 2015 and October 2016 An issue
is that the treated and the control group may be very different We attempt to guarantee that the
common support condition is satisfied by excluding very small and very large shops107 For similar
reasons we also exclude shops which were not open even on Saturdays either before or during the
policy108
An important part of the analysis is the definition of the treated group As we do not observe directly
the area and the ownership of the shop we rely on the change in the number of days open We
consider a shop treated if it was open for at least 30 days per month before the policy (in median) and
it was open for less than 26 days after the policy was introduced (again in median)109 The control
group consists of other firms in the sample
Taking a look at the number of days open for the two groups reveals that compliance was very high
More than 95 percent of the shops that had been open on Sundays before the policy were closed on
Sundays during the whole policy period More than 95 percent of shops in the control group were
closed on Sundays both before and after the policy There are few firms which deviated from this
pattern by for example opening on Sundays when the policy started110
106 In other 4-digit sectors either there are too few firms or nearly all of them are treated (clothes shoes etc) or none of them (fuel)
107 Based on the 5th and 95th percentiles of the median sales distribution based on sales before the policy Unfortunately we do not have other measures of shop size
108 More precisely we exclude shops for which the median monthly days open was below 21 days either before or during the policy
109 A potential worry with this approach is that some shops may have closed voluntarily when the policy was introduced We cannot exclude this possibility but this may not be that important for the relatively large shops in the sample One can expect that voluntary Sunday closure would not start exactly at the beginning of the policy but rather after a period of gathering information about consumer demand on Sunday By checking the monthly distribution of the number of days open we find only few firms which changed their behaviour in this respect during the policy
110 Note that many small shops remained open on Sundays but most of them are missing from our restricted sample because of small median sales
Productivity evolution and reallocation in retail trade
134
Figure 815 reports descriptive statistics of the key variables Panel A) compares the evolution of
average sales of the treated and the control group before during and after the introduction of the
policy The dynamics of sales growth was remarkably similar before the policy was introduced
suggesting that the parallel trend assumption was satisfied Average sales in the control group are
somewhat higher during the policy suggesting some reallocation of market share to that group After
the policy the treated group seems to slightly overperform the control group
Part B) of Figure 818 shows the evolution of average sales per day open Again the pre-policy trends
are similar for the two groups Sales per day increases significantly for both groups during the policy
consumers did their Sunday shopping on other days The increase is substantially larger for the treated
group showing that most of the former Sunday shopping took place in the same shop but on other
days of the week The fact that there is an increase in the control group shows that part of the former
Sunday shopping was reallocated to these shops Interestingly the sales per day advantage of the
treated group remained even after the policy was abandoned As we will see the main reason for this
is that after abandoning the policy some of the shops remained closed
Figure 818 The evolution of key variables in the treated group and the control group groceries
A) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
135
B) Sales per day
While these patterns are suggestive the data allow us to conduct a more precise econometric event
study exercise We do so by creating a number of quarterly event study dummies to capture the
differential dynamics of the treated and control groups We define the variable lsquoevent timersquo which
shows the number of months since the policy started (it is zero in March 2015) This variable takes
negative values before that date We define quarterly dummies based on the event time variable For
example the first treatment quarter dummy is one when event time is 0 1 or 2 and the firm is in the
treated group The first pre-treatment dummy takes the value of 1 when event time is -1 -2 or -3 and
the firm is in the treated group
We run the following regression to estimate these trends
119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591
120591 + 120583119894119895 + 120575119905 + 휀119894119895119905
In this regression the dependent variable is days open ln(monthly sales) and ln(salesdays open) 119894
denotes firms 119895 shops and 119905 time measured in month while 120591 is event time in quarters The variables
of interest are the full set of event study dummies The base category will be the second pre-trend
dummy (event time -4 -5 or -6) The motivation for this choice is that the policy was announced in
this period (December 2014) hence the first pre-trend period the beginning of 2015 may include
preparation for the policy 120583119894119895 are shop fixed effects to control for shop heterogeneity 120575119905 are time
(monthly) fixed effects which control both for seasonality and macro shocks When we run the
regression by pooling different 4-digit industries we allow these dummies to vary across industries In
a more demanding specification we also include firm-time fixed effects and identify from the
differences across the treated and non-treated shops of the same firm in the same month We cluster
standard errors at the shop-level
Figure 819 summarizes the main results for the whole retail sector while the regressions are reported
in Table A71 in the Appendix Panel A) shows the results for days open with the right-hand panel
including firm-time fixed effects We see that on average treated firms cut the number of days open
by 2-3 days relative to the control group ndash the effect is more pronounced with firm fixed effects There
Productivity evolution and reallocation in retail trade
136
is practically no pre-trend and the timing of the reduction of days open is strongly in line with the
introduction of the policy The number of days open increases sharply after the end of the policy but
only to below pre-policy levels This suggests that some shops did not re-open on Sundays after the
policy probably because they learned that their sales did not suffer much
Panel B) shows the behaviour of average monthly sales Again there is no evidence for a pre-trend
During the policy treated firms experienced a 2-3 percent lower sales growth relative to the control
group This shows how much of sales was re-allocated to other shops Post-policy variables suggest
full recovery to pre-policy levels
Panel C) of the same figure shows the effect of the policy on sales per day open This variable
increased by 5-10 percent in the treated group relative to the control group The bulk of consumers
seem to have remained loyal to their familiar shops and simply made their shopping on other days
This may have also been helped by longer opening hours on other days of the week and further efforts
made by shops to retain their customers Sales per day remain higher even after the end of the policy
most likely because some shops did not re-open on Sundays but probably also because of
organizational changes during the policy
Figure 819 Event study results for the whole retail sector
A) Days
Productivity differences in Hungary and mechanisms of TFP growth slowdown
137
B) Sales
C) Sales per day
Notes This figure presents point estimates and 95 confidence intervals from the event study regression showing
the evolution of number of days open sales and sales per day of the treated group compared to the control group
as described in the text All specifications include shop fixed effects The left panel regressions also include 4-digit
industry-time fixed effects while the right side panels include firm-time dummies
Productivity evolution and reallocation in retail trade
138
Figure 820 re-estimates the same regressions for groceries where the policy was most relevant The
regression results are reported in Table A72 in the Appendix We find very similar results to the whole
retail sector The only exception is that the evolution of post-policy behaviour of sales is less clear
Figure 820 Event study results for NACE 4711
A) Days
B) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
139
C) Sales per day
Notes The figure above presents point estimates and 95 confidence intervals from the event study regression
showing the evolution of number of days open sales and sales per day of the treated group compared to the
control group as described in the text All specifications include shop fixed effects The left panel regressions also
include 4-digit industry-time fixed effects while the right side panels include firm-time dummies
A possible concern with these estimates is that the increase in sales per day may result from a simple
composition effect If sales are usually very small on Sundays anyway then closing on Sundays may
mechanically increase average daily sales We check for this possibility by estimating sales on different
days of the week from the pre-policy period While we do not observe the sales on each day of the
week we observe sales in different months with a different combination of days We rely on this
variation to estimate a regression of the following form
ln 119904119886119897119890119904119894119895119905 = 120573 lowast 119883119905 + 120574 lowast 119889119886119905119890119905 + 120583119894119895 + 휀119894119895119905
where 119883119905 is a vector of variables containing the number of Mondays Tuesdays etc in month 119905 We
also control for the number of holidays in the month We control for seasonality by including dummies
for December January and summer months The regression also includes firm fixed effects and is
estimated on the period 2009-2014 120574 lowast 119889119886119905119890119905 is a linear trend The estimated results are reported in
Table A73 in the Appendix
The regression shows that sales on Sundays were not that small namely similar to a typical Monday
or Wednesday Thus the composition effect is unlikely to affect the results much To check for the
relevance of these composition effects Figure 821 A) reports sales predicted from the above
regression for the treated group (by setting the number of Sundays to be zero during the policy)
Therefore the `predictedrsquo line shows what would have happened if sales had remained the same on
Productivity evolution and reallocation in retail trade
140
non-Sundays during the policy The actual line is clearly above the predicted one suggesting that sales
on other days have increased
Panel B) of Figure 821 shows how sales per day would have evolved based on a similar regression
Note that predicted sales per day are slightly larger during the policy than beforehand thanks to the
mechanical composition effect resulting from the slightly lower sales on Sundays Actual sales per day
however are substantially higher than this simple prediction showing again that sales per day
increased on other days of the week
Figure 821 The evolution of the variables versus prediction
A) Sales
B) Sales per day
Productivity differences in Hungary and mechanisms of TFP growth slowdown
141
All in all the mandatory Sunday closing of shops was effective in terms of compliance It did not have
strong reallocative effects with a 2-3 percent fall in sales in the treated group Consumers seem to
have remained mostly loyal to the shop they had frequented and made their shopping on other days
of the week at the same shop Interestingly some of the shops seem to have learned that it is optimal
to remain closed on Sundays even after the policy was cancelled
88 Conclusions
In line with the main message of other parts of this study there are huge productivity differences
across firms within the retail sector There is a strong duality between small and large firms both in
terms of productivity and margins Consumers are likely to pay significantly lower prices in the shops
of large firms Many of the large firms are multinationals which had expanded rapidly before the crisis
At the other end of the range the exceptionally low performance of very small firms seems to be a
significant issue Many technologies applied by the most productive retailers could be adapted
relatively easily by some of the less productive firms Increasing absorptive capacity and effective
financing could help in promoting this Still many of the low-productivity very small shops may not be
viable in the long run
A key pattern observed is the increasing concentration of the retail sector pre-crisis resulting from the
expansion of large chains and foreign firms These trends seem to have stopped or slowed down after
the crisis In line with this pattern the contribution of reallocation decreased post-crisis relative to
earlier periods While many factors can play a role in this pattern it may be related to the different
size-dependent policies introduced after 2010 While these developments may help smaller retail firms
consumers may face higher prices in the long run
Not all the policies introduced can be properly evaluated based on the data at hand especially because
multiple policies were introduced at the same time with some of them affecting only few firms We
were able to analyse precisely the effects of mandatory Sunday closing based on store level data We
found that a relatively small share of the demand was lost by the treated shops and the majority of
consumers simply switched to shopping at the same place on other days Interestingly some of the
treated shops found it optimal not to re-open on Sundays even when the policy was reversed
Additionally retailers and wholesalers also play a large and increasing role in mediating imports and
exports We found a large increase in goods imported directly by retailers rather than indirectly via
wholesalers This was mainly driven by large foreign firms and may have benefited their consumers
thanks to a lower degree of double marginalisation Both the number of exporting firms and the
amount exported by wholesalers and retailers increased most likely benefitting from easy access to
markets of other EU member states and probably from the opportunities provided by e-commerce
This can benefit both the exporting firms and the Hungarian producers who can more easily reach
foreign markets with the help of these intermediaries Policies may help retailers to internationalise by
making international sales especially on the internet even easier
Conclusions
142
9 CONCLUSIONS
The results of this report confirm that Hungary is atypical because of the relatively poor productivity
performance of frontier firms Importantly contrary to a strong version of the duality concept this is
not a result of Hungarian frontier firms being on the global frontier typically they are quite far away
from it This robust pattern underlines that besides helping non-frontier firms policies may also have
to focus on the performance of the frontier group A transparent environment with a strong rule of law
complemented by a well-educated workforce and a strong innovation system is key for providing
incentives to invest into the most advanced technologies
The analysis in this report reinforces the impression that there is a large productivity gap between
globally engaged or owned and other firms the gap being about 35 percent in manufacturing and
above 60 percent in services This gap seems to be roughly constant in the period under study The
firm-level analysis in Chapter 7 also reveals that one of the mechanisms which conserves the gap is
that foreign frontier firms are able to increase their productivity more than their domestic counterparts
even from frontier levels These findings reinforce the importance of well-designed policies that are
able to help domestic firms to catch up with foreign firms A key precondition for domestic firms to
build linkages with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A more specific
conclusion is the importance of enabling high-productivity domestic firms to improve their productivity
levels even further
The large within-industry productivity dispersion the relatively low (though not extreme in
international comparison) allocative efficiency documented in some of the industries the strong
positive contribution of reallocation to total TFP growth before the crisis and the relatively low entry
rate imply that policies promoting reallocation have a potential to increase aggregate productivity
levels significantly These policies can include improving general framework conditions by cutting
administrative costs reducing entry and exit barriers and using a neutral regulation The fact that
capital market distortions still appear to be significantly above their pre-crisis levels implies that
policies that reduce financial frictions may help the reallocation process The fact that exporters tend to
expand faster relative to non-exporters indicates that access to EU and global markets generates a
strong and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general traded and
more knowledge-intensive sectors fared better both in terms of productivity growth and allocative
efficiency The difference between traded and non-traded sectors points again to the importance of
global competition in promoting higher productivity and more efficient allocation of resources This also
implies that adopting policies that focus on innovation or reallocation in services may be especially
important given the large number of people working in those sectors The better performance of and
reallocation into more knowledge-intensive sectors underlines the importance of education policies
aimed at developing up-to-date and flexible skills and innovation policies that help improve the
knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people working at
firms with at least a few employees and those working in very small firms or self-employed The latter
category represents 30-50 percent of people engaged in some important industries Inclusive policies
may attempt to generate supportive conditions for these people by providing knowledge and training
as well as helping them to find jobs with wider perspectives or to set up well-operating firms The large
share of these unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a characteristic difference between the pre-crisis
period characterised by strong reallocation mainly via the expansion of large foreign-owned chains
Productivity differences in Hungary and mechanisms of TFP growth slowdown
143
and the post-crisis period with a stagnating share of large chains This break is likely to be linked to
post-crisis policies favouring smaller firms While halting further concentration in a country with
already one of the highest share of multinationals in this sector can have a number of benefits it is
likely to lead to higher prices and lower industry-level productivity growth in the long run Policies
should balance carefully between these trade-offs Another key pattern identified is the increasing role
of retailers (and wholesalers) in trade intermediation both on the import and export side Policymakers
should encourage these trends and design policies which provide capabilities for such firms to enter
international markets probably via e-commerce
References
144
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Ahn J Khandelwal A K and Wei S J (2011) ldquoThe role of intermediaries in facilitating traderdquo Journal of International Economics 84(1) 73-85
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Andrews D Criscuolo C and Gal P (2015) ldquoFrontier firms technology diffusion and public policy
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Antildeoacuten Higoacuten D Mantildeez J A Rochina-Barrachina M E Sanchis A and Sanchis-Llopis J A (2017)
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Arrobbio A Barros A C H Beauchard R F Berg A S Brumby J Fortin H Garrido J Kikeri
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httpdocumentsworldbankorgcurateden228331468169750340Corporate-governance-of-state-
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Atkin D Faber B and Gonzalez-Navarro M (2018) ldquoRetail globalization and household welfare
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Baily M N Hulten C Campbell D Bresnahan T and Caves R (1992) ldquoProductivity dynamics in
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
145
Basker E and Van P H (2010) ldquoImports lsquoЯrsquo us Retail chains as platforms for developing country importsrdquo American Economic Review 100(2) 414-18
Beacutekeacutes G Kleinert J and Toubal F (2009) ldquoSpillovers from multinationals to heterogeneous
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Beacutekeacutes G Murakoumlzy B and Harasztosi P (2011) ldquoFirms and products in international trade
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Bellone F Musso P Nesta L and Warzynski F (2014) ldquoInternational trade and firm-level
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Biesebroeck J V (2008) ldquoAggregating and decomposing productivityrdquo Review of Business and
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David H Dorn D and Hanson G H (2013) ldquoThe China syndrome Local labor market effects of
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Earle J S and Telegdy A (2008) ldquoOwnership and wages Estimating public-private and foreign-
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
147
Fazekas K (2017) ldquoHungarian labour marketrdquo Centre for Economic and Regional Studies Hungarian
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Foster L Haltiwanger J and Krizan C J (2006) ldquoMarket selection reallocation and restructuring
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Foster L Haltiwanger J and Syverson C (2008) Reallocation firm turnover and efficiency
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Foster L Haltiwanger J C and Krizan C J (2001) ldquoAggregate productivity growth Lessons from
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Gamberoni E Gartner C Giordano C and Lopez-Garcia P (2016) ldquoIs corruption efficiency-
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Gamberoni E Giordano C and Lopez-Garcia P (2016) ldquoCapital and labour (mis)allocation in the
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Garicano L Lelarge C and Van Reenen J (2016) ldquoFirm size distortions and the productivity
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Girma S (2005) ldquoAbsorptive capacity and productivity spillovers from FDI A threshold regression
analysisrdquo Oxford Bulletin of Economics and Statistics 67(3) 281-306
Girma S and Goumlrg H (2007) ldquoEvaluating the foreign ownership wage premium using a difference-
in-differences matching approachrdquo Journal of International Economics 72(1) 97-112
Girma S Thompson S and Wright P W (2002) ldquoWhy are productivity and wages higher in foreign
firmsrdquo Economic and Social Review 33(1) 93-100
Gopinath G Kalemli-Ozcan S Karabarbounis L and Villegas-Sanchez C (2017) ldquoCapital allocation
and productivity in South Europerdquo Quarterly Journal of Economics 132(4) 1915-1967
Gorodnichenko Y Revoltella D Svejnar J and Weiss C T (2018) ldquoResource misallocation in
European firms The role of constraints firm characteristics and managerial decisionsrdquo NBER Working
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Griliches Z and Regev H (1995) ldquoFirm productivity in Israeli industry 1979-1988rdquo Journal of
Econometrics 65(1) 175ndash203
Guner N Ventura G and Xu Y (2008) ldquoMacroeconomic implications of size-dependent policiesrdquo Review of Economic Dynamics 11(4) 721-744
Halpern L Koren M and Szeidl A (2015) ldquoImported inputs and productivityrdquo American Economic
Review 105(12) 3660-3703
Halpern L and Murakoumlzy B (2007) ldquoDoes distance matter in spilloverrdquo Economics of Transition
15(4) 781-805
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Harasztosi P (2011) ldquoGrowth in Hungary 1994-2008 The role of capital labour productivity and
reallocationrdquo MNB Working Papers 201112
Harasztosi P and Lindner A (2017) ldquoWho Pays for the Minimum Wagerdquo Mimeo
Haskel J and Sadun R (2012) ldquoRegulation and UK retailing productivity Evidence from microdatardquo Economica 79(315) 425-448
Haskel J E Pereira S C and Slaughter M J (2007) ldquoDoes inward foreign direct investment boost
the productivity of domestic firmsrdquo The Review of Economics and Statistics 89(3) 482-496
Hausmann R and Rodrik D (2003) ldquoEconomic development as self-discoveryrdquo Journal of
Development Economics 72(2) 603-633
Hausmann R Hwang J and Rodrik D (2007) ldquoWhat you export mattersrdquo Journal of Economic
Growth 12(1) 1-25
Herrendorf B Rogerson R and Valentinyi A (2014) ldquoGrowth and structural transformationrdquo
Handbook of Economic Growth (Vol 2) Elsevier 855-941
Hopenhayn H A (2014) ldquoFirms misallocation and aggregate productivity A reviewrdquo Annual Review
of Economics 6(1) 735-770
Hornok C and Murakoumlzy B (2018) ldquoMarkups of exporters and importers Evidence from Hungaryrdquo
The Scandinavian Journal of Economics forthcoming
Hsieh C T and Klenow P J (2009) Misallocation and manufacturing TFP in China and Indiardquo The
Quarterly Journal of Economics 124(4) 1403-1448
Hsieh C T and Olken B A (2014) ldquoThe missing missing middlerdquo Journal of Economic Perspectives 28(3) 89-108
Huttunen K (2007) ldquoThe effect of foreign acquisition on employment and wages Evidence from Finnish establishmentsrdquo The Review of Economics and Statistics 89(3) 497-509 Inklaar R and Timmer M P (2008) ldquoGGDC productivity level database International comparisons of output inputs and productivity at the industry levelrdquo Groningen Growth and Development Centre Research Memorandum GD-104 University of Groningen Groningen
Inklaar R and Timmer M P (2009) ldquoProductivity convergence across industries and countries The
importance of theory-based measurementrdquo Macroeconomic Dynamics 13(S2) 218-240
Iwasaki I Csizmadia P Illeacutessy M Makoacute C and Szanyi M (2012) ldquoThe nested variable model of
FDI spillover effects Estimation using Hungarian panel datardquo International Economic Journal 26(4)
673-709
Javorcik B S (2004) ldquoDoes foreign direct investment increase the productivity of domestic firms In
search of spillovers through backward linkagesrdquo American Economic Review 94(3) 605-627
Productivity differences in Hungary and mechanisms of TFP growth slowdown
149
Javorcik B S and Spatareanu M (2011) ldquoDoes it matter where you come from Vertical spillovers
from Foreign Direct Investment and the origin of investorsrdquo Journal of Development Economics 96(1)
126-138
Jaumlger K (2017) ldquoEU KLEMS growth and productivity accounts 2017 releaserdquo Statistical Module
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Wolf Z (2004) ldquoInvestment behavior user cost and monetary policy transmission The case of
Hungaryrdquo MNB Working Papers 200412
Kertesi G and Koumlllő J (2004) ldquoFighting low equilibriarsquo by doubling the minimum wage Hungarys
experimentrdquo IZA Discussion Papers (No 970)
Konings J (2001) ldquoThe effects of Foreign Direct Investment on domestic firmsrdquo Economics of
Transition 9(3) 619-633
Koumlllő J (2010) ldquoHungary The consequences of doubling the minimum wagerdquo In D Vaughan-
Whitehead (Ed) The Minimum Wage Revisited in the Enlarged EU Chapter 8 Edward Elgar
Publishing Cheltenham UK
Kugler M (2006) ldquoSpillovers from Foreign Direct Investment Within or between industriesrdquo Journal
of Development Economics 80(2) 444-477
Kuusk A Staehr K and Varblane U (2017) ldquoSectoral change and labour productivity growth
during boom bust and recovery in Central and Eastern Europerdquo Economic Change and Restructuring
50(1) 21-43
Levinsohn J and Petrin A (2003) ldquoEstimating production functions using inputs to control for
unobservablesrdquo The Review of Economic Studies 70(2) 317-341
Lin P Liu Z and Zhang Y (2009) ldquoDo Chinese domestic firms benefit from FDI inflow Evidence
of horizontal and vertical spilloversrdquo China Economic Review 20(4) 677-691
McGowan M A Andrews D and Millot V (2017) ldquoThe walking dead Zombie firms and productivity
performance in OECD countriesrdquo OECD Economics Department Working Papers (No 1372)
McMillan M Rodrik D and Sepulveda C (2017) ldquoStructural change fundamentals and growth A
framework and case studiesrdquo NBER Working Papers (No w23378) National Bureau of Economic
Research University of Chicago Press Chicago
Melitz J (2003) ldquoThe impact of trade on intra-industry reallocations and aggregate industry
productivityrdquo Econometrica 71(6) 1695-1725
Nicolini M and Resmini L (2010) ldquoFDI spillovers in new EU member statesrdquo Economics of
Transition 18(3) 487-511
OECD (2016) ldquoThe productivity-inclusiveness nexus Preliminary versionrdquo OECD Publishing Paris
httpdxdoiorg1017879789264258303-en
Olley G and Pakes A (1996) ldquoThe dynamics of productivity in the telecommunications equipment
industryrdquo Econometrica 64(6) 1263-1297
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Rev2 to NACE Rev1rdquo Working Papers (No 1502) University of Urbino Carlo Bo
Petrin A and Levinsohn J (2012) ldquoMeasuring aggregate productivity growth using plant‐level datardquo
The RAND Journal of Economics 43(4) 705-725
Petrin A Reiter J and White K (2011) ldquoThe impact of plant-level resource reallocations and
technical progress on US macroeconomic growthrdquo Review of Economic Dynamics 14(1) 3ndash26
Raff H and Schmitt N (2016) ldquoRetailing and international traderdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 157-179
Ratchford B T (2016) ldquoRetail productivityrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 54-72
Restuccia D and Rogerson R (2017) ldquoThe causes and costs of misallocationrdquo Journal of Economic
Perspectives 31(3) 151-74
Rovigatti G and Mollisi V (2016) ldquoPRODEST Stata module for production function estimation based
on the control function approachrdquo Statistical Software Components S458239 Boston College
Department of Economics Revised 12 Jun 2017 Accessed October 26 2017
httpsideasrepecorgcbocbocodes458239html
Sadun R (2015) ldquoDoes planning regulation protect independent retailersrdquo Review of Economics and Statistics 97(5) 983-1001
Scarpetta S Hemmings P Tressel T and Woo J (2002) ldquoThe role of policy and institutions for
productivity and firm dynamics Evidence from micro and industry datardquo OECD Economics Department
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httpdxdoiorg102139ssrn308680
Smith H (2016) ldquoThe economics of retailer-supplier pricing relationships Theory and evidencerdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 97-136
Smeets R (2008) ldquoCollecting the pieces of the FDI knowledge spillovers puzzlerdquo The World Bank
Research Observer 23(2) 107-138
Syverson C (2011) ldquoWhat determines productivityrdquo Journal of Economic Literature 49(2) 326-65
Taglioni D and Winkler D (2016) ldquoMaking global value chains work for developmentrdquo The World
Bank Issue 143 1-10
Topalova P and Khandelwal A (2011) ldquoTrade liberalization and firm productivity The case of
Indiardquo Review of Economics and Statistics 93(3) 995-1009
Viviano E (2008) ldquoEntry regulations and labour market outcomes Evidence from the Italian retail trade sectorrdquo Labour Economics 15(6) 1200-1222
Vogel A and Wagner J (2010) ldquoHigher productivity in importing German manufacturing firms Self-selection learning from importing or bothrdquo Review of World Economics 145(4) 641-665
Wagner J (2007) ldquoExports and productivity A survey of the evidence from firm‐level datardquo The
World Economy 30(1) 60-82
Productivity differences in Hungary and mechanisms of TFP growth slowdown
151
Wooldridge J M (2009) ldquoOn estimating firm-level production functions using proxy variables to
control for unobservablesrdquo Economics Letters 104(3) 112-114
Zhang Y Li H Li Y and Zhou L A (2010) ldquoFDI spillovers in an emerging market The role of
foreign firms country origin diversity and domestic firms absorptive capacityrdquo Strategic Management
Journal 31(9) 969-989
Appendix
152
APPENDIX
A3 Chapter 3 Internationally comparable data sources and methodology
A31 EU KLEMS amp OECD STAN
The EU KLEMS project aimed at creating a database on measures of economic growth productivity
employment creation capital formation and technological change at the industry level for all European
Union member states from 1970 onwards The database provides an important input to policy
evaluation in particular for the assessment of the goals concerning competitiveness and economic
growth potential as established by the Lisbon and Barcelona summit goals
The input measures include various categories of capital labour energy material and service inputs
Productivity measures have also been developed in particular with growth accounting techniques
Several measures on knowledge creation have also been constructed
The basic data of the EU KLEMS is also available in the OECD STAN database sometimes in a more up
to date version We have downloaded the following variables from there
- EMPE Number of employees
- EMPN Number of persons engaged ndash total employment
- SELF Number of self-employed
- VALU Value added current prices (millions of national currency)
- VALK Value added volumes (current price of the reference year 2010 millions)
- VALP Value added deflators (reference year 2010 = 100))
Labour productivity is defined as gross value added at constant prices divided by the number of
persons engaged In order to create comparative labour productivity levels we used the 2005
benchmark from the GGDC Productivity Level Database111 This project provides productivity levels
relative to the USA that can be used together with EU KLEMS growth accounts to create comparable
productivity level extrapolations (Inklaar and Timmer 2008 Inklaar and Timmer 2009)
A32 OECD Structural and Demographic Business Statistics
The OECD Structural and Demographic Business Statistics (SDBS) consists of two databases the
OECD Business Demography Indicators (BDI) and the OECD Structural Business Statistics (SBS)
The OECD Business Demography Indicators (BDI) database contains data on births and deaths of
enterprises their life expectancy and the important role they play in economic growth and
productivity The OECD Structural Business Statistics (SBS) database features the data collection
of the Statistics Directorate relating to a number of key variables such as for example value added
operating surplus employment and the number of business units broken down by ISIC Rev 4
industry groups referred to as the Structural Statistics on Industry and Services (SSIS) database and
by economic sector and enterprise size class referred to as the Business Statistics by Size Class (BSC)
database For most countries the main sources of information used in the compilation of structural
business statistics are business surveys economic censuses and business registers
111 More information can be found on the homepage of GGDC Production Level Database
httpswwwrugnlggdcproductivitypldearlier-release
Productivity differences in Hungary and mechanisms of TFP growth slowdown
153
The statistical population is composed of enterprises (or establishments when no data on enterprises
are available) In the case of BDI database the population contains all enterprises including non-
employers ie enterprises with no employees while the population of SBS contains only the employer
enterprises ie firms with at least one employee
Birth rate of all enterprises is the ratio of the number of enterprise births and the number of
enterprises active in the reference period Births do not include entries into the population due to
mergers break-ups the split-off or restructuring of a set of enterprises It does not include entries
into a sub-population resulting only from a change of activity (Source BDI)
Death rate of all enterprises is the ratio of the number of enterprise deaths and the number of
enterprises active in the reference period Deaths do not include exits from the population due to
mergers take-overs break-ups or the restructuring of a set of enterprises It does not include exits
from a sub-population resulting only from a change of activity An enterprise is included in the count of
deaths only if it is not reactivated within two years Equally a reactivation within two years is not
counted as a birth (Source BDI)
Number of enterprises is a count of the number of enterprises active during at least a part of the
reference period (Source SBS)
A33 OECD Productivity Frontier
The OECD productivity frontier dataset is based on AMADEUSORBIS and calculates comparable labour
productivity and TFP (MFP) measures across countries The project aims at defining the most
productive (frontier) enterprises both globally and for every country at the 2-digit industry level
(Andrews et al 2016)
Here we use data kindly provided by the OECD for the global and the Hungarian national productivity
frontier Two types of productivity measures are presented labour productivity and Wooldridge MFP
Both frontier series are defined as the average of log-productivity of the top 10 within each 2-digit
industry and year To make this measure less sensitive to expanding coverage over time the 10 is
chosen based on the median number of observations within a 2-digit industry The median for each 2
digit industry is calculated over all the years retained in the analysis
A key issue with AMADEUSORBIS with regard to Hungary is its changing coverage (see Box in Chapter
2) This makes these comparisons meaningful only from 20082009 onwards The underlying sample
includes all firms that over their observed lifespan had at least 20 employees on average
To arrive at internationally comparable real series 2-digit country specific industry value added and
investment deflators were used (2005 = 1) and the monetary values were converted to 2005 USDs
using industry level PPPs from the Groningen Growth and Development Centrersquos Productivity Level
Database112
112 For more information visit the Centrersquos homepage httpswwwrugnlggdcproductivitypld
Appendix
154
A4 Chapter 4 Evolution of the Productivity Distribution
Table A41 Average TFP growth with alternative TFP measures
A) Market economy
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 19 74 16 60
2006 93 119 95 97
2007 39 56 49 65
2008 -10 -04 -06 01
2009 -69 -82 -65 -63
2010 11 80 05 60
2011 34 40 31 45
2012 21 01 24 18
2013 30 22 22 22
2014 40 59 36 48
2015 52 49 50 43
2016 20 03 25 12
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Manufacturing
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 20 114 24 127
2006 114 149 118 137
2007 78 71 86 98
2008 17 -17 32 -11
2009 -133 -117 -120 -87
2010 80 173 85 178
2011 04 18 01 25
2012 -02 -58 07 -38
2013 -12 05 -15 16
2014 -01 27 01 34
2015 30 14 34 19
2016 04 -23 14 -05
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Productivity differences in Hungary and mechanisms of TFP growth slowdown
155
C) Market services
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 10 32 06 01
2006 79 90 82 64
2007 24 48 35 44
2008 -21 -03 -19 05
2009 -52 -71 -51 -54
2010 -11 26 -19 -05
2011 43 57 40 57
2012 30 48 31 57
2013 39 29 31 25
2014 46 78 39 55
2015 54 72 52 58
2016 25 20 29 23
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table presents growth rates of TFP estimated with the translog ACF estimator and the Fixed Effects
estimator for lsquomarket industriesrsquo (see Section 25) The sample does not include agriculture mining and financial
services Services include construction and utilities
Appendix
156
Table A42 Unweighted TFP growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 21 -02 -09 144
2006 118 143 58 47
2007 59 43 90 348
2008 -09 79 17 111
2009 -53 -191 -197 -139
2010 80 76 85 130
2011 -22 17 10 153
2012 01 14 -57 -06
2013 -38 20 -38 54
2014 -03 -05 08 33
2015 61 04 -19 132
2016 09 -10 12 91
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Market Services
Year KIS LKIS Construction Utilities
2005 127 16 -01 -46
2006 166 75 94 66
2007 13 58 60 16
2008 -16 14 -37 -28
2009 -63 -94 -15 44
2010 54 12 -08 23
2011 97 46 77 -29
2012 12 74 06 -57
2013 12 30 60 -71
2014 78 89 65 -31
2015 106 70 22 12
2016 16 31 -47 37
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the unweighted average ACF TFP growth rate by technology category (see Section 25)
Only firms with at least 5 employees The sample does not include agriculture and financial services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
157
Table A43 Employment-weighted labour productivity growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 172 32 73 300
2006 266 114 54 10
2007 121 52 69 243
2008 -25 -17 -03 126
2009 31 -151 -186 35
2010 135 114 199 207
2011 -33 -10 96 96
2012 03 -34 -32 -226
2013 -35 22 26 253
2014 33 19 53 94
2015 82 -04 -06 102
2016 34 18 08 -110
Average
2004-2007 186 66 65 184
2007-2010 47 -18 03 123
2010-2013 -21 02 24 35
2013-2016 28 14 20 85
B) Services
Year KIS LKIS Construction Utilities
2005 127 -05 41 -31
2006 166 75 21 54
2007 13 11 25 -36
2008 -16 -19 05 -02
2009 -63 -117 09 04
2010 54 -01 -05 13
2011 97 47 54 13
2012 12 62 19 -47
2013 12 21 62 -44
2014 78 55 64 -39
2015 106 54 07 65
2016 16 49 -60 43
Average
2004-2007 102 27 29 -04
2007-2010 -08 -46 03 05
2010-2013 40 48 24 -01
2013-2016 53 45 18 06
Notes This table shows the employment-weighted average LP growth rate by technology category (see Section
25) Only firms with at least 5 employees The sample does not include agriculture and financial services
Appendix
158
Table A44 The share of firms in the top decile ()
A) By size
2004 2007 2010 2013 2016
5-9 emp 1049 1051 1043 1096 1045
10-19 emp 954 962 92 904 92
20-49 emp 994 903 939 856 998
50-99 emp 896 1024 1188 1009 1096
100- emp 721 81 839 748 728
B) By ownership
2004 2007 2010 2013 2016
Domestic 833 818 814 824 837
Foreign 2344 2499 2422 2384 2488
State 554 728 81 575 695
C) By region
2004 2007 2010 2013 2016
Central HU 567 568 59 56 549
Northern
Hungary 195 116 19 208 224
Northern
Great Plain 161 178 239 23 249
Southern
Great Plain 137 118 17 258 179
Central
Transdanubia 276 33 332 369 332
Western
Transdanubia 311 283 244 361 444
Southern
Transdanubia 184 201 235 143 181
Notes Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
159
Figure A41 Persistence of top decile status
Notes This figure shows how many of top decile firms in year 2010 were frontier in 2013 how many exited and
how many continued as non-frontier The first panel shows this transition matrix for different 3-year periods
Appendix
160
A5 Chapter 5 Allocative Efficiency
Table A51 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168
C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209
D - Electricity gas
steam and AC
6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743
E - Water supply
sewerage waste
6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872
F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023
G - Wholesale and
retail trade
6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303
H - Transportation
and storage
6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985
I - Accommodation
food service
6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250
J - Information and
Communication
6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046
M - Professional
Scientific and Tech Act
6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075
N - Administrative and support service
6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
161
Table A52 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692
C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109
D - Electricity
gas steam and
AC
6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291
E - Water supply
sewerage waste
6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916
F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436
G - Wholesale and
retail trade
6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260
H - Transportation
and storage
6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776
I -
Accommodation
food service
6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899
J - Information
and
Communication
6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800
M - Professional
Scientific and
Tech Act
6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242
N - Administrative
and support service
6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
162
Table A53 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covar
iance
unweight
ed LP
weighted
LP
covar
iance
B - Mining and quarrying 7509 8072 0564 8038 8583 0546 8378 9440 1063 8609 9028 0419
C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796
D - Electricity gas steam and
AC
9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197
E - Water supply sewerage waste
8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703
F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096
G - Wholesale and retail trade 7955 8252 0297 8036 8452 0415 7955 8307 0352 8197 8589 0392
H - Transportation and
storage
8364 8475 0110 8300 8525 0224 8194 7698 -
0496
8292 7289 -
1003
I - Accommodation food
service
7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651
J - Information and Communication
8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988
M - Professional Scientific and Tech Act
8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406
N - Administrative and
support service
7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238
Productivity differences in Hungary and mechanisms of TFP growth slowdown
163
Table A54 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance
B - Mining and
quarrying
7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358
C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960
D - Electricity gas
steam and AC
9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289
E - Water supply
sewerage waste
8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064
F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159
G - Wholesale and
retail trade
7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037
H - Transportation
and storage
8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022
I - Accommodation
food service
7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363
J - Information and
Communication
7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263
M - Professional
Scientific and Tech
Act
7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431
N - Administrative
and support service
7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216
Appendix
164
Table A55 Allocative efficiency based on Hsieh-Klenow (2009) ndash 1 digit industries
Distortions in 2001 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4802 1540 0299 0803 19127 1152
C - Manufacturing 5620 1300 0425 0818 12807 1008
D - Electricity gas steam and AC 6760 0503 0591 0456 6171 0784
E - Water supply sewerage waste 6629 0599 0103 1127 6245 1248
F - Construction 6706 0818 0280 0954 21186 1227
G - Wholesale and retail trade 7225 1088 0395 1007 21997 1211
H - Transportation and storage 6073 0984 -0154 1647 15193 1144
I - Accommodation food service 6201 0684 -0025 0919 7951 1263
J - Information and Communication 5499 1273 0549 0603 5387 1265
M - Professional Scientific and Tech Act 6961 0920 0253 1062 45052 1293
N - Administrative and support service 6778 1237 0084 1020 42372 1546
Productivity differences in Hungary and mechanisms of TFP growth slowdown
165
Table A55- continuedhellip
Distortions in 2005 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4211 1121 0269 0669 12217 0953
C - Manufacturing 5919 1173 0497 0890 13439 0998
D - Electricity gas steam and AC 6569 0880 0596 0553 6400 1181
E - Water supply sewerage waste 6433 0722 0091 1277 9084 1126
F - Construction 6794 0744 0155 0947 20440 1099
G - Wholesale and retail trade 7497 1199 0392 0771 20492 1543
H - Transportation and storage 6305 1063 0017 1205 11362 1232
I - Accommodation food service 6085 0660 0098 1287 5680 1239
J - Information and Communication 5867 1337 0608 0637 6375 1481
M - Professional Scientific and Tech Act 6926 0951 0129 1118 50400 1474
N - Administrative and support service 6904 1206 -0004 1055 47387 1649
Appendix
166
Table A55- continuedhellip
Distortions in 2010 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales
taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4219 0669 -0104 0759 11170 1012
C - Manufacturing 6024 1201 0523 0740 12732 1001
D - Electricity gas steam and AC 7260 1273 0813 0433 12091 1565
E - Water supply sewerage waste 6474 0700 0123 0965 13717 1279
F - Construction 6621 0775 0200 1075 30395 1437
G - Wholesale and retail trade 7471 1230 0310 0842 22833 1527
H - Transportation and storage 6517 1250 0123 1030 9632 1459
I - Accommodation food service 6080 0704 0001 1060 5570 1341
J - Information and Communication 5989 1245 0581 0870 11895 1572
M - Professional Scientific and Tech Act 7076 1042 0130 1077 78642 1486
Productivity differences in Hungary and mechanisms of TFP growth slowdown
167
Table A55- continuedhellip
Distortions in 2016 Productivity Productivity dispersion
Median implicit sales
taxes
Dispersion of implicit sales
taxes
Median implicit cost
of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4484 0705 0264 0601 13655 0812
C - Manufacturing 6022 1110 0514 0971 11130 1074
D - Electricity gas steam and AC 7341 0966 0724 0307 36231 2054
E - Water supply sewerage waste 6363 0763 0015 1134 15926 1399
F - Construction 6938 0809 0298 0868 28761 1453
G - Wholesale and retail trade 7511 1005 0312 0959 26886 1576
H - Transportation and storage 6656 0972 0104 1078 16755 1745
I - Accommodation food service 6492 0672 0163 0943 6439 1443
J - Information and Communication 6211 1165 0422 0747 23648 1609
M - Professional Scientific and Tech Act 7188 0956 0148 1223 72383 1567
N - Administrative and support service 7112 1219 -0081 1109 98641 1801
Notes Total factor productivity is measured by the method of Ackerberg et al (2015) See Chapter 52 for details
Appendix
168
Appendix Figure 51 Weighted and unweighted labour productivity by 2-digit industry 2016 firms with at least 5 employees
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted log labour productivity in 2016 while the horizontal axis shows its
weighted log labour productivity in the same year We have omitted industries with less than 1000 observations
Productivity differences in Hungary and mechanisms of TFP growth slowdown
169
Appendix Figure 52 The relationship between weighted and unweighted labour productivity by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted labour productivity levels run at the 2-digit industry level
separately for 2005 2010 and 2016
Appendix
170
Appendix Figure 53 the change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences between the weighted and unweighted
labour productivity) in 2010 while the vertical axis shows the same quantity in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
171
A6 Chapter 6 Reallocation
Table A61 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -93 -38 10 -65 -02 -10 50 -43
C Manufacturing 108 23 48 36 -02 -11 03 05
D Electricity gas 08 07 05 -04 26 -06 22 10
E Water supply sewerage 17 -17 31 03 08 -09 09 09
F Construction 26 04 08 13 -14 -02 -19 07
G Wholesale and retail trade 30 03 11 16 -55 -08 -65 18
H Transportation and storage -21 14 -43 08 -39 10 -57 08
I Accommodation 68 -07 53 22 -44 00 -37 -07
J ICT 96 10 63 23 29 -24 35 18
M Professional scientific 39 -13 35 17 -38 -04 -26 -08
N Administrative and support 104 11 37 56 -49 -02 -04 -43
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying 08 11 09 -12 41 60 -30 10
C Manufacturing -18 07 -30 05 10 -08 22 -04
D Electricity gas -26 26 -70 18 74 07 31 36
E Water supply sewerage -08 15 -05 -18 -04 05 00 -09
F Construction 42 03 26 13 01 06 -16 11
G Wholesale and retail trade 54 01 32 21 68 04 56 07
H Transportation and storage 89 14 51 25 21 -30 06 45
I Accommodation 85 -05 59 32 51 -03 46 08
J ICT 19 -02 11 10 47 -02 38 11
M Professional scientific 69 05 12 53 30 -04 18 16
N Administrative and support 50 00 36 14 106 01 87 18
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
172
Table A62 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -253 -59 -19 -175 73 -02 39 36
C Manufacturing 105 20 51 34 06 -14 03 16
D Electricity gas 06 09 03 -06 14 -14 23 05
E Water supply sewerage 21 -12 32 02 -06 -09 00 03
F Construction 35 07 12 16 -23 -03 -24 04
G Wholesale and retail trade 27 06 06 16 -39 03 -59 17
H Transportation and storage -34 17 -58 06 -33 14 -58 11
I Accommodation 67 -09 50 26 -42 03 -39 -05
J ICT 85 14 39 32 27 -14 21 19
M Professional scientific 46 -07 28 25 -28 -03 -22 -03
N Administrative and support 122 29 28 65 -49 00 -09 -40
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -08 11 07 -26 24 -03 06 22
C Manufacturing -12 06 -25 07 06 -04 11 -01
D Electricity gas 16 16 -15 15 30 00 25 06
E Water supply sewerage -07 15 -02 -20 -14 -01 02 -15
F Construction 45 03 22 20 10 02 -05 12
G Wholesale and retail trade 45 02 21 22 68 04 53 10
H Transportation and storage 85 13 45 27 75 00 08 66
I Accommodation 81 -04 54 30 51 -02 42 11
J ICT 13 00 00 13 49 10 33 06
M Professional scientific 64 08 11 45 32 00 14 18
N Administrative and support 50 08 15 27 80 19 49 12
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
173
Table A63 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 93 24 44 26 105 12 59 34
C Manufacturing 132 34 54 44 08 19 -12 01
D Electricity gas 13 -04 09 08 41 02 25 14
E Water supply sewerage 45 -02 37 09 -08 -09 04 -03
F Construction 24 07 10 07 -01 07 -09 01
G Wholesale and retail trade 38 08 12 18 -67 -04 -73 10
H Transportation and storage -25 06 -28 -04 -47 03 -56 06
I Accommodation 59 -03 56 07 -74 -12 -41 -21
J ICT 58 19 80 -40 20 -21 40 00
M Professional scientific 61 11 34 16 -67 02 -26 -43
N Administrative and support 61 -20 38 43 -63 -24 -11 -29
2010-2013 2013-2016
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 26 04 -01 24 -29 17 -28 -18
C Manufacturing 00 14 -21 07 33 16 19 -01
D Electricity gas -43 26 -85 16 90 25 13 52
E Water supply sewerage -20 -07 -08 -05 04 -03 06 01
F Construction 40 05 26 10 -03 05 -05 -03
G Wholesale and retail trade 49 05 31 13 68 13 57 -01
H Transportation and storage 59 12 46 01 09 -27 15 20
I Accommodation 74 -07 55 26 47 -08 54 02
J ICT 16 -07 11 12 22 -25 42 05
M Professional scientific 70 20 16 33 45 13 25 06
N Administrative and support 61 08 31 21 81 -11 76 15
Appendix
174
Table A64 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 48 15 -01 34 70 17 53 00
C Manufacturing 132 32 56 45 16 14 -03 05
D Electricity gas 14 -03 05 11 35 -07 30 12
E Water supply sewerage 48 00 39 09 -10 -06 03 -07
F Construction 28 10 14 04 03 06 -14 11
G Wholesale and retail trade 38 10 07 21 -47 07 -61 07
H Transportation and storage -35 09 -40 -04 -41 06 -57 10
I Accommodation 62 -03 52 12 -65 -09 -43 -13
J ICT 00 -15 49 -34 03 -22 14 12
M Professional scientific 75 20 27 28 -46 02 -27 -22
N Administrative and support 91 -05 25 71 -60 -11 -07 -42
2010-2013 2013-2016
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 33 -11 04 40 50 28 05 17
C Manufacturing 06 13 -15 07 28 12 16 00
D Electricity gas 16 18 -26 25 23 17 02 05
E Water supply sewerage -17 -06 -05 -05 04 -04 08 00
F Construction 44 05 26 14 03 02 07 -07
G Wholesale and retail trade 37 05 17 15 65 12 54 -01
H Transportation and storage 56 11 42 04 46 -07 16 36
I Accommodation 70 -07 52 25 44 -07 51 01
J ICT 26 07 04 16 17 -20 37 00
M Professional scientific 56 17 11 28 52 16 23 13
N Administrative and support 65 17 27 22 59 06 41 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
175
A7 Chapter 7 Firm-level productivity growth and dynamics
A71 Productivity growth
Table A71 Relationship between lagged productivity level and subsequent productivity
growth over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 -0188 -0203 -0203
(000550) (000558) (000551)
TFP in t-1 Year 2006 -0222 -0238 -0235
(000518) (000525) (000519)
TFP in t-1 Year 2009 -0143 -0159 -0155
(000570) (000579) (000572)
TFP in t-1 Year 2012 -0156 -0172 -0171
(000516) (000524) (000517)
Year 2003 -00313 -00297
(000507) (000510)
Year 2006 -0184 -0183
(000489) (000491)
Year 2009 -00766 -00762
(000492) (000493)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 114200 113900 113900
R-squared 0061 0067 0084
Appendix
176
Table A72 Relationship between lagged productivity levels and subsequent productivity
growth by size and age
Dep var TFP growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 -0170 -0186 -0213 -0223
(000561) (000578) (00155) (00155)
TFP in t-1 Group 2 00397 00243 -000502 -000776
(00146) (00147) (00213) (00213)
TFP in t-1 Group 3 00793 00652 00725 00600
(00221) (00222) (00164) (00165)
TFP in t-1 Group 4 00753 00666
(00244) (00247)
Group 2 00227 000593 -0000410 0000118
(000940) (000963) (00162) (00162)
Group 3 00216 -000934 00235 00220
(00150) (00154) (00131) (00132)
Group 4 00235 -00351
(00157) (00169)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Observations 30135 30062 30135 30062
R-squared 0056 0073 0056 0073
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees size group 4 is
100+ employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5
years age group 3 is firms older than 5 The baseline category is firms of 2-3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
177
Table A73 Differences in productivity growth by ownership group within different firm
groups
Dep var TFP growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 00476
(00114)
Foreign Non-exporter 00573
(00213)
Foreign Exporter 00610
(00139)
Foreign Size group 1 00295
(00162)
Foreign Size group 2 00849
(00243)
Foreign Size group 3 000361
(00340)
Foreign Size group 4 00662
(00318)
Foreign Age group 1 0119
(00381)
Foreign Age group 2 -00117
(00363)
Foreign Age group 3 00467
(00124)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 31642 31642 31642 31274
R-squared 0032 0033 0033 0033
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column 2 and size and age group dummies in columns 3 and 4 respectively
Appendix
178
Table A74 Relationship between lagged productivity levels and subsequent productivity
growth by ownership and exporter status over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
Firm categories by
foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003
00577 00607 00141 00214
(00151) (00151) (00124) (00124)
TFP in t-1 Firm group Year 2006
00703 0101 00361 00558
(00152) (00152) (00118) (00118)
TFP in t-1 Firm group Year 2009
00338 00306 00450 00406
(00153) (00153) (00122) (00121)
TFP in t-1 Firm group Year 2012
00758 00436 00474 00321
(00146) (00146) (00109) (00109)
Firm group Year 2003
00978 00756 00286 000961
(00128) (00130) (000912) (000977)
Firm group Year 2006
-00290 -00145 -00592 -00411
(00133) (00135) (000871) (000932)
Firm group Year 2009
0114 0116 00502 00457
(00124) (00127) (000824) (000881)
Firm group Year 2012
0120 0120 00234 00155
(00126) (00129) (000782) (000835)
Year FE YES YES
Industry FE YES YES
Firm-level controls
YES YES YES YES
Region FE YES YES
Industry-year FE
YES YES
Observations 112374 112374 113900 113900
R-squared 0066 0085 0065 0085
Notes Firm group refers to foreign ownership in columns (1) and (2) and to exporter status in
columns (3) and (4)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
179
A72 Employment growth
Table A75 Relationship between lagged productivity levels and subsequent employment
growth over time
Dep var employment growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0113 0113 0113
(000472) (000478) (000475)
TFP in t-1 Year 2006 0120 0120 0119
(000434) (000439) (000437)
TFP in t-1 Year 2009 0109 0109 0107
(000479) (000485) (000482)
TFP in t-1 Year 2012 00982 00958 00956
(000442) (000448) (000445)
Year 2003 -00171 -00125
(000441) (000444)
Year 2006 -0134 -0128
(000422) (000423)
Year 2009 -00899 -00873
(000425) (000426)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 123900 123574 123574
R-squared 0042 0049 0054
Appendix
180
Table A76 Relationship between lagged productivity levels and subsequent employment
growth over time with alternative employment growth measures including exiting firms
Dep var employment growth from t to t+3 (including exiting firms (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0156 0147 0148
(000641) (000647) (000644)
TFP in t-1 Year 2006 0134 0127 0128
(000581) (000587) (000584)
TFP in t-1 Year 2009 0139 0132 0134
(000648) (000655) (000651)
TFP in t-1 Year 2012 0132 0126 0127
(000618) (000624) (000621)
Year 2003 -00765 -00618
(000617) (000619)
Year 2006 -0220 -0211
(000586) (000587)
Year 2009 -0177 -0173
(000591) (000590)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 143011 142638 142638
R-squared 0037 0047 0051
Productivity differences in Hungary and mechanisms of TFP growth slowdown
181
Table A77 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status with alternative employment growth measures
including exiting firms
Dep var employment growth from t to t+3 (including exiting firms t=2012)
VARIABLES (1) (2) (3) (4) (5) (6)
TFP in t-1 0134 0130 0134 0137 0134 0136
(000651) (000660) (000722) (000729) (000764) (000767)
TFP in t-1 Foreign -00109 -00138 00116 000347
(00166) (00167) (00289) (00289)
TFP in t-1 Exporter -00371 -00256 -00304 -00226
(00124) (00126) (00148) (00148)
TFP in t-1 Foreign exporter -00222 -00165
(00364) (00365)
Foreign -00254 -00351 -0102 -00739
(00151) (00156) (00254) (00256)
Exporter 00998 00982 00940 00889
(00100) (00102) (00106) (00107)
Foreign exporter 00855 00605
(00312) (00315)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 34980 34980 35564 35473 34980 34980
R-squared 0031 0051 0037 0054 0034 0052
Appendix
182
Table A78 Differences in employment growth by exporter status within different firm
groups
Dep var employment growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Exporter 00876
(000741)
Exporter Domestic 00893
(000788)
Exporter Foreign 00703
(00207)
Exporter Size group 1 00858
(000850)
Exporter Size group 2 00872
(00159)
Exporter Size group 3 0154
(00276)
Exporter Size group 4 00345
(00329)
Exporter Age group 1 00968
(00230)
Exporter Age group 2 0139
(00212)
Exporter Age group 3 00810
(000801)
industry-region FE YES YES YES YES
Firm-group indicators YES YES YES
Observations 34418 33909 34418 33989
R-squared 0034 0034 0034 0036
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
183
Table A79 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status over time
Dep var Employment growth from t to t+3 (t=2003200620092012)
Firm categories by foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003 000927 00131 00178 00190
(00129) (00130) (00107) (00107)
TFP in t-1 Firm group Year 2006 00137 00103 00130 000821
(00126) (00127) (00101) (00101)
TFP in t-1 Firm group Year 2009 -00778 -00676 -00498 -00426
(00129) (00130) (00104) (00104)
TFP in t-1 Firm group Year 2012 -00389 -00321 -00350 -00306
(00126) (00126) (000942) (000942)
Firm group Year 2003 -00601 -00332 000244 00299
(00110) (00113) (000795) (000856)
Firm group Year 2006 -00159 -000559 00640 00786
(00112) (00115) (000752) (000807)
Firm group Year 2009 00404 00249 0111 00882
(00106) (00109) (000714) (000767)
Firm group Year 2012 -00102 -00116 00747 00607
(00110) (00112) (000684) (000735)
Year FE YES YES
Industry FE YES YES
Firm-level controls YES YES YES YES
Region FE YES YES
Industry-year FE YES YES
Observations 121954 121954 123574 123574
R-squared 0046 0055 0045 0055
Notes Firm group refers to foreign ownership in columns (1) and (2) and exporter status in columns
(3) and (4)
Appendix
184
A73 Entry and exit
Table A710 Entry and exit premium by ownership and exporter status
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
VARIABLES (1) (2) (3) (4) (5) (6)
Entry Domestic 00363 00433 Exit Domestic -0165 -0161 Exit Non-exporter
-0172 -0186
(00103) (00102) (00112) (00112) (00122) (00121)
Entry Foreign 0414 0354 Exit Foreign 0255 0203 Exit Exporter
0171 0126
(00284) (00281) (00311) (00309) (00213) (00211)
Incumbent Foreign
0512 0461 Continuing Foreign
0465 0411 Continuing Exporter
0279 0232
(00122) (00129) (00123) (00131) (000887) (000926)
Industry FE YES Industry FE YES Industry FE YES
Industry-region FE YES Industry-region FE
YES Industry-region FE
YES
Firm-level controls YES Firm-level controls
YES Firm-level controls
YES
Observations 44231 44231 Observations 38367 38367 Observations 39020 38916
R-squared 0355 0383 R-squared 0339 0369 R-squared 0331 0370
Table A711 Differences in productivity levels by ownership group within different firm
groups
Depvar TFP in year t (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 0429
(00118)
Foreign Non-exporter 0278
(00206)
Foreign Exporter 0397
(00146)
Foreign Size group 1 0523
(00162)
Foreign Size group 2 0472
(00254)
Foreign Size group 3 0416
(00363)
Foreign Size group 4 0235
(00341)
Foreign Age group 1 0258
(00352)
Foreign Age group 2 0381
(00356)
Foreign Age group 3 0460
(00131)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 38367 38367 38367 37822
R-squared 0350 0361 0353 0356
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
185
Table A712 Entry and exit premium by ownership and exporter status over time
Depvar TFP in year t (t=2006200920122015 for entry and t=2003200620092012 for exit)
VARIABLES (1) (2) VARIABLES (3) (4) VARIABLES (5) (6)
Entry Domestic Year 2006
-00510 -00403 Exit Domestic 2003 -0187 -0188 Exit Non-exporter 2003 -0197 -0198
(000924) (000923) (00107) (00106) (00114) (00113)
Entry Domestic Year 2009
00244 00230 Exit Domestic 2006 -00996 -0101 Exit Non-exporter 2006 -0114 -0118
(000999) (000996) (000917) (000911) (000977) (000971)
Entry Domestic Year 2012
00594 00515 Exit Domestic 2009 -0105 -0113 Exit Non-exporter 2009 -0116 -0123
(000985) (000983) (000942) (000937) (00101) (00101)
Entry Domestic Year 2015
00475 00392 Exit Domestic 2012 -0140 -0150 Exit Non-exporter 2012 -0167 -0174
(000998) (000999) (00111) (00110) (00119) (00119)
Entry Foreign Year 2006
0374 0313 Exit Foreign 2003 0116 00940 Exit Exporter 2003 00659 00517
(00265) (00264) (00264) (00263) (00196) (00197)
Entry Foreign Year 2009
0423 0410 Exit Foreign 2006 0199 0153 Exit Exporter 2006 0194 0165
(00257) (00257) (00267) (00265) (00183) (00183)
Entry Foreign Year 2012
0342 0334 Exit Foreign 2009 0197 0184 Exit Exporter 2009 00720 00760
(00279) (00278) (00278) (00277) (00185) (00185)
Entry Foreign Year 2015
0382 0365 Exit Foreign 2012 0217 0223 Exit Exporter 2012 0114 0137
(00276) (00275) (00307) (00305) (00208) (00208)
Incumbent Foreign Year 2006
0485 0428 Continuing Foreign 2003 0416 0386 Continuing Exporter 2003 0278 0257
(00122) (00124) (00124) (00126) (000943) (000994)
Incumbent Foreign Year 2009
0410 0391 Continuing Foreign 2006 0498 0446 Continuing Exporter 2006 0317 0280
(00120) (00122) (00122) (00124) (000895) (000943)
Incumbent Foreign Year 2012
0436 0439 Continuing Foreign 2009 0414 0404 Continuing Exporter 2009 0194 0201
(00122) (00124) (00119) (00122) (000867) (000915)
Incumbent Foreign Year 2015
0471 0476 Continuing Foreign 2012 0412 0422 Continuing Exporter 2012 0211 0239
(00118) (00120) (00120) (00122) (000827) (000876)
Year FE YES Year FE YES Year FE YES
Industry FE YES Industry FE YES Industry FE YES
Firm-level controls YES YES Firm-level controls YES YES Firm-level controls YES YES
Industry-year FE YES Industry-year FE YES Industry-year FE YES
Region FE YES Region FE YES Region FE YES
Observations 164136 164136 Observations 155657 155657 Observations 157711 157711
R-squared 0369 0380 R-squared 0373 0386 R-squared 0374 0387
Table A713 Entry and exit premium by size and age
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
Firm categories by size age
VARIABLES (1) (2) VARIABLES (3) (4) (5) (6)
Entry Group 1 00233 00151 Exit Group 1 -0170 -0171 -0214 -0210
(00108) (00105) (00121) (00118) (00250) (00241)
Entry Group 2 0106 000987 Exit Group 2 -0201 -0260 -0286 -0260
(00298) (00289) (00280) (00272) (00271) (00261)
Entry Group 3 0124 00204 Exit Group 3 -0152 -0245 -0219 -0207
(00574) (00556) (00479) (00464) (00179) (00173)
Entry Group 4 0123 -00552 Exit Group 4 -0291 -0453
(00720) (00697) (00532) (00517)
Incumbent Group 2 00137 -00620 Continuing Group 2 -00108 -00902 -00277 -00256
(00104) (00101) (00111) (00109) (00170) (00164)
Incumbent Group 3 00163 -0130 Continuing Group 3 000582 -0148 -00759 -00758
(00170) (00168) (00179) (00176) (00131) (00127)
Incumbent Group 4 00150 -0268 Continuing Group 4 -00159 -0293
(00181) (00185) (00188) (00192)
Industry FE YES Industry FE YES YES
Industry-region FE YES Industry-region FE YES YES
Firm-level controls YES Firm-level controls YES YES
Observations 46160 46034 39020 38916 38459 38357
R-squared 0296 0355 0311 0369 0315 0374
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is 50-99
employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is firms of 4-5
years age group 3 is firms older than 5 years
Figure A71 Share of exiting firms in the subsequent 3 years by lagged productivity levels in
different periods
A8 Chapter 8 Retail
Appendix Table A81 Event study regression for the whole retail industry
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 0005 0014 0012 0030 -0230 -0511 (0006) (0005) (0005) (0005) (0030) (0049)
pre_trend_treated3 -0010 -0003 -0008 -0003 -0037 -0031 (0006) (0008) (0006) (0008) (0030) (0056)
pre_trend_treated4 -0020 -0010 -0014 0000 -0209 -0299 (0006) (0007) (0006) (0007) (0029) (0051)
pre_trend_treated5 0004 0011 0006 0017 -0128 -0258 (0007) (0008) (0007) (0008) (0034) (0064)
pre_trend_treated6 -0008 0001 -0004 0008 -0129 -0222 (0007) (0008) (0006) (0008) (0035) (0065)
pre_trend_treated7 0001 -0001 0016 0017 -0365 -0496 (0010) (0013) (0010) (0012) (0053) (0072)
trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)
trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)
trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)
trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)
post_trend_treated1 -0039 -0006 -0007 0044 -0885 -1394 (0012) (0012) (0012) (0011) (0061) (0096)
post_trend_treated2 0022 0003 0044 0048 -0665 -1273 (0012) (0012) (0011) (0011) (0068) (0100)
post_trend_treated3 -0001 0004 0035 0058 -0993 -1531 (0012) (0012) (0012) (0012) (0058) (0092)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 225866 209604 225860 209598 225908 209647
R-squared 0958 0978 0961 0980 0684 0809
Appendix
188
Appendix Table A82 Event study regression for NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 -0008 -0004 -0002 0016 -0189 -0576 (0004) (0005) (0004) (0005) (0033) (0055)
pre_trend_treated3 -0016 -0018 -0013 -0014 -0057 -0064 (0006) (0011) (0006) (0010) (0034) (0059)
pre_trend_treated4 -0010 -0005 -0002 0008 -0236 -0351 (0004) (0007) (0004) (0007) (0034) (0060)
pre_trend_treated5 -0004 0002 -0001 0010 -0129 -0304 (0006) (0008) (0006) (0008) (0037) (0068)
pre_trend_treated6 0011 0000 0018 0011 -0173 -0307 (0007) (0009) (0007) (0009) (0045) (0085)
pre_trend_treated7 -0016 -0032 0002 -0007 -0433 -0640 (0010) (0016) (0009) (0015) (0068) (0091)
trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)
trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)
trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)
trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)
post_trend_treated1 -0009 -0032 0033 0036 -1163 -1875 (0008) (0012) (0008) (0011) (0084) (0118)
post_trend_treated2 0057 -0024 0087 0041 -0888 -1810 (0014) (0013) (0012) (0012) (0097) (0121)
post_trend_treated3 0014 -0031 0060 0044 -1255 -2040 (0011) (0013) (0010) (0012) (0079) (0108)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 94740 87533 94737 87530 94740 87533
R-squared 0968 0982 0973 0985 0642 0809
Appendix Table A83 Sales and the number of different days in a month
(1)
Dependent ln sales
Sunday 0049 (0001)
Saturday 0059 (0001)
Friday 0054 (0001)
Thursday 0050 (0001)
Wednesday 0053 (0001)
Tuesday 0060 (0001)
Monday 0048 (0001)
holiday 0008 (0000)
Jan -0169 (0002)
Dec 0138 (0003)
summer 0032 (0002)
date -0000 (0000)
Observations 463345
R-squared 0970
Appendix
190
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doi 10287333213
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33-E
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ISBN 978-92-79-73462-5 doi 10287333213
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
Table of contents
EXECUTIVE SUMMARY I
1 INTRODUCTION 1
2 DATA SOURCES 4
21 Cleaning the data and defining industry categories 5
22 Productivity estimation 6
23 Estimation sample 10
24 Firm-level variables 13
25 Industry categorization 16
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON 18
31 Convergence 18
32 Within-industry heterogeneity 24
33 Firm dynamics 28
34 Conclusions 31
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION 32
41 Context 32
42 Aggregate productivity and the self-employed 33
43 The evolution of productivity distribution in Hungary 36
44 Duality in productivity and productivity growth 47
45 Conclusions 56
5 ALLOCATIVE EFFICIENCY 58
51 Olley-Pakes efficiency 58
52 Product market and capital market distortions 62
53 Conclusions 70
6 REALLOCATION 73
61 Reallocation across industries 73
62 Reallocation across firms 76
63 Failure of reallocation Zombie firms 82
64 Conclusions 86
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS 89
71 Productivity growth 89
72 Employment growth 95
73 Entry and exit 99
74 Conclusions 103
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE 104
81 Context 104
82 Data 108
83 General trends 110
84 Allocative efficiency and reallocation 118
85 Trade 123
86 Policies Crisis taxes 131
87 Policies Mandatory Sunday closing 133
88 Conclusions 141
9 CONCLUSIONS 142
REFERENCES 144
APPENDIX 152
A3 Chapter 3 Internationally comparable data sources and methodology 152
A31 EU KLEMS amp OECD STAN 152
A32 OECD Structural and Demographic Business Statistics 152
A33 OECD Productivity Frontier 153
A4 Chapter 4 Evolution of the Productivity Distribution 154
A5 Chapter 5 Allocative Efficiency 160
A6 Chapter 6 Reallocation 171
A7 Chapter 7 Firm-level productivity growth and dynamics 175
A71 Productivity growth 175
A72 Employment growth 179
A73 Entry and exit 184
A8 Chapter 8 Retail 187
Productivity differences in Hungary and mechanisms of TFP growth slowdown
i
EXECUTIVE SUMMARY
Slow post-crisis total factor productivity (hereafter TFP) growth is a significant policy
challenge for many European countries in general and for Hungary in particular This
report aims at providing a comprehensive analysis of the processes behind productivity
growth slowdown in Hungary based on micro-data from administrative sources between
2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions A focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact detail of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Executive Summary
ii
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides an
exhaustive picture of Hungarian businesses It is important to keep in mind though that it
omits two important parts of the economy the overwhelming majority of the non-market
sector (including public works) and the self-employed Given the number of people
employed in these two sectors their performance has a strong effect on macro numbers
The available albeit scarce data for the self-employed qualify the findings by suggesting
that the measured productivity level and growth of this group is considerably below than
that of double-entry bookkeeping firms ndash implying that within-industry productivity
dispersion may be even larger than what is indicated by the balance sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
of capital increase on average its rise was disproportionally larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
Productivity differences in Hungary and mechanisms of TFP growth slowdown
iii
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly adding little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterized by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers have increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
The results of this report confirm that Hungary is atypical because of the relatively poor
productivity performance of frontier firms Importantly contrary to a strong version of the
duality concept this is not a result of Hungarian frontier firms being on the global frontier
typically they are quite far away from it This robust pattern underlines that besides
helping non-frontier firms policy may also have to focus on the performance of the
frontier group A transparent environment with a strong rule of law complemented by a
well-educated workforce and a robust innovation system is key for providing incentives to
invest into the most advanced technologies
Executive Summary
iv
The analysis in this report reinforces the impression that there is a large productivity gap
between globally engaged or owned and other firms the gap being about 35 percent in
manufacturing and above 60 percent in services This gap seems to be roughly constant in
the period under study The firm-level analysis in Chapter 7 also reveals that one of the
mechanisms which conserves the gap is that foreign frontier firms are able to increase
their productivity more than their domestic counterparts even from frontier levels These
findings reinforce the importance of well-designed policies that are able to help domestic
firms to catch up with foreign firms A key precondition for domestic firms to build linkages
with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A
more specific conclusion is the importance of enabling high-productivity domestic firms to
improve their productivity levels even further
The large within-industry productivity dispersion the relatively low (though not extreme
in international comparison) allocative efficiency documented in some of the industries the
strong positive contribution of reallocation to total TFP growth before the crisis and the
relatively low entry rate imply that policies promoting reallocation have a potential to
increase aggregate productivity levels significantly These policies can include improving
the general framework conditions by cutting administrative costs reducing entry and exit
barriers and using a neutral regulation The fact that capital market distortions still appear
to be significantly above their pre-crisis levels impliesthat policies that reduce financial
frictions may help the reallocation process The fact that exporters tend to expand faster
relative to non-exporters suggests that access to EU and global markets generate a strong
and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general
traded and more knowledge-intensive sectors fared better both in terms of productivity
growth and allocative efficiency The difference between traded and non-traded sectors
points once again to the importance of global competition in promoting higher productivity
and more efficient allocation of resources This also implies that adopting policies that
focus on innovation or reallocation in services may be especially important given the large
number of people working in those sectors The better performance of and reallocation into
more knowledge-intensive sectors underline the importance of education policies aimed at
developing up-to-date and flexible skills and the significance of innovation policies that
help to improve the knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people
working at firms with at least a few employees and those working in very small firms or as
self-employed The latter category represents 30-50 percent of the people engaged in
some important industries Inclusive policies may attempt to generate supportive
conditions for these people by providing knowledge and training as well as helping them
find jobs with wider perspectives or set up a well-operating firm The large share of these
unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a marked difference between the pre-
crisis period characterised by strong reallocation mainly via the expansion of large
foreign-owned chains and the post-crisis period with a stagnating share of large chains
This break is likely to be linked to post-crisis policies favouring smaller firms While halting
further concentration in a country with already one of the highest share of multinationals
in this sector can have a number of benefits in the long run it is likely to lead to higher
prices and lower industry-level productivity growth Policies should balance carefully
between these trade-offs Another key pattern identified is the increasing role of retailers
(and wholesalers) in trade intermediation both on the import and export side
Policymakers should encourage these trends and design policies which provide capabilities
for such firms to enter international markets probably via e-commerce
Productivity differences in Hungary and mechanisms of TFP growth slowdown
1
1 INTRODUCTION
Slow post-crisis TFP growth is a significant policy challenge for many European countries in
general and for Hungary in particular This report aims at providing a comprehensive
analysis of the processes behind productivity growth slowdown in Hungary based on
micro-data from administrative sources between 2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions The focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact details of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Introduction
2
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides a very
detailed and comprehensive picture of the Hungarian business economy It is important to
keep in mind though that it omits two important parts of the economy the overwhelming
majority of the non-market sector (including public works) and the self-employed Given
the number of people employed in these two sectors their performance has a strong effect
on macro numbers The available albeit scarce data for the self-employed qualify the
findings by suggesting that the measured productivity levels and growth of this group are
considerably below those of double-entry bookkeeping firms ndash implying that within-
industry productivity dispersion may even be larger than what is indicated by the balance
sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries the frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
Productivity differences in Hungary and mechanisms of TFP growth slowdown
3
of capital increase on average its rise was disproportionately larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly contributing little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterised by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
Data Sources
4
BOX 21 AMADEUS and the NAV balance sheet data
An alternative and frequently used source of balance sheet data is the AMADEUS dataset
In this box we compare the data about Hungary with the dataset used in this report
namely the administrative NAV panel
AMADEUS is a firm-level dataset collected and issued by Bureau Van Dijk a Moodyrsquos
Analytics Company It contains comprehensive financial information on around 21 million
companies across Europe with a focus on private company information It includes
information about company financials in a standard format (which makes it comparable
across countries) directors stock prices and detailed corporate ownership structures
(Global Ultimate Owners and subsidiaries) Financial information on firms consists of data
from balance sheets profit and loss statements and standard ratios Non-mandatory cells
are however often missing (eg employment) Therefore the drawbacks of this
database are that it is not representative and that not all firms provide enough
information to analyse issues such as productivity or TFP
Table B21 shows the coverage of AMADEUS (the number of firms as a share of the firms
in the administrative NAV data) by year and size category In earlier years the AMADEUS
sample consisted of mostly large firms but even the coverage of larger firms was
relatively low Recently the expanding coverage has made the AMADEUS sample more
representative While the smallest firms are still undersampled the coverage of firms with
more than 5 employees has reached nearly 100 (In some cases it is even above 100
because of slight differences in the number of employees reported)
The two databases also differ in terms of the variables they include The NAV data are
more detailed in terms of assets and liabilities AMADEUS in contrast provides more
information on ownership It defines the Global Ultimate Owner (GUO) for each company
and analyses their shareholding structure Ownership share is given in percentages and
in addition the degree of independence is also given
Our main aim in this report is to estimate productivity and its change reliably and
representatively for different types of firms small and large This requires a decent
coverage of all types of firms and reliable information on their finances for a number of
periods Because of this we prefer to use the NAV database with its large and universal
coverage and the rich information on firm inputs and outputs
2 DATA SOURCES
The main database we use in this project is the balance sheet panel of Hungarian firms
between 2000-2016 The balance sheet dataset is an administrative panel collected by the
National Tax Authority (NAV formerly APEH) from corporate tax declarations The
database includes the balance sheet and profit amp loss statements of all double-entry
bookkeeping Hungarian enterprises between 2000 and 2016 (see Section 42 for a brief
discussion of the size and the performance of the not double-entry bookkeeping sector of
the Hungarian economy) Besides key financial variables the database includes the
industry code of the firm the number of its employees its date of foundation the location
of its headquarters and whether it is domestically- or foreign-owned for each year
Productivity differences in Hungary and mechanisms of TFP growth slowdown
5
21 Cleaning the data and defining industry categories
We have taken a number of steps to clean the key variables in the balance sheet panel
First we impute missing observations for firms with more than 10 employees in the
preceding and following years For continuous variables we use the average of the
previous and following year values For categorical variables we use the value from the
previous year Similarly we impute missing data using lagged values for two of the largest
firms in year 2016
Then a baseline cleaning is applied to the values of all the financial variables to correct for
possible mistakes of reporting in HUF rather than 1000 HUF or for extremely small or big
values in the data Employment and sales are cleaned of extreme values and outliers
Suspiciously large jumps followed by another jump into the opposite direction are
smoothed by the average of the previous and following years Regarding capital stocks we
use the sum of tangible and intangible assets Whenever intangible assets are missing we
input a zero
We deflate the different variables with the appropriate price indices from the OECD STAN
which includes value added capital intermediate input and output price deflators at 2-
digit industry level1
Regarding industry codes the database in general includes the 2-digit industry code of a
firm in each year based on the actual industry classification system 4-digit industry codes
are only available between 2000 and 20052 We harmonize to NACE Rev 2 codes by using
1 A few industries are merged in the EU-KLEMS We will call this 64 category classification ldquo2-digitrdquo
industry in what follows
2 The database available in the CSO which we will use for Task 3 includes 4-digit codes for all years
BOX 21 Amadeus and the NAV database (cont)
Table B21 Coverage by employment categories (AmadeusNAV)
Year 1 emp 2-5 emp
6-10 emp
11-20 emp
21-49 emp
50-249 emp
250 lt emp
Total
2004 005 028 092 105 160 312 642 043
2005 010 050 169 288 483 1066 2227 108
2006 017 087 315 553 966 1935 3632 192
2007 2209 3006 4384 5249 5743 6082 7412 3135
2008 098 324 951 1692 2840 4868 7827 576
2009 5962 6070 7217 7428 7831 7798 9336 6301
2010 2142 4685 7034 7540 8424 8228 9634 4175
2011 2277 4736 7064 7753 8521 8657 9681 4220
2012 9397 8298 9305 9484 9507 9159 10121 8990
2013 7274 8140 9423 9981 9747 9445 10312 8044
Notes This table shows the number of observations in AMADEUS as a percentage of observations in the
NAV data for each year-size category cell
Data Sources
6
concordances from Eurostat3 We use these harmonized codes whenever we define deciles
and the frontier or within-industry variables so that NACE revisions should not affect the
results Finally we split those firms which switch from manufacturing to services or vice
versa adding separate firm identifiers for the two periods4
22 Productivity estimation
From many perspectives the most robust and convenient measure of productivity is
labour productivity We calculate this variable simply as the log of value added per
employee At the same time the key shortcoming of labour productivity is that it does not
reflect the differences in capital intensity across firms Total Factor Productivity (TFP) aims
to control for this issue We estimate TFP with the method of Ackerberg et al (2015) ndash we
refer to it as ACF ndash which can be regarded as the state of the art In the Appendix we
also provide robustness checks using different productivity measures
Technically firm-level TFP estimation involves estimating a production function
119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)
where i indexes firms t indexes years 119871119894119905 is the number of employees and 119870119894119905 is the capital
stock of firm i in year t In this specification the residual of the equation 휀119894119905 is the
estimated TFP for firm i in year t 120573119897 and 120573119896 are the output elasticities in the production
function reflecting the marginal product of labour and capital and the optimal capital
intensity
Estimating firm-level production functions involves several choices First it is usually
important to include year fixed effects in order to control for macro- or industry level
shocks Second industries may differ in their optimal capital intensity ie the coefficients
of the two variables To handle this we estimate the production function separately for
each 2-digit NACE industry Third financial data reported by small firms may not be very
accurate Including them into the sample on which the production function is estimated
may introduce bias into that regression Hence we estimate the production functions only
on the sample of firms with at least 5 employees but also predict the TFP for smaller firms
Fourth the Cobb-Douglas production function may be too restrictive in some cases but it is
possible to estimate more flexible functions (eg translog)
A key problem with firm-level TFP estimation is that input use (119871119894119905 and 119870119894119905) can be
correlated with the residual TFP Consequently OLS estimation may yield biased
coefficients The bias arises from attributing part of the productivity advantage to the
higher input use of more productive firms A simple and robust solution for this issue is to
estimate the production function with a fixed effects estimator This method controls for
endogeneity resulting from unobserved time-invariant firm characteristics
3 Because of the changes in the Hungarian industry classification in 2003 and 2008 industry code harmonization is required The Hungarian industry classification system (TEAOR) corresponds to NACE Rev 1 between 1998 and 2002 to NACE Rev11 from 2003 to 2007 and to NACE Rev 2 from 2008 onwards The conversion of industry codes in 2000-2002 to NACE Rev 11 is relatively straightforward and efficient thanks to the 4-digit codes The conversion from NACE Rev 11 to
NACE Rev 2 is less so as 4-digit codes are only available until 2005 Hence for each firm we assume that its 4-digit industry remained the same in the period of 2005-2007 and use this 4-digit industry for the conversion After these conversions we clean industry codes ignoring those changes when firms switch industries for 1-3 years and then switch back This process leads to a harmonized 2-digit NACE Rev 2 code for each year
4 After industry cleaning this can only happen either at the beginning or the end of the period when the firm is observed or if the firm switches industry for a period longer than 3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
7
A second and related problem is that input use can also be correlated with time-variant
productivity shocks This type of endogeneity is not corrected by the fixed effects
estimator More specifically managers (unlike economists analysing the balance sheet)
may observe productivity shocks at the beginning of the year and adjust the flexible inputs
(labour in our case) accordingly As a result we may falsely ascribe a productivity
improvement to the increase in labour input The recent best practice of handling this
issue is the control function approach in which one controls for the productivity shock by
using a proxy for it in an initial step The proxy is another flexible input usually materials
or energy use As we have reliable data on materials we will use that variable
In this report we rely on the method of Ackerberg et al (2015)5 Importantly with this
method the production function coefficient estimates are close to what is expected6 and
the returns to scale are slightly above one (typically between 1 and 12 see Figure 21)7 8
After estimating the coefficients we simply calculate the estimated TFP for firm i in year t
by subtracting the product of inputs and the estimated elasticities
119879119865119875119894119905 = 119871119899 119881119860119894119905 minus 120573 lowast 119897119899 119871119894119905 minus 120573 lowast 119897119899 119870119894119905 (22)
In this way we calculate a TFP level (rather than its value relative to year and industry
fixed effects) which is important when calculating productivity changes Note that the
calculated productivity changes are very similar to the logic of the Solow residual
When interpreting productivity estimates it is important to remember that both the labour
productivity and TFP estimates are value added-based measures In other words in cross-
sectional comparisons they show how many forints or euros (rather than cars or apples)
are produced with a given amount of inputs Therefore value added based productivity
reflects both physical productivity and markups9
5 We have estimated all of these with the prodest (Rovigatti and Mollisi 2016) command in Stata
6 Reassuringly Ackerberg et al (2015) themselves report some production function estimates using data from Chile and their estimated coefficients are similar to what we get 08-09 for labour and about 02 for capital
7 We also control for attrition of firms from the sample but this does not affect the estimates significantly
8 The Levinsohn-Petrin (2003) and Wooldridge (2009) production function estimates are less attractive Most importantly the estimated returns to scale are well below 1 typically between 07 and 08 These implausibly low returns to scale imply an implausibly high TFP for larger firms with their TFP advantage being many times their labour productivity advantage even though they employ much more capital per worker The implausibly low returns to scale strongly affect our calculations In such a framework if a firm doubles all of its inputs and outputs its estimated TFP increases by about 30 percent even though it transforms inputs into outputs in the same way In
