polarizing growth, equalizing recession: technologies and ... · polarizing growth, equalizing...

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Polarizing growth, equalizing recession: technologies and skills in European

employment

Valeria Cirillo, Leopoldo Nascia, Mario Pianta

Abstract

The aim of this work is to shed light on employment polarization considering employment changes between sectors

and upskilling/downskilling trends within sectors. In the long run (1999-2011), an important part of upskilling

registered in manufacturing is also explained by structural change, that is employment movement toward services

and employment contraction in manual workers. We detail the analysis considering the relevance of cycles on

employment and polarization investigating the relationship between polarization trends and technology in Europe in

upswings (2002-2005) and downswings (2006-2011). We consider technology as a differentiated process focusing on

product and process innovations. We analyze polarization firstly applying a within-between decomposition in order

to account for intersectoral and intrasectoral employment variations. Then, we use an econometric model to

investigate the impact of different technological strategies on polarization trends. In order to account for economic

cycles, we split the sample in two periods 2002-2005 and 2006-2011. We found a significative and positive impact of

cost-competitiveness technological strategies on polarization and a negative impact of product-based innovations on

polarization trends during the upswing. On the contrary, during downswings most innovations are process-oriented

and they have a negative impact on employment due to restructuring plans performed by firms. We detect an

overall increase in polarization during upswings for both manufacturing and services which is reverted during

downswings basically due to drastic cuts in manual workers in manufacturing industries. Services continue to

polarize also during downswings.

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1. Motivation and Conceptual Framework

The aim of this piece of work is to shed light on the polarization trends detected in Europe in the last decade. We

focus on the relationship between innovation and employment at sectoral level considering professions, cycles and

technologies.

Professions matter

The relationship between technology and employment cannot be studied regardless of the skill

composition of the workforce. Most of approaches have focused on the effects of innovation on the quality

of employment. When they assume equilibrium in the labor market, they investigate the impact of

innovation on the relative composition of the labor supply. From this point of view, technological

unemployment does not exist and the only impact of technology on jobs is related to a specific demand for

skills/unskilled workers. The introduction of technology modifies the skill composition of employment

through a demand/supply effect.

Under this framework a large literature mainly from US and UK has recognized a skill-biased technical

change focusing on the increasing gap in terms of compensation/demand of skilled/unskilled workers.

According to this literature, the introduction of innovation broadly defined has favored skilled rather than

unskilled workers. Two main reasons have been recognized to explain the increasing demand for skilled

workers: trade and technology.

Focusing on the latter, technology has been seen as a complement for human capital and assumed to take

a factor-augmenting form complementing high skilled workers. The job opportunities for blue collars in the

labor market worsen and the resulting inequality is presented as a 'natural' effect of technological change.

In a standard demand/supply framework, the adoption of a new production technique requiring skilled

workers increases their relative demand compared to unskilled workers. If there is a shift upward of the

supply curve of human capital (skilled workers), the initial skill premia should be reduced. This framework

is applied to explain changes in returns to skills and evolution of earnings inequality and it is known in

Labor Economics as “canonical model”. It assumes the presence of two different skill groups performing

different and imperfectly substitutable tasks or producing two imperfectly substitutable goods (Acemoglu,

Autor, 2011). According to this perspective, the return to skills is determined by a race between the

increase in the supply of skills in the labor market and technical change assumed to be skill biased (Goldin

and Katz, 2008). Among others, Katz and Murphy (1992), Autor et al. (1998, 2008), Carneiro and Lee

(2009), Katz et al. (1995), Davis (1992), Murphy et al. (1998), Card and Lemieux (2001), Fitzenberger and

Kohn (2006) have supported this approach.

The canonical model has been widely used by labor economists to explain wage inequality trends during

the 1980s in developed countries, but the theory fails to give a proper answer to inequality trends during

the 1990s, when employment growth was polarized with employment share of high-skilled and low-skilled

occupations expanding, and employment share of middle-skilled occupations contracting. Furthermore,

the canonical model does not account for other important evidences such as declines in real wages of low

skill workers, rapid diffusion of new technologies directly substituting capital for labor in tasks performed

by moderately skilled workers, expansion of offshoring allowing foreign labor to substitute for domestic

workers in specific tasks (Acemoglu, Autor, 2011, p. 1044). The new theory based on these empirical

patterns is called “job polarizarion” and it has widely substituted and augmented the canonical model in

order to make it more suitable to explain new trends in labor market.

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Besides the task-based framework formally systematized by Acemoglu and Autor (2011), an increasing

branch of literature has recognized the rise of polarization in labor market.

In terms of evidence, Dustmann et al., 2008, found evidence for increasing wage inequality and

polarization for Germany; Machin, 2011, for UK; Centeno and Novo, 2009, for Portugal; Michaels, Natray,

Van Reenen, 2010, for Europe as a whole using EU-KLEMS aggregate data. In the empirical literature, it is

quite commonly recognized a polarization trend both for employment and wages however, different

explanations have been proposed to account for it (routinization, consumption spillovers,

The routinization hypothesis is actually a refinement of the skill-biased technical change explanation and it

strictly derives from the task-based framework described above. It has been proposed by Autor, Levy and

Murnane (2003), according to it routine workers have been replaced by machines contrary to abstract jobs.

This explanation mostly relies on the introduction of information technology (ICT) experimenting a secular

decline in real costs and for then incenting employers to substitute expensive labor for new computers.

The result is a contraction of the employment in the middle of the wage distribution, or better by middle

skilled workers (clerical and administrative occupations) performing cognitive and manual jobs, “routine

jobs”.

Some authors focus on consumption spillovers as main driver of polarization (Manning, 2004, Mazzolari

and Ragusa 2012, Leonardi, 2010). Mazzolari and Ragusa (2012), for example, working on census data

show that since 1980 in the United States low skill workers have been increasingly employed in the

provision of non-tradeable time-intensive services. The idea is that skilled workers demand more of these

time-intensive services, then wage gains at the top of the wage distribution are expected to raise the

consumption of these services. According to this hypothesis, a sort of “trickle-down effect” should be

verified due to the wage gains experienced by skilled workers that might affect also low-skill labor markets.

This approach is complementary to the skill-biased technological change hypothesis because it recognizes

the employment and earning opportunities experienced by skilled workers during ’80 and it justifies the

employment and wage growth of unskilled workers by a consumption spillover effects during ’90. Sharing

this idea, Manning (2004) emphasizes the dependence of unskilled employment opportunities to physical

proximity of skilled more likely to buy in service time in order to free themselves from home production

tasks. Basically, this branch of studies focus on cleaning, restaurant work and other low-skill jobs on which

technology has little impact (