productivity decompositions for example size and growth are mechanically related to TFP leading to overestimating allocative efficiency
9 Recent literature has emphasized the difference between value added-based (revenue) and physical productivity and has also proposed a number of methods to distinguish between the two (see Foster et al 2008 Hsieh and Klenow 2009 Syverson 2011 Bellone et al 2014 De Loecker and Goldberg 2014) Hornok and Murakoumlzy (2018) also apply such methods to investigate the markup differences of Hungarian importers and exporters
Data Sources
8
Figure 21 ACF production function coefficients
A) Manufacturing
B) Services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
9
We take some additional steps to clean our raw productivity estimates First we winsorize
productivity at the lowest and highest percentile of the 2-digit industry-year-specific
distribution of firms with at least 5 employees We fill out gaps of 1 or 2 years in the
productivity variable by using linear approximation Finally we clean the productivity of
firms with at least 5 employees based on changes We smooth large 1-year jumps10 and
disregard productivity values if there is a large jump after entry or before exit11
Table 21 presents the average labour productivity and TFP by 1-digit NACE categories in
2004 and 2016
Table 21 Average productivity measures by 1-digit industry in 2004 and 2016
unweighted
Labour productivity Total factor productivity
2004 2016 2004 2016
NACE Description Mean Stdev Mean Stdev Mean Stdev Mean Stdev
B Mining 797 088 867 086 408 087 441 065
C Manufacturing 777 087 806 079 581 079 598 077
D Electricity gas steam 929 106 953 138 629 091 634 132
E Water supply sewerage waste
812 085 830 089 604 091 593 094
F Construction 773 080 803 072 620 071 646 066
G Wholesale and retail trade 804 102 825 090 652 093 678 081
H Transportation and storage 841 071 837 072 625 067 623 072
I Accommodation 710 075 752 080 594 068 640 071
J ICT 834 094 862 090 631 101 669 098
M Professional scientific and technical activities
815 087 844 088 636 087 673 088
N Administrative and support services
763 098 792 094 640 107 662 113
Total 789 095 815 087 620 090 647 087
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
10 We replace 119910119905 with 119910119905minus1+119910119905+1
2 if abs(119910119905 minus 119910119905minus1)gt1 abs(119910119905+1 minus 119910119905minus1)le 05 abs(119910119905minus1 minus 119910119905minus2)le 03 timesabs(119910119905 minus
119910119905minus1) abs(119910119905+2 minus 119910119905+1) le 03 timesabs(119910119905 minus 119910119905minus1) where 119910119905 denotes a productivity measure in logs of year
t Corresponding conditions are modified to abs(119910119905+1 minus 119910119905minus1) le 1 abs(119910119905+1 minus 119910119905minus1) le 03 times abs(119910119905 minus119910119905minus1) in the second observed year and in the year before the last observed one
11 abs(119910119905 minus 119910119905minus1)gt15
Data Sources
10
23 Estimation sample
Next we introduce some restrictions to define our baseline sample As our aim is to focus
on the market economy we constrain our sample based on industry and legal form We
keep only the market economy according to the OECD definition dropping observations in
agriculture and in non-market services (NACE Rev 2 categories 53 84-94 and 96-99)
We also drop financial and insurance activities12 as well as observations for which industry
is missing even after cleaning
We also drop firms which functioned as non-profit budgetary institutions or institutions
with technical codes at any time during the observed period We also drop firms which
never reported positive employment We refer to the remaining sample as the baseline
sample
Our main sample used for most of the calculations and for the estimations consists of
observations with at least 5 employees a non-missing total factor productivity value and
no remaining large productivity jumps13 We refer to the resulting sample as our main
sample Excluding the smallest firms has multiple advantages First exclusion of small
firms reduces measurement error as the smallest firms are the most likely to misreport
Additionally one-employee firms cannot be told apart from the self-employed who create
a firm for administrative reasons but clearly do not operate as an ordinary firm The
existence of such firms as well as their financial variables are likely to be strongly
determined by the differential in the tax treatment of personal versus corporate incomes
Because of these reasons both productivity levels and productivity changes may be
measured with an excessive amount of noise for the very small firms and therefore we
exclude them from our main analysis
Table 22 shows the distribution of firms by size category in our baseline sample Clearly
our sample expands strongly between 2000 and 2004 which is mainly a result of legal
changes requiring a larger group of firms to use double-entry bookkeeping While this
expansion is the strongest for the smallest firms it also affects a large number of firms
with up to 20 employees This artificial `entryrsquo of firms can bias estimates of productivity
growth (yielding a negative composition effect) and its decomposition (a negative entry
effect) For this reason in many cases we will start our analysis in 2004
Figure 22 investigates how much the exclusion of very small firms matters It shows that
while the share of 0 and 1 employee firms was between 50 and 60 percent their share in
terms of employment and sales was only around 5-6 percent hence even after their
exclusion our sample captures much of the national output We however report
robustness checks for our main results with all firms with a positive number of employees
in the Appendix
12 We decide to drop the financial sector because of conceptual and measurement problems of defining the productivity of financial firms especially during the crisis It might also distort the aggregate results Dropping these firms also corresponds to the usual practice (eg McGowan et al 2017) However including financial firms does not have a significant impact on our main results
13 We exclude firms that had a log productivity change higher than 15 in absolute value at any one time We also exclude firms switching between manufacturing and services more than twice
Productivity differences in Hungary and mechanisms of TFP growth slowdown
11
Table 22 Distribution of firm size by employment categories
Year 0 emp 1 emp 2-4 emp 5-9 emp 10-19 emp 20-49 emp 50-99 emp 100 lt emp Total
2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739
2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987
2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568
2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376
2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880
2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844
2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671
2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709
2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318
2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113
2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104
2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509
2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335
2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665
2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678
2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519
2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727
Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742
Notes The sample is our baseline sample (see Section 23) also including observations without an
estimated TFP
Figure 22 The share of 0 and 1 employee firms in the number of firms employees and
sales
Data Sources
12
Table 23 shows the number of observations lost because of missing values cleaning and
sample restrictions compared to the original data Dropping firms based on industry and
legal form as well as firms which never report positive number of employees does not
reduce the sample considerably The baseline data contains about 23 of the firms in the
original data The coverage in terms of total employment or value added is even higher
While the reduced sample of firms with at least 5 employees contains only about 20 of
the original number of firms the coverage of total employment and value added is still
above 70 We lose an additional 4 of firms which have no estimated TFP (negative
value added or missing capital) or which have large TFP jumps over time The
corresponding reduction in employment and value added coverage is about 20 and 15
percentage points respectively14 In the main sample we capture almost 23 of the total
employment and value added which we have in the original data
Table 23 Change in sample size and coverage after introducing restrictions
Number of
firms
Total
employment
Total value
added
Original data (after imputing observations) 1000 1000 1000
Drop agriculture and missing industry 952 954 984
Drop non-market services 845 895 948
Drop based on legal form 844 885 946
Drop firms which never had positive
employment
708 885 935
Keep only market economy according to OECD 667 859 912
Drop financial and insurance activities 652 830 790
Baseline sample 652 830 790
Keep observations with at least 5 employees 196 726 723
Keep firms which have no big TFP jump and
observations with non-missing TFP
157 600 647
Main sample 157 600 647
Table 24 shows the share of observations in the main sample by 1-digit NACE industry
The industry composition is quite stable over time Wholesale and retail trade has the
largest share close to 13 followed by manufacturing (21-31) construction (13-14)
and professional scientific and technical activities (7-9) The largest decline over time
was in manufacturing (from 31 to 21) Construction transport and storage
accommodation professional scientific and technical activities and administrative and
support services increased their share by more than one percentage point
14 While this cleaning certainly drops a large number of firms this is standard practice when the aim is to capture and decompose aggregate dynamics
Productivity differences in Hungary and mechanisms of TFP growth slowdown
13
Table 24 The share of observations by industry
NACE Description 2000 2004 2008 2012 2016
B Mining 029 025 024 021 018
C Manufacturing 3085 2636 2352 2280 2122
D Electricity gas steam 021 027 026 025 024
E Water supply sewerage waste
103 112 114 127 098
F Construction 1263 1439 1447 1263 1375
G Wholesale and retail trade 3207 3026 2954 3005 3034
H Transportation and storage 479 551 606 642 683
I Accommodation 477 617 650 719 783
J ICT 351 328 396 403 400
M Professional scientific and
technical activities 688 691 859 915 891
N Administrative and support services
297 547 572 601 572
Total 100 100 100 100 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
24 Firm-level variables
For the present analysis we create firm groups based on different firm characteristics In
this subsection we explain these groupings and provide descriptive statistics
The database includes information on direct ownership Based on this one can identify
firms which are domestically-owned15 foreign-owned or state-owned (including municipal
ownership) We identify a firm as foreign-owned if the foreign share is above 10 percent
Similarly we classify a firm as state-owned if the state-owned share is above 50
percent16 Based on these definitions in 2016 nearly 10 percent of firms were foreign-
owned while the share of state-owned firms was about 1 percent (Table 25) Both foreign
and state ownership is more frequent in larger firms therefore foreign and state share is
higher in terms of employment 373 percent of employees work in foreign-owned firms
and 66 percent in state-owned ones Foreign ownership was concentrated in mining and
manufacturing electricity generation and distribution trade and ICT State ownership was
high in electricity generation and distribution and in utilities The fact that state-owned
firms are concentrated in these two industries limits the possibilities of how the effects of
state ownership and the effect of the peculiarities of these highly regulated industries can
be distinguished from each other Therefore in most cases we will not present results
separately for state-owned firms (except for Section 44)
15 For brevity we will mainly refer to domestically-owned private firms simply as domestically-owned
16 Only 15 of firms with more than 10 percent foreign share report a foreign share between 10 and 51 percent Re-classifing them as domestic does not affect our main results
Data Sources
14
Table 25 Share of state- and foreign-owned firms with at least 5 employees 2016
A) Number of firms
NACE Sector Domestic Foreign State Total
B Mining 8228 1772 000 100
C Manufacturing 8432 1522 046 100
D Electricity gas steam 5631 1942 2427 100
E Water supply sewerage waste 6351 450 3199 100
F Construction 9746 192 062 100
G Wholesale and retail 8957 1006 037 100
H Transportation 9005 890 105 100
I Accommodation 9411 467 121 100
J ICT 8314 1541 145 100
M Professional scientific and technical activities
8982 915 102 100
N Administrative and support services
8991 798 211 100
Total 8937 953 110 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
B) Employment
NACE Sector Domestic Foreign State Total
B Mining 725 275 00 100
C Manufacturing 437 552 12 100
D Electricity gas steam 674 234 91 100
E Water supply sewerage waste 189 32 780 100
F Construction 899 74 28 100
G Wholesale and retail 660 334 06 100
H Transportation 474 199 327 100
I Accommodation 867 111 22 100
J ICT 424 546 30 100
M Professional scientific and technical activities
650 331 20 100
N Administrative and support services
681 258 61 100
Total 560 373 66 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
The data include direct information on export sales and we classify a firm as an exporter
in a given year if its export sales are positive Table 26 shows the share of observations
both by ownership (foreign or private domestic) and exporter status The distribution of
firms across the four groups is stable over time Overall 65-75 of the firms are owned
domestically and supply only the domestic market The share of foreign firms decreased
from 143 in 2000 to 96 in 2016 After an initial decline the share of exporters
increased from 26 in 2000 to 315 by 2016 More than 23 of the foreign firms export
while the same ratio for domestic firms is less than 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
15
Table 26 Yearly share of observations by ownership and exporter status
Year Foreign
exporter
Foreign
non-
exporter
Domestic
exporter
Domestic
non-
exporter
2000 92 51 168 690
2001 89 46 172 693
2002 84 43 173 701
2003 79 40 164 717
2004 71 36 157 736
2005 70 34 160 736
2006 69 34 163 734
2007 73 32 180 715
2008 74 34 186 706
2009 78 36 195 692
2010 77 34 202 687
2011 78 32 215 675
2012 81 31 229 659
2013 79 30 236 655
2014 74 33 233 660
2015 71 31 238 659
2016 70 26 245 658
Total 76 35 196 692
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded
Table 27 presents some baseline descriptive statistics for the four firm groups created by
ownership and exporter status We define age using the year of foundation of the firm On
average foreign exporter firms are the largest and the most productive Within both
categories exporter firms are older larger and more productive in line with similar
patterns in other countries17 We will analyse differences further in Section 44
17 See for example Bernard-Jensen (1999)
Data Sources
16
Table 27 Average characteristics by ownership and exporter status in year 2004 and
2016
Foreign exporter
Foreign non-exporter
Domestic exporter
Domestic non-exporter
Year 2004
N of employees 1385 511 451 165
(5689) (2410) (1396) (404)
Labour productivity 877 825 830 769
(101) (120) (087) (086)
TFP ACF 666 660 634 611
(111) (112) (091) (084)
Age
101 85 99 85
(42) (46) (43) (43)
Year 2016
N of employees 1619 338 344 151
(6246) (1257) (1290) (405)
Labour productivity 906 839 844 793
(088) (115) (075) (080)
TFP ACF 696 684 651 639
(113) (109) (086) (080)
Age 160 105 149 124
(82) (75) (76) (75)
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded Standard deviations are in
parentheses
25 Industry categorization
As we have mentioned already the main industry identifier is the 2-digit NACE Rev 2
industry classification These are hierarchically ordered into 1-digit categories
These categories however do not always lend themselves to easy interpretation On the
one hand one may want to distinguish between different types of manufacturing activities
Here a key question concerns the knowledge intensity or the high-techness of the activity
On the other hand sometimes it is useful to aggregate some of the service activities to
obtain more easily interpretable results
In order to do this we use Eurostatrsquos high-tech aggregation of manufacturing and services
by NACE Rev 2 which we will call industry type18 Note that these sets of industries
include only activities carried out in market industries (ie 10 to 82 NACE Rev 2 industry
codes) When using these categories we do not include firms in non-market sectors like
education (85) or arts entertainment and recreation (90 to 93) (See Table 28)
We would like to point out that while the Eurostat categories clearly reflect the global
technology and knowledge intensity of each industry the actual activity conducted in a
given country may differ from the technology category of the industry This issue is highly
relevant in Hungary where MNEs in high-tech industries operate affiliates conducting
assembly activities in Hungary without much RampD or innovation Still we find this
categorization a good way of aggregating data but still preserving some heterogeneity
18 Retrieved from httpeceuropaeueurostatcachemetadataAnnexeshtec_esms_an3pdf
Productivity differences in Hungary and mechanisms of TFP growth slowdown
17
Table 28 Industry categorization
Manufacturing NACE Rev 2 codes
High-technology manuf 21 26
Medium-high technology manuf 20 27 to 30
Medium-low technology manuf 19 22 to 25 33
Low technology manuf 10 to 18 31 to 32
Services
Knowledge-intensive services (KIS) 50 to 51 58 to 63 64 to 66 69 to 75 78 80
Less knowledge-intensive services (LKIS) 45 to 47 49 52 55 to 56 77 79 81 82
Utilities 35 to 39
Construction 41 to 43
Productivity Trends Hungary in International Comparison
18
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON
The main aim of this chapter is to summarize existing evidence on Hungarian productivity
trends based on internationally comparable databases which include either industry-level
or micro-aggregated information The specificities and similarities of Hungary to
comparable countries will both guide and frame our analysis in the remaining chapters
which use Hungarian micro-data
31 Convergence
The fundamental question regarding the productivity evolution of Hungary or other less
developed EU member countries is whether productivity catches up with the most
developed countries at least in the medium or long run We investigate such medium- or
long-run trends in this subsection by analysing the evolution of relative productivity which
is defined as the level of labour productivity compared to one of the key economies of the
EU Germany (at ppp exchange rates) Figure 31 presents such a comparison of the
labour productivity levels of Hungary the Czech Republic Poland and Slovakia We use the
OECD STAN database for this exercise and present trends for as many years as possible to
reflect long-run developments
Figure 31 Relative labour productivity level (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN and GGDC Productivity Level Database The
market economy excludes real estate For more details see Appendix A3
Let us start with the evolution of aggregate labour productivity According to Figure 31 all
of these countries seemed to be on the road to convergence to frontier countries in terms
of labour productivity before the financial crisis In particular labour productivity in
Hungary increased from 50 percent of the German level in 1998 to 65 percent in 2008 A
similar pre-crisis convergence can be observed in all three comparator countries19
19 Note that TFP is not available for Hungary in the EU KLEMS after 2008 Therefore we restrict this
international comparison to labour productivity
Productivity differences in Hungary and mechanisms of TFP growth slowdown
19
Note that the labour productivity decline during the crisis does not show up in the above
figure because it also affected the baseline country Post-crisis Hungarian labour
productivity (relative to Germany) remained flat stabilizing at around 65 percent While
this is similar to the productivity evolution of the Czech Republic it differs remarkably from
Poland and Slovakia which were able to close their productivity gap relative to Germany
by about 5 percentage points between 2009 and 2015 This slowdown of aggregate
productivity growth and the lack of further convergence from previous levels is actually
the main motivation for this study
A key question is whether the slowdown characterises the whole economy or it is
constrained to some of the sectors or types of enterprises The first dimension is to
distinguish between the state sector and the market economy According to OECD STAN
non-market sectors accounted for about 27 percent of all employment in 201520 The
second panel of Figure 31 restricts the sample to the lsquomarket economyrsquo21 Interestingly
productivity differences relative to Germany are larger in the market economy compared
to the whole economy suggesting that the productivity levels of the public sector in the
two countries appear to be closer to each other In Hungary the relative productivity of
the market economy follows a very similar trend to the whole economy with about 10 pp
relative productivity increase between 1998 and 2005 and stagnation post-crisis With the
exception of Slovakia post-crisis productivity growth is also flat in the comparator
countries
Figure 32 Relative labour productivity in manufacturing and business services
Germany=100
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN Business services excludes real estate For
more details see Appendix A3
20 According to the EU KLEMS this share has remained more or less constant since 2003
21 This includes NACE Rev 2 Codes 5-82 except real estate (68)
Productivity Trends Hungary in International Comparison
20
The market economy can be further disaggregated into manufacturing and business
services (Figure 32) There is strong evidence of catching up in manufacturing between
1995 and 2008 when relative productivity increased by more than 10 percentage points
Relative productivity fell immediately after the crisis with positive growth after 2011
reaching pre-crisis (relative) levels by 2015 Comparator countries which started from
much lower levels caught up faster pre-crisis and faced a much smaller fall around the
crisis years In other words comparator countries have caught up with Hungary in terms
of manufacturing productivity but there is no evidence for a sharp break in the trend post-
crisis
This contrasts sharply with business services where a period of catch-up until 2005 was
followed by a substantial decline in relative labour productivity This is also in strong
contrast with the comparator countries where relative productivity of business services
either increased (Czech Republic and Poland) or stagnated (in Slovakia) Business services
appear to be a key source of aggregate productivity slowdown
Figure 33 presents productivity dynamics in four specific industries to substantiate the
more aggregated picture with some more concrete examples The first two examples are
manufacturing industries namely the textiles and the automotive industry The relative
productivity level of textiles stagnated during the crisis at quite low levels fell during the
crisis followed by some growth from 2012 In motor vehicles relative productivity
increased by nearly 10 percentage points relative to Germany between 2001 and 2009
followed by a significant fall around the crisis and a strong recovery from 2012 The
picture is also varied in services In retail and wholesale there had been some productivity
improvement before the crisis followed by a declining trend post-crisis Both the level and
dynamics of relative productivity compares unfavourably to the comparator countries In
professional services relative labour productivity had grown quickly until 2011 followed
by a declining trend
Figure 33 Relative labour productivity evolution (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
21
Similar observations can be made when analysing the relative productivity of all types of
industries (Figure 34) The difference in productivity levels relative to Germany tends to
be larger in manufacturing than in services Light industries have especially low relative
productivity levels In terms of productivity growth we see mostly positive trends in most
manufacturing industries and a less clear picture in services with a decline or stagnation
in many service industries
Figure 34 Labour productivity of different industries relative to Germany 2005 and 2015
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix A3
Even in countries and industries with a relatively low level of average productivity it is
possible that a segment of the economy operates at world-class levels or shows fast
convergence to that This possibility may be especially relevant in economies where a
number of large and probably foreign-owned firms operate together with many smaller
domestically-owned firms which is certainly the case in Hungary One approach to
investigate this possibility was suggested and implemented by the OECD (Andrews et al
2017) This approach builds on cross-country micro-data to calculate the productivity of
the most productive firms in the world (global frontier) and compare it with the
productivity of the most productive firms in a country (national frontier)
Figure 35 shows these comparisons based on the OECDrsquos calculations22 In particular the
horizontal axis shows how productive Hungarian frontier firms are relative to the global
22 We would like to thank Peter Gal and his colleagues in the OECD for sharing these data with us In
this version global frontier is defined as the top 10 percent most productive firms worldwide
while the national frontier is the top 10 percent within the country according to ORBIS See
Appendix 3 and Box 41 for details on these data
Productivity Trends Hungary in International Comparison
22
frontier (100 is the global frontier) while the vertical axis compares Hungarian and global
non-frontier firms The figures suggest a number of conclusions To start with the frontier
productivity gap is strongly associated with the non-frontier productivity gap showing that
in industries where the typical firms are of relatively low productivity so are the frontier
firms Importantly the slope of the fitted line (06) is well below 1 suggesting that on
average there is a smaller gap between a top global and a top Hungarian firm than
between a typical (non-frontier) global firm and a typical Hungarian firm This is in line
with the duality hypothesis
That said one has to emphasise that the picture does not support a ldquostrong versionrdquo of the
duality hypothesis ie that the best Hungarian firms operate at world-class productivity
levels Even in manufacturing Hungarian frontier firms typically produce 40-60 percent
less value added per employee compared to the global frontier (good examples are
machinery (28) and motor vehicles (29)) The smallest gaps appear in a few relatively
low-tech service industries (trade and repair of vehicles (45) or warehousing (52)) where
frontier productivity is actually above the global frontier23
The observation that such large productivity differences exist between global frontier and
Hungarian frontier firms even within relatively narrowly defined industries suggests that
the low relative productivity of the Hungarian market economy is not a consequence of
industry composition ndash it mainly results from within-industry gaps Importantly these
main patterns are very similar and independent of how productivity is measured (labour
productivity or TFP) namely they are not a consequence of capital intensity differences
Finally by and large there is no evidence for convergence of frontier firms to the global
frontier between 2009 and 201324 If anything the gap between the global and the
Hungarian frontier widened in this period while the difference between the global and the
Hungarian frontier was 34 percent in the median industry in 2009 it widened to 38 by
2013
23 Naturally this is likely to be the case in other similar countries Still in different discussions it is often supposed implicitly that the best Hungarian firms are indistinguishable from the global frontier
24 Prior to 20082009 the coverage of ORBIS the source for the OECD calculations is fairly limited for
Hungary hence those calculations are less reliable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
23
Figure 35 Productivity of Hungarian frontier and non-frontier firms relative to firms in
other countries (2013)
A) Labour productivity
B) TFP
Notes The industry codes are 2-digit NACE Rev 2 codes We have omitted industries with only few
observations (less than 5 Hungarian frontier firms) in the case of labour productivity outliers we
ignored those where the HU frontier was measured to be more productive than 125 percent of the
global frontier (ICT real estate and office administration services) Note that there are fewer
observations regarding TFP than labour productivity Source Data provided by the OECD calculated
from Andrews et al (2017) For more information see Appendix 3
Productivity Trends Hungary in International Comparison
24
We can draw a number of conclusions from these calculations First while Hungaryrsquos
labour productivity had been catching up similarly to other CEE countries to more
advanced economies before the crisis there was a trend break after the crisis especially
compared to Poland and Slovakia Only part of the productivity slowdown could be
explained by a slowdown in non-market sectors but there is also a pronounced slowdown
in the market economy This is not the result of having a combination of a few firms with
world-class productivity and many less efficient SMEs ndash actually the productivity of
frontier firms is only about 40-50 percent of global leaders even in industries where the
Hungarian frontier consists of many multinational firms There is no evidence that
Hungarian frontier firms were catching up with global leaders between 2009 and 2015
32 Within-industry heterogeneity
Since the beginning of the 2000s with the availability of detailed micro-data sets at the
firm-level it has become clear that within-industry heterogeneity in terms of productivity
is significantly larger than heterogeneity differences across industries (Bernard et al
2003 Bernard et al 2007 Bernard et al 2012 OECD 2017) Many factors have been
proposed which may generate and sustain the observed large productivity differences
including managerial practices different quality of labour capital and knowledge as well as
a number of external factors The exact role of different factors is an active area of
research (Syverson 2011) Recent research also hints at increasing dispersion within
sectors (Berlingieri et al 2017b)
In 2011 the level of the p90p10 ratio (90th and 10th percentile of productivity
distribution) was high in Hungary relative to other OECD countries taking a value of 279
in manufacturing and 329 in services (Table 31) These numbers are in logs representing
about 20-fold differences These numbers are similar to Chile and Indonesia A similar
pattern emerges with respect to TFP
Table 31 Productivity p90p10 ratio by country (2011)
Country
Year 2011
Log LP 90-10 ratio Log MFP 90-10 ratio
Manufacturing Services Manufacturing Services
Australia 187 205 190 212
Austria 196 242 - -
Belgium 160 174 180 178
Chile 300 353 307 387
Denmark 146 196 132 180
Finland 117 138 119 134
France 135 181 140 178
Hungary 279 329 254 286
Indonesia 311 - 341 -
Italy 166 201 160 188
Japan 126 138 117 138
Netherlands 200 298 227 227
New Zealand 184 209 192 208
sNorway 173 217 187 208
Portugal 188 265 188 275
Sweden 145 186 159 245
Notes This is a reproduction of Table 6 from Berlingieri et al (2017a) Note that the OECD uses the
term lsquoMFPrsquo (Multi-factor productivity) in the same sense as we use TFP in this report
Second as seen in Table 32 similarly to other OECD countries the overwhelming
majority of productivity differences results from within- rather than across-sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
25
differences The share of within-sector differences is 79 in manufacturing and 99 in
services The manufacturing share is close to the average of the countries in the sample
while the services share is at the high end
Table 32 Share of within-sector variance in total LP dispersion by country (2011)
Country
Year 2011
LP Dispersion
Manufacturing Services
Australia 98 99
Austria 86 90
Belgium 76 88
Chile 90 97
Denmark 84 63
Finland 65 76
France 63 85
Hungary 79 99
Indonesia 79 -
Italy 82 65
Japan 75 89
Netherlands 80 71
Norway 83 73
Portugal 62 76
Sweden 53 74
Notes This is a reproduction of Table 7 from Berlingieri et al (2017a)
These figures suggest that within-industry productivity dispersion is relatively high in
Hungary but it is not out of the range of countries at a similar level of development Still
these overall dispersion measures may not capture the duality between firms of different
sizes and ownership Internationally comparable data regarding productivity of firms in
different size classes is available from the OECD Structural and Demographic Business
Statistics (Figure 36) Size is strongly associated with productivity large firms are 45
times and 18 times as productive as very small firms in manufacturing and services
respectively However large these premia are not out of the range of similar countries in
services it is very similar to other CEE countries while in manufacturing it is at the high
end of the distribution but not extreme
Another relevant pattern in Figure 36 is that productivity differences by size are very
different between CEE countries and Western European countries This observation may
partly reflect the importance of large and productive multinational firms in CEE countries
but can also be a more or less automatic consequence of the fact that firm size distribution
significantly differs between the two groups of countries (Figure 37) Typically the share
of very small firms is larger in less developed economies leading to a more skewed firm
size distribution Such a distribution which is associated with a larger number of small
firms within size classes (the majority of firms with 1-9 employees in CEE employs only 1-
2 employees) leads to larger differences across size classes and larger within-industry
productivity dispersion The massive share of very small firms in these countries also
reflects that many of the lsquomicro-enterprisesrsquo (with only 1-2 employees) do not operate as
proper firms they behave more like individual entrepreneurs
Productivity Trends Hungary in International Comparison
26
Figure 36 Value added per person employed by size class (1-9 persons employed=100)
A) Manufacturing
B) Services of the business economy
Notes Value added per person employed defined as value added at factor costs divided by the
number of persons engaged in the reference period Economic sector lsquoManufacturingrsquo comprises
Divisions 10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business
economyrsquo comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities
of holding companies Source OECD SDBS For more details see Appendix 3 Main sample for 2015
Productivity differences in Hungary and mechanisms of TFP growth slowdown
27
Figure 37 Firm distribution by size class (2015)
A) Manufacturing
B) Services of the business economy
Notes Only enterprises with at least one employee are included lsquoManufacturingrsquo comprises Divisions
10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business economyrsquo
comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities of holding
companies Source OECD SDBS For more details see Appendix 3 Main sample
Productivity Trends Hungary in International Comparison
28
The main conclusion from investigating within-industry differences across firms is that both
the productivity dispersion and the productivity advantage of large firms is indeed
relatively large in international comparison but these numbers are not radically different
from similar countries Nevertheless differences in firm size distribution between more
and less developed countries go a long way towards explaining the differences between
Western European and CEE countries
33 Firm dynamics
A potential reason for declining productivity growth may be weak dynamics including low
entry and exit rates as well as slower reallocation The OECD Structural and Demographic
Business Statistics database provides international comparisons of entry and exit rates and
their changes across countries (Figure 38 and Figure 39)
In general both exit and entry rates are higher in CEE countries relative to Western
European economies25 This stronger dynamism may reflect stronger growth but it is also
affected (in a mechanistic way) by the differences in firm size distribution Importantly in
a cross-section entry and exit rates are strongly correlated suggesting that they capture
the same general aspect of firm dynamics Services are more dynamic than
manufacturing once again partly because of the different size distributions
Within CEE countries entry and exit rates seem to be associated with productivity growth
(and level) Countries with stronger post-crisis productivity growth (Poland Slovakia and
Romania) exhibit significantly higher entry and exit rates while those with less dynamic
productivity growth (Hungary and the Czech Republic) have lower churning This provides
some evidence that lower entry and exit rates may be systematically related to the weaker
productivity performance of these countries We will take a more detailed look at the
relationship between entry and exit and productivity growth in Chapters 6 and 7
When comparing 2012 and 2015 the pictures provide evidence for increased entry and
decreased exit in parallel with recovery and better growth prospects Still entry rates
remain one of the lowest in CEE indicating that entry and dynamic young firms may
contribute less to productivity growth in Hungary compared to other CEE countries
25 Note that these OECD statistics include all enterprises (even those with no employees) hence
changes in the tax treatment of firms relative to individual entrepreneurs may affect measured
dynamics Also firm death is defined based on the rsquodeathrsquo of the legal entity which may happen
many years after stopping production For more information see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
29
Figure 38 Birth rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Birth rate is defined as the number of enterprise births divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers The
economic sector lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo
comprises Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix A3
Productivity Trends Hungary in International Comparison
30
Figure 39 Death rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Death rate is defined as the number of enterprise deaths divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers
Poland has no available data for 2015 so the 2014 value is reported The economic sector
lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo comprises
Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
31
34 Conclusions
In international comparison productivity slowdown after the crisis was especially severe in
Hungary both in manufacturing and services There are large productivity differences
within industries and also between small and large firms While these are at the high end
in international comparison they are not extreme compared to similar countries A
comparison to the global frontier suggests that even top Hungarian firms are significantly
behind top global firms in terms of productivity These facts provide a motivation for our
analysis of the evolution of the shape of the productivity distribution in Chapter 4
International comparison of firm dynamics suggests that ndash similarly to other CEE countries
ndash Hungarian industries are more dynamic than their Western European counterparts but
entry and exit rates in Hungary and the Czech Republic are below the average of CEE
countries This motivates our investigation of the contribution of entry and exit to
productivity growth in Chapters 6 and 7
Evolution of the Productivity Distribution
32
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION
41 Context
The study of within-industry productivity differences is motivated by two concepts First
the OECD (2016) argues that one of the key issues of recent developments in productivity
growth is that there is a strong divergence between the productivity evolution of frontier
firms and other firms However this same publication reports that Hungary seems to be
an exception to this trend with slow productivity growth at the frontier and faster
productivity growth of less productive firms suggesting some within-industry catch-up
(Figure 41) We look into the particulars behind this phenomenon by following the
evolution of the average productivity of different deciles in the productivity distribution
Second as we have already mentioned a key concept of the Hungarian (and CEE) policy
debate is the lsquodualityrsquo of smalldomestically-owned and largeforeign-owned firms The
large gap between the two types of firms presents a challenge for policy but it also
indicates an opportunity for domestic firms to catch up with foreign firms which may use
more productive technology (still far in terms of productivity from the global frontier see
Chapter 31) The evolution of the productivity gap (or premium) between small and large
firms as well as domestic and foreign firms informs us about whether firms on the lsquowrong
sidersquo of the duality are able to catch up with the firms at the national frontier
The duality debate frames productivity differences partly as a consequence of the lsquomissingrsquo
medium-sized (domestic) firms Hsieh and Olken (2014) argue that in less productive
economies the full firm size distribution is shifted to the left because of the constraints on
the growth of small firms Thus according to this view the productivity difference is not a
result of too few medium sized firms but of too few firms which are not small
Figure 41 Divergence in labour productivity performance
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
33
B) Non-financial Services
Notes This is a reproduction of Figure 16 from OECD (2016)
In this chapter we investigate how the shape of the productivity distribution evolved over
the years Section 42 contrasts the development of firms with other types of economic
entities Section 43 analyses how average productivity and productivity deciles evolved
while 44 investigates the duality based on size and ownership
42 Aggregate productivity and the self-employed
Before turning to the productivity distribution of firms it is worthwhile to describe how the
productivity level and evolution of firms ndash and in particular double-entry bookkeeping
enterprises ndash differ from other entities in particular the self-employed Given the large
number of people employed by those entities this exercise can reveal a lot both about
productivity dispersion and the drivers of aggregate productivity growth
Let us motivate this investigation by comparing aggregate statistics (derived from data
applicable to all people engaged in an industry) with patterns calculated from our NAV data
(which includes only double-entry bookkeeping firms) Figure 42 shows the labour
productivity growth reported by OECD STAN and the evolution of the average labour
productivity as calculated from the NAV data weighted by sales and employment (Figure
42) According to the Figure while these series co-move they do so with some
discrepancies While productivity dynamics in Manufacturing are very similar across all
samples the relationship is looser for services and for the market economy with the NAV
series notably exhibiting less pronounced post-crisis slowdown than the OECD STAN data
Evolution of the Productivity Distribution
34
Figure 42 Cumulative labour productivity growth according to OECD STAN and the NAV
sample
There can be many reasons behind the differences between these series (see Biesebroeck
2008) but arguably one of the main factors is the discrepancy in the number of
employees in the two databases Firms in the full NAV database employed 24 million
people in 2015 compared with 286 million employed and 325 million lsquoengagedrsquo in the
market economy according to the OECD STAN One source of this difference may be that
while some unofficially employed workers report their true employment status in LFS
(Labour Force Survey) ndash which serves as the basis for our aggregate data ndash they do not
appear in any official registers and such the NAV data Benedek et al (2013) reaffirming
the statement compare LFS employment data with tax registers and show that 16-18
percent of jobs are not declared to the tax authorities
Even more importantly from our perspective the NAV data by definition includes no
information on the self-employed and typically small non-double-entry bookkeeping firms
operating under special taxation The distinct productivity dynamics of these two groups
along with changes in undeclared employment may explain another part of the difference
Obtaining direct information on this issue would be of great interest but acquiring it is far
from straightforward Some information on these entities is available from the Register of
Economic Organizations (Gazdasaacutegi Szervezetek Regisztere GSZR) which is available
between 2012 and 2015 Most importantly this database provides us with information on
the number of employees and sales updated annually This in and of itself does not allow
us to estimate productivity properly but with its help we can calculate a crude proxy
sales per employee for illustration
Table 41 reports26 the number of employees and the average sales per worker values for
three groups The first is the group of double-entry bookkeeping firms (ie the firms who
26 These tables were calculated as follows First we combined the GSZR and NAV databases for years 2012 and 2015 Observing that about 80 percent of the firms present in the NAV sample are also present in the GSZR register we restricted our sample to the entities who are listed in the GSZR so that our variables would be commensurable From this collection we selected those who
Productivity differences in Hungary and mechanisms of TFP growth slowdown
35
are present in the NAV data) the second is the category of the self-employed (ie those
who are registered as individual entrepreneurs) and the third category is that of lsquoother
firmsrsquo (ie entities who are registered as firms in the GFO (Gazdaacutelkodaacutesi Forma) coding
system but are not categorised as self-employed and are not following a double-entry
bookkeeping method) We distinguish between manufacturing and other industries of the
market economy27 We supply figures for the earliest and latest years for which data are
available The tables reveal two important observations
First according to the GSZR about 30 percent of reported employees in Manufacturing
and 50 percent of reported employees in other industries work outside the double-entry
bookkeeping group Importantly these numbers may be overestimates because the GSZR
may report the same person in multiple entities for example when they work part-time or
switch jobs within the year That said both the EU KLEMS and the GSZR suggest that a
large share of people work outside the double-entry bookkeeping group in the market
economy
Second while sales per worker is not drastically different between double-entry
bookkeeping firms and other firms the difference between firms and the self-employed is
between 6-10-fold This difference in sales per employee may represent 2-3-fold labour
productivity differences between people employed by firms and the self-employed on
average28
Third the dynamics of sales per worker differ markedly between double-entry
bookkeeping firms and other entities while it increased by 40 percent in the NAV sample
between 2012 and 2015 it stagnated for the self-employed This may results from a
number of factors ranging from composition effects changes in tax regulations or low
productivity growth Still the low measured productivity growth of this sector of the
economy may be an important factor behind the slower post-crisis aggregate productivity
growth in services compared to the NAV sample Table 41 illustrates this for the sales per
worker measure While it grew by 40 percent in the lsquoOtherrsquo category between 2012 and
2015 based on the NAV sample its lsquoaggregatersquo growth was only 6 percent during the same
period
Obviously one cannot draw far reaching conclusions from such statistics given the
immense measurement problems Still these patterns suggest that in a sense the duality
between firms and the self-employed may constitute a similarly deep divide to the one
belong to the lsquomarket economyrsquo (as defined in Chapter 2) and are registered as lsquofirmsrsquo according to GFO coding system (ie have 1-digit GFO codes 1 or 2) We tagged the firms present in the NAV sample as lsquodouble-entry bookkeeping firmsrsquo and marked those who have 2-digit GFO codes equalling to 23 as lsquoself-employedrsquo We categorised the rest of our sample as lsquoother firmsrsquo Further we distinguished between manufacturing and other market economy firms based on their NACE codes and then calculated for sales per worker measures on the level of each observation finally to compute for yearly aggregates for each group as indicated above
27 Notably in line with the definition in Chapter 2 these lsquoother industriesrsquo do not include agriculture
28 Needless to say this cannot be easily mapped into productivity differences given that firms using more intermediate inputs are more likely to choose double-entry bookkeping (and hence pay
taxes based on profits) rather than simplified taxes (and pay taxes based on sales) Still one can do the following back of the envelope calculation In the NAV sample the average ratio of material expenditure over sales was 066 both in 2012 and 2015 Therefore value added per employee (or labour productivity) could be about a third of the sales per employee variable If one conservativelly assumes that the self-employed have zero material costs their labour productivity is the same as their sales per employee index Based on this simple calculation the 6-10-fold difference in sales per employee map to at least 2-3-fold differences in labour productivity
Evolution of the Productivity Distribution
36
that exist between globally integrated and domestic-oriented firms Consequently policies
can be formulated with an explicit focus on this group
Table 41 Number of employees and sales per employee for different entities
Number of employees
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 621229 627391 1325299 1196332
Other firm 289636 296921 698326 771930
Self-employed 72674 74325 620699 638001
Total 983539 998637 2644324 2606263
Average sales per employee (HUF million)
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 140 199 196 278
Other firm 151 146 196 201
Self-employed 25 25 29 28
Total 92 99 105 111
43 The evolution of productivity distribution in Hungary
Average productivity
Let us continue by investigating the evolution of average productivity Table 42 presents
the average labour productivity and TFP growth rates for the market economy
manufacturing and services as defined in Chapter 2 We report both unweighted and
labour-weighted productivity growth for each year
Let us start with the whole market economy Between 2004 and 2007 both labour
productivity and TFP was growing strongly by 7-8 percent on average as expected in a
catching up economy (as we have seen in Chapter 3) Importantly the weighted growth
rate was higher than the unweighted one suggesting that reallocation played a positive
role in aggregate productivity growth (see Section 62 for more details)
During the crisis we see a slight productivity decline in 2008 a sharp fall of about 8
percent in 2009 followed by a strong recovery in 2010 The 2010 productivity recovery
resulted from the productivity growth of large firms unweighted average productivity
growth was very slow This suggests an asymmetry in recovering from the crisis-related
productivity decline
Post-crisis all measures document a slowdown in productivity growth with typical growth
rates between 25-35 percent Notably weighted productivity growth measures were
similar to unweighted ones in the wake of the crisis suggesting deterioration in the
efficiency of the reallocation process The 2010-2013 and 2013-2016 periods seem to be
quite similar to each other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
37
Importantly while labour productivity and TFP dynamics differ to some extent the overall
picture is very similar for the two productivity measures This is in line with the hypothesis
that any productivity slowdown is not merely a consequence of lower capital stock growth
The results are similar when using alternative TFP estimators (see Table A41 in the
Appendix)
Table 42 Labour productivity and (ACF) TFP growth in the sample
A) Market economy
Year LP TFP
unweighted emp w unweighted emp w
2005 20 58 19 74
2006 92 91 93 119
2007 53 60 39 56
2008 -10 -08 -10 -04
2009 -70 -81 -69 -82
2010 -05 44 11 80
2011 25 45 34 40
2012 25 22 21 01
2013 19 25 30 22
2014 39 45 40 59
2015 51 50 52 49
2016 36 19 20 03
Average
2004-2007 55 70 50 83
2007-2010 -28 -15 -23 -02
2010-2013 23 34 33 29
2013-2016 36 35 35 33
B) Manufacturing
Year LP TFP
unweighted emp w unweighted emp w
2005 37 148 20 114
2006 124 163 114 149
2007 100 114 78 71
2008 25 -03 17 -17
2009 -115 -94 -133 -117
2010 82 161 80 173
2011 -02 34 04 18
2012 05 -46 -02 -58
2013 -14 31 -12 05
2014 11 48 -01 27
2015 38 37 30 14
2016 26 01 04 -23
Average
2004-2007 87 141 71 111
2007-2010 -02 22 -12 13
2010-2013 -04 17 04 -03
2013-2016 15 29 05 06
Evolution of the Productivity Distribution
38
C) Market services
Year
LP TFP
unweighted emp w unweighted emp w
2005 12 -04 10 32
2006 80 47 79 90
2007 39 25 24 48
2008 -22 -06 -21 -03
2009 -57 -68 -52 -71
2010 -29 -17 -11 26
2011 33 49 43 57
2012 31 60 30 48
2013 29 21 39 29
2014 46 45 46 78
2015 54 58 54 72
2016 39 30 25 20
Average
2004-2007 43 23 38 57
2007-2010 -36 -31 -28 -16
2010-2013 31 44 39 51
2013-2016 42 39 41 50
Notes This figure presents growth rates of labour productivity and aggregate TFP for lsquomarket
industriesrsquo (see section 25) The sample does not include agriculture mining and financial services
Services include construction and utilities Only firms with at least 5 employees
Comparing manufacturing and services shows a key dichotomy between the two large
sectors In Manufacturing productivity growth was strong before the crisis with above 10
percent average weighted growth rates This fell to very low levels after 2010 Similarly to
the whole market economy reallocation processes had been more efficient before 2008 In
contrast for services no clear structural break appears around the time of the crisis either
in terms of pre- and post-crisis growth rates or reallocation efficiency
Table 43 looks into industry differences in more detail The picture is similar for
manufacturing industries in the various technology categories with a very substantial
slowdown in productivity growth Productivity growth was fastest in high-tech both before
and after the crisis Services are a bit more heterogeneous High-tech services behaved
similarly to high-tech manufacturing with strong pre-crisis growth (around 10 percent on
average) followed by a slowdown to growth rates around 5 percent per year In less
knowledge-intensive services which represent the majority of business service
employment growth rates were similar before and after the crisis (around 5 percent)29
Lastly we see moderate growth rates and then some slowdown in construction and
utilities
29 Note however that this may not be the case for the self-employed as has been discussed in the previous chapter
Productivity differences in Hungary and mechanisms of TFP growth slowdown
39
Table 43 TFP growth by type of industry (employment-weighted ACF TFP)
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 124 19 66 274 2006 240 137 39 33
2007 74 02 41 221
2008 -45 23 -15 59
2009 05 -191 -218 48
2010 135 111 264 168
2011 -45 18 34 100
2012 -15 -24 -83 -181
2013 -41 37 -22 125
2014 06 07 27 86
2015 65 01 -54 80
2016 -02 04 -27 -91
Average 2005-2007 146 53 49 176
2007-2010 32 -19 10 92
2010-2013 -34 07 -21 20
2013-2016 07 12 -19 50
B) Services
Year KIS LKIS Construction Utilities
2005 127 16 34 -48
2006 166 75 30 67
2007 13 58 42 29
2008 -16 14 -72 -26
2009 -63 -94 -04 25
2010 54 12 09 05
2011 97 46 65 29
2012 12 74 13 -22
2013 12 30 63 -07
2014 78 89 65 -81
2015 106 70 14 54
2016 16 31 -47 39
Average
2005-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the sales-weighted average ACF TFP growth rate by technology category (see
Section 25) Only firms with at least 5 employees The sample does not include agriculture mining
and financial services
In general patterns are similar for the unweighted measures (See Table A42 in the
Appendix) with weaker pre-crisis growth in manufacturing where reallocation seems to
have mattered most Labour productivity behaved similarly to TFP (See Table A43 in the
Appendix)
Evolution of the Productivity Distribution
40
Frontier firms
The key motivation for this investigation is to understand better how productivity dynamics
of lsquofrontierrsquo firms differ from firms in other parts of the productivity distribution Defining
frontier firms is not a straightforward task (Andrews et al 2017) Inevitably all such
attempts have to face the trade-off between a narrow definition which may to a large
extent capture the behaviour of outliers and a broader definition which may include
many firms which are very far from the actual frontier
One can find a sensible compromise between the too narrow and the too broad definitions
by following the OECD practice (Andrews et al 2017) This solves the problem of
including small firms with potentially large noise by restricting the sample to firms with at
least 20 employees on average in the sample period Frontier is defined as the top 5
percent of such firms for each industry-year combination An additional issue is that the
number of observations may change across years This is solved by calculating the top 5
based on the median number of observations per year We will call these firms frontier
firms
An alternative definition is simply to define the top decile within the productivity
distribution in industry-year combination as frontier based on our main sample We will
employ this strategy as well for the sake of comparison
Table 44 investigates the prevalence of frontier firms in different groups30 The probability
of being frontier is not related strongly to size A foreign-owned firm is 3-4 times more
likely to be frontier than a domestically-owned private firm State-owned firms are similar
to privately owned domestic firms in this respect As a result about half of the frontier
firms are foreign-owned Finally frontier firms are substantially more prevalent in the
more developed regions of the country especially in Central Hungary These patterns are
quite stable throughout the years and they prevail in a multiple regression analysis The
top decile of the productivity distribution has a similar composition (see Table A44 in the
Appendix)31
Table 44 The share of frontier firms () among firms with at least 20 employees
A) By size
2004 2007 2010 2013 2016
20-49 emp 357 327 34 362 329
50-99 emp 401 468 542 486 555
100- emp 293 358 414 42 462
B) By ownership
2004 2007 2010 2013 2016
Domestic 213 194 236 272 289
Foreign 873 955 896 82 821
State 181 211 166 167 263
30 Note that we restrict the sample to firms with at least 20 employees because the definition of frontier requires to have at least 20 employees on average
31 When the definition is based on labour productivity the share of frontier firms increases with size The foreign advantage is also larger
Productivity differences in Hungary and mechanisms of TFP growth slowdown
41
C) By region
2004 2007 2010 2013 2016
Central HU 596 621 652 552 579
Northern Hungary 174 104 176 237 168
Northern Great Plain 152 195 199 38 268
Southern Great Plain 128 127 18 277 224
Central Transdanubia 296 27 32 359 322
Western Transdanubia 408 313 305 433 395
Southern
Transdanubia 131 081 188 159 211
Another key question is the extent to which frontier status is persistent Figure 43 shows
a transition matrix ie it considers frontier firms in year t and reports their status in t+3
Do they remain frontier or become a non-frontier firms or exit the market altogether
Overall the 3-year persistence of the frontier status is around 45 percent ndash nearly half of
frontier firms will also be frontier 3 years later This is a bit higher than what is found in
other countries Antildeoacuten Higoacuten et al (2017) for example report that about half of all
national frontier firms remain on the frontier for a year but only about 20 percent for 5
years The persistence of frontier status remained largely unchanged across the years
Frontier status is more persistent for foreign and exporter firms The transition matrix of
top decile firms is similar with slightly weaker persistence (Figure A41 in the Appendix)
Figure 43 Transition matrix for frontier firms
Notes This figure shows how many of the frontier firms in year 2010 were still frontier in 2013 how
many exited and how many continued as non-frontier Only firms with at least 20 employees The first
panel shows this transition matrix for various 3-year periods
Evolution of the Productivity Distribution
42
Productivity evolution across deciles
The figures in this section compare the average productivity of frontier firms of the top
decile of the productivity distribution lsquohigh productivity firmsrsquo (8th and 9th deciles) lsquotypical
firmsrsquo (4th to 6th deciles) and lsquolow productivityrsquo firms (2nd and 3rd deciles) all of these
defined at the year-NACE 2 level This approach follows closely that of the OECD (2016)
Also we use the 8 lsquotechnologicalrsquo industry categories introduced in Section 25 to condense
information but still allow for heterogeneity across industries
Let us start with comparing TFP levels (Figure 44) and their cumulative changes (Figure
45) at the different parts of the productivity distribution (note that the vertical axes differ
across sectors) TFP levels are measured in natural logarithms For example in low-tech
manufacturing the difference between low-productivity firms and the frontier is about 2 log
points or more than 7-fold32 Within-industry productivity differentials are much larger
than across-industry differences or changes From a methodological point of view in most
industries frontier firms co-move with the top percentiles but there are a few exceptions
most prominently high-tech manufacturing
The overall productivity evolution is much in line with the averages reported in Table 42
There is strong pre-crisis growth in Manufacturing followed by a fall in 2009 and sluggish
growth afterwards High-tech manufacturing is a partial exception from this trend
Productivity growth actually accelerated after the crisis in services
Figure 44 TFP levels in various types of industries
A) Manufacturing
32 1198902 asymp 74
Productivity differences in Hungary and mechanisms of TFP growth slowdown
43
B) Services
Notes This figure shows the evolution of the (unweighted) average ACF TFP level of the different
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles with at least 20 employees on average lsquotop decilersquo is the 10th decile lsquohighrsquo
is the 8-9th decile typical is the 4-6th deciles while `lowrsquo is 2-3rd deciles Main sample The industry
categories are described in Section 25 The sample includes the sectors of the market economy
except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less knowledge-
intensive services
Most importantly we do not find evidence for an increasing gap between frontier and other
firms (in line with OECD 2016) in any of the industries Within manufacturing there is
convergence between frontier and non-frontier firms in medium-low and high-tech
industries However this is not robust for the alternative definition of frontier (top decile)
which moves strongly together with other deciles Based on this one may say that there is
no robust evidence either for convergence or divergence in manufacturing There are some
signs of convergence pre-crisis in knowledge-intensive and less knowledge-intensive
services as well as in construction followed by stronger productivity growth in the highest
quartiles post-crisis Importantly any convergence or divergence appears to be small
relative to already existing differences
Evolution of the Productivity Distribution
44
Figure 45 Cumulative TFP growth since 2004
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is the 10th
decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main sample The
industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less
knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
45
The picture is somewhat different when labour productivity is considered (Figure 46) In
this case the difference in growth rates between frontier and other firms is more
pronounced than in the case of TFP One can plausibly claim that less productive deciles of
the distribution caught up somewhat with the most productive firms in high-tech
manufacturing in the two service sectors and also in construction This suggests that
capital deepening by less productive firms (or low investment by frontier firms) may lead
to some convergence in terms of labour productivity but less so in terms of TFP33
Figure 46 Cumulative labour productivity growth since 2004 for labour productivity
deciles
A) Manufacturing
33 Note that these figures are the most directly comparable ones to Figure 41 which also presents results for labour productivity In line with that figure we find evidence for convergence between the median firm and frontier firms We also find that low-productivity firms converge The most important reason for this is that we exclude firms with less than 5 employees from our sample
Evolution of the Productivity Distribution
46
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average labour productivity level
for various deciles of the productivity distribution within each 2-digit industry-year combination
lsquoFrontier firmsrsquo are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo
is the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample The industry categories are described in Section 25 The sample includes the sectors of the
market economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo
Less knowledge-intensive services
Figure 47 zooms in to a few industries of interest which both confirm and qualify the
overall picture In textiles (a low-tech industry) frontier firms did not increase their
productivity in the period under study while lower productivity deciles experienced a
cumulative 40-50 percent productivity growth leading to an overall positive growth As
Section 61 discusses employment decline and firm exit were high in this industry
therefore the improvement of lower deciles may partly result from the exit of the lowest
productivity firms In machinery (a medium-high tech industry) all productivity deciles
had experienced strong TFP growth before the crisis and a significant fall during the crisis
followed by slow growth In this industry the full distribution has moved together
In retail (which is a member of the less knowledge-intensive services) TFP had grown to
some extent prior to the crisis followed by a large fall around the crisis and some growth
since 2012 Interestingly the fall was much larger and persistent for the most productive
firms while typical and low-productivity firms were able to maintain their pre-crisis
productivity levels The weak productivity performance of the top decile may have partly
resulted from regulatory changes and could have had large aggregate consequences given
the large employment share of retail (see Chapter 8) In lsquoComputer programming
consultancy and related activitiesrsquo there was a cumulative TFP increase of about 30 percent
since 2004 for all deciles without signs of convergence or divergence
Productivity differences in Hungary and mechanisms of TFP growth slowdown
47
Figure 47 Cumulative TFP growth since 2004 selected industries
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination in four industries
lsquoFrontier firmsrsquo are in the 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is
the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample
44 Duality in productivity and productivity growth
Besides the evolution of the overall shape of productivity distribution it is important to
understand the lsquodualityrsquo of productivity with respect to ownership
As a starting point Figure 48 shows the distribution of TFP and the natural logarithm of
the average wage for our main sample34 We filter out 2-digit industry fixed effects from
the two variables to control for industry-level differences
Comparing private domestic and foreign-owned firms one can make a number of
observations The foreign-owned distribution clearly stochastically dominates the
productivity and wage distribution of domestically-owned firms On average foreign firms
have 40 percent higher TFP and pay 75 percent higher wages than domestically-owned
firms in the same industry That said the within-group heterogeneity is larger than the
across-group heterogeneity generating a substantial overlap between the two
distributions For example 30 percent of domestically-owned firms are more productive
than the median foreign firm The averages between the two groups differ substantially
but there are many productive domestically-owned firms and unproductive foreign ones
34 Result for other TFP measures are very similar
Evolution of the Productivity Distribution
48
Another interesting difference between the distributions is that the foreign-owned
distribution is substantially more dispersed than the domestically-owned one (its standard
deviation is 23 percent larger) suggesting more technological heterogeneity within the
foreign-owned group This may suggest that this group includes both firms with world-
class technology and plants utilizing low-cost labour in a relatively unproductive way That
said the distribution is clearly not bi-modal there are no clearly distinguishable clusters of
high-tech and low-tech firms They operate along a continuum
Comparing state-owned firms to the other two groups shows that they are more similar to
the domestically-owned private firms with two interesting twists35 First the low-
productivity left tail of state-owned firms is much thicker than that of the privately owned
domestic firms Many state-owned firms operate with very low productivity levels (see also
Section 63) As a result the average productivity of these firms is 25 percent lower
compared to privately-owned domestic firms in the same industry
The second twist is that even though state-owned firms tend to be substantially less
productive than privately owned domestic firms they pay on average 25 percent higher
wages This may be a consequence of differences in worker composition but may also
suggest that these firms face soft budget constraints and their employees are able to
capture a larger slice from a smaller pie
Figure 48 Distribution of TFP and average wage by ownership (cleaned from industry-
year effects) 2016
Notes This figure shows the distribution of productivity and ln average wage after filtering out
industry-year fixed effects from it Domestically-owned is domestic privately-owned Main sample
35 Note that the sample of state owned firms is much smaller than the other two groups and operates in very specific indutries This may affect the distribution
Productivity differences in Hungary and mechanisms of TFP growth slowdown
49
Figure 49 shows the evolution of the productivity distributions across years Note that in
order to illustrate shifts in time industry-year fixed effects are not filtered out from this
figure Therefore comparing the distributions with Figure 47 shows how much industry
composition matters
Panel A) illustrates the productivity evolution of domestic private firms The shape of this
distribution remained remarkably similar across years There are clear rightward shifts
between 2004-2008 and 2012-2016 while the distribution did not change during the crisis
period Similar patterns can be observed regarding foreign-owned firms This distribution
was always more dispersed than the domestic one with little changes in its standard
deviation across years
The shape of the state-owned productivity distribution is more peculiar Most visibly it had
been bi-modal before the crisis This is mainly a consequence of industry composition the
low productivity part representing some utilities While the bi-modality disappeared post-
crisis the low-productivity tail of the distribution became thicker Finally we do not see
any rightward shift in this distribution there was little productivity improvement in this
small segment of the economy
Figure 49 Evolution of the distribution of TFP by ownership
A) Domestic private
Evolution of the Productivity Distribution
50
B) Foreign
C) State
Notes This figure shows the distribution of TFP Domestically-owned is domestic privately-owned
Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
51
BOX 41 Duality between domestic and foreign-owned firms in an international context
We are not the first to document the substantial wage and productivity advantage of foreign firms Earle
and Telegdy (2008) by using NAV data between 1986-2003 show that foreign-owned firms were almost
twice as productive as domestic private firms (measured in terms of labour productivity) and also paid
40 higher wages when controlling for employee characteristics A substantial part of this premium
results from foreign owners acquiring more productive firms (mostly during the privatisation process)
but even after controlling for this selection process the foreign wage premium remains 14 Similar
results are found by Telegdy et al (2012) when using the longer period between 1986 and 2008
Foreign-owned firms tend to have positive productivity and wage premia in most countries developed or
emerging Among others Aitken et al (1996) show that foreign-owned firms have higher productivity
and wages in Mexico and Venezuela even after controlling for firm size skill mix and capital intensity
Conyon et al (2002) use acquisitions in the UK in 1989-1994 to find that foreign firms pay 34 higher
wages which can be fully attributed to their 13 higher productivity Girma et al (2002) have a similar
result showing that foreign firms in the UK have 8-15 higher productivity which leads to 4-5 higher
wages Using UK data from 1981-1994 Girma and Goumlrg (2007) find wage differentials of a similar
magnitude but heterogeneous with regard to the source country of the foreign investor Huttunen
(2007) looks at Finland and finds 26-37 wage premium of firms 3 years after being acquired by
foreign investors In the Central-Eastern-European region Djankov and Hoekman (2000) show that
foreign investment in the 90s increased the productivity of recipient firms in the Czech Republic
Governments aim to attract foreign direct investment (FDI) as it is assumed to have a positive impact
on the domestic economy From an economic point of view it is justifiable to provide incentives to
foreign investors if their investments have positive spillovers to domestic firms increasing their
productivity The higher productivity of foreign-owned firms which is documented in the previously
mentioned studies is a necessary condition for that At the same time if foreign firms establish no links
with domestic firms there is only limited opportunity for knowledge spillovers In this case the inflow of
foreign investments results in a dual structure of the economy
Evidence is rather mixed on FDI spillovers to domestic firms in the same industry because a negative
competition effect might dominate the positive technology or knowledge effect Haskel et al (2007) find
that a 10-percentage-point increase in the share of foreign ownership increases the TFP of domestic
firms in the same industry by 05 in the UK Konings (2001) finds negative spillovers for Bulgaria and
Romania and no spillovers for Poland Positive spillovers in vertically related industries are much more
general Using Lithuanian data Javorcik (2004) shows that one standard deviation increase in the foreign
share of an industry is associated with 15 increase in the output of domestic firms operating in the
supplier industry Similarly Kugler (2006) finds no within-industry spillovers but positive spillovers in
vertically related industries in Colombia
Evolution of the Productivity Distribution
52
Let us turn to industry differences in duality The substantial difference between the average TFP of
domestic and foreign-owned firms is present in all kinds of industries (Figure 410 and 411) In
manufacturing the percentage difference is about 34 percent (a log difference of 03) while it is
around 65-100 percent in services Significantly the cumulative TFP growth of the two types of firms
was very similar by the end of the period There is no evidence for the catching-up of domestic firms
with foreign ones The duality in this respect does not seem to diminish substantially
The TFP gap between foreign and domestic firms is amplified by the much higher capital intensity of
foreign firms (Figure 412) In manufacturing foreign firms employed more than twice as much capital
per employee than domestic firms While the capital intensity of both domestic and foreign-owned
firms increased steadily during the period in that sector the gap remained constant showing little
catching-up of domestic firms in terms of capital deepening In a sharp contrast there was a decrease
in the capitallabour ratio in services and this phenomenon took place quicker in the case of foreign
firms
This picture is reinforced at the industry level (Figure 413) In textiles foreign firms invested more
than domestic ones leading to significant capital deepening for that group of firms In machinery both
groups of firms increased their capital intensity to a similar extent In retail foreign firms had invested
much before the crisis but cut their investments deeply after that while the capital intensity of
domestic firms remained mostly flat In programming capital intensity declined slightly following the
crisis
BOX 41 Duality between domestic and foreign-owned firms in an international context
(cont)
Looking at Hungarian data several papers show the existence of positive FDI spillovers to domestic
firms Halpern and Murakoumlzy (2007) find significantly positive spillovers in the supplier industry but
no evidence for within-industry spillovers Beacutekeacutes et al (2009) find a negative effect on low-
productivity firms in the same industry while the spillover effect is positive for high-productivity
firms Iwasaki et al (2012) find positive spillovers even within the same industry conditional on the
proximity in product and technological space At the same time Bisztray (2016) shows that the
large-scale foreign direct investment of Audi did not increase the productivity of domestic firms in
the supplier industry
We know from the literature that the effect of FDI on domestic firms is highly heterogeneous even in
the supplier industry (see Smeets 2008 for a review) A crucial precondition of positive spillovers is
the absorptive capacity of the domestic firms (Crespo-Fontoura 2007) Using data from Bulgaria
Poland and Romania Nicolini and Resmini (2010) show that firm size matters as well Additionally
they find within-industry spillovers in labour-intensive sectors and cross-industry spillovers in high-
tech sectors Also the characteristics of the foreign investment play an important role in the
magnitude of the spillover effect Javorcik (2004) estimates a positive effect on the productivity of
domestic firms only in the case of shared foreign and domestic ownership but not for fully foreign-
owned firms Javorcik and Spatareanu (2011) show that the distance of the investorrsquos country is
also important as investors from far-away countries establish more links with local suppliers In line
with that they estimate positive vertical spillovers from US investors but not from European
investors in Romania Lin et al (2009) show that vertical FDI spillovers in China are weaker for
export-oriented FDI compared to domestic-oriented
Productivity differences in Hungary and mechanisms of TFP growth slowdown
53
Figure 410 TFP levels of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the (unweighted) average ACF TFP level of foreign and domestically-owned firms Main
sample The industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge- intensive services lsquoLKISrsquo Less knowledge-
intensive services
Evolution of the Productivity Distribution
54
Figure 411 Cumulated TFP growth of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level of foreign and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
55
Figure 412 Capital intensity of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the average capital intensity (log tangible and intangible assetsemployee) of foreign- and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Evolution of the Productivity Distribution
56
Figure 413 Cumulative change in capital intensity of foreign and domestic firms selected industries
Notes This figure shows the (unweighted) average capital intensity (log tangible and intangible assetsemployee)
of foreign and domestically-owned firms since 2004 in four industries Main sample
45 Conclusions
Our investigation of the evolution of productivity distribution has yielded a number of relevant
conclusions which will inform the work conducted in the remaining sections In line with international
evidence we have found that productivity dispersion within industries is many times larger than the
differences between industries Importantly Hungary seems to be an exception to the international
trend of frontier firms diverging from the rest of the economy ndash if anything there is evidence for the
low productivity growth of frontier firms and for some catching-up by others
OECD (2016 Figure 16) has found such a pattern only in Hungary and Italy with divergence in all the
other countries under study (Austria Belgium Canada Chile Denmark Finland France Japan
Norway and Sweden) We find two kinds of explanations plausible First in Hungary (unlike most other
countries in that sample) national frontier firms are quite far away from the global frontier As
Andrews et al (2015) argue the productivity divergence mainly arises between global frontier firms
and the rest If national frontier firms are far away from the global frontier they may find themselves
on the wrong side of global divergence Second it is possible that the policies and institutional
environment for national firms in Hungary is less conducive to adopt local frontier technologies A way
to learn more about the background of this result would be to use cross-country micro-data to study
the behaviour of frontier firms in even more countries including other CEE countries
The low productivity growth of Hungarian national frontier firms constrains productivity growth
directly Furthermore if national frontier firms do not adopt the most developed technology potential
spillovers to other firms will also remain limited Andrews et al (2015) have shown that good
Productivity differences in Hungary and mechanisms of TFP growth slowdown
57
framework conditions (most importantly good regulatory practices in upstream sectors) and innovation
related policies such as providing incentives for RampD and building a more robust national innovation
system are associated with a stronger catch-up of national frontier firms to the global frontier
The results reveal that duality especially between foreign and domestic firms is substantial and there
is no evidence for catching-up by domestic firms The gap is especially large in the service industries
That said the gap between the two groups can be bridged indeed the productivity differences
between the two groups are smaller than within them Duality while a sign of inefficiency also
provides an opportunity for domestic firms to tap into the knowledge base possessed by their foreign-
owned counterparts and to integrate into global value chains by relying on the links of foreign firms
While efficient strategies aiming at maximizing the benefits from FDI and global value chains may
differ across countries there are a few policy options which unambiguously help countries in benefiting
from the presence of multinational firms A robust result of the recent spillover literature is that
domestic firms need strong absorptive capacity including technological knowledge and a skilled
workforce to be able to benefit from the presence of foreign-owned firms (Girma 2005 Crespo and
Fontoura 2007 Zhang et al 2010) One dimension of absorptive capacity building is creating an
effective innovation system with a strong knowledge base and easy access to that knowledge Another
dimension is developing technological and management capabilities which enable firms to understand
and apply advanced knowledge Such capabilities are essential both for technological upgrading and for
integrating into global value chains (Taglioni and Winkler 2016)
An important caveat regarding these results is that they are limited to double-entry bookkeeping firms
We have emphasised that a large share of people work outside the double-entry bookkeeping entities
included in our sample While data are scarce about the productivity of these entities available
information suggests that both the levels and dynamics of productivity may differ radically between
double-entry bookkeeping firms and other entities If so inclusive policies could focus on providing
skills and opportunities for the self-employed
State-owned firms constitute a small part of the Hungarian market economy but such firms are
prevalent in some industries including utilities The productivity of some of these firms is very low
when compared to the productivity of privately-owned firms while they pay higher wages Both of
these phenomena hint at soft budget constraints and other inefficiencies Policies aiming at providing
better incentives either by improving corporate governance of state-owned firms (Arrobio et al 2014)
or by creating framework conditions more conducive to competition may help in in promoting
productivity growth in these important industries
Allocative efficiency
58
5 ALLOCATIVE EFFICIENCY
A key insight of recent productivity research is that differences in productivity levels across countries
largely result from the inefficient allocation of resources across firms rather than from differences in
the productivity of lsquotypical firmsrsquo both in cross-section (Hsieh and Klenow 2009 Restrucca and
Rogerson 2017) and in time-series (Gopintah et al 2017) Inefficient allocation refers to the
phenomenon that low-productivity firms possess a large amount of capital and labour (rather than
shrinking or exiting) or when firms with similar marginal products use a different amount or
composition of inputs
In this chapter we employ two strategies to quantify the extent of such distortions The first strategy
proposed by Olley and Pakes (1996) simply asks whether more productive firms are larger A more
positive covariance between productivity and employment suggests a better allocation of resources
across firms and higher industry level (labour-weighted) productivity (even when holding the
unweighted productivity level unchanged) The Olley-Pakes method is generally agnostic about the
specific nature of distortions but measures their results in an intuitive and robust way at the industry-
year level
Hsieh and Klenow (2009) attempt to identify the sources of distortions36 In particular they argue that
firms can face two main distortions product market distortion (modelled as an implicit sales tax and
identified from the wedge between labour costs and value added) and capital market distortion
(modelled as an implicit capital tax and identified from differences in the cost share of capital) These
variables can be measured at the firm-level Industry-level distortions can be quantified both as the
average of firm-level distortions and also as the dispersion of firm-level measures
This chapter describes these measures at the industry-year level Section 51 presents the Olley-Pakes
covariance terms while Section 52 implements the Hsieh-Klenow method
51 Olley-Pakes efficiency
The Olley-Pakes (also called static) approach of productivity decomposition consists of decomposing
the aggregated (industry-region-level) productivity which is the weighted average of firm-level
productivity levels into the unweighted average firm-level productivity and the covariance between
productivity and firm size (Olley and Pakes 1996) The latter term reflects how efficiently resources in
this case labour are allocated across firms A more positive covariance between size and productivity
reflects stronger allocative efficiency
Let us start with cross-country evidence from the OECD (Andrews and Criscuolo 2013) According to
this source in 2005 static allocative efficiency in Hungarian manufacturing (the covariance term) was
positive but slightly below the average of OECD countries similar to Portugal and Italy (Figure 51)37
Allocative efficiency in services was negative one of the lowest of the countries in the sample
(Andrews and Cingano 2014 Figure 10) showing that less productive firms tended to be larger in the
service sector Andrews and Cingano (2014) also show that the relatively low allocative efficiency in
Hungary is partly explained by policies including product market regulation and creditor protection
36 For an overview of the reallocation literature see Hoppenhayn (2014)
37 Note that these calculations use the ORBISAMADEUS database covering a relatively small fraction of larger
Hungarian firms in 2005 (about 3300 firms) see Box 21
Productivity differences in Hungary and mechanisms of TFP growth slowdown
59
Figure 51 Static allocative efficiency in Hungarian Manufacturing (2005)
Notes This figure is a reproduction of Figure 7 from Andrews and Criscuolo (2013)
Let us turn to our data The logic of the static decomposition is presented in Figure 52 for our main
sample by 2-digit industry38 The horizontal axis shows the unweighted average log labour productivity
of each industry while the vertical axis shows the productivity weighted by employment If all firms
were of equal size (or at least firm size was independent of productivity) weighted and unweighted
productivity would be equal ie all industries would be on the 45-degree line If size and productivity
were positively correlated the weighted productivity would be larger than the unweighted one The
difference between the weighted and unweighted average is the covariance between size and
productivity This measure of allocative efficiency is equal to the vertical distance between each point
and the 45-degree line Allocative efficiency contributes positively to industry productivity in industries
above the 45-degree line while it has a negative contribution for industries below the line
For example in the manufacture of machineries (28) the unweighted average productivity is 646
while the weighted average productivity is 665 Allocative efficiency resulting from more productive
machine manufacturers being larger contributes with 019 to the aggregate productivity of this
industry An industry with negative allocative efficiency is warehousing (52) where the lower
productivity of larger firms contributes negatively to aggregate productivity (the unweighted
productivity being 681 and the weighted only 585)
38 Appendix Table A51-Table A56 summarise the Olley-Pakes (1996) measures by industry
Allocative efficiency
60
Importantly allocative efficiency is positive in most industries It is especially high in the most
knowledge-intensive services (scientific research (72) employment activities (78)) in service
industries with a few large firms (broadcasting (60) telecom (61)) and in key manufacturing
industries beverages (11) chemicals (20) machinery production (28) and vehicle production (29) In
a few industries low-productivity firms tend to be larger Prominent examples are professional
services advertising (69) and legal and accounting activities (73) services with many state-owned
firms transportation (39) waste management (49) and logistics (52) In line with OECD evidence
allocative efficiency tends to be more positive in manufacturing compared to services
Finally Figure A51 in the Appendix shows that allocative efficiency is significantly higher when labour
productivity is considered rather than TFP almost every industry has larger weighted labour
productivity than unweighted labour productivity This difference simply results from the positive
association between productivity and capital intensity
Figure 52 Weighted and unweighted TFP by 2-digit industry 2015 main sample
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted TFP while the vertical axis
shows its weighted TFP in the same year We have omitted industries with less than 1000 observations TFP is
estimated using the method of Ackerberg et al (2015)
Another conclusion that can be drawn from Figure 52 is that allocative efficiency is higher in sectors
with higher unweighted productivity represented by the fitted line in the figure In other words high
firm-level efficiency seems to move together with higher allocative efficiency in the industry One
mechanism behind this relationship may be that incentives for technology upgrading are stronger
when the reallocation process is more effective (Restruccia and Rogerson 2017) but stronger
international competition can also affect positively both within-firm productivity dynamics and
reallocation across firms In Figure 53 we investigate whether this relationship changed between
years The figure shows that the positive relationship between unweighted productivity and allocative
Productivity differences in Hungary and mechanisms of TFP growth slowdown
61
efficiency did not change substantially over time This relationship is similar when labour productivity is
considered (see Figure A52 in the Appendix)
Figure 53 The relationship between weighted and unweighted TFP by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted TFP levels run at the
2-digit industry level separately for 2005 2010 and 2016 TFP is estimated using the method of Ackerberg et al
(2015)
From the perspective of productivity slowdown a key question is whether allocative efficiency
deteriorated in some industries following the crisis Figure 54 shows the allocative efficiency of each
2-digit industry in 2010 and 2016 The axes here represent the distances from the 45-degree line in
Figure 52 If an industry is on the 45-degree line of this figure its allocative efficiency remained
unchanged in the period if an industry is above the line its allocative efficiency was better in 2016
compared to 2010 The first conclusion that can be drawn is that levels of allocative efficiency are
persistent industries cluster around the 45-degree line Also the fitted line shows that allocative
efficiency grew somewhat faster in industries where allocative efficiency was worse and this
relationship is statistically significant Therefore productivity growth decline is unlikely to be the result
of rapidly worsening allocative efficiency
One can however identify a couple of industries where substantial changes took place The machinery
industry (28) for example became more efficient partly because of the entry of new large foreign-
owned firms Office administration (82) and management activities (70) also increased their allocative
efficiency This is most likely due to the entry of large shared service providers Allocative efficiency
decreased in land transportation (39) waste management (49) and warehousing (52)
The evaluation of allocative efficiency in labour productivity shows similar patterns (Table A53 in the
Appendix)
Allocative efficiency
62
Figure 54 The change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences
between the weighted and unweighted TFP) in 2010 while the vertical axis shows the same quantity in 2016 TFP is
estimated using the method of Ackerberg et al (2015)
52 Product market and capital market distortions
The Olley-Pakes static decomposition framework can quantify the overall allocative efficiency of sectors
but it is incapable of informing us about the nature of distortions In this section we implement the
methodology of Hsieh and Klenow (2009)39 to distinguish between product and capital market
distortions This distinction is of much interest given that the crisis and its aftermath ran parallel with
both financial market frictions and changes in product market regulation
The logic of the Hsieh and Klenow (2009) method is the following Under the assumptions of
monopolistic competition on product markets (similarly to Melitz 2003) and frictionless labour
markets the marginal product of labour and capital should be equalized across firms in the absence of
market distortions In turn if the production function is Cobb-Douglas the equality of marginal
products implies that the share of labour costs in value added and capital intensity (capitallabour)
should be equalized across firms Under product market distortions (modelled with a firm-specific
implicit lsquosales taxrsquo or a negative rent) the wedge between labour costs and value added will differ
across firms because firms facing lower implicit taxes charge higher markups The more heterogeneous
the lsquosales taxrsquo is the larger the dispersion of the wedge Capital market distortions are modelled as
39 The Hsieh-Klenow approach has been criticized recently by Haltiwanger et al (2018)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
63
implicit firm-specific capital tax rates Firms facing different capital tax rates choose different capital
intensity levels and hence different capitallabour cost ratios Therefore the dispersion of capital
intensity (or more precisely the cost share of capital) reflects the dispersion of capital tax rates
Note that the implicit taxes proxy multiple sources of distortions from which differences in explicit
taxes represent only a small part The implicit lsquosales taxrsquo includes the cost of complying with different
types of regulations size-dependent regulation the effect of fixed costs and market power The
implicit lsquocapital taxrsquo includes for instance the full cost of accessing financing possible subsidies for
investment or differences in tax incentives to invest These implicit taxes provide a convenient way of
summarizing markup differences and differences in access to capital
As a result the dispersion of the wedge and capital intensity reflect how heterogeneous the two
implicit tax rates are More heterogeneity in implicit tax rates in turn implies more disperse total factor
productivity within industry40 and a less efficient allocation of resources In other words similarly
productive firms (having also similar marginal products of inputs) choose very different input quantities
and combinations
Product market distortions
We start our empirical investigation by calculating the rents (1-implicit sales tax rate) for every firm by
a proxy for markups41
1 minus 120591119884119904119894 =120590
120590minus1
120573119871119904+120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119881119860119904119894 (51)
where 120591119884119904119894 shows the size of the implicit lsquosales taxrsquo (or product market distortion) for firm i in sector s
120590 denotes the elasticity of substitution between firms by consumers and 120573119871119904 and 120573119870119904 are the
coefficients of labour and capital in the production function We follow the calibration of Hsieh and
Klenow (2009)42 and set 120590 = 3 while we plug in 120573119871119904 and 120573119870119904 using our production function estimation of
Section 22 119881119860119904119894 represents the real value added of the firm i in sector s while 119871119886119887119888119900119904119905119904119894 is labour
related expenses for firm i in sector s
The equation reflects the intuition that firms facing a lower implicit sales tax can charge higher
markups and as a result will pay a lower share of their value added to their employees Note that the
level of 120591119884119904119894 depends on a number of parameters and may be driven by differences in for example the
elasticity of substitution Therefore we will normalize the values of this estimate when comparing
typical distortions across industries
Figure 55 summarizes the implicit sales taxes by industry (120591119884119904119894) We standardise the values of 120591119884119904119894 by
subtracting the market level median from the firm-level implicit sales taxes and plot the median of
40 Appendix Table A57 summarises the dispersion of TFP within industry Note that dispersion in labour productivity
(log-value added per worker) is not necessarily related to product market distortions as firms with various
labour productivity may have the same TFP if the production function does not have the property of constant
return to scale
41 Hsieh and Klenow (2009) Equation 18
42 The predicted value of product market distortions crucially depends on the elasticity of substitution However the differences in 120591119884119904119894 across industries and years measures the changes in product market distortions even if the
elasticity of substitution is miscalibrated
Allocative efficiency
64
these standardised values by industry If the standardised bar is positive (negative) than the median
firm in the industry faces a higher (lower) implicit sales tax than the median firm in the economy We
find that product market distortions tend to be larger in highly regulated industries (energy
transportation ICT) while they tend to be lower in less regulated ones with strong competition
including manufacturing accommodation and administrative services The difference between
industries is non-trivial the difference between highly regulated sectors and manufacturing is
equivalent to an extra 10-20 percentage `sales tax ratersquo
The ranking of the industries (with the exception of energy) remained similar between 2006 and 2016
but differences became somewhat larger with a relative decrease in implicit taxes in manufacturing
and administrative services and an increase in transportation and ICT43
Figure 55 Implicit sales taxes (120591119884119904119894) by industry
Notes The figure above shows the median size of product market rents in 2006 and 2016 Industries with positive
tax measures can achieve rents below the market average due to product market distortions
The previous exercise has investigated across-industry differences A further question is whether firms
face different tax rates even within industries because of for example size-dependent taxes This is a
key measure to examine whether resources are misallocated across firms within industries Our
measure for this is the standard deviation of ln(1 minus 120591119884119904119894) (Figure 56)44 This dispersion is substantial
43 We report these measures in more detail in Table A55 of the Appendix
44 Also note that this measure of dispersion is independent of the elasticity of substitution and the production function parameters
Productivity differences in Hungary and mechanisms of TFP growth slowdown
65
with the standard deviation equivalent to a 100 percent sales tax45 Within-industry differences in this
variable are similar across industries with a relatively small dispersion only in mining and energy
Figure 56 Standard deviation of implicit sales tax rates (ln(1 minus 120591119884119904119894)) by industry
Notes The figure shows the within industry product market distortions in 2006 and 2016 Resources are less
effectively distributed in industries with larger distortion measures
Capital market distortions
Distortions on the capital market are identified from how the ratio of expenses on labour and capital
(capital intensity in cost terms) differ from what is predicted by the production function with no capital
tax46
119877(1 + 120591119870119904119894) =120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119870119904119894 (52)
The left hand side of this equation represents the implicit cost of capital for firm i in sector s backed
out from the capital intensity of the firm If it is 01 the firm faces an implicit lsquointerest ratersquo of 10
percent if it is 02 the lsquointerest ratersquo is 20 This can be decomposed into 119877 the frictionless user
45 Similar differences have been found in other countries as well and they are in line with the vast degree of heterogeneity in terms of size and productivity within industries
46 Hsieh and Klenow (2009) Equation 19
Allocative efficiency
66
costs47 of capital (having the same unit of measurement) multiplied by 1 plus the implicit lsquocapital tax
ratersquo 120591119870119904119894 which is firm-specific48
Similarly to the product market equation 120573119871119904 denotes the labour elasticity of the production function
120573119870119904 is the capital elasticity of the production function and 119871119886119887119888119900119904119905119904119894 is the total labour cost for firm i in
sector s The denominator consists the capital stock of the firm (119870119904119894)
It is not common in the literature to report 120591119870119904119894 because its absolute value depends crucially on the
calibration of the rental rate of capital This is an issue because it is hard to obtain reliable information
on the frictionless rate of capital which most likely changed substantially between the pre-crisis
disinflationary period and the wake of the crisis Besides 120591119870119904119894 takes extremely large values for firms
with low level of capital (eg if the firm rents its capital instead of owning it) Note that the levels of
this variable are identified from the difference between the observed capital intensity (in cost terms)
and the optimal one implied from the production function Therefore we prefer to report the more
easily interpretable implicit median cost of capital 119877(1 + 120591119870119904119894) by industry49
While we find differences and changes in the implicit cost of capital informative it is not a direct
measure of capital market distortions because it can also reflect differences in the user cost of capital
across industries and years However the ratio (or log difference) of the implicit cost of capital
between two firms measures the difference between their respective implicit capital tax rates (or more
precisely between their 1 + 120591119870119904119894) As a result the standard deviation of the log implicit cost of capital
provides a pure measure of the dispersion of implicit capital taxes independently from the exact value
of 119877 Its interpretation is the relative standard deviation of the user cost of capital which is identified
from the dispersion of capital intensities
Figure 57 summarizes the median size of implicit cost of capital across industries50 Administrative and
professional services and ICT seem to pay the highest implicit cost for capital it is above 40 percent in
these industries As opposed to these utilities accommodation and food services face implicit costs of
capital below 20 percent The large differences in access to capital across industries are likely to result
mainly from differences in the size and age distribution of firms as well as from the different share of
tangible capital in different industries Moreover the median implicit cost of capital rose practically in
all service industries but decreased slightly in manufacturing
47 The rental price of capital covers the interest rate and the depreciation of capital stock
48 If one is willing to assume a specific value for the frictionless user cost of capital it is easy to back out 120591119870119904119894 For
example if the implicit cost of capital for firm 119894 (the left hand side) is 02 and (following Hsieh and Klenow 2009) one sets R = 01 then 120591119870119904119894 = 1 meaning that firm 119894 can obtain capital at a 10 percentage points higher interest rate
relative to the frictionless rate
49 The median of 119877(1 minus 120591119870119904119894) is less dependent on the extreme values of the distribution than the average so it is a
more precise measure of capital market distortions a typical firm faces than the average of it
50 We can validate our implicit capital cost measure by comparing our results to Kaacutetay and Wolf (2004) According to their estimates (using a different methodology) the median user cost of capital was 189 percent between 1993 and 2002 Our results have similar magnitude as the median implicit cost of capital was 255 percent in 2006 and 287 percent in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
67
Figure 57 Median implicit cost of capital by industry
Notes The figure shows the average size of capital market distortions in 2006 and 2016 Industries with larger
distortion measures are more constrained in accessing capital due to capital market distortions
Again the differences in typical capital costs across industries are much smaller than differences across
firms within an industry (see Figure 58) In industries where median implicit capital costs are lower
the dispersion of those costs also tends to be smaller the estimated cost of accessing capital is
significantly more unequal in the retail sector and administrative services relative to manufacturing
The notable exemption is the energy sector which has the lowest median and the largest dispersion in
the implicit cost of capital reflecting a relatively low level of capital costs resulting from predictable
tangible capital intensive activities
Allocative efficiency
68
Figure 58 The standard deviation of the estimated implicit cost of capital by industry
Notes The figure shows the standard deviations of capital market distortions log (119877(1 + 120591119870119904119894)) in 2006 and 2016
Most importantly capital market distortions increased within nearly all industries both in terms of
their levels and dispersion Hungary is not an exception in this respect This trend has been
documented in other countries where FDI played important role in economic growth A key study on
this topic is Gopinath et al (2017) who show that large capital inflows and credit market constraints
of small firms jointly increased capital market distortions in Spain This evidence suggests that the
crisis led to similar developments in Hungary making capital costs more unequal by generating
financial frictions This inefficiency seems to have resulted in the misallocation of capital in all types of
industries
A key question from a policy perspective is whether one can identify types of firms which faced a
systematically large increase in their cost of capital We follow the approach of Gorodnichenko et al
(2018) who quantified the misallocation of capital at the firm-level and found that small and young
firms faced an exceptionally high cost of capital We follow this strategy to identify observables which
are likely to be related to the level and change of capital costs
Figure 59 plots the relationship between firm age firm size and the estimated implicit cost of capital
119877(1 + 120591119870119904119894) The figure sorts the firms into twenty equally-sized bins by age and size and plots the
median implicit cost of capital separately for 2006 and 2016 Panel (a) highlights that the implicit cost
of capital was decreasing with firm age even before the crisis with young firms facing about 25
percentage points higher capital costs compared to firms older than 10 years This function became
dramatically steeper by 2016 when the median `oldrsquo firm (more than 10 years old) faced an implicit
capital cost of 25 percent a median 5-year old firm paid 50 percent and a very young firm faced more
than a 100 percent implicit cost of capital This figure suggests that capital market frictions generate
important constraints for entry and the growth of small firms hindering reallocation and innovation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
69
Panel (b) of Figure 59 visualizes the relationship between employment and the implicit cost of capital
We find that firms with more than 20 employees faced an implicit cost of capital below the median of
the whole sample both in 2006 and 2016 As opposed to this small firms faced above the median
implicit cost of capital in 2006 and suffered from a disproportionally large increase in the next decade
This again constrains the growth of small firms relative to their larger peers
Figure 59 The evolution of the implicit cost of capital by age and firm size
A) Age of firms
B) Size of firms
Notes The figure shows the median implicit cost of capital 119877(1 + 120591119870119904119894) by age and size categories
Allocative efficiency
70
The results presented above have shown two patterns an increasing dispersion of the implicit cost of
capital on the one hand and a steeper gradient between observables (age and size) and capital taxes
on the other A natural question is whether increased financial friction led to larger differences in
access to capital along observables One explanation for this could be that banks have become more
wary about allocating capital to say firms operating in industries with much intangible capital The
alternative is that the increased variance in capital access reflects mainly differences along unobserved
dimensions by for example more scrutiny of managers when deciding about firm loans These two
possibilities can have different policy implications In the former case for example policymakers may
promote access to capital for specific groups of firms
Table 51 presents regressions with the implicit capital cost as a dependent variable and key firm-level
characteristics as explanatory variables Our first conclusion is that the regressions explain only a
relatively small part (less than 20 percent) of the variation in the implicit cost of capital the
overwhelming majority of the variation arises from unobservables In this sense policies targeting
specific types of firms may have a limited effect
That said the explanatory power of observables increased by around a third between 2006 and 2016
While the explanatory power of industry dummies slightly decreased that of age increased
substantially from 2 percent to 57 percent The explanatory power of size was much smaller in both
periods suggesting that its correlation with the implicit cost of capital may be confounded by its
correlation with age and industry This evidence together with Figure 59 suggests that indeed
capital access by young firms deteriorated substantially after the crisis
Table 51 Variance decomposition of implicit cost of capital
Variance in 2006 Variance in 2016
Variance
component
Share
of total
Variance
component
Share
of total
Total Variance of log-implicit
cost of capital
2126 100 2443 100
Components of Variance
Variance of age 0042 20 0140 57
Variance of size 0006 03 0002 01
Variance of ownership 0012 06 0022 09
Variance of region 0012 06 0025 10
Variance of industry 0202 95 0180 74
Residual 1830 861 1995 817
Notes Control variables are dummies for age ownership (private foreign or state-owned) region (7 NUTS2
region) and 2 digit industry
53 Conclusions
This section summarises the static measures of allocative efficiency by industry types (Table 52) A
key pattern that emerges is that resources are allocated more efficiently in the manufacturing sectors
First on average the OP covariance is strongly positive within manufacturing while it is very close to
zero in less knowledge-intensive services The Hsieh-Klenow (2009) efficiency measures suggest that
product market distortions are similar across sectors but capital market distortions are significantly
lower in manufacturing These findings are in line with the disciplinary effect of international
competition in the traded sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
71
Table 52 Allocative efficiency within industries sectors (2016)
Industry type TFP
level
in
2016
TFP
growth
between
2011 and
2016
Olley-
Pakes
allocative
efficiency
Dispersion
of implicit
sales taxes
Dispersion
of implicit
cost of
capital
Low-tech mfg 5694 0027 0197 111 146
Medium-low tech mfg 6081 0027 0017 102 142
Medium-high tech mfg 6129 -0093 0119 111 131
High-tech mfg 6708 0276 0072 107 145
Total manufacturing 5942 0021 0242 107 143
Knowledge-intensive serv 6706 0225 0403 106 166
Less knowledge-intensive serv 6566 021 -0081 108 159
Construction 6411 0082 0023 109 148
Utilities 5949 -0138 0801 093 155
Total services 6598 0212 0055 108 160
Notes The table summarises the allocative efficiency measures by broad industry categories The dispersion of
implicit sales taxes is measured by the standard deviation of 119897119899(1 minus 120591119884119904119894) while the dispersion of the implicit cost of
capital is measured by the standard deviation of ln (119877(1 + 120591119870119904119894))
Capital market distortions became more severe in the wake of the financial crisis while there was no
such trend in terms of product market distortions This finding is in line with results for Southern
Europe (Gamberoni et al 2016a Gopinath et al 2017) and CEE countries in general (Gamberoni et
al 2016b) (see Figure 510) This suggests that the financial intermediation system is still less
effective relative to its pre-crisis performance
Investigating at the firm-level we found that the deterioration of the financial conditions did not hit all
firms equally In particular young firms were hit especially hard by ever increasing capital costs even
though many policy tools were introduced to help such firms including the subsidized access to capital
by the Central Bank (eg the NHP program) Deteriorating access to capital by young firms can be
especially harmful for reallocation often driven by dynamic young firms Policies aimed at promoting
equal and efficient access to capital especially for young firms may help to reduce these inefficiencies
Given the magnitude of the still existing allocative inefficiency policies which support reallocation could
have a significant positive effect on aggregate productivity A key conclusion of recent research is that
firm-specific distortions which may result from discretionary policies or non-transparent regulations
have a quantitatively significant effect on aggregate productivity (Hsieh and Klenow 2009 Bartelsman
et al 2013 Restuccia and Rogerson 2017) In particular size-dependent taxes and regulations
(Garicano et al 2016) ineffective labour and product market regulations and FDI barriers (Andrews
and Cingano 2014) have been shown to be negatively associated with allocative efficiency and its
improvement Gamberoni et al (2016b) also demonstrate that higher corruption levels slow down the
improvement of allocative efficiency Chapter 8 will investigate the effects of such policies in more
detail using the example of the retail industry
The specific pattern showing that capital distortions are relatively high and have increased in Hungary
(similarly to other CEE and Southern European countries) suggests that policies which facilitate the
reduction of financial frictions and provide symmetric access to capital for all firms could improve
allocative efficiency to a large degree Specifically policies should attempt to facilitate capital flows to
Allocative efficiency
72
more efficient firms even if young rather than to firms with a higher net worth or more tangible
assets (Gopinath et al 2017)
Figure 510 Capital and labour misallocation in CEE countries country-specific weighted average
across sectors
Notes This is a reproduction of Figure 1 from Gamberoni et al (2016b)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
73
6 REALLOCATION
After investigating the level of allocative efficiency in Chapter 5 namely a lsquostaticrsquo approach we now
turn to a dynamic view focusing on how much reallocation across industries (Section 61) and firms
(Section 62) contributed to aggregate and sectoral productivity growth
61 Reallocation across industries
An important channel behind the relationship between economic development and productivity is the
structural change of the economy first from agriculture to manufacturing and then from
manufacturing to services (Herrendorf et al 2014 McMillan et al 2017) But at higher levels of
development economic growth is also associated with reallocation across industries within these broad
sectors primarily from more traditional to more knowledge-intensive ones (Hausmann and Rodrik
2003 Hausmann et al 2007) Kuunk et al (2017) demonstrate that in terms of its contribution to
productivity growth across-industry reallocation within sectors dominated reallocation across sectors
in CEE countries In this subsection we take a brief look at the importance of this process in Hungary
by quantifying the reallocation of employees across and within 2-digit industries
Table 61 shows how the employment share of different industries in our main sample changed over
time51 The most important pattern is a pronounced shift from manufacturing to services until 2010
and near-constant sectoral shares after that In particular the share of manufacturing decreased by
nearly a quarter from 38 percent to 32 percent between 2004 and 2010 but this number remained
unchanged in the years following the crisis The crisis seems to have constituted a structural break in
this process
A more detailed look at the composition of industries shows that ndash in net terms ndash this structural
change was driven by a transition of employment from low-tech manufacturing52 to both knowledge-
intensive and less knowledge-intensive services while the employment share of the more high-tech
manufacturing industries remained practically unchanged After 2010 the structure of manufacturing
remained mainly unchanged in this aspect with no further shift away from low-tech manufacturing
activities Within services we see a continuous increase in the share of knowledge-intensive services
both before and after the crisis In the 12 years under study the employment share of knowledge-
intensive services increased by 6 percentage points or nearly 60 percent
51 Note that these calculations in line with other parts of this report apply to the firm sector of the Hungarian
economy ie ignore the self-employed (see Section 42) When taking into account the self-employed the share of
services and sectoral share follow somewhat different dynamics
52 One factor behind this process might have been the almost doubling of the minimum wage in 2000 and 2001
(Koumlllő 2010 Harasztosi and Lindner 2017) and a growing import competition in the light industries (David et al
2013)
Reallocation
74
Table 61 Employment in different sectors (main sample)
2004 2007 2010 2013 2016
Low-tech mfg 152 117 107 105 100
Medium-low tech mfg 89 92 91 96 98
Medium-high tech mfg 94 96 82 89 93
High-tech mfg 49 49 44 39 35
Total manufacturing 384 355 324 329 327
Knowledge-intensive serv 107 128 149 158 167
Less knowledge-intensive serv 382 395 410 405 397
Construction 86 88 83 75 75
Utilities 40 34 34 33 34
Total services 616 645 676 671 673
Notes This table shows employment shares by industry type (see Section 25) for the full sample
To provide a more detailed picture Figure 61 illustrates how employment growth in different 2-digit
industries is associated with their initial productivity level (Figure 61) In particular if more productive
sectors increase their employment share faster aggregate productivity should grow
Figure 61 Employment change as a function of initial TFP
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
75
B) Services
Notes Industries are 2-digit NACE Rev 2 industries The fitted line is weighted with initial employment Main
sample
To quantify whether across-industry reallocation matters we decompose the aggregate productivity
growth observed in our sample into the contributions of cross-industry reallocation and within-industry
productivity growth We divide our sample into three-year periods and calculate the average yearly
productivity growth by periods
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119901119894119905minus3)119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+ sum 119905119891119901119894119905minus3 lowast (119904ℎ119886119903119890119894119905 minus 119904ℎ119886119903119890119894119905minus3)119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
(61)
where the left hand side variable is the change in aggregate TFP between years 119905 minus 3 and 119905 119904ℎ119886119903119890119894119905 is
the share of the (2-digit) industry i in year t in the total employment and 119905119891119901119894119905 is average TFP of the
industry The first term on the right side is the within-industry TFP growth weighted by initial market
shares and the second term is the between effect capturing whether more productive industries have
increased their employment shares53
The decomposition in Figure 62 presents the result of this reallocation exercise for annualized growth
rates Its interpretation is the following between 2004 and 2007 average annual productivity growth
was nearly 8 percent in the total economy Around 7 percentage points from it is explained by within-
industry developments and only about 1 percentage point by reallocation across industries
53 This decomposition gives a comprehensive measure of the reallocation between industries but it is unable to
show the importance of firm exits and entries We investigate this in the next section
Reallocation
76
In general the figure shows that within-industry reallocation rather than cross-industry
developments played the key role in aggregate productivity growth Furthermore in line with Table
61 the contribution of between-industry reallocation was effectively zero post-crisis During the crisis
cross-industry productivity growth contributed positively to aggregate productivity growth while within
industry reallocation dramatically lowered aggregate productivity
This overall picture suggests that the flow of resources from light industries to other manufacturing
the growing share of services and especially knowledge-intensive services were a detectable though
not dominant driver of productivity growth only before 2010 Within-industry developments were
quantitatively more important throughout the whole period under study
This latter finding hints at a deterioration in the environment determining the reallocation process
post-crisis This seems to be the case for the whole economy but the negative contribution of
reallocation is more pronounced in manufacturing
Figure 62 Across and within industry productivity growth annualized log
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth main
sample
62 Reallocation across firms
In this subsection we take a look at the role of reallocation from a different perspective Rather than
focusing on whether the resources flow across industries we take a firm-level focus and decompose
TFP growth to within and across firm components The usefulness of this approach lies in the fact that
it sheds more light on the flexibility and efficiency of the process determining resource flows across
firms and also allows us to distinguish between resource flows across continuing firms on the one hand
and entry and exit on the other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
77
There are two general methods of measuring the reallocation of resources from less efficient to more
efficient firms The first method quantifies the labour and capital gains of more efficient firms directly
(Harasztosi 2011 Petrin et al 2011 Petrin and Levinson 2012) The second method is based on
product-market developments allocation of resources improves if the market share of high
productivity firms increases (Baily et al 1992 Griliches and Regev 1995 Brown and Earle 2008)
We adopt this second method as it can quantify directly the TFP contribution of firm entries and exits
To begin with we decompose the aggregate TFP growth between years t and t-3 based on the method
of Foster et al (2001) and Foster et al (2008)
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905minus3 lowast ∆119905119891119901119894119905minus3119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
+ sum (119905119891119901119894119905minus3 minus 119905119891119905minus3 + ∆119905119891119901119894119905) lowast ∆119904ℎ119886119903119890119894119905minus3119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+
sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119905minus3)119894isin119873⏟ 119890119899119905119903119910 119890119891119891119890119888119905
+sum 119904ℎ119886119903119890119894119905minus3 lowast (119905119891119901119894119905minus3 minus 119905119891119905minus3)119894isin119873⏟ 119890119909119894119905 119890119891119891119890119888119905
where the left hand side variable is the average annual aggregate TFP growth between years t-3 and t
and 119905119891119905 is the employment weighted average aggregate TFP while 119905119891119901119894119905 is the TFP of firm i in year t
119904ℎ119886119903119890119894119905 denotes the employment share of firm i in year t The first and second sum contain every firm
while the third sum consists of only firms which enter between years t-3 and t and the fourth sum
consists firms which leave the market between years t-3 and t
Each element of this decomposition has an intuitive economic interpretation In order of inclusion
these are i) within-firm TFP growth weighted by initial market shares ii) between effect capturing
whether initially more productive firms have raised their market shares and whether firms with
increasing productivity also expand (cross effect) and the iii) entry effect and iv) exit effect We pull
the last two terms together and interpret it as net entry effect which captures whether more
productive firms entered than exited54
Figure 63 summarizes the results for the market economy Before the crisis all three components
contributed positively to aggregate productivity growth Reallocation both across continuing firms and
on the margin of entry and exit was an important driver of productivity growth Productivity growth
was negative during the crisis as we have seen in Section 43 This was a result of strong negative
within-firm growth partly counterbalanced by positive reallocation Within-firm growth was still
sluggish immediately after the crisis but reallocation was relatively intensive and efficient Within-firm
growth recovered after 2013 and the importance of reallocation decreased Still the contribution of all
three components is substantially smaller relative to pre-crisis suggesting that the productivity
slowdown results from a combination of low within-firm growth and less effective reallocation
54 Note that these quantities cannot be easily linked to the withinacross industry decomposition of the previous
section Across firm reallocation and the entry effect can take place both across and within sectors
(62)
Reallocation
78
Figure 63 Dynamic decomposition annualized log main sample
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth by 3-
year periods main sample
Figure 64 repeats the decomposition exercise for each industry type For ease of interpretation (and
to get more stable results) we aggregate the three non-high-tech manufacturing sectors for these
calculations
As we have seen in Section 43 productivity dynamics differed markedly across these sectors Still
there are some common patterns First the strong pre-crisis productivity growth resulted from a
combination of strong within-firm productivity growth and efficient reallocation The sectors differ in
terms of the weights of these forces reallocation (especially entry) was most important in non-high-
tech manufacturing while within-firm growth dominated in high-tech manufacturing In services the
two components were of roughly equal importance
As we have seen productivity increased even during the crisis in high-tech manufacturing as a
combination of within and across productivity growth In other industries productivity growth was
negative during the crisis In non-high-tech manufacturing a strongly negative within growth was
somewhat counterbalanced by positive reallocation In contrast we find evidence for a negative
reallocation effect in services during the crisis
Immediately following the crisis (2010-2013) within growth remained sluggish but reallocation
resulting from firm entry and exit intensified especially in non-high-tech manufacturing and high-tech
services By 2013-2016 within growth recovered and the effect of reallocation became smaller
Productivity differences in Hungary and mechanisms of TFP growth slowdown
79
Figure 64 Dynamic decomposition by sector
A) High-tech Manufacturing
B) Non-high-tech Manufacturing
Reallocation
80
C) Knowledge-intensive services (KIS)
D) Not knowledge-intensive services (NKIS)
Notes This figure shows the Foster et al (2008) type dynamic decomposition of the productivity growth in our
sample for 3 periods by broad sectors as defined by the EurostatOECD (httpeceuropaeueurostatstatistics-
explainedindexphpGlossaryHigh-tech)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
81
One of the main messages of our analysis in Section 44 has been the large and persistent duality
between globally oriented and other firms This motivates our investigation of the extent to which
exporters and foreign-owned firms contributed to productivity growth and also whether reallocation
via the expansion of the more productive group contributed to aggregate productivity growth In order
to investigate these questions we decompose aggregate productivity growth into three parts the
within contribution of exporters (in the starting period) the within-contribution of non-exporters and
the reallocation between the two groups (which mainly reflects the change in the market share of
exporters) We conduct a similar analysis between foreign and domestically-owned firms
Table 62 shows the decomposition by export status Pre-crisis exporters contributed substantially
more to productivity growth than non-exporters both in manufacturing and services The reallocation
of resources to exporters mattered little Exporters were still capable of improving their productivity
levels during the crisis though it was not enough at the aggregate to counterbalance the falling
productivity of non-exporters Post-crisis the productivity growth of exporters slowed down and
aggregate growth was mainly driven by productivity changes within the non-exporter group
Productivity growth became much less exporter-driven post-crisis
Table 62 TFP growth decomposition by exporter status annualized log
2004-2007
Total Exporter Non-exporter Across
Market economy 793 577 226 -009
Manufacturing 1053 877 164 011
Market services 606 387 222 -003
2007-2010
Total Exporter Non-exporter Across
Market economy -045 091 -136 000
Manufacturing 054 021 007 027
Market services -178 129 -305 -002
2010-2013
Total Exporter Non-exporter Across
Market economy 206 033 144 029
Manufacturing -122 -129 -012 019
Market services 471 077 289 106
2013-2016
Total Exporter Non-exporter Across
Market economy 362 155 206 001
Manufacturing 057 049 011 -003
Market services 650 243 382 025
Notes This table decomposes the sales-weighted productivity growth into within-exporter within-non-exporter
contributions and the contribution of the reallocation between the two groups main sample
Table 63 decomposes productivity growth by ownership The picture is similar to the exporter
decomposition with a key contribution of foreign-owned firms to productivity growth pre-crisis and a
much smaller contribution after that Again reallocation of resources to foreign-owned firms played a
limited role in productivity growth
Reallocation
82
Table 63 TFP growth decomposition by ownership status annualized log
2004-2007
Total Foreign Domestic Across
Market economy 793 255 516 022
Manufacturing 1053 375 619 059
Market services 606 148 383 075
2007-2010
Total Foreign Domestic Across
Market economy -045 018 -076 013
Manufacturing 054 081 -034 007
Market services -178 -131 -120 073
2010-2013
Total Foreign Domestic Across
Market economy 206 003 199 005
Manufacturing -122 -152 027 003
Market services 471 138 336 -003
2010-2016
Total Foreign Domestic Across
Market economy 362 106 264 -008
Manufacturing 057 011 040 005
Market services 650 234 452 -037
Notes This table decomposes the sales-weighted productivity growth into within-foreign within-domestic
contributions and the contribution of the reallocation between the two groups Main sample
63 Failure of reallocation Zombie firms
Following the crisis it was suggested that weak productivity performance could be linked to the
survival of unprofitable and ineffective firms The presence of many such firms limits the access of
better-managed firms to capital and generates congestion in the product markets which limits entry
(Caballero et al 2008) McGowan et al (2017) have argued and provided evidence that the share of
such ldquozombie firmsrdquo has risen since the middle of the 2000s and that the higher share of such firms is
associated with lower productivity growth and investment at the industry level
Given the productivity slowdown in Hungary and the extent to which the financial crisis has affected
bank lending it is of interest to see whether the prevalence of ldquozombie firmsrdquo increased
disproportionately after the crisis
Figure 65 shows the share of ldquozombie firmsrdquo in 9 OECD countries from McGowan et al (2017) The
share of such firms in the full sample increased from just below 3 percent in 2003 to 5 percent in
2013 The rise was especially noticeable in Spain and Italy where in 2013 the share of firms reached
11 and 5 percent respectively Even more importantly the employment share of ldquozombie firmsrdquo rose
above 15 percent in both countries by 2013 possibly generating significant effects for other firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
83
Figure 65 Share of ldquozombie firmsrdquo in some OECD countries
Notes This is a reproduction of Figure 5A from McGowan et al (2017) Country codes should be interpreted as
follows BEL ndash Belgium ESP ndash Spain FIN ndash Finland FRA ndash France GBR ndash Great Britain ITA ndash Italy KOR ndash South
Korea SWE ndash Sweden SVN ndash Slovenia
Our basic definition of ldquozombie firmsrdquo follows McGowan et al (2017) for comparability We define a
firm as a zombie if it is at least 10 years old and its interest coverage ratio (the ratio of operating
income to interest expenses) has been below one for the last three years A limitation of this definition
is that interest expenses are not reported (or missing) for many smaller firms which only submit a
less detailed financial statement (or have no bank financing) To overcome this problem we also
categorize firms as zombies if their operating profit is negative for three subsequent years In such
cases the coverage ratio is not defined but the firmrsquos income is clearly not enough to cover its interest
expenses Note that this is a very conservative definition ndash one could also input interest expenses for
external capital for firms with missing interest expenditures (Figure 66)55
55 In actual fact 95 percent of zombies defined in this manner have negative profits
Reallocation
84
Figure 66 Share of ldquozombie firmsrdquo in Hungary
Notes Main sample
The patterns are the following First the share of ldquozombie firmsrdquo among firms with at least 5
employees was relatively high even before the crisis reaching about 8 percent by 2006 This increased
slightly in the wake of the crisis but started to decline after that falling to 3 percent in 2016 ldquoZombie
employmentrdquo fluctuated around 12-15 percent in most years with a steep decline after 2014
Put in an international context it is clear that the existence of ldquozombie firmsrdquo is a relatively big issue in
Hungary with their employment share at the highest end of the distribution of the OECD countries
examined by McGowan et al (2017) The prevalence of such firms however had been relatively high
even before the crisis with a relatively moderate growth between 2009 and 2011 followed by a
significant fall of the share of these firms Therefore ldquozombie firmsrdquo may have constrained productivity
growth in Hungary in the whole period but it is unlikely that an increase in zombie share is a key
explanation for productivity slowdown following the crisis
Table 64 shows the employment share of zombie firms in different dimensions One can see a U-
shaped relationship in terms of size with the largest zombie share among the smallest and the largest
firms The somewhat larger share of zombies among small firms may be explained by the tendency of
such firms to report losses in order to evade business taxes Large firms may be able to operate
persistently under losses either because of their accumulated savings or even more likely because of
the deep pockets of their owners This is also suggested by part B) which shows that a firm is more
Productivity differences in Hungary and mechanisms of TFP growth slowdown
85
likely to be a zombie if owned by the state56 or by foreigners In the latter case profit-shifting motives
may also play a role in reporting losses for sustained periods in Hungary Finally zombies are more
prevalent in services compared to manufacturing and in low-tech industries compared to high-tech
Table 64 Zombie employment by size ownership and industry
A) By size
2004 2007 2010 2013 2016
5-9 emp 62 751 862 802 411
10-19 emp 618 626 679 652 297
20-49 emp 607 554 698 648 296
50-99 emp 711 685 792 829 438
100- emp 2005 1839 1559 1489 456
Total 1548 1367 1229 1194 417
B) By ownership
2004 2007 2010 2013 2016
Domestic 66 671 769 614 253
Foreign 989 1011 1139 1577 585
State 6289 5954 4127 2792 7
Total 155 1369 1228 1194 417
C) By type of industry
2004 2007 2010 2013 2016
Low-tech mfg 1236 1255 1149 906 371
Medium-low tech mfg 897 515 846 974 634
Medium-high tech mfg 542 407 936 486 269
High-tech mfg 427 1392 429 268 1
KIS 2098 737 875 1408 592
LKIS 282 2327 187 1867 365
Construction 258 394 339 515 352
Utilities 297 1031 705 593 756
Total 1553 1372 1229 1194 418
Notes Main sample
Importantly all these patterns persist in multiple regressions when one includes all these variables at
the same time together with other controls (ie larger firms are more likely to be zombies even when
controlling for ownership) In such regressions (lag) productivity is the strongest predictor of not
56 Obviously the extreme employment share of state-owned zombie firms partly results from the massive size of some large utilities including the national railways and the Hungarian Post
Reallocation
86
becoming a zombie firm later one standard deviation higher productivity is associated with a 5
percentage point lower probability of becoming a zombie in the next period Note however that
productivity is actually a close measure of profitability hence this finding mostly reflects a mechanical
relationship of high profitability firms being less likely to become low profitability firms in the future
Figure 67 shows a 3-year transition matrix for zombie firms ie the share of year t zombie firms
which ldquorecoverrdquo remain zombies or exit from the market by year t+3 One cannot see radical changes
across years with somewhat more firms recovering and less exiting in later periods In line with the
argument about deeper pockets larger firms are more likely to remain zombies and less likely to exit
than smaller ones This is related to ownership foreign (and to a smaller extent state-owned) firms
are more likely to remain zombies There also seems to be a characteristic difference between
manufacturing and services manufacturing firms seem to be less likely to lsquorecoverrsquo and more likely to
exit suggesting more persistence of low performance in that sector
Figure 67 What happens to zombie firms within 3 years (2010)
Notes Main sample
64 Conclusions
In line with the immense within-industry productivity heterogeneity documented in Chapter 4 and 5
we find that while there was some reallocation across sectors in the economy the overwhelming
majority of productivity growth took place within industries This emphasizes the usefulness of policies
which promote productivity growth in a sector-neutral way rather than prioritizing some sectors of the
economy
In line with the lower efficiency of capital allocation post-crisis we have found that by and large both
within-firm productivity growth and reallocation across firms and industries became less efficient post-
crisis relative to its pre-crisis level This may reflect the presence of policies which promote specific
sectors or inhibit the growth and entry of more productive firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
87
In terms of the participation of global networks we have found that at least pre-crisis exporters and
foreign-owned firms contributed significantly to productivity growth Post-crisis the productivity
contribution of internationalized firms became much less substantial Adopting policies that create an
environment which is favourable for innovative investments and does not hamper the expansion of
globally oriented firms may contribute substantially to strengthening productivity growth
The presence of firms which are loss making for an extended period of time suggests a serious failure
of the reallocation process The share of such firms was relatively high in Hungary employing well
above 10 percent of the employees in our sample in most years This level was already high pre-crisis
and increased further during the crisis but there has been substantial improvement in recent years
The problem is more severe for larger firms and state owned firms Improving the corporate
governance of these firms and the effectiveness of the banking system may help in further alleviating
the problem
Andrews et al (2017) argue that the presence of zombie firms ndash and other barriers to firm dynamics ndash
is heavily related to the efficiency of insolvency regimes and the effectiveness of the banking system
Figure 68 shows an insolvency regime index developed by the OECD (the higher the index value the
slower the restructuring) Hungary is one of the countries with the weakest insolvency systems with
all sub-measures taking high values This coupled with the presence of weak banks can be one of the
reasons for the permanently high zombie firm share as well as the increasingly inefficient capital
allocation across firms Therefore insolvency reform complemented with policies aimed at improving
bank forbearance can help to reduce the presence of zombie firms The presence of zombie firms may
also be related to the large share of bank financing Promoting market-based financing including bond
and venture capital markets may also help to diminish the problem
Figure 68 Insolvency regimes across countries
Notes This chart is a reproduction of Andrews et al (2017)rsquos original except for being restricted to European
states only The stacked bars represent the 3 main components of a countrys insolvency index for the year 2016
while the diamond figure indicates these measures aggregate for the year 2010 The authors constructed these
figures with the help of an OECD questionnaire Each measure is associated with a factor that in the long term is
thought to reduce a countrys business dynamism and consequently hamper its proclivity for productivity growth
The first one Personal costs of insolvency stands for environmental factors which could curb a failed
entrepreneurs ability to start new businesses in the future The second measure Lack of prevention and
streamlining indicates whether there are sufficient practices in place for the early detection and resolution of
Reallocation
88
financial distress Thirdly Barriers to restructuring shows how easy it is for a firm suffering from short-term
financial troubles to restructure its debt Country codes should be interpreted as follows GBR ndash Great Britian FRA
ndash France DNK ndash Denmark DEU ndash Germany ESP ndash Spain FIN ndash Finland IRL ndash Ireland SVN ndash Slovenia PRT ndash
Portugal AUT ndash Austria GRC ndash Greece SVK ndash Slovakia ITA ndash Italy LVA ndash Latvia POL ndash Poland NOR ndash Norway
SWE ndash Sweden LTU ndash Lithuania BEL ndash Belgium CZE ndash Czech Republic MLD ndash Moldova HUN ndash Hungary EST ndash
Estonia
Productivity differences in Hungary and mechanisms of TFP growth slowdown
89
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS
The main aim of this section is to investigate the micro-level processes which underlie the patterns
documented at the industry level in the previous chapters (especially in Chapter 6) by presenting a few
descriptive relationships between firm characteristics and firm dynamics More specifically we would
like to understand how various firm characteristics are associated with the observed patterns of
productivity and employment growth to illustrate the micro-level processes behind within-firm
productivity growth and reallocation Additionally we look at which types of firms enter and exit in
order to shed light on how they contribute to changes in the average productivity level
We seek to answer three main questions First was there a structural break either in the productivity
growth or in the reallocation process after the crisis which may have contributed to the productivity
growth slowdown Second do we see a structural difference in these processes along the main
dimensions of the lsquodualityrsquo of the Hungarian economy eg the characteristic differences between
globally involved large firms and their domestic market oriented peers Third can we find peculiar
patterns which may explain the unusual evolution of productivity quintiles namely the slow
productivity growth of frontier firms relative to less productive firms (as documented in detail in
Section 44)
In terms of firm characteristics we focus on variables which are likely to be related to the duality (see
Section 44) ownership size age and exporter status We do firm-level regression analyses which
allows us to use a rich set of controls and fixed effects Additionally we look at the interaction of the
different characteristics to get an even more precise picture about the main factors driving productivity
growth and reallocation
The structure of this chapter follows closely the logic of the dynamic productivity decomposition
exercise in Chapter 6 In Section 71 we investigate the determinants of within-firm productivity
growth In Section 72 we explore how firm characteristics are related to future employment growth ndash
ie to between firm reallocation ndash followed by the analysis of entry and exit in Section 73
71 Productivity growth
Questions and descriptive patterns
A key relationship of interest is how future productivity growth is related to current productivity levels
Our main motivation to study this question is that it can shed light on the extent of convergence to
more productive firms within the industry If there is a tendency for low-productivity firms to catch up
the productivity growth of such firms will be higher We analyse this relationship for the whole
economy and will also split the sample along different dimensions We are particularly interested in
three questions First is there a difference between the productivity growth rates of firms along some
dimensions even when controlling for productivity We think that this question is highly relevant but
will also qualify the findings of for example Section 32 where we compared firms with different
ownership structures and of different sizes with each other unconditionally which may mask the
different composition of the two groups in terms of initial productivity levels Second we are interested
in whether the slope of the relationship between the initial productivity level and subsequent
productivity growth differ along observable dimensions Is it the case for example that domestically-
owned firms face a productivity ceiling beyond which they cannot improve their efficiency any further
while foreign firms are better able to push forward even starting from very high productivity levels
Third we would like to find out whether there are structural changes in this relationship which may be
associated with the productivity slowdown following the crisis
Firm-level Productivity growth and dynamics
90
While the main mechanism behind this relationship is likely to result from a process of convergence
between firms the measured relationship can also partly arise from a mechanical negative relationship
coming from regression to the mean A large positive measurement error in productivity in year t
automatically generates a large negative growth rate from t As we are interested in the convergence
process rather than the mechanics of the regression to the mean we look at the relationship between
lagged productivity levels and 3-year productivity growth We assume that regression to the mean
resulting from measurement errors is less likely to show up when the productivity level is lagged An
additional limitation of this exercise is survivorship bias because lower productivity firms are more
likely to exit if they are unable to improve their productivity level We will analyse exit and entry
separately in Section 73
First to see the overall patterns we present the relationship between initial productivity levels and
productivity growth in the following 3 years in a non-parametric way (see Figure 71) To do so we
classify firms within each industry into 20 quantiles based on productivity in the previous year For
example we show how productivity growth between 2012 and 2015 is related to productivity levels in
2011 For each quantile we calculate average growth after partialling out 2-digit industry fixed
effects We show this relationship for different years to see whether there is a structural change in the
within-firm productivity growth process57 We demean lagged productivity levels by 2-digit industry
and year so zero on the horizontal axis corresponds to the mean productivity level We take four
periods pre-crisis (2003-2006) crisis (2006-2009) post-crisis (2009-2012) and recent (2012-
2015)58
Figure 71 shows that the relationship between previous productivity levels (on the horizontal axis) and
subsequent 3-year growth (on the vertical axis) can be well approximated with a linear relationship
We see a pronounced negative relationship in all periods reflecting that (surviving) lower productivity
firms increase their productivity faster than more productive firms generating some within-firm
convergence in the sample of continuing firms The slope of the relationship ie the productivity
growth premium of less productive firms is quite stable across non-crisis years but differs markedly in
the crisis showing that the crisis-related productivity decline was more severe for more productive
firms probably because these firms had been hit the hardest by the collapse of global trade59 Note
that this is much in line with the slow productivity growth of frontier firms in the same period
documented in Section 43 Figure 44 In normal times macro conditions seem to shift the whole line
up or down rather than rotate it The average 3-year productivity growth rate is the lowest during the
crisis and is still low in the post-crisis period but there is no difference between the pre-crisis and the
recent periods60
57 As in the previous chapters we use our main sample (see Chapter 2) in which we only consider firms with at
least 5 employees and measure productivity with the method of Ackerberg Caves and Frazer (2015)
58 Note that to measure subsequent growth we need three years following the base year when the level of
productivity is measured Consequently the last year we include is 2012 ndash and follow what happens to firms
between 2012 and 2015
59 More exit of low-productivity firms during the crisis may have also introduced a survivorship bias but as the
patterns in Figure A71 of the Appendix show this seems not to be the case
60 Table A71 of the Appendix shows the same patterns from a regression
Productivity differences in Hungary and mechanisms of TFP growth slowdown
91
Figure 71 The relationship between lagged productivity levels and subsequent productivity growth
over time
Notes This figure shows how the log of productivity in t-1 (on the horizontal axis demeaned by 2-digit industry
and year) is related to productivity growth between t and t+3 Each dot represents one of 20 quantiles of the
productivity level distribution and the average 3-year growth rate of firms within that quantile including 2-digit
industry fixed effects
Estimation
After establishing a linear relationship between lagged productivity level and subsequent growth we
look at the role of firm characteristics in productivity growth We do it in two steps First we look at
cross-sectional patterns taking the most recent period (2012-2015) We ask if there is a difference
between firm groups in productivity growth for the average firm (ie a firm having industry-average
productivity) and if there is a difference in the convergence pattern These two aspects correspond to
differences in the level and the slope of the line We estimate the following regressions
1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905
119896
119870
119896=1
+ 1205732(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1) +sum1205733119896(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1)119866119894119905
119896
119870
119896=1
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
We denote productivity of firm i in year t with 119905119891119901119894119905 1198893_119905119891119901119894119905 stands for 3-year productivity growth
119905119891119901 119895(119894)119905minus1 is the year-specific average lagged productivity in industry j of firm i G is a firm characteristic
(eg ownership or size) which contains K categories (eg one ownership group foreign or three size
categories) 119883119894119905 is a set of additional firm-level controls (these can be size age ownership or exporter
status) 120572119895(119894) is industry or industry-region fixed effects and 휀119894119905 is the error term Then 1205731119896 measures the
productivity-growth difference for average-productivity firms in firm group 119866119896 (eg foreign) compared
to average-productivity firms in the baseline category (eg domestic) 1205733119896 measures the difference in
the convergence patterns between firm group 119866119896 and the baseline category
(71)
Firm-level Productivity growth and dynamics
92
Second we also check dynamic patterns to see how the role of these firm characteristics changed over
time taking the same periods as in Figure 71 The baseline regression for comparing productivity
dynamics across years is as follows
1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905
119897
119897=200320062009
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
As before 1198893_119905119891119901119894119905 is the 3-year productivity growth of firm i from year t to t+3 and 119905119891119901119894119905minus1 denotes the
productivity level of firm i in t-1 119905119891119901 119895(119894)119905minus1119897 denotes the year-specific average lagged productivity in
industry j which firm i belongs to 119863119905119897 is an indicator for year l 119883119894119905 is a set of firm-specific time-variant
controls and 120572119895(119894) is industry or industry-region fixed effects as in the previous specification 1205731119897
measures the difference between the productivity growth of firms with industry-average productivity in
year l and in year 2012 The difference comes from two sources industry-level average productivity
levels could change over time and productivity growth for firms with the same productivity level could
also vary As we are interested in how the productivity growth of the average firm changed over time
we will not separate these two effects 1205732119897 measures the slope of the relationship between lagged
productivity levels and subsequent productivity growth in year l Comparing the different 1205732119897 coefficients
shows how the process of convergence between low- and high-productivity firms changed over time
We take a similar approach when we compare group-specific productivity dynamics over time We
interact group indicators demeaned productivity levels and the interaction of the two from the static
regression with a full set of year dummies and include year dummies separately as well
1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905
119896119863119905119897
119870
119896=1119897=2003200620092012
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ sum sum1205733119896119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119866119894119905119896
119870
119896=1
119863119905119897
119897=2003200620092012
+ sum 1205734119897119863119905
119897
119897=200320062009
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
Comparing 1205731119896119897 coefficients for different l-s shows how the difference between average-productivity
firms in the baseline category and in group k changed over time Similarly comparing 1205733119896119897 coefficients
with different l-s shows how convergence differences between the baseline category and group k firms
evolved over time These specifications allow us to add industry-year fixed effects so we can also
control for industry-specific trends
Results
Figure 72 shows the non-parametric relationships by firm characteristics creating scatter plots which
show productivity quantiles separately by firm groups These figures hint at the fact that on average
foreign-owned and exporter firms experience higher productivity growth conditional on initial
productivity levels In addition the relationship between the initial productivity level and subsequent
growth is weaker for foreign-owned firms suggesting that even highly productive foreign firms are
able to raise their productivity further while similar domestic firms have a harder time doing so Size
groups and age groups are similar to each other though the smallest firms have stronger convergence
patterns than the largest
We can discover the same scenarios using regression analysis in which we can control for the
abovementioned firm characteristics and fixed effects (Table 71) The most important conclusion is
that average-productivity foreign-owned firms raise their productivity faster relative to similar
domestic firms by about 10 percentage points Average exporters also have a TFP growth advantage
(72)
(73)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
93
relative to non-exporters but this premium disappears when we control for ownership We find some
evidence for a positive interaction between productivity levels and foreign ownership in line with lower
constraints for further TFP growth in the case of foreign frontier firms The same pattern applies to
exporter firms
Figure 72 The relationship between lagged productivity levels and subsequent productivity growth by
firm group
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to log productivity growth
between t and t+3 Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-
year growth rate of firms within that quantile including 2-digit industry fixed effects
The similar results for exporters and foreign-owned firms ndash and the strong correlation between foreign
ownership and exporter status ndash raise the question does this difference arise from foreign ownership
or exporting or do both variables have an independent effect Table A73 in the Appendix examines
this question only to find that the foreign premium in average productivity growth unconditional on
the productivity level is there both for exporters and non-exporters and is higher for younger and
smaller firms When we look at how the relationship between lagged productivity level and subsequent
growth differs by both characteristics at the same time we find that both foreign ownership and
exporter status matter but for different aspects of the relationship The difference in the slope of the
relationship comes both from the foreign-owned and from exporters compared to low-productivity
firms of the same category high-productivity firms grow relatively faster if they are foreign-owned The
same is true for comparing exporters and non-exporters At the same time the average difference in
Firm-level Productivity growth and dynamics
94
productivity growth comes from foreign ownership firms with industry-average productivity levels
have a higher productivity growth if they are foreign There is no significant additional effect for foreign
exporters on top of adding up foreign and exporter premia either in average productivity growth or in
convergence61
The TFP growth advantage of foreign-owned firms even when compared to domestically-owned firms
with the same productivity level points at a mechanism that reinforces the already existing duality
when domestic firms reach frontier productivity levels their TFP growth slows down much more than
that of foreign firms This self-reinforcing mechanism may be behind the non-convergence between
foreign and domestic firms (Section 44) With regard to size and age we find that high-productivity
firms have a relatively greater chance to increase their productivity if they are larger or older
compared to their smaller and younger counterparts (see Table A72 in the Appendix)
Table 71 The relationship between lagged productivity levels and subsequent productivity growth by
ownership and exporter status
Dep var TFP growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190
(000556) (000568) (000622) (000629) (000656) (000663)
TFP in t-1 Foreign 00475 00418 00433 00423
(00140) (00142) (00252) (00253)
TFP in t-1 Exporter 00477 00287 00216 00224
(00104) (00106) (00125) (00126)
TFP in t-1 Foreign exporter -000697 -00151
(00312) (00314)
Foreign 0117 0109 0120 0104
(00121) (00132) (00223) (00226)
Exporter 00240 000260 000401 0000919
(000791) (000845) (000851) (000881)
Foreign exporter -000670 000871
(00267) (00272)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 29717 29717 30135 30062 29717 29717
R-squared 0060 0072 0056 0073 0060 0072
61 Looking at the same patterns over time (Table A74 in the Appendix) suggests that higher average productivity
growth is a rather stable feature of foreign firms The only exception was the crisis period when it disappeared
Splitting the sample by broad sectors shows that foreign firms have higher average productivity growth both in
manufacturing and services The difference in within-group convergence patterns stayed the same for the
foreign The same is true for exporters except for the pre-crisis period when the coefficient is not significant
Productivity differences in Hungary and mechanisms of TFP growth slowdown
95
72 Employment growth
Question and descriptive patterns
The relationship between initial productivity levels and subsequent employment growth shows the
reallocation of continuing firms Between-firm reallocation results from more productive firms growing
faster In this subsection we ask how between-firm reallocation changed over time and how
reallocation patterns vary by different firm characteristics
To measure reallocation we use a similar approach to that in the previous subsection but the
lsquodependentrsquo variable will be 3-year employment growth in log terms rather than productivity growth
The slope of the estimated relationship reflects the employment growth advantage of more productive
firms or the strength of ldquocreative destructionrdquo among surviving firms Shifts in the level show changes
in the average growth rate
We calculate the 3-year employment growth using the formula119871119905+3minus119871119905
(119871119905+3+119871119905)2 where 119871119905 is the number of
employees in year t This formula shows the percentage increase in employment from year t to t+3
compared to the average size in year t and t+3 This measure performs better for smaller firms than a
simple log difference in employment as it does not result in extremely high numbers with a low initial
employment level62 In all the regressions of this subsection we control for exact firm size using the
logarithm of the number of employees
Figure 73 Reallocation by year
Notes The figure shows how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
62 Additionally while the baseline estimates are only for continuing firms this measure allows us to include firms
exiting in the period (t+1t+3) as well in some robustness checks In these cases we take Lt+3 = 0
Firm-level Productivity growth and dynamics
96
Figure 73 illustrates the patterns in the data non-parametrically The relationship between previous-
year productivity levels and subsequent employment growth is positive in all years This shows that in
line with the creative destruction hypothesis more productive firms are more likely to grow in the
subsequent three years The figure doesnrsquot show characteristic changes in the reallocation process
across years the slope of the curves being similar to each other Our regression estimates presented
in the Appendix (Tables A75 and A76) support that reallocation patterns are stable over time63 The
average growth rate of typical firms naturally follows the macro cycle strongly ndash aggregate changes
seem to shift the line up or down but do not seem to rotate it In other words with this approach we
do not find evidence for a structural change in the reallocation process therefore it is unlikely that
such a change should explain satisfactorily the productivity slowdown
We create similar figures for the most recent period (2012-2015) by different firm characteristics
(Figure 74) The most important result is that exporters grow significantly faster than non-exporters
when controlling for their initial productivity This leads to reallocation from non-exporters to
exporters Given that the productivity advantage of exporters is in the order of 30-100 percent in the
different industries (see Section 43) this reallocation process can yield enormous productivity gains
The slope of the curve is also less steep for exporters suggesting that their expansion is less
dependent on their productivity level relative to domestic firms in other words reallocation within the
exporter group is weaker relative to non-exporters
63 As before the relationship between lagged productivity levels and subsequent employment growth can be
properly approximated by a linear function
Productivity differences in Hungary and mechanisms of TFP growth slowdown
97
Figure 74 Reallocation by firm groups
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
Firm-level Productivity growth and dynamics
98
Estimation results
Table 72 Reallocation by ownership and exporter status
Dep var employment growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 0105 0102 0105 0107 0107 0108
(000484) (000493) (000539) (000546) (000570) (000575)
TFP in t-1 Foreign
-00369 -00328 -00252 -00224
(00123) (00124) (00217) (00217)
TFP in t-1 Exporter
-00344 -00298 -00250 -00249
(000913) (000932) (00109) (00110)
TFP in t-1 Foreign exporter
-0000806 000194
(00271) (00272)
Foreign 000105 -000863 -000786 -0000106
(00112) (00116) (00194) (00196)
Exporter 00586 00635 00653 00672
(000738) (000754) (000777) (000786)
Foreign exporter
-000647 -00123
(00234) (00238)
Industry FE YES YES YES
Industry-region FE
YES YES YES
Firm-level controls
YES YES YES
Log of employees
YES YES YES YES YES YES
Observations 31662 31662 32124 32043 31662 31662
R-squared 0035 0049 0038 0051 0037 0049
Looking at the regression results (Table 72) confirms our previous findings even after controlling for
fixed effects Exporters with an average productivity level grow about 6 percentage points faster than
non-exporters hinting at strong positive reallocation between the two groups with slightly weaker
reallocation within the exporter group64 At the same time average-productivity foreign-owned firms
do not have higher employment growth than domestic ones Similarly to productivity growth we find
no extra premium for foreign exporters65 66 Overall these results emphasise that participation in
64 We define exporters based on their export activity in year t so the group of exporters also includes those firms
which export in t but not any more afterwards This means that a worse subsequent performance ndash lower
growth and exiting from exporting ndash has no effect on our exporter classification
65 The main patterns concerning employment growth of average-productivity firms are robust to modifying the
employment growth measure in such a way that it includes exits as a full employment decline (See Table A77
in the Appendix) In this version employment growth of foreign firms is significantly lower overall but this is
counterbalanced by the significantly positive coefficient of the foreign exporter indicator
66 We show in Table A78 of the Appendix that the higher average growth of exporters is present in all size (except
for the largest) age and ownership groups Dynamic patterns suggest (in Table A79 of the Appendix) that the
higher growth rate of average-productivity exporters is robust over time This result is also robust to splitting
the sample into manufacturing and services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
99
international markets is an important driver of industry and aggregate productivity growth in Hungary
by providing opportunities for exporters to expand as Section 62 has documented
As Table 73 shows competitive pressure also seems to affect more the growth prospects of smaller
firms the relationship between initial TFP levels and employment growth is significantly stronger for
smaller firms Between-firm reallocation appears to be much stronger for smaller firms while less
productive large firms are unlikely to contract even if they are inefficient conditional on survival
There are no clear patterns by age groups
Table 73 Reallocation by size and age group
Dep var employment growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 0112 0110 00875 00863
(000486) (000502) (00133) (00133)
TFP in t-1 Group 2 -00455 -00431 00445 00384
(00128) (00129) (00182) (00183)
TFP in t-1 Group 3 -00745 -00748 000733 000822
(00194) (00196) (00141) (00142)
TFP in t-1 Group 4 -00953 -00957
(00218) (00220)
Group 2 -000596 -000810 -00188 -00212
(00122) (00123) (00141) (00141)
Group 3 -000928 -00157 -00115 -00169
(00199) (00201) (00114) (00115)
Group 4 00328 00280
(00276) (00278)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Log of employees YES YES YES YES
Observations 32124 32043 32124 32043
R-squared 0038 0052 0037 0051
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees and size group 4 is 100+
employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5 years age group 3 is
firms older than 5 The baseline category is firms of 2-3 years
73 Entry and exit
Questions
This subsection aims at investigating which firms enter and exit and in particular how productive
those firms are relative to continuing firms This corresponds to the micro-level equivalent of the net
entry effect (see Chapter 6) The motivation for the micro-level investigation is that in this manner we
Firm-level Productivity growth and dynamics
100
can study which firm-level factors determine the type of firms that enter and exit and control for
industry heterogeneity
Our approach is similar to the previous section with the main difference being that this time the
dependent variable is productivity while the variables of interest are entry and exit dummies Their
coefficients show the productivity lsquopremiarsquo (often negative) of new entrants and exiting firms relative
to continuing firms These premia are especially useful to answer two kinds of questions First their
magnitude and size inform us about how entry and exit contribute to productivity growth Second
changes in these premia are also indicative of the changes in the costs of entry and the survival of
low-productivity firms
Estimation
To use a symmetric approach we define entrants and exiting firms using a 3-year interval An entrant
is a firm that has entered in the previous 3 years67 This means we look at the productivity of firms in
year t and compare it between incumbents (ie firms older than 4 years) and entrants (ie firms being
2-4 years old) In a similar way we compare the productivity in year t of firms exiting in the following
3 years (ie the last time the firm reports positive employment is in the period (t t+2) and non-
exiting firms (firms still reporting positive employment in year t+3)
As before we start with a static approach looking at the productivity premium of entrants and exiting
firms in the most recent period (taking year 2015 for 2012-2014 entrants and 2012 for firms exiting in
2013-2015) Then as a dynamic approach we take all four time periods as before and interact the
premia with year dummies The static regression we estimate is as follows
119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905
119896119873119864119894119905119870119896=1 + 1205732119866119894119905
0119864119894119905 + sum 1205733119896119866119894119905
119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)
119905119891119901119894119905 is the productivity of firm i in year t (measured in logarithm) 119866119894119905119896 is the k-th category (eg size
category 5 with more than 100 employees) in a grouping according to firm characteristics G (eg
size) and 1198661198941199050 is the baseline category (eg firms with 5-49 employees) 119864119894119905 stands for entrant or
exiting firm dummy in the different specifications and 119873119864119894119905 are incumbent or continuing firms
accordingly Then 1205732 measures the entry or exit premium for firms in the baseline category and 1205733119896
measures the same premium for firms in category k of grouping G Both premia are calculated
compared to incumbentscontinuing firms in the baseline category 1198661198941199050 1205731
119896 measures the productivity
advantage or disadvantage of incumbentcontinuing firms in category k of grouping G also compared
to the average productivity level in the baseline group As before 119883119894119905 includes additional firm-level
characteristics and 120572119895(119894) is industry or industry-region fixed effects In those versions where we include
industry fixed effects we identify from within-industry differences This means that 1205733119896 measures the
same entry or exit premium for firms in category k of grouping G compared to incumbentscontinuing
firms in the same category As before we create the dynamic version of the above regression by
interacting 119866119894119905119896119873119864119894119905 119866119894119905
0119864119894119905 and 119866119894119905119896119864119894119905 with year dummies and including year dummies separately as well
67 We consider firms changing industry from manufacturing to services or vice versa as exitors and new entrants at the same time (see Chapter 2)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
101
Results
Table 74 shows how the productivity premium of entrants and exiting firms changed over time In
these specifications we compare the yearly average productivity of incumbents and entering or exiting
firms separately in each year The point estimates suggest that entering firms were about 2-4 percent
more productive than incumbents except for the 5 percent productivity disadvantage in the pre-crisis
period while exiting firms were 10-20 percent less productive than the continuing firms The
productivity advantage of entrants and the disadvantage of exiting firms did not change radically
during our time period This difference constitutes a potential for positive net entry effects in terms of
reallocation The exact value of the net entry effect also depends on the share of employees affected
by entry and exit While the premia of entering and exiting firms remained roughly the same in the
different periods exit and entry rates changed (see Section 33) which results in positive net entry
effects before the crisis and negative effects after that (see Section 62)
Table 74 Productivity premium of entering and exiting firms over time
Dep var TFP in year t
Firm group Entry Exit
(1) (2) (3) (4) (5) (6)
EntrantExitorPeriod 2003-2006
-00532 -00535 -00465 -0214 -0199 -0200
(000906) (000873) (000879) (00102) (000987) (000989)
EntrantExitorPeriod 2006-2009
00269 00212 00232 -0135 -0117 -0119
(000967) (000931) (000936) (000897) (000865) (000865)
EntrantExitorPeriod 2009-2012
00369 00444 00360 -0133 -0114 -0121
(000960) (000926) (000931) (000920) (000889) (000889)
EntrantExitorPeriod
2012-2015
00374 00324 00252 -0171 -0150 -0157
(000971) (000936) (000944) (00107) (00103) (00103)
Period 2003-2006 -0115 -00927 -00711 -00445
(000519) (000501) (000556) (000537)
Period 2006-2009 -0175 -0169 -000972 00122
(000522) (000503) (000537) (000518)
Period 2009-2012 -0116 -0117 -00580 -00528
(000528) (000508) (000543) (000522)
Year FE YES YES YES YES
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES YES YES
Industry-year FE YES YES
Observations 166607 166168 166168 158143 157711 157711
R-squared 0327 0380 0380 0332 0385 0387
Firm-level Productivity growth and dynamics
102
Next we focus on the most recent period and look at the productivity differences of entrants and
exiting firms by different firm groups
Figure 75 Productivity premium for entering and exiting firms by ownership
Figure 75 presents the premia of domestic and foreign entering and exiting firms relative to domestic
incumbents As we saw in Section 44 foreign firms are on average more productive than domestic
ones68 foreign incumbents have on average a premium of 669 Compared to domestic incumbents
foreign entrants have 513 higher productivity There is also a positive productivity premium of 29
for exiting foreign firms Similarly the productivity of exiting exporters is 186 higher than that of
continuing non-exporters69 This means that domestic incumbent firms can survive longer even with a
lower level of productivity Consequently having many foreign entrants has a positive effect on
average productivity while on average foreign exits do not affect average productivity70 71
68 Table A711 shows that the productivity advantage of foreign-owned firms is present in all size and age groups as well as both within the exporter and non-exporter firm groups
69 Table A710 of the Appendix shows the estimation results with standard errors
70 As Table A712 of the Appendix shows foreign entrant premium and the premium of continuing or exiting
foreign and exporter firms seem fairly stable over time The positive premium of entering and exiting foreign
firms is also robust for splitting the sample into manufacturing and services
71 As Table A713 of the Appendix shows there is no considerable difference in the productivity disadvantage of
exiting firms by size or age group
Productivity differences in Hungary and mechanisms of TFP growth slowdown
103
74 Conclusions
One contribution of this chapter is that we have documented that one of the factors behind the
sustained duality in productivity between foreign and domestically-owned firms is that foreign-owned
firms tend to be more capable of upgrading their productivity even from already high productivity
levels Similar patterns apply to the more globally oriented exporters This mechanism underlines the
importance of policies that promote absorptive capacity-building (see Section 45) a strong knowledge
base easy access to external knowledge and flexible and advanced skills are especially important
when upgrading productivity beyond already high levels
We have also found strong reallocation from non-exporters to exporters Given the high productivity
premia of exporters in Hungary (Beacutekeacutes et al 2011) and in general (Wagner 2007) such a
reallocation can lead to substantial improvement in aggregate productivity (and as we have seen in
Section 62 it did to some extent before the crisis) These results emphasise that participation in
international markets is an important driver of industry and aggregate productivity growth in Hungary
because it provides valuable opportunities for exporters to expand Note that this reallocation effect of
international openness has been in the focus of the recent literature on international trade (Melitz
2003 Bernard et al 2006 Amiti and Konings 2007 Topalova and Khandelwal 2011 De Loecker
2011) Note also that the asymmetric expansion possibilities of exporters and domestic firms also
amplify the duality between the two groups
The analysis of entry and exit has revealed that entrants are somewhat more productive than
incumbents even a few years after entry Exiting firms are significantly less productive This on the
one hand implies that exit and entry is a substantial source of reallocation (as Section 62 has shown)
On the other hand the low productivity of exiting firms also suggests that domestic firms can survive
long even with relatively low productivity levels maybe because of inefficiencies in the capital
allocation process including the insolvency regime
Productivity evolution and reallocation in retail trade
104
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE
The previous chapters have presented a number of results on the productivity and growth in different
sectors of the economy The aim of this chapter is to look deeper into one of the key sectors of the
economy namely retail trade for more detailed insights
Two main reasons have motivated us to choose the retail sector First retail is a key sector of the
economy which provides jobs for a great many people and influences what consumers can buy and at
what prices Retail (and wholesale) does not only interact with consumers it is a key supplier of inputs
while beig a buyer of outputs for all other firms in the market economy72 The degree to which it is
capable of supplying a large variety of intermediate inputs at reasonable prices is an important
determinant of the productivity of firms relying on these sources Its market structure also affects
fundamentally the incentives that producers experience73
The second reason is that there have been a number of regulatory changes in the retail sector in
Hungary in recent years While these policies had multiple motivations one of their common features
is that they are size-dependent either explicitly or implicitly As such they have a potential to increase
the costs of larger firms and influence the reallocation process in favour of smaller mostly
domestically-owned firms This may matter as international evidence has shown that much of retail
productivity growth in recent decades has resulted from the expansion of large store chains (Foster et
al 2006) Exactly because of the strong links between retail and other industries regulatory
restrictions in retail represent nearly a third of all service-related restrictions which are carried over to
other sectors of the economy74
The structure of this chapter is the following Section 81 describes the policy context of Hungarian
retailing Section 82 introduces the available datasets Section 83 describes the major developments
in retail productivity Section 84 describes trends in reallocation The last three sections describe three
specific questions Section 85 analyses the role of retailers and wholesalers in importing and
exporting Section 86 provides a few illustrative statistics on how size-dependent taxes could have
affected reallocation and prices Finally Section 87 evaluates a specific policy namely the mandatory
Sunday closing of larger shops Section 88 concludes
81 Context
The retail industry is an important employer in all EU member states and Hungary is not an exception
Its employment share in our sample has been around 12 percent (Figure 81) Similarly to the EU as a
whole retail productivity is below the average of the market economy therefore its GDP share is
below its employment share Still it represented 6-7 percent of total value added in our sample
72 See EC (2018) for the importance of the retail industry in Europe
73 See Smith (2016) for a review of this literature
74 EC (2018) p 5
Productivity differences in Hungary and mechanisms of TFP growth slowdown
105
Figure 81 The share of retail and wholesale firms in market economy value added and employment
Notes Full sample with at least 1 employee in any of the years
The largest sub-industry within retail is groceries (NACE 4711) Its share of the total turnover around
40 percent is at the lower end of the EU distribution75 Given its importance (and the large sample size
within it) we will often study only groceries in our empirical analyses
Measuring the restrictiveness of different regulations in any sector of the economy is not an easy task
The European Commission has designed a ldquoRetail Restrictiveness Indicatorrdquo to quantify the potential
effect of these regulations in force at the end of 2017 (see Figure 82) The higher values of the
indicator indicate more restrictive regulations76 According to this indicator the restrictiveness of retail
regulation in Hungary is slightly below the EU average and similar to other CEE countries
The indicator distinguishes between regulations related to the establishment of shops on the one hand
and those related to their operation on the other In Hungary there are few operations restrictions
(mainly restrictions on distribution channels) while entry is regulated more heavily mostly by size-
related restrictions and requirements for economic data
75 EC (2018) p 4
76 There is ample empirical evidence that entry barriers planning regulations and operating restrictions are related to productivity and prices in retail Some examples are Bertrand and Kramarz (2002) Viviano (2008) Haskel and Sadun (2012) Sadun (2015) Daveri et al (2016)
Productivity evolution and reallocation in retail trade
106
Figure 82 Retail Restrictiveness Indicator
Notes This is a reproduction of Figure 8 from EC (2018)
While regulation in Hungary is not especially restrictive a number of new measures were introduced
following the crisis (see Box 81) While these have various motivations a common feature of most of
them is that they are size-dependent As such they may distort competition and constrain reallocation
to larger firms
One type of size-dependent policies is size-dependent taxes Crisis taxes introduced right after the
crisis (and phased out in 2013) were highly progressive in sales volume Local business taxes have
been similarly progressive in total sales at the firm-level since 2013 Other size-dependent policies are
restrictions on the establishment of shops or their operation The Plaza Stop law constrained the
establishment of malls larger than 300 m2 Another peculiar policy was requiring larger shops to close
on Sundays between March 2015 and April 2016
Quantifying the effect of such policies is not an easy task In some cases it is not possible with the
data at hand to identify the shops and firms which were affected by the different types of taxes For
example without knowing the exact location of the establishment it is not possible to identify which
firms operate in malls and hence could have been affected by the Plaza Stop law As we discuss in
Section 85 the highest bracket of the crisis tax only affected 6 firms and thus it is hard to run
statistical tests with an appropriate power In contrast some of the effects of the mandatory Sunday
closing policy can be very effectively estimated based on shop-level data
Therefore we will apply two complementary strategies The first is to investigate whether there are
trend breaks in the reallocation process following the crisis when many of the new policies were
Productivity differences in Hungary and mechanisms of TFP growth slowdown
107
introduced While we find suggestive changes around the crisis one cannot make casual statements
based on this strategy given the number of other changes in the economy The second strategy is to
examine specific policies where a credible differences-in-differences identification is possible
Unfortunately this strategy is basically limited to Sunday closing
BOX 81 Size-dependent taxes and regulations in the retail sector
This box describes a number of size-dependent taxes and regulations which could be linked to the retail data and investigated during this exercise The list is only indicative and will be appended by desk research and possibly interviews
2010-2013 crisis taxes
Crisis taxes were introduced in 2010 and were in force (mostly) until 2013 They affected the energy telecom and retail sector as their base was operating profits resulting from these activities The tax rate was strongly progressive for retail
Below 500m HUF 0
Between 500m and 30bn HUF 01
Between 30bn and 100bn HUF 04
Above 100bn HUF 25
Between March 15 2015 and April 23 2016 Sunday closing for larger and non-employee owned retail stores
The 2014 CII law which came into force on March 15 2015 banned shops with a retail space of more than 400 square meters to open on Sundays with some exceptions most notably the new tobacco shops Smaller shops could only open if their workers had at least a 20 stake in the
business or if they were close relatives of the owner The law was repealed in 2016
2013-today Progressive local business tax
The base of local business tax is the ldquoadjustedrdquo revenue of firms This usually means revenue minus material expenditures but regulation stipulating the exact method of calculation has changed a number of times since the introduction of this type of tax In 2013 a progressive
element was introduced by making the definition of the cost of purchased goods size-dependent In particular smaller firms can now deduct more of their expenditures than larger ones The deductible part is
Below 500m HUF of net sales 100
Between 500m and 20bn HUF 85
Between 20bn and 80bn HUF 75
Above 80bn HUF 70 of the cost of goods is eligible
Productivity evolution and reallocation in retail trade
108
82 Data
We rely on two main data sources in this chapter The first one is the NAV balance sheet data
described in detail in Chapter 2 Based on the industry code identifier we restrict the sample to firms
in industry 47 retail There are a few firms which switch to this category from other industries (mainly
wholesale of food manufacturing) We keep the whole history of these firms throughout the analysis
Second we use a retail-specific survey conducted by the Hungarian Central Statistical Office which
samples firms and collects data for all shops of the sampled firm77 Firms included in the sample are
compelled by law to submit monthly reports on their turnover and 4-digit industry-codes plus for all
of their stores information about these entitiesrsquo location (municipality) identification number 4-digit
77 httpswwwkshhudocshuninfo02osap2018kerdoivk181045pdf
BOX 81 Size-dependent taxes and regulations in the retail sector (cont)
2013-today Licensing of tobacco wholesale and retail
On 22 April 2013 in line with Act CXXXIV ldquoon reducing smoking prevalence among young people
and the retail of tobacco productsrdquo (adopted by the Hungarian Parliament on 11 September
2012) the National Tobacco Trading Non-profit Company (a 100 government-owned joint-stock
company controlled by the relevant minister under the mandate of this law) was established
From then on only special ldquonational tobacco shopsrdquo licensed by the state have been allowed to
sell tobacco products These shops enjoy a number of benefits compared to other shops
Exempted from the Sunday closing for retail shops
National tobacco shops are exempted from the ban on selling alcohol after 10pm rarr in effect
tobacco shops do not come under the ruling of the commercial law Local municipalities can
otherwise regulate shops based on that law
2011- today ldquoPlaza Stoprdquo Law
The so-called Plaza Stop Law (the 2011 CLXVI Law) came into force in January 2012 It
prohibits the construction of new retail facilities or the expansion of any already existing one with
a leasable area of more than 300 msup2 Exemptions could be granted to certain developments by a
committee of ministry officials and with the approval of the Minister of National Economy
In 2013 the law was extended to include building conversions In February 2015 a new
amendment was ratified which basically renewed the effect of the 2011 law and introduced some
modifications to it Now retail facilities with a floor space of less than 400m2 can be built without
any special procedure Furthermore the right to grant exemptions was given to a special
administrative department which is supplemented by a committee made up of delegated
members of different ministries
Productivity differences in Hungary and mechanisms of TFP growth slowdown
109
industry-code sales and the monthly number of days spent open The sample consists of all larger
retail firms78 and a representative sample of other firms re-sampled on an annual basis
An important consequence of this design is that we observe each of the shops of the sampled firms
This is valuable in two respects First with this information it is possible to calculate the number of
shops and average shop size at the firm-level Second one can identify new and exiting shops for
firms which are in the sample continuously ie larger firms Further with the help of the firmsrsquo
identification number we are also able to link this information to data from the NAV database for
qualified analysis
Two caveats may be mentioned here First the re-sampling of the representative part of the sample
prevents us from following small firms through the entire sampling frame Second in the beginning of
2012 there was a switchover in the coding of shop-level identification numbers which prevents us
from linking shops before and after
As mentioned above the database also includes information on the industry classification of the shop
In most of our exercises we restrict the sample to grocery stores more formally bdquoRetail sale in non-
specialised stores with food beverages or tobacco predominatingrdquo (NACE 4711) Table 81 shows the
sample size of the merged database for groceries We have classified firms according to the number of
shops they have and report their number and their storesrsquo number according to these categories
Table 81 The number of firms and the number of shops in different size categories in Groceries
1 shop 2-4 shops 5-9 shops 10-49 shops gt50 shops year firm shop firm shop firm shop firm shop firm shop
2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446
2006 573 573 51 131 59 430 111 2008 30 1548
2007 546 546 53 125 60 429 110 1987 33 1634
2008 628 628 45 102 50 350 104 1823 21 1574
2009 527 527 33 72 41 290 99 1879 24 1754
2010 472 472 22 49 32 238 94 1793 22 1968
2011 537 537 14 30 29 212 92 1758 22 2027
2012 374 374 30 68 49 335 88 1643 23 2107
2013 503 503 48 121 42 277 88 1622 25 2094
2014 410 410 106 239 48 320 81 1530 24 2054
2015 512 512 135 311 42 292 80 1544 30 2090
2016 518 518 120 292 37 271 77 1457 23 2022
A key distinction in this merged database is the one between shops and firms Sales employment
ownership is observable only at the firm-year level so these variables are the same for each of the
shops of a firm for a calendar year Shops are only observable for sampled firms but we observe
sales and the number of days they were open at a monthly regularity As a result even if one runs
regressions at the shop-month-level productivity and employment can only vary at the firm-year
level For this reason we always cluster the standard errors at the firm or firm-year level
78 Larger firms are defined as having more than 7 stores in operation or with a number of employees of more than 50 and at least 6 stores or with a significantly large store in a product category
Productivity evolution and reallocation in retail trade
110
While balance sheet data includes information on exports it does not inform us about imports In
Section 84 we use detailed trade data to analyse importing by wholesale and retail firms This is
reported at the importer firm-product (8 digit Harmonized System)-country of origin level Most
importantly we can link this information to the balance sheet of the firm This is collected by a survey
following the European Unionrsquos practice79 We aggregate these data to the firm-year level but
distinguish between consumer goods capital goods and intermediate inputs used in further production
by relying on the correspondence table of the Eurostat between the Harmonized System and Broad
Economic Category classifications
83 General trends
Let us start with describing the firm size distribution across years (see Table 82) for firms with at least
one employee Similarly to other EU countries the majority of firms in retail are very small in
different years between 70-75 percent of retail firms employed less than 5 people80 The share of firms
with more than 50 employees fluctuated at around 1 percent
As one would expect larger firms have a significantly larger weight in terms of employment and sales
The top 05 percent of firms employed more than 30 percent of all employees in each year The
employment-share of these top firms increased nearly monotonically from 33 percent in 2004 to more
than 38 percent in 2011 when it reached its peak This was followed by a slightly declining trend to
357 percent in 2016 This time path represents the gradual expansion of large chains both organically
and via the acquisition of stores81 up to the crisis when this trend seems to have ended
The market share of large firms is even larger reaching 45 percent in 2016 The difference between
the employment share and sales share shows that large retail firms are substantially more efficient ndash
at least in terms of sales over employees ndash than the average firm At the other extreme the smallest
retail firms generate only 126 percent of sales with 20 percent of employees suggesting that in 2016
each of their employees sold only about half of the average The patterns are similar in other years
Efficiency differences are large in this sector though not larger than in most other sectors of the
economy (see Chapter 4)
79 See httpeceuropaeueurostatstatistics-explainedindexphpInternational_trade_statistics_-_background An important limitation of these data is that firms only report transactions above a specific size This may bias estimates of firm-level importing downward for small firms
80 As we discussed in Section 42 the NAV sample includes only double-entry bookeeping firms while the unemployed and people working in firms with simplified accounting are omitted from these data These people are likely to work in small economic units with low productivity levels
81 The increasing share of large retailers is a general trend globally see Ellickson (2016)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
111
Table 82 Share of firms in different size categories (at least 1 employee)
A) Number of firms
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100
2006 7333 1613 687 252 065 049 100
2007 7369 1597 674 244 067 050 100
2008 7410 1571 667 236 067 048 100
2009 7522 1535 614 221 059 048 100
2010 7551 1508 640 201 057 044 100
2011 7591 1493 623 194 057 041 100
2012 7625 1506 568 204 055 042 100
2013 7526 1596 577 207 052 042 100
2014 7380 1684 628 210 056 042 100
2015 7239 1771 661 232 056 041 100
2016 7163 1804 676 252 063 043 100
B) Employment
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000
2006 2034 1475 1269 1025 679 3525 10000
2007 2026 1443 1241 984 669 3644 10000
2008 2020 1391 1138 910 579 3980 10000
2009 2002 1427 1244 870 570 3789 10000
2010 2099 1443 1243 862 577 3731 10000
2011 2144 1458 1119 895 555 3830 10000
2012 2144 1541 1131 906 541 3713 10000
2013 2168 1591 1204 896 555 3650 10000
2014 2104 1644 1236 970 548 3538 10000
2015 2064 1620 1216 1006 588 3570 10000
2016 1999 1620 1216 1006 588 3570 10000
C) Sales
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000
2006 1114 1278 1587 1025 776 4226 10000
2007 1110 1266 1543 956 855 4268 10000
2008 1112 1524 1045 891 531 4906 10000
2009 1104 1641 1073 856 718 4607 10000
2010 1104 1521 1103 930 693 4594 10000
2011 1160 1577 1004 934 624 4714 10000
2012 1146 1647 1003 913 629 4578 10000
2013 1229 1411 1090 945 709 4360 10000
2014 1486 1472 1093 1001 752 4389 10000
2015 1293 1458 1118 985 660 4512 10000
2016 1266 1458 1118 985 660 4512 10000
Productivity evolution and reallocation in retail trade
112
Figure 83 presents C5 concentration measures82 for the full retail sector and for some of its
subsectors83 The share of the top 5 retail firms was around 30 percent of total retail sales
Concentration was increasing pre-crisis from 30 percent in 2003 to 35 percent in 2009 Concentration
decreased and returned to its 2003 value by 2014 The latter trend as we will discuss in Section 85
may be associated with size-dependent policies
The various sub-industries exhibit different patterns in terms of concentration Let us start with
groceries Pre-crisis the dynamics in this subsector was driven mainly by the expansion of large
chains Consequently concentration was strongly increasing with C5 growing from 50 percent in 2003
to more than 60 percent by 2008 Concentration in this subsector was rising further post-crisis but at
a somewhat slower pace We observe a similar pattern of increasing concentration with a trend break
around the crisis in sales of books and clothes in specialized stores In these sub-industries
establishment regulations like the Plaza Stop law could have played a more important role in the trend
break than taxes Specialized cosmetics retailing was already very highly concentrated at the
beginning of the period and remained largely unchanged
Figure 83 Concentration in retail and various sub-industries
This observation motivates a more detailed look at different measures of efficiency and prices Panel A)
of Table 83 calculates the average TFP levels84 both for different size categories and for the
aggregate Note that TFP calculated from balance sheet data is revenue productivity measuring the
82 Calculated as the sales share of the 5 firms with the largest sales
83 We rely on a slighly different version of the NAV data for this exercise which includes 4-digit identifiers but only runs until 2014
84 See Section 22 on details of TFP estimation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
113
amount of revenue produced by an input bundle Consequently it does not only measure physical
productivity (units sold per unit of input) but also markups This distinction is especially important in
retail85
Let us start with the two aggregate series one unweighted and the other weighted by employment
The employment-weighted series has higher values because more productive firms tend to be larger
(see Section 51) The two series follow a parallel trend suggesting that the correlation between size
and productivity did not change radically TFP has increased by about 15 percent from 2004 to 2006
remained constant until 2008 fallen by around 10 percent in 2009 and then started to grow by 4-5
percent each year from 2011
Note that this productivity evolution is similar to what is reported by OECD STAN before and during the
crisis but the post-crisis recovery in our data is much more pronounced (Figure 84) As we have
discussed in detail in Section 42 this is most likely a result of the large number of self-employed and
the distinct productivity level and evolution of that group86 Productivity has been definitely increasing
since 2011 in our sample
Interestingly TFP is not increasing monotonically with firm size There is a clear 25-30 percentage
point difference between the smallest firms (1-4 employees) and firms in other size categories which
have a similar TFP to each other Besides differences in efficiency this may also be partly explained by
the tax avoiding behaviour of the smallest firms ie under-reporting sales or over-reporting costs
Panel B) of Table 83 investigates gross margins These are calculated as
119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
which is the margin that retailer 119894 realises in year 119905 on the cost it pays for the sold goods in
percentage A value of 20 shows that the price the consumer pays is 20 percent higher than what the
retailer paid for the goods87 Note that gross margins reflect a combination of two factors `physical
productivityrsquo (how much capital and labour is needed for a given amount of sales) and markups Still
gross margins are of interest because they are the closest proxy available in financial statements of
prices paid by customers
We can make two key observations First on average (weighted) margins increased from about 155
percent to 19 percent during the period under study with a fall during the crisis Second margins
were about 5 percentage points higher in the smallest retail firms compared to larger ones
Interestingly during and immediately after the crisis (between 2009 and 2012) the margins of the
largest firms were substantially lower than those of other firms This is because the margins of the
largest firms actually fell during this period while that of smaller firms remained roughly constant
85 Measurement of productivity in retail raises a number of conceptual and measurement issues (Ratchford 2016) Two main problems are the measurement of output (conceptually retail services) and of the inputs used (for example shop area) In practice however such detailed data are not available and it is standard to use TFP
86 The figures for retail are similar to those for the whole of the service industry About a third of all people engaged are self-employed operating at a significantly lower productivity level than retail firms The productivity of the self-employed did not grow between 2012 and 2015
87 We winsorise it at the 5th and 95th percentiles Note that the cost of goods sold would be preferable to material costs but that is often missing from the data especially for small firms
Productivity evolution and reallocation in retail trade
114
Most likely larger firms were able to cut markups while smaller firms with already lower markups
were not able to do so
As we have mentioned above margins reflect a combination of cost factors and market power The
gross operating rate88 attempts to control for labour costs and shows margins after personal cost
119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
with value added calculated as discussed in Chapter 2 In international comparison gross operating
rates are relatively low in Hungary89 These rates show a clear downward trend with time Gross
operating margins are clearly decreasing with firm size showing that larger firms operate with large
scale and low margins Similarly to the gross margin we see a fall during the crisis
Figure 84 Productivity evolution in the NAV sample and the OECD STAN
88 httpeceuropaeueurostatstatistics-explainedindexphpGlossaryGross_operating_rate_-_SBS
89 As shown by EC (2018) Figure 2 Our weighted estimates are similar to what is reported there based on Eurostat data
Productivity differences in Hungary and mechanisms of TFP growth slowdown
115
Table 83 Performance and margins (at least 1 employee)
A) TFP
B) Gross Margin
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 1769 1548 1793 1883 1584 1495 1735 1554
2005 1839 1571 1858 1878 1504 1666 1794 1584
2006 2040 1658 1842 1879 1599 1660 1956 1653
2007 2220 1720 1875 1940 1664 1702 2104 1712
2008 2192 1792 1872 1992 1522 1739 2096 1728
2009 2084 1719 1864 1967 1599 1527 2006 1630
2010 2096 1749 1867 2102 1664 1535 2024 1682
2011 2172 1765 1862 2003 1759 1558 2084 1728
2012 2203 1740 1828 1913 1969 1541 2102 1733
2013 2243 1734 1806 2018 1999 1794 2128 1790
2014 2363 1789 1817 2149 2022 1869 2223 1855
2015 2390 1803 1885 2165 2215 1987 2245 1928
2016 2379 1799 1911 2172 2403 2170 2237 1948
C) Gross operating rate
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
unweighted weighted
2004 651 701 663 677 479 480 658 611
2005 827 757 777 712 526 560 806 644
2006 908 773 777 753 579 797 870 706
2007 807 635 653 704 500 580 764 606
2008 665 588 577 610 422 478 643 525
2009 618 535 538 522 351 339 595 456
2010 669 587 629 626 421 341 650 510
2011 698 617 591 637 410 393 676 542
2012 770 643 599 624 485 404 735 577
2013 735 594 555 631 478 369 697 530
2014 745 578 568 612 446 398 700 531
2015 766 609 625 646 547 452 724 579
2016 759 597 621 632 609 487 715 588
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 599 633 637 628 612 623 610 626
2005 610 635 643 628 616 626 619 631
2006 623 646 651 640 630 642 631 643
2007 624 643 650 641 627 643 631 642
2008 625 645 647 640 630 640 631 642
2009 619 641 642 631 619 630 626 630
2010 617 641 643 634 622 631 625 631
2011 621 643 645 637 629 639 628 639
2012 624 646 644 637 629 639 631 644
2013 626 647 642 640 637 642 632 645
2014 628 648 650 645 640 648 634 648
2015 638 658 658 655 652 659 644 659
2016 643 661 665 659 655 661 649 665
Productivity evolution and reallocation in retail trade
116
As Section 44 has shown for the market economy in general the Hungarian economy can be
characterised by a strong duality between foreign and domestically-owned firms Retail is one of the
sectors where this is the most transparent with many small domestic firms operating alongside large
multinational super- and hypermarket chains90 Figure 83 shows the share of foreign-owned firms in
terms of number employment and market share Foreign-owned retail firms are substantially larger
than domestic ones between 5-7 percent of firms are foreign-owned but they employ around 30
percent of employees and realise around 40 percent of sales This also implies that the salesworker
share is also larger in foreign firms than in domestic ones This results from the larger typical size of
foreign firms when controlling for size salesworker is not higher for foreign firms The market share
of foreign-owned firms is at the top of the distribution in EU countries with a larger foreign share only
in Latvia and Poland91
Figure 85 shows an inverted U-shaped pattern with an increasing market share of foreign firms until
2009 followed by a fall of nearly 5 percentage points between 2013 and 2016 This fall in foreign share
ran parallel with the introduction of policies favouring smaller firms in various ways
Figure 85 Share of foreign firms with at least 1 employee
There is much variation behind the overall pattern as Figure 86 illustrates plotting the market share
of foreign firms across sub-industries In groceries foreign share fluctuated around 70 percent It was
90 There is limited literature on the spillover effects generated by multinational retailers See for example Atkin et al (2018)
91 See EC (2018) Figure 2
Productivity differences in Hungary and mechanisms of TFP growth slowdown
117
rising slightly pre-crisis in parallel with the increasing concentration of the industry The increase of
the market share of foreign firms was the strongest in clothes reflecting the expansion of different
multinational chains mainly in plazas The increasing trend observable for the category seems to have
broken around 2012 which coincides with the introduction of the Plaza Stop regulation Foreign
market share was always high in the highly concentrated cosmetics sector A few foreign chains were
dominant in this sector throughout the period Foreign share actually decreased sharply in books and
newspapers
Figure 86 Foreign share in sub-industries
A key question when evaluating the expansion of foreign firms is their performance Foreign retail
firms are substantially more productive than domestic ones (Figure 87) With the exception of the
crisis years labour productivity advantage was between 60-80 percent while the TFP advantage was
between 20-40 percent The TFP advantage is smaller because of the larger capital intensity of foreign
firms These productivity premia are not purely a consequence of the larger size of foreign firms this
pattern is robust to controlling for firm size There is no clear trend in the premia they were declining
before the crisis (suggesting that domestically-owned firms were catching up) and rising after it The
figure also shows a large decline in the premia in the crisis years This is likely to be a consequence of
more pro-cyclical margins of foreign firms which are captured by revenue productivity measures
Productivity evolution and reallocation in retail trade
118
Figure 87 Productivity premia of foreign firms labour weighted
The main message of this section is that similarly to other industries large productivity differences
persist in retail These differences are primarily associated with size larger firms are more productive
and charge lower margins The performance of very small shops and the self-employed looks
especially weak The pre-crisis period was characterised by an expansion of large and foreign firms
but this growth stopped after 2010
84 Allocative efficiency and reallocation
In this section we follow the approach of Chapters 5 and 6 in analysing allocative efficiency and
reallocation with a focus on the retail industry
Chapter 5 showed that an important metric of allocative efficiency at any point in time is the degree of
co-variance of productivity and size which is directly related to aggregate productivity Figure 88
shows the elasticity of the number of employees with respect to labour productivity and TFP A more
positive relationship represents a more efficient allocation of labour across firms92 The figure shows
these relationships both for the full sample (of firms with at least 1 employee) and the main sample
(firms with at least 5 employees)
The elasticity depends crucially both on the sample and the productivity measure We find that the
correlations are much stronger when the full sample is considered rather than the base sample This
reflects our findings in Table 83 namely that the smallest firms differ substantially from other firms
92 These are coefficients from separate yearly univariate regressions with ln number of employees on the left hand side and productivity as the explanatory variable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
119
while firms with at least 5 employees are quite similar to each other The labour productivity premium
of larger firms is greater than their TFP premium reflecting their higher capital intensity
The key insight from Table 83 is that most of the Olley-Pakes correlation or measured allocative
efficiency results from the fact that very small firms are of very low productivity Within the group of
firms with at least 5 employees the correlation between TFP and size is practically zero There is a
positive although small correlation within the group between employment and labour productivity93
There is also no key trend in this measure of allocative efficiency some measures show improvement
while others a deterioration94
Figure 88 The elasticity of employment with respect to productivity main sample
Figure 89 performs the dynamic (Foster-type) productivity decomposition for the retail industry The
picture is not very different from the patterns found for services in general (see Figure 64) Pre-crisis
parallel with the strong growth of large chains growth was mainly driven by reallocation primarily in
the form of firm entry The crisis was accompanied by an annual 5 percent fall in productivity driven
by within-firm productivity decline As we have seen in Table 83 this was most likely the results of
margin-cutting by large firms Between 2010-2013 within-firm productivity growth and net entry
contributed similarly to the (relatively low) productivity growth Productivity growth sped up between
2013-2016 mainly driven by the within-firm component with little reallocation The trend break in the
growth of large chains is clearly reflected in this decomposition
93 As we have discussed in Chaper 5 this is not exceptional ndash actually similar correlations are found in services in other European contries
94 These low levels of allocative efficiency are in line with international evidence In fact these correlations have been negative in the majority of EU member states (EC 2018 p 7)
Productivity evolution and reallocation in retail trade
120
Figure 89 Dynamic decomposition of productivity growth in retail
While these results are informative about reallocation at the firm-level the shop-level data enable us
to investigate reallocation at a more detailed level These data enable us to investigate whether key
firm or shop-level variables are related to opening new shops closing shops or the growth of the shops
of continuing firms We investigate these questions in the paragraphs that follow
The simplest way to explore the shop-extensive margin or the change in the number of shops is to
aggregate the shop-level data to the firm-level In particular we calculate the change in the number of
shops the number of new shops and the number of old shops for each firm 119894 and year 119905 Denoting
these variables which show changes between year 119905 and 119905 + 1 by 119910119894119905 we run the following firm-level
regressions
119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905
where 119883119894119905 is a vector of firm-level variables These proxy productivity (by ln labour productivity) and
size (by the number of shops of the firm and the average sales per shop) 120575119905 is a full set of year
dummies
When estimating these equations one has to make a number of compromises Most importantly one
can only observe the change in shop numbers when the firm is present in the sample both in year 119905
and 119905 + 1 Otherwise one cannot be sure whether all the shops were closed or simply not sampled in
119905 + 1 Unfortunately this is a serious restriction for two reasons First one cannot observe the exit or
Productivity differences in Hungary and mechanisms of TFP growth slowdown
121
entry only survival for single-shop firms95 Second we also miss when a multi-shop firm exits with all
its shops
One also has to make a number of further methodological choices We restrict our sample to groceries
which is a relatively homogeneous group with many observations Another choice is that even though
we observe shops on a monthly basis we consider only year-to-year changes between May and the
following May Running the regressions on the monthly data would inflate artificially the number of
observations and introduce important methodological problems including seasonality
Table 84 presents the results In column (1) the dependent variable is the (net) change in the
number of shops The results suggest that productivity is of limited importance as a determinant of
change in shop numbers but size matters Firms with more and larger shops were more likely to
expand in terms of opening new shops Foreign firms expand faster because they are larger
conditional on size ownership does not matter Size is correlated both with shop opening and closing
firms with a larger average shop size are more likely to open new shops while chains with more shops
are less likely to close existing ones
Table 84 Determinants of the change in the number of shops at the firm-level groceries
(1) (2) (3)
Dependent Change in
number of
shops
New
shops
Closed
shops
Labour productivity 0001 0025 -0011
(0024) (0014) (0018)
Foreign-owned -0087 -0042 0019
(0064) (0034) (0045)
ln( (average
salesshop)
0056 0034 -0017
(0016) (0010) (0014)
5-9 shops 0176 0001 -0126
(0070) (0036) (0054)
10-49 shops 0231 -0009 -0167
(0067) (0034) (0052)
more than 50 shops 0194 0002 -0109
(0071) (0038) (0055)
Year FE yes yes yes
Observations 815 815 815
R-squared 0105 0093 0084
Notes One observation is a firm-year Standard errors are clustered at the firm-level
One may get a more detailed picture by investigating at the shop-level Here we can straightforwardly
estimate both the exit part of the extensive margin (did a specific shop close) and the intensive
margin (did the shop extend its sales)
95 For this reason we drop single-shop firms altogether from the analysis
Productivity evolution and reallocation in retail trade
122
In particular we run regressions of the following form
119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905
where 119894 denotes firms 119895 shops and 119905 years The outcome variable 119910119894119895119905 is either a dummy showing
that the shop closed96 between 119905 and 119905 + 1 or represents the growth of (log) sales of the shop 119883119894119905 are
firm-level variables such as productivity while 119885119894119895119905 are shop-level variables such as shop-level sales
The same restrictions apply as in the previous case
Table 85 reports basic regressions We run both the exit and sales growth regressions for three
subperiods 2004-2007 2008-2010 and 2012-2015 Our main question is whether one can identify
any changes in the relocation process across these subperiods
Let us start with the exit regressions Similarly to the firm-level results we find that productivity and
ownership are not associated with the probability of exit Shop size is significantly related to closing a
shop twice as large sales are associated with 5 percentage points lower probability of the event
occurring This relationship became stronger by the third period The number of shops of the firm is
also negatively associated with the probability of closing the shops and this effect only became
significant post-crisis In addition the explanatory power of the regression is also higher by nearly 50
percent in this last period compared to the earlier ones To sum up we find that the size of the shop
and the turned out to be more important post-crisis making such shops less likely to close
In contrast to the exit equation we do not find significant effects in the growth regressions Neither
size nor productivity seem to be related to growth at the shop-level
To sum up the level of allocative efficiency in retail is relatively low ndash similarly to other European
countries ndash and one cannot see a significant change in this respect Pre-crisis when large chains
expanded rapidly reallocation played a significant role in aggregate productivity growth while within-
firm growth became dominant after the crisis Shop-level data suggest that the expansion in terms of
number of shops is mainly determined by firm size rather than productivity and ownership Sales
growth of existing shops does not seem to be related to size ownership or productivity The lack of
evidence for a relationship between opening new shops or the growth of existing shops is much in line
with the low measured allocative efficiency in the industry
96 We run linear probability models for shop exits Probit models yield similar results
Productivity differences in Hungary and mechanisms of TFP growth slowdown
123
Table 85 Probability of closing a shop and growth regression NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent Closing the shop Growth
Period 2004-
2007
2008-
2010
2012-
2015
2004-
2007
2008-
2010
2012-
2015
labour productivity 0005 -0021 0102 0034 -0012 0097
(0009) (0015) (0087) (0018) (0022) (0063)
foreign-owned -0005 0063 0056 0016 -0014 -0035
(0023) (0045) (0057) (0023) (0055) (0066)
ln sales -0026 -0029 -0057 -0018 -0010 0010
(0006) (0007) (0021) (0007) (0017) (0005)
5-9 shops -0014 -0066 -0111 0061 -0035 -0055
(0026) (0051) (0043) (0046) (0044) (0026)
10-49 shops -0045 -0114 -0149 0046 -0038 -0023
(0024) (0048) (0039) (0044) (0043) (0017)
more than 50
shops
0001 -0077 -0134 0061 -0027 -0016
(0029) (0048) (0040) (0045) (0044) (0022)
Observations 15374 10946 15038 14025 10120 13458
R-squared 0030 0053 0073 0023 0041 0121
Notes OLS regressions run at the shop-year level only for firms present both in t and t+1 In columns (1)-(3) the
dependent variable is a dummy indicating whether the shop closes between t and t+1 while in columns (4)-(6) it is
the growth rate of sales between t and t+1 The explanatory variables are measured at year t The number of
shops variables are dummies representing the number of shops of the firm County and year fixed effects are
included Period 1 2004-2007 Period 2 2008-2010 period 3 2012-2015 Standard errors are clustered at the
firm-level
85 Trade
In small open economies a very important function of the wholesale and retail sector is the
intermediation of international trade for consumers and firms The operation and efficiency of these
industries can have a strong impact on aggregate welfare and productivity by determining both the
cost and variety of imported goods available as well as the cost of exporting products (Raff and
Schmitt 2016)
Many interesting questions emerge in this framework One of the key issues is the problem of double
marginalisation In the case of consumers (and consumer goods) one dimension of this question is
whether retailers import products directly or via wholesalers If retailers find it very hard to import
directly (because of say large fixed costs) double marginalisation can raise prices for consumers
Through this channel lower trade cost of retailers can benefit consumers As a result the share of
consumer goods imported directly by retailers may be an important proxy for the lower prevalence of
double marginalisation
In the case of intermediate inputs manufacturing firms face the choice of importing the product
directly (and paying the fixed costs of doing so) or relying on an intermediary Again reduced fixed
cost may make imported goods cheaper contributing positively to productivity growth Access to
imported intermediate inputs has been shown to be strongly correlated with the productivity of
Hungarian manufacturing firms (Halpern et al 2015)
Productivity evolution and reallocation in retail trade
124
The question of duality is also highly relevant in this context Multinational retailers can easily rely on
producers abroad hence their expansion can have important effects on Hungarian producers
Domestic chains on the other hand may find it hard to import a large variety of foreign products
which may result in a reduced choice set for consumers
Ultimately it is the questions above that motivate our investigation of importing and exporting by
wholesalers and retailers Our data are exceptionally suitable for this exercise Given that firm balance
sheets can be linked to detailed export and import data one can quantify the amount of products
imported and exported from different product categories by wholesalers and retailers
An important methodological note is that we only observe direct imports in the trade data The most
important consequence of this limitation is that while in actual fact the share of imported goods on a
retailerrsquos shelf is a combination of goods imported directly by the retailer and those imported by a
wholesaler and sold to the retailer with the data we are only able to observe the former (Basker and
Van 2010) Also note that in contrast to imports exports are reported in the balance sheet
Therefore we will use this source of information when analysing exporting
Importing
To start with Figure 810 shows the share of retailers and wholesalers from the total Hungarian
imports of different types of goods In terms of all imports the share of these two groups of firms
fluctuated around 25 percent with a slightly decreasing trend The bulk of the imports were conducted
by manufacturing firms with an especially large share by multinational affiliates strongly integrated
into global value chains for example in the automotive industry Overall wholesalersrsquo imports were
about 5 times larger than those of retailers97
Naturally wholesalers and retailers dominate the importing of consumer goods by a share of around
70 percent A key trend here is the increasing share of retailers In 2004 21 percent of intermediated
trade (imports of wholesalers and retailers) were imported by retailers which increased gradually to
33 by 2015 This is a significant shift which reflects in part the expansion of multinational retail
chains but probably also easier access to imports by retailers
The share of intermediated trade was around 20 percent both for intermediate inputs and capital
goods dominated by wholesalers This reflects that in aggregate terms the overwhelming majority of
goods used by firms in production are imported directly The share of intermediated trade decreased
strongly following the crisis from 20 percent in 2010 to 13 percent in 2015 Given the skewed size
distribution of manufacturing firms this does not mean that most firms import their inputs directly
many smaller firms rely strongly on trade intermediaries when purchasing their inputs
Figure 811 looks into the trends behind consumer goods imports in more detail The left hand side
figure shows the share of imports compared to the total cost of goods sold (COGS) by wholesalers and
retailers98 We find that this ratio is roughly constant for wholesalers namely around 10 percent99
97 This can be compared to the results of Bernard et al (2010) who report that retailers and firms active both in retail and wholesale represent 14 percent of importing firms and 9 percent of imports in the US
98 In particular we calculate total consumer goods imports for wholesale and retail firms and divide it with the sum cost of goods sold across all retailers
99 Needless to say wholesalers also import other type of goods which are part of their cost of goods sold This ratio was 36 percent in 2015 showing that more than a third of their sales was imported
Productivity differences in Hungary and mechanisms of TFP growth slowdown
125
This contrasts sharply with retailers where the share of directly imported goods nearly doubled
between 2005 and 2015 from 6 percent to 11 percent100 This corresponds to a substantial increase in
the share of imported goods offered to consumers by retailers and an increasing share of this volume
is imported directly by the retailer presumably with a smaller degree of double marginalisation
One can also decompose the increasing direct import share of retailers to its different margins One
possibility is that - probably thanks to the declining fixed costs of importing - more and more retailers
started to import (an extensive margin effect) The right panel of Figure 811 shows that this is not the
case the share of directly importing retailers stagnated at about 8 percent of firms (with at least 5
employees) in the whole period Instead the rise of direct imports was driven by the intensive margin
or the average direct import per retailer Other regressions (not reported) suggest that this does result
mainly from the increased imports of large retailers
Figure 810 Share of wholesale retail and other firmsrsquo imports relative to total imports across
product categories
100 Again considering all goods the importcost of goods sold ratio increased from 11 to 18 percent for retailers
Productivity evolution and reallocation in retail trade
126
Figure 811 The share of consumer goods imports relative to the cost of goods sold and the share of
direct consumer goods importers by industry
Notes Firms with at least 5 employees
Figure 812 distinguishes between foreign and domestically-owned retail firms Both the share of
importers and their intensive margins are much higher for foreign-owned firms in the industry The
share of consumer goods imports in foreign firms in terms of cost nearly tripled between 2005 and
2015 from 7 to 21 percent101 compared to the 2-5 percent increase for domestically-owned firms
The increase in imports by retailers hence was mainly driven by multinationals
101 A similar increase from 18 percent in 2005 to 32 percent in 2015 can be observed when non- consumer goods are considered
Productivity differences in Hungary and mechanisms of TFP growth slowdown
127
Figure 812 The share of consumer goods imports relative to cost of goods sold and the share of
direct consumer goods importers by ownership
Notes Firms with at least 5 employees
Table 86 presents the cross-sectional linear regressions in order to investigate the premia of importers
among retailers along several dimensions In these regressions the dependent variable is a dummy
which shows whether a firm imports at least 1 percent of its cost of goods sold102 We find substantial
and highly significant premia in terms of size productivity and ownership 100 percent higher
productivity translates into about 5 percentage points higher probability of importing This premium
was increasing significantly between 2005 and 2015 showing a stronger self-selection of more
productive retailers into direct importing Foreign retailers are 20-25 percentage points more likely to
import on average A doubling of employees is associated with around 9 percentage points higher
probability of importing103
102 These are linear probability models but probit specifications yield similar marginal effects
103 Similar premia are found for importers in most industries and are mainly explained by the fixed costs of importing (Vogel and Wagner 2010)
Productivity evolution and reallocation in retail trade
128
Table 86 Determinants of importing linear probability models Retailers
(1) (2) (3) (4) (5)
Year 2005 2008 2010 2012 2015
Dependent Imports at least 1 percent of purchases
Labour productivity 0050 0054 0047 0059 0065
(0003) (0003) (0003) (0003) (0003)
Foreign-owned 0249 0196 0224 0238 0217
(0014) (0011) (0012) (0012) (0012)
Ln employees 0082 0082 0075 0073 0088
(0004) (0004) (0004) (0004) (0004)
Constant -0470 -0536 -0464 -0551 -0637
(0027) (0026) (0027) (0028) (0027)
Observations 7467 7977 7400 7122 8308
R-squared 0116 0130 0127 0140 0143
Notes Firms with at least 5 employees These are cross-sectional regressions where the dependent variable is
dummy representing whether the firm imports at least 1 percent of its cost of goods sold
Exporting
Wholesalers and retailers can also play a significant role as export intermediaries Extended export
activities of these firms can be an important source of growth for these firms but can also benefit
many smaller producers who would not find it profitable to export directly (Ahn et al 2011)
Figure 813 shows that 85-90 percent of exporting was conducted directly by producers rather than by
wholesalers or retailers The share of intermediated exports was constant pre-crisis but started to fall
after 2012
Productivity differences in Hungary and mechanisms of TFP growth slowdown
129
Figure 813 Share of wholesale retail and other firmsrsquo exports relative to total exports of firms
Many wholesalers and retailers started to export in the period under study (Figure 814) The share of
exporters in wholesale firms increased from 25 percent in 2005 to 35 percent in 2015 while the share
of exporting retailers doubled in this period The share of exports in the turnover of these firms also
increased substantially
Figure 814 Share of exports relative to turnover and share of exporters by industry
While foreign-owned firms are about 4 times more likely to export than domestic ones entry into
exporting was not limited to foreign-owned firms (Figure 815) the share of exporters among
domestically-owned firms doubled between 2005 and 2015 This was paralleled with an increase in the
share of exports relative to total turnover
Productivity evolution and reallocation in retail trade
130
Figure 815 Share of exports relative to turnover and share of exporters by ownership for the retail
sector
Table 87 reports linear probability models with export status as the dependent variable More
productive larger and foreign-owned firms are more likely to export In general both the size and
labour productivity premia increased between 2005 and 2015 once again suggesting stronger self-
selection based on these variables
Table 87 Determinants of exporting linear probability models retail
(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015
Dependent Exports at least 1 percent of total revenue
Labour
productivity
0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)
Foreign-owned 0083 0137 0141 0119 0107
(0009) (0011) (0013) (0013) (0012)
Ln employees 0019 0029 0028 0031 0034
(0003) (0004) (0004) (0004) (0004)
Constant -0159 -0277 -0271 -0321 -0317
(0018) (0026) (0027) (0029) (0028)
Observations 7622 7976 7663 7384 8730
R-squared 0028 0045 0041 0041 0036
This section has shown that the role of retailers in international trade is becoming more and more
important in Hungary This can have many benefits from providing a larger variety of potentially lower
priced goods to consumers to letting smaller producers reach foreign markets Increasing exports
mostly reflect opportunities provided by European integration and the internet but policies can also
help firms to become more adapt at utilising these opportunities
Productivity differences in Hungary and mechanisms of TFP growth slowdown
131
86 Policies Crisis taxes
As we have described briefly in Section 81 some of the new policies introduced after the crisis were
size-dependent either explicitly or implicitly The crisis taxes and the local business tax104 were based
on explicitly taxing large firms at higher rates Such policies can have substantial effects at the sectoral
level (Guner et al 2008)
Evaluating the effects of these taxes is not a straightforward task A possible approach was followed in
Section 84 where we have investigated the reallocation process in detail While such an approach is
not capable of identifying the causal effects of specific policies it may provide a broad picture The
results most importantly Figure 88 suggest that the importance of the reallocation process declined
relative to within-firm productivity growth Still this could have resulted from many reasons other than
policy changes
A more direct approach is to identify specific firms which were affected by a policy and to compare
their behaviour to similar firms not affected by the policy Such a diff-in-diff approach may be an
effective policy evaluation tool when there are sharp breakpoints in the tax schedule with enough
`treatedrsquo and control firms in the two groups
As for the crisis taxes the only sharp discontinuity was at the top rate when the tax rate increased
from 04 to 25 percent of profits The cutoff was at HUF 100bn and according to our data altogether
6 retail firms qualified for inclusion in this group This sample size does not allow for a statistically
powerful test
Still a few graphs may illustrate the processes First the market share of these large mainly
multinational firms were expanding quickly before 2010 and stagnated afterwards (Figure 816)
Second we can illustrate some of the key performance measures discussed in Section 83 Figure 817
compares the treated firms to a control group consisting of firms with at least 100 employees We find
that the premium of the treated group in terms of both productivity measures and margins were
higher between 2010 and 2013 than before or after105 As we have discussed earlier at least in the
short term these revenue-based measures are likely to reflect changes in prices Hence this figure
hints at increased prices in the treated group relative to the control group suggesting that treated
firms passed on the tax to consumers Note that these differences are not statistically significant and
to reiterate may have resulted from many other factors rather than just the effects of this specific
policy
104 The effect of the local business tax is much harder to test given its more continuous nature
105 Note that the margin premia are in fact negative in line with the lower margins charged by the largest firms
Productivity evolution and reallocation in retail trade
132
Figure 816 Sales and employment share of firms in the top bracket of the crisis tax
Notes Full sample
Figure 817 Margin TFP and labour productivity advantage of firms in the top bracket of the crisis tax
firms with more than 100 employees
Productivity differences in Hungary and mechanisms of TFP growth slowdown
133
87 Policies Mandatory Sunday closing
One of the most characteristic non-tax based size-dependent policies was mandatory Sunday closing of
larger shops introduced in March 2015 and reversed in April 2016 While the policy had multiple aims
it was partly motivated by supporting smaller and family-owned shops In this section we investigate
two outcomes related to this policy First we aim at understanding its reallocation effects ie the
extent to which the market share of treated shops lost market share Second we are interested in the
extent to which consumption was reallocated to other days of the week
The shop-level data is ideal to investigate the effects of this policy First the policy was defined at the
shop- rather than the firm-level We can identify the affected shops precisely based on the number of
days they were open Second many shops have been affected by this policy making the test
powerful Third the policy has a clearly defined beginning and end making a difference in differences
strategy feasible
Our empirical approach starts with restricting the sample to comparable firms First we investigate
mainly grocery shops where we have sizable treated and control groups106 In the sample we include
only shops which were continuously in the sample between January 2015 and October 2016 An issue
is that the treated and the control group may be very different We attempt to guarantee that the
common support condition is satisfied by excluding very small and very large shops107 For similar
reasons we also exclude shops which were not open even on Saturdays either before or during the
policy108
An important part of the analysis is the definition of the treated group As we do not observe directly
the area and the ownership of the shop we rely on the change in the number of days open We
consider a shop treated if it was open for at least 30 days per month before the policy (in median) and
it was open for less than 26 days after the policy was introduced (again in median)109 The control
group consists of other firms in the sample
Taking a look at the number of days open for the two groups reveals that compliance was very high
More than 95 percent of the shops that had been open on Sundays before the policy were closed on
Sundays during the whole policy period More than 95 percent of shops in the control group were
closed on Sundays both before and after the policy There are few firms which deviated from this
pattern by for example opening on Sundays when the policy started110
106 In other 4-digit sectors either there are too few firms or nearly all of them are treated (clothes shoes etc) or none of them (fuel)
107 Based on the 5th and 95th percentiles of the median sales distribution based on sales before the policy Unfortunately we do not have other measures of shop size
108 More precisely we exclude shops for which the median monthly days open was below 21 days either before or during the policy
109 A potential worry with this approach is that some shops may have closed voluntarily when the policy was introduced We cannot exclude this possibility but this may not be that important for the relatively large shops in the sample One can expect that voluntary Sunday closure would not start exactly at the beginning of the policy but rather after a period of gathering information about consumer demand on Sunday By checking the monthly distribution of the number of days open we find only few firms which changed their behaviour in this respect during the policy
110 Note that many small shops remained open on Sundays but most of them are missing from our restricted sample because of small median sales
Productivity evolution and reallocation in retail trade
134
Figure 815 reports descriptive statistics of the key variables Panel A) compares the evolution of
average sales of the treated and the control group before during and after the introduction of the
policy The dynamics of sales growth was remarkably similar before the policy was introduced
suggesting that the parallel trend assumption was satisfied Average sales in the control group are
somewhat higher during the policy suggesting some reallocation of market share to that group After
the policy the treated group seems to slightly overperform the control group
Part B) of Figure 818 shows the evolution of average sales per day open Again the pre-policy trends
are similar for the two groups Sales per day increases significantly for both groups during the policy
consumers did their Sunday shopping on other days The increase is substantially larger for the treated
group showing that most of the former Sunday shopping took place in the same shop but on other
days of the week The fact that there is an increase in the control group shows that part of the former
Sunday shopping was reallocated to these shops Interestingly the sales per day advantage of the
treated group remained even after the policy was abandoned As we will see the main reason for this
is that after abandoning the policy some of the shops remained closed
Figure 818 The evolution of key variables in the treated group and the control group groceries
A) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
135
B) Sales per day
While these patterns are suggestive the data allow us to conduct a more precise econometric event
study exercise We do so by creating a number of quarterly event study dummies to capture the
differential dynamics of the treated and control groups We define the variable lsquoevent timersquo which
shows the number of months since the policy started (it is zero in March 2015) This variable takes
negative values before that date We define quarterly dummies based on the event time variable For
example the first treatment quarter dummy is one when event time is 0 1 or 2 and the firm is in the
treated group The first pre-treatment dummy takes the value of 1 when event time is -1 -2 or -3 and
the firm is in the treated group
We run the following regression to estimate these trends
119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591
120591 + 120583119894119895 + 120575119905 + 휀119894119895119905
In this regression the dependent variable is days open ln(monthly sales) and ln(salesdays open) 119894
denotes firms 119895 shops and 119905 time measured in month while 120591 is event time in quarters The variables
of interest are the full set of event study dummies The base category will be the second pre-trend
dummy (event time -4 -5 or -6) The motivation for this choice is that the policy was announced in
this period (December 2014) hence the first pre-trend period the beginning of 2015 may include
preparation for the policy 120583119894119895 are shop fixed effects to control for shop heterogeneity 120575119905 are time
(monthly) fixed effects which control both for seasonality and macro shocks When we run the
regression by pooling different 4-digit industries we allow these dummies to vary across industries In
a more demanding specification we also include firm-time fixed effects and identify from the
differences across the treated and non-treated shops of the same firm in the same month We cluster
standard errors at the shop-level
Figure 819 summarizes the main results for the whole retail sector while the regressions are reported
in Table A71 in the Appendix Panel A) shows the results for days open with the right-hand panel
including firm-time fixed effects We see that on average treated firms cut the number of days open
by 2-3 days relative to the control group ndash the effect is more pronounced with firm fixed effects There
Productivity evolution and reallocation in retail trade
136
is practically no pre-trend and the timing of the reduction of days open is strongly in line with the
introduction of the policy The number of days open increases sharply after the end of the policy but
only to below pre-policy levels This suggests that some shops did not re-open on Sundays after the
policy probably because they learned that their sales did not suffer much
Panel B) shows the behaviour of average monthly sales Again there is no evidence for a pre-trend
During the policy treated firms experienced a 2-3 percent lower sales growth relative to the control
group This shows how much of sales was re-allocated to other shops Post-policy variables suggest
full recovery to pre-policy levels
Panel C) of the same figure shows the effect of the policy on sales per day open This variable
increased by 5-10 percent in the treated group relative to the control group The bulk of consumers
seem to have remained loyal to their familiar shops and simply made their shopping on other days
This may have also been helped by longer opening hours on other days of the week and further efforts
made by shops to retain their customers Sales per day remain higher even after the end of the policy
most likely because some shops did not re-open on Sundays but probably also because of
organizational changes during the policy
Figure 819 Event study results for the whole retail sector
A) Days
Productivity differences in Hungary and mechanisms of TFP growth slowdown
137
B) Sales
C) Sales per day
Notes This figure presents point estimates and 95 confidence intervals from the event study regression showing
the evolution of number of days open sales and sales per day of the treated group compared to the control group
as described in the text All specifications include shop fixed effects The left panel regressions also include 4-digit
industry-time fixed effects while the right side panels include firm-time dummies
Productivity evolution and reallocation in retail trade
138
Figure 820 re-estimates the same regressions for groceries where the policy was most relevant The
regression results are reported in Table A72 in the Appendix We find very similar results to the whole
retail sector The only exception is that the evolution of post-policy behaviour of sales is less clear
Figure 820 Event study results for NACE 4711
A) Days
B) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
139
C) Sales per day
Notes The figure above presents point estimates and 95 confidence intervals from the event study regression
showing the evolution of number of days open sales and sales per day of the treated group compared to the
control group as described in the text All specifications include shop fixed effects The left panel regressions also
include 4-digit industry-time fixed effects while the right side panels include firm-time dummies
A possible concern with these estimates is that the increase in sales per day may result from a simple
composition effect If sales are usually very small on Sundays anyway then closing on Sundays may
mechanically increase average daily sales We check for this possibility by estimating sales on different
days of the week from the pre-policy period While we do not observe the sales on each day of the
week we observe sales in different months with a different combination of days We rely on this
variation to estimate a regression of the following form
ln 119904119886119897119890119904119894119895119905 = 120573 lowast 119883119905 + 120574 lowast 119889119886119905119890119905 + 120583119894119895 + 휀119894119895119905
where 119883119905 is a vector of variables containing the number of Mondays Tuesdays etc in month 119905 We
also control for the number of holidays in the month We control for seasonality by including dummies
for December January and summer months The regression also includes firm fixed effects and is
estimated on the period 2009-2014 120574 lowast 119889119886119905119890119905 is a linear trend The estimated results are reported in
Table A73 in the Appendix
The regression shows that sales on Sundays were not that small namely similar to a typical Monday
or Wednesday Thus the composition effect is unlikely to affect the results much To check for the
relevance of these composition effects Figure 821 A) reports sales predicted from the above
regression for the treated group (by setting the number of Sundays to be zero during the policy)
Therefore the `predictedrsquo line shows what would have happened if sales had remained the same on
Productivity evolution and reallocation in retail trade
140
non-Sundays during the policy The actual line is clearly above the predicted one suggesting that sales
on other days have increased
Panel B) of Figure 821 shows how sales per day would have evolved based on a similar regression
Note that predicted sales per day are slightly larger during the policy than beforehand thanks to the
mechanical composition effect resulting from the slightly lower sales on Sundays Actual sales per day
however are substantially higher than this simple prediction showing again that sales per day
increased on other days of the week
Figure 821 The evolution of the variables versus prediction
A) Sales
B) Sales per day
Productivity differences in Hungary and mechanisms of TFP growth slowdown
141
All in all the mandatory Sunday closing of shops was effective in terms of compliance It did not have
strong reallocative effects with a 2-3 percent fall in sales in the treated group Consumers seem to
have remained mostly loyal to the shop they had frequented and made their shopping on other days
of the week at the same shop Interestingly some of the shops seem to have learned that it is optimal
to remain closed on Sundays even after the policy was cancelled
88 Conclusions
In line with the main message of other parts of this study there are huge productivity differences
across firms within the retail sector There is a strong duality between small and large firms both in
terms of productivity and margins Consumers are likely to pay significantly lower prices in the shops
of large firms Many of the large firms are multinationals which had expanded rapidly before the crisis
At the other end of the range the exceptionally low performance of very small firms seems to be a
significant issue Many technologies applied by the most productive retailers could be adapted
relatively easily by some of the less productive firms Increasing absorptive capacity and effective
financing could help in promoting this Still many of the low-productivity very small shops may not be
viable in the long run
A key pattern observed is the increasing concentration of the retail sector pre-crisis resulting from the
expansion of large chains and foreign firms These trends seem to have stopped or slowed down after
the crisis In line with this pattern the contribution of reallocation decreased post-crisis relative to
earlier periods While many factors can play a role in this pattern it may be related to the different
size-dependent policies introduced after 2010 While these developments may help smaller retail firms
consumers may face higher prices in the long run
Not all the policies introduced can be properly evaluated based on the data at hand especially because
multiple policies were introduced at the same time with some of them affecting only few firms We
were able to analyse precisely the effects of mandatory Sunday closing based on store level data We
found that a relatively small share of the demand was lost by the treated shops and the majority of
consumers simply switched to shopping at the same place on other days Interestingly some of the
treated shops found it optimal not to re-open on Sundays even when the policy was reversed
Additionally retailers and wholesalers also play a large and increasing role in mediating imports and
exports We found a large increase in goods imported directly by retailers rather than indirectly via
wholesalers This was mainly driven by large foreign firms and may have benefited their consumers
thanks to a lower degree of double marginalisation Both the number of exporting firms and the
amount exported by wholesalers and retailers increased most likely benefitting from easy access to
markets of other EU member states and probably from the opportunities provided by e-commerce
This can benefit both the exporting firms and the Hungarian producers who can more easily reach
foreign markets with the help of these intermediaries Policies may help retailers to internationalise by
making international sales especially on the internet even easier
Conclusions
142
9 CONCLUSIONS
The results of this report confirm that Hungary is atypical because of the relatively poor productivity
performance of frontier firms Importantly contrary to a strong version of the duality concept this is
not a result of Hungarian frontier firms being on the global frontier typically they are quite far away
from it This robust pattern underlines that besides helping non-frontier firms policies may also have
to focus on the performance of the frontier group A transparent environment with a strong rule of law
complemented by a well-educated workforce and a strong innovation system is key for providing
incentives to invest into the most advanced technologies
The analysis in this report reinforces the impression that there is a large productivity gap between
globally engaged or owned and other firms the gap being about 35 percent in manufacturing and
above 60 percent in services This gap seems to be roughly constant in the period under study The
firm-level analysis in Chapter 7 also reveals that one of the mechanisms which conserves the gap is
that foreign frontier firms are able to increase their productivity more than their domestic counterparts
even from frontier levels These findings reinforce the importance of well-designed policies that are
able to help domestic firms to catch up with foreign firms A key precondition for domestic firms to
build linkages with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A more specific
conclusion is the importance of enabling high-productivity domestic firms to improve their productivity
levels even further
The large within-industry productivity dispersion the relatively low (though not extreme in
international comparison) allocative efficiency documented in some of the industries the strong
positive contribution of reallocation to total TFP growth before the crisis and the relatively low entry
rate imply that policies promoting reallocation have a potential to increase aggregate productivity
levels significantly These policies can include improving general framework conditions by cutting
administrative costs reducing entry and exit barriers and using a neutral regulation The fact that
capital market distortions still appear to be significantly above their pre-crisis levels implies that
policies that reduce financial frictions may help the reallocation process The fact that exporters tend to
expand faster relative to non-exporters indicates that access to EU and global markets generates a
strong and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general traded and
more knowledge-intensive sectors fared better both in terms of productivity growth and allocative
efficiency The difference between traded and non-traded sectors points again to the importance of
global competition in promoting higher productivity and more efficient allocation of resources This also
implies that adopting policies that focus on innovation or reallocation in services may be especially
important given the large number of people working in those sectors The better performance of and
reallocation into more knowledge-intensive sectors underlines the importance of education policies
aimed at developing up-to-date and flexible skills and innovation policies that help improve the
knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people working at
firms with at least a few employees and those working in very small firms or self-employed The latter
category represents 30-50 percent of people engaged in some important industries Inclusive policies
may attempt to generate supportive conditions for these people by providing knowledge and training
as well as helping them to find jobs with wider perspectives or to set up well-operating firms The large
share of these unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a characteristic difference between the pre-crisis
period characterised by strong reallocation mainly via the expansion of large foreign-owned chains
Productivity differences in Hungary and mechanisms of TFP growth slowdown
143
and the post-crisis period with a stagnating share of large chains This break is likely to be linked to
post-crisis policies favouring smaller firms While halting further concentration in a country with
already one of the highest share of multinationals in this sector can have a number of benefits it is
likely to lead to higher prices and lower industry-level productivity growth in the long run Policies
should balance carefully between these trade-offs Another key pattern identified is the increasing role
of retailers (and wholesalers) in trade intermediation both on the import and export side Policymakers
should encourage these trends and design policies which provide capabilities for such firms to enter
international markets probably via e-commerce
References
144
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analysisrdquo Oxford Bulletin of Economics and Statistics 67(3) 281-306
Girma S and Goumlrg H (2007) ldquoEvaluating the foreign ownership wage premium using a difference-
in-differences matching approachrdquo Journal of International Economics 72(1) 97-112
Girma S Thompson S and Wright P W (2002) ldquoWhy are productivity and wages higher in foreign
firmsrdquo Economic and Social Review 33(1) 93-100
Gopinath G Kalemli-Ozcan S Karabarbounis L and Villegas-Sanchez C (2017) ldquoCapital allocation
and productivity in South Europerdquo Quarterly Journal of Economics 132(4) 1915-1967
Gorodnichenko Y Revoltella D Svejnar J and Weiss C T (2018) ldquoResource misallocation in
European firms The role of constraints firm characteristics and managerial decisionsrdquo NBER Working
Papers (No w24444) National Bureau of Economic Research University of Chicago Press Chicago
Griliches Z and Regev H (1995) ldquoFirm productivity in Israeli industry 1979-1988rdquo Journal of
Econometrics 65(1) 175ndash203
Guner N Ventura G and Xu Y (2008) ldquoMacroeconomic implications of size-dependent policiesrdquo Review of Economic Dynamics 11(4) 721-744
Halpern L Koren M and Szeidl A (2015) ldquoImported inputs and productivityrdquo American Economic
Review 105(12) 3660-3703
Halpern L and Murakoumlzy B (2007) ldquoDoes distance matter in spilloverrdquo Economics of Transition
15(4) 781-805
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Haltiwanger J Kulick R and Syverson C (2018) ldquoMisallocation measures The distortion that ate
the residualrdquo NBER Working Papers (No w24199) National Bureau of Economic Research University
of Chicago Press Chicago
Harasztosi P (2011) ldquoGrowth in Hungary 1994-2008 The role of capital labour productivity and
reallocationrdquo MNB Working Papers 201112
Harasztosi P and Lindner A (2017) ldquoWho Pays for the Minimum Wagerdquo Mimeo
Haskel J and Sadun R (2012) ldquoRegulation and UK retailing productivity Evidence from microdatardquo Economica 79(315) 425-448
Haskel J E Pereira S C and Slaughter M J (2007) ldquoDoes inward foreign direct investment boost
the productivity of domestic firmsrdquo The Review of Economics and Statistics 89(3) 482-496
Hausmann R and Rodrik D (2003) ldquoEconomic development as self-discoveryrdquo Journal of
Development Economics 72(2) 603-633
Hausmann R Hwang J and Rodrik D (2007) ldquoWhat you export mattersrdquo Journal of Economic
Growth 12(1) 1-25
Herrendorf B Rogerson R and Valentinyi A (2014) ldquoGrowth and structural transformationrdquo
Handbook of Economic Growth (Vol 2) Elsevier 855-941
Hopenhayn H A (2014) ldquoFirms misallocation and aggregate productivity A reviewrdquo Annual Review
of Economics 6(1) 735-770
Hornok C and Murakoumlzy B (2018) ldquoMarkups of exporters and importers Evidence from Hungaryrdquo
The Scandinavian Journal of Economics forthcoming
Hsieh C T and Klenow P J (2009) Misallocation and manufacturing TFP in China and Indiardquo The
Quarterly Journal of Economics 124(4) 1403-1448
Hsieh C T and Olken B A (2014) ldquoThe missing missing middlerdquo Journal of Economic Perspectives 28(3) 89-108
Huttunen K (2007) ldquoThe effect of foreign acquisition on employment and wages Evidence from Finnish establishmentsrdquo The Review of Economics and Statistics 89(3) 497-509 Inklaar R and Timmer M P (2008) ldquoGGDC productivity level database International comparisons of output inputs and productivity at the industry levelrdquo Groningen Growth and Development Centre Research Memorandum GD-104 University of Groningen Groningen
Inklaar R and Timmer M P (2009) ldquoProductivity convergence across industries and countries The
importance of theory-based measurementrdquo Macroeconomic Dynamics 13(S2) 218-240
Iwasaki I Csizmadia P Illeacutessy M Makoacute C and Szanyi M (2012) ldquoThe nested variable model of
FDI spillover effects Estimation using Hungarian panel datardquo International Economic Journal 26(4)
673-709
Javorcik B S (2004) ldquoDoes foreign direct investment increase the productivity of domestic firms In
search of spillovers through backward linkagesrdquo American Economic Review 94(3) 605-627
Productivity differences in Hungary and mechanisms of TFP growth slowdown
149
Javorcik B S and Spatareanu M (2011) ldquoDoes it matter where you come from Vertical spillovers
from Foreign Direct Investment and the origin of investorsrdquo Journal of Development Economics 96(1)
126-138
Jaumlger K (2017) ldquoEU KLEMS growth and productivity accounts 2017 releaserdquo Statistical Module
Retrieved from httpwwweuklemsnettcb2017metholology_eu20klems_2017pdfKaacutetay G and
Wolf Z (2004) ldquoInvestment behavior user cost and monetary policy transmission The case of
Hungaryrdquo MNB Working Papers 200412
Kertesi G and Koumlllő J (2004) ldquoFighting low equilibriarsquo by doubling the minimum wage Hungarys
experimentrdquo IZA Discussion Papers (No 970)
Konings J (2001) ldquoThe effects of Foreign Direct Investment on domestic firmsrdquo Economics of
Transition 9(3) 619-633
Koumlllő J (2010) ldquoHungary The consequences of doubling the minimum wagerdquo In D Vaughan-
Whitehead (Ed) The Minimum Wage Revisited in the Enlarged EU Chapter 8 Edward Elgar
Publishing Cheltenham UK
Kugler M (2006) ldquoSpillovers from Foreign Direct Investment Within or between industriesrdquo Journal
of Development Economics 80(2) 444-477
Kuusk A Staehr K and Varblane U (2017) ldquoSectoral change and labour productivity growth
during boom bust and recovery in Central and Eastern Europerdquo Economic Change and Restructuring
50(1) 21-43
Levinsohn J and Petrin A (2003) ldquoEstimating production functions using inputs to control for
unobservablesrdquo The Review of Economic Studies 70(2) 317-341
Lin P Liu Z and Zhang Y (2009) ldquoDo Chinese domestic firms benefit from FDI inflow Evidence
of horizontal and vertical spilloversrdquo China Economic Review 20(4) 677-691
McGowan M A Andrews D and Millot V (2017) ldquoThe walking dead Zombie firms and productivity
performance in OECD countriesrdquo OECD Economics Department Working Papers (No 1372)
McMillan M Rodrik D and Sepulveda C (2017) ldquoStructural change fundamentals and growth A
framework and case studiesrdquo NBER Working Papers (No w23378) National Bureau of Economic
Research University of Chicago Press Chicago
Melitz J (2003) ldquoThe impact of trade on intra-industry reallocations and aggregate industry
productivityrdquo Econometrica 71(6) 1695-1725
Nicolini M and Resmini L (2010) ldquoFDI spillovers in new EU member statesrdquo Economics of
Transition 18(3) 487-511
OECD (2016) ldquoThe productivity-inclusiveness nexus Preliminary versionrdquo OECD Publishing Paris
httpdxdoiorg1017879789264258303-en
Olley G and Pakes A (1996) ldquoThe dynamics of productivity in the telecommunications equipment
industryrdquo Econometrica 64(6) 1263-1297
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Perani G and Cirillo V (2015) ldquoMatching industry classifications A method for converting NACE
Rev2 to NACE Rev1rdquo Working Papers (No 1502) University of Urbino Carlo Bo
Petrin A and Levinsohn J (2012) ldquoMeasuring aggregate productivity growth using plant‐level datardquo
The RAND Journal of Economics 43(4) 705-725
Petrin A Reiter J and White K (2011) ldquoThe impact of plant-level resource reallocations and
technical progress on US macroeconomic growthrdquo Review of Economic Dynamics 14(1) 3ndash26
Raff H and Schmitt N (2016) ldquoRetailing and international traderdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 157-179
Ratchford B T (2016) ldquoRetail productivityrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 54-72
Restuccia D and Rogerson R (2017) ldquoThe causes and costs of misallocationrdquo Journal of Economic
Perspectives 31(3) 151-74
Rovigatti G and Mollisi V (2016) ldquoPRODEST Stata module for production function estimation based
on the control function approachrdquo Statistical Software Components S458239 Boston College
Department of Economics Revised 12 Jun 2017 Accessed October 26 2017
httpsideasrepecorgcbocbocodes458239html
Sadun R (2015) ldquoDoes planning regulation protect independent retailersrdquo Review of Economics and Statistics 97(5) 983-1001
Scarpetta S Hemmings P Tressel T and Woo J (2002) ldquoThe role of policy and institutions for
productivity and firm dynamics Evidence from micro and industry datardquo OECD Economics Department
Working Papers (No 329) Available at SSRN httpsssrncomabstract=308680 or
httpdxdoiorg102139ssrn308680
Smith H (2016) ldquoThe economics of retailer-supplier pricing relationships Theory and evidencerdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 97-136
Smeets R (2008) ldquoCollecting the pieces of the FDI knowledge spillovers puzzlerdquo The World Bank
Research Observer 23(2) 107-138
Syverson C (2011) ldquoWhat determines productivityrdquo Journal of Economic Literature 49(2) 326-65
Taglioni D and Winkler D (2016) ldquoMaking global value chains work for developmentrdquo The World
Bank Issue 143 1-10
Topalova P and Khandelwal A (2011) ldquoTrade liberalization and firm productivity The case of
Indiardquo Review of Economics and Statistics 93(3) 995-1009
Viviano E (2008) ldquoEntry regulations and labour market outcomes Evidence from the Italian retail trade sectorrdquo Labour Economics 15(6) 1200-1222
Vogel A and Wagner J (2010) ldquoHigher productivity in importing German manufacturing firms Self-selection learning from importing or bothrdquo Review of World Economics 145(4) 641-665
Wagner J (2007) ldquoExports and productivity A survey of the evidence from firm‐level datardquo The
World Economy 30(1) 60-82
Productivity differences in Hungary and mechanisms of TFP growth slowdown
151
Wooldridge J M (2009) ldquoOn estimating firm-level production functions using proxy variables to
control for unobservablesrdquo Economics Letters 104(3) 112-114
Zhang Y Li H Li Y and Zhou L A (2010) ldquoFDI spillovers in an emerging market The role of
foreign firms country origin diversity and domestic firms absorptive capacityrdquo Strategic Management
Journal 31(9) 969-989
Appendix
152
APPENDIX
A3 Chapter 3 Internationally comparable data sources and methodology
A31 EU KLEMS amp OECD STAN
The EU KLEMS project aimed at creating a database on measures of economic growth productivity
employment creation capital formation and technological change at the industry level for all European
Union member states from 1970 onwards The database provides an important input to policy
evaluation in particular for the assessment of the goals concerning competitiveness and economic
growth potential as established by the Lisbon and Barcelona summit goals
The input measures include various categories of capital labour energy material and service inputs
Productivity measures have also been developed in particular with growth accounting techniques
Several measures on knowledge creation have also been constructed
The basic data of the EU KLEMS is also available in the OECD STAN database sometimes in a more up
to date version We have downloaded the following variables from there
- EMPE Number of employees
- EMPN Number of persons engaged ndash total employment
- SELF Number of self-employed
- VALU Value added current prices (millions of national currency)
- VALK Value added volumes (current price of the reference year 2010 millions)
- VALP Value added deflators (reference year 2010 = 100))
Labour productivity is defined as gross value added at constant prices divided by the number of
persons engaged In order to create comparative labour productivity levels we used the 2005
benchmark from the GGDC Productivity Level Database111 This project provides productivity levels
relative to the USA that can be used together with EU KLEMS growth accounts to create comparable
productivity level extrapolations (Inklaar and Timmer 2008 Inklaar and Timmer 2009)
A32 OECD Structural and Demographic Business Statistics
The OECD Structural and Demographic Business Statistics (SDBS) consists of two databases the
OECD Business Demography Indicators (BDI) and the OECD Structural Business Statistics (SBS)
The OECD Business Demography Indicators (BDI) database contains data on births and deaths of
enterprises their life expectancy and the important role they play in economic growth and
productivity The OECD Structural Business Statistics (SBS) database features the data collection
of the Statistics Directorate relating to a number of key variables such as for example value added
operating surplus employment and the number of business units broken down by ISIC Rev 4
industry groups referred to as the Structural Statistics on Industry and Services (SSIS) database and
by economic sector and enterprise size class referred to as the Business Statistics by Size Class (BSC)
database For most countries the main sources of information used in the compilation of structural
business statistics are business surveys economic censuses and business registers
111 More information can be found on the homepage of GGDC Production Level Database
httpswwwrugnlggdcproductivitypldearlier-release
Productivity differences in Hungary and mechanisms of TFP growth slowdown
153
The statistical population is composed of enterprises (or establishments when no data on enterprises
are available) In the case of BDI database the population contains all enterprises including non-
employers ie enterprises with no employees while the population of SBS contains only the employer
enterprises ie firms with at least one employee
Birth rate of all enterprises is the ratio of the number of enterprise births and the number of
enterprises active in the reference period Births do not include entries into the population due to
mergers break-ups the split-off or restructuring of a set of enterprises It does not include entries
into a sub-population resulting only from a change of activity (Source BDI)
Death rate of all enterprises is the ratio of the number of enterprise deaths and the number of
enterprises active in the reference period Deaths do not include exits from the population due to
mergers take-overs break-ups or the restructuring of a set of enterprises It does not include exits
from a sub-population resulting only from a change of activity An enterprise is included in the count of
deaths only if it is not reactivated within two years Equally a reactivation within two years is not
counted as a birth (Source BDI)
Number of enterprises is a count of the number of enterprises active during at least a part of the
reference period (Source SBS)
A33 OECD Productivity Frontier
The OECD productivity frontier dataset is based on AMADEUSORBIS and calculates comparable labour
productivity and TFP (MFP) measures across countries The project aims at defining the most
productive (frontier) enterprises both globally and for every country at the 2-digit industry level
(Andrews et al 2016)
Here we use data kindly provided by the OECD for the global and the Hungarian national productivity
frontier Two types of productivity measures are presented labour productivity and Wooldridge MFP
Both frontier series are defined as the average of log-productivity of the top 10 within each 2-digit
industry and year To make this measure less sensitive to expanding coverage over time the 10 is
chosen based on the median number of observations within a 2-digit industry The median for each 2
digit industry is calculated over all the years retained in the analysis
A key issue with AMADEUSORBIS with regard to Hungary is its changing coverage (see Box in Chapter
2) This makes these comparisons meaningful only from 20082009 onwards The underlying sample
includes all firms that over their observed lifespan had at least 20 employees on average
To arrive at internationally comparable real series 2-digit country specific industry value added and
investment deflators were used (2005 = 1) and the monetary values were converted to 2005 USDs
using industry level PPPs from the Groningen Growth and Development Centrersquos Productivity Level
Database112
112 For more information visit the Centrersquos homepage httpswwwrugnlggdcproductivitypld
Appendix
154
A4 Chapter 4 Evolution of the Productivity Distribution
Table A41 Average TFP growth with alternative TFP measures
A) Market economy
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 19 74 16 60
2006 93 119 95 97
2007 39 56 49 65
2008 -10 -04 -06 01
2009 -69 -82 -65 -63
2010 11 80 05 60
2011 34 40 31 45
2012 21 01 24 18
2013 30 22 22 22
2014 40 59 36 48
2015 52 49 50 43
2016 20 03 25 12
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Manufacturing
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 20 114 24 127
2006 114 149 118 137
2007 78 71 86 98
2008 17 -17 32 -11
2009 -133 -117 -120 -87
2010 80 173 85 178
2011 04 18 01 25
2012 -02 -58 07 -38
2013 -12 05 -15 16
2014 -01 27 01 34
2015 30 14 34 19
2016 04 -23 14 -05
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Productivity differences in Hungary and mechanisms of TFP growth slowdown
155
C) Market services
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 10 32 06 01
2006 79 90 82 64
2007 24 48 35 44
2008 -21 -03 -19 05
2009 -52 -71 -51 -54
2010 -11 26 -19 -05
2011 43 57 40 57
2012 30 48 31 57
2013 39 29 31 25
2014 46 78 39 55
2015 54 72 52 58
2016 25 20 29 23
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table presents growth rates of TFP estimated with the translog ACF estimator and the Fixed Effects
estimator for lsquomarket industriesrsquo (see Section 25) The sample does not include agriculture mining and financial
services Services include construction and utilities
Appendix
156
Table A42 Unweighted TFP growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 21 -02 -09 144
2006 118 143 58 47
2007 59 43 90 348
2008 -09 79 17 111
2009 -53 -191 -197 -139
2010 80 76 85 130
2011 -22 17 10 153
2012 01 14 -57 -06
2013 -38 20 -38 54
2014 -03 -05 08 33
2015 61 04 -19 132
2016 09 -10 12 91
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Market Services
Year KIS LKIS Construction Utilities
2005 127 16 -01 -46
2006 166 75 94 66
2007 13 58 60 16
2008 -16 14 -37 -28
2009 -63 -94 -15 44
2010 54 12 -08 23
2011 97 46 77 -29
2012 12 74 06 -57
2013 12 30 60 -71
2014 78 89 65 -31
2015 106 70 22 12
2016 16 31 -47 37
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the unweighted average ACF TFP growth rate by technology category (see Section 25)
Only firms with at least 5 employees The sample does not include agriculture and financial services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
157
Table A43 Employment-weighted labour productivity growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 172 32 73 300
2006 266 114 54 10
2007 121 52 69 243
2008 -25 -17 -03 126
2009 31 -151 -186 35
2010 135 114 199 207
2011 -33 -10 96 96
2012 03 -34 -32 -226
2013 -35 22 26 253
2014 33 19 53 94
2015 82 -04 -06 102
2016 34 18 08 -110
Average
2004-2007 186 66 65 184
2007-2010 47 -18 03 123
2010-2013 -21 02 24 35
2013-2016 28 14 20 85
B) Services
Year KIS LKIS Construction Utilities
2005 127 -05 41 -31
2006 166 75 21 54
2007 13 11 25 -36
2008 -16 -19 05 -02
2009 -63 -117 09 04
2010 54 -01 -05 13
2011 97 47 54 13
2012 12 62 19 -47
2013 12 21 62 -44
2014 78 55 64 -39
2015 106 54 07 65
2016 16 49 -60 43
Average
2004-2007 102 27 29 -04
2007-2010 -08 -46 03 05
2010-2013 40 48 24 -01
2013-2016 53 45 18 06
Notes This table shows the employment-weighted average LP growth rate by technology category (see Section
25) Only firms with at least 5 employees The sample does not include agriculture and financial services
Appendix
158
Table A44 The share of firms in the top decile ()
A) By size
2004 2007 2010 2013 2016
5-9 emp 1049 1051 1043 1096 1045
10-19 emp 954 962 92 904 92
20-49 emp 994 903 939 856 998
50-99 emp 896 1024 1188 1009 1096
100- emp 721 81 839 748 728
B) By ownership
2004 2007 2010 2013 2016
Domestic 833 818 814 824 837
Foreign 2344 2499 2422 2384 2488
State 554 728 81 575 695
C) By region
2004 2007 2010 2013 2016
Central HU 567 568 59 56 549
Northern
Hungary 195 116 19 208 224
Northern
Great Plain 161 178 239 23 249
Southern
Great Plain 137 118 17 258 179
Central
Transdanubia 276 33 332 369 332
Western
Transdanubia 311 283 244 361 444
Southern
Transdanubia 184 201 235 143 181
Notes Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
159
Figure A41 Persistence of top decile status
Notes This figure shows how many of top decile firms in year 2010 were frontier in 2013 how many exited and
how many continued as non-frontier The first panel shows this transition matrix for different 3-year periods
Appendix
160
A5 Chapter 5 Allocative Efficiency
Table A51 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168
C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209
D - Electricity gas
steam and AC
6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743
E - Water supply
sewerage waste
6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872
F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023
G - Wholesale and
retail trade
6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303
H - Transportation
and storage
6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985
I - Accommodation
food service
6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250
J - Information and
Communication
6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046
M - Professional
Scientific and Tech Act
6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075
N - Administrative and support service
6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
161
Table A52 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692
C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109
D - Electricity
gas steam and
AC
6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291
E - Water supply
sewerage waste
6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916
F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436
G - Wholesale and
retail trade
6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260
H - Transportation
and storage
6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776
I -
Accommodation
food service
6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899
J - Information
and
Communication
6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800
M - Professional
Scientific and
Tech Act
6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242
N - Administrative
and support service
6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
162
Table A53 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covar
iance
unweight
ed LP
weighted
LP
covar
iance
B - Mining and quarrying 7509 8072 0564 8038 8583 0546 8378 9440 1063 8609 9028 0419
C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796
D - Electricity gas steam and
AC
9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197
E - Water supply sewerage waste
8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703
F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096
G - Wholesale and retail trade 7955 8252 0297 8036 8452 0415 7955 8307 0352 8197 8589 0392
H - Transportation and
storage
8364 8475 0110 8300 8525 0224 8194 7698 -
0496
8292 7289 -
1003
I - Accommodation food
service
7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651
J - Information and Communication
8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988
M - Professional Scientific and Tech Act
8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406
N - Administrative and
support service
7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238
Productivity differences in Hungary and mechanisms of TFP growth slowdown
163
Table A54 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance
B - Mining and
quarrying
7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358
C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960
D - Electricity gas
steam and AC
9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289
E - Water supply
sewerage waste
8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064
F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159
G - Wholesale and
retail trade
7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037
H - Transportation
and storage
8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022
I - Accommodation
food service
7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363
J - Information and
Communication
7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263
M - Professional
Scientific and Tech
Act
7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431
N - Administrative
and support service
7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216
Appendix
164
Table A55 Allocative efficiency based on Hsieh-Klenow (2009) ndash 1 digit industries
Distortions in 2001 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4802 1540 0299 0803 19127 1152
C - Manufacturing 5620 1300 0425 0818 12807 1008
D - Electricity gas steam and AC 6760 0503 0591 0456 6171 0784
E - Water supply sewerage waste 6629 0599 0103 1127 6245 1248
F - Construction 6706 0818 0280 0954 21186 1227
G - Wholesale and retail trade 7225 1088 0395 1007 21997 1211
H - Transportation and storage 6073 0984 -0154 1647 15193 1144
I - Accommodation food service 6201 0684 -0025 0919 7951 1263
J - Information and Communication 5499 1273 0549 0603 5387 1265
M - Professional Scientific and Tech Act 6961 0920 0253 1062 45052 1293
N - Administrative and support service 6778 1237 0084 1020 42372 1546
Productivity differences in Hungary and mechanisms of TFP growth slowdown
165
Table A55- continuedhellip
Distortions in 2005 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4211 1121 0269 0669 12217 0953
C - Manufacturing 5919 1173 0497 0890 13439 0998
D - Electricity gas steam and AC 6569 0880 0596 0553 6400 1181
E - Water supply sewerage waste 6433 0722 0091 1277 9084 1126
F - Construction 6794 0744 0155 0947 20440 1099
G - Wholesale and retail trade 7497 1199 0392 0771 20492 1543
H - Transportation and storage 6305 1063 0017 1205 11362 1232
I - Accommodation food service 6085 0660 0098 1287 5680 1239
J - Information and Communication 5867 1337 0608 0637 6375 1481
M - Professional Scientific and Tech Act 6926 0951 0129 1118 50400 1474
N - Administrative and support service 6904 1206 -0004 1055 47387 1649
Appendix
166
Table A55- continuedhellip
Distortions in 2010 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales
taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4219 0669 -0104 0759 11170 1012
C - Manufacturing 6024 1201 0523 0740 12732 1001
D - Electricity gas steam and AC 7260 1273 0813 0433 12091 1565
E - Water supply sewerage waste 6474 0700 0123 0965 13717 1279
F - Construction 6621 0775 0200 1075 30395 1437
G - Wholesale and retail trade 7471 1230 0310 0842 22833 1527
H - Transportation and storage 6517 1250 0123 1030 9632 1459
I - Accommodation food service 6080 0704 0001 1060 5570 1341
J - Information and Communication 5989 1245 0581 0870 11895 1572
M - Professional Scientific and Tech Act 7076 1042 0130 1077 78642 1486
Productivity differences in Hungary and mechanisms of TFP growth slowdown
167
Table A55- continuedhellip
Distortions in 2016 Productivity Productivity dispersion
Median implicit sales
taxes
Dispersion of implicit sales
taxes
Median implicit cost
of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4484 0705 0264 0601 13655 0812
C - Manufacturing 6022 1110 0514 0971 11130 1074
D - Electricity gas steam and AC 7341 0966 0724 0307 36231 2054
E - Water supply sewerage waste 6363 0763 0015 1134 15926 1399
F - Construction 6938 0809 0298 0868 28761 1453
G - Wholesale and retail trade 7511 1005 0312 0959 26886 1576
H - Transportation and storage 6656 0972 0104 1078 16755 1745
I - Accommodation food service 6492 0672 0163 0943 6439 1443
J - Information and Communication 6211 1165 0422 0747 23648 1609
M - Professional Scientific and Tech Act 7188 0956 0148 1223 72383 1567
N - Administrative and support service 7112 1219 -0081 1109 98641 1801
Notes Total factor productivity is measured by the method of Ackerberg et al (2015) See Chapter 52 for details
Appendix
168
Appendix Figure 51 Weighted and unweighted labour productivity by 2-digit industry 2016 firms with at least 5 employees
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted log labour productivity in 2016 while the horizontal axis shows its
weighted log labour productivity in the same year We have omitted industries with less than 1000 observations
Productivity differences in Hungary and mechanisms of TFP growth slowdown
169
Appendix Figure 52 The relationship between weighted and unweighted labour productivity by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted labour productivity levels run at the 2-digit industry level
separately for 2005 2010 and 2016
Appendix
170
Appendix Figure 53 the change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences between the weighted and unweighted
labour productivity) in 2010 while the vertical axis shows the same quantity in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
171
A6 Chapter 6 Reallocation
Table A61 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -93 -38 10 -65 -02 -10 50 -43
C Manufacturing 108 23 48 36 -02 -11 03 05
D Electricity gas 08 07 05 -04 26 -06 22 10
E Water supply sewerage 17 -17 31 03 08 -09 09 09
F Construction 26 04 08 13 -14 -02 -19 07
G Wholesale and retail trade 30 03 11 16 -55 -08 -65 18
H Transportation and storage -21 14 -43 08 -39 10 -57 08
I Accommodation 68 -07 53 22 -44 00 -37 -07
J ICT 96 10 63 23 29 -24 35 18
M Professional scientific 39 -13 35 17 -38 -04 -26 -08
N Administrative and support 104 11 37 56 -49 -02 -04 -43
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying 08 11 09 -12 41 60 -30 10
C Manufacturing -18 07 -30 05 10 -08 22 -04
D Electricity gas -26 26 -70 18 74 07 31 36
E Water supply sewerage -08 15 -05 -18 -04 05 00 -09
F Construction 42 03 26 13 01 06 -16 11
G Wholesale and retail trade 54 01 32 21 68 04 56 07
H Transportation and storage 89 14 51 25 21 -30 06 45
I Accommodation 85 -05 59 32 51 -03 46 08
J ICT 19 -02 11 10 47 -02 38 11
M Professional scientific 69 05 12 53 30 -04 18 16
N Administrative and support 50 00 36 14 106 01 87 18
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
172
Table A62 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -253 -59 -19 -175 73 -02 39 36
C Manufacturing 105 20 51 34 06 -14 03 16
D Electricity gas 06 09 03 -06 14 -14 23 05
E Water supply sewerage 21 -12 32 02 -06 -09 00 03
F Construction 35 07 12 16 -23 -03 -24 04
G Wholesale and retail trade 27 06 06 16 -39 03 -59 17
H Transportation and storage -34 17 -58 06 -33 14 -58 11
I Accommodation 67 -09 50 26 -42 03 -39 -05
J ICT 85 14 39 32 27 -14 21 19
M Professional scientific 46 -07 28 25 -28 -03 -22 -03
N Administrative and support 122 29 28 65 -49 00 -09 -40
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -08 11 07 -26 24 -03 06 22
C Manufacturing -12 06 -25 07 06 -04 11 -01
D Electricity gas 16 16 -15 15 30 00 25 06
E Water supply sewerage -07 15 -02 -20 -14 -01 02 -15
F Construction 45 03 22 20 10 02 -05 12
G Wholesale and retail trade 45 02 21 22 68 04 53 10
H Transportation and storage 85 13 45 27 75 00 08 66
I Accommodation 81 -04 54 30 51 -02 42 11
J ICT 13 00 00 13 49 10 33 06
M Professional scientific 64 08 11 45 32 00 14 18
N Administrative and support 50 08 15 27 80 19 49 12
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
173
Table A63 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 93 24 44 26 105 12 59 34
C Manufacturing 132 34 54 44 08 19 -12 01
D Electricity gas 13 -04 09 08 41 02 25 14
E Water supply sewerage 45 -02 37 09 -08 -09 04 -03
F Construction 24 07 10 07 -01 07 -09 01
G Wholesale and retail trade 38 08 12 18 -67 -04 -73 10
H Transportation and storage -25 06 -28 -04 -47 03 -56 06
I Accommodation 59 -03 56 07 -74 -12 -41 -21
J ICT 58 19 80 -40 20 -21 40 00
M Professional scientific 61 11 34 16 -67 02 -26 -43
N Administrative and support 61 -20 38 43 -63 -24 -11 -29
2010-2013 2013-2016
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 26 04 -01 24 -29 17 -28 -18
C Manufacturing 00 14 -21 07 33 16 19 -01
D Electricity gas -43 26 -85 16 90 25 13 52
E Water supply sewerage -20 -07 -08 -05 04 -03 06 01
F Construction 40 05 26 10 -03 05 -05 -03
G Wholesale and retail trade 49 05 31 13 68 13 57 -01
H Transportation and storage 59 12 46 01 09 -27 15 20
I Accommodation 74 -07 55 26 47 -08 54 02
J ICT 16 -07 11 12 22 -25 42 05
M Professional scientific 70 20 16 33 45 13 25 06
N Administrative and support 61 08 31 21 81 -11 76 15
Appendix
174
Table A64 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 48 15 -01 34 70 17 53 00
C Manufacturing 132 32 56 45 16 14 -03 05
D Electricity gas 14 -03 05 11 35 -07 30 12
E Water supply sewerage 48 00 39 09 -10 -06 03 -07
F Construction 28 10 14 04 03 06 -14 11
G Wholesale and retail trade 38 10 07 21 -47 07 -61 07
H Transportation and storage -35 09 -40 -04 -41 06 -57 10
I Accommodation 62 -03 52 12 -65 -09 -43 -13
J ICT 00 -15 49 -34 03 -22 14 12
M Professional scientific 75 20 27 28 -46 02 -27 -22
N Administrative and support 91 -05 25 71 -60 -11 -07 -42
2010-2013 2013-2016
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 33 -11 04 40 50 28 05 17
C Manufacturing 06 13 -15 07 28 12 16 00
D Electricity gas 16 18 -26 25 23 17 02 05
E Water supply sewerage -17 -06 -05 -05 04 -04 08 00
F Construction 44 05 26 14 03 02 07 -07
G Wholesale and retail trade 37 05 17 15 65 12 54 -01
H Transportation and storage 56 11 42 04 46 -07 16 36
I Accommodation 70 -07 52 25 44 -07 51 01
J ICT 26 07 04 16 17 -20 37 00
M Professional scientific 56 17 11 28 52 16 23 13
N Administrative and support 65 17 27 22 59 06 41 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
175
A7 Chapter 7 Firm-level productivity growth and dynamics
A71 Productivity growth
Table A71 Relationship between lagged productivity level and subsequent productivity
growth over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 -0188 -0203 -0203
(000550) (000558) (000551)
TFP in t-1 Year 2006 -0222 -0238 -0235
(000518) (000525) (000519)
TFP in t-1 Year 2009 -0143 -0159 -0155
(000570) (000579) (000572)
TFP in t-1 Year 2012 -0156 -0172 -0171
(000516) (000524) (000517)
Year 2003 -00313 -00297
(000507) (000510)
Year 2006 -0184 -0183
(000489) (000491)
Year 2009 -00766 -00762
(000492) (000493)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 114200 113900 113900
R-squared 0061 0067 0084
Appendix
176
Table A72 Relationship between lagged productivity levels and subsequent productivity
growth by size and age
Dep var TFP growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 -0170 -0186 -0213 -0223
(000561) (000578) (00155) (00155)
TFP in t-1 Group 2 00397 00243 -000502 -000776
(00146) (00147) (00213) (00213)
TFP in t-1 Group 3 00793 00652 00725 00600
(00221) (00222) (00164) (00165)
TFP in t-1 Group 4 00753 00666
(00244) (00247)
Group 2 00227 000593 -0000410 0000118
(000940) (000963) (00162) (00162)
Group 3 00216 -000934 00235 00220
(00150) (00154) (00131) (00132)
Group 4 00235 -00351
(00157) (00169)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Observations 30135 30062 30135 30062
R-squared 0056 0073 0056 0073
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees size group 4 is
100+ employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5
years age group 3 is firms older than 5 The baseline category is firms of 2-3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
177
Table A73 Differences in productivity growth by ownership group within different firm
groups
Dep var TFP growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 00476
(00114)
Foreign Non-exporter 00573
(00213)
Foreign Exporter 00610
(00139)
Foreign Size group 1 00295
(00162)
Foreign Size group 2 00849
(00243)
Foreign Size group 3 000361
(00340)
Foreign Size group 4 00662
(00318)
Foreign Age group 1 0119
(00381)
Foreign Age group 2 -00117
(00363)
Foreign Age group 3 00467
(00124)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 31642 31642 31642 31274
R-squared 0032 0033 0033 0033
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column 2 and size and age group dummies in columns 3 and 4 respectively
Appendix
178
Table A74 Relationship between lagged productivity levels and subsequent productivity
growth by ownership and exporter status over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
Firm categories by
foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003
00577 00607 00141 00214
(00151) (00151) (00124) (00124)
TFP in t-1 Firm group Year 2006
00703 0101 00361 00558
(00152) (00152) (00118) (00118)
TFP in t-1 Firm group Year 2009
00338 00306 00450 00406
(00153) (00153) (00122) (00121)
TFP in t-1 Firm group Year 2012
00758 00436 00474 00321
(00146) (00146) (00109) (00109)
Firm group Year 2003
00978 00756 00286 000961
(00128) (00130) (000912) (000977)
Firm group Year 2006
-00290 -00145 -00592 -00411
(00133) (00135) (000871) (000932)
Firm group Year 2009
0114 0116 00502 00457
(00124) (00127) (000824) (000881)
Firm group Year 2012
0120 0120 00234 00155
(00126) (00129) (000782) (000835)
Year FE YES YES
Industry FE YES YES
Firm-level controls
YES YES YES YES
Region FE YES YES
Industry-year FE
YES YES
Observations 112374 112374 113900 113900
R-squared 0066 0085 0065 0085
Notes Firm group refers to foreign ownership in columns (1) and (2) and to exporter status in
columns (3) and (4)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
179
A72 Employment growth
Table A75 Relationship between lagged productivity levels and subsequent employment
growth over time
Dep var employment growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0113 0113 0113
(000472) (000478) (000475)
TFP in t-1 Year 2006 0120 0120 0119
(000434) (000439) (000437)
TFP in t-1 Year 2009 0109 0109 0107
(000479) (000485) (000482)
TFP in t-1 Year 2012 00982 00958 00956
(000442) (000448) (000445)
Year 2003 -00171 -00125
(000441) (000444)
Year 2006 -0134 -0128
(000422) (000423)
Year 2009 -00899 -00873
(000425) (000426)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 123900 123574 123574
R-squared 0042 0049 0054
Appendix
180
Table A76 Relationship between lagged productivity levels and subsequent employment
growth over time with alternative employment growth measures including exiting firms
Dep var employment growth from t to t+3 (including exiting firms (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0156 0147 0148
(000641) (000647) (000644)
TFP in t-1 Year 2006 0134 0127 0128
(000581) (000587) (000584)
TFP in t-1 Year 2009 0139 0132 0134
(000648) (000655) (000651)
TFP in t-1 Year 2012 0132 0126 0127
(000618) (000624) (000621)
Year 2003 -00765 -00618
(000617) (000619)
Year 2006 -0220 -0211
(000586) (000587)
Year 2009 -0177 -0173
(000591) (000590)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 143011 142638 142638
R-squared 0037 0047 0051
Productivity differences in Hungary and mechanisms of TFP growth slowdown
181
Table A77 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status with alternative employment growth measures
including exiting firms
Dep var employment growth from t to t+3 (including exiting firms t=2012)
VARIABLES (1) (2) (3) (4) (5) (6)
TFP in t-1 0134 0130 0134 0137 0134 0136
(000651) (000660) (000722) (000729) (000764) (000767)
TFP in t-1 Foreign -00109 -00138 00116 000347
(00166) (00167) (00289) (00289)
TFP in t-1 Exporter -00371 -00256 -00304 -00226
(00124) (00126) (00148) (00148)
TFP in t-1 Foreign exporter -00222 -00165
(00364) (00365)
Foreign -00254 -00351 -0102 -00739
(00151) (00156) (00254) (00256)
Exporter 00998 00982 00940 00889
(00100) (00102) (00106) (00107)
Foreign exporter 00855 00605
(00312) (00315)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 34980 34980 35564 35473 34980 34980
R-squared 0031 0051 0037 0054 0034 0052
Appendix
182
Table A78 Differences in employment growth by exporter status within different firm
groups
Dep var employment growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Exporter 00876
(000741)
Exporter Domestic 00893
(000788)
Exporter Foreign 00703
(00207)
Exporter Size group 1 00858
(000850)
Exporter Size group 2 00872
(00159)
Exporter Size group 3 0154
(00276)
Exporter Size group 4 00345
(00329)
Exporter Age group 1 00968
(00230)
Exporter Age group 2 0139
(00212)
Exporter Age group 3 00810
(000801)
industry-region FE YES YES YES YES
Firm-group indicators YES YES YES
Observations 34418 33909 34418 33989
R-squared 0034 0034 0034 0036
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
183
Table A79 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status over time
Dep var Employment growth from t to t+3 (t=2003200620092012)
Firm categories by foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003 000927 00131 00178 00190
(00129) (00130) (00107) (00107)
TFP in t-1 Firm group Year 2006 00137 00103 00130 000821
(00126) (00127) (00101) (00101)
TFP in t-1 Firm group Year 2009 -00778 -00676 -00498 -00426
(00129) (00130) (00104) (00104)
TFP in t-1 Firm group Year 2012 -00389 -00321 -00350 -00306
(00126) (00126) (000942) (000942)
Firm group Year 2003 -00601 -00332 000244 00299
(00110) (00113) (000795) (000856)
Firm group Year 2006 -00159 -000559 00640 00786
(00112) (00115) (000752) (000807)
Firm group Year 2009 00404 00249 0111 00882
(00106) (00109) (000714) (000767)
Firm group Year 2012 -00102 -00116 00747 00607
(00110) (00112) (000684) (000735)
Year FE YES YES
Industry FE YES YES
Firm-level controls YES YES YES YES
Region FE YES YES
Industry-year FE YES YES
Observations 121954 121954 123574 123574
R-squared 0046 0055 0045 0055
Notes Firm group refers to foreign ownership in columns (1) and (2) and exporter status in columns
(3) and (4)
Appendix
184
A73 Entry and exit
Table A710 Entry and exit premium by ownership and exporter status
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
VARIABLES (1) (2) (3) (4) (5) (6)
Entry Domestic 00363 00433 Exit Domestic -0165 -0161 Exit Non-exporter
-0172 -0186
(00103) (00102) (00112) (00112) (00122) (00121)
Entry Foreign 0414 0354 Exit Foreign 0255 0203 Exit Exporter
0171 0126
(00284) (00281) (00311) (00309) (00213) (00211)
Incumbent Foreign
0512 0461 Continuing Foreign
0465 0411 Continuing Exporter
0279 0232
(00122) (00129) (00123) (00131) (000887) (000926)
Industry FE YES Industry FE YES Industry FE YES
Industry-region FE YES Industry-region FE
YES Industry-region FE
YES
Firm-level controls YES Firm-level controls
YES Firm-level controls
YES
Observations 44231 44231 Observations 38367 38367 Observations 39020 38916
R-squared 0355 0383 R-squared 0339 0369 R-squared 0331 0370
Table A711 Differences in productivity levels by ownership group within different firm
groups
Depvar TFP in year t (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 0429
(00118)
Foreign Non-exporter 0278
(00206)
Foreign Exporter 0397
(00146)
Foreign Size group 1 0523
(00162)
Foreign Size group 2 0472
(00254)
Foreign Size group 3 0416
(00363)
Foreign Size group 4 0235
(00341)
Foreign Age group 1 0258
(00352)
Foreign Age group 2 0381
(00356)
Foreign Age group 3 0460
(00131)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 38367 38367 38367 37822
R-squared 0350 0361 0353 0356
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
185
Table A712 Entry and exit premium by ownership and exporter status over time
Depvar TFP in year t (t=2006200920122015 for entry and t=2003200620092012 for exit)
VARIABLES (1) (2) VARIABLES (3) (4) VARIABLES (5) (6)
Entry Domestic Year 2006
-00510 -00403 Exit Domestic 2003 -0187 -0188 Exit Non-exporter 2003 -0197 -0198
(000924) (000923) (00107) (00106) (00114) (00113)
Entry Domestic Year 2009
00244 00230 Exit Domestic 2006 -00996 -0101 Exit Non-exporter 2006 -0114 -0118
(000999) (000996) (000917) (000911) (000977) (000971)
Entry Domestic Year 2012
00594 00515 Exit Domestic 2009 -0105 -0113 Exit Non-exporter 2009 -0116 -0123
(000985) (000983) (000942) (000937) (00101) (00101)
Entry Domestic Year 2015
00475 00392 Exit Domestic 2012 -0140 -0150 Exit Non-exporter 2012 -0167 -0174
(000998) (000999) (00111) (00110) (00119) (00119)
Entry Foreign Year 2006
0374 0313 Exit Foreign 2003 0116 00940 Exit Exporter 2003 00659 00517
(00265) (00264) (00264) (00263) (00196) (00197)
Entry Foreign Year 2009
0423 0410 Exit Foreign 2006 0199 0153 Exit Exporter 2006 0194 0165
(00257) (00257) (00267) (00265) (00183) (00183)
Entry Foreign Year 2012
0342 0334 Exit Foreign 2009 0197 0184 Exit Exporter 2009 00720 00760
(00279) (00278) (00278) (00277) (00185) (00185)
Entry Foreign Year 2015
0382 0365 Exit Foreign 2012 0217 0223 Exit Exporter 2012 0114 0137
(00276) (00275) (00307) (00305) (00208) (00208)
Incumbent Foreign Year 2006
0485 0428 Continuing Foreign 2003 0416 0386 Continuing Exporter 2003 0278 0257
(00122) (00124) (00124) (00126) (000943) (000994)
Incumbent Foreign Year 2009
0410 0391 Continuing Foreign 2006 0498 0446 Continuing Exporter 2006 0317 0280
(00120) (00122) (00122) (00124) (000895) (000943)
Incumbent Foreign Year 2012
0436 0439 Continuing Foreign 2009 0414 0404 Continuing Exporter 2009 0194 0201
(00122) (00124) (00119) (00122) (000867) (000915)
Incumbent Foreign Year 2015
0471 0476 Continuing Foreign 2012 0412 0422 Continuing Exporter 2012 0211 0239
(00118) (00120) (00120) (00122) (000827) (000876)
Year FE YES Year FE YES Year FE YES
Industry FE YES Industry FE YES Industry FE YES
Firm-level controls YES YES Firm-level controls YES YES Firm-level controls YES YES
Industry-year FE YES Industry-year FE YES Industry-year FE YES
Region FE YES Region FE YES Region FE YES
Observations 164136 164136 Observations 155657 155657 Observations 157711 157711
R-squared 0369 0380 R-squared 0373 0386 R-squared 0374 0387
Table A713 Entry and exit premium by size and age
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
Firm categories by size age
VARIABLES (1) (2) VARIABLES (3) (4) (5) (6)
Entry Group 1 00233 00151 Exit Group 1 -0170 -0171 -0214 -0210
(00108) (00105) (00121) (00118) (00250) (00241)
Entry Group 2 0106 000987 Exit Group 2 -0201 -0260 -0286 -0260
(00298) (00289) (00280) (00272) (00271) (00261)
Entry Group 3 0124 00204 Exit Group 3 -0152 -0245 -0219 -0207
(00574) (00556) (00479) (00464) (00179) (00173)
Entry Group 4 0123 -00552 Exit Group 4 -0291 -0453
(00720) (00697) (00532) (00517)
Incumbent Group 2 00137 -00620 Continuing Group 2 -00108 -00902 -00277 -00256
(00104) (00101) (00111) (00109) (00170) (00164)
Incumbent Group 3 00163 -0130 Continuing Group 3 000582 -0148 -00759 -00758
(00170) (00168) (00179) (00176) (00131) (00127)
Incumbent Group 4 00150 -0268 Continuing Group 4 -00159 -0293
(00181) (00185) (00188) (00192)
Industry FE YES Industry FE YES YES
Industry-region FE YES Industry-region FE YES YES
Firm-level controls YES Firm-level controls YES YES
Observations 46160 46034 39020 38916 38459 38357
R-squared 0296 0355 0311 0369 0315 0374
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is 50-99
employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is firms of 4-5
years age group 3 is firms older than 5 years
Figure A71 Share of exiting firms in the subsequent 3 years by lagged productivity levels in
different periods
A8 Chapter 8 Retail
Appendix Table A81 Event study regression for the whole retail industry
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 0005 0014 0012 0030 -0230 -0511 (0006) (0005) (0005) (0005) (0030) (0049)
pre_trend_treated3 -0010 -0003 -0008 -0003 -0037 -0031 (0006) (0008) (0006) (0008) (0030) (0056)
pre_trend_treated4 -0020 -0010 -0014 0000 -0209 -0299 (0006) (0007) (0006) (0007) (0029) (0051)
pre_trend_treated5 0004 0011 0006 0017 -0128 -0258 (0007) (0008) (0007) (0008) (0034) (0064)
pre_trend_treated6 -0008 0001 -0004 0008 -0129 -0222 (0007) (0008) (0006) (0008) (0035) (0065)
pre_trend_treated7 0001 -0001 0016 0017 -0365 -0496 (0010) (0013) (0010) (0012) (0053) (0072)
trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)
trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)
trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)
trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)
post_trend_treated1 -0039 -0006 -0007 0044 -0885 -1394 (0012) (0012) (0012) (0011) (0061) (0096)
post_trend_treated2 0022 0003 0044 0048 -0665 -1273 (0012) (0012) (0011) (0011) (0068) (0100)
post_trend_treated3 -0001 0004 0035 0058 -0993 -1531 (0012) (0012) (0012) (0012) (0058) (0092)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 225866 209604 225860 209598 225908 209647
R-squared 0958 0978 0961 0980 0684 0809
Appendix
188
Appendix Table A82 Event study regression for NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 -0008 -0004 -0002 0016 -0189 -0576 (0004) (0005) (0004) (0005) (0033) (0055)
pre_trend_treated3 -0016 -0018 -0013 -0014 -0057 -0064 (0006) (0011) (0006) (0010) (0034) (0059)
pre_trend_treated4 -0010 -0005 -0002 0008 -0236 -0351 (0004) (0007) (0004) (0007) (0034) (0060)
pre_trend_treated5 -0004 0002 -0001 0010 -0129 -0304 (0006) (0008) (0006) (0008) (0037) (0068)
pre_trend_treated6 0011 0000 0018 0011 -0173 -0307 (0007) (0009) (0007) (0009) (0045) (0085)
pre_trend_treated7 -0016 -0032 0002 -0007 -0433 -0640 (0010) (0016) (0009) (0015) (0068) (0091)
trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)
trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)
trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)
trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)
post_trend_treated1 -0009 -0032 0033 0036 -1163 -1875 (0008) (0012) (0008) (0011) (0084) (0118)
post_trend_treated2 0057 -0024 0087 0041 -0888 -1810 (0014) (0013) (0012) (0012) (0097) (0121)
post_trend_treated3 0014 -0031 0060 0044 -1255 -2040 (0011) (0013) (0010) (0012) (0079) (0108)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 94740 87533 94737 87530 94740 87533
R-squared 0968 0982 0973 0985 0642 0809
Appendix Table A83 Sales and the number of different days in a month
(1)
Dependent ln sales
Sunday 0049 (0001)
Saturday 0059 (0001)
Friday 0054 (0001)
Thursday 0050 (0001)
Wednesday 0053 (0001)
Tuesday 0060 (0001)
Monday 0048 (0001)
holiday 0008 (0000)
Jan -0169 (0002)
Dec 0138 (0003)
summer 0032 (0002)
date -0000 (0000)
Observations 463345
R-squared 0970
Appendix
190
HOW TO OBTAIN EU PUBLICATIONS
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(httpeeaseuropaeudelegationsindex_enhtm)
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doi 10287333213
ET-0
4-1
7-8
33-E
N-N
Productivity differences in Hungary and mechanisms of TFP growth slowdown
Table of contents
EXECUTIVE SUMMARY I
1 INTRODUCTION 1
2 DATA SOURCES 4
21 Cleaning the data and defining industry categories 5
22 Productivity estimation 6
23 Estimation sample 10
24 Firm-level variables 13
25 Industry categorization 16
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON 18
31 Convergence 18
32 Within-industry heterogeneity 24
33 Firm dynamics 28
34 Conclusions 31
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION 32
41 Context 32
42 Aggregate productivity and the self-employed 33
43 The evolution of productivity distribution in Hungary 36
44 Duality in productivity and productivity growth 47
45 Conclusions 56
5 ALLOCATIVE EFFICIENCY 58
51 Olley-Pakes efficiency 58
52 Product market and capital market distortions 62
53 Conclusions 70
6 REALLOCATION 73
61 Reallocation across industries 73
62 Reallocation across firms 76
63 Failure of reallocation Zombie firms 82
64 Conclusions 86
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS 89
71 Productivity growth 89
72 Employment growth 95
73 Entry and exit 99
74 Conclusions 103
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE 104
81 Context 104
82 Data 108
83 General trends 110
84 Allocative efficiency and reallocation 118
85 Trade 123
86 Policies Crisis taxes 131
87 Policies Mandatory Sunday closing 133
88 Conclusions 141
9 CONCLUSIONS 142
REFERENCES 144
APPENDIX 152
A3 Chapter 3 Internationally comparable data sources and methodology 152
A31 EU KLEMS amp OECD STAN 152
A32 OECD Structural and Demographic Business Statistics 152
A33 OECD Productivity Frontier 153
A4 Chapter 4 Evolution of the Productivity Distribution 154
A5 Chapter 5 Allocative Efficiency 160
A6 Chapter 6 Reallocation 171
A7 Chapter 7 Firm-level productivity growth and dynamics 175
A71 Productivity growth 175
A72 Employment growth 179
A73 Entry and exit 184
A8 Chapter 8 Retail 187
Productivity differences in Hungary and mechanisms of TFP growth slowdown
i
EXECUTIVE SUMMARY
Slow post-crisis total factor productivity (hereafter TFP) growth is a significant policy
challenge for many European countries in general and for Hungary in particular This
report aims at providing a comprehensive analysis of the processes behind productivity
growth slowdown in Hungary based on micro-data from administrative sources between
2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions A focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact detail of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Executive Summary
ii
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides an
exhaustive picture of Hungarian businesses It is important to keep in mind though that it
omits two important parts of the economy the overwhelming majority of the non-market
sector (including public works) and the self-employed Given the number of people
employed in these two sectors their performance has a strong effect on macro numbers
The available albeit scarce data for the self-employed qualify the findings by suggesting
that the measured productivity level and growth of this group is considerably below than
that of double-entry bookkeeping firms ndash implying that within-industry productivity
dispersion may be even larger than what is indicated by the balance sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
of capital increase on average its rise was disproportionally larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
Productivity differences in Hungary and mechanisms of TFP growth slowdown
iii
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly adding little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterized by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers have increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
The results of this report confirm that Hungary is atypical because of the relatively poor
productivity performance of frontier firms Importantly contrary to a strong version of the
duality concept this is not a result of Hungarian frontier firms being on the global frontier
typically they are quite far away from it This robust pattern underlines that besides
helping non-frontier firms policy may also have to focus on the performance of the
frontier group A transparent environment with a strong rule of law complemented by a
well-educated workforce and a robust innovation system is key for providing incentives to
invest into the most advanced technologies
Executive Summary
iv
The analysis in this report reinforces the impression that there is a large productivity gap
between globally engaged or owned and other firms the gap being about 35 percent in
manufacturing and above 60 percent in services This gap seems to be roughly constant in
the period under study The firm-level analysis in Chapter 7 also reveals that one of the
mechanisms which conserves the gap is that foreign frontier firms are able to increase
their productivity more than their domestic counterparts even from frontier levels These
findings reinforce the importance of well-designed policies that are able to help domestic
firms to catch up with foreign firms A key precondition for domestic firms to build linkages
with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A
more specific conclusion is the importance of enabling high-productivity domestic firms to
improve their productivity levels even further
The large within-industry productivity dispersion the relatively low (though not extreme
in international comparison) allocative efficiency documented in some of the industries the
strong positive contribution of reallocation to total TFP growth before the crisis and the
relatively low entry rate imply that policies promoting reallocation have a potential to
increase aggregate productivity levels significantly These policies can include improving
the general framework conditions by cutting administrative costs reducing entry and exit
barriers and using a neutral regulation The fact that capital market distortions still appear
to be significantly above their pre-crisis levels impliesthat policies that reduce financial
frictions may help the reallocation process The fact that exporters tend to expand faster
relative to non-exporters suggests that access to EU and global markets generate a strong
and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general
traded and more knowledge-intensive sectors fared better both in terms of productivity
growth and allocative efficiency The difference between traded and non-traded sectors
points once again to the importance of global competition in promoting higher productivity
and more efficient allocation of resources This also implies that adopting policies that
focus on innovation or reallocation in services may be especially important given the large
number of people working in those sectors The better performance of and reallocation into
more knowledge-intensive sectors underline the importance of education policies aimed at
developing up-to-date and flexible skills and the significance of innovation policies that
help to improve the knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people
working at firms with at least a few employees and those working in very small firms or as
self-employed The latter category represents 30-50 percent of the people engaged in
some important industries Inclusive policies may attempt to generate supportive
conditions for these people by providing knowledge and training as well as helping them
find jobs with wider perspectives or set up a well-operating firm The large share of these
unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a marked difference between the pre-
crisis period characterised by strong reallocation mainly via the expansion of large
foreign-owned chains and the post-crisis period with a stagnating share of large chains
This break is likely to be linked to post-crisis policies favouring smaller firms While halting
further concentration in a country with already one of the highest share of multinationals
in this sector can have a number of benefits in the long run it is likely to lead to higher
prices and lower industry-level productivity growth Policies should balance carefully
between these trade-offs Another key pattern identified is the increasing role of retailers
(and wholesalers) in trade intermediation both on the import and export side
Policymakers should encourage these trends and design policies which provide capabilities
for such firms to enter international markets probably via e-commerce
Productivity differences in Hungary and mechanisms of TFP growth slowdown
1
1 INTRODUCTION
Slow post-crisis TFP growth is a significant policy challenge for many European countries in
general and for Hungary in particular This report aims at providing a comprehensive
analysis of the processes behind productivity growth slowdown in Hungary based on
micro-data from administrative sources between 2001-2016
In particular the report aims to contribute to four ongoing debates First it attempts to
document the productivity growth slowdown in detail to uncover potential sources of
heterogeneity Besides documenting differences across industries it also makes an effort
to identify how the whole shape of the productivity distribution evolved along different
dimensions The focus on the whole distribution is motivated inter alia by recent findings
that in many countries productivity slowdown has resulted from a combination of healthy
productivity growth of frontier firms coupled with an increasing gap between frontier and
non-frontier firms (Andrews et al 2017) Interestingly this does not seem to be the case
in Hungary (OECD 2016) where frontier firm productivity growth has actually been
similar to or slower than that of other firms Understanding the exact details of this
phenomenon is of much interest given that slow frontier firm productivity growth
necessitates different policies from those that intend to close the gap between frontier and
non-frontier firms
The second overarching question related to frontier and non-frontier firms is the idea of
the so-called duality in Hungary The concept of duality emphasises the large differences in
terms of productivity and wages between globally oriented often foreign-owned large
firms and the rest of the economy Duality also refers to the lack of interconnectedness
between these two groups of firms in terms of supplier-buyer linkages and worker flows
which limits positive intergroup spillovers One version of the duality concept also asserts
that the lsquoglobalrsquo sector is as productive as the global frontier In this report we will use a
number of methods and perspectives to provide evidence for the different dimensions of
this duality and investigate whether there is evidence for a narrowing gap Duality is an
important concept motivating many economic policy decisions therefore understanding its
causes and evolution is of considerable policy interest
The third group of questions relates to how efficiently resources are allocated across firms
Similarly to other countries within-industry productivity differences are at least a
magnitude larger than between-industry differences This implies that the efficiency of the
allocation of resources within an industry (ie whether more productive firms have access
to more labour and capital) matters much for aggregate productivity Two recent
developments might have affected allocative efficiency First the crisis put an immense
pressure on financial intermediation which could have distorted capital allocation decisions
(Gopinath et al 2017) Second Hungary has introduced a number of new policy tools
some of which are size-dependent or target only a subset of firms within an industry
Finally the report is interested in the extent to which sectors and industries differ in terms
of productivity and firm dynamics One useful distinction here is between the traded and
non-traded sectors of the economy In traded sectors international competition can
provide powerful incentives for firms to invest into more productive technologies and
competitive pressure can also drive a more efficient allocation of resources by providing
opportunities for more efficient firms to grow and by forcing less efficient firms out of the
market Another operative distinction between industries is the role of knowledge in
production Knowledge-intensive sectors may exhibit different dynamics thanks to the
more significant role of technological differences and change
Introduction
2
In the paper we use a number of different approaches to shed light on the various aspects
of these overarching research questions The basis of our research is a set of
administrative micro-data of all double-entry bookkeeping enterprises in Hungary We
introduce these data in Chapter 2 of this report in detail The database provides a very
detailed and comprehensive picture of the Hungarian business economy It is important to
keep in mind though that it omits two important parts of the economy the overwhelming
majority of the non-market sector (including public works) and the self-employed Given
the number of people employed in these two sectors their performance has a strong effect
on macro numbers The available albeit scarce data for the self-employed qualify the
findings by suggesting that the measured productivity levels and growth of this group are
considerably below those of double-entry bookkeeping firms ndash implying that within-
industry productivity dispersion may even be larger than what is indicated by the balance
sheet data
Chapter 3 provides a context for our investigation by presenting internationally
comparable micro-data based information on different dimensions of productivity levels
growth dispersion and dynamics These comparisons primarily illustrate that Hungarian
productivity developments and patterns are well within the range found in similar
countries but in some respects ndash including the extent of productivity slowdown or the
relatively low entry and exit rates ndash they differ markedly from the averages of the
countryrsquos peer group OECD data also reveal that the level of productivity is relatively low
even at the top of the national distribution Hungarian frontier firms lag considerably
behind the global frontier
Chapter 4 analyses how the shape of productivity distribution evolved by reporting
productivity dynamics in the different deciles This analysis confirms that within-sector
productivity dispersion is indeed many times larger than across-industry differences The
analysis also reveals that in most industries the frontier firmsrsquo productivity increased at
similar or lower rates than that of other deciles of the productivity distribution This makes
Hungary an exception from the general pattern of divergence between frontier and other
firms The report suggests that the main reason for this is that most of the Hungarian
frontier firms are far away from the global frontier
A similar approach reveals the importance of duality in terms of ownership About 50
percent of frontier firms are foreign-owned and on average they are 30 percent more
productive and pay 70 percent higher wages than domestically-owned firms The report
also finds little evidence for convergence This gap between averages however does not
imply a complete separation between the two groups many domestically-owned firms are
more productive than the typical foreign firm and vice versa The productivity distribution
of foreign firms is more dispersed than that of the domestically-owned showing more
technological heterogeneity within this group Regarding the distinction between private
and public enterprises ndash another possible dimension of duality ndash the report finds that
there are relatively few state-owned firms in Hungary and they are mainly concentrated in
utilities The performance of these firms lags behind privately-owned firms and the gap
has not been decreasing
The large productivity dispersion in the report motivates the analysis of allocative
efficiency in Chapter 5 It relies on the Olley-Pakes (1996) approach to quantify the extent
to which more productive firms possess more resources and applies the Hsieh-Klenow
(2009) methodology to distinguish between product and capital market distortions Both
approaches suggest significantly higher efficiency in traded sectors Static allocative
efficiency varies substantially across industries but appears to be quite persistent with
little change during the period under study The strain on financial intermediation that
accompanied the crisis increased the misallocation of capital Not only did the implicit cost
Productivity differences in Hungary and mechanisms of TFP growth slowdown
3
of capital increase on average its rise was disproportionately larger for young firms
potentially constraining the reallocation process by the growth of new enterprises
The static analysis of allocative efficiency is complemented by a dynamic approach to
productivity decomposition in Chapter 6 Reallocation across industries played a relatively
small role in aggregate productivity growth throughout the period under study most
productivity increase resulted from within-industry developments Pre-crisis within-
industry growth was dominated by reallocation but within-firm productivity growth was
also substantial During the crisis a large within-firm productivity decline was only partly
counterbalanced by reallocation across firms Post-crisis the contribution of the
reallocation process deteriorated significantly contributing little to aggregate productivity
growth In particular globally integrated firms contributed a lot to productivity growth pre-
crisis but their contribution declined after the crisis Chapter 6 also identifies a peculiar
source for the failure of the reallocation process namely the survival of large
permanently loss-making firms (dubbed as ldquozombie firmsrdquo) These employed well above 10
percent of all employees in most years even before the crisis One can however observe
some improvement in recent years in this respect
While the investigation of allocative efficiency and reallocation uses micro-data based
industry-level measures Chapter 7 examines these processes at the firm-level by relating
productivity to future productivity and employment growth as well as entry and exit This
approach can control for both industry- and firm-level heterogeneity Although these
dynamic processes are remarkably similar before and after the crisis the analysis reveals
characteristic differences between globally engaged and domestic-oriented firms relevant
for the duality debate In particular foreign firms near the Hungarian productivity frontier
seem to be able to increase their productivity further while similarly productive domestic
firms find such improvements much harder to achieve In terms of reallocation exporting
firms grow significantly faster than non-exporters (even of the same productivity)
suggesting reallocation to exporters
Besides presenting the trends in the full market economy a specific industry retail trade
is analysed in detail in Chapter 8 A key pattern observed in that industry is a
characteristic trend break around the crisis The pre-crisis period was characterised by
increasing concentration resulting from the expansion of large chains and foreign firms
These trends seem to have stopped or slowed down after the crisis In line with this
pattern the contribution of reallocation decreased post-crisis relative to earlier periods
While many factors can play a role in such a pattern it may be related to the different
size-dependent policies introduced after 2010 While smaller retail firms may benefit from
these developments consumers may face higher prices in the long run
The retail and wholesale sectors are also of interest as they play a large and increasing
role in mediating imports and exports for the market economy There was a large increase
in goods imported directly by retailers rather than indirectly via wholesalers This was
mainly driven by large foreign firms and may have benefited their consumers thanks to a
lower degree of double marginalisation and a wider choice Both the number of exporting
firms and the amount exported by wholesalers and retailers increased most likely
benefitting from easy access to the Common Market and the opportunities provided by e-
commerce Exports by wholesalers and retailers can be an important channel for smaller
producers to reach foreign markets more easily
Data Sources
4
BOX 21 AMADEUS and the NAV balance sheet data
An alternative and frequently used source of balance sheet data is the AMADEUS dataset
In this box we compare the data about Hungary with the dataset used in this report
namely the administrative NAV panel
AMADEUS is a firm-level dataset collected and issued by Bureau Van Dijk a Moodyrsquos
Analytics Company It contains comprehensive financial information on around 21 million
companies across Europe with a focus on private company information It includes
information about company financials in a standard format (which makes it comparable
across countries) directors stock prices and detailed corporate ownership structures
(Global Ultimate Owners and subsidiaries) Financial information on firms consists of data
from balance sheets profit and loss statements and standard ratios Non-mandatory cells
are however often missing (eg employment) Therefore the drawbacks of this
database are that it is not representative and that not all firms provide enough
information to analyse issues such as productivity or TFP
Table B21 shows the coverage of AMADEUS (the number of firms as a share of the firms
in the administrative NAV data) by year and size category In earlier years the AMADEUS
sample consisted of mostly large firms but even the coverage of larger firms was
relatively low Recently the expanding coverage has made the AMADEUS sample more
representative While the smallest firms are still undersampled the coverage of firms with
more than 5 employees has reached nearly 100 (In some cases it is even above 100
because of slight differences in the number of employees reported)
The two databases also differ in terms of the variables they include The NAV data are
more detailed in terms of assets and liabilities AMADEUS in contrast provides more
information on ownership It defines the Global Ultimate Owner (GUO) for each company
and analyses their shareholding structure Ownership share is given in percentages and
in addition the degree of independence is also given
Our main aim in this report is to estimate productivity and its change reliably and
representatively for different types of firms small and large This requires a decent
coverage of all types of firms and reliable information on their finances for a number of
periods Because of this we prefer to use the NAV database with its large and universal
coverage and the rich information on firm inputs and outputs
2 DATA SOURCES
The main database we use in this project is the balance sheet panel of Hungarian firms
between 2000-2016 The balance sheet dataset is an administrative panel collected by the
National Tax Authority (NAV formerly APEH) from corporate tax declarations The
database includes the balance sheet and profit amp loss statements of all double-entry
bookkeeping Hungarian enterprises between 2000 and 2016 (see Section 42 for a brief
discussion of the size and the performance of the not double-entry bookkeeping sector of
the Hungarian economy) Besides key financial variables the database includes the
industry code of the firm the number of its employees its date of foundation the location
of its headquarters and whether it is domestically- or foreign-owned for each year
Productivity differences in Hungary and mechanisms of TFP growth slowdown
5
21 Cleaning the data and defining industry categories
We have taken a number of steps to clean the key variables in the balance sheet panel
First we impute missing observations for firms with more than 10 employees in the
preceding and following years For continuous variables we use the average of the
previous and following year values For categorical variables we use the value from the
previous year Similarly we impute missing data using lagged values for two of the largest
firms in year 2016
Then a baseline cleaning is applied to the values of all the financial variables to correct for
possible mistakes of reporting in HUF rather than 1000 HUF or for extremely small or big
values in the data Employment and sales are cleaned of extreme values and outliers
Suspiciously large jumps followed by another jump into the opposite direction are
smoothed by the average of the previous and following years Regarding capital stocks we
use the sum of tangible and intangible assets Whenever intangible assets are missing we
input a zero
We deflate the different variables with the appropriate price indices from the OECD STAN
which includes value added capital intermediate input and output price deflators at 2-
digit industry level1
Regarding industry codes the database in general includes the 2-digit industry code of a
firm in each year based on the actual industry classification system 4-digit industry codes
are only available between 2000 and 20052 We harmonize to NACE Rev 2 codes by using
1 A few industries are merged in the EU-KLEMS We will call this 64 category classification ldquo2-digitrdquo
industry in what follows
2 The database available in the CSO which we will use for Task 3 includes 4-digit codes for all years
BOX 21 Amadeus and the NAV database (cont)
Table B21 Coverage by employment categories (AmadeusNAV)
Year 1 emp 2-5 emp
6-10 emp
11-20 emp
21-49 emp
50-249 emp
250 lt emp
Total
2004 005 028 092 105 160 312 642 043
2005 010 050 169 288 483 1066 2227 108
2006 017 087 315 553 966 1935 3632 192
2007 2209 3006 4384 5249 5743 6082 7412 3135
2008 098 324 951 1692 2840 4868 7827 576
2009 5962 6070 7217 7428 7831 7798 9336 6301
2010 2142 4685 7034 7540 8424 8228 9634 4175
2011 2277 4736 7064 7753 8521 8657 9681 4220
2012 9397 8298 9305 9484 9507 9159 10121 8990
2013 7274 8140 9423 9981 9747 9445 10312 8044
Notes This table shows the number of observations in AMADEUS as a percentage of observations in the
NAV data for each year-size category cell
Data Sources
6
concordances from Eurostat3 We use these harmonized codes whenever we define deciles
and the frontier or within-industry variables so that NACE revisions should not affect the
results Finally we split those firms which switch from manufacturing to services or vice
versa adding separate firm identifiers for the two periods4
22 Productivity estimation
From many perspectives the most robust and convenient measure of productivity is
labour productivity We calculate this variable simply as the log of value added per
employee At the same time the key shortcoming of labour productivity is that it does not
reflect the differences in capital intensity across firms Total Factor Productivity (TFP) aims
to control for this issue We estimate TFP with the method of Ackerberg et al (2015) ndash we
refer to it as ACF ndash which can be regarded as the state of the art In the Appendix we
also provide robustness checks using different productivity measures
Technically firm-level TFP estimation involves estimating a production function
119871119899 119881119860119894119905 = 120573119897 lowast 119897119899 119871119894119905 + 120573119896 lowast 119897119899 119870119894119905 + 휀119894119905 (21)
where i indexes firms t indexes years 119871119894119905 is the number of employees and 119870119894119905 is the capital
stock of firm i in year t In this specification the residual of the equation 휀119894119905 is the
estimated TFP for firm i in year t 120573119897 and 120573119896 are the output elasticities in the production
function reflecting the marginal product of labour and capital and the optimal capital
intensity
Estimating firm-level production functions involves several choices First it is usually
important to include year fixed effects in order to control for macro- or industry level
shocks Second industries may differ in their optimal capital intensity ie the coefficients
of the two variables To handle this we estimate the production function separately for
each 2-digit NACE industry Third financial data reported by small firms may not be very
accurate Including them into the sample on which the production function is estimated
may introduce bias into that regression Hence we estimate the production functions only
on the sample of firms with at least 5 employees but also predict the TFP for smaller firms
Fourth the Cobb-Douglas production function may be too restrictive in some cases but it is
possible to estimate more flexible functions (eg translog)
A key problem with firm-level TFP estimation is that input use (119871119894119905 and 119870119894119905) can be
correlated with the residual TFP Consequently OLS estimation may yield biased
coefficients The bias arises from attributing part of the productivity advantage to the
higher input use of more productive firms A simple and robust solution for this issue is to
estimate the production function with a fixed effects estimator This method controls for
endogeneity resulting from unobserved time-invariant firm characteristics
3 Because of the changes in the Hungarian industry classification in 2003 and 2008 industry code harmonization is required The Hungarian industry classification system (TEAOR) corresponds to NACE Rev 1 between 1998 and 2002 to NACE Rev11 from 2003 to 2007 and to NACE Rev 2 from 2008 onwards The conversion of industry codes in 2000-2002 to NACE Rev 11 is relatively straightforward and efficient thanks to the 4-digit codes The conversion from NACE Rev 11 to
NACE Rev 2 is less so as 4-digit codes are only available until 2005 Hence for each firm we assume that its 4-digit industry remained the same in the period of 2005-2007 and use this 4-digit industry for the conversion After these conversions we clean industry codes ignoring those changes when firms switch industries for 1-3 years and then switch back This process leads to a harmonized 2-digit NACE Rev 2 code for each year
4 After industry cleaning this can only happen either at the beginning or the end of the period when the firm is observed or if the firm switches industry for a period longer than 3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
7
A second and related problem is that input use can also be correlated with time-variant
productivity shocks This type of endogeneity is not corrected by the fixed effects
estimator More specifically managers (unlike economists analysing the balance sheet)
may observe productivity shocks at the beginning of the year and adjust the flexible inputs
(labour in our case) accordingly As a result we may falsely ascribe a productivity
improvement to the increase in labour input The recent best practice of handling this
issue is the control function approach in which one controls for the productivity shock by
using a proxy for it in an initial step The proxy is another flexible input usually materials
or energy use As we have reliable data on materials we will use that variable
In this report we rely on the method of Ackerberg et al (2015)5 Importantly with this
method the production function coefficient estimates are close to what is expected6 and
the returns to scale are slightly above one (typically between 1 and 12 see Figure 21)7 8
After estimating the coefficients we simply calculate the estimated TFP for firm i in year t
by subtracting the product of inputs and the estimated elasticities
119879119865119875119894119905 = 119871119899 119881119860119894119905 minus 120573 lowast 119897119899 119871119894119905 minus 120573 lowast 119897119899 119870119894119905 (22)
In this way we calculate a TFP level (rather than its value relative to year and industry
fixed effects) which is important when calculating productivity changes Note that the
calculated productivity changes are very similar to the logic of the Solow residual
When interpreting productivity estimates it is important to remember that both the labour
productivity and TFP estimates are value added-based measures In other words in cross-
sectional comparisons they show how many forints or euros (rather than cars or apples)
are produced with a given amount of inputs Therefore value added based productivity
reflects both physical productivity and markups9
5 We have estimated all of these with the prodest (Rovigatti and Mollisi 2016) command in Stata
6 Reassuringly Ackerberg et al (2015) themselves report some production function estimates using data from Chile and their estimated coefficients are similar to what we get 08-09 for labour and about 02 for capital
7 We also control for attrition of firms from the sample but this does not affect the estimates significantly
8 The Levinsohn-Petrin (2003) and Wooldridge (2009) production function estimates are less attractive Most importantly the estimated returns to scale are well below 1 typically between 07 and 08 These implausibly low returns to scale imply an implausibly high TFP for larger firms with their TFP advantage being many times their labour productivity advantage even though they employ much more capital per worker The implausibly low returns to scale strongly affect our calculations In such a framework if a firm doubles all of its inputs and outputs its estimated TFP increases by about 30 percent even though it transforms inputs into outputs in the same way In
productivity decompositions for example size and growth are mechanically related to TFP leading to overestimating allocative efficiency
9 Recent literature has emphasized the difference between value added-based (revenue) and physical productivity and has also proposed a number of methods to distinguish between the two (see Foster et al 2008 Hsieh and Klenow 2009 Syverson 2011 Bellone et al 2014 De Loecker and Goldberg 2014) Hornok and Murakoumlzy (2018) also apply such methods to investigate the markup differences of Hungarian importers and exporters
Data Sources
8
Figure 21 ACF production function coefficients
A) Manufacturing
B) Services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
9
We take some additional steps to clean our raw productivity estimates First we winsorize
productivity at the lowest and highest percentile of the 2-digit industry-year-specific
distribution of firms with at least 5 employees We fill out gaps of 1 or 2 years in the
productivity variable by using linear approximation Finally we clean the productivity of
firms with at least 5 employees based on changes We smooth large 1-year jumps10 and
disregard productivity values if there is a large jump after entry or before exit11
Table 21 presents the average labour productivity and TFP by 1-digit NACE categories in
2004 and 2016
Table 21 Average productivity measures by 1-digit industry in 2004 and 2016
unweighted
Labour productivity Total factor productivity
2004 2016 2004 2016
NACE Description Mean Stdev Mean Stdev Mean Stdev Mean Stdev
B Mining 797 088 867 086 408 087 441 065
C Manufacturing 777 087 806 079 581 079 598 077
D Electricity gas steam 929 106 953 138 629 091 634 132
E Water supply sewerage waste
812 085 830 089 604 091 593 094
F Construction 773 080 803 072 620 071 646 066
G Wholesale and retail trade 804 102 825 090 652 093 678 081
H Transportation and storage 841 071 837 072 625 067 623 072
I Accommodation 710 075 752 080 594 068 640 071
J ICT 834 094 862 090 631 101 669 098
M Professional scientific and technical activities
815 087 844 088 636 087 673 088
N Administrative and support services
763 098 792 094 640 107 662 113
Total 789 095 815 087 620 090 647 087
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
10 We replace 119910119905 with 119910119905minus1+119910119905+1
2 if abs(119910119905 minus 119910119905minus1)gt1 abs(119910119905+1 minus 119910119905minus1)le 05 abs(119910119905minus1 minus 119910119905minus2)le 03 timesabs(119910119905 minus
119910119905minus1) abs(119910119905+2 minus 119910119905+1) le 03 timesabs(119910119905 minus 119910119905minus1) where 119910119905 denotes a productivity measure in logs of year
t Corresponding conditions are modified to abs(119910119905+1 minus 119910119905minus1) le 1 abs(119910119905+1 minus 119910119905minus1) le 03 times abs(119910119905 minus119910119905minus1) in the second observed year and in the year before the last observed one
11 abs(119910119905 minus 119910119905minus1)gt15
Data Sources
10
23 Estimation sample
Next we introduce some restrictions to define our baseline sample As our aim is to focus
on the market economy we constrain our sample based on industry and legal form We
keep only the market economy according to the OECD definition dropping observations in
agriculture and in non-market services (NACE Rev 2 categories 53 84-94 and 96-99)
We also drop financial and insurance activities12 as well as observations for which industry
is missing even after cleaning
We also drop firms which functioned as non-profit budgetary institutions or institutions
with technical codes at any time during the observed period We also drop firms which
never reported positive employment We refer to the remaining sample as the baseline
sample
Our main sample used for most of the calculations and for the estimations consists of
observations with at least 5 employees a non-missing total factor productivity value and
no remaining large productivity jumps13 We refer to the resulting sample as our main
sample Excluding the smallest firms has multiple advantages First exclusion of small
firms reduces measurement error as the smallest firms are the most likely to misreport
Additionally one-employee firms cannot be told apart from the self-employed who create
a firm for administrative reasons but clearly do not operate as an ordinary firm The
existence of such firms as well as their financial variables are likely to be strongly
determined by the differential in the tax treatment of personal versus corporate incomes
Because of these reasons both productivity levels and productivity changes may be
measured with an excessive amount of noise for the very small firms and therefore we
exclude them from our main analysis
Table 22 shows the distribution of firms by size category in our baseline sample Clearly
our sample expands strongly between 2000 and 2004 which is mainly a result of legal
changes requiring a larger group of firms to use double-entry bookkeeping While this
expansion is the strongest for the smallest firms it also affects a large number of firms
with up to 20 employees This artificial `entryrsquo of firms can bias estimates of productivity
growth (yielding a negative composition effect) and its decomposition (a negative entry
effect) For this reason in many cases we will start our analysis in 2004
Figure 22 investigates how much the exclusion of very small firms matters It shows that
while the share of 0 and 1 employee firms was between 50 and 60 percent their share in
terms of employment and sales was only around 5-6 percent hence even after their
exclusion our sample captures much of the national output We however report
robustness checks for our main results with all firms with a positive number of employees
in the Appendix
12 We decide to drop the financial sector because of conceptual and measurement problems of defining the productivity of financial firms especially during the crisis It might also distort the aggregate results Dropping these firms also corresponds to the usual practice (eg McGowan et al 2017) However including financial firms does not have a significant impact on our main results
13 We exclude firms that had a log productivity change higher than 15 in absolute value at any one time We also exclude firms switching between manufacturing and services more than twice
Productivity differences in Hungary and mechanisms of TFP growth slowdown
11
Table 22 Distribution of firm size by employment categories
Year 0 emp 1 emp 2-4 emp 5-9 emp 10-19 emp 20-49 emp 50-99 emp 100 lt emp Total
2000 12 867 24 481 33 924 17 009 10 806 6 911 2 457 2 284 110 739
2001 20 300 34 394 39 499 18 545 11 343 7 136 2 454 2 316 135 987
2002 25 356 40 087 43 466 19 738 11 976 7 224 2 413 2 308 152 568
2003 29 655 45 057 47 472 21 491 12 656 7 319 2 465 2 261 168 376
2004 39 126 68 895 66 787 26 069 13 603 7 645 2 489 2 266 226 880
2005 15 920 65 818 66 403 26 963 14 096 7 897 2 523 2 224 201 844
2006 15 204 70 888 66 885 27 368 14 388 8 112 2 558 2 268 207 671
2007 17 633 72 953 66 969 27 610 14 481 8 120 2 657 2 286 212 709
2008 38 502 78 158 70 284 28 370 14 822 8 146 2 731 2 305 243 318
2009 41 561 82 903 70 096 27 421 14 011 7 500 2 458 2 163 248 113
2010 44 792 84 957 71 362 27 635 14 720 7 103 2 404 2 131 255 104
2011 41 769 91 358 72 333 27 842 14 633 6 988 2 403 2 183 259 509
2012 39 146 94 201 71 926 26 924 13 432 7 128 2 388 2 190 257 335
2013 39 606 89 736 71 607 27 415 13 397 7 336 2 376 2 192 253 665
2014 38 016 87 540 72 157 28 532 14 133 7 620 2 460 2 220 252 678
2015 38 569 79 881 72 003 29 375 14 831 8 059 2 546 2 255 247 519
2016 39 034 72 965 67 691 28 210 14 192 7 844 2 562 2 229 234 727
Total 537 056 1 184 272 1 070 864 436 517 231 520 128 088 42 344 38 081 3 668 742
Notes The sample is our baseline sample (see Section 23) also including observations without an
estimated TFP
Figure 22 The share of 0 and 1 employee firms in the number of firms employees and
sales
Data Sources
12
Table 23 shows the number of observations lost because of missing values cleaning and
sample restrictions compared to the original data Dropping firms based on industry and
legal form as well as firms which never report positive number of employees does not
reduce the sample considerably The baseline data contains about 23 of the firms in the
original data The coverage in terms of total employment or value added is even higher
While the reduced sample of firms with at least 5 employees contains only about 20 of
the original number of firms the coverage of total employment and value added is still
above 70 We lose an additional 4 of firms which have no estimated TFP (negative
value added or missing capital) or which have large TFP jumps over time The
corresponding reduction in employment and value added coverage is about 20 and 15
percentage points respectively14 In the main sample we capture almost 23 of the total
employment and value added which we have in the original data
Table 23 Change in sample size and coverage after introducing restrictions
Number of
firms
Total
employment
Total value
added
Original data (after imputing observations) 1000 1000 1000
Drop agriculture and missing industry 952 954 984
Drop non-market services 845 895 948
Drop based on legal form 844 885 946
Drop firms which never had positive
employment
708 885 935
Keep only market economy according to OECD 667 859 912
Drop financial and insurance activities 652 830 790
Baseline sample 652 830 790
Keep observations with at least 5 employees 196 726 723
Keep firms which have no big TFP jump and
observations with non-missing TFP
157 600 647
Main sample 157 600 647
Table 24 shows the share of observations in the main sample by 1-digit NACE industry
The industry composition is quite stable over time Wholesale and retail trade has the
largest share close to 13 followed by manufacturing (21-31) construction (13-14)
and professional scientific and technical activities (7-9) The largest decline over time
was in manufacturing (from 31 to 21) Construction transport and storage
accommodation professional scientific and technical activities and administrative and
support services increased their share by more than one percentage point
14 While this cleaning certainly drops a large number of firms this is standard practice when the aim is to capture and decompose aggregate dynamics
Productivity differences in Hungary and mechanisms of TFP growth slowdown
13
Table 24 The share of observations by industry
NACE Description 2000 2004 2008 2012 2016
B Mining 029 025 024 021 018
C Manufacturing 3085 2636 2352 2280 2122
D Electricity gas steam 021 027 026 025 024
E Water supply sewerage waste
103 112 114 127 098
F Construction 1263 1439 1447 1263 1375
G Wholesale and retail trade 3207 3026 2954 3005 3034
H Transportation and storage 479 551 606 642 683
I Accommodation 477 617 650 719 783
J ICT 351 328 396 403 400
M Professional scientific and
technical activities 688 691 859 915 891
N Administrative and support services
297 547 572 601 572
Total 100 100 100 100 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
24 Firm-level variables
For the present analysis we create firm groups based on different firm characteristics In
this subsection we explain these groupings and provide descriptive statistics
The database includes information on direct ownership Based on this one can identify
firms which are domestically-owned15 foreign-owned or state-owned (including municipal
ownership) We identify a firm as foreign-owned if the foreign share is above 10 percent
Similarly we classify a firm as state-owned if the state-owned share is above 50
percent16 Based on these definitions in 2016 nearly 10 percent of firms were foreign-
owned while the share of state-owned firms was about 1 percent (Table 25) Both foreign
and state ownership is more frequent in larger firms therefore foreign and state share is
higher in terms of employment 373 percent of employees work in foreign-owned firms
and 66 percent in state-owned ones Foreign ownership was concentrated in mining and
manufacturing electricity generation and distribution trade and ICT State ownership was
high in electricity generation and distribution and in utilities The fact that state-owned
firms are concentrated in these two industries limits the possibilities of how the effects of
state ownership and the effect of the peculiarities of these highly regulated industries can
be distinguished from each other Therefore in most cases we will not present results
separately for state-owned firms (except for Section 44)
15 For brevity we will mainly refer to domestically-owned private firms simply as domestically-owned
16 Only 15 of firms with more than 10 percent foreign share report a foreign share between 10 and 51 percent Re-classifing them as domestic does not affect our main results
Data Sources
14
Table 25 Share of state- and foreign-owned firms with at least 5 employees 2016
A) Number of firms
NACE Sector Domestic Foreign State Total
B Mining 8228 1772 000 100
C Manufacturing 8432 1522 046 100
D Electricity gas steam 5631 1942 2427 100
E Water supply sewerage waste 6351 450 3199 100
F Construction 9746 192 062 100
G Wholesale and retail 8957 1006 037 100
H Transportation 9005 890 105 100
I Accommodation 9411 467 121 100
J ICT 8314 1541 145 100
M Professional scientific and technical activities
8982 915 102 100
N Administrative and support services
8991 798 211 100
Total 8937 953 110 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
B) Employment
NACE Sector Domestic Foreign State Total
B Mining 725 275 00 100
C Manufacturing 437 552 12 100
D Electricity gas steam 674 234 91 100
E Water supply sewerage waste 189 32 780 100
F Construction 899 74 28 100
G Wholesale and retail 660 334 06 100
H Transportation 474 199 327 100
I Accommodation 867 111 22 100
J ICT 424 546 30 100
M Professional scientific and technical activities
650 331 20 100
N Administrative and support services
681 258 61 100
Total 560 373 66 100
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP
The data include direct information on export sales and we classify a firm as an exporter
in a given year if its export sales are positive Table 26 shows the share of observations
both by ownership (foreign or private domestic) and exporter status The distribution of
firms across the four groups is stable over time Overall 65-75 of the firms are owned
domestically and supply only the domestic market The share of foreign firms decreased
from 143 in 2000 to 96 in 2016 After an initial decline the share of exporters
increased from 26 in 2000 to 315 by 2016 More than 23 of the foreign firms export
while the same ratio for domestic firms is less than 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
15
Table 26 Yearly share of observations by ownership and exporter status
Year Foreign
exporter
Foreign
non-
exporter
Domestic
exporter
Domestic
non-
exporter
2000 92 51 168 690
2001 89 46 172 693
2002 84 43 173 701
2003 79 40 164 717
2004 71 36 157 736
2005 70 34 160 736
2006 69 34 163 734
2007 73 32 180 715
2008 74 34 186 706
2009 78 36 195 692
2010 77 34 202 687
2011 78 32 215 675
2012 81 31 229 659
2013 79 30 236 655
2014 74 33 233 660
2015 71 31 238 659
2016 70 26 245 658
Total 76 35 196 692
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded
Table 27 presents some baseline descriptive statistics for the four firm groups created by
ownership and exporter status We define age using the year of foundation of the firm On
average foreign exporter firms are the largest and the most productive Within both
categories exporter firms are older larger and more productive in line with similar
patterns in other countries17 We will analyse differences further in Section 44
17 See for example Bernard-Jensen (1999)
Data Sources
16
Table 27 Average characteristics by ownership and exporter status in year 2004 and
2016
Foreign exporter
Foreign non-exporter
Domestic exporter
Domestic non-exporter
Year 2004
N of employees 1385 511 451 165
(5689) (2410) (1396) (404)
Labour productivity 877 825 830 769
(101) (120) (087) (086)
TFP ACF 666 660 634 611
(111) (112) (091) (084)
Age
101 85 99 85
(42) (46) (43) (43)
Year 2016
N of employees 1619 338 344 151
(6246) (1257) (1290) (405)
Labour productivity 906 839 844 793
(088) (115) (075) (080)
TFP ACF 696 684 651 639
(113) (109) (086) (080)
Age 160 105 149 124
(82) (75) (76) (75)
Notes The sample is our main sample (see Section 23) including observations with at least 5
employees and with an estimated TFP state-owned firms excluded Standard deviations are in
parentheses
25 Industry categorization
As we have mentioned already the main industry identifier is the 2-digit NACE Rev 2
industry classification These are hierarchically ordered into 1-digit categories
These categories however do not always lend themselves to easy interpretation On the
one hand one may want to distinguish between different types of manufacturing activities
Here a key question concerns the knowledge intensity or the high-techness of the activity
On the other hand sometimes it is useful to aggregate some of the service activities to
obtain more easily interpretable results
In order to do this we use Eurostatrsquos high-tech aggregation of manufacturing and services
by NACE Rev 2 which we will call industry type18 Note that these sets of industries
include only activities carried out in market industries (ie 10 to 82 NACE Rev 2 industry
codes) When using these categories we do not include firms in non-market sectors like
education (85) or arts entertainment and recreation (90 to 93) (See Table 28)
We would like to point out that while the Eurostat categories clearly reflect the global
technology and knowledge intensity of each industry the actual activity conducted in a
given country may differ from the technology category of the industry This issue is highly
relevant in Hungary where MNEs in high-tech industries operate affiliates conducting
assembly activities in Hungary without much RampD or innovation Still we find this
categorization a good way of aggregating data but still preserving some heterogeneity
18 Retrieved from httpeceuropaeueurostatcachemetadataAnnexeshtec_esms_an3pdf
Productivity differences in Hungary and mechanisms of TFP growth slowdown
17
Table 28 Industry categorization
Manufacturing NACE Rev 2 codes
High-technology manuf 21 26
Medium-high technology manuf 20 27 to 30
Medium-low technology manuf 19 22 to 25 33
Low technology manuf 10 to 18 31 to 32
Services
Knowledge-intensive services (KIS) 50 to 51 58 to 63 64 to 66 69 to 75 78 80
Less knowledge-intensive services (LKIS) 45 to 47 49 52 55 to 56 77 79 81 82
Utilities 35 to 39
Construction 41 to 43
Productivity Trends Hungary in International Comparison
18
3 PRODUCTIVITY TRENDS HUNGARY IN INTERNATIONAL COMPARISON
The main aim of this chapter is to summarize existing evidence on Hungarian productivity
trends based on internationally comparable databases which include either industry-level
or micro-aggregated information The specificities and similarities of Hungary to
comparable countries will both guide and frame our analysis in the remaining chapters
which use Hungarian micro-data
31 Convergence
The fundamental question regarding the productivity evolution of Hungary or other less
developed EU member countries is whether productivity catches up with the most
developed countries at least in the medium or long run We investigate such medium- or
long-run trends in this subsection by analysing the evolution of relative productivity which
is defined as the level of labour productivity compared to one of the key economies of the
EU Germany (at ppp exchange rates) Figure 31 presents such a comparison of the
labour productivity levels of Hungary the Czech Republic Poland and Slovakia We use the
OECD STAN database for this exercise and present trends for as many years as possible to
reflect long-run developments
Figure 31 Relative labour productivity level (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN and GGDC Productivity Level Database The
market economy excludes real estate For more details see Appendix A3
Let us start with the evolution of aggregate labour productivity According to Figure 31 all
of these countries seemed to be on the road to convergence to frontier countries in terms
of labour productivity before the financial crisis In particular labour productivity in
Hungary increased from 50 percent of the German level in 1998 to 65 percent in 2008 A
similar pre-crisis convergence can be observed in all three comparator countries19
19 Note that TFP is not available for Hungary in the EU KLEMS after 2008 Therefore we restrict this
international comparison to labour productivity
Productivity differences in Hungary and mechanisms of TFP growth slowdown
19
Note that the labour productivity decline during the crisis does not show up in the above
figure because it also affected the baseline country Post-crisis Hungarian labour
productivity (relative to Germany) remained flat stabilizing at around 65 percent While
this is similar to the productivity evolution of the Czech Republic it differs remarkably from
Poland and Slovakia which were able to close their productivity gap relative to Germany
by about 5 percentage points between 2009 and 2015 This slowdown of aggregate
productivity growth and the lack of further convergence from previous levels is actually
the main motivation for this study
A key question is whether the slowdown characterises the whole economy or it is
constrained to some of the sectors or types of enterprises The first dimension is to
distinguish between the state sector and the market economy According to OECD STAN
non-market sectors accounted for about 27 percent of all employment in 201520 The
second panel of Figure 31 restricts the sample to the lsquomarket economyrsquo21 Interestingly
productivity differences relative to Germany are larger in the market economy compared
to the whole economy suggesting that the productivity levels of the public sector in the
two countries appear to be closer to each other In Hungary the relative productivity of
the market economy follows a very similar trend to the whole economy with about 10 pp
relative productivity increase between 1998 and 2005 and stagnation post-crisis With the
exception of Slovakia post-crisis productivity growth is also flat in the comparator
countries
Figure 32 Relative labour productivity in manufacturing and business services
Germany=100
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN Business services excludes real estate For
more details see Appendix A3
20 According to the EU KLEMS this share has remained more or less constant since 2003
21 This includes NACE Rev 2 Codes 5-82 except real estate (68)
Productivity Trends Hungary in International Comparison
20
The market economy can be further disaggregated into manufacturing and business
services (Figure 32) There is strong evidence of catching up in manufacturing between
1995 and 2008 when relative productivity increased by more than 10 percentage points
Relative productivity fell immediately after the crisis with positive growth after 2011
reaching pre-crisis (relative) levels by 2015 Comparator countries which started from
much lower levels caught up faster pre-crisis and faced a much smaller fall around the
crisis years In other words comparator countries have caught up with Hungary in terms
of manufacturing productivity but there is no evidence for a sharp break in the trend post-
crisis
This contrasts sharply with business services where a period of catch-up until 2005 was
followed by a substantial decline in relative labour productivity This is also in strong
contrast with the comparator countries where relative productivity of business services
either increased (Czech Republic and Poland) or stagnated (in Slovakia) Business services
appear to be a key source of aggregate productivity slowdown
Figure 33 presents productivity dynamics in four specific industries to substantiate the
more aggregated picture with some more concrete examples The first two examples are
manufacturing industries namely the textiles and the automotive industry The relative
productivity level of textiles stagnated during the crisis at quite low levels fell during the
crisis followed by some growth from 2012 In motor vehicles relative productivity
increased by nearly 10 percentage points relative to Germany between 2001 and 2009
followed by a significant fall around the crisis and a strong recovery from 2012 The
picture is also varied in services In retail and wholesale there had been some productivity
improvement before the crisis followed by a declining trend post-crisis Both the level and
dynamics of relative productivity compares unfavourably to the comparator countries In
professional services relative labour productivity had grown quickly until 2011 followed
by a declining trend
Figure 33 Relative labour productivity evolution (Germany=100)
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
21
Similar observations can be made when analysing the relative productivity of all types of
industries (Figure 34) The difference in productivity levels relative to Germany tends to
be larger in manufacturing than in services Light industries have especially low relative
productivity levels In terms of productivity growth we see mostly positive trends in most
manufacturing industries and a less clear picture in services with a decline or stagnation
in many service industries
Figure 34 Labour productivity of different industries relative to Germany 2005 and 2015
Notes Labour productivity is defined as value added at constant prices per number of persons
engaged Source Own calculations based on OECD STAN For more details see Appendix A3
Even in countries and industries with a relatively low level of average productivity it is
possible that a segment of the economy operates at world-class levels or shows fast
convergence to that This possibility may be especially relevant in economies where a
number of large and probably foreign-owned firms operate together with many smaller
domestically-owned firms which is certainly the case in Hungary One approach to
investigate this possibility was suggested and implemented by the OECD (Andrews et al
2017) This approach builds on cross-country micro-data to calculate the productivity of
the most productive firms in the world (global frontier) and compare it with the
productivity of the most productive firms in a country (national frontier)
Figure 35 shows these comparisons based on the OECDrsquos calculations22 In particular the
horizontal axis shows how productive Hungarian frontier firms are relative to the global
22 We would like to thank Peter Gal and his colleagues in the OECD for sharing these data with us In
this version global frontier is defined as the top 10 percent most productive firms worldwide
while the national frontier is the top 10 percent within the country according to ORBIS See
Appendix 3 and Box 41 for details on these data
Productivity Trends Hungary in International Comparison
22
frontier (100 is the global frontier) while the vertical axis compares Hungarian and global
non-frontier firms The figures suggest a number of conclusions To start with the frontier
productivity gap is strongly associated with the non-frontier productivity gap showing that
in industries where the typical firms are of relatively low productivity so are the frontier
firms Importantly the slope of the fitted line (06) is well below 1 suggesting that on
average there is a smaller gap between a top global and a top Hungarian firm than
between a typical (non-frontier) global firm and a typical Hungarian firm This is in line
with the duality hypothesis
That said one has to emphasise that the picture does not support a ldquostrong versionrdquo of the
duality hypothesis ie that the best Hungarian firms operate at world-class productivity
levels Even in manufacturing Hungarian frontier firms typically produce 40-60 percent
less value added per employee compared to the global frontier (good examples are
machinery (28) and motor vehicles (29)) The smallest gaps appear in a few relatively
low-tech service industries (trade and repair of vehicles (45) or warehousing (52)) where
frontier productivity is actually above the global frontier23
The observation that such large productivity differences exist between global frontier and
Hungarian frontier firms even within relatively narrowly defined industries suggests that
the low relative productivity of the Hungarian market economy is not a consequence of
industry composition ndash it mainly results from within-industry gaps Importantly these
main patterns are very similar and independent of how productivity is measured (labour
productivity or TFP) namely they are not a consequence of capital intensity differences
Finally by and large there is no evidence for convergence of frontier firms to the global
frontier between 2009 and 201324 If anything the gap between the global and the
Hungarian frontier widened in this period while the difference between the global and the
Hungarian frontier was 34 percent in the median industry in 2009 it widened to 38 by
2013
23 Naturally this is likely to be the case in other similar countries Still in different discussions it is often supposed implicitly that the best Hungarian firms are indistinguishable from the global frontier
24 Prior to 20082009 the coverage of ORBIS the source for the OECD calculations is fairly limited for
Hungary hence those calculations are less reliable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
23
Figure 35 Productivity of Hungarian frontier and non-frontier firms relative to firms in
other countries (2013)
A) Labour productivity
B) TFP
Notes The industry codes are 2-digit NACE Rev 2 codes We have omitted industries with only few
observations (less than 5 Hungarian frontier firms) in the case of labour productivity outliers we
ignored those where the HU frontier was measured to be more productive than 125 percent of the
global frontier (ICT real estate and office administration services) Note that there are fewer
observations regarding TFP than labour productivity Source Data provided by the OECD calculated
from Andrews et al (2017) For more information see Appendix 3
Productivity Trends Hungary in International Comparison
24
We can draw a number of conclusions from these calculations First while Hungaryrsquos
labour productivity had been catching up similarly to other CEE countries to more
advanced economies before the crisis there was a trend break after the crisis especially
compared to Poland and Slovakia Only part of the productivity slowdown could be
explained by a slowdown in non-market sectors but there is also a pronounced slowdown
in the market economy This is not the result of having a combination of a few firms with
world-class productivity and many less efficient SMEs ndash actually the productivity of
frontier firms is only about 40-50 percent of global leaders even in industries where the
Hungarian frontier consists of many multinational firms There is no evidence that
Hungarian frontier firms were catching up with global leaders between 2009 and 2015
32 Within-industry heterogeneity
Since the beginning of the 2000s with the availability of detailed micro-data sets at the
firm-level it has become clear that within-industry heterogeneity in terms of productivity
is significantly larger than heterogeneity differences across industries (Bernard et al
2003 Bernard et al 2007 Bernard et al 2012 OECD 2017) Many factors have been
proposed which may generate and sustain the observed large productivity differences
including managerial practices different quality of labour capital and knowledge as well as
a number of external factors The exact role of different factors is an active area of
research (Syverson 2011) Recent research also hints at increasing dispersion within
sectors (Berlingieri et al 2017b)
In 2011 the level of the p90p10 ratio (90th and 10th percentile of productivity
distribution) was high in Hungary relative to other OECD countries taking a value of 279
in manufacturing and 329 in services (Table 31) These numbers are in logs representing
about 20-fold differences These numbers are similar to Chile and Indonesia A similar
pattern emerges with respect to TFP
Table 31 Productivity p90p10 ratio by country (2011)
Country
Year 2011
Log LP 90-10 ratio Log MFP 90-10 ratio
Manufacturing Services Manufacturing Services
Australia 187 205 190 212
Austria 196 242 - -
Belgium 160 174 180 178
Chile 300 353 307 387
Denmark 146 196 132 180
Finland 117 138 119 134
France 135 181 140 178
Hungary 279 329 254 286
Indonesia 311 - 341 -
Italy 166 201 160 188
Japan 126 138 117 138
Netherlands 200 298 227 227
New Zealand 184 209 192 208
sNorway 173 217 187 208
Portugal 188 265 188 275
Sweden 145 186 159 245
Notes This is a reproduction of Table 6 from Berlingieri et al (2017a) Note that the OECD uses the
term lsquoMFPrsquo (Multi-factor productivity) in the same sense as we use TFP in this report
Second as seen in Table 32 similarly to other OECD countries the overwhelming
majority of productivity differences results from within- rather than across-sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
25
differences The share of within-sector differences is 79 in manufacturing and 99 in
services The manufacturing share is close to the average of the countries in the sample
while the services share is at the high end
Table 32 Share of within-sector variance in total LP dispersion by country (2011)
Country
Year 2011
LP Dispersion
Manufacturing Services
Australia 98 99
Austria 86 90
Belgium 76 88
Chile 90 97
Denmark 84 63
Finland 65 76
France 63 85
Hungary 79 99
Indonesia 79 -
Italy 82 65
Japan 75 89
Netherlands 80 71
Norway 83 73
Portugal 62 76
Sweden 53 74
Notes This is a reproduction of Table 7 from Berlingieri et al (2017a)
These figures suggest that within-industry productivity dispersion is relatively high in
Hungary but it is not out of the range of countries at a similar level of development Still
these overall dispersion measures may not capture the duality between firms of different
sizes and ownership Internationally comparable data regarding productivity of firms in
different size classes is available from the OECD Structural and Demographic Business
Statistics (Figure 36) Size is strongly associated with productivity large firms are 45
times and 18 times as productive as very small firms in manufacturing and services
respectively However large these premia are not out of the range of similar countries in
services it is very similar to other CEE countries while in manufacturing it is at the high
end of the distribution but not extreme
Another relevant pattern in Figure 36 is that productivity differences by size are very
different between CEE countries and Western European countries This observation may
partly reflect the importance of large and productive multinational firms in CEE countries
but can also be a more or less automatic consequence of the fact that firm size distribution
significantly differs between the two groups of countries (Figure 37) Typically the share
of very small firms is larger in less developed economies leading to a more skewed firm
size distribution Such a distribution which is associated with a larger number of small
firms within size classes (the majority of firms with 1-9 employees in CEE employs only 1-
2 employees) leads to larger differences across size classes and larger within-industry
productivity dispersion The massive share of very small firms in these countries also
reflects that many of the lsquomicro-enterprisesrsquo (with only 1-2 employees) do not operate as
proper firms they behave more like individual entrepreneurs
Productivity Trends Hungary in International Comparison
26
Figure 36 Value added per person employed by size class (1-9 persons employed=100)
A) Manufacturing
B) Services of the business economy
Notes Value added per person employed defined as value added at factor costs divided by the
number of persons engaged in the reference period Economic sector lsquoManufacturingrsquo comprises
Divisions 10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business
economyrsquo comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities
of holding companies Source OECD SDBS For more details see Appendix 3 Main sample for 2015
Productivity differences in Hungary and mechanisms of TFP growth slowdown
27
Figure 37 Firm distribution by size class (2015)
A) Manufacturing
B) Services of the business economy
Notes Only enterprises with at least one employee are included lsquoManufacturingrsquo comprises Divisions
10-33 in the 2-digit ISIC Rev 4 industry classification while lsquoServices of the business economyrsquo
comprises Divisions 45-82 in the 2-digit ISIC Rev 4 industry classification except activities of holding
companies Source OECD SDBS For more details see Appendix 3 Main sample
Productivity Trends Hungary in International Comparison
28
The main conclusion from investigating within-industry differences across firms is that both
the productivity dispersion and the productivity advantage of large firms is indeed
relatively large in international comparison but these numbers are not radically different
from similar countries Nevertheless differences in firm size distribution between more
and less developed countries go a long way towards explaining the differences between
Western European and CEE countries
33 Firm dynamics
A potential reason for declining productivity growth may be weak dynamics including low
entry and exit rates as well as slower reallocation The OECD Structural and Demographic
Business Statistics database provides international comparisons of entry and exit rates and
their changes across countries (Figure 38 and Figure 39)
In general both exit and entry rates are higher in CEE countries relative to Western
European economies25 This stronger dynamism may reflect stronger growth but it is also
affected (in a mechanistic way) by the differences in firm size distribution Importantly in
a cross-section entry and exit rates are strongly correlated suggesting that they capture
the same general aspect of firm dynamics Services are more dynamic than
manufacturing once again partly because of the different size distributions
Within CEE countries entry and exit rates seem to be associated with productivity growth
(and level) Countries with stronger post-crisis productivity growth (Poland Slovakia and
Romania) exhibit significantly higher entry and exit rates while those with less dynamic
productivity growth (Hungary and the Czech Republic) have lower churning This provides
some evidence that lower entry and exit rates may be systematically related to the weaker
productivity performance of these countries We will take a more detailed look at the
relationship between entry and exit and productivity growth in Chapters 6 and 7
When comparing 2012 and 2015 the pictures provide evidence for increased entry and
decreased exit in parallel with recovery and better growth prospects Still entry rates
remain one of the lowest in CEE indicating that entry and dynamic young firms may
contribute less to productivity growth in Hungary compared to other CEE countries
25 Note that these OECD statistics include all enterprises (even those with no employees) hence
changes in the tax treatment of firms relative to individual entrepreneurs may affect measured
dynamics Also firm death is defined based on the rsquodeathrsquo of the legal entity which may happen
many years after stopping production For more information see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
29
Figure 38 Birth rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Birth rate is defined as the number of enterprise births divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers The
economic sector lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo
comprises Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix A3
Productivity Trends Hungary in International Comparison
30
Figure 39 Death rate of all enterprises
A) Manufacturing
B) Services of the business economy
Notes Death rate is defined as the number of enterprise deaths divided by the number of enterprises
active in the reference period The population contains all enterprises including non-employers
Poland has no available data for 2015 so the 2014 value is reported The economic sector
lsquoManufacturingrsquo comprises Divisions 10-33 while lsquoServices of the business economyrsquo comprises
Divisions 45-82 (except activities of holding companies) in the 2-digit ISIC Rev 4 industry
classification Source OECD SDBS For more details see Appendix 3
Productivity differences in Hungary and mechanisms of TFP growth slowdown
31
34 Conclusions
In international comparison productivity slowdown after the crisis was especially severe in
Hungary both in manufacturing and services There are large productivity differences
within industries and also between small and large firms While these are at the high end
in international comparison they are not extreme compared to similar countries A
comparison to the global frontier suggests that even top Hungarian firms are significantly
behind top global firms in terms of productivity These facts provide a motivation for our
analysis of the evolution of the shape of the productivity distribution in Chapter 4
International comparison of firm dynamics suggests that ndash similarly to other CEE countries
ndash Hungarian industries are more dynamic than their Western European counterparts but
entry and exit rates in Hungary and the Czech Republic are below the average of CEE
countries This motivates our investigation of the contribution of entry and exit to
productivity growth in Chapters 6 and 7
Evolution of the Productivity Distribution
32
4 EVOLUTION OF THE PRODUCTIVITY DISTRIBUTION
41 Context
The study of within-industry productivity differences is motivated by two concepts First
the OECD (2016) argues that one of the key issues of recent developments in productivity
growth is that there is a strong divergence between the productivity evolution of frontier
firms and other firms However this same publication reports that Hungary seems to be
an exception to this trend with slow productivity growth at the frontier and faster
productivity growth of less productive firms suggesting some within-industry catch-up
(Figure 41) We look into the particulars behind this phenomenon by following the
evolution of the average productivity of different deciles in the productivity distribution
Second as we have already mentioned a key concept of the Hungarian (and CEE) policy
debate is the lsquodualityrsquo of smalldomestically-owned and largeforeign-owned firms The
large gap between the two types of firms presents a challenge for policy but it also
indicates an opportunity for domestic firms to catch up with foreign firms which may use
more productive technology (still far in terms of productivity from the global frontier see
Chapter 31) The evolution of the productivity gap (or premium) between small and large
firms as well as domestic and foreign firms informs us about whether firms on the lsquowrong
sidersquo of the duality are able to catch up with the firms at the national frontier
The duality debate frames productivity differences partly as a consequence of the lsquomissingrsquo
medium-sized (domestic) firms Hsieh and Olken (2014) argue that in less productive
economies the full firm size distribution is shifted to the left because of the constraints on
the growth of small firms Thus according to this view the productivity difference is not a
result of too few medium sized firms but of too few firms which are not small
Figure 41 Divergence in labour productivity performance
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
33
B) Non-financial Services
Notes This is a reproduction of Figure 16 from OECD (2016)
In this chapter we investigate how the shape of the productivity distribution evolved over
the years Section 42 contrasts the development of firms with other types of economic
entities Section 43 analyses how average productivity and productivity deciles evolved
while 44 investigates the duality based on size and ownership
42 Aggregate productivity and the self-employed
Before turning to the productivity distribution of firms it is worthwhile to describe how the
productivity level and evolution of firms ndash and in particular double-entry bookkeeping
enterprises ndash differ from other entities in particular the self-employed Given the large
number of people employed by those entities this exercise can reveal a lot both about
productivity dispersion and the drivers of aggregate productivity growth
Let us motivate this investigation by comparing aggregate statistics (derived from data
applicable to all people engaged in an industry) with patterns calculated from our NAV data
(which includes only double-entry bookkeeping firms) Figure 42 shows the labour
productivity growth reported by OECD STAN and the evolution of the average labour
productivity as calculated from the NAV data weighted by sales and employment (Figure
42) According to the Figure while these series co-move they do so with some
discrepancies While productivity dynamics in Manufacturing are very similar across all
samples the relationship is looser for services and for the market economy with the NAV
series notably exhibiting less pronounced post-crisis slowdown than the OECD STAN data
Evolution of the Productivity Distribution
34
Figure 42 Cumulative labour productivity growth according to OECD STAN and the NAV
sample
There can be many reasons behind the differences between these series (see Biesebroeck
2008) but arguably one of the main factors is the discrepancy in the number of
employees in the two databases Firms in the full NAV database employed 24 million
people in 2015 compared with 286 million employed and 325 million lsquoengagedrsquo in the
market economy according to the OECD STAN One source of this difference may be that
while some unofficially employed workers report their true employment status in LFS
(Labour Force Survey) ndash which serves as the basis for our aggregate data ndash they do not
appear in any official registers and such the NAV data Benedek et al (2013) reaffirming
the statement compare LFS employment data with tax registers and show that 16-18
percent of jobs are not declared to the tax authorities
Even more importantly from our perspective the NAV data by definition includes no
information on the self-employed and typically small non-double-entry bookkeeping firms
operating under special taxation The distinct productivity dynamics of these two groups
along with changes in undeclared employment may explain another part of the difference
Obtaining direct information on this issue would be of great interest but acquiring it is far
from straightforward Some information on these entities is available from the Register of
Economic Organizations (Gazdasaacutegi Szervezetek Regisztere GSZR) which is available
between 2012 and 2015 Most importantly this database provides us with information on
the number of employees and sales updated annually This in and of itself does not allow
us to estimate productivity properly but with its help we can calculate a crude proxy
sales per employee for illustration
Table 41 reports26 the number of employees and the average sales per worker values for
three groups The first is the group of double-entry bookkeeping firms (ie the firms who
26 These tables were calculated as follows First we combined the GSZR and NAV databases for years 2012 and 2015 Observing that about 80 percent of the firms present in the NAV sample are also present in the GSZR register we restricted our sample to the entities who are listed in the GSZR so that our variables would be commensurable From this collection we selected those who
Productivity differences in Hungary and mechanisms of TFP growth slowdown
35
are present in the NAV data) the second is the category of the self-employed (ie those
who are registered as individual entrepreneurs) and the third category is that of lsquoother
firmsrsquo (ie entities who are registered as firms in the GFO (Gazdaacutelkodaacutesi Forma) coding
system but are not categorised as self-employed and are not following a double-entry
bookkeeping method) We distinguish between manufacturing and other industries of the
market economy27 We supply figures for the earliest and latest years for which data are
available The tables reveal two important observations
First according to the GSZR about 30 percent of reported employees in Manufacturing
and 50 percent of reported employees in other industries work outside the double-entry
bookkeeping group Importantly these numbers may be overestimates because the GSZR
may report the same person in multiple entities for example when they work part-time or
switch jobs within the year That said both the EU KLEMS and the GSZR suggest that a
large share of people work outside the double-entry bookkeeping group in the market
economy
Second while sales per worker is not drastically different between double-entry
bookkeeping firms and other firms the difference between firms and the self-employed is
between 6-10-fold This difference in sales per employee may represent 2-3-fold labour
productivity differences between people employed by firms and the self-employed on
average28
Third the dynamics of sales per worker differ markedly between double-entry
bookkeeping firms and other entities while it increased by 40 percent in the NAV sample
between 2012 and 2015 it stagnated for the self-employed This may results from a
number of factors ranging from composition effects changes in tax regulations or low
productivity growth Still the low measured productivity growth of this sector of the
economy may be an important factor behind the slower post-crisis aggregate productivity
growth in services compared to the NAV sample Table 41 illustrates this for the sales per
worker measure While it grew by 40 percent in the lsquoOtherrsquo category between 2012 and
2015 based on the NAV sample its lsquoaggregatersquo growth was only 6 percent during the same
period
Obviously one cannot draw far reaching conclusions from such statistics given the
immense measurement problems Still these patterns suggest that in a sense the duality
between firms and the self-employed may constitute a similarly deep divide to the one
belong to the lsquomarket economyrsquo (as defined in Chapter 2) and are registered as lsquofirmsrsquo according to GFO coding system (ie have 1-digit GFO codes 1 or 2) We tagged the firms present in the NAV sample as lsquodouble-entry bookkeeping firmsrsquo and marked those who have 2-digit GFO codes equalling to 23 as lsquoself-employedrsquo We categorised the rest of our sample as lsquoother firmsrsquo Further we distinguished between manufacturing and other market economy firms based on their NACE codes and then calculated for sales per worker measures on the level of each observation finally to compute for yearly aggregates for each group as indicated above
27 Notably in line with the definition in Chapter 2 these lsquoother industriesrsquo do not include agriculture
28 Needless to say this cannot be easily mapped into productivity differences given that firms using more intermediate inputs are more likely to choose double-entry bookkeping (and hence pay
taxes based on profits) rather than simplified taxes (and pay taxes based on sales) Still one can do the following back of the envelope calculation In the NAV sample the average ratio of material expenditure over sales was 066 both in 2012 and 2015 Therefore value added per employee (or labour productivity) could be about a third of the sales per employee variable If one conservativelly assumes that the self-employed have zero material costs their labour productivity is the same as their sales per employee index Based on this simple calculation the 6-10-fold difference in sales per employee map to at least 2-3-fold differences in labour productivity
Evolution of the Productivity Distribution
36
that exist between globally integrated and domestic-oriented firms Consequently policies
can be formulated with an explicit focus on this group
Table 41 Number of employees and sales per employee for different entities
Number of employees
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 621229 627391 1325299 1196332
Other firm 289636 296921 698326 771930
Self-employed 72674 74325 620699 638001
Total 983539 998637 2644324 2606263
Average sales per employee (HUF million)
Manufacturing Other
2012 2015 2012 2015
Double-entry bookkeeping firm 140 199 196 278
Other firm 151 146 196 201
Self-employed 25 25 29 28
Total 92 99 105 111
43 The evolution of productivity distribution in Hungary
Average productivity
Let us continue by investigating the evolution of average productivity Table 42 presents
the average labour productivity and TFP growth rates for the market economy
manufacturing and services as defined in Chapter 2 We report both unweighted and
labour-weighted productivity growth for each year
Let us start with the whole market economy Between 2004 and 2007 both labour
productivity and TFP was growing strongly by 7-8 percent on average as expected in a
catching up economy (as we have seen in Chapter 3) Importantly the weighted growth
rate was higher than the unweighted one suggesting that reallocation played a positive
role in aggregate productivity growth (see Section 62 for more details)
During the crisis we see a slight productivity decline in 2008 a sharp fall of about 8
percent in 2009 followed by a strong recovery in 2010 The 2010 productivity recovery
resulted from the productivity growth of large firms unweighted average productivity
growth was very slow This suggests an asymmetry in recovering from the crisis-related
productivity decline
Post-crisis all measures document a slowdown in productivity growth with typical growth
rates between 25-35 percent Notably weighted productivity growth measures were
similar to unweighted ones in the wake of the crisis suggesting deterioration in the
efficiency of the reallocation process The 2010-2013 and 2013-2016 periods seem to be
quite similar to each other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
37
Importantly while labour productivity and TFP dynamics differ to some extent the overall
picture is very similar for the two productivity measures This is in line with the hypothesis
that any productivity slowdown is not merely a consequence of lower capital stock growth
The results are similar when using alternative TFP estimators (see Table A41 in the
Appendix)
Table 42 Labour productivity and (ACF) TFP growth in the sample
A) Market economy
Year LP TFP
unweighted emp w unweighted emp w
2005 20 58 19 74
2006 92 91 93 119
2007 53 60 39 56
2008 -10 -08 -10 -04
2009 -70 -81 -69 -82
2010 -05 44 11 80
2011 25 45 34 40
2012 25 22 21 01
2013 19 25 30 22
2014 39 45 40 59
2015 51 50 52 49
2016 36 19 20 03
Average
2004-2007 55 70 50 83
2007-2010 -28 -15 -23 -02
2010-2013 23 34 33 29
2013-2016 36 35 35 33
B) Manufacturing
Year LP TFP
unweighted emp w unweighted emp w
2005 37 148 20 114
2006 124 163 114 149
2007 100 114 78 71
2008 25 -03 17 -17
2009 -115 -94 -133 -117
2010 82 161 80 173
2011 -02 34 04 18
2012 05 -46 -02 -58
2013 -14 31 -12 05
2014 11 48 -01 27
2015 38 37 30 14
2016 26 01 04 -23
Average
2004-2007 87 141 71 111
2007-2010 -02 22 -12 13
2010-2013 -04 17 04 -03
2013-2016 15 29 05 06
Evolution of the Productivity Distribution
38
C) Market services
Year
LP TFP
unweighted emp w unweighted emp w
2005 12 -04 10 32
2006 80 47 79 90
2007 39 25 24 48
2008 -22 -06 -21 -03
2009 -57 -68 -52 -71
2010 -29 -17 -11 26
2011 33 49 43 57
2012 31 60 30 48
2013 29 21 39 29
2014 46 45 46 78
2015 54 58 54 72
2016 39 30 25 20
Average
2004-2007 43 23 38 57
2007-2010 -36 -31 -28 -16
2010-2013 31 44 39 51
2013-2016 42 39 41 50
Notes This figure presents growth rates of labour productivity and aggregate TFP for lsquomarket
industriesrsquo (see section 25) The sample does not include agriculture mining and financial services
Services include construction and utilities Only firms with at least 5 employees
Comparing manufacturing and services shows a key dichotomy between the two large
sectors In Manufacturing productivity growth was strong before the crisis with above 10
percent average weighted growth rates This fell to very low levels after 2010 Similarly to
the whole market economy reallocation processes had been more efficient before 2008 In
contrast for services no clear structural break appears around the time of the crisis either
in terms of pre- and post-crisis growth rates or reallocation efficiency
Table 43 looks into industry differences in more detail The picture is similar for
manufacturing industries in the various technology categories with a very substantial
slowdown in productivity growth Productivity growth was fastest in high-tech both before
and after the crisis Services are a bit more heterogeneous High-tech services behaved
similarly to high-tech manufacturing with strong pre-crisis growth (around 10 percent on
average) followed by a slowdown to growth rates around 5 percent per year In less
knowledge-intensive services which represent the majority of business service
employment growth rates were similar before and after the crisis (around 5 percent)29
Lastly we see moderate growth rates and then some slowdown in construction and
utilities
29 Note however that this may not be the case for the self-employed as has been discussed in the previous chapter
Productivity differences in Hungary and mechanisms of TFP growth slowdown
39
Table 43 TFP growth by type of industry (employment-weighted ACF TFP)
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 124 19 66 274 2006 240 137 39 33
2007 74 02 41 221
2008 -45 23 -15 59
2009 05 -191 -218 48
2010 135 111 264 168
2011 -45 18 34 100
2012 -15 -24 -83 -181
2013 -41 37 -22 125
2014 06 07 27 86
2015 65 01 -54 80
2016 -02 04 -27 -91
Average 2005-2007 146 53 49 176
2007-2010 32 -19 10 92
2010-2013 -34 07 -21 20
2013-2016 07 12 -19 50
B) Services
Year KIS LKIS Construction Utilities
2005 127 16 34 -48
2006 166 75 30 67
2007 13 58 42 29
2008 -16 14 -72 -26
2009 -63 -94 -04 25
2010 54 12 09 05
2011 97 46 65 29
2012 12 74 13 -22
2013 12 30 63 -07
2014 78 89 65 -81
2015 106 70 14 54
2016 16 31 -47 39
Average
2005-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the sales-weighted average ACF TFP growth rate by technology category (see
Section 25) Only firms with at least 5 employees The sample does not include agriculture mining
and financial services
In general patterns are similar for the unweighted measures (See Table A42 in the
Appendix) with weaker pre-crisis growth in manufacturing where reallocation seems to
have mattered most Labour productivity behaved similarly to TFP (See Table A43 in the
Appendix)
Evolution of the Productivity Distribution
40
Frontier firms
The key motivation for this investigation is to understand better how productivity dynamics
of lsquofrontierrsquo firms differ from firms in other parts of the productivity distribution Defining
frontier firms is not a straightforward task (Andrews et al 2017) Inevitably all such
attempts have to face the trade-off between a narrow definition which may to a large
extent capture the behaviour of outliers and a broader definition which may include
many firms which are very far from the actual frontier
One can find a sensible compromise between the too narrow and the too broad definitions
by following the OECD practice (Andrews et al 2017) This solves the problem of
including small firms with potentially large noise by restricting the sample to firms with at
least 20 employees on average in the sample period Frontier is defined as the top 5
percent of such firms for each industry-year combination An additional issue is that the
number of observations may change across years This is solved by calculating the top 5
based on the median number of observations per year We will call these firms frontier
firms
An alternative definition is simply to define the top decile within the productivity
distribution in industry-year combination as frontier based on our main sample We will
employ this strategy as well for the sake of comparison
Table 44 investigates the prevalence of frontier firms in different groups30 The probability
of being frontier is not related strongly to size A foreign-owned firm is 3-4 times more
likely to be frontier than a domestically-owned private firm State-owned firms are similar
to privately owned domestic firms in this respect As a result about half of the frontier
firms are foreign-owned Finally frontier firms are substantially more prevalent in the
more developed regions of the country especially in Central Hungary These patterns are
quite stable throughout the years and they prevail in a multiple regression analysis The
top decile of the productivity distribution has a similar composition (see Table A44 in the
Appendix)31
Table 44 The share of frontier firms () among firms with at least 20 employees
A) By size
2004 2007 2010 2013 2016
20-49 emp 357 327 34 362 329
50-99 emp 401 468 542 486 555
100- emp 293 358 414 42 462
B) By ownership
2004 2007 2010 2013 2016
Domestic 213 194 236 272 289
Foreign 873 955 896 82 821
State 181 211 166 167 263
30 Note that we restrict the sample to firms with at least 20 employees because the definition of frontier requires to have at least 20 employees on average
31 When the definition is based on labour productivity the share of frontier firms increases with size The foreign advantage is also larger
Productivity differences in Hungary and mechanisms of TFP growth slowdown
41
C) By region
2004 2007 2010 2013 2016
Central HU 596 621 652 552 579
Northern Hungary 174 104 176 237 168
Northern Great Plain 152 195 199 38 268
Southern Great Plain 128 127 18 277 224
Central Transdanubia 296 27 32 359 322
Western Transdanubia 408 313 305 433 395
Southern
Transdanubia 131 081 188 159 211
Another key question is the extent to which frontier status is persistent Figure 43 shows
a transition matrix ie it considers frontier firms in year t and reports their status in t+3
Do they remain frontier or become a non-frontier firms or exit the market altogether
Overall the 3-year persistence of the frontier status is around 45 percent ndash nearly half of
frontier firms will also be frontier 3 years later This is a bit higher than what is found in
other countries Antildeoacuten Higoacuten et al (2017) for example report that about half of all
national frontier firms remain on the frontier for a year but only about 20 percent for 5
years The persistence of frontier status remained largely unchanged across the years
Frontier status is more persistent for foreign and exporter firms The transition matrix of
top decile firms is similar with slightly weaker persistence (Figure A41 in the Appendix)
Figure 43 Transition matrix for frontier firms
Notes This figure shows how many of the frontier firms in year 2010 were still frontier in 2013 how
many exited and how many continued as non-frontier Only firms with at least 20 employees The first
panel shows this transition matrix for various 3-year periods
Evolution of the Productivity Distribution
42
Productivity evolution across deciles
The figures in this section compare the average productivity of frontier firms of the top
decile of the productivity distribution lsquohigh productivity firmsrsquo (8th and 9th deciles) lsquotypical
firmsrsquo (4th to 6th deciles) and lsquolow productivityrsquo firms (2nd and 3rd deciles) all of these
defined at the year-NACE 2 level This approach follows closely that of the OECD (2016)
Also we use the 8 lsquotechnologicalrsquo industry categories introduced in Section 25 to condense
information but still allow for heterogeneity across industries
Let us start with comparing TFP levels (Figure 44) and their cumulative changes (Figure
45) at the different parts of the productivity distribution (note that the vertical axes differ
across sectors) TFP levels are measured in natural logarithms For example in low-tech
manufacturing the difference between low-productivity firms and the frontier is about 2 log
points or more than 7-fold32 Within-industry productivity differentials are much larger
than across-industry differences or changes From a methodological point of view in most
industries frontier firms co-move with the top percentiles but there are a few exceptions
most prominently high-tech manufacturing
The overall productivity evolution is much in line with the averages reported in Table 42
There is strong pre-crisis growth in Manufacturing followed by a fall in 2009 and sluggish
growth afterwards High-tech manufacturing is a partial exception from this trend
Productivity growth actually accelerated after the crisis in services
Figure 44 TFP levels in various types of industries
A) Manufacturing
32 1198902 asymp 74
Productivity differences in Hungary and mechanisms of TFP growth slowdown
43
B) Services
Notes This figure shows the evolution of the (unweighted) average ACF TFP level of the different
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles with at least 20 employees on average lsquotop decilersquo is the 10th decile lsquohighrsquo
is the 8-9th decile typical is the 4-6th deciles while `lowrsquo is 2-3rd deciles Main sample The industry
categories are described in Section 25 The sample includes the sectors of the market economy
except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less knowledge-
intensive services
Most importantly we do not find evidence for an increasing gap between frontier and other
firms (in line with OECD 2016) in any of the industries Within manufacturing there is
convergence between frontier and non-frontier firms in medium-low and high-tech
industries However this is not robust for the alternative definition of frontier (top decile)
which moves strongly together with other deciles Based on this one may say that there is
no robust evidence either for convergence or divergence in manufacturing There are some
signs of convergence pre-crisis in knowledge-intensive and less knowledge-intensive
services as well as in construction followed by stronger productivity growth in the highest
quartiles post-crisis Importantly any convergence or divergence appears to be small
relative to already existing differences
Evolution of the Productivity Distribution
44
Figure 45 Cumulative TFP growth since 2004
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination lsquoFrontier firmsrsquo
are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is the 10th
decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main sample The
industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo Less
knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
45
The picture is somewhat different when labour productivity is considered (Figure 46) In
this case the difference in growth rates between frontier and other firms is more
pronounced than in the case of TFP One can plausibly claim that less productive deciles of
the distribution caught up somewhat with the most productive firms in high-tech
manufacturing in the two service sectors and also in construction This suggests that
capital deepening by less productive firms (or low investment by frontier firms) may lead
to some convergence in terms of labour productivity but less so in terms of TFP33
Figure 46 Cumulative labour productivity growth since 2004 for labour productivity
deciles
A) Manufacturing
33 Note that these figures are the most directly comparable ones to Figure 41 which also presents results for labour productivity In line with that figure we find evidence for convergence between the median firm and frontier firms We also find that low-productivity firms converge The most important reason for this is that we exclude firms with less than 5 employees from our sample
Evolution of the Productivity Distribution
46
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average labour productivity level
for various deciles of the productivity distribution within each 2-digit industry-year combination
lsquoFrontier firmsrsquo are in the top 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo
is the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample The industry categories are described in Section 25 The sample includes the sectors of the
market economy except agriculture mining and finance lsquoKISrsquo Knowledge-intensive services lsquoLKISrsquo
Less knowledge-intensive services
Figure 47 zooms in to a few industries of interest which both confirm and qualify the
overall picture In textiles (a low-tech industry) frontier firms did not increase their
productivity in the period under study while lower productivity deciles experienced a
cumulative 40-50 percent productivity growth leading to an overall positive growth As
Section 61 discusses employment decline and firm exit were high in this industry
therefore the improvement of lower deciles may partly result from the exit of the lowest
productivity firms In machinery (a medium-high tech industry) all productivity deciles
had experienced strong TFP growth before the crisis and a significant fall during the crisis
followed by slow growth In this industry the full distribution has moved together
In retail (which is a member of the less knowledge-intensive services) TFP had grown to
some extent prior to the crisis followed by a large fall around the crisis and some growth
since 2012 Interestingly the fall was much larger and persistent for the most productive
firms while typical and low-productivity firms were able to maintain their pre-crisis
productivity levels The weak productivity performance of the top decile may have partly
resulted from regulatory changes and could have had large aggregate consequences given
the large employment share of retail (see Chapter 8) In lsquoComputer programming
consultancy and related activitiesrsquo there was a cumulative TFP increase of about 30 percent
since 2004 for all deciles without signs of convergence or divergence
Productivity differences in Hungary and mechanisms of TFP growth slowdown
47
Figure 47 Cumulative TFP growth since 2004 selected industries
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level for various
deciles of the productivity distribution within each 2-digit industry-year combination in four industries
lsquoFrontier firmsrsquo are in the 5 percentiles of firms with at least 20 employees on average lsquotop decilersquo is
the 10th decile lsquohighrsquo is the 8-9th decile typical is the 4-6th deciles while low is 2-3rd deciles Main
sample
44 Duality in productivity and productivity growth
Besides the evolution of the overall shape of productivity distribution it is important to
understand the lsquodualityrsquo of productivity with respect to ownership
As a starting point Figure 48 shows the distribution of TFP and the natural logarithm of
the average wage for our main sample34 We filter out 2-digit industry fixed effects from
the two variables to control for industry-level differences
Comparing private domestic and foreign-owned firms one can make a number of
observations The foreign-owned distribution clearly stochastically dominates the
productivity and wage distribution of domestically-owned firms On average foreign firms
have 40 percent higher TFP and pay 75 percent higher wages than domestically-owned
firms in the same industry That said the within-group heterogeneity is larger than the
across-group heterogeneity generating a substantial overlap between the two
distributions For example 30 percent of domestically-owned firms are more productive
than the median foreign firm The averages between the two groups differ substantially
but there are many productive domestically-owned firms and unproductive foreign ones
34 Result for other TFP measures are very similar
Evolution of the Productivity Distribution
48
Another interesting difference between the distributions is that the foreign-owned
distribution is substantially more dispersed than the domestically-owned one (its standard
deviation is 23 percent larger) suggesting more technological heterogeneity within the
foreign-owned group This may suggest that this group includes both firms with world-
class technology and plants utilizing low-cost labour in a relatively unproductive way That
said the distribution is clearly not bi-modal there are no clearly distinguishable clusters of
high-tech and low-tech firms They operate along a continuum
Comparing state-owned firms to the other two groups shows that they are more similar to
the domestically-owned private firms with two interesting twists35 First the low-
productivity left tail of state-owned firms is much thicker than that of the privately owned
domestic firms Many state-owned firms operate with very low productivity levels (see also
Section 63) As a result the average productivity of these firms is 25 percent lower
compared to privately-owned domestic firms in the same industry
The second twist is that even though state-owned firms tend to be substantially less
productive than privately owned domestic firms they pay on average 25 percent higher
wages This may be a consequence of differences in worker composition but may also
suggest that these firms face soft budget constraints and their employees are able to
capture a larger slice from a smaller pie
Figure 48 Distribution of TFP and average wage by ownership (cleaned from industry-
year effects) 2016
Notes This figure shows the distribution of productivity and ln average wage after filtering out
industry-year fixed effects from it Domestically-owned is domestic privately-owned Main sample
35 Note that the sample of state owned firms is much smaller than the other two groups and operates in very specific indutries This may affect the distribution
Productivity differences in Hungary and mechanisms of TFP growth slowdown
49
Figure 49 shows the evolution of the productivity distributions across years Note that in
order to illustrate shifts in time industry-year fixed effects are not filtered out from this
figure Therefore comparing the distributions with Figure 47 shows how much industry
composition matters
Panel A) illustrates the productivity evolution of domestic private firms The shape of this
distribution remained remarkably similar across years There are clear rightward shifts
between 2004-2008 and 2012-2016 while the distribution did not change during the crisis
period Similar patterns can be observed regarding foreign-owned firms This distribution
was always more dispersed than the domestic one with little changes in its standard
deviation across years
The shape of the state-owned productivity distribution is more peculiar Most visibly it had
been bi-modal before the crisis This is mainly a consequence of industry composition the
low productivity part representing some utilities While the bi-modality disappeared post-
crisis the low-productivity tail of the distribution became thicker Finally we do not see
any rightward shift in this distribution there was little productivity improvement in this
small segment of the economy
Figure 49 Evolution of the distribution of TFP by ownership
A) Domestic private
Evolution of the Productivity Distribution
50
B) Foreign
C) State
Notes This figure shows the distribution of TFP Domestically-owned is domestic privately-owned
Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
51
BOX 41 Duality between domestic and foreign-owned firms in an international context
We are not the first to document the substantial wage and productivity advantage of foreign firms Earle
and Telegdy (2008) by using NAV data between 1986-2003 show that foreign-owned firms were almost
twice as productive as domestic private firms (measured in terms of labour productivity) and also paid
40 higher wages when controlling for employee characteristics A substantial part of this premium
results from foreign owners acquiring more productive firms (mostly during the privatisation process)
but even after controlling for this selection process the foreign wage premium remains 14 Similar
results are found by Telegdy et al (2012) when using the longer period between 1986 and 2008
Foreign-owned firms tend to have positive productivity and wage premia in most countries developed or
emerging Among others Aitken et al (1996) show that foreign-owned firms have higher productivity
and wages in Mexico and Venezuela even after controlling for firm size skill mix and capital intensity
Conyon et al (2002) use acquisitions in the UK in 1989-1994 to find that foreign firms pay 34 higher
wages which can be fully attributed to their 13 higher productivity Girma et al (2002) have a similar
result showing that foreign firms in the UK have 8-15 higher productivity which leads to 4-5 higher
wages Using UK data from 1981-1994 Girma and Goumlrg (2007) find wage differentials of a similar
magnitude but heterogeneous with regard to the source country of the foreign investor Huttunen
(2007) looks at Finland and finds 26-37 wage premium of firms 3 years after being acquired by
foreign investors In the Central-Eastern-European region Djankov and Hoekman (2000) show that
foreign investment in the 90s increased the productivity of recipient firms in the Czech Republic
Governments aim to attract foreign direct investment (FDI) as it is assumed to have a positive impact
on the domestic economy From an economic point of view it is justifiable to provide incentives to
foreign investors if their investments have positive spillovers to domestic firms increasing their
productivity The higher productivity of foreign-owned firms which is documented in the previously
mentioned studies is a necessary condition for that At the same time if foreign firms establish no links
with domestic firms there is only limited opportunity for knowledge spillovers In this case the inflow of
foreign investments results in a dual structure of the economy
Evidence is rather mixed on FDI spillovers to domestic firms in the same industry because a negative
competition effect might dominate the positive technology or knowledge effect Haskel et al (2007) find
that a 10-percentage-point increase in the share of foreign ownership increases the TFP of domestic
firms in the same industry by 05 in the UK Konings (2001) finds negative spillovers for Bulgaria and
Romania and no spillovers for Poland Positive spillovers in vertically related industries are much more
general Using Lithuanian data Javorcik (2004) shows that one standard deviation increase in the foreign
share of an industry is associated with 15 increase in the output of domestic firms operating in the
supplier industry Similarly Kugler (2006) finds no within-industry spillovers but positive spillovers in
vertically related industries in Colombia
Evolution of the Productivity Distribution
52
Let us turn to industry differences in duality The substantial difference between the average TFP of
domestic and foreign-owned firms is present in all kinds of industries (Figure 410 and 411) In
manufacturing the percentage difference is about 34 percent (a log difference of 03) while it is
around 65-100 percent in services Significantly the cumulative TFP growth of the two types of firms
was very similar by the end of the period There is no evidence for the catching-up of domestic firms
with foreign ones The duality in this respect does not seem to diminish substantially
The TFP gap between foreign and domestic firms is amplified by the much higher capital intensity of
foreign firms (Figure 412) In manufacturing foreign firms employed more than twice as much capital
per employee than domestic firms While the capital intensity of both domestic and foreign-owned
firms increased steadily during the period in that sector the gap remained constant showing little
catching-up of domestic firms in terms of capital deepening In a sharp contrast there was a decrease
in the capitallabour ratio in services and this phenomenon took place quicker in the case of foreign
firms
This picture is reinforced at the industry level (Figure 413) In textiles foreign firms invested more
than domestic ones leading to significant capital deepening for that group of firms In machinery both
groups of firms increased their capital intensity to a similar extent In retail foreign firms had invested
much before the crisis but cut their investments deeply after that while the capital intensity of
domestic firms remained mostly flat In programming capital intensity declined slightly following the
crisis
BOX 41 Duality between domestic and foreign-owned firms in an international context
(cont)
Looking at Hungarian data several papers show the existence of positive FDI spillovers to domestic
firms Halpern and Murakoumlzy (2007) find significantly positive spillovers in the supplier industry but
no evidence for within-industry spillovers Beacutekeacutes et al (2009) find a negative effect on low-
productivity firms in the same industry while the spillover effect is positive for high-productivity
firms Iwasaki et al (2012) find positive spillovers even within the same industry conditional on the
proximity in product and technological space At the same time Bisztray (2016) shows that the
large-scale foreign direct investment of Audi did not increase the productivity of domestic firms in
the supplier industry
We know from the literature that the effect of FDI on domestic firms is highly heterogeneous even in
the supplier industry (see Smeets 2008 for a review) A crucial precondition of positive spillovers is
the absorptive capacity of the domestic firms (Crespo-Fontoura 2007) Using data from Bulgaria
Poland and Romania Nicolini and Resmini (2010) show that firm size matters as well Additionally
they find within-industry spillovers in labour-intensive sectors and cross-industry spillovers in high-
tech sectors Also the characteristics of the foreign investment play an important role in the
magnitude of the spillover effect Javorcik (2004) estimates a positive effect on the productivity of
domestic firms only in the case of shared foreign and domestic ownership but not for fully foreign-
owned firms Javorcik and Spatareanu (2011) show that the distance of the investorrsquos country is
also important as investors from far-away countries establish more links with local suppliers In line
with that they estimate positive vertical spillovers from US investors but not from European
investors in Romania Lin et al (2009) show that vertical FDI spillovers in China are weaker for
export-oriented FDI compared to domestic-oriented
Productivity differences in Hungary and mechanisms of TFP growth slowdown
53
Figure 410 TFP levels of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the (unweighted) average ACF TFP level of foreign and domestically-owned firms Main
sample The industry categories are described in Section 25 The sample includes the sectors of the market
economy except agriculture mining and finance lsquoKISrsquo Knowledge- intensive services lsquoLKISrsquo Less knowledge-
intensive services
Evolution of the Productivity Distribution
54
Figure 411 Cumulated TFP growth of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the cumulative growth of the (unweighted) average ACF TFP level of foreign and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
55
Figure 412 Capital intensity of foreign and domestic firms
A) Manufacturing
B) Services
Notes This figure shows the average capital intensity (log tangible and intangible assetsemployee) of foreign- and
domestically-owned firms since 2004 Main sample The industry categories are described in Section 25 The
sample includes the sectors of the market economy except agriculture mining and finance lsquoKISrsquo Knowledge-
intensive services lsquoLKISrsquo Less knowledge-intensive services
Evolution of the Productivity Distribution
56
Figure 413 Cumulative change in capital intensity of foreign and domestic firms selected industries
Notes This figure shows the (unweighted) average capital intensity (log tangible and intangible assetsemployee)
of foreign and domestically-owned firms since 2004 in four industries Main sample
45 Conclusions
Our investigation of the evolution of productivity distribution has yielded a number of relevant
conclusions which will inform the work conducted in the remaining sections In line with international
evidence we have found that productivity dispersion within industries is many times larger than the
differences between industries Importantly Hungary seems to be an exception to the international
trend of frontier firms diverging from the rest of the economy ndash if anything there is evidence for the
low productivity growth of frontier firms and for some catching-up by others
OECD (2016 Figure 16) has found such a pattern only in Hungary and Italy with divergence in all the
other countries under study (Austria Belgium Canada Chile Denmark Finland France Japan
Norway and Sweden) We find two kinds of explanations plausible First in Hungary (unlike most other
countries in that sample) national frontier firms are quite far away from the global frontier As
Andrews et al (2015) argue the productivity divergence mainly arises between global frontier firms
and the rest If national frontier firms are far away from the global frontier they may find themselves
on the wrong side of global divergence Second it is possible that the policies and institutional
environment for national firms in Hungary is less conducive to adopt local frontier technologies A way
to learn more about the background of this result would be to use cross-country micro-data to study
the behaviour of frontier firms in even more countries including other CEE countries
The low productivity growth of Hungarian national frontier firms constrains productivity growth
directly Furthermore if national frontier firms do not adopt the most developed technology potential
spillovers to other firms will also remain limited Andrews et al (2015) have shown that good
Productivity differences in Hungary and mechanisms of TFP growth slowdown
57
framework conditions (most importantly good regulatory practices in upstream sectors) and innovation
related policies such as providing incentives for RampD and building a more robust national innovation
system are associated with a stronger catch-up of national frontier firms to the global frontier
The results reveal that duality especially between foreign and domestic firms is substantial and there
is no evidence for catching-up by domestic firms The gap is especially large in the service industries
That said the gap between the two groups can be bridged indeed the productivity differences
between the two groups are smaller than within them Duality while a sign of inefficiency also
provides an opportunity for domestic firms to tap into the knowledge base possessed by their foreign-
owned counterparts and to integrate into global value chains by relying on the links of foreign firms
While efficient strategies aiming at maximizing the benefits from FDI and global value chains may
differ across countries there are a few policy options which unambiguously help countries in benefiting
from the presence of multinational firms A robust result of the recent spillover literature is that
domestic firms need strong absorptive capacity including technological knowledge and a skilled
workforce to be able to benefit from the presence of foreign-owned firms (Girma 2005 Crespo and
Fontoura 2007 Zhang et al 2010) One dimension of absorptive capacity building is creating an
effective innovation system with a strong knowledge base and easy access to that knowledge Another
dimension is developing technological and management capabilities which enable firms to understand
and apply advanced knowledge Such capabilities are essential both for technological upgrading and for
integrating into global value chains (Taglioni and Winkler 2016)
An important caveat regarding these results is that they are limited to double-entry bookkeeping firms
We have emphasised that a large share of people work outside the double-entry bookkeeping entities
included in our sample While data are scarce about the productivity of these entities available
information suggests that both the levels and dynamics of productivity may differ radically between
double-entry bookkeeping firms and other entities If so inclusive policies could focus on providing
skills and opportunities for the self-employed
State-owned firms constitute a small part of the Hungarian market economy but such firms are
prevalent in some industries including utilities The productivity of some of these firms is very low
when compared to the productivity of privately-owned firms while they pay higher wages Both of
these phenomena hint at soft budget constraints and other inefficiencies Policies aiming at providing
better incentives either by improving corporate governance of state-owned firms (Arrobio et al 2014)
or by creating framework conditions more conducive to competition may help in in promoting
productivity growth in these important industries
Allocative efficiency
58
5 ALLOCATIVE EFFICIENCY
A key insight of recent productivity research is that differences in productivity levels across countries
largely result from the inefficient allocation of resources across firms rather than from differences in
the productivity of lsquotypical firmsrsquo both in cross-section (Hsieh and Klenow 2009 Restrucca and
Rogerson 2017) and in time-series (Gopintah et al 2017) Inefficient allocation refers to the
phenomenon that low-productivity firms possess a large amount of capital and labour (rather than
shrinking or exiting) or when firms with similar marginal products use a different amount or
composition of inputs
In this chapter we employ two strategies to quantify the extent of such distortions The first strategy
proposed by Olley and Pakes (1996) simply asks whether more productive firms are larger A more
positive covariance between productivity and employment suggests a better allocation of resources
across firms and higher industry level (labour-weighted) productivity (even when holding the
unweighted productivity level unchanged) The Olley-Pakes method is generally agnostic about the
specific nature of distortions but measures their results in an intuitive and robust way at the industry-
year level
Hsieh and Klenow (2009) attempt to identify the sources of distortions36 In particular they argue that
firms can face two main distortions product market distortion (modelled as an implicit sales tax and
identified from the wedge between labour costs and value added) and capital market distortion
(modelled as an implicit capital tax and identified from differences in the cost share of capital) These
variables can be measured at the firm-level Industry-level distortions can be quantified both as the
average of firm-level distortions and also as the dispersion of firm-level measures
This chapter describes these measures at the industry-year level Section 51 presents the Olley-Pakes
covariance terms while Section 52 implements the Hsieh-Klenow method
51 Olley-Pakes efficiency
The Olley-Pakes (also called static) approach of productivity decomposition consists of decomposing
the aggregated (industry-region-level) productivity which is the weighted average of firm-level
productivity levels into the unweighted average firm-level productivity and the covariance between
productivity and firm size (Olley and Pakes 1996) The latter term reflects how efficiently resources in
this case labour are allocated across firms A more positive covariance between size and productivity
reflects stronger allocative efficiency
Let us start with cross-country evidence from the OECD (Andrews and Criscuolo 2013) According to
this source in 2005 static allocative efficiency in Hungarian manufacturing (the covariance term) was
positive but slightly below the average of OECD countries similar to Portugal and Italy (Figure 51)37
Allocative efficiency in services was negative one of the lowest of the countries in the sample
(Andrews and Cingano 2014 Figure 10) showing that less productive firms tended to be larger in the
service sector Andrews and Cingano (2014) also show that the relatively low allocative efficiency in
Hungary is partly explained by policies including product market regulation and creditor protection
36 For an overview of the reallocation literature see Hoppenhayn (2014)
37 Note that these calculations use the ORBISAMADEUS database covering a relatively small fraction of larger
Hungarian firms in 2005 (about 3300 firms) see Box 21
Productivity differences in Hungary and mechanisms of TFP growth slowdown
59
Figure 51 Static allocative efficiency in Hungarian Manufacturing (2005)
Notes This figure is a reproduction of Figure 7 from Andrews and Criscuolo (2013)
Let us turn to our data The logic of the static decomposition is presented in Figure 52 for our main
sample by 2-digit industry38 The horizontal axis shows the unweighted average log labour productivity
of each industry while the vertical axis shows the productivity weighted by employment If all firms
were of equal size (or at least firm size was independent of productivity) weighted and unweighted
productivity would be equal ie all industries would be on the 45-degree line If size and productivity
were positively correlated the weighted productivity would be larger than the unweighted one The
difference between the weighted and unweighted average is the covariance between size and
productivity This measure of allocative efficiency is equal to the vertical distance between each point
and the 45-degree line Allocative efficiency contributes positively to industry productivity in industries
above the 45-degree line while it has a negative contribution for industries below the line
For example in the manufacture of machineries (28) the unweighted average productivity is 646
while the weighted average productivity is 665 Allocative efficiency resulting from more productive
machine manufacturers being larger contributes with 019 to the aggregate productivity of this
industry An industry with negative allocative efficiency is warehousing (52) where the lower
productivity of larger firms contributes negatively to aggregate productivity (the unweighted
productivity being 681 and the weighted only 585)
38 Appendix Table A51-Table A56 summarise the Olley-Pakes (1996) measures by industry
Allocative efficiency
60
Importantly allocative efficiency is positive in most industries It is especially high in the most
knowledge-intensive services (scientific research (72) employment activities (78)) in service
industries with a few large firms (broadcasting (60) telecom (61)) and in key manufacturing
industries beverages (11) chemicals (20) machinery production (28) and vehicle production (29) In
a few industries low-productivity firms tend to be larger Prominent examples are professional
services advertising (69) and legal and accounting activities (73) services with many state-owned
firms transportation (39) waste management (49) and logistics (52) In line with OECD evidence
allocative efficiency tends to be more positive in manufacturing compared to services
Finally Figure A51 in the Appendix shows that allocative efficiency is significantly higher when labour
productivity is considered rather than TFP almost every industry has larger weighted labour
productivity than unweighted labour productivity This difference simply results from the positive
association between productivity and capital intensity
Figure 52 Weighted and unweighted TFP by 2-digit industry 2015 main sample
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted TFP while the vertical axis
shows its weighted TFP in the same year We have omitted industries with less than 1000 observations TFP is
estimated using the method of Ackerberg et al (2015)
Another conclusion that can be drawn from Figure 52 is that allocative efficiency is higher in sectors
with higher unweighted productivity represented by the fitted line in the figure In other words high
firm-level efficiency seems to move together with higher allocative efficiency in the industry One
mechanism behind this relationship may be that incentives for technology upgrading are stronger
when the reallocation process is more effective (Restruccia and Rogerson 2017) but stronger
international competition can also affect positively both within-firm productivity dynamics and
reallocation across firms In Figure 53 we investigate whether this relationship changed between
years The figure shows that the positive relationship between unweighted productivity and allocative
Productivity differences in Hungary and mechanisms of TFP growth slowdown
61
efficiency did not change substantially over time This relationship is similar when labour productivity is
considered (see Figure A52 in the Appendix)
Figure 53 The relationship between weighted and unweighted TFP by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted TFP levels run at the
2-digit industry level separately for 2005 2010 and 2016 TFP is estimated using the method of Ackerberg et al
(2015)
From the perspective of productivity slowdown a key question is whether allocative efficiency
deteriorated in some industries following the crisis Figure 54 shows the allocative efficiency of each
2-digit industry in 2010 and 2016 The axes here represent the distances from the 45-degree line in
Figure 52 If an industry is on the 45-degree line of this figure its allocative efficiency remained
unchanged in the period if an industry is above the line its allocative efficiency was better in 2016
compared to 2010 The first conclusion that can be drawn is that levels of allocative efficiency are
persistent industries cluster around the 45-degree line Also the fitted line shows that allocative
efficiency grew somewhat faster in industries where allocative efficiency was worse and this
relationship is statistically significant Therefore productivity growth decline is unlikely to be the result
of rapidly worsening allocative efficiency
One can however identify a couple of industries where substantial changes took place The machinery
industry (28) for example became more efficient partly because of the entry of new large foreign-
owned firms Office administration (82) and management activities (70) also increased their allocative
efficiency This is most likely due to the entry of large shared service providers Allocative efficiency
decreased in land transportation (39) waste management (49) and warehousing (52)
The evaluation of allocative efficiency in labour productivity shows similar patterns (Table A53 in the
Appendix)
Allocative efficiency
62
Figure 54 The change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences
between the weighted and unweighted TFP) in 2010 while the vertical axis shows the same quantity in 2016 TFP is
estimated using the method of Ackerberg et al (2015)
52 Product market and capital market distortions
The Olley-Pakes static decomposition framework can quantify the overall allocative efficiency of sectors
but it is incapable of informing us about the nature of distortions In this section we implement the
methodology of Hsieh and Klenow (2009)39 to distinguish between product and capital market
distortions This distinction is of much interest given that the crisis and its aftermath ran parallel with
both financial market frictions and changes in product market regulation
The logic of the Hsieh and Klenow (2009) method is the following Under the assumptions of
monopolistic competition on product markets (similarly to Melitz 2003) and frictionless labour
markets the marginal product of labour and capital should be equalized across firms in the absence of
market distortions In turn if the production function is Cobb-Douglas the equality of marginal
products implies that the share of labour costs in value added and capital intensity (capitallabour)
should be equalized across firms Under product market distortions (modelled with a firm-specific
implicit lsquosales taxrsquo or a negative rent) the wedge between labour costs and value added will differ
across firms because firms facing lower implicit taxes charge higher markups The more heterogeneous
the lsquosales taxrsquo is the larger the dispersion of the wedge Capital market distortions are modelled as
39 The Hsieh-Klenow approach has been criticized recently by Haltiwanger et al (2018)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
63
implicit firm-specific capital tax rates Firms facing different capital tax rates choose different capital
intensity levels and hence different capitallabour cost ratios Therefore the dispersion of capital
intensity (or more precisely the cost share of capital) reflects the dispersion of capital tax rates
Note that the implicit taxes proxy multiple sources of distortions from which differences in explicit
taxes represent only a small part The implicit lsquosales taxrsquo includes the cost of complying with different
types of regulations size-dependent regulation the effect of fixed costs and market power The
implicit lsquocapital taxrsquo includes for instance the full cost of accessing financing possible subsidies for
investment or differences in tax incentives to invest These implicit taxes provide a convenient way of
summarizing markup differences and differences in access to capital
As a result the dispersion of the wedge and capital intensity reflect how heterogeneous the two
implicit tax rates are More heterogeneity in implicit tax rates in turn implies more disperse total factor
productivity within industry40 and a less efficient allocation of resources In other words similarly
productive firms (having also similar marginal products of inputs) choose very different input quantities
and combinations
Product market distortions
We start our empirical investigation by calculating the rents (1-implicit sales tax rate) for every firm by
a proxy for markups41
1 minus 120591119884119904119894 =120590
120590minus1
120573119871119904+120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119881119860119904119894 (51)
where 120591119884119904119894 shows the size of the implicit lsquosales taxrsquo (or product market distortion) for firm i in sector s
120590 denotes the elasticity of substitution between firms by consumers and 120573119871119904 and 120573119870119904 are the
coefficients of labour and capital in the production function We follow the calibration of Hsieh and
Klenow (2009)42 and set 120590 = 3 while we plug in 120573119871119904 and 120573119870119904 using our production function estimation of
Section 22 119881119860119904119894 represents the real value added of the firm i in sector s while 119871119886119887119888119900119904119905119904119894 is labour
related expenses for firm i in sector s
The equation reflects the intuition that firms facing a lower implicit sales tax can charge higher
markups and as a result will pay a lower share of their value added to their employees Note that the
level of 120591119884119904119894 depends on a number of parameters and may be driven by differences in for example the
elasticity of substitution Therefore we will normalize the values of this estimate when comparing
typical distortions across industries
Figure 55 summarizes the implicit sales taxes by industry (120591119884119904119894) We standardise the values of 120591119884119904119894 by
subtracting the market level median from the firm-level implicit sales taxes and plot the median of
40 Appendix Table A57 summarises the dispersion of TFP within industry Note that dispersion in labour productivity
(log-value added per worker) is not necessarily related to product market distortions as firms with various
labour productivity may have the same TFP if the production function does not have the property of constant
return to scale
41 Hsieh and Klenow (2009) Equation 18
42 The predicted value of product market distortions crucially depends on the elasticity of substitution However the differences in 120591119884119904119894 across industries and years measures the changes in product market distortions even if the
elasticity of substitution is miscalibrated
Allocative efficiency
64
these standardised values by industry If the standardised bar is positive (negative) than the median
firm in the industry faces a higher (lower) implicit sales tax than the median firm in the economy We
find that product market distortions tend to be larger in highly regulated industries (energy
transportation ICT) while they tend to be lower in less regulated ones with strong competition
including manufacturing accommodation and administrative services The difference between
industries is non-trivial the difference between highly regulated sectors and manufacturing is
equivalent to an extra 10-20 percentage `sales tax ratersquo
The ranking of the industries (with the exception of energy) remained similar between 2006 and 2016
but differences became somewhat larger with a relative decrease in implicit taxes in manufacturing
and administrative services and an increase in transportation and ICT43
Figure 55 Implicit sales taxes (120591119884119904119894) by industry
Notes The figure above shows the median size of product market rents in 2006 and 2016 Industries with positive
tax measures can achieve rents below the market average due to product market distortions
The previous exercise has investigated across-industry differences A further question is whether firms
face different tax rates even within industries because of for example size-dependent taxes This is a
key measure to examine whether resources are misallocated across firms within industries Our
measure for this is the standard deviation of ln(1 minus 120591119884119904119894) (Figure 56)44 This dispersion is substantial
43 We report these measures in more detail in Table A55 of the Appendix
44 Also note that this measure of dispersion is independent of the elasticity of substitution and the production function parameters
Productivity differences in Hungary and mechanisms of TFP growth slowdown
65
with the standard deviation equivalent to a 100 percent sales tax45 Within-industry differences in this
variable are similar across industries with a relatively small dispersion only in mining and energy
Figure 56 Standard deviation of implicit sales tax rates (ln(1 minus 120591119884119904119894)) by industry
Notes The figure shows the within industry product market distortions in 2006 and 2016 Resources are less
effectively distributed in industries with larger distortion measures
Capital market distortions
Distortions on the capital market are identified from how the ratio of expenses on labour and capital
(capital intensity in cost terms) differ from what is predicted by the production function with no capital
tax46
119877(1 + 120591119870119904119894) =120573119870119904
120573119871119904
119871119886119887119888119900119904119905119904119894
119870119904119894 (52)
The left hand side of this equation represents the implicit cost of capital for firm i in sector s backed
out from the capital intensity of the firm If it is 01 the firm faces an implicit lsquointerest ratersquo of 10
percent if it is 02 the lsquointerest ratersquo is 20 This can be decomposed into 119877 the frictionless user
45 Similar differences have been found in other countries as well and they are in line with the vast degree of heterogeneity in terms of size and productivity within industries
46 Hsieh and Klenow (2009) Equation 19
Allocative efficiency
66
costs47 of capital (having the same unit of measurement) multiplied by 1 plus the implicit lsquocapital tax
ratersquo 120591119870119904119894 which is firm-specific48
Similarly to the product market equation 120573119871119904 denotes the labour elasticity of the production function
120573119870119904 is the capital elasticity of the production function and 119871119886119887119888119900119904119905119904119894 is the total labour cost for firm i in
sector s The denominator consists the capital stock of the firm (119870119904119894)
It is not common in the literature to report 120591119870119904119894 because its absolute value depends crucially on the
calibration of the rental rate of capital This is an issue because it is hard to obtain reliable information
on the frictionless rate of capital which most likely changed substantially between the pre-crisis
disinflationary period and the wake of the crisis Besides 120591119870119904119894 takes extremely large values for firms
with low level of capital (eg if the firm rents its capital instead of owning it) Note that the levels of
this variable are identified from the difference between the observed capital intensity (in cost terms)
and the optimal one implied from the production function Therefore we prefer to report the more
easily interpretable implicit median cost of capital 119877(1 + 120591119870119904119894) by industry49
While we find differences and changes in the implicit cost of capital informative it is not a direct
measure of capital market distortions because it can also reflect differences in the user cost of capital
across industries and years However the ratio (or log difference) of the implicit cost of capital
between two firms measures the difference between their respective implicit capital tax rates (or more
precisely between their 1 + 120591119870119904119894) As a result the standard deviation of the log implicit cost of capital
provides a pure measure of the dispersion of implicit capital taxes independently from the exact value
of 119877 Its interpretation is the relative standard deviation of the user cost of capital which is identified
from the dispersion of capital intensities
Figure 57 summarizes the median size of implicit cost of capital across industries50 Administrative and
professional services and ICT seem to pay the highest implicit cost for capital it is above 40 percent in
these industries As opposed to these utilities accommodation and food services face implicit costs of
capital below 20 percent The large differences in access to capital across industries are likely to result
mainly from differences in the size and age distribution of firms as well as from the different share of
tangible capital in different industries Moreover the median implicit cost of capital rose practically in
all service industries but decreased slightly in manufacturing
47 The rental price of capital covers the interest rate and the depreciation of capital stock
48 If one is willing to assume a specific value for the frictionless user cost of capital it is easy to back out 120591119870119904119894 For
example if the implicit cost of capital for firm 119894 (the left hand side) is 02 and (following Hsieh and Klenow 2009) one sets R = 01 then 120591119870119904119894 = 1 meaning that firm 119894 can obtain capital at a 10 percentage points higher interest rate
relative to the frictionless rate
49 The median of 119877(1 minus 120591119870119904119894) is less dependent on the extreme values of the distribution than the average so it is a
more precise measure of capital market distortions a typical firm faces than the average of it
50 We can validate our implicit capital cost measure by comparing our results to Kaacutetay and Wolf (2004) According to their estimates (using a different methodology) the median user cost of capital was 189 percent between 1993 and 2002 Our results have similar magnitude as the median implicit cost of capital was 255 percent in 2006 and 287 percent in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
67
Figure 57 Median implicit cost of capital by industry
Notes The figure shows the average size of capital market distortions in 2006 and 2016 Industries with larger
distortion measures are more constrained in accessing capital due to capital market distortions
Again the differences in typical capital costs across industries are much smaller than differences across
firms within an industry (see Figure 58) In industries where median implicit capital costs are lower
the dispersion of those costs also tends to be smaller the estimated cost of accessing capital is
significantly more unequal in the retail sector and administrative services relative to manufacturing
The notable exemption is the energy sector which has the lowest median and the largest dispersion in
the implicit cost of capital reflecting a relatively low level of capital costs resulting from predictable
tangible capital intensive activities
Allocative efficiency
68
Figure 58 The standard deviation of the estimated implicit cost of capital by industry
Notes The figure shows the standard deviations of capital market distortions log (119877(1 + 120591119870119904119894)) in 2006 and 2016
Most importantly capital market distortions increased within nearly all industries both in terms of
their levels and dispersion Hungary is not an exception in this respect This trend has been
documented in other countries where FDI played important role in economic growth A key study on
this topic is Gopinath et al (2017) who show that large capital inflows and credit market constraints
of small firms jointly increased capital market distortions in Spain This evidence suggests that the
crisis led to similar developments in Hungary making capital costs more unequal by generating
financial frictions This inefficiency seems to have resulted in the misallocation of capital in all types of
industries
A key question from a policy perspective is whether one can identify types of firms which faced a
systematically large increase in their cost of capital We follow the approach of Gorodnichenko et al
(2018) who quantified the misallocation of capital at the firm-level and found that small and young
firms faced an exceptionally high cost of capital We follow this strategy to identify observables which
are likely to be related to the level and change of capital costs
Figure 59 plots the relationship between firm age firm size and the estimated implicit cost of capital
119877(1 + 120591119870119904119894) The figure sorts the firms into twenty equally-sized bins by age and size and plots the
median implicit cost of capital separately for 2006 and 2016 Panel (a) highlights that the implicit cost
of capital was decreasing with firm age even before the crisis with young firms facing about 25
percentage points higher capital costs compared to firms older than 10 years This function became
dramatically steeper by 2016 when the median `oldrsquo firm (more than 10 years old) faced an implicit
capital cost of 25 percent a median 5-year old firm paid 50 percent and a very young firm faced more
than a 100 percent implicit cost of capital This figure suggests that capital market frictions generate
important constraints for entry and the growth of small firms hindering reallocation and innovation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
69
Panel (b) of Figure 59 visualizes the relationship between employment and the implicit cost of capital
We find that firms with more than 20 employees faced an implicit cost of capital below the median of
the whole sample both in 2006 and 2016 As opposed to this small firms faced above the median
implicit cost of capital in 2006 and suffered from a disproportionally large increase in the next decade
This again constrains the growth of small firms relative to their larger peers
Figure 59 The evolution of the implicit cost of capital by age and firm size
A) Age of firms
B) Size of firms
Notes The figure shows the median implicit cost of capital 119877(1 + 120591119870119904119894) by age and size categories
Allocative efficiency
70
The results presented above have shown two patterns an increasing dispersion of the implicit cost of
capital on the one hand and a steeper gradient between observables (age and size) and capital taxes
on the other A natural question is whether increased financial friction led to larger differences in
access to capital along observables One explanation for this could be that banks have become more
wary about allocating capital to say firms operating in industries with much intangible capital The
alternative is that the increased variance in capital access reflects mainly differences along unobserved
dimensions by for example more scrutiny of managers when deciding about firm loans These two
possibilities can have different policy implications In the former case for example policymakers may
promote access to capital for specific groups of firms
Table 51 presents regressions with the implicit capital cost as a dependent variable and key firm-level
characteristics as explanatory variables Our first conclusion is that the regressions explain only a
relatively small part (less than 20 percent) of the variation in the implicit cost of capital the
overwhelming majority of the variation arises from unobservables In this sense policies targeting
specific types of firms may have a limited effect
That said the explanatory power of observables increased by around a third between 2006 and 2016
While the explanatory power of industry dummies slightly decreased that of age increased
substantially from 2 percent to 57 percent The explanatory power of size was much smaller in both
periods suggesting that its correlation with the implicit cost of capital may be confounded by its
correlation with age and industry This evidence together with Figure 59 suggests that indeed
capital access by young firms deteriorated substantially after the crisis
Table 51 Variance decomposition of implicit cost of capital
Variance in 2006 Variance in 2016
Variance
component
Share
of total
Variance
component
Share
of total
Total Variance of log-implicit
cost of capital
2126 100 2443 100
Components of Variance
Variance of age 0042 20 0140 57
Variance of size 0006 03 0002 01
Variance of ownership 0012 06 0022 09
Variance of region 0012 06 0025 10
Variance of industry 0202 95 0180 74
Residual 1830 861 1995 817
Notes Control variables are dummies for age ownership (private foreign or state-owned) region (7 NUTS2
region) and 2 digit industry
53 Conclusions
This section summarises the static measures of allocative efficiency by industry types (Table 52) A
key pattern that emerges is that resources are allocated more efficiently in the manufacturing sectors
First on average the OP covariance is strongly positive within manufacturing while it is very close to
zero in less knowledge-intensive services The Hsieh-Klenow (2009) efficiency measures suggest that
product market distortions are similar across sectors but capital market distortions are significantly
lower in manufacturing These findings are in line with the disciplinary effect of international
competition in the traded sector
Productivity differences in Hungary and mechanisms of TFP growth slowdown
71
Table 52 Allocative efficiency within industries sectors (2016)
Industry type TFP
level
in
2016
TFP
growth
between
2011 and
2016
Olley-
Pakes
allocative
efficiency
Dispersion
of implicit
sales taxes
Dispersion
of implicit
cost of
capital
Low-tech mfg 5694 0027 0197 111 146
Medium-low tech mfg 6081 0027 0017 102 142
Medium-high tech mfg 6129 -0093 0119 111 131
High-tech mfg 6708 0276 0072 107 145
Total manufacturing 5942 0021 0242 107 143
Knowledge-intensive serv 6706 0225 0403 106 166
Less knowledge-intensive serv 6566 021 -0081 108 159
Construction 6411 0082 0023 109 148
Utilities 5949 -0138 0801 093 155
Total services 6598 0212 0055 108 160
Notes The table summarises the allocative efficiency measures by broad industry categories The dispersion of
implicit sales taxes is measured by the standard deviation of 119897119899(1 minus 120591119884119904119894) while the dispersion of the implicit cost of
capital is measured by the standard deviation of ln (119877(1 + 120591119870119904119894))
Capital market distortions became more severe in the wake of the financial crisis while there was no
such trend in terms of product market distortions This finding is in line with results for Southern
Europe (Gamberoni et al 2016a Gopinath et al 2017) and CEE countries in general (Gamberoni et
al 2016b) (see Figure 510) This suggests that the financial intermediation system is still less
effective relative to its pre-crisis performance
Investigating at the firm-level we found that the deterioration of the financial conditions did not hit all
firms equally In particular young firms were hit especially hard by ever increasing capital costs even
though many policy tools were introduced to help such firms including the subsidized access to capital
by the Central Bank (eg the NHP program) Deteriorating access to capital by young firms can be
especially harmful for reallocation often driven by dynamic young firms Policies aimed at promoting
equal and efficient access to capital especially for young firms may help to reduce these inefficiencies
Given the magnitude of the still existing allocative inefficiency policies which support reallocation could
have a significant positive effect on aggregate productivity A key conclusion of recent research is that
firm-specific distortions which may result from discretionary policies or non-transparent regulations
have a quantitatively significant effect on aggregate productivity (Hsieh and Klenow 2009 Bartelsman
et al 2013 Restuccia and Rogerson 2017) In particular size-dependent taxes and regulations
(Garicano et al 2016) ineffective labour and product market regulations and FDI barriers (Andrews
and Cingano 2014) have been shown to be negatively associated with allocative efficiency and its
improvement Gamberoni et al (2016b) also demonstrate that higher corruption levels slow down the
improvement of allocative efficiency Chapter 8 will investigate the effects of such policies in more
detail using the example of the retail industry
The specific pattern showing that capital distortions are relatively high and have increased in Hungary
(similarly to other CEE and Southern European countries) suggests that policies which facilitate the
reduction of financial frictions and provide symmetric access to capital for all firms could improve
allocative efficiency to a large degree Specifically policies should attempt to facilitate capital flows to
Allocative efficiency
72
more efficient firms even if young rather than to firms with a higher net worth or more tangible
assets (Gopinath et al 2017)
Figure 510 Capital and labour misallocation in CEE countries country-specific weighted average
across sectors
Notes This is a reproduction of Figure 1 from Gamberoni et al (2016b)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
73
6 REALLOCATION
After investigating the level of allocative efficiency in Chapter 5 namely a lsquostaticrsquo approach we now
turn to a dynamic view focusing on how much reallocation across industries (Section 61) and firms
(Section 62) contributed to aggregate and sectoral productivity growth
61 Reallocation across industries
An important channel behind the relationship between economic development and productivity is the
structural change of the economy first from agriculture to manufacturing and then from
manufacturing to services (Herrendorf et al 2014 McMillan et al 2017) But at higher levels of
development economic growth is also associated with reallocation across industries within these broad
sectors primarily from more traditional to more knowledge-intensive ones (Hausmann and Rodrik
2003 Hausmann et al 2007) Kuunk et al (2017) demonstrate that in terms of its contribution to
productivity growth across-industry reallocation within sectors dominated reallocation across sectors
in CEE countries In this subsection we take a brief look at the importance of this process in Hungary
by quantifying the reallocation of employees across and within 2-digit industries
Table 61 shows how the employment share of different industries in our main sample changed over
time51 The most important pattern is a pronounced shift from manufacturing to services until 2010
and near-constant sectoral shares after that In particular the share of manufacturing decreased by
nearly a quarter from 38 percent to 32 percent between 2004 and 2010 but this number remained
unchanged in the years following the crisis The crisis seems to have constituted a structural break in
this process
A more detailed look at the composition of industries shows that ndash in net terms ndash this structural
change was driven by a transition of employment from low-tech manufacturing52 to both knowledge-
intensive and less knowledge-intensive services while the employment share of the more high-tech
manufacturing industries remained practically unchanged After 2010 the structure of manufacturing
remained mainly unchanged in this aspect with no further shift away from low-tech manufacturing
activities Within services we see a continuous increase in the share of knowledge-intensive services
both before and after the crisis In the 12 years under study the employment share of knowledge-
intensive services increased by 6 percentage points or nearly 60 percent
51 Note that these calculations in line with other parts of this report apply to the firm sector of the Hungarian
economy ie ignore the self-employed (see Section 42) When taking into account the self-employed the share of
services and sectoral share follow somewhat different dynamics
52 One factor behind this process might have been the almost doubling of the minimum wage in 2000 and 2001
(Koumlllő 2010 Harasztosi and Lindner 2017) and a growing import competition in the light industries (David et al
2013)
Reallocation
74
Table 61 Employment in different sectors (main sample)
2004 2007 2010 2013 2016
Low-tech mfg 152 117 107 105 100
Medium-low tech mfg 89 92 91 96 98
Medium-high tech mfg 94 96 82 89 93
High-tech mfg 49 49 44 39 35
Total manufacturing 384 355 324 329 327
Knowledge-intensive serv 107 128 149 158 167
Less knowledge-intensive serv 382 395 410 405 397
Construction 86 88 83 75 75
Utilities 40 34 34 33 34
Total services 616 645 676 671 673
Notes This table shows employment shares by industry type (see Section 25) for the full sample
To provide a more detailed picture Figure 61 illustrates how employment growth in different 2-digit
industries is associated with their initial productivity level (Figure 61) In particular if more productive
sectors increase their employment share faster aggregate productivity should grow
Figure 61 Employment change as a function of initial TFP
A) Manufacturing
Productivity differences in Hungary and mechanisms of TFP growth slowdown
75
B) Services
Notes Industries are 2-digit NACE Rev 2 industries The fitted line is weighted with initial employment Main
sample
To quantify whether across-industry reallocation matters we decompose the aggregate productivity
growth observed in our sample into the contributions of cross-industry reallocation and within-industry
productivity growth We divide our sample into three-year periods and calculate the average yearly
productivity growth by periods
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119901119894119905minus3)119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+ sum 119905119891119901119894119905minus3 lowast (119904ℎ119886119903119890119894119905 minus 119904ℎ119886119903119890119894119905minus3)119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
(61)
where the left hand side variable is the change in aggregate TFP between years 119905 minus 3 and 119905 119904ℎ119886119903119890119894119905 is
the share of the (2-digit) industry i in year t in the total employment and 119905119891119901119894119905 is average TFP of the
industry The first term on the right side is the within-industry TFP growth weighted by initial market
shares and the second term is the between effect capturing whether more productive industries have
increased their employment shares53
The decomposition in Figure 62 presents the result of this reallocation exercise for annualized growth
rates Its interpretation is the following between 2004 and 2007 average annual productivity growth
was nearly 8 percent in the total economy Around 7 percentage points from it is explained by within-
industry developments and only about 1 percentage point by reallocation across industries
53 This decomposition gives a comprehensive measure of the reallocation between industries but it is unable to
show the importance of firm exits and entries We investigate this in the next section
Reallocation
76
In general the figure shows that within-industry reallocation rather than cross-industry
developments played the key role in aggregate productivity growth Furthermore in line with Table
61 the contribution of between-industry reallocation was effectively zero post-crisis During the crisis
cross-industry productivity growth contributed positively to aggregate productivity growth while within
industry reallocation dramatically lowered aggregate productivity
This overall picture suggests that the flow of resources from light industries to other manufacturing
the growing share of services and especially knowledge-intensive services were a detectable though
not dominant driver of productivity growth only before 2010 Within-industry developments were
quantitatively more important throughout the whole period under study
This latter finding hints at a deterioration in the environment determining the reallocation process
post-crisis This seems to be the case for the whole economy but the negative contribution of
reallocation is more pronounced in manufacturing
Figure 62 Across and within industry productivity growth annualized log
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth main
sample
62 Reallocation across firms
In this subsection we take a look at the role of reallocation from a different perspective Rather than
focusing on whether the resources flow across industries we take a firm-level focus and decompose
TFP growth to within and across firm components The usefulness of this approach lies in the fact that
it sheds more light on the flexibility and efficiency of the process determining resource flows across
firms and also allows us to distinguish between resource flows across continuing firms on the one hand
and entry and exit on the other
Productivity differences in Hungary and mechanisms of TFP growth slowdown
77
There are two general methods of measuring the reallocation of resources from less efficient to more
efficient firms The first method quantifies the labour and capital gains of more efficient firms directly
(Harasztosi 2011 Petrin et al 2011 Petrin and Levinson 2012) The second method is based on
product-market developments allocation of resources improves if the market share of high
productivity firms increases (Baily et al 1992 Griliches and Regev 1995 Brown and Earle 2008)
We adopt this second method as it can quantify directly the TFP contribution of firm entries and exits
To begin with we decompose the aggregate TFP growth between years t and t-3 based on the method
of Foster et al (2001) and Foster et al (2008)
∆119905119891119905 = sum 119904ℎ119886119903119890119894119905minus3 lowast ∆119905119891119901119894119905minus3119894⏟ 119908119894119905ℎ119894119899 119890119891119891119890119888119905
+ sum (119905119891119901119894119905minus3 minus 119905119891119905minus3 + ∆119905119891119901119894119905) lowast ∆119904ℎ119886119903119890119894119905minus3119894⏟ 119887119890119905119908119890119890119899 119890119891119891119890119888119905
+
sum 119904ℎ119886119903119890119894119905 lowast (119905119891119901119894119905 minus 119905119891119905minus3)119894isin119873⏟ 119890119899119905119903119910 119890119891119891119890119888119905
+sum 119904ℎ119886119903119890119894119905minus3 lowast (119905119891119901119894119905minus3 minus 119905119891119905minus3)119894isin119873⏟ 119890119909119894119905 119890119891119891119890119888119905
where the left hand side variable is the average annual aggregate TFP growth between years t-3 and t
and 119905119891119905 is the employment weighted average aggregate TFP while 119905119891119901119894119905 is the TFP of firm i in year t
119904ℎ119886119903119890119894119905 denotes the employment share of firm i in year t The first and second sum contain every firm
while the third sum consists of only firms which enter between years t-3 and t and the fourth sum
consists firms which leave the market between years t-3 and t
Each element of this decomposition has an intuitive economic interpretation In order of inclusion
these are i) within-firm TFP growth weighted by initial market shares ii) between effect capturing
whether initially more productive firms have raised their market shares and whether firms with
increasing productivity also expand (cross effect) and the iii) entry effect and iv) exit effect We pull
the last two terms together and interpret it as net entry effect which captures whether more
productive firms entered than exited54
Figure 63 summarizes the results for the market economy Before the crisis all three components
contributed positively to aggregate productivity growth Reallocation both across continuing firms and
on the margin of entry and exit was an important driver of productivity growth Productivity growth
was negative during the crisis as we have seen in Section 43 This was a result of strong negative
within-firm growth partly counterbalanced by positive reallocation Within-firm growth was still
sluggish immediately after the crisis but reallocation was relatively intensive and efficient Within-firm
growth recovered after 2013 and the importance of reallocation decreased Still the contribution of all
three components is substantially smaller relative to pre-crisis suggesting that the productivity
slowdown results from a combination of low within-firm growth and less effective reallocation
54 Note that these quantities cannot be easily linked to the withinacross industry decomposition of the previous
section Across firm reallocation and the entry effect can take place both across and within sectors
(62)
Reallocation
78
Figure 63 Dynamic decomposition annualized log main sample
Notes This figure shows the Foster et al (2008) type dynamic decomposition of sales-weighted TFP growth by 3-
year periods main sample
Figure 64 repeats the decomposition exercise for each industry type For ease of interpretation (and
to get more stable results) we aggregate the three non-high-tech manufacturing sectors for these
calculations
As we have seen in Section 43 productivity dynamics differed markedly across these sectors Still
there are some common patterns First the strong pre-crisis productivity growth resulted from a
combination of strong within-firm productivity growth and efficient reallocation The sectors differ in
terms of the weights of these forces reallocation (especially entry) was most important in non-high-
tech manufacturing while within-firm growth dominated in high-tech manufacturing In services the
two components were of roughly equal importance
As we have seen productivity increased even during the crisis in high-tech manufacturing as a
combination of within and across productivity growth In other industries productivity growth was
negative during the crisis In non-high-tech manufacturing a strongly negative within growth was
somewhat counterbalanced by positive reallocation In contrast we find evidence for a negative
reallocation effect in services during the crisis
Immediately following the crisis (2010-2013) within growth remained sluggish but reallocation
resulting from firm entry and exit intensified especially in non-high-tech manufacturing and high-tech
services By 2013-2016 within growth recovered and the effect of reallocation became smaller
Productivity differences in Hungary and mechanisms of TFP growth slowdown
79
Figure 64 Dynamic decomposition by sector
A) High-tech Manufacturing
B) Non-high-tech Manufacturing
Reallocation
80
C) Knowledge-intensive services (KIS)
D) Not knowledge-intensive services (NKIS)
Notes This figure shows the Foster et al (2008) type dynamic decomposition of the productivity growth in our
sample for 3 periods by broad sectors as defined by the EurostatOECD (httpeceuropaeueurostatstatistics-
explainedindexphpGlossaryHigh-tech)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
81
One of the main messages of our analysis in Section 44 has been the large and persistent duality
between globally oriented and other firms This motivates our investigation of the extent to which
exporters and foreign-owned firms contributed to productivity growth and also whether reallocation
via the expansion of the more productive group contributed to aggregate productivity growth In order
to investigate these questions we decompose aggregate productivity growth into three parts the
within contribution of exporters (in the starting period) the within-contribution of non-exporters and
the reallocation between the two groups (which mainly reflects the change in the market share of
exporters) We conduct a similar analysis between foreign and domestically-owned firms
Table 62 shows the decomposition by export status Pre-crisis exporters contributed substantially
more to productivity growth than non-exporters both in manufacturing and services The reallocation
of resources to exporters mattered little Exporters were still capable of improving their productivity
levels during the crisis though it was not enough at the aggregate to counterbalance the falling
productivity of non-exporters Post-crisis the productivity growth of exporters slowed down and
aggregate growth was mainly driven by productivity changes within the non-exporter group
Productivity growth became much less exporter-driven post-crisis
Table 62 TFP growth decomposition by exporter status annualized log
2004-2007
Total Exporter Non-exporter Across
Market economy 793 577 226 -009
Manufacturing 1053 877 164 011
Market services 606 387 222 -003
2007-2010
Total Exporter Non-exporter Across
Market economy -045 091 -136 000
Manufacturing 054 021 007 027
Market services -178 129 -305 -002
2010-2013
Total Exporter Non-exporter Across
Market economy 206 033 144 029
Manufacturing -122 -129 -012 019
Market services 471 077 289 106
2013-2016
Total Exporter Non-exporter Across
Market economy 362 155 206 001
Manufacturing 057 049 011 -003
Market services 650 243 382 025
Notes This table decomposes the sales-weighted productivity growth into within-exporter within-non-exporter
contributions and the contribution of the reallocation between the two groups main sample
Table 63 decomposes productivity growth by ownership The picture is similar to the exporter
decomposition with a key contribution of foreign-owned firms to productivity growth pre-crisis and a
much smaller contribution after that Again reallocation of resources to foreign-owned firms played a
limited role in productivity growth
Reallocation
82
Table 63 TFP growth decomposition by ownership status annualized log
2004-2007
Total Foreign Domestic Across
Market economy 793 255 516 022
Manufacturing 1053 375 619 059
Market services 606 148 383 075
2007-2010
Total Foreign Domestic Across
Market economy -045 018 -076 013
Manufacturing 054 081 -034 007
Market services -178 -131 -120 073
2010-2013
Total Foreign Domestic Across
Market economy 206 003 199 005
Manufacturing -122 -152 027 003
Market services 471 138 336 -003
2010-2016
Total Foreign Domestic Across
Market economy 362 106 264 -008
Manufacturing 057 011 040 005
Market services 650 234 452 -037
Notes This table decomposes the sales-weighted productivity growth into within-foreign within-domestic
contributions and the contribution of the reallocation between the two groups Main sample
63 Failure of reallocation Zombie firms
Following the crisis it was suggested that weak productivity performance could be linked to the
survival of unprofitable and ineffective firms The presence of many such firms limits the access of
better-managed firms to capital and generates congestion in the product markets which limits entry
(Caballero et al 2008) McGowan et al (2017) have argued and provided evidence that the share of
such ldquozombie firmsrdquo has risen since the middle of the 2000s and that the higher share of such firms is
associated with lower productivity growth and investment at the industry level
Given the productivity slowdown in Hungary and the extent to which the financial crisis has affected
bank lending it is of interest to see whether the prevalence of ldquozombie firmsrdquo increased
disproportionately after the crisis
Figure 65 shows the share of ldquozombie firmsrdquo in 9 OECD countries from McGowan et al (2017) The
share of such firms in the full sample increased from just below 3 percent in 2003 to 5 percent in
2013 The rise was especially noticeable in Spain and Italy where in 2013 the share of firms reached
11 and 5 percent respectively Even more importantly the employment share of ldquozombie firmsrdquo rose
above 15 percent in both countries by 2013 possibly generating significant effects for other firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
83
Figure 65 Share of ldquozombie firmsrdquo in some OECD countries
Notes This is a reproduction of Figure 5A from McGowan et al (2017) Country codes should be interpreted as
follows BEL ndash Belgium ESP ndash Spain FIN ndash Finland FRA ndash France GBR ndash Great Britain ITA ndash Italy KOR ndash South
Korea SWE ndash Sweden SVN ndash Slovenia
Our basic definition of ldquozombie firmsrdquo follows McGowan et al (2017) for comparability We define a
firm as a zombie if it is at least 10 years old and its interest coverage ratio (the ratio of operating
income to interest expenses) has been below one for the last three years A limitation of this definition
is that interest expenses are not reported (or missing) for many smaller firms which only submit a
less detailed financial statement (or have no bank financing) To overcome this problem we also
categorize firms as zombies if their operating profit is negative for three subsequent years In such
cases the coverage ratio is not defined but the firmrsquos income is clearly not enough to cover its interest
expenses Note that this is a very conservative definition ndash one could also input interest expenses for
external capital for firms with missing interest expenditures (Figure 66)55
55 In actual fact 95 percent of zombies defined in this manner have negative profits
Reallocation
84
Figure 66 Share of ldquozombie firmsrdquo in Hungary
Notes Main sample
The patterns are the following First the share of ldquozombie firmsrdquo among firms with at least 5
employees was relatively high even before the crisis reaching about 8 percent by 2006 This increased
slightly in the wake of the crisis but started to decline after that falling to 3 percent in 2016 ldquoZombie
employmentrdquo fluctuated around 12-15 percent in most years with a steep decline after 2014
Put in an international context it is clear that the existence of ldquozombie firmsrdquo is a relatively big issue in
Hungary with their employment share at the highest end of the distribution of the OECD countries
examined by McGowan et al (2017) The prevalence of such firms however had been relatively high
even before the crisis with a relatively moderate growth between 2009 and 2011 followed by a
significant fall of the share of these firms Therefore ldquozombie firmsrdquo may have constrained productivity
growth in Hungary in the whole period but it is unlikely that an increase in zombie share is a key
explanation for productivity slowdown following the crisis
Table 64 shows the employment share of zombie firms in different dimensions One can see a U-
shaped relationship in terms of size with the largest zombie share among the smallest and the largest
firms The somewhat larger share of zombies among small firms may be explained by the tendency of
such firms to report losses in order to evade business taxes Large firms may be able to operate
persistently under losses either because of their accumulated savings or even more likely because of
the deep pockets of their owners This is also suggested by part B) which shows that a firm is more
Productivity differences in Hungary and mechanisms of TFP growth slowdown
85
likely to be a zombie if owned by the state56 or by foreigners In the latter case profit-shifting motives
may also play a role in reporting losses for sustained periods in Hungary Finally zombies are more
prevalent in services compared to manufacturing and in low-tech industries compared to high-tech
Table 64 Zombie employment by size ownership and industry
A) By size
2004 2007 2010 2013 2016
5-9 emp 62 751 862 802 411
10-19 emp 618 626 679 652 297
20-49 emp 607 554 698 648 296
50-99 emp 711 685 792 829 438
100- emp 2005 1839 1559 1489 456
Total 1548 1367 1229 1194 417
B) By ownership
2004 2007 2010 2013 2016
Domestic 66 671 769 614 253
Foreign 989 1011 1139 1577 585
State 6289 5954 4127 2792 7
Total 155 1369 1228 1194 417
C) By type of industry
2004 2007 2010 2013 2016
Low-tech mfg 1236 1255 1149 906 371
Medium-low tech mfg 897 515 846 974 634
Medium-high tech mfg 542 407 936 486 269
High-tech mfg 427 1392 429 268 1
KIS 2098 737 875 1408 592
LKIS 282 2327 187 1867 365
Construction 258 394 339 515 352
Utilities 297 1031 705 593 756
Total 1553 1372 1229 1194 418
Notes Main sample
Importantly all these patterns persist in multiple regressions when one includes all these variables at
the same time together with other controls (ie larger firms are more likely to be zombies even when
controlling for ownership) In such regressions (lag) productivity is the strongest predictor of not
56 Obviously the extreme employment share of state-owned zombie firms partly results from the massive size of some large utilities including the national railways and the Hungarian Post
Reallocation
86
becoming a zombie firm later one standard deviation higher productivity is associated with a 5
percentage point lower probability of becoming a zombie in the next period Note however that
productivity is actually a close measure of profitability hence this finding mostly reflects a mechanical
relationship of high profitability firms being less likely to become low profitability firms in the future
Figure 67 shows a 3-year transition matrix for zombie firms ie the share of year t zombie firms
which ldquorecoverrdquo remain zombies or exit from the market by year t+3 One cannot see radical changes
across years with somewhat more firms recovering and less exiting in later periods In line with the
argument about deeper pockets larger firms are more likely to remain zombies and less likely to exit
than smaller ones This is related to ownership foreign (and to a smaller extent state-owned) firms
are more likely to remain zombies There also seems to be a characteristic difference between
manufacturing and services manufacturing firms seem to be less likely to lsquorecoverrsquo and more likely to
exit suggesting more persistence of low performance in that sector
Figure 67 What happens to zombie firms within 3 years (2010)
Notes Main sample
64 Conclusions
In line with the immense within-industry productivity heterogeneity documented in Chapter 4 and 5
we find that while there was some reallocation across sectors in the economy the overwhelming
majority of productivity growth took place within industries This emphasizes the usefulness of policies
which promote productivity growth in a sector-neutral way rather than prioritizing some sectors of the
economy
In line with the lower efficiency of capital allocation post-crisis we have found that by and large both
within-firm productivity growth and reallocation across firms and industries became less efficient post-
crisis relative to its pre-crisis level This may reflect the presence of policies which promote specific
sectors or inhibit the growth and entry of more productive firms
Productivity differences in Hungary and mechanisms of TFP growth slowdown
87
In terms of the participation of global networks we have found that at least pre-crisis exporters and
foreign-owned firms contributed significantly to productivity growth Post-crisis the productivity
contribution of internationalized firms became much less substantial Adopting policies that create an
environment which is favourable for innovative investments and does not hamper the expansion of
globally oriented firms may contribute substantially to strengthening productivity growth
The presence of firms which are loss making for an extended period of time suggests a serious failure
of the reallocation process The share of such firms was relatively high in Hungary employing well
above 10 percent of the employees in our sample in most years This level was already high pre-crisis
and increased further during the crisis but there has been substantial improvement in recent years
The problem is more severe for larger firms and state owned firms Improving the corporate
governance of these firms and the effectiveness of the banking system may help in further alleviating
the problem
Andrews et al (2017) argue that the presence of zombie firms ndash and other barriers to firm dynamics ndash
is heavily related to the efficiency of insolvency regimes and the effectiveness of the banking system
Figure 68 shows an insolvency regime index developed by the OECD (the higher the index value the
slower the restructuring) Hungary is one of the countries with the weakest insolvency systems with
all sub-measures taking high values This coupled with the presence of weak banks can be one of the
reasons for the permanently high zombie firm share as well as the increasingly inefficient capital
allocation across firms Therefore insolvency reform complemented with policies aimed at improving
bank forbearance can help to reduce the presence of zombie firms The presence of zombie firms may
also be related to the large share of bank financing Promoting market-based financing including bond
and venture capital markets may also help to diminish the problem
Figure 68 Insolvency regimes across countries
Notes This chart is a reproduction of Andrews et al (2017)rsquos original except for being restricted to European
states only The stacked bars represent the 3 main components of a countrys insolvency index for the year 2016
while the diamond figure indicates these measures aggregate for the year 2010 The authors constructed these
figures with the help of an OECD questionnaire Each measure is associated with a factor that in the long term is
thought to reduce a countrys business dynamism and consequently hamper its proclivity for productivity growth
The first one Personal costs of insolvency stands for environmental factors which could curb a failed
entrepreneurs ability to start new businesses in the future The second measure Lack of prevention and
streamlining indicates whether there are sufficient practices in place for the early detection and resolution of
Reallocation
88
financial distress Thirdly Barriers to restructuring shows how easy it is for a firm suffering from short-term
financial troubles to restructure its debt Country codes should be interpreted as follows GBR ndash Great Britian FRA
ndash France DNK ndash Denmark DEU ndash Germany ESP ndash Spain FIN ndash Finland IRL ndash Ireland SVN ndash Slovenia PRT ndash
Portugal AUT ndash Austria GRC ndash Greece SVK ndash Slovakia ITA ndash Italy LVA ndash Latvia POL ndash Poland NOR ndash Norway
SWE ndash Sweden LTU ndash Lithuania BEL ndash Belgium CZE ndash Czech Republic MLD ndash Moldova HUN ndash Hungary EST ndash
Estonia
Productivity differences in Hungary and mechanisms of TFP growth slowdown
89
7 FIRM-LEVEL PRODUCTIVITY GROWTH AND DYNAMICS
The main aim of this section is to investigate the micro-level processes which underlie the patterns
documented at the industry level in the previous chapters (especially in Chapter 6) by presenting a few
descriptive relationships between firm characteristics and firm dynamics More specifically we would
like to understand how various firm characteristics are associated with the observed patterns of
productivity and employment growth to illustrate the micro-level processes behind within-firm
productivity growth and reallocation Additionally we look at which types of firms enter and exit in
order to shed light on how they contribute to changes in the average productivity level
We seek to answer three main questions First was there a structural break either in the productivity
growth or in the reallocation process after the crisis which may have contributed to the productivity
growth slowdown Second do we see a structural difference in these processes along the main
dimensions of the lsquodualityrsquo of the Hungarian economy eg the characteristic differences between
globally involved large firms and their domestic market oriented peers Third can we find peculiar
patterns which may explain the unusual evolution of productivity quintiles namely the slow
productivity growth of frontier firms relative to less productive firms (as documented in detail in
Section 44)
In terms of firm characteristics we focus on variables which are likely to be related to the duality (see
Section 44) ownership size age and exporter status We do firm-level regression analyses which
allows us to use a rich set of controls and fixed effects Additionally we look at the interaction of the
different characteristics to get an even more precise picture about the main factors driving productivity
growth and reallocation
The structure of this chapter follows closely the logic of the dynamic productivity decomposition
exercise in Chapter 6 In Section 71 we investigate the determinants of within-firm productivity
growth In Section 72 we explore how firm characteristics are related to future employment growth ndash
ie to between firm reallocation ndash followed by the analysis of entry and exit in Section 73
71 Productivity growth
Questions and descriptive patterns
A key relationship of interest is how future productivity growth is related to current productivity levels
Our main motivation to study this question is that it can shed light on the extent of convergence to
more productive firms within the industry If there is a tendency for low-productivity firms to catch up
the productivity growth of such firms will be higher We analyse this relationship for the whole
economy and will also split the sample along different dimensions We are particularly interested in
three questions First is there a difference between the productivity growth rates of firms along some
dimensions even when controlling for productivity We think that this question is highly relevant but
will also qualify the findings of for example Section 32 where we compared firms with different
ownership structures and of different sizes with each other unconditionally which may mask the
different composition of the two groups in terms of initial productivity levels Second we are interested
in whether the slope of the relationship between the initial productivity level and subsequent
productivity growth differ along observable dimensions Is it the case for example that domestically-
owned firms face a productivity ceiling beyond which they cannot improve their efficiency any further
while foreign firms are better able to push forward even starting from very high productivity levels
Third we would like to find out whether there are structural changes in this relationship which may be
associated with the productivity slowdown following the crisis
Firm-level Productivity growth and dynamics
90
While the main mechanism behind this relationship is likely to result from a process of convergence
between firms the measured relationship can also partly arise from a mechanical negative relationship
coming from regression to the mean A large positive measurement error in productivity in year t
automatically generates a large negative growth rate from t As we are interested in the convergence
process rather than the mechanics of the regression to the mean we look at the relationship between
lagged productivity levels and 3-year productivity growth We assume that regression to the mean
resulting from measurement errors is less likely to show up when the productivity level is lagged An
additional limitation of this exercise is survivorship bias because lower productivity firms are more
likely to exit if they are unable to improve their productivity level We will analyse exit and entry
separately in Section 73
First to see the overall patterns we present the relationship between initial productivity levels and
productivity growth in the following 3 years in a non-parametric way (see Figure 71) To do so we
classify firms within each industry into 20 quantiles based on productivity in the previous year For
example we show how productivity growth between 2012 and 2015 is related to productivity levels in
2011 For each quantile we calculate average growth after partialling out 2-digit industry fixed
effects We show this relationship for different years to see whether there is a structural change in the
within-firm productivity growth process57 We demean lagged productivity levels by 2-digit industry
and year so zero on the horizontal axis corresponds to the mean productivity level We take four
periods pre-crisis (2003-2006) crisis (2006-2009) post-crisis (2009-2012) and recent (2012-
2015)58
Figure 71 shows that the relationship between previous productivity levels (on the horizontal axis) and
subsequent 3-year growth (on the vertical axis) can be well approximated with a linear relationship
We see a pronounced negative relationship in all periods reflecting that (surviving) lower productivity
firms increase their productivity faster than more productive firms generating some within-firm
convergence in the sample of continuing firms The slope of the relationship ie the productivity
growth premium of less productive firms is quite stable across non-crisis years but differs markedly in
the crisis showing that the crisis-related productivity decline was more severe for more productive
firms probably because these firms had been hit the hardest by the collapse of global trade59 Note
that this is much in line with the slow productivity growth of frontier firms in the same period
documented in Section 43 Figure 44 In normal times macro conditions seem to shift the whole line
up or down rather than rotate it The average 3-year productivity growth rate is the lowest during the
crisis and is still low in the post-crisis period but there is no difference between the pre-crisis and the
recent periods60
57 As in the previous chapters we use our main sample (see Chapter 2) in which we only consider firms with at
least 5 employees and measure productivity with the method of Ackerberg Caves and Frazer (2015)
58 Note that to measure subsequent growth we need three years following the base year when the level of
productivity is measured Consequently the last year we include is 2012 ndash and follow what happens to firms
between 2012 and 2015
59 More exit of low-productivity firms during the crisis may have also introduced a survivorship bias but as the
patterns in Figure A71 of the Appendix show this seems not to be the case
60 Table A71 of the Appendix shows the same patterns from a regression
Productivity differences in Hungary and mechanisms of TFP growth slowdown
91
Figure 71 The relationship between lagged productivity levels and subsequent productivity growth
over time
Notes This figure shows how the log of productivity in t-1 (on the horizontal axis demeaned by 2-digit industry
and year) is related to productivity growth between t and t+3 Each dot represents one of 20 quantiles of the
productivity level distribution and the average 3-year growth rate of firms within that quantile including 2-digit
industry fixed effects
Estimation
After establishing a linear relationship between lagged productivity level and subsequent growth we
look at the role of firm characteristics in productivity growth We do it in two steps First we look at
cross-sectional patterns taking the most recent period (2012-2015) We ask if there is a difference
between firm groups in productivity growth for the average firm (ie a firm having industry-average
productivity) and if there is a difference in the convergence pattern These two aspects correspond to
differences in the level and the slope of the line We estimate the following regressions
1198893_119905119891119901119894119905 = 1205730 +sum1205731119896119866119894119905
119896
119870
119896=1
+ 1205732(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1) +sum1205733119896(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1)119866119894119905
119896
119870
119896=1
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
We denote productivity of firm i in year t with 119905119891119901119894119905 1198893_119905119891119901119894119905 stands for 3-year productivity growth
119905119891119901 119895(119894)119905minus1 is the year-specific average lagged productivity in industry j of firm i G is a firm characteristic
(eg ownership or size) which contains K categories (eg one ownership group foreign or three size
categories) 119883119894119905 is a set of additional firm-level controls (these can be size age ownership or exporter
status) 120572119895(119894) is industry or industry-region fixed effects and 휀119894119905 is the error term Then 1205731119896 measures the
productivity-growth difference for average-productivity firms in firm group 119866119896 (eg foreign) compared
to average-productivity firms in the baseline category (eg domestic) 1205733119896 measures the difference in
the convergence patterns between firm group 119866119896 and the baseline category
(71)
Firm-level Productivity growth and dynamics
92
Second we also check dynamic patterns to see how the role of these firm characteristics changed over
time taking the same periods as in Figure 71 The baseline regression for comparing productivity
dynamics across years is as follows
1198893_119905119891119901119894119905 = 1205730 + sum 1205731119897119863119905
119897
119897=200320062009
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
As before 1198893_119905119891119901119894119905 is the 3-year productivity growth of firm i from year t to t+3 and 119905119891119901119894119905minus1 denotes the
productivity level of firm i in t-1 119905119891119901 119895(119894)119905minus1119897 denotes the year-specific average lagged productivity in
industry j which firm i belongs to 119863119905119897 is an indicator for year l 119883119894119905 is a set of firm-specific time-variant
controls and 120572119895(119894) is industry or industry-region fixed effects as in the previous specification 1205731119897
measures the difference between the productivity growth of firms with industry-average productivity in
year l and in year 2012 The difference comes from two sources industry-level average productivity
levels could change over time and productivity growth for firms with the same productivity level could
also vary As we are interested in how the productivity growth of the average firm changed over time
we will not separate these two effects 1205732119897 measures the slope of the relationship between lagged
productivity levels and subsequent productivity growth in year l Comparing the different 1205732119897 coefficients
shows how the process of convergence between low- and high-productivity firms changed over time
We take a similar approach when we compare group-specific productivity dynamics over time We
interact group indicators demeaned productivity levels and the interaction of the two from the static
regression with a full set of year dummies and include year dummies separately as well
1198893_119905119891119901119894119905 = 1205730 + sum sum1205731119896119897119866119894119905
119896119863119905119897
119870
119896=1119897=2003200620092012
+ sum 1205732119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119863119905119897
119897=2003200620092012
+ sum sum1205733119896119897(119905119891119901119894119905minus1 minus 119905119891119901 119895(119894)119905minus1
119897 )119866119894119905119896
119870
119896=1
119863119905119897
119897=2003200620092012
+ sum 1205734119897119863119905
119897
119897=200320062009
+ 119883119894119905 + 120572119895(119894) + 휀119894119905
Comparing 1205731119896119897 coefficients for different l-s shows how the difference between average-productivity
firms in the baseline category and in group k changed over time Similarly comparing 1205733119896119897 coefficients
with different l-s shows how convergence differences between the baseline category and group k firms
evolved over time These specifications allow us to add industry-year fixed effects so we can also
control for industry-specific trends
Results
Figure 72 shows the non-parametric relationships by firm characteristics creating scatter plots which
show productivity quantiles separately by firm groups These figures hint at the fact that on average
foreign-owned and exporter firms experience higher productivity growth conditional on initial
productivity levels In addition the relationship between the initial productivity level and subsequent
growth is weaker for foreign-owned firms suggesting that even highly productive foreign firms are
able to raise their productivity further while similar domestic firms have a harder time doing so Size
groups and age groups are similar to each other though the smallest firms have stronger convergence
patterns than the largest
We can discover the same scenarios using regression analysis in which we can control for the
abovementioned firm characteristics and fixed effects (Table 71) The most important conclusion is
that average-productivity foreign-owned firms raise their productivity faster relative to similar
domestic firms by about 10 percentage points Average exporters also have a TFP growth advantage
(72)
(73)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
93
relative to non-exporters but this premium disappears when we control for ownership We find some
evidence for a positive interaction between productivity levels and foreign ownership in line with lower
constraints for further TFP growth in the case of foreign frontier firms The same pattern applies to
exporter firms
Figure 72 The relationship between lagged productivity levels and subsequent productivity growth by
firm group
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to log productivity growth
between t and t+3 Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-
year growth rate of firms within that quantile including 2-digit industry fixed effects
The similar results for exporters and foreign-owned firms ndash and the strong correlation between foreign
ownership and exporter status ndash raise the question does this difference arise from foreign ownership
or exporting or do both variables have an independent effect Table A73 in the Appendix examines
this question only to find that the foreign premium in average productivity growth unconditional on
the productivity level is there both for exporters and non-exporters and is higher for younger and
smaller firms When we look at how the relationship between lagged productivity level and subsequent
growth differs by both characteristics at the same time we find that both foreign ownership and
exporter status matter but for different aspects of the relationship The difference in the slope of the
relationship comes both from the foreign-owned and from exporters compared to low-productivity
firms of the same category high-productivity firms grow relatively faster if they are foreign-owned The
same is true for comparing exporters and non-exporters At the same time the average difference in
Firm-level Productivity growth and dynamics
94
productivity growth comes from foreign ownership firms with industry-average productivity levels
have a higher productivity growth if they are foreign There is no significant additional effect for foreign
exporters on top of adding up foreign and exporter premia either in average productivity growth or in
convergence61
The TFP growth advantage of foreign-owned firms even when compared to domestically-owned firms
with the same productivity level points at a mechanism that reinforces the already existing duality
when domestic firms reach frontier productivity levels their TFP growth slows down much more than
that of foreign firms This self-reinforcing mechanism may be behind the non-convergence between
foreign and domestic firms (Section 44) With regard to size and age we find that high-productivity
firms have a relatively greater chance to increase their productivity if they are larger or older
compared to their smaller and younger counterparts (see Table A72 in the Appendix)
Table 71 The relationship between lagged productivity levels and subsequent productivity growth by
ownership and exporter status
Dep var TFP growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 -0180 -0184 -0177 -0187 -0186 -0190
(000556) (000568) (000622) (000629) (000656) (000663)
TFP in t-1 Foreign 00475 00418 00433 00423
(00140) (00142) (00252) (00253)
TFP in t-1 Exporter 00477 00287 00216 00224
(00104) (00106) (00125) (00126)
TFP in t-1 Foreign exporter -000697 -00151
(00312) (00314)
Foreign 0117 0109 0120 0104
(00121) (00132) (00223) (00226)
Exporter 00240 000260 000401 0000919
(000791) (000845) (000851) (000881)
Foreign exporter -000670 000871
(00267) (00272)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 29717 29717 30135 30062 29717 29717
R-squared 0060 0072 0056 0073 0060 0072
61 Looking at the same patterns over time (Table A74 in the Appendix) suggests that higher average productivity
growth is a rather stable feature of foreign firms The only exception was the crisis period when it disappeared
Splitting the sample by broad sectors shows that foreign firms have higher average productivity growth both in
manufacturing and services The difference in within-group convergence patterns stayed the same for the
foreign The same is true for exporters except for the pre-crisis period when the coefficient is not significant
Productivity differences in Hungary and mechanisms of TFP growth slowdown
95
72 Employment growth
Question and descriptive patterns
The relationship between initial productivity levels and subsequent employment growth shows the
reallocation of continuing firms Between-firm reallocation results from more productive firms growing
faster In this subsection we ask how between-firm reallocation changed over time and how
reallocation patterns vary by different firm characteristics
To measure reallocation we use a similar approach to that in the previous subsection but the
lsquodependentrsquo variable will be 3-year employment growth in log terms rather than productivity growth
The slope of the estimated relationship reflects the employment growth advantage of more productive
firms or the strength of ldquocreative destructionrdquo among surviving firms Shifts in the level show changes
in the average growth rate
We calculate the 3-year employment growth using the formula119871119905+3minus119871119905
(119871119905+3+119871119905)2 where 119871119905 is the number of
employees in year t This formula shows the percentage increase in employment from year t to t+3
compared to the average size in year t and t+3 This measure performs better for smaller firms than a
simple log difference in employment as it does not result in extremely high numbers with a low initial
employment level62 In all the regressions of this subsection we control for exact firm size using the
logarithm of the number of employees
Figure 73 Reallocation by year
Notes The figure shows how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
62 Additionally while the baseline estimates are only for continuing firms this measure allows us to include firms
exiting in the period (t+1t+3) as well in some robustness checks In these cases we take Lt+3 = 0
Firm-level Productivity growth and dynamics
96
Figure 73 illustrates the patterns in the data non-parametrically The relationship between previous-
year productivity levels and subsequent employment growth is positive in all years This shows that in
line with the creative destruction hypothesis more productive firms are more likely to grow in the
subsequent three years The figure doesnrsquot show characteristic changes in the reallocation process
across years the slope of the curves being similar to each other Our regression estimates presented
in the Appendix (Tables A75 and A76) support that reallocation patterns are stable over time63 The
average growth rate of typical firms naturally follows the macro cycle strongly ndash aggregate changes
seem to shift the line up or down but do not seem to rotate it In other words with this approach we
do not find evidence for a structural change in the reallocation process therefore it is unlikely that
such a change should explain satisfactorily the productivity slowdown
We create similar figures for the most recent period (2012-2015) by different firm characteristics
(Figure 74) The most important result is that exporters grow significantly faster than non-exporters
when controlling for their initial productivity This leads to reallocation from non-exporters to
exporters Given that the productivity advantage of exporters is in the order of 30-100 percent in the
different industries (see Section 43) this reallocation process can yield enormous productivity gains
The slope of the curve is also less steep for exporters suggesting that their expansion is less
dependent on their productivity level relative to domestic firms in other words reallocation within the
exporter group is weaker relative to non-exporters
63 As before the relationship between lagged productivity levels and subsequent employment growth can be
properly approximated by a linear function
Productivity differences in Hungary and mechanisms of TFP growth slowdown
97
Figure 74 Reallocation by firm groups
By ownership By exporter status
By size By age
Notes These figures show how the log of productivity in t-1 (horizontal axis) is related to employment growth
between t and t+3 (demeaned using industry-specific average employment growth throughout the whole period)
Each dot represents one of 20 quantiles of the productivity level distribution and the average 3-year employment
growth rate of firms within that quantile including 2-digit industry fixed effects
Firm-level Productivity growth and dynamics
98
Estimation results
Table 72 Reallocation by ownership and exporter status
Dep var employment growth from t to t+3 (t=2012)
Variables (1) (2) (3) (4) (5) (6)
TFP in t-1 0105 0102 0105 0107 0107 0108
(000484) (000493) (000539) (000546) (000570) (000575)
TFP in t-1 Foreign
-00369 -00328 -00252 -00224
(00123) (00124) (00217) (00217)
TFP in t-1 Exporter
-00344 -00298 -00250 -00249
(000913) (000932) (00109) (00110)
TFP in t-1 Foreign exporter
-0000806 000194
(00271) (00272)
Foreign 000105 -000863 -000786 -0000106
(00112) (00116) (00194) (00196)
Exporter 00586 00635 00653 00672
(000738) (000754) (000777) (000786)
Foreign exporter
-000647 -00123
(00234) (00238)
Industry FE YES YES YES
Industry-region FE
YES YES YES
Firm-level controls
YES YES YES
Log of employees
YES YES YES YES YES YES
Observations 31662 31662 32124 32043 31662 31662
R-squared 0035 0049 0038 0051 0037 0049
Looking at the regression results (Table 72) confirms our previous findings even after controlling for
fixed effects Exporters with an average productivity level grow about 6 percentage points faster than
non-exporters hinting at strong positive reallocation between the two groups with slightly weaker
reallocation within the exporter group64 At the same time average-productivity foreign-owned firms
do not have higher employment growth than domestic ones Similarly to productivity growth we find
no extra premium for foreign exporters65 66 Overall these results emphasise that participation in
64 We define exporters based on their export activity in year t so the group of exporters also includes those firms
which export in t but not any more afterwards This means that a worse subsequent performance ndash lower
growth and exiting from exporting ndash has no effect on our exporter classification
65 The main patterns concerning employment growth of average-productivity firms are robust to modifying the
employment growth measure in such a way that it includes exits as a full employment decline (See Table A77
in the Appendix) In this version employment growth of foreign firms is significantly lower overall but this is
counterbalanced by the significantly positive coefficient of the foreign exporter indicator
66 We show in Table A78 of the Appendix that the higher average growth of exporters is present in all size (except
for the largest) age and ownership groups Dynamic patterns suggest (in Table A79 of the Appendix) that the
higher growth rate of average-productivity exporters is robust over time This result is also robust to splitting
the sample into manufacturing and services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
99
international markets is an important driver of industry and aggregate productivity growth in Hungary
by providing opportunities for exporters to expand as Section 62 has documented
As Table 73 shows competitive pressure also seems to affect more the growth prospects of smaller
firms the relationship between initial TFP levels and employment growth is significantly stronger for
smaller firms Between-firm reallocation appears to be much stronger for smaller firms while less
productive large firms are unlikely to contract even if they are inefficient conditional on survival
There are no clear patterns by age groups
Table 73 Reallocation by size and age group
Dep var employment growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 0112 0110 00875 00863
(000486) (000502) (00133) (00133)
TFP in t-1 Group 2 -00455 -00431 00445 00384
(00128) (00129) (00182) (00183)
TFP in t-1 Group 3 -00745 -00748 000733 000822
(00194) (00196) (00141) (00142)
TFP in t-1 Group 4 -00953 -00957
(00218) (00220)
Group 2 -000596 -000810 -00188 -00212
(00122) (00123) (00141) (00141)
Group 3 -000928 -00157 -00115 -00169
(00199) (00201) (00114) (00115)
Group 4 00328 00280
(00276) (00278)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Log of employees YES YES YES YES
Observations 32124 32043 32124 32043
R-squared 0038 0052 0037 0051
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees and size group 4 is 100+
employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5 years age group 3 is
firms older than 5 The baseline category is firms of 2-3 years
73 Entry and exit
Questions
This subsection aims at investigating which firms enter and exit and in particular how productive
those firms are relative to continuing firms This corresponds to the micro-level equivalent of the net
entry effect (see Chapter 6) The motivation for the micro-level investigation is that in this manner we
Firm-level Productivity growth and dynamics
100
can study which firm-level factors determine the type of firms that enter and exit and control for
industry heterogeneity
Our approach is similar to the previous section with the main difference being that this time the
dependent variable is productivity while the variables of interest are entry and exit dummies Their
coefficients show the productivity lsquopremiarsquo (often negative) of new entrants and exiting firms relative
to continuing firms These premia are especially useful to answer two kinds of questions First their
magnitude and size inform us about how entry and exit contribute to productivity growth Second
changes in these premia are also indicative of the changes in the costs of entry and the survival of
low-productivity firms
Estimation
To use a symmetric approach we define entrants and exiting firms using a 3-year interval An entrant
is a firm that has entered in the previous 3 years67 This means we look at the productivity of firms in
year t and compare it between incumbents (ie firms older than 4 years) and entrants (ie firms being
2-4 years old) In a similar way we compare the productivity in year t of firms exiting in the following
3 years (ie the last time the firm reports positive employment is in the period (t t+2) and non-
exiting firms (firms still reporting positive employment in year t+3)
As before we start with a static approach looking at the productivity premium of entrants and exiting
firms in the most recent period (taking year 2015 for 2012-2014 entrants and 2012 for firms exiting in
2013-2015) Then as a dynamic approach we take all four time periods as before and interact the
premia with year dummies The static regression we estimate is as follows
119905119891119901119894119905 = 1205730 + sum 1205731119896119866119894119905
119896119873119864119894119905119870119896=1 + 1205732119866119894119905
0119864119894119905 + sum 1205733119896119866119894119905
119896119864119894119905119870119896=1 + 119883119894119905 + 120572119895(119894) + 휀119894119905 (74)
119905119891119901119894119905 is the productivity of firm i in year t (measured in logarithm) 119866119894119905119896 is the k-th category (eg size
category 5 with more than 100 employees) in a grouping according to firm characteristics G (eg
size) and 1198661198941199050 is the baseline category (eg firms with 5-49 employees) 119864119894119905 stands for entrant or
exiting firm dummy in the different specifications and 119873119864119894119905 are incumbent or continuing firms
accordingly Then 1205732 measures the entry or exit premium for firms in the baseline category and 1205733119896
measures the same premium for firms in category k of grouping G Both premia are calculated
compared to incumbentscontinuing firms in the baseline category 1198661198941199050 1205731
119896 measures the productivity
advantage or disadvantage of incumbentcontinuing firms in category k of grouping G also compared
to the average productivity level in the baseline group As before 119883119894119905 includes additional firm-level
characteristics and 120572119895(119894) is industry or industry-region fixed effects In those versions where we include
industry fixed effects we identify from within-industry differences This means that 1205733119896 measures the
same entry or exit premium for firms in category k of grouping G compared to incumbentscontinuing
firms in the same category As before we create the dynamic version of the above regression by
interacting 119866119894119905119896119873119864119894119905 119866119894119905
0119864119894119905 and 119866119894119905119896119864119894119905 with year dummies and including year dummies separately as well
67 We consider firms changing industry from manufacturing to services or vice versa as exitors and new entrants at the same time (see Chapter 2)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
101
Results
Table 74 shows how the productivity premium of entrants and exiting firms changed over time In
these specifications we compare the yearly average productivity of incumbents and entering or exiting
firms separately in each year The point estimates suggest that entering firms were about 2-4 percent
more productive than incumbents except for the 5 percent productivity disadvantage in the pre-crisis
period while exiting firms were 10-20 percent less productive than the continuing firms The
productivity advantage of entrants and the disadvantage of exiting firms did not change radically
during our time period This difference constitutes a potential for positive net entry effects in terms of
reallocation The exact value of the net entry effect also depends on the share of employees affected
by entry and exit While the premia of entering and exiting firms remained roughly the same in the
different periods exit and entry rates changed (see Section 33) which results in positive net entry
effects before the crisis and negative effects after that (see Section 62)
Table 74 Productivity premium of entering and exiting firms over time
Dep var TFP in year t
Firm group Entry Exit
(1) (2) (3) (4) (5) (6)
EntrantExitorPeriod 2003-2006
-00532 -00535 -00465 -0214 -0199 -0200
(000906) (000873) (000879) (00102) (000987) (000989)
EntrantExitorPeriod 2006-2009
00269 00212 00232 -0135 -0117 -0119
(000967) (000931) (000936) (000897) (000865) (000865)
EntrantExitorPeriod 2009-2012
00369 00444 00360 -0133 -0114 -0121
(000960) (000926) (000931) (000920) (000889) (000889)
EntrantExitorPeriod
2012-2015
00374 00324 00252 -0171 -0150 -0157
(000971) (000936) (000944) (00107) (00103) (00103)
Period 2003-2006 -0115 -00927 -00711 -00445
(000519) (000501) (000556) (000537)
Period 2006-2009 -0175 -0169 -000972 00122
(000522) (000503) (000537) (000518)
Period 2009-2012 -0116 -0117 -00580 -00528
(000528) (000508) (000543) (000522)
Year FE YES YES YES YES
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES YES YES
Industry-year FE YES YES
Observations 166607 166168 166168 158143 157711 157711
R-squared 0327 0380 0380 0332 0385 0387
Firm-level Productivity growth and dynamics
102
Next we focus on the most recent period and look at the productivity differences of entrants and
exiting firms by different firm groups
Figure 75 Productivity premium for entering and exiting firms by ownership
Figure 75 presents the premia of domestic and foreign entering and exiting firms relative to domestic
incumbents As we saw in Section 44 foreign firms are on average more productive than domestic
ones68 foreign incumbents have on average a premium of 669 Compared to domestic incumbents
foreign entrants have 513 higher productivity There is also a positive productivity premium of 29
for exiting foreign firms Similarly the productivity of exiting exporters is 186 higher than that of
continuing non-exporters69 This means that domestic incumbent firms can survive longer even with a
lower level of productivity Consequently having many foreign entrants has a positive effect on
average productivity while on average foreign exits do not affect average productivity70 71
68 Table A711 shows that the productivity advantage of foreign-owned firms is present in all size and age groups as well as both within the exporter and non-exporter firm groups
69 Table A710 of the Appendix shows the estimation results with standard errors
70 As Table A712 of the Appendix shows foreign entrant premium and the premium of continuing or exiting
foreign and exporter firms seem fairly stable over time The positive premium of entering and exiting foreign
firms is also robust for splitting the sample into manufacturing and services
71 As Table A713 of the Appendix shows there is no considerable difference in the productivity disadvantage of
exiting firms by size or age group
Productivity differences in Hungary and mechanisms of TFP growth slowdown
103
74 Conclusions
One contribution of this chapter is that we have documented that one of the factors behind the
sustained duality in productivity between foreign and domestically-owned firms is that foreign-owned
firms tend to be more capable of upgrading their productivity even from already high productivity
levels Similar patterns apply to the more globally oriented exporters This mechanism underlines the
importance of policies that promote absorptive capacity-building (see Section 45) a strong knowledge
base easy access to external knowledge and flexible and advanced skills are especially important
when upgrading productivity beyond already high levels
We have also found strong reallocation from non-exporters to exporters Given the high productivity
premia of exporters in Hungary (Beacutekeacutes et al 2011) and in general (Wagner 2007) such a
reallocation can lead to substantial improvement in aggregate productivity (and as we have seen in
Section 62 it did to some extent before the crisis) These results emphasise that participation in
international markets is an important driver of industry and aggregate productivity growth in Hungary
because it provides valuable opportunities for exporters to expand Note that this reallocation effect of
international openness has been in the focus of the recent literature on international trade (Melitz
2003 Bernard et al 2006 Amiti and Konings 2007 Topalova and Khandelwal 2011 De Loecker
2011) Note also that the asymmetric expansion possibilities of exporters and domestic firms also
amplify the duality between the two groups
The analysis of entry and exit has revealed that entrants are somewhat more productive than
incumbents even a few years after entry Exiting firms are significantly less productive This on the
one hand implies that exit and entry is a substantial source of reallocation (as Section 62 has shown)
On the other hand the low productivity of exiting firms also suggests that domestic firms can survive
long even with relatively low productivity levels maybe because of inefficiencies in the capital
allocation process including the insolvency regime
Productivity evolution and reallocation in retail trade
104
8 PRODUCTIVITY EVOLUTION AND REALLOCATION IN RETAIL TRADE
The previous chapters have presented a number of results on the productivity and growth in different
sectors of the economy The aim of this chapter is to look deeper into one of the key sectors of the
economy namely retail trade for more detailed insights
Two main reasons have motivated us to choose the retail sector First retail is a key sector of the
economy which provides jobs for a great many people and influences what consumers can buy and at
what prices Retail (and wholesale) does not only interact with consumers it is a key supplier of inputs
while beig a buyer of outputs for all other firms in the market economy72 The degree to which it is
capable of supplying a large variety of intermediate inputs at reasonable prices is an important
determinant of the productivity of firms relying on these sources Its market structure also affects
fundamentally the incentives that producers experience73
The second reason is that there have been a number of regulatory changes in the retail sector in
Hungary in recent years While these policies had multiple motivations one of their common features
is that they are size-dependent either explicitly or implicitly As such they have a potential to increase
the costs of larger firms and influence the reallocation process in favour of smaller mostly
domestically-owned firms This may matter as international evidence has shown that much of retail
productivity growth in recent decades has resulted from the expansion of large store chains (Foster et
al 2006) Exactly because of the strong links between retail and other industries regulatory
restrictions in retail represent nearly a third of all service-related restrictions which are carried over to
other sectors of the economy74
The structure of this chapter is the following Section 81 describes the policy context of Hungarian
retailing Section 82 introduces the available datasets Section 83 describes the major developments
in retail productivity Section 84 describes trends in reallocation The last three sections describe three
specific questions Section 85 analyses the role of retailers and wholesalers in importing and
exporting Section 86 provides a few illustrative statistics on how size-dependent taxes could have
affected reallocation and prices Finally Section 87 evaluates a specific policy namely the mandatory
Sunday closing of larger shops Section 88 concludes
81 Context
The retail industry is an important employer in all EU member states and Hungary is not an exception
Its employment share in our sample has been around 12 percent (Figure 81) Similarly to the EU as a
whole retail productivity is below the average of the market economy therefore its GDP share is
below its employment share Still it represented 6-7 percent of total value added in our sample
72 See EC (2018) for the importance of the retail industry in Europe
73 See Smith (2016) for a review of this literature
74 EC (2018) p 5
Productivity differences in Hungary and mechanisms of TFP growth slowdown
105
Figure 81 The share of retail and wholesale firms in market economy value added and employment
Notes Full sample with at least 1 employee in any of the years
The largest sub-industry within retail is groceries (NACE 4711) Its share of the total turnover around
40 percent is at the lower end of the EU distribution75 Given its importance (and the large sample size
within it) we will often study only groceries in our empirical analyses
Measuring the restrictiveness of different regulations in any sector of the economy is not an easy task
The European Commission has designed a ldquoRetail Restrictiveness Indicatorrdquo to quantify the potential
effect of these regulations in force at the end of 2017 (see Figure 82) The higher values of the
indicator indicate more restrictive regulations76 According to this indicator the restrictiveness of retail
regulation in Hungary is slightly below the EU average and similar to other CEE countries
The indicator distinguishes between regulations related to the establishment of shops on the one hand
and those related to their operation on the other In Hungary there are few operations restrictions
(mainly restrictions on distribution channels) while entry is regulated more heavily mostly by size-
related restrictions and requirements for economic data
75 EC (2018) p 4
76 There is ample empirical evidence that entry barriers planning regulations and operating restrictions are related to productivity and prices in retail Some examples are Bertrand and Kramarz (2002) Viviano (2008) Haskel and Sadun (2012) Sadun (2015) Daveri et al (2016)
Productivity evolution and reallocation in retail trade
106
Figure 82 Retail Restrictiveness Indicator
Notes This is a reproduction of Figure 8 from EC (2018)
While regulation in Hungary is not especially restrictive a number of new measures were introduced
following the crisis (see Box 81) While these have various motivations a common feature of most of
them is that they are size-dependent As such they may distort competition and constrain reallocation
to larger firms
One type of size-dependent policies is size-dependent taxes Crisis taxes introduced right after the
crisis (and phased out in 2013) were highly progressive in sales volume Local business taxes have
been similarly progressive in total sales at the firm-level since 2013 Other size-dependent policies are
restrictions on the establishment of shops or their operation The Plaza Stop law constrained the
establishment of malls larger than 300 m2 Another peculiar policy was requiring larger shops to close
on Sundays between March 2015 and April 2016
Quantifying the effect of such policies is not an easy task In some cases it is not possible with the
data at hand to identify the shops and firms which were affected by the different types of taxes For
example without knowing the exact location of the establishment it is not possible to identify which
firms operate in malls and hence could have been affected by the Plaza Stop law As we discuss in
Section 85 the highest bracket of the crisis tax only affected 6 firms and thus it is hard to run
statistical tests with an appropriate power In contrast some of the effects of the mandatory Sunday
closing policy can be very effectively estimated based on shop-level data
Therefore we will apply two complementary strategies The first is to investigate whether there are
trend breaks in the reallocation process following the crisis when many of the new policies were
Productivity differences in Hungary and mechanisms of TFP growth slowdown
107
introduced While we find suggestive changes around the crisis one cannot make casual statements
based on this strategy given the number of other changes in the economy The second strategy is to
examine specific policies where a credible differences-in-differences identification is possible
Unfortunately this strategy is basically limited to Sunday closing
BOX 81 Size-dependent taxes and regulations in the retail sector
This box describes a number of size-dependent taxes and regulations which could be linked to the retail data and investigated during this exercise The list is only indicative and will be appended by desk research and possibly interviews
2010-2013 crisis taxes
Crisis taxes were introduced in 2010 and were in force (mostly) until 2013 They affected the energy telecom and retail sector as their base was operating profits resulting from these activities The tax rate was strongly progressive for retail
Below 500m HUF 0
Between 500m and 30bn HUF 01
Between 30bn and 100bn HUF 04
Above 100bn HUF 25
Between March 15 2015 and April 23 2016 Sunday closing for larger and non-employee owned retail stores
The 2014 CII law which came into force on March 15 2015 banned shops with a retail space of more than 400 square meters to open on Sundays with some exceptions most notably the new tobacco shops Smaller shops could only open if their workers had at least a 20 stake in the
business or if they were close relatives of the owner The law was repealed in 2016
2013-today Progressive local business tax
The base of local business tax is the ldquoadjustedrdquo revenue of firms This usually means revenue minus material expenditures but regulation stipulating the exact method of calculation has changed a number of times since the introduction of this type of tax In 2013 a progressive
element was introduced by making the definition of the cost of purchased goods size-dependent In particular smaller firms can now deduct more of their expenditures than larger ones The deductible part is
Below 500m HUF of net sales 100
Between 500m and 20bn HUF 85
Between 20bn and 80bn HUF 75
Above 80bn HUF 70 of the cost of goods is eligible
Productivity evolution and reallocation in retail trade
108
82 Data
We rely on two main data sources in this chapter The first one is the NAV balance sheet data
described in detail in Chapter 2 Based on the industry code identifier we restrict the sample to firms
in industry 47 retail There are a few firms which switch to this category from other industries (mainly
wholesale of food manufacturing) We keep the whole history of these firms throughout the analysis
Second we use a retail-specific survey conducted by the Hungarian Central Statistical Office which
samples firms and collects data for all shops of the sampled firm77 Firms included in the sample are
compelled by law to submit monthly reports on their turnover and 4-digit industry-codes plus for all
of their stores information about these entitiesrsquo location (municipality) identification number 4-digit
77 httpswwwkshhudocshuninfo02osap2018kerdoivk181045pdf
BOX 81 Size-dependent taxes and regulations in the retail sector (cont)
2013-today Licensing of tobacco wholesale and retail
On 22 April 2013 in line with Act CXXXIV ldquoon reducing smoking prevalence among young people
and the retail of tobacco productsrdquo (adopted by the Hungarian Parliament on 11 September
2012) the National Tobacco Trading Non-profit Company (a 100 government-owned joint-stock
company controlled by the relevant minister under the mandate of this law) was established
From then on only special ldquonational tobacco shopsrdquo licensed by the state have been allowed to
sell tobacco products These shops enjoy a number of benefits compared to other shops
Exempted from the Sunday closing for retail shops
National tobacco shops are exempted from the ban on selling alcohol after 10pm rarr in effect
tobacco shops do not come under the ruling of the commercial law Local municipalities can
otherwise regulate shops based on that law
2011- today ldquoPlaza Stoprdquo Law
The so-called Plaza Stop Law (the 2011 CLXVI Law) came into force in January 2012 It
prohibits the construction of new retail facilities or the expansion of any already existing one with
a leasable area of more than 300 msup2 Exemptions could be granted to certain developments by a
committee of ministry officials and with the approval of the Minister of National Economy
In 2013 the law was extended to include building conversions In February 2015 a new
amendment was ratified which basically renewed the effect of the 2011 law and introduced some
modifications to it Now retail facilities with a floor space of less than 400m2 can be built without
any special procedure Furthermore the right to grant exemptions was given to a special
administrative department which is supplemented by a committee made up of delegated
members of different ministries
Productivity differences in Hungary and mechanisms of TFP growth slowdown
109
industry-code sales and the monthly number of days spent open The sample consists of all larger
retail firms78 and a representative sample of other firms re-sampled on an annual basis
An important consequence of this design is that we observe each of the shops of the sampled firms
This is valuable in two respects First with this information it is possible to calculate the number of
shops and average shop size at the firm-level Second one can identify new and exiting shops for
firms which are in the sample continuously ie larger firms Further with the help of the firmsrsquo
identification number we are also able to link this information to data from the NAV database for
qualified analysis
Two caveats may be mentioned here First the re-sampling of the representative part of the sample
prevents us from following small firms through the entire sampling frame Second in the beginning of
2012 there was a switchover in the coding of shop-level identification numbers which prevents us
from linking shops before and after
As mentioned above the database also includes information on the industry classification of the shop
In most of our exercises we restrict the sample to grocery stores more formally bdquoRetail sale in non-
specialised stores with food beverages or tobacco predominatingrdquo (NACE 4711) Table 81 shows the
sample size of the merged database for groceries We have classified firms according to the number of
shops they have and report their number and their storesrsquo number according to these categories
Table 81 The number of firms and the number of shops in different size categories in Groceries
1 shop 2-4 shops 5-9 shops 10-49 shops gt50 shops year firm shop firm shop firm shop firm shop firm shop
2004 646 646 110 274 73 508 131 2281 36 1334 2005 592 592 125 306 63 466 122 2232 35 1446
2006 573 573 51 131 59 430 111 2008 30 1548
2007 546 546 53 125 60 429 110 1987 33 1634
2008 628 628 45 102 50 350 104 1823 21 1574
2009 527 527 33 72 41 290 99 1879 24 1754
2010 472 472 22 49 32 238 94 1793 22 1968
2011 537 537 14 30 29 212 92 1758 22 2027
2012 374 374 30 68 49 335 88 1643 23 2107
2013 503 503 48 121 42 277 88 1622 25 2094
2014 410 410 106 239 48 320 81 1530 24 2054
2015 512 512 135 311 42 292 80 1544 30 2090
2016 518 518 120 292 37 271 77 1457 23 2022
A key distinction in this merged database is the one between shops and firms Sales employment
ownership is observable only at the firm-year level so these variables are the same for each of the
shops of a firm for a calendar year Shops are only observable for sampled firms but we observe
sales and the number of days they were open at a monthly regularity As a result even if one runs
regressions at the shop-month-level productivity and employment can only vary at the firm-year
level For this reason we always cluster the standard errors at the firm or firm-year level
78 Larger firms are defined as having more than 7 stores in operation or with a number of employees of more than 50 and at least 6 stores or with a significantly large store in a product category
Productivity evolution and reallocation in retail trade
110
While balance sheet data includes information on exports it does not inform us about imports In
Section 84 we use detailed trade data to analyse importing by wholesale and retail firms This is
reported at the importer firm-product (8 digit Harmonized System)-country of origin level Most
importantly we can link this information to the balance sheet of the firm This is collected by a survey
following the European Unionrsquos practice79 We aggregate these data to the firm-year level but
distinguish between consumer goods capital goods and intermediate inputs used in further production
by relying on the correspondence table of the Eurostat between the Harmonized System and Broad
Economic Category classifications
83 General trends
Let us start with describing the firm size distribution across years (see Table 82) for firms with at least
one employee Similarly to other EU countries the majority of firms in retail are very small in
different years between 70-75 percent of retail firms employed less than 5 people80 The share of firms
with more than 50 employees fluctuated at around 1 percent
As one would expect larger firms have a significantly larger weight in terms of employment and sales
The top 05 percent of firms employed more than 30 percent of all employees in each year The
employment-share of these top firms increased nearly monotonically from 33 percent in 2004 to more
than 38 percent in 2011 when it reached its peak This was followed by a slightly declining trend to
357 percent in 2016 This time path represents the gradual expansion of large chains both organically
and via the acquisition of stores81 up to the crisis when this trend seems to have ended
The market share of large firms is even larger reaching 45 percent in 2016 The difference between
the employment share and sales share shows that large retail firms are substantially more efficient ndash
at least in terms of sales over employees ndash than the average firm At the other extreme the smallest
retail firms generate only 126 percent of sales with 20 percent of employees suggesting that in 2016
each of their employees sold only about half of the average The patterns are similar in other years
Efficiency differences are large in this sector though not larger than in most other sectors of the
economy (see Chapter 4)
79 See httpeceuropaeueurostatstatistics-explainedindexphpInternational_trade_statistics_-_background An important limitation of these data is that firms only report transactions above a specific size This may bias estimates of firm-level importing downward for small firms
80 As we discussed in Section 42 the NAV sample includes only double-entry bookeeping firms while the unemployed and people working in firms with simplified accounting are omitted from these data These people are likely to work in small economic units with low productivity levels
81 The increasing share of large retailers is a general trend globally see Ellickson (2016)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
111
Table 82 Share of firms in different size categories (at least 1 employee)
A) Number of firms
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 7400 1588 652 240 069 051 100 2005 7316 1633 686 245 071 049 100
2006 7333 1613 687 252 065 049 100
2007 7369 1597 674 244 067 050 100
2008 7410 1571 667 236 067 048 100
2009 7522 1535 614 221 059 048 100
2010 7551 1508 640 201 057 044 100
2011 7591 1493 623 194 057 041 100
2012 7625 1506 568 204 055 042 100
2013 7526 1596 577 207 052 042 100
2014 7380 1684 628 210 056 042 100
2015 7239 1771 661 232 056 041 100
2016 7163 1804 676 252 063 043 100
B) Employment
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 2142 1545 1318 1048 724 3270 10000 2005 2095 1505 1294 1067 668 3433 10000
2006 2034 1475 1269 1025 679 3525 10000
2007 2026 1443 1241 984 669 3644 10000
2008 2020 1391 1138 910 579 3980 10000
2009 2002 1427 1244 870 570 3789 10000
2010 2099 1443 1243 862 577 3731 10000
2011 2144 1458 1119 895 555 3830 10000
2012 2144 1541 1131 906 541 3713 10000
2013 2168 1591 1204 896 555 3650 10000
2014 2104 1644 1236 970 548 3538 10000
2015 2064 1620 1216 1006 588 3570 10000
2016 1999 1620 1216 1006 588 3570 10000
C) Sales
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
total
2004 1233 1368 1623 942 885 4036 10000 2005 1146 1330 1664 984 816 4092 10000
2006 1114 1278 1587 1025 776 4226 10000
2007 1110 1266 1543 956 855 4268 10000
2008 1112 1524 1045 891 531 4906 10000
2009 1104 1641 1073 856 718 4607 10000
2010 1104 1521 1103 930 693 4594 10000
2011 1160 1577 1004 934 624 4714 10000
2012 1146 1647 1003 913 629 4578 10000
2013 1229 1411 1090 945 709 4360 10000
2014 1486 1472 1093 1001 752 4389 10000
2015 1293 1458 1118 985 660 4512 10000
2016 1266 1458 1118 985 660 4512 10000
Productivity evolution and reallocation in retail trade
112
Figure 83 presents C5 concentration measures82 for the full retail sector and for some of its
subsectors83 The share of the top 5 retail firms was around 30 percent of total retail sales
Concentration was increasing pre-crisis from 30 percent in 2003 to 35 percent in 2009 Concentration
decreased and returned to its 2003 value by 2014 The latter trend as we will discuss in Section 85
may be associated with size-dependent policies
The various sub-industries exhibit different patterns in terms of concentration Let us start with
groceries Pre-crisis the dynamics in this subsector was driven mainly by the expansion of large
chains Consequently concentration was strongly increasing with C5 growing from 50 percent in 2003
to more than 60 percent by 2008 Concentration in this subsector was rising further post-crisis but at
a somewhat slower pace We observe a similar pattern of increasing concentration with a trend break
around the crisis in sales of books and clothes in specialized stores In these sub-industries
establishment regulations like the Plaza Stop law could have played a more important role in the trend
break than taxes Specialized cosmetics retailing was already very highly concentrated at the
beginning of the period and remained largely unchanged
Figure 83 Concentration in retail and various sub-industries
This observation motivates a more detailed look at different measures of efficiency and prices Panel A)
of Table 83 calculates the average TFP levels84 both for different size categories and for the
aggregate Note that TFP calculated from balance sheet data is revenue productivity measuring the
82 Calculated as the sales share of the 5 firms with the largest sales
83 We rely on a slighly different version of the NAV data for this exercise which includes 4-digit identifiers but only runs until 2014
84 See Section 22 on details of TFP estimation
Productivity differences in Hungary and mechanisms of TFP growth slowdown
113
amount of revenue produced by an input bundle Consequently it does not only measure physical
productivity (units sold per unit of input) but also markups This distinction is especially important in
retail85
Let us start with the two aggregate series one unweighted and the other weighted by employment
The employment-weighted series has higher values because more productive firms tend to be larger
(see Section 51) The two series follow a parallel trend suggesting that the correlation between size
and productivity did not change radically TFP has increased by about 15 percent from 2004 to 2006
remained constant until 2008 fallen by around 10 percent in 2009 and then started to grow by 4-5
percent each year from 2011
Note that this productivity evolution is similar to what is reported by OECD STAN before and during the
crisis but the post-crisis recovery in our data is much more pronounced (Figure 84) As we have
discussed in detail in Section 42 this is most likely a result of the large number of self-employed and
the distinct productivity level and evolution of that group86 Productivity has been definitely increasing
since 2011 in our sample
Interestingly TFP is not increasing monotonically with firm size There is a clear 25-30 percentage
point difference between the smallest firms (1-4 employees) and firms in other size categories which
have a similar TFP to each other Besides differences in efficiency this may also be partly explained by
the tax avoiding behaviour of the smallest firms ie under-reporting sales or over-reporting costs
Panel B) of Table 83 investigates gross margins These are calculated as
119866119903119900119904119904 119872119886119903119892119894119899119894119905 =119904119886119897119890119904119894119905 minus119898119886119905119890119903119894119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
which is the margin that retailer 119894 realises in year 119905 on the cost it pays for the sold goods in
percentage A value of 20 shows that the price the consumer pays is 20 percent higher than what the
retailer paid for the goods87 Note that gross margins reflect a combination of two factors `physical
productivityrsquo (how much capital and labour is needed for a given amount of sales) and markups Still
gross margins are of interest because they are the closest proxy available in financial statements of
prices paid by customers
We can make two key observations First on average (weighted) margins increased from about 155
percent to 19 percent during the period under study with a fall during the crisis Second margins
were about 5 percentage points higher in the smallest retail firms compared to larger ones
Interestingly during and immediately after the crisis (between 2009 and 2012) the margins of the
largest firms were substantially lower than those of other firms This is because the margins of the
largest firms actually fell during this period while that of smaller firms remained roughly constant
85 Measurement of productivity in retail raises a number of conceptual and measurement issues (Ratchford 2016) Two main problems are the measurement of output (conceptually retail services) and of the inputs used (for example shop area) In practice however such detailed data are not available and it is standard to use TFP
86 The figures for retail are similar to those for the whole of the service industry About a third of all people engaged are self-employed operating at a significantly lower productivity level than retail firms The productivity of the self-employed did not grow between 2012 and 2015
87 We winsorise it at the 5th and 95th percentiles Note that the cost of goods sold would be preferable to material costs but that is often missing from the data especially for small firms
Productivity evolution and reallocation in retail trade
114
Most likely larger firms were able to cut markups while smaller firms with already lower markups
were not able to do so
As we have mentioned above margins reflect a combination of cost factors and market power The
gross operating rate88 attempts to control for labour costs and shows margins after personal cost
119892119903119900119904119904 119900119901119890119903119886119905119894119899119892 119903119886119905119890119894119905 =119907119886119897119906119890 119886119889119889119890119889119894119905 minus 119901119890119903119904119900119899119886119897 119888119900119904119905119894119905
119904119886119897119890119904119894119905
with value added calculated as discussed in Chapter 2 In international comparison gross operating
rates are relatively low in Hungary89 These rates show a clear downward trend with time Gross
operating margins are clearly decreasing with firm size showing that larger firms operate with large
scale and low margins Similarly to the gross margin we see a fall during the crisis
Figure 84 Productivity evolution in the NAV sample and the OECD STAN
88 httpeceuropaeueurostatstatistics-explainedindexphpGlossaryGross_operating_rate_-_SBS
89 As shown by EC (2018) Figure 2 Our weighted estimates are similar to what is reported there based on Eurostat data
Productivity differences in Hungary and mechanisms of TFP growth slowdown
115
Table 83 Performance and margins (at least 1 employee)
A) TFP
B) Gross Margin
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 1769 1548 1793 1883 1584 1495 1735 1554
2005 1839 1571 1858 1878 1504 1666 1794 1584
2006 2040 1658 1842 1879 1599 1660 1956 1653
2007 2220 1720 1875 1940 1664 1702 2104 1712
2008 2192 1792 1872 1992 1522 1739 2096 1728
2009 2084 1719 1864 1967 1599 1527 2006 1630
2010 2096 1749 1867 2102 1664 1535 2024 1682
2011 2172 1765 1862 2003 1759 1558 2084 1728
2012 2203 1740 1828 1913 1969 1541 2102 1733
2013 2243 1734 1806 2018 1999 1794 2128 1790
2014 2363 1789 1817 2149 2022 1869 2223 1855
2015 2390 1803 1885 2165 2215 1987 2245 1928
2016 2379 1799 1911 2172 2403 2170 2237 1948
C) Gross operating rate
Size 1-4
emp
5-9
emp
10-19
emp
20-49
emp
50-99
emp
100+
emp
unweighted weighted
2004 651 701 663 677 479 480 658 611
2005 827 757 777 712 526 560 806 644
2006 908 773 777 753 579 797 870 706
2007 807 635 653 704 500 580 764 606
2008 665 588 577 610 422 478 643 525
2009 618 535 538 522 351 339 595 456
2010 669 587 629 626 421 341 650 510
2011 698 617 591 637 410 393 676 542
2012 770 643 599 624 485 404 735 577
2013 735 594 555 631 478 369 697 530
2014 745 578 568 612 446 398 700 531
2015 766 609 625 646 547 452 724 579
2016 759 597 621 632 609 487 715 588
Size 1-4 emp
5-9 emp
10-19 emp
20-49 emp
50-99 emp
100+ emp
unweighted weighted
2004 599 633 637 628 612 623 610 626
2005 610 635 643 628 616 626 619 631
2006 623 646 651 640 630 642 631 643
2007 624 643 650 641 627 643 631 642
2008 625 645 647 640 630 640 631 642
2009 619 641 642 631 619 630 626 630
2010 617 641 643 634 622 631 625 631
2011 621 643 645 637 629 639 628 639
2012 624 646 644 637 629 639 631 644
2013 626 647 642 640 637 642 632 645
2014 628 648 650 645 640 648 634 648
2015 638 658 658 655 652 659 644 659
2016 643 661 665 659 655 661 649 665
Productivity evolution and reallocation in retail trade
116
As Section 44 has shown for the market economy in general the Hungarian economy can be
characterised by a strong duality between foreign and domestically-owned firms Retail is one of the
sectors where this is the most transparent with many small domestic firms operating alongside large
multinational super- and hypermarket chains90 Figure 83 shows the share of foreign-owned firms in
terms of number employment and market share Foreign-owned retail firms are substantially larger
than domestic ones between 5-7 percent of firms are foreign-owned but they employ around 30
percent of employees and realise around 40 percent of sales This also implies that the salesworker
share is also larger in foreign firms than in domestic ones This results from the larger typical size of
foreign firms when controlling for size salesworker is not higher for foreign firms The market share
of foreign-owned firms is at the top of the distribution in EU countries with a larger foreign share only
in Latvia and Poland91
Figure 85 shows an inverted U-shaped pattern with an increasing market share of foreign firms until
2009 followed by a fall of nearly 5 percentage points between 2013 and 2016 This fall in foreign share
ran parallel with the introduction of policies favouring smaller firms in various ways
Figure 85 Share of foreign firms with at least 1 employee
There is much variation behind the overall pattern as Figure 86 illustrates plotting the market share
of foreign firms across sub-industries In groceries foreign share fluctuated around 70 percent It was
90 There is limited literature on the spillover effects generated by multinational retailers See for example Atkin et al (2018)
91 See EC (2018) Figure 2
Productivity differences in Hungary and mechanisms of TFP growth slowdown
117
rising slightly pre-crisis in parallel with the increasing concentration of the industry The increase of
the market share of foreign firms was the strongest in clothes reflecting the expansion of different
multinational chains mainly in plazas The increasing trend observable for the category seems to have
broken around 2012 which coincides with the introduction of the Plaza Stop regulation Foreign
market share was always high in the highly concentrated cosmetics sector A few foreign chains were
dominant in this sector throughout the period Foreign share actually decreased sharply in books and
newspapers
Figure 86 Foreign share in sub-industries
A key question when evaluating the expansion of foreign firms is their performance Foreign retail
firms are substantially more productive than domestic ones (Figure 87) With the exception of the
crisis years labour productivity advantage was between 60-80 percent while the TFP advantage was
between 20-40 percent The TFP advantage is smaller because of the larger capital intensity of foreign
firms These productivity premia are not purely a consequence of the larger size of foreign firms this
pattern is robust to controlling for firm size There is no clear trend in the premia they were declining
before the crisis (suggesting that domestically-owned firms were catching up) and rising after it The
figure also shows a large decline in the premia in the crisis years This is likely to be a consequence of
more pro-cyclical margins of foreign firms which are captured by revenue productivity measures
Productivity evolution and reallocation in retail trade
118
Figure 87 Productivity premia of foreign firms labour weighted
The main message of this section is that similarly to other industries large productivity differences
persist in retail These differences are primarily associated with size larger firms are more productive
and charge lower margins The performance of very small shops and the self-employed looks
especially weak The pre-crisis period was characterised by an expansion of large and foreign firms
but this growth stopped after 2010
84 Allocative efficiency and reallocation
In this section we follow the approach of Chapters 5 and 6 in analysing allocative efficiency and
reallocation with a focus on the retail industry
Chapter 5 showed that an important metric of allocative efficiency at any point in time is the degree of
co-variance of productivity and size which is directly related to aggregate productivity Figure 88
shows the elasticity of the number of employees with respect to labour productivity and TFP A more
positive relationship represents a more efficient allocation of labour across firms92 The figure shows
these relationships both for the full sample (of firms with at least 1 employee) and the main sample
(firms with at least 5 employees)
The elasticity depends crucially both on the sample and the productivity measure We find that the
correlations are much stronger when the full sample is considered rather than the base sample This
reflects our findings in Table 83 namely that the smallest firms differ substantially from other firms
92 These are coefficients from separate yearly univariate regressions with ln number of employees on the left hand side and productivity as the explanatory variable
Productivity differences in Hungary and mechanisms of TFP growth slowdown
119
while firms with at least 5 employees are quite similar to each other The labour productivity premium
of larger firms is greater than their TFP premium reflecting their higher capital intensity
The key insight from Table 83 is that most of the Olley-Pakes correlation or measured allocative
efficiency results from the fact that very small firms are of very low productivity Within the group of
firms with at least 5 employees the correlation between TFP and size is practically zero There is a
positive although small correlation within the group between employment and labour productivity93
There is also no key trend in this measure of allocative efficiency some measures show improvement
while others a deterioration94
Figure 88 The elasticity of employment with respect to productivity main sample
Figure 89 performs the dynamic (Foster-type) productivity decomposition for the retail industry The
picture is not very different from the patterns found for services in general (see Figure 64) Pre-crisis
parallel with the strong growth of large chains growth was mainly driven by reallocation primarily in
the form of firm entry The crisis was accompanied by an annual 5 percent fall in productivity driven
by within-firm productivity decline As we have seen in Table 83 this was most likely the results of
margin-cutting by large firms Between 2010-2013 within-firm productivity growth and net entry
contributed similarly to the (relatively low) productivity growth Productivity growth sped up between
2013-2016 mainly driven by the within-firm component with little reallocation The trend break in the
growth of large chains is clearly reflected in this decomposition
93 As we have discussed in Chaper 5 this is not exceptional ndash actually similar correlations are found in services in other European contries
94 These low levels of allocative efficiency are in line with international evidence In fact these correlations have been negative in the majority of EU member states (EC 2018 p 7)
Productivity evolution and reallocation in retail trade
120
Figure 89 Dynamic decomposition of productivity growth in retail
While these results are informative about reallocation at the firm-level the shop-level data enable us
to investigate reallocation at a more detailed level These data enable us to investigate whether key
firm or shop-level variables are related to opening new shops closing shops or the growth of the shops
of continuing firms We investigate these questions in the paragraphs that follow
The simplest way to explore the shop-extensive margin or the change in the number of shops is to
aggregate the shop-level data to the firm-level In particular we calculate the change in the number of
shops the number of new shops and the number of old shops for each firm 119894 and year 119905 Denoting
these variables which show changes between year 119905 and 119905 + 1 by 119910119894119905 we run the following firm-level
regressions
119910119894119905 = 120573119883119894119905 + 120575119905 + 휀119894119905
where 119883119894119905 is a vector of firm-level variables These proxy productivity (by ln labour productivity) and
size (by the number of shops of the firm and the average sales per shop) 120575119905 is a full set of year
dummies
When estimating these equations one has to make a number of compromises Most importantly one
can only observe the change in shop numbers when the firm is present in the sample both in year 119905
and 119905 + 1 Otherwise one cannot be sure whether all the shops were closed or simply not sampled in
119905 + 1 Unfortunately this is a serious restriction for two reasons First one cannot observe the exit or
Productivity differences in Hungary and mechanisms of TFP growth slowdown
121
entry only survival for single-shop firms95 Second we also miss when a multi-shop firm exits with all
its shops
One also has to make a number of further methodological choices We restrict our sample to groceries
which is a relatively homogeneous group with many observations Another choice is that even though
we observe shops on a monthly basis we consider only year-to-year changes between May and the
following May Running the regressions on the monthly data would inflate artificially the number of
observations and introduce important methodological problems including seasonality
Table 84 presents the results In column (1) the dependent variable is the (net) change in the
number of shops The results suggest that productivity is of limited importance as a determinant of
change in shop numbers but size matters Firms with more and larger shops were more likely to
expand in terms of opening new shops Foreign firms expand faster because they are larger
conditional on size ownership does not matter Size is correlated both with shop opening and closing
firms with a larger average shop size are more likely to open new shops while chains with more shops
are less likely to close existing ones
Table 84 Determinants of the change in the number of shops at the firm-level groceries
(1) (2) (3)
Dependent Change in
number of
shops
New
shops
Closed
shops
Labour productivity 0001 0025 -0011
(0024) (0014) (0018)
Foreign-owned -0087 -0042 0019
(0064) (0034) (0045)
ln( (average
salesshop)
0056 0034 -0017
(0016) (0010) (0014)
5-9 shops 0176 0001 -0126
(0070) (0036) (0054)
10-49 shops 0231 -0009 -0167
(0067) (0034) (0052)
more than 50 shops 0194 0002 -0109
(0071) (0038) (0055)
Year FE yes yes yes
Observations 815 815 815
R-squared 0105 0093 0084
Notes One observation is a firm-year Standard errors are clustered at the firm-level
One may get a more detailed picture by investigating at the shop-level Here we can straightforwardly
estimate both the exit part of the extensive margin (did a specific shop close) and the intensive
margin (did the shop extend its sales)
95 For this reason we drop single-shop firms altogether from the analysis
Productivity evolution and reallocation in retail trade
122
In particular we run regressions of the following form
119910119894119895119905 = 120573119883119894119905 + 120574119885119894119895119905 + 120575119905 + 휀119894119895119905
where 119894 denotes firms 119895 shops and 119905 years The outcome variable 119910119894119895119905 is either a dummy showing
that the shop closed96 between 119905 and 119905 + 1 or represents the growth of (log) sales of the shop 119883119894119905 are
firm-level variables such as productivity while 119885119894119895119905 are shop-level variables such as shop-level sales
The same restrictions apply as in the previous case
Table 85 reports basic regressions We run both the exit and sales growth regressions for three
subperiods 2004-2007 2008-2010 and 2012-2015 Our main question is whether one can identify
any changes in the relocation process across these subperiods
Let us start with the exit regressions Similarly to the firm-level results we find that productivity and
ownership are not associated with the probability of exit Shop size is significantly related to closing a
shop twice as large sales are associated with 5 percentage points lower probability of the event
occurring This relationship became stronger by the third period The number of shops of the firm is
also negatively associated with the probability of closing the shops and this effect only became
significant post-crisis In addition the explanatory power of the regression is also higher by nearly 50
percent in this last period compared to the earlier ones To sum up we find that the size of the shop
and the turned out to be more important post-crisis making such shops less likely to close
In contrast to the exit equation we do not find significant effects in the growth regressions Neither
size nor productivity seem to be related to growth at the shop-level
To sum up the level of allocative efficiency in retail is relatively low ndash similarly to other European
countries ndash and one cannot see a significant change in this respect Pre-crisis when large chains
expanded rapidly reallocation played a significant role in aggregate productivity growth while within-
firm growth became dominant after the crisis Shop-level data suggest that the expansion in terms of
number of shops is mainly determined by firm size rather than productivity and ownership Sales
growth of existing shops does not seem to be related to size ownership or productivity The lack of
evidence for a relationship between opening new shops or the growth of existing shops is much in line
with the low measured allocative efficiency in the industry
96 We run linear probability models for shop exits Probit models yield similar results
Productivity differences in Hungary and mechanisms of TFP growth slowdown
123
Table 85 Probability of closing a shop and growth regression NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent Closing the shop Growth
Period 2004-
2007
2008-
2010
2012-
2015
2004-
2007
2008-
2010
2012-
2015
labour productivity 0005 -0021 0102 0034 -0012 0097
(0009) (0015) (0087) (0018) (0022) (0063)
foreign-owned -0005 0063 0056 0016 -0014 -0035
(0023) (0045) (0057) (0023) (0055) (0066)
ln sales -0026 -0029 -0057 -0018 -0010 0010
(0006) (0007) (0021) (0007) (0017) (0005)
5-9 shops -0014 -0066 -0111 0061 -0035 -0055
(0026) (0051) (0043) (0046) (0044) (0026)
10-49 shops -0045 -0114 -0149 0046 -0038 -0023
(0024) (0048) (0039) (0044) (0043) (0017)
more than 50
shops
0001 -0077 -0134 0061 -0027 -0016
(0029) (0048) (0040) (0045) (0044) (0022)
Observations 15374 10946 15038 14025 10120 13458
R-squared 0030 0053 0073 0023 0041 0121
Notes OLS regressions run at the shop-year level only for firms present both in t and t+1 In columns (1)-(3) the
dependent variable is a dummy indicating whether the shop closes between t and t+1 while in columns (4)-(6) it is
the growth rate of sales between t and t+1 The explanatory variables are measured at year t The number of
shops variables are dummies representing the number of shops of the firm County and year fixed effects are
included Period 1 2004-2007 Period 2 2008-2010 period 3 2012-2015 Standard errors are clustered at the
firm-level
85 Trade
In small open economies a very important function of the wholesale and retail sector is the
intermediation of international trade for consumers and firms The operation and efficiency of these
industries can have a strong impact on aggregate welfare and productivity by determining both the
cost and variety of imported goods available as well as the cost of exporting products (Raff and
Schmitt 2016)
Many interesting questions emerge in this framework One of the key issues is the problem of double
marginalisation In the case of consumers (and consumer goods) one dimension of this question is
whether retailers import products directly or via wholesalers If retailers find it very hard to import
directly (because of say large fixed costs) double marginalisation can raise prices for consumers
Through this channel lower trade cost of retailers can benefit consumers As a result the share of
consumer goods imported directly by retailers may be an important proxy for the lower prevalence of
double marginalisation
In the case of intermediate inputs manufacturing firms face the choice of importing the product
directly (and paying the fixed costs of doing so) or relying on an intermediary Again reduced fixed
cost may make imported goods cheaper contributing positively to productivity growth Access to
imported intermediate inputs has been shown to be strongly correlated with the productivity of
Hungarian manufacturing firms (Halpern et al 2015)
Productivity evolution and reallocation in retail trade
124
The question of duality is also highly relevant in this context Multinational retailers can easily rely on
producers abroad hence their expansion can have important effects on Hungarian producers
Domestic chains on the other hand may find it hard to import a large variety of foreign products
which may result in a reduced choice set for consumers
Ultimately it is the questions above that motivate our investigation of importing and exporting by
wholesalers and retailers Our data are exceptionally suitable for this exercise Given that firm balance
sheets can be linked to detailed export and import data one can quantify the amount of products
imported and exported from different product categories by wholesalers and retailers
An important methodological note is that we only observe direct imports in the trade data The most
important consequence of this limitation is that while in actual fact the share of imported goods on a
retailerrsquos shelf is a combination of goods imported directly by the retailer and those imported by a
wholesaler and sold to the retailer with the data we are only able to observe the former (Basker and
Van 2010) Also note that in contrast to imports exports are reported in the balance sheet
Therefore we will use this source of information when analysing exporting
Importing
To start with Figure 810 shows the share of retailers and wholesalers from the total Hungarian
imports of different types of goods In terms of all imports the share of these two groups of firms
fluctuated around 25 percent with a slightly decreasing trend The bulk of the imports were conducted
by manufacturing firms with an especially large share by multinational affiliates strongly integrated
into global value chains for example in the automotive industry Overall wholesalersrsquo imports were
about 5 times larger than those of retailers97
Naturally wholesalers and retailers dominate the importing of consumer goods by a share of around
70 percent A key trend here is the increasing share of retailers In 2004 21 percent of intermediated
trade (imports of wholesalers and retailers) were imported by retailers which increased gradually to
33 by 2015 This is a significant shift which reflects in part the expansion of multinational retail
chains but probably also easier access to imports by retailers
The share of intermediated trade was around 20 percent both for intermediate inputs and capital
goods dominated by wholesalers This reflects that in aggregate terms the overwhelming majority of
goods used by firms in production are imported directly The share of intermediated trade decreased
strongly following the crisis from 20 percent in 2010 to 13 percent in 2015 Given the skewed size
distribution of manufacturing firms this does not mean that most firms import their inputs directly
many smaller firms rely strongly on trade intermediaries when purchasing their inputs
Figure 811 looks into the trends behind consumer goods imports in more detail The left hand side
figure shows the share of imports compared to the total cost of goods sold (COGS) by wholesalers and
retailers98 We find that this ratio is roughly constant for wholesalers namely around 10 percent99
97 This can be compared to the results of Bernard et al (2010) who report that retailers and firms active both in retail and wholesale represent 14 percent of importing firms and 9 percent of imports in the US
98 In particular we calculate total consumer goods imports for wholesale and retail firms and divide it with the sum cost of goods sold across all retailers
99 Needless to say wholesalers also import other type of goods which are part of their cost of goods sold This ratio was 36 percent in 2015 showing that more than a third of their sales was imported
Productivity differences in Hungary and mechanisms of TFP growth slowdown
125
This contrasts sharply with retailers where the share of directly imported goods nearly doubled
between 2005 and 2015 from 6 percent to 11 percent100 This corresponds to a substantial increase in
the share of imported goods offered to consumers by retailers and an increasing share of this volume
is imported directly by the retailer presumably with a smaller degree of double marginalisation
One can also decompose the increasing direct import share of retailers to its different margins One
possibility is that - probably thanks to the declining fixed costs of importing - more and more retailers
started to import (an extensive margin effect) The right panel of Figure 811 shows that this is not the
case the share of directly importing retailers stagnated at about 8 percent of firms (with at least 5
employees) in the whole period Instead the rise of direct imports was driven by the intensive margin
or the average direct import per retailer Other regressions (not reported) suggest that this does result
mainly from the increased imports of large retailers
Figure 810 Share of wholesale retail and other firmsrsquo imports relative to total imports across
product categories
100 Again considering all goods the importcost of goods sold ratio increased from 11 to 18 percent for retailers
Productivity evolution and reallocation in retail trade
126
Figure 811 The share of consumer goods imports relative to the cost of goods sold and the share of
direct consumer goods importers by industry
Notes Firms with at least 5 employees
Figure 812 distinguishes between foreign and domestically-owned retail firms Both the share of
importers and their intensive margins are much higher for foreign-owned firms in the industry The
share of consumer goods imports in foreign firms in terms of cost nearly tripled between 2005 and
2015 from 7 to 21 percent101 compared to the 2-5 percent increase for domestically-owned firms
The increase in imports by retailers hence was mainly driven by multinationals
101 A similar increase from 18 percent in 2005 to 32 percent in 2015 can be observed when non- consumer goods are considered
Productivity differences in Hungary and mechanisms of TFP growth slowdown
127
Figure 812 The share of consumer goods imports relative to cost of goods sold and the share of
direct consumer goods importers by ownership
Notes Firms with at least 5 employees
Table 86 presents the cross-sectional linear regressions in order to investigate the premia of importers
among retailers along several dimensions In these regressions the dependent variable is a dummy
which shows whether a firm imports at least 1 percent of its cost of goods sold102 We find substantial
and highly significant premia in terms of size productivity and ownership 100 percent higher
productivity translates into about 5 percentage points higher probability of importing This premium
was increasing significantly between 2005 and 2015 showing a stronger self-selection of more
productive retailers into direct importing Foreign retailers are 20-25 percentage points more likely to
import on average A doubling of employees is associated with around 9 percentage points higher
probability of importing103
102 These are linear probability models but probit specifications yield similar marginal effects
103 Similar premia are found for importers in most industries and are mainly explained by the fixed costs of importing (Vogel and Wagner 2010)
Productivity evolution and reallocation in retail trade
128
Table 86 Determinants of importing linear probability models Retailers
(1) (2) (3) (4) (5)
Year 2005 2008 2010 2012 2015
Dependent Imports at least 1 percent of purchases
Labour productivity 0050 0054 0047 0059 0065
(0003) (0003) (0003) (0003) (0003)
Foreign-owned 0249 0196 0224 0238 0217
(0014) (0011) (0012) (0012) (0012)
Ln employees 0082 0082 0075 0073 0088
(0004) (0004) (0004) (0004) (0004)
Constant -0470 -0536 -0464 -0551 -0637
(0027) (0026) (0027) (0028) (0027)
Observations 7467 7977 7400 7122 8308
R-squared 0116 0130 0127 0140 0143
Notes Firms with at least 5 employees These are cross-sectional regressions where the dependent variable is
dummy representing whether the firm imports at least 1 percent of its cost of goods sold
Exporting
Wholesalers and retailers can also play a significant role as export intermediaries Extended export
activities of these firms can be an important source of growth for these firms but can also benefit
many smaller producers who would not find it profitable to export directly (Ahn et al 2011)
Figure 813 shows that 85-90 percent of exporting was conducted directly by producers rather than by
wholesalers or retailers The share of intermediated exports was constant pre-crisis but started to fall
after 2012
Productivity differences in Hungary and mechanisms of TFP growth slowdown
129
Figure 813 Share of wholesale retail and other firmsrsquo exports relative to total exports of firms
Many wholesalers and retailers started to export in the period under study (Figure 814) The share of
exporters in wholesale firms increased from 25 percent in 2005 to 35 percent in 2015 while the share
of exporting retailers doubled in this period The share of exports in the turnover of these firms also
increased substantially
Figure 814 Share of exports relative to turnover and share of exporters by industry
While foreign-owned firms are about 4 times more likely to export than domestic ones entry into
exporting was not limited to foreign-owned firms (Figure 815) the share of exporters among
domestically-owned firms doubled between 2005 and 2015 This was paralleled with an increase in the
share of exports relative to total turnover
Productivity evolution and reallocation in retail trade
130
Figure 815 Share of exports relative to turnover and share of exporters by ownership for the retail
sector
Table 87 reports linear probability models with export status as the dependent variable More
productive larger and foreign-owned firms are more likely to export In general both the size and
labour productivity premia increased between 2005 and 2015 once again suggesting stronger self-
selection based on these variables
Table 87 Determinants of exporting linear probability models retail
(1) (2) (3) (4) (5) Year 2005 2008 2010 2012 2015
Dependent Exports at least 1 percent of total revenue
Labour
productivity
0020 0035 0036 0043 0041 (0002) (0003) (0003) (0004) (0003)
Foreign-owned 0083 0137 0141 0119 0107
(0009) (0011) (0013) (0013) (0012)
Ln employees 0019 0029 0028 0031 0034
(0003) (0004) (0004) (0004) (0004)
Constant -0159 -0277 -0271 -0321 -0317
(0018) (0026) (0027) (0029) (0028)
Observations 7622 7976 7663 7384 8730
R-squared 0028 0045 0041 0041 0036
This section has shown that the role of retailers in international trade is becoming more and more
important in Hungary This can have many benefits from providing a larger variety of potentially lower
priced goods to consumers to letting smaller producers reach foreign markets Increasing exports
mostly reflect opportunities provided by European integration and the internet but policies can also
help firms to become more adapt at utilising these opportunities
Productivity differences in Hungary and mechanisms of TFP growth slowdown
131
86 Policies Crisis taxes
As we have described briefly in Section 81 some of the new policies introduced after the crisis were
size-dependent either explicitly or implicitly The crisis taxes and the local business tax104 were based
on explicitly taxing large firms at higher rates Such policies can have substantial effects at the sectoral
level (Guner et al 2008)
Evaluating the effects of these taxes is not a straightforward task A possible approach was followed in
Section 84 where we have investigated the reallocation process in detail While such an approach is
not capable of identifying the causal effects of specific policies it may provide a broad picture The
results most importantly Figure 88 suggest that the importance of the reallocation process declined
relative to within-firm productivity growth Still this could have resulted from many reasons other than
policy changes
A more direct approach is to identify specific firms which were affected by a policy and to compare
their behaviour to similar firms not affected by the policy Such a diff-in-diff approach may be an
effective policy evaluation tool when there are sharp breakpoints in the tax schedule with enough
`treatedrsquo and control firms in the two groups
As for the crisis taxes the only sharp discontinuity was at the top rate when the tax rate increased
from 04 to 25 percent of profits The cutoff was at HUF 100bn and according to our data altogether
6 retail firms qualified for inclusion in this group This sample size does not allow for a statistically
powerful test
Still a few graphs may illustrate the processes First the market share of these large mainly
multinational firms were expanding quickly before 2010 and stagnated afterwards (Figure 816)
Second we can illustrate some of the key performance measures discussed in Section 83 Figure 817
compares the treated firms to a control group consisting of firms with at least 100 employees We find
that the premium of the treated group in terms of both productivity measures and margins were
higher between 2010 and 2013 than before or after105 As we have discussed earlier at least in the
short term these revenue-based measures are likely to reflect changes in prices Hence this figure
hints at increased prices in the treated group relative to the control group suggesting that treated
firms passed on the tax to consumers Note that these differences are not statistically significant and
to reiterate may have resulted from many other factors rather than just the effects of this specific
policy
104 The effect of the local business tax is much harder to test given its more continuous nature
105 Note that the margin premia are in fact negative in line with the lower margins charged by the largest firms
Productivity evolution and reallocation in retail trade
132
Figure 816 Sales and employment share of firms in the top bracket of the crisis tax
Notes Full sample
Figure 817 Margin TFP and labour productivity advantage of firms in the top bracket of the crisis tax
firms with more than 100 employees
Productivity differences in Hungary and mechanisms of TFP growth slowdown
133
87 Policies Mandatory Sunday closing
One of the most characteristic non-tax based size-dependent policies was mandatory Sunday closing of
larger shops introduced in March 2015 and reversed in April 2016 While the policy had multiple aims
it was partly motivated by supporting smaller and family-owned shops In this section we investigate
two outcomes related to this policy First we aim at understanding its reallocation effects ie the
extent to which the market share of treated shops lost market share Second we are interested in the
extent to which consumption was reallocated to other days of the week
The shop-level data is ideal to investigate the effects of this policy First the policy was defined at the
shop- rather than the firm-level We can identify the affected shops precisely based on the number of
days they were open Second many shops have been affected by this policy making the test
powerful Third the policy has a clearly defined beginning and end making a difference in differences
strategy feasible
Our empirical approach starts with restricting the sample to comparable firms First we investigate
mainly grocery shops where we have sizable treated and control groups106 In the sample we include
only shops which were continuously in the sample between January 2015 and October 2016 An issue
is that the treated and the control group may be very different We attempt to guarantee that the
common support condition is satisfied by excluding very small and very large shops107 For similar
reasons we also exclude shops which were not open even on Saturdays either before or during the
policy108
An important part of the analysis is the definition of the treated group As we do not observe directly
the area and the ownership of the shop we rely on the change in the number of days open We
consider a shop treated if it was open for at least 30 days per month before the policy (in median) and
it was open for less than 26 days after the policy was introduced (again in median)109 The control
group consists of other firms in the sample
Taking a look at the number of days open for the two groups reveals that compliance was very high
More than 95 percent of the shops that had been open on Sundays before the policy were closed on
Sundays during the whole policy period More than 95 percent of shops in the control group were
closed on Sundays both before and after the policy There are few firms which deviated from this
pattern by for example opening on Sundays when the policy started110
106 In other 4-digit sectors either there are too few firms or nearly all of them are treated (clothes shoes etc) or none of them (fuel)
107 Based on the 5th and 95th percentiles of the median sales distribution based on sales before the policy Unfortunately we do not have other measures of shop size
108 More precisely we exclude shops for which the median monthly days open was below 21 days either before or during the policy
109 A potential worry with this approach is that some shops may have closed voluntarily when the policy was introduced We cannot exclude this possibility but this may not be that important for the relatively large shops in the sample One can expect that voluntary Sunday closure would not start exactly at the beginning of the policy but rather after a period of gathering information about consumer demand on Sunday By checking the monthly distribution of the number of days open we find only few firms which changed their behaviour in this respect during the policy
110 Note that many small shops remained open on Sundays but most of them are missing from our restricted sample because of small median sales
Productivity evolution and reallocation in retail trade
134
Figure 815 reports descriptive statistics of the key variables Panel A) compares the evolution of
average sales of the treated and the control group before during and after the introduction of the
policy The dynamics of sales growth was remarkably similar before the policy was introduced
suggesting that the parallel trend assumption was satisfied Average sales in the control group are
somewhat higher during the policy suggesting some reallocation of market share to that group After
the policy the treated group seems to slightly overperform the control group
Part B) of Figure 818 shows the evolution of average sales per day open Again the pre-policy trends
are similar for the two groups Sales per day increases significantly for both groups during the policy
consumers did their Sunday shopping on other days The increase is substantially larger for the treated
group showing that most of the former Sunday shopping took place in the same shop but on other
days of the week The fact that there is an increase in the control group shows that part of the former
Sunday shopping was reallocated to these shops Interestingly the sales per day advantage of the
treated group remained even after the policy was abandoned As we will see the main reason for this
is that after abandoning the policy some of the shops remained closed
Figure 818 The evolution of key variables in the treated group and the control group groceries
A) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
135
B) Sales per day
While these patterns are suggestive the data allow us to conduct a more precise econometric event
study exercise We do so by creating a number of quarterly event study dummies to capture the
differential dynamics of the treated and control groups We define the variable lsquoevent timersquo which
shows the number of months since the policy started (it is zero in March 2015) This variable takes
negative values before that date We define quarterly dummies based on the event time variable For
example the first treatment quarter dummy is one when event time is 0 1 or 2 and the firm is in the
treated group The first pre-treatment dummy takes the value of 1 when event time is -1 -2 or -3 and
the firm is in the treated group
We run the following regression to estimate these trends
119910119894119895119905 = sum 120573120591119890119907119890119899119905 119904119905119906119889119910 119889119906119898119898119910119894119895119905120591
120591 + 120583119894119895 + 120575119905 + 휀119894119895119905
In this regression the dependent variable is days open ln(monthly sales) and ln(salesdays open) 119894
denotes firms 119895 shops and 119905 time measured in month while 120591 is event time in quarters The variables
of interest are the full set of event study dummies The base category will be the second pre-trend
dummy (event time -4 -5 or -6) The motivation for this choice is that the policy was announced in
this period (December 2014) hence the first pre-trend period the beginning of 2015 may include
preparation for the policy 120583119894119895 are shop fixed effects to control for shop heterogeneity 120575119905 are time
(monthly) fixed effects which control both for seasonality and macro shocks When we run the
regression by pooling different 4-digit industries we allow these dummies to vary across industries In
a more demanding specification we also include firm-time fixed effects and identify from the
differences across the treated and non-treated shops of the same firm in the same month We cluster
standard errors at the shop-level
Figure 819 summarizes the main results for the whole retail sector while the regressions are reported
in Table A71 in the Appendix Panel A) shows the results for days open with the right-hand panel
including firm-time fixed effects We see that on average treated firms cut the number of days open
by 2-3 days relative to the control group ndash the effect is more pronounced with firm fixed effects There
Productivity evolution and reallocation in retail trade
136
is practically no pre-trend and the timing of the reduction of days open is strongly in line with the
introduction of the policy The number of days open increases sharply after the end of the policy but
only to below pre-policy levels This suggests that some shops did not re-open on Sundays after the
policy probably because they learned that their sales did not suffer much
Panel B) shows the behaviour of average monthly sales Again there is no evidence for a pre-trend
During the policy treated firms experienced a 2-3 percent lower sales growth relative to the control
group This shows how much of sales was re-allocated to other shops Post-policy variables suggest
full recovery to pre-policy levels
Panel C) of the same figure shows the effect of the policy on sales per day open This variable
increased by 5-10 percent in the treated group relative to the control group The bulk of consumers
seem to have remained loyal to their familiar shops and simply made their shopping on other days
This may have also been helped by longer opening hours on other days of the week and further efforts
made by shops to retain their customers Sales per day remain higher even after the end of the policy
most likely because some shops did not re-open on Sundays but probably also because of
organizational changes during the policy
Figure 819 Event study results for the whole retail sector
A) Days
Productivity differences in Hungary and mechanisms of TFP growth slowdown
137
B) Sales
C) Sales per day
Notes This figure presents point estimates and 95 confidence intervals from the event study regression showing
the evolution of number of days open sales and sales per day of the treated group compared to the control group
as described in the text All specifications include shop fixed effects The left panel regressions also include 4-digit
industry-time fixed effects while the right side panels include firm-time dummies
Productivity evolution and reallocation in retail trade
138
Figure 820 re-estimates the same regressions for groceries where the policy was most relevant The
regression results are reported in Table A72 in the Appendix We find very similar results to the whole
retail sector The only exception is that the evolution of post-policy behaviour of sales is less clear
Figure 820 Event study results for NACE 4711
A) Days
B) Sales
Productivity differences in Hungary and mechanisms of TFP growth slowdown
139
C) Sales per day
Notes The figure above presents point estimates and 95 confidence intervals from the event study regression
showing the evolution of number of days open sales and sales per day of the treated group compared to the
control group as described in the text All specifications include shop fixed effects The left panel regressions also
include 4-digit industry-time fixed effects while the right side panels include firm-time dummies
A possible concern with these estimates is that the increase in sales per day may result from a simple
composition effect If sales are usually very small on Sundays anyway then closing on Sundays may
mechanically increase average daily sales We check for this possibility by estimating sales on different
days of the week from the pre-policy period While we do not observe the sales on each day of the
week we observe sales in different months with a different combination of days We rely on this
variation to estimate a regression of the following form
ln 119904119886119897119890119904119894119895119905 = 120573 lowast 119883119905 + 120574 lowast 119889119886119905119890119905 + 120583119894119895 + 휀119894119895119905
where 119883119905 is a vector of variables containing the number of Mondays Tuesdays etc in month 119905 We
also control for the number of holidays in the month We control for seasonality by including dummies
for December January and summer months The regression also includes firm fixed effects and is
estimated on the period 2009-2014 120574 lowast 119889119886119905119890119905 is a linear trend The estimated results are reported in
Table A73 in the Appendix
The regression shows that sales on Sundays were not that small namely similar to a typical Monday
or Wednesday Thus the composition effect is unlikely to affect the results much To check for the
relevance of these composition effects Figure 821 A) reports sales predicted from the above
regression for the treated group (by setting the number of Sundays to be zero during the policy)
Therefore the `predictedrsquo line shows what would have happened if sales had remained the same on
Productivity evolution and reallocation in retail trade
140
non-Sundays during the policy The actual line is clearly above the predicted one suggesting that sales
on other days have increased
Panel B) of Figure 821 shows how sales per day would have evolved based on a similar regression
Note that predicted sales per day are slightly larger during the policy than beforehand thanks to the
mechanical composition effect resulting from the slightly lower sales on Sundays Actual sales per day
however are substantially higher than this simple prediction showing again that sales per day
increased on other days of the week
Figure 821 The evolution of the variables versus prediction
A) Sales
B) Sales per day
Productivity differences in Hungary and mechanisms of TFP growth slowdown
141
All in all the mandatory Sunday closing of shops was effective in terms of compliance It did not have
strong reallocative effects with a 2-3 percent fall in sales in the treated group Consumers seem to
have remained mostly loyal to the shop they had frequented and made their shopping on other days
of the week at the same shop Interestingly some of the shops seem to have learned that it is optimal
to remain closed on Sundays even after the policy was cancelled
88 Conclusions
In line with the main message of other parts of this study there are huge productivity differences
across firms within the retail sector There is a strong duality between small and large firms both in
terms of productivity and margins Consumers are likely to pay significantly lower prices in the shops
of large firms Many of the large firms are multinationals which had expanded rapidly before the crisis
At the other end of the range the exceptionally low performance of very small firms seems to be a
significant issue Many technologies applied by the most productive retailers could be adapted
relatively easily by some of the less productive firms Increasing absorptive capacity and effective
financing could help in promoting this Still many of the low-productivity very small shops may not be
viable in the long run
A key pattern observed is the increasing concentration of the retail sector pre-crisis resulting from the
expansion of large chains and foreign firms These trends seem to have stopped or slowed down after
the crisis In line with this pattern the contribution of reallocation decreased post-crisis relative to
earlier periods While many factors can play a role in this pattern it may be related to the different
size-dependent policies introduced after 2010 While these developments may help smaller retail firms
consumers may face higher prices in the long run
Not all the policies introduced can be properly evaluated based on the data at hand especially because
multiple policies were introduced at the same time with some of them affecting only few firms We
were able to analyse precisely the effects of mandatory Sunday closing based on store level data We
found that a relatively small share of the demand was lost by the treated shops and the majority of
consumers simply switched to shopping at the same place on other days Interestingly some of the
treated shops found it optimal not to re-open on Sundays even when the policy was reversed
Additionally retailers and wholesalers also play a large and increasing role in mediating imports and
exports We found a large increase in goods imported directly by retailers rather than indirectly via
wholesalers This was mainly driven by large foreign firms and may have benefited their consumers
thanks to a lower degree of double marginalisation Both the number of exporting firms and the
amount exported by wholesalers and retailers increased most likely benefitting from easy access to
markets of other EU member states and probably from the opportunities provided by e-commerce
This can benefit both the exporting firms and the Hungarian producers who can more easily reach
foreign markets with the help of these intermediaries Policies may help retailers to internationalise by
making international sales especially on the internet even easier
Conclusions
142
9 CONCLUSIONS
The results of this report confirm that Hungary is atypical because of the relatively poor productivity
performance of frontier firms Importantly contrary to a strong version of the duality concept this is
not a result of Hungarian frontier firms being on the global frontier typically they are quite far away
from it This robust pattern underlines that besides helping non-frontier firms policies may also have
to focus on the performance of the frontier group A transparent environment with a strong rule of law
complemented by a well-educated workforce and a strong innovation system is key for providing
incentives to invest into the most advanced technologies
The analysis in this report reinforces the impression that there is a large productivity gap between
globally engaged or owned and other firms the gap being about 35 percent in manufacturing and
above 60 percent in services This gap seems to be roughly constant in the period under study The
firm-level analysis in Chapter 7 also reveals that one of the mechanisms which conserves the gap is
that foreign frontier firms are able to increase their productivity more than their domestic counterparts
even from frontier levels These findings reinforce the importance of well-designed policies that are
able to help domestic firms to catch up with foreign firms A key precondition for domestic firms to
build linkages with foreign firms and to benefit more from their presence is a high level of absorptive
capacity High skills and an efficient innovation system can support this aim as well A more specific
conclusion is the importance of enabling high-productivity domestic firms to improve their productivity
levels even further
The large within-industry productivity dispersion the relatively low (though not extreme in
international comparison) allocative efficiency documented in some of the industries the strong
positive contribution of reallocation to total TFP growth before the crisis and the relatively low entry
rate imply that policies promoting reallocation have a potential to increase aggregate productivity
levels significantly These policies can include improving general framework conditions by cutting
administrative costs reducing entry and exit barriers and using a neutral regulation The fact that
capital market distortions still appear to be significantly above their pre-crisis levels implies that
policies that reduce financial frictions may help the reallocation process The fact that exporters tend to
expand faster relative to non-exporters indicates that access to EU and global markets generates a
strong and positive reallocation effect
Throughout our analysis we have found significant differences across sectors In general traded and
more knowledge-intensive sectors fared better both in terms of productivity growth and allocative
efficiency The difference between traded and non-traded sectors points again to the importance of
global competition in promoting higher productivity and more efficient allocation of resources This also
implies that adopting policies that focus on innovation or reallocation in services may be especially
important given the large number of people working in those sectors The better performance of and
reallocation into more knowledge-intensive sectors underlines the importance of education policies
aimed at developing up-to-date and flexible skills and innovation policies that help improve the
knowledge base and the functioning of the innovation system
Available evidence suggests a wide gap in the productivity level and earnings of people working at
firms with at least a few employees and those working in very small firms or self-employed The latter
category represents 30-50 percent of people engaged in some important industries Inclusive policies
may attempt to generate supportive conditions for these people by providing knowledge and training
as well as helping them to find jobs with wider perspectives or to set up well-operating firms The large
share of these unproductive economic entities holds back productivity growth even at the macro-level
The specific analysis of the retail sector has shown a characteristic difference between the pre-crisis
period characterised by strong reallocation mainly via the expansion of large foreign-owned chains
Productivity differences in Hungary and mechanisms of TFP growth slowdown
143
and the post-crisis period with a stagnating share of large chains This break is likely to be linked to
post-crisis policies favouring smaller firms While halting further concentration in a country with
already one of the highest share of multinationals in this sector can have a number of benefits it is
likely to lead to higher prices and lower industry-level productivity growth in the long run Policies
should balance carefully between these trade-offs Another key pattern identified is the increasing role
of retailers (and wholesalers) in trade intermediation both on the import and export side Policymakers
should encourage these trends and design policies which provide capabilities for such firms to enter
international markets probably via e-commerce
References
144
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Antildeoacuten Higoacuten D Mantildeez J A Rochina-Barrachina M E Sanchis A and Sanchis-Llopis J A (2017)
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Arrobbio A Barros A C H Beauchard R F Berg A S Brumby J Fortin H Garrido J Kikeri
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httpdocumentsworldbankorgcurateden228331468169750340Corporate-governance-of-state-
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Atkin D Faber B and Gonzalez-Navarro M (2018) ldquoRetail globalization and household welfare
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
145
Basker E and Van P H (2010) ldquoImports lsquoЯrsquo us Retail chains as platforms for developing country importsrdquo American Economic Review 100(2) 414-18
Beacutekeacutes G Kleinert J and Toubal F (2009) ldquoSpillovers from multinationals to heterogeneous
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Biesebroeck J V (2008) ldquoAggregating and decomposing productivityrdquo Review of Business and
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Bisztray M (2016) ldquoThe effect of FDI on local suppliers Evidence from Audi in Hungaryrdquo IEHAS
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David H Dorn D and Hanson G H (2013) ldquoThe China syndrome Local labor market effects of
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
147
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Foster L Haltiwanger J and Krizan C J (2006) ldquoMarket selection reallocation and restructuring
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Garicano L Lelarge C and Van Reenen J (2016) ldquoFirm size distortions and the productivity
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Girma S (2005) ldquoAbsorptive capacity and productivity spillovers from FDI A threshold regression
analysisrdquo Oxford Bulletin of Economics and Statistics 67(3) 281-306
Girma S and Goumlrg H (2007) ldquoEvaluating the foreign ownership wage premium using a difference-
in-differences matching approachrdquo Journal of International Economics 72(1) 97-112
Girma S Thompson S and Wright P W (2002) ldquoWhy are productivity and wages higher in foreign
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Gopinath G Kalemli-Ozcan S Karabarbounis L and Villegas-Sanchez C (2017) ldquoCapital allocation
and productivity in South Europerdquo Quarterly Journal of Economics 132(4) 1915-1967
Gorodnichenko Y Revoltella D Svejnar J and Weiss C T (2018) ldquoResource misallocation in
European firms The role of constraints firm characteristics and managerial decisionsrdquo NBER Working
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Griliches Z and Regev H (1995) ldquoFirm productivity in Israeli industry 1979-1988rdquo Journal of
Econometrics 65(1) 175ndash203
Guner N Ventura G and Xu Y (2008) ldquoMacroeconomic implications of size-dependent policiesrdquo Review of Economic Dynamics 11(4) 721-744
Halpern L Koren M and Szeidl A (2015) ldquoImported inputs and productivityrdquo American Economic
Review 105(12) 3660-3703
Halpern L and Murakoumlzy B (2007) ldquoDoes distance matter in spilloverrdquo Economics of Transition
15(4) 781-805
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Harasztosi P (2011) ldquoGrowth in Hungary 1994-2008 The role of capital labour productivity and
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Harasztosi P and Lindner A (2017) ldquoWho Pays for the Minimum Wagerdquo Mimeo
Haskel J and Sadun R (2012) ldquoRegulation and UK retailing productivity Evidence from microdatardquo Economica 79(315) 425-448
Haskel J E Pereira S C and Slaughter M J (2007) ldquoDoes inward foreign direct investment boost
the productivity of domestic firmsrdquo The Review of Economics and Statistics 89(3) 482-496
Hausmann R and Rodrik D (2003) ldquoEconomic development as self-discoveryrdquo Journal of
Development Economics 72(2) 603-633
Hausmann R Hwang J and Rodrik D (2007) ldquoWhat you export mattersrdquo Journal of Economic
Growth 12(1) 1-25
Herrendorf B Rogerson R and Valentinyi A (2014) ldquoGrowth and structural transformationrdquo
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Hopenhayn H A (2014) ldquoFirms misallocation and aggregate productivity A reviewrdquo Annual Review
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Hornok C and Murakoumlzy B (2018) ldquoMarkups of exporters and importers Evidence from Hungaryrdquo
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Hsieh C T and Klenow P J (2009) Misallocation and manufacturing TFP in China and Indiardquo The
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Hsieh C T and Olken B A (2014) ldquoThe missing missing middlerdquo Journal of Economic Perspectives 28(3) 89-108
Huttunen K (2007) ldquoThe effect of foreign acquisition on employment and wages Evidence from Finnish establishmentsrdquo The Review of Economics and Statistics 89(3) 497-509 Inklaar R and Timmer M P (2008) ldquoGGDC productivity level database International comparisons of output inputs and productivity at the industry levelrdquo Groningen Growth and Development Centre Research Memorandum GD-104 University of Groningen Groningen
Inklaar R and Timmer M P (2009) ldquoProductivity convergence across industries and countries The
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673-709
Javorcik B S (2004) ldquoDoes foreign direct investment increase the productivity of domestic firms In
search of spillovers through backward linkagesrdquo American Economic Review 94(3) 605-627
Productivity differences in Hungary and mechanisms of TFP growth slowdown
149
Javorcik B S and Spatareanu M (2011) ldquoDoes it matter where you come from Vertical spillovers
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Hungaryrdquo MNB Working Papers 200412
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experimentrdquo IZA Discussion Papers (No 970)
Konings J (2001) ldquoThe effects of Foreign Direct Investment on domestic firmsrdquo Economics of
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Kugler M (2006) ldquoSpillovers from Foreign Direct Investment Within or between industriesrdquo Journal
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50(1) 21-43
Levinsohn J and Petrin A (2003) ldquoEstimating production functions using inputs to control for
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Lin P Liu Z and Zhang Y (2009) ldquoDo Chinese domestic firms benefit from FDI inflow Evidence
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McGowan M A Andrews D and Millot V (2017) ldquoThe walking dead Zombie firms and productivity
performance in OECD countriesrdquo OECD Economics Department Working Papers (No 1372)
McMillan M Rodrik D and Sepulveda C (2017) ldquoStructural change fundamentals and growth A
framework and case studiesrdquo NBER Working Papers (No w23378) National Bureau of Economic
Research University of Chicago Press Chicago
Melitz J (2003) ldquoThe impact of trade on intra-industry reallocations and aggregate industry
productivityrdquo Econometrica 71(6) 1695-1725
Nicolini M and Resmini L (2010) ldquoFDI spillovers in new EU member statesrdquo Economics of
Transition 18(3) 487-511
OECD (2016) ldquoThe productivity-inclusiveness nexus Preliminary versionrdquo OECD Publishing Paris
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Raff H and Schmitt N (2016) ldquoRetailing and international traderdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 157-179
Ratchford B T (2016) ldquoRetail productivityrdquo In E Basker (Ed) Handbook on the economics of retailing and distribution Edward Elgar Publishing Cheltenham UK 54-72
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Productivity differences in Hungary and mechanisms of TFP growth slowdown
151
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Appendix
152
APPENDIX
A3 Chapter 3 Internationally comparable data sources and methodology
A31 EU KLEMS amp OECD STAN
The EU KLEMS project aimed at creating a database on measures of economic growth productivity
employment creation capital formation and technological change at the industry level for all European
Union member states from 1970 onwards The database provides an important input to policy
evaluation in particular for the assessment of the goals concerning competitiveness and economic
growth potential as established by the Lisbon and Barcelona summit goals
The input measures include various categories of capital labour energy material and service inputs
Productivity measures have also been developed in particular with growth accounting techniques
Several measures on knowledge creation have also been constructed
The basic data of the EU KLEMS is also available in the OECD STAN database sometimes in a more up
to date version We have downloaded the following variables from there
- EMPE Number of employees
- EMPN Number of persons engaged ndash total employment
- SELF Number of self-employed
- VALU Value added current prices (millions of national currency)
- VALK Value added volumes (current price of the reference year 2010 millions)
- VALP Value added deflators (reference year 2010 = 100))
Labour productivity is defined as gross value added at constant prices divided by the number of
persons engaged In order to create comparative labour productivity levels we used the 2005
benchmark from the GGDC Productivity Level Database111 This project provides productivity levels
relative to the USA that can be used together with EU KLEMS growth accounts to create comparable
productivity level extrapolations (Inklaar and Timmer 2008 Inklaar and Timmer 2009)
A32 OECD Structural and Demographic Business Statistics
The OECD Structural and Demographic Business Statistics (SDBS) consists of two databases the
OECD Business Demography Indicators (BDI) and the OECD Structural Business Statistics (SBS)
The OECD Business Demography Indicators (BDI) database contains data on births and deaths of
enterprises their life expectancy and the important role they play in economic growth and
productivity The OECD Structural Business Statistics (SBS) database features the data collection
of the Statistics Directorate relating to a number of key variables such as for example value added
operating surplus employment and the number of business units broken down by ISIC Rev 4
industry groups referred to as the Structural Statistics on Industry and Services (SSIS) database and
by economic sector and enterprise size class referred to as the Business Statistics by Size Class (BSC)
database For most countries the main sources of information used in the compilation of structural
business statistics are business surveys economic censuses and business registers
111 More information can be found on the homepage of GGDC Production Level Database
httpswwwrugnlggdcproductivitypldearlier-release
Productivity differences in Hungary and mechanisms of TFP growth slowdown
153
The statistical population is composed of enterprises (or establishments when no data on enterprises
are available) In the case of BDI database the population contains all enterprises including non-
employers ie enterprises with no employees while the population of SBS contains only the employer
enterprises ie firms with at least one employee
Birth rate of all enterprises is the ratio of the number of enterprise births and the number of
enterprises active in the reference period Births do not include entries into the population due to
mergers break-ups the split-off or restructuring of a set of enterprises It does not include entries
into a sub-population resulting only from a change of activity (Source BDI)
Death rate of all enterprises is the ratio of the number of enterprise deaths and the number of
enterprises active in the reference period Deaths do not include exits from the population due to
mergers take-overs break-ups or the restructuring of a set of enterprises It does not include exits
from a sub-population resulting only from a change of activity An enterprise is included in the count of
deaths only if it is not reactivated within two years Equally a reactivation within two years is not
counted as a birth (Source BDI)
Number of enterprises is a count of the number of enterprises active during at least a part of the
reference period (Source SBS)
A33 OECD Productivity Frontier
The OECD productivity frontier dataset is based on AMADEUSORBIS and calculates comparable labour
productivity and TFP (MFP) measures across countries The project aims at defining the most
productive (frontier) enterprises both globally and for every country at the 2-digit industry level
(Andrews et al 2016)
Here we use data kindly provided by the OECD for the global and the Hungarian national productivity
frontier Two types of productivity measures are presented labour productivity and Wooldridge MFP
Both frontier series are defined as the average of log-productivity of the top 10 within each 2-digit
industry and year To make this measure less sensitive to expanding coverage over time the 10 is
chosen based on the median number of observations within a 2-digit industry The median for each 2
digit industry is calculated over all the years retained in the analysis
A key issue with AMADEUSORBIS with regard to Hungary is its changing coverage (see Box in Chapter
2) This makes these comparisons meaningful only from 20082009 onwards The underlying sample
includes all firms that over their observed lifespan had at least 20 employees on average
To arrive at internationally comparable real series 2-digit country specific industry value added and
investment deflators were used (2005 = 1) and the monetary values were converted to 2005 USDs
using industry level PPPs from the Groningen Growth and Development Centrersquos Productivity Level
Database112
112 For more information visit the Centrersquos homepage httpswwwrugnlggdcproductivitypld
Appendix
154
A4 Chapter 4 Evolution of the Productivity Distribution
Table A41 Average TFP growth with alternative TFP measures
A) Market economy
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 19 74 16 60
2006 93 119 95 97
2007 39 56 49 65
2008 -10 -04 -06 01
2009 -69 -82 -65 -63
2010 11 80 05 60
2011 34 40 31 45
2012 21 01 24 18
2013 30 22 22 22
2014 40 59 36 48
2015 52 49 50 43
2016 20 03 25 12
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Manufacturing
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 20 114 24 127
2006 114 149 118 137
2007 78 71 86 98
2008 17 -17 32 -11
2009 -133 -117 -120 -87
2010 80 173 85 178
2011 04 18 01 25
2012 -02 -58 07 -38
2013 -12 05 -15 16
2014 -01 27 01 34
2015 30 14 34 19
2016 04 -23 14 -05
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Productivity differences in Hungary and mechanisms of TFP growth slowdown
155
C) Market services
Year ACF translog Fixed effects
unweighted emp w unweighted emp w
2005 10 32 06 01
2006 79 90 82 64
2007 24 48 35 44
2008 -21 -03 -19 05
2009 -52 -71 -51 -54
2010 -11 26 -19 -05
2011 43 57 40 57
2012 30 48 31 57
2013 39 29 31 25
2014 46 78 39 55
2015 54 72 52 58
2016 25 20 29 23
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table presents growth rates of TFP estimated with the translog ACF estimator and the Fixed Effects
estimator for lsquomarket industriesrsquo (see Section 25) The sample does not include agriculture mining and financial
services Services include construction and utilities
Appendix
156
Table A42 Unweighted TFP growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 21 -02 -09 144
2006 118 143 58 47
2007 59 43 90 348
2008 -09 79 17 111
2009 -53 -191 -197 -139
2010 80 76 85 130
2011 -22 17 10 153
2012 01 14 -57 -06
2013 -38 20 -38 54
2014 -03 -05 08 33
2015 61 04 -19 132
2016 09 -10 12 91
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
B) Market Services
Year KIS LKIS Construction Utilities
2005 127 16 -01 -46
2006 166 75 94 66
2007 13 58 60 16
2008 -16 14 -37 -28
2009 -63 -94 -15 44
2010 54 12 -08 23
2011 97 46 77 -29
2012 12 74 06 -57
2013 12 30 60 -71
2014 78 89 65 -31
2015 106 70 22 12
2016 16 31 -47 37
Average
2004-2007 102 50 35 16
2007-2010 -08 -23 -22 02
2010-2013 40 57 29 02
2013-2016 53 55 24 01
Notes This table shows the unweighted average ACF TFP growth rate by technology category (see Section 25)
Only firms with at least 5 employees The sample does not include agriculture and financial services
Productivity differences in Hungary and mechanisms of TFP growth slowdown
157
Table A43 Employment-weighted labour productivity growth for different industry types
A) Manufacturing
Year Low-tech Medium-low Medium-high High
2005 172 32 73 300
2006 266 114 54 10
2007 121 52 69 243
2008 -25 -17 -03 126
2009 31 -151 -186 35
2010 135 114 199 207
2011 -33 -10 96 96
2012 03 -34 -32 -226
2013 -35 22 26 253
2014 33 19 53 94
2015 82 -04 -06 102
2016 34 18 08 -110
Average
2004-2007 186 66 65 184
2007-2010 47 -18 03 123
2010-2013 -21 02 24 35
2013-2016 28 14 20 85
B) Services
Year KIS LKIS Construction Utilities
2005 127 -05 41 -31
2006 166 75 21 54
2007 13 11 25 -36
2008 -16 -19 05 -02
2009 -63 -117 09 04
2010 54 -01 -05 13
2011 97 47 54 13
2012 12 62 19 -47
2013 12 21 62 -44
2014 78 55 64 -39
2015 106 54 07 65
2016 16 49 -60 43
Average
2004-2007 102 27 29 -04
2007-2010 -08 -46 03 05
2010-2013 40 48 24 -01
2013-2016 53 45 18 06
Notes This table shows the employment-weighted average LP growth rate by technology category (see Section
25) Only firms with at least 5 employees The sample does not include agriculture and financial services
Appendix
158
Table A44 The share of firms in the top decile ()
A) By size
2004 2007 2010 2013 2016
5-9 emp 1049 1051 1043 1096 1045
10-19 emp 954 962 92 904 92
20-49 emp 994 903 939 856 998
50-99 emp 896 1024 1188 1009 1096
100- emp 721 81 839 748 728
B) By ownership
2004 2007 2010 2013 2016
Domestic 833 818 814 824 837
Foreign 2344 2499 2422 2384 2488
State 554 728 81 575 695
C) By region
2004 2007 2010 2013 2016
Central HU 567 568 59 56 549
Northern
Hungary 195 116 19 208 224
Northern
Great Plain 161 178 239 23 249
Southern
Great Plain 137 118 17 258 179
Central
Transdanubia 276 33 332 369 332
Western
Transdanubia 311 283 244 361 444
Southern
Transdanubia 184 201 235 143 181
Notes Main sample
Productivity differences in Hungary and mechanisms of TFP growth slowdown
159
Figure A41 Persistence of top decile status
Notes This figure shows how many of top decile firms in year 2010 were frontier in 2013 how many exited and
how many continued as non-frontier The first panel shows this transition matrix for different 3-year periods
Appendix
160
A5 Chapter 5 Allocative Efficiency
Table A51 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3443 2878 -0565 4178 4479 0301 4163 4518 0355 4241 4409 0168
C - Manufacturing 5675 5668 -0007 5779 5864 0085 5916 6219 0303 5938 6147 0209
D - Electricity gas
steam and AC
6376 6949 0574 6132 6440 0308 6310 6681 0371 6291 7034 0743
E - Water supply
sewerage waste
6357 6788 0431 5933 6445 0513 6081 6578 0497 5855 6727 0872
F - Construction 6215 6384 0169 6176 6477 0301 6262 6453 0191 6411 6433 0023
G - Wholesale and
retail trade
6413 6573 0160 6497 6756 0259 6460 6759 0299 6727 7030 0303
H - Transportation
and storage
6303 5586 -0717 6145 5663 -0482 6094 5345 -0749 6196 5211 -0985
I - Accommodation
food service
6155 6347 0192 5925 6156 0231 5937 6418 0481 6328 6578 0250
J - Information and
Communication
6301 5674 -0626 6228 5956 -0272 6244 6278 0034 6598 6552 -0046
M - Professional
Scientific and Tech Act
6467 6429 -0038 6387 6490 0103 6455 6420 -0035 6691 6766 0075
N - Administrative and support service
6402 6698 0296 6404 6878 0475 6370 7299 0928 6571 7597 1026
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
161
Table A52 Allocative efficiency in TFP based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance unweighted
TFP
weighted
TFP
covariance
B - Mining and
quarrying
3563 4253 0690 4174 5801 1627 4080 6943 2862 4299 6991 2692
C - Manufacturing 5715 6856 1140 5795 7062 1267 5958 8580 2622 5992 8100 2109
D - Electricity
gas steam and
AC
6371 8325 1954 6246 8740 2493 6387 12670 6283 6177 12468 6291
E - Water supply
sewerage waste
6368 8298 1930 5914 7845 1930 5960 9136 3176 5846 8761 2916
F - Construction 6242 8765 2523 6183 8267 2084 6298 9577 3280 6504 8940 2436
G - Wholesale and
retail trade
6366 9258 2892 6373 9019 2646 6340 10597 4257 6614 9873 3260
H - Transportation
and storage
6255 7213 0958 6064 7041 0977 5980 7629 1648 6113 6889 0776
I -
Accommodation
food service
6209 10150 3942 5993 8265 2272 5990 10103 4113 6380 9279 2899
J - Information
and
Communication
6438 8174 1736 6231 8052 1820 6312 10443 4131 6664 10463 3800
M - Professional
Scientific and
Tech Act
6544 8764 2221 6365 8298 1933 6485 9932 3447 6754 9996 3242
N - Administrative
and support service
6308 9688 3380 6248 9186 2938 6160 11654 5495 6328 11367 5039
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
162
Table A53 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries firms with more than 5 employees
Year 2001 2005 2010 2015
Industry unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covari
ance
unweigh
ted LP
weighted
LP
covar
iance
unweight
ed LP
weighted
LP
covar
iance
B - Mining and quarrying 7509 8072 0564 8038 8583 0546 8378 9440 1063 8609 9028 0419
C - Manufacturing 7609 8136 0527 7762 8369 0607 7947 8775 0828 8016 8812 0796
D - Electricity gas steam and
AC
9208 10320 1112 9180 9859 0679 9373 10234 0861 9391 10588 1197
E - Water supply sewerage waste
8156 8782 0626 8149 8661 0512 8253 8784 0531 8255 8959 0703
F - Construction 7768 8130 0362 7669 8175 0507 7750 8090 0341 7954 8050 0096
G - Wholesale and retail trade 7955 8252 0297 8036 8452 0415 7955 8307 0352 8197 8589 0392
H - Transportation and
storage
8364 8475 0110 8300 8525 0224 8194 7698 -
0496
8292 7289 -
1003
I - Accommodation food
service
7404 8272 0868 7074 7828 0753 7021 7811 0790 7421 8072 0651
J - Information and Communication
8315 9062 0747 8284 9146 0863 8244 9387 1143 8549 9537 0988
M - Professional Scientific and Tech Act
8255 8513 0258 8171 8572 0401 8149 8529 0379 8368 8774 0406
N - Administrative and
support service
7760 7807 0047 7603 7550 -0053 7571 7662 0091 7835 8073 0238
Productivity differences in Hungary and mechanisms of TFP growth slowdown
163
Table A54 Allocative efficiency in labour productivity based on Olley-Pakes (1996) ndash 1 digit industries full sample
Year 2001 2005 2010 2015
Industry unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance unweighted
labour productivity
weighted
labour productivity
covariance
B - Mining and
quarrying
7539 11520 3982 7982 11003 3021 8288 13580 5292 8427 13784 5358
C - Manufacturing 7521 9579 2058 7520 9473 1953 7668 10917 3249 7785 10746 2960
D - Electricity gas
steam and AC
9140 12271 3132 9205 13334 4129 9200 17723 8522 8735 16024 7289
E - Water supply
sewerage waste
8095 10391 2296 8014 10044 2030 8047 11383 3336 8101 11165 3064
F - Construction 7560 10292 2732 7373 9273 1900 7456 10217 2761 7758 9917 2159
G - Wholesale and
retail trade
7734 10790 3056 7656 10152 2496 7546 11064 3518 7867 10903 3037
H - Transportation
and storage
8137 10473 2336 8010 9991 1981 7830 9988 2158 7993 9015 1022
I - Accommodation
food service
7249 12529 5280 6888 9652 2765 6816 10665 3849 7275 10638 3363
J - Information and
Communication
7917 11871 3954 7724 11079 3355 7675 13079 5404 8059 13321 5263
M - Professional
Scientific and Tech
Act
7925 10792 2867 7671 9983 2312 7652 11200 3548 7957 11387 3431
N - Administrative
and support service
7600 10409 2809 7453 9257 1804 7393 10724 3332 7692 10908 3216
Appendix
164
Table A55 Allocative efficiency based on Hsieh-Klenow (2009) ndash 1 digit industries
Distortions in 2001 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4802 1540 0299 0803 19127 1152
C - Manufacturing 5620 1300 0425 0818 12807 1008
D - Electricity gas steam and AC 6760 0503 0591 0456 6171 0784
E - Water supply sewerage waste 6629 0599 0103 1127 6245 1248
F - Construction 6706 0818 0280 0954 21186 1227
G - Wholesale and retail trade 7225 1088 0395 1007 21997 1211
H - Transportation and storage 6073 0984 -0154 1647 15193 1144
I - Accommodation food service 6201 0684 -0025 0919 7951 1263
J - Information and Communication 5499 1273 0549 0603 5387 1265
M - Professional Scientific and Tech Act 6961 0920 0253 1062 45052 1293
N - Administrative and support service 6778 1237 0084 1020 42372 1546
Productivity differences in Hungary and mechanisms of TFP growth slowdown
165
Table A55- continuedhellip
Distortions in 2005 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4211 1121 0269 0669 12217 0953
C - Manufacturing 5919 1173 0497 0890 13439 0998
D - Electricity gas steam and AC 6569 0880 0596 0553 6400 1181
E - Water supply sewerage waste 6433 0722 0091 1277 9084 1126
F - Construction 6794 0744 0155 0947 20440 1099
G - Wholesale and retail trade 7497 1199 0392 0771 20492 1543
H - Transportation and storage 6305 1063 0017 1205 11362 1232
I - Accommodation food service 6085 0660 0098 1287 5680 1239
J - Information and Communication 5867 1337 0608 0637 6375 1481
M - Professional Scientific and Tech Act 6926 0951 0129 1118 50400 1474
N - Administrative and support service 6904 1206 -0004 1055 47387 1649
Appendix
166
Table A55- continuedhellip
Distortions in 2010 Productivity Productivity dispersion
Median implicit sales taxes
Dispersion of implicit sales
taxes
Median implicit cost of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4219 0669 -0104 0759 11170 1012
C - Manufacturing 6024 1201 0523 0740 12732 1001
D - Electricity gas steam and AC 7260 1273 0813 0433 12091 1565
E - Water supply sewerage waste 6474 0700 0123 0965 13717 1279
F - Construction 6621 0775 0200 1075 30395 1437
G - Wholesale and retail trade 7471 1230 0310 0842 22833 1527
H - Transportation and storage 6517 1250 0123 1030 9632 1459
I - Accommodation food service 6080 0704 0001 1060 5570 1341
J - Information and Communication 5989 1245 0581 0870 11895 1572
M - Professional Scientific and Tech Act 7076 1042 0130 1077 78642 1486
Productivity differences in Hungary and mechanisms of TFP growth slowdown
167
Table A55- continuedhellip
Distortions in 2016 Productivity Productivity dispersion
Median implicit sales
taxes
Dispersion of implicit sales
taxes
Median implicit cost
of capital
Dispersion of implicit cost of
capital
B - Mining and quarrying 4484 0705 0264 0601 13655 0812
C - Manufacturing 6022 1110 0514 0971 11130 1074
D - Electricity gas steam and AC 7341 0966 0724 0307 36231 2054
E - Water supply sewerage waste 6363 0763 0015 1134 15926 1399
F - Construction 6938 0809 0298 0868 28761 1453
G - Wholesale and retail trade 7511 1005 0312 0959 26886 1576
H - Transportation and storage 6656 0972 0104 1078 16755 1745
I - Accommodation food service 6492 0672 0163 0943 6439 1443
J - Information and Communication 6211 1165 0422 0747 23648 1609
M - Professional Scientific and Tech Act 7188 0956 0148 1223 72383 1567
N - Administrative and support service 7112 1219 -0081 1109 98641 1801
Notes Total factor productivity is measured by the method of Ackerberg et al (2015) See Chapter 52 for details
Appendix
168
Appendix Figure 51 Weighted and unweighted labour productivity by 2-digit industry 2016 firms with at least 5 employees
Notes All points represent a 2-digit industry The horizontal axis shows its unweighted log labour productivity in 2016 while the horizontal axis shows its
weighted log labour productivity in the same year We have omitted industries with less than 1000 observations
Productivity differences in Hungary and mechanisms of TFP growth slowdown
169
Appendix Figure 52 The relationship between weighted and unweighted labour productivity by year
Notes This figure shows the fitted lines from regressions between weighted and unweighted labour productivity levels run at the 2-digit industry level
separately for 2005 2010 and 2016
Appendix
170
Appendix Figure 53 the change in allocative efficiency by 2-digit industry
Notes All points represent a 2-digit industry The horizontal axis shows the OP allocative efficiency (the differences between the weighted and unweighted
labour productivity) in 2010 while the vertical axis shows the same quantity in 2016
Productivity differences in Hungary and mechanisms of TFP growth slowdown
171
A6 Chapter 6 Reallocation
Table A61 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -93 -38 10 -65 -02 -10 50 -43
C Manufacturing 108 23 48 36 -02 -11 03 05
D Electricity gas 08 07 05 -04 26 -06 22 10
E Water supply sewerage 17 -17 31 03 08 -09 09 09
F Construction 26 04 08 13 -14 -02 -19 07
G Wholesale and retail trade 30 03 11 16 -55 -08 -65 18
H Transportation and storage -21 14 -43 08 -39 10 -57 08
I Accommodation 68 -07 53 22 -44 00 -37 -07
J ICT 96 10 63 23 29 -24 35 18
M Professional scientific 39 -13 35 17 -38 -04 -26 -08
N Administrative and support 104 11 37 56 -49 -02 -04 -43
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying 08 11 09 -12 41 60 -30 10
C Manufacturing -18 07 -30 05 10 -08 22 -04
D Electricity gas -26 26 -70 18 74 07 31 36
E Water supply sewerage -08 15 -05 -18 -04 05 00 -09
F Construction 42 03 26 13 01 06 -16 11
G Wholesale and retail trade 54 01 32 21 68 04 56 07
H Transportation and storage 89 14 51 25 21 -30 06 45
I Accommodation 85 -05 59 32 51 -03 46 08
J ICT 19 -02 11 10 47 -02 38 11
M Professional scientific 69 05 12 53 30 -04 18 16
N Administrative and support 50 00 36 14 106 01 87 18
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Appendix
172
Table A62 Decomposition of growth in TFP based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -253 -59 -19 -175 73 -02 39 36
C Manufacturing 105 20 51 34 06 -14 03 16
D Electricity gas 06 09 03 -06 14 -14 23 05
E Water supply sewerage 21 -12 32 02 -06 -09 00 03
F Construction 35 07 12 16 -23 -03 -24 04
G Wholesale and retail trade 27 06 06 16 -39 03 -59 17
H Transportation and storage -34 17 -58 06 -33 14 -58 11
I Accommodation 67 -09 50 26 -42 03 -39 -05
J ICT 85 14 39 32 27 -14 21 19
M Professional scientific 46 -07 28 25 -28 -03 -22 -03
N Administrative and support 122 29 28 65 -49 00 -09 -40
2010-2013 2013-2016
teaor_1d TFP growth Within Between Net entry TFP growth Within Between Net entry
B Mining and quarrying -08 11 07 -26 24 -03 06 22
C Manufacturing -12 06 -25 07 06 -04 11 -01
D Electricity gas 16 16 -15 15 30 00 25 06
E Water supply sewerage -07 15 -02 -20 -14 -01 02 -15
F Construction 45 03 22 20 10 02 -05 12
G Wholesale and retail trade 45 02 21 22 68 04 53 10
H Transportation and storage 85 13 45 27 75 00 08 66
I Accommodation 81 -04 54 30 51 -02 42 11
J ICT 13 00 00 13 49 10 33 06
M Professional scientific 64 08 11 45 32 00 14 18
N Administrative and support 50 08 15 27 80 19 49 12
Notes Total factor productivity is measured by the method of Ackerberg et al (2015)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
173
Table A63 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries full sample
2004-2007 2007-2010
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 93 24 44 26 105 12 59 34
C Manufacturing 132 34 54 44 08 19 -12 01
D Electricity gas 13 -04 09 08 41 02 25 14
E Water supply sewerage 45 -02 37 09 -08 -09 04 -03
F Construction 24 07 10 07 -01 07 -09 01
G Wholesale and retail trade 38 08 12 18 -67 -04 -73 10
H Transportation and storage -25 06 -28 -04 -47 03 -56 06
I Accommodation 59 -03 56 07 -74 -12 -41 -21
J ICT 58 19 80 -40 20 -21 40 00
M Professional scientific 61 11 34 16 -67 02 -26 -43
N Administrative and support 61 -20 38 43 -63 -24 -11 -29
2010-2013 2013-2016
LP growth Within Between Net entry LP growth Within Between Net entry
B Mining and quarrying 26 04 -01 24 -29 17 -28 -18
C Manufacturing 00 14 -21 07 33 16 19 -01
D Electricity gas -43 26 -85 16 90 25 13 52
E Water supply sewerage -20 -07 -08 -05 04 -03 06 01
F Construction 40 05 26 10 -03 05 -05 -03
G Wholesale and retail trade 49 05 31 13 68 13 57 -01
H Transportation and storage 59 12 46 01 09 -27 15 20
I Accommodation 74 -07 55 26 47 -08 54 02
J ICT 16 -07 11 12 22 -25 42 05
M Professional scientific 70 20 16 33 45 13 25 06
N Administrative and support 61 08 31 21 81 -11 76 15
Appendix
174
Table A64 Decomposition of growth in labour productivity based on Foster et al (2008) ndash 1 digit industries main sample
2004-2007 2007-2010
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 48 15 -01 34 70 17 53 00
C Manufacturing 132 32 56 45 16 14 -03 05
D Electricity gas 14 -03 05 11 35 -07 30 12
E Water supply sewerage 48 00 39 09 -10 -06 03 -07
F Construction 28 10 14 04 03 06 -14 11
G Wholesale and retail trade 38 10 07 21 -47 07 -61 07
H Transportation and storage -35 09 -40 -04 -41 06 -57 10
I Accommodation 62 -03 52 12 -65 -09 -43 -13
J ICT 00 -15 49 -34 03 -22 14 12
M Professional scientific 75 20 27 28 -46 02 -27 -22
N Administrative and support 91 -05 25 71 -60 -11 -07 -42
2010-2013 2013-2016
LP growth Within Between Net entry LP gtowth Within Between Net entry
B Mining and quarrying 33 -11 04 40 50 28 05 17
C Manufacturing 06 13 -15 07 28 12 16 00
D Electricity gas 16 18 -26 25 23 17 02 05
E Water supply sewerage -17 -06 -05 -05 04 -04 08 00
F Construction 44 05 26 14 03 02 07 -07
G Wholesale and retail trade 37 05 17 15 65 12 54 -01
H Transportation and storage 56 11 42 04 46 -07 16 36
I Accommodation 70 -07 52 25 44 -07 51 01
J ICT 26 07 04 16 17 -20 37 00
M Professional scientific 56 17 11 28 52 16 23 13
N Administrative and support 65 17 27 22 59 06 41 13
Productivity differences in Hungary and mechanisms of TFP growth slowdown
175
A7 Chapter 7 Firm-level productivity growth and dynamics
A71 Productivity growth
Table A71 Relationship between lagged productivity level and subsequent productivity
growth over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 -0188 -0203 -0203
(000550) (000558) (000551)
TFP in t-1 Year 2006 -0222 -0238 -0235
(000518) (000525) (000519)
TFP in t-1 Year 2009 -0143 -0159 -0155
(000570) (000579) (000572)
TFP in t-1 Year 2012 -0156 -0172 -0171
(000516) (000524) (000517)
Year 2003 -00313 -00297
(000507) (000510)
Year 2006 -0184 -0183
(000489) (000491)
Year 2009 -00766 -00762
(000492) (000493)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 114200 113900 113900
R-squared 0061 0067 0084
Appendix
176
Table A72 Relationship between lagged productivity levels and subsequent productivity
growth by size and age
Dep var TFP growth from t to t+3 (t=2012)
Firm categories by size age
VARIABLES (1) (2) (3) (4)
TFP in t-1 -0170 -0186 -0213 -0223
(000561) (000578) (00155) (00155)
TFP in t-1 Group 2 00397 00243 -000502 -000776
(00146) (00147) (00213) (00213)
TFP in t-1 Group 3 00793 00652 00725 00600
(00221) (00222) (00164) (00165)
TFP in t-1 Group 4 00753 00666
(00244) (00247)
Group 2 00227 000593 -0000410 0000118
(000940) (000963) (00162) (00162)
Group 3 00216 -000934 00235 00220
(00150) (00154) (00131) (00132)
Group 4 00235 -00351
(00157) (00169)
Industry FE YES YES
Industry-region FE YES YES
Firm-level controls YES YES
Observations 30135 30062 30135 30062
R-squared 0056 0073 0056 0073
Notes Size group 2 is firms with 20-49 employees size group 3 is 50-99 employees size group 4 is
100+ employees The baseline category is firms with 5-19 employees Age group 2 is firms of 4-5
years age group 3 is firms older than 5 The baseline category is firms of 2-3 years
Productivity differences in Hungary and mechanisms of TFP growth slowdown
177
Table A73 Differences in productivity growth by ownership group within different firm
groups
Dep var TFP growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 00476
(00114)
Foreign Non-exporter 00573
(00213)
Foreign Exporter 00610
(00139)
Foreign Size group 1 00295
(00162)
Foreign Size group 2 00849
(00243)
Foreign Size group 3 000361
(00340)
Foreign Size group 4 00662
(00318)
Foreign Age group 1 0119
(00381)
Foreign Age group 2 -00117
(00363)
Foreign Age group 3 00467
(00124)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 31642 31642 31642 31274
R-squared 0032 0033 0033 0033
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column 2 and size and age group dummies in columns 3 and 4 respectively
Appendix
178
Table A74 Relationship between lagged productivity levels and subsequent productivity
growth by ownership and exporter status over time
Dep var TFP growth from t to t+3 (t=2003200620092012)
Firm categories by
foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003
00577 00607 00141 00214
(00151) (00151) (00124) (00124)
TFP in t-1 Firm group Year 2006
00703 0101 00361 00558
(00152) (00152) (00118) (00118)
TFP in t-1 Firm group Year 2009
00338 00306 00450 00406
(00153) (00153) (00122) (00121)
TFP in t-1 Firm group Year 2012
00758 00436 00474 00321
(00146) (00146) (00109) (00109)
Firm group Year 2003
00978 00756 00286 000961
(00128) (00130) (000912) (000977)
Firm group Year 2006
-00290 -00145 -00592 -00411
(00133) (00135) (000871) (000932)
Firm group Year 2009
0114 0116 00502 00457
(00124) (00127) (000824) (000881)
Firm group Year 2012
0120 0120 00234 00155
(00126) (00129) (000782) (000835)
Year FE YES YES
Industry FE YES YES
Firm-level controls
YES YES YES YES
Region FE YES YES
Industry-year FE
YES YES
Observations 112374 112374 113900 113900
R-squared 0066 0085 0065 0085
Notes Firm group refers to foreign ownership in columns (1) and (2) and to exporter status in
columns (3) and (4)
Productivity differences in Hungary and mechanisms of TFP growth slowdown
179
A72 Employment growth
Table A75 Relationship between lagged productivity levels and subsequent employment
growth over time
Dep var employment growth from t to t+3 (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0113 0113 0113
(000472) (000478) (000475)
TFP in t-1 Year 2006 0120 0120 0119
(000434) (000439) (000437)
TFP in t-1 Year 2009 0109 0109 0107
(000479) (000485) (000482)
TFP in t-1 Year 2012 00982 00958 00956
(000442) (000448) (000445)
Year 2003 -00171 -00125
(000441) (000444)
Year 2006 -0134 -0128
(000422) (000423)
Year 2009 -00899 -00873
(000425) (000426)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 123900 123574 123574
R-squared 0042 0049 0054
Appendix
180
Table A76 Relationship between lagged productivity levels and subsequent employment
growth over time with alternative employment growth measures including exiting firms
Dep var employment growth from t to t+3 (including exiting firms (t=2003200620092012)
VARIABLES (1) (2) (3)
TFP in t-1 Year 2003 0156 0147 0148
(000641) (000647) (000644)
TFP in t-1 Year 2006 0134 0127 0128
(000581) (000587) (000584)
TFP in t-1 Year 2009 0139 0132 0134
(000648) (000655) (000651)
TFP in t-1 Year 2012 0132 0126 0127
(000618) (000624) (000621)
Year 2003 -00765 -00618
(000617) (000619)
Year 2006 -0220 -0211
(000586) (000587)
Year 2009 -0177 -0173
(000591) (000590)
Year FE YES YES
Industry FE YES
Industry-region FE YES
Firm-level controls YES YES
Region FE YES
Industry-year FE YES
Observations 143011 142638 142638
R-squared 0037 0047 0051
Productivity differences in Hungary and mechanisms of TFP growth slowdown
181
Table A77 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status with alternative employment growth measures
including exiting firms
Dep var employment growth from t to t+3 (including exiting firms t=2012)
VARIABLES (1) (2) (3) (4) (5) (6)
TFP in t-1 0134 0130 0134 0137 0134 0136
(000651) (000660) (000722) (000729) (000764) (000767)
TFP in t-1 Foreign -00109 -00138 00116 000347
(00166) (00167) (00289) (00289)
TFP in t-1 Exporter -00371 -00256 -00304 -00226
(00124) (00126) (00148) (00148)
TFP in t-1 Foreign exporter -00222 -00165
(00364) (00365)
Foreign -00254 -00351 -0102 -00739
(00151) (00156) (00254) (00256)
Exporter 00998 00982 00940 00889
(00100) (00102) (00106) (00107)
Foreign exporter 00855 00605
(00312) (00315)
Industry FE YES YES YES
Industry-region FE YES YES YES
Firm-level controls YES YES YES
Observations 34980 34980 35564 35473 34980 34980
R-squared 0031 0051 0037 0054 0034 0052
Appendix
182
Table A78 Differences in employment growth by exporter status within different firm
groups
Dep var employment growth from t to t+3 (t=2012)
VARIABLES (1) (2) (3) (4)
Exporter 00876
(000741)
Exporter Domestic 00893
(000788)
Exporter Foreign 00703
(00207)
Exporter Size group 1 00858
(000850)
Exporter Size group 2 00872
(00159)
Exporter Size group 3 0154
(00276)
Exporter Size group 4 00345
(00329)
Exporter Age group 1 00968
(00230)
Exporter Age group 2 0139
(00212)
Exporter Age group 3 00810
(000801)
industry-region FE YES YES YES YES
Firm-group indicators YES YES YES
Observations 34418 33909 34418 33989
R-squared 0034 0034 0034 0036
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
183
Table A79 Relationship between lagged productivity levels and subsequent employment
growth by ownership and exporter status over time
Dep var Employment growth from t to t+3 (t=2003200620092012)
Firm categories by foreign ownership exporter status
VARIABLES (1) (2) (3) (4)
TFP in t-1 Firm group Year 2003 000927 00131 00178 00190
(00129) (00130) (00107) (00107)
TFP in t-1 Firm group Year 2006 00137 00103 00130 000821
(00126) (00127) (00101) (00101)
TFP in t-1 Firm group Year 2009 -00778 -00676 -00498 -00426
(00129) (00130) (00104) (00104)
TFP in t-1 Firm group Year 2012 -00389 -00321 -00350 -00306
(00126) (00126) (000942) (000942)
Firm group Year 2003 -00601 -00332 000244 00299
(00110) (00113) (000795) (000856)
Firm group Year 2006 -00159 -000559 00640 00786
(00112) (00115) (000752) (000807)
Firm group Year 2009 00404 00249 0111 00882
(00106) (00109) (000714) (000767)
Firm group Year 2012 -00102 -00116 00747 00607
(00110) (00112) (000684) (000735)
Year FE YES YES
Industry FE YES YES
Firm-level controls YES YES YES YES
Region FE YES YES
Industry-year FE YES YES
Observations 121954 121954 123574 123574
R-squared 0046 0055 0045 0055
Notes Firm group refers to foreign ownership in columns (1) and (2) and exporter status in columns
(3) and (4)
Appendix
184
A73 Entry and exit
Table A710 Entry and exit premium by ownership and exporter status
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
VARIABLES (1) (2) (3) (4) (5) (6)
Entry Domestic 00363 00433 Exit Domestic -0165 -0161 Exit Non-exporter
-0172 -0186
(00103) (00102) (00112) (00112) (00122) (00121)
Entry Foreign 0414 0354 Exit Foreign 0255 0203 Exit Exporter
0171 0126
(00284) (00281) (00311) (00309) (00213) (00211)
Incumbent Foreign
0512 0461 Continuing Foreign
0465 0411 Continuing Exporter
0279 0232
(00122) (00129) (00123) (00131) (000887) (000926)
Industry FE YES Industry FE YES Industry FE YES
Industry-region FE YES Industry-region FE
YES Industry-region FE
YES
Firm-level controls YES Firm-level controls
YES Firm-level controls
YES
Observations 44231 44231 Observations 38367 38367 Observations 39020 38916
R-squared 0355 0383 R-squared 0339 0369 R-squared 0331 0370
Table A711 Differences in productivity levels by ownership group within different firm
groups
Depvar TFP in year t (t=2012)
VARIABLES (1) (2) (3) (4)
Foreign 0429
(00118)
Foreign Non-exporter 0278
(00206)
Foreign Exporter 0397
(00146)
Foreign Size group 1 0523
(00162)
Foreign Size group 2 0472
(00254)
Foreign Size group 3 0416
(00363)
Foreign Size group 4 0235
(00341)
Foreign Age group 1 0258
(00352)
Foreign Age group 2 0381
(00356)
Foreign Age group 3 0460
(00131)
Industry-region FE YES YES YES YES
Firm group indicators YES YES YES
Observations 38367 38367 38367 37822
R-squared 0350 0361 0353 0356
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is
50-99 employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is
firms of 4-5 years age group 3 is firms older than 5 years Firm group indicators refer to an exporter
dummy in column (2) and size and age group dummies in columns (3) and (4) respectively
Productivity differences in Hungary and mechanisms of TFP growth slowdown
185
Table A712 Entry and exit premium by ownership and exporter status over time
Depvar TFP in year t (t=2006200920122015 for entry and t=2003200620092012 for exit)
VARIABLES (1) (2) VARIABLES (3) (4) VARIABLES (5) (6)
Entry Domestic Year 2006
-00510 -00403 Exit Domestic 2003 -0187 -0188 Exit Non-exporter 2003 -0197 -0198
(000924) (000923) (00107) (00106) (00114) (00113)
Entry Domestic Year 2009
00244 00230 Exit Domestic 2006 -00996 -0101 Exit Non-exporter 2006 -0114 -0118
(000999) (000996) (000917) (000911) (000977) (000971)
Entry Domestic Year 2012
00594 00515 Exit Domestic 2009 -0105 -0113 Exit Non-exporter 2009 -0116 -0123
(000985) (000983) (000942) (000937) (00101) (00101)
Entry Domestic Year 2015
00475 00392 Exit Domestic 2012 -0140 -0150 Exit Non-exporter 2012 -0167 -0174
(000998) (000999) (00111) (00110) (00119) (00119)
Entry Foreign Year 2006
0374 0313 Exit Foreign 2003 0116 00940 Exit Exporter 2003 00659 00517
(00265) (00264) (00264) (00263) (00196) (00197)
Entry Foreign Year 2009
0423 0410 Exit Foreign 2006 0199 0153 Exit Exporter 2006 0194 0165
(00257) (00257) (00267) (00265) (00183) (00183)
Entry Foreign Year 2012
0342 0334 Exit Foreign 2009 0197 0184 Exit Exporter 2009 00720 00760
(00279) (00278) (00278) (00277) (00185) (00185)
Entry Foreign Year 2015
0382 0365 Exit Foreign 2012 0217 0223 Exit Exporter 2012 0114 0137
(00276) (00275) (00307) (00305) (00208) (00208)
Incumbent Foreign Year 2006
0485 0428 Continuing Foreign 2003 0416 0386 Continuing Exporter 2003 0278 0257
(00122) (00124) (00124) (00126) (000943) (000994)
Incumbent Foreign Year 2009
0410 0391 Continuing Foreign 2006 0498 0446 Continuing Exporter 2006 0317 0280
(00120) (00122) (00122) (00124) (000895) (000943)
Incumbent Foreign Year 2012
0436 0439 Continuing Foreign 2009 0414 0404 Continuing Exporter 2009 0194 0201
(00122) (00124) (00119) (00122) (000867) (000915)
Incumbent Foreign Year 2015
0471 0476 Continuing Foreign 2012 0412 0422 Continuing Exporter 2012 0211 0239
(00118) (00120) (00120) (00122) (000827) (000876)
Year FE YES Year FE YES Year FE YES
Industry FE YES Industry FE YES Industry FE YES
Firm-level controls YES YES Firm-level controls YES YES Firm-level controls YES YES
Industry-year FE YES Industry-year FE YES Industry-year FE YES
Region FE YES Region FE YES Region FE YES
Observations 164136 164136 Observations 155657 155657 Observations 157711 157711
R-squared 0369 0380 R-squared 0373 0386 R-squared 0374 0387
Table A713 Entry and exit premium by size and age
Depvar TFP in year t (t=2015 for entry and t=2012 for exit)
Firm categories by size age
VARIABLES (1) (2) VARIABLES (3) (4) (5) (6)
Entry Group 1 00233 00151 Exit Group 1 -0170 -0171 -0214 -0210
(00108) (00105) (00121) (00118) (00250) (00241)
Entry Group 2 0106 000987 Exit Group 2 -0201 -0260 -0286 -0260
(00298) (00289) (00280) (00272) (00271) (00261)
Entry Group 3 0124 00204 Exit Group 3 -0152 -0245 -0219 -0207
(00574) (00556) (00479) (00464) (00179) (00173)
Entry Group 4 0123 -00552 Exit Group 4 -0291 -0453
(00720) (00697) (00532) (00517)
Incumbent Group 2 00137 -00620 Continuing Group 2 -00108 -00902 -00277 -00256
(00104) (00101) (00111) (00109) (00170) (00164)
Incumbent Group 3 00163 -0130 Continuing Group 3 000582 -0148 -00759 -00758
(00170) (00168) (00179) (00176) (00131) (00127)
Incumbent Group 4 00150 -0268 Continuing Group 4 -00159 -0293
(00181) (00185) (00188) (00192)
Industry FE YES Industry FE YES YES
Industry-region FE YES Industry-region FE YES YES
Firm-level controls YES Firm-level controls YES YES
Observations 46160 46034 39020 38916 38459 38357
R-squared 0296 0355 0311 0369 0315 0374
Notes Size group 1 is firms with 5-19 employees size group 2 is 20-49 employees size group 3 is 50-99
employees size group 4 is 100+ employees Age group 1 is firms of 2-3 years age group 2 is firms of 4-5
years age group 3 is firms older than 5 years
Figure A71 Share of exiting firms in the subsequent 3 years by lagged productivity levels in
different periods
A8 Chapter 8 Retail
Appendix Table A81 Event study regression for the whole retail industry
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 0005 0014 0012 0030 -0230 -0511 (0006) (0005) (0005) (0005) (0030) (0049)
pre_trend_treated3 -0010 -0003 -0008 -0003 -0037 -0031 (0006) (0008) (0006) (0008) (0030) (0056)
pre_trend_treated4 -0020 -0010 -0014 0000 -0209 -0299 (0006) (0007) (0006) (0007) (0029) (0051)
pre_trend_treated5 0004 0011 0006 0017 -0128 -0258 (0007) (0008) (0007) (0008) (0034) (0064)
pre_trend_treated6 -0008 0001 -0004 0008 -0129 -0222 (0007) (0008) (0006) (0008) (0035) (0065)
pre_trend_treated7 0001 -0001 0016 0017 -0365 -0496 (0010) (0013) (0010) (0012) (0053) (0072)
trend_treated1 -0029 -0021 0041 0075 -1933 -2621 (0005) (0007) (0005) (0007) (0045) (0075)
trend_treated2 -0043 -0043 0042 0076 -2271 -3089 (0007) (0011) (0007) (0010) (0051) (0087)
trend_treated3 -0021 -0030 0070 0090 -2424 -3172 (0005) (0008) (0005) (0008) (0056) (0088)
trend_treated4 -0017 -0009 0059 0099 -2086 -2895 (0008) (0010) (0008) (0009) (0048) (0077)
post_trend_treated1 -0039 -0006 -0007 0044 -0885 -1394 (0012) (0012) (0012) (0011) (0061) (0096)
post_trend_treated2 0022 0003 0044 0048 -0665 -1273 (0012) (0012) (0011) (0011) (0068) (0100)
post_trend_treated3 -0001 0004 0035 0058 -0993 -1531 (0012) (0012) (0012) (0012) (0058) (0092)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 225866 209604 225860 209598 225908 209647
R-squared 0958 0978 0961 0980 0684 0809
Appendix
188
Appendix Table A82 Event study regression for NACE 4711
(1) (2) (3) (4) (5) (6)
Dependent lsales lsales sales_day sales_day days_open days_open
pre_trend_treated1 -0008 -0004 -0002 0016 -0189 -0576 (0004) (0005) (0004) (0005) (0033) (0055)
pre_trend_treated3 -0016 -0018 -0013 -0014 -0057 -0064 (0006) (0011) (0006) (0010) (0034) (0059)
pre_trend_treated4 -0010 -0005 -0002 0008 -0236 -0351 (0004) (0007) (0004) (0007) (0034) (0060)
pre_trend_treated5 -0004 0002 -0001 0010 -0129 -0304 (0006) (0008) (0006) (0008) (0037) (0068)
pre_trend_treated6 0011 0000 0018 0011 -0173 -0307 (0007) (0009) (0007) (0009) (0045) (0085)
pre_trend_treated7 -0016 -0032 0002 -0007 -0433 -0640 (0010) (0016) (0009) (0015) (0068) (0091)
trend_treated1 -0017 -0034 0058 0079 -2059 -3065 (0005) (0006) (0005) (0006) (0053) (0078)
trend_treated2 -0039 -0065 0049 0067 -2363 -3518 (0007) (0013) (0007) (0012) (0059) (0094)
trend_treated3 -0021 -0047 0075 0086 -2580 -3593 (0006) (0009) (0006) (0009) (0061) (0082)
trend_treated4 -0022 -0044 0067 0086 -2379 -3482 (0007) (0011) (0007) (0009) (0057) (0079)
post_trend_treated1 -0009 -0032 0033 0036 -1163 -1875 (0008) (0012) (0008) (0011) (0084) (0118)
post_trend_treated2 0057 -0024 0087 0041 -0888 -1810 (0014) (0013) (0012) (0012) (0097) (0121)
post_trend_treated3 0014 -0031 0060 0044 -1255 -2040 (0011) (0013) (0010) (0012) (0079) (0108)
Shop FE yes yes yes yes yes yes
Firm-year FE no yes no yes no yes
Observations 94740 87533 94737 87530 94740 87533
R-squared 0968 0982 0973 0985 0642 0809
Appendix Table A83 Sales and the number of different days in a month
(1)
Dependent ln sales
Sunday 0049 (0001)
Saturday 0059 (0001)
Friday 0054 (0001)
Thursday 0050 (0001)
Wednesday 0053 (0001)
Tuesday 0060 (0001)
Monday 0048 (0001)
holiday 0008 (0000)
Jan -0169 (0002)
Dec 0138 (0003)
summer 0032 (0002)
date -0000 (0000)
Observations 463345
R-squared 0970
Appendix
190
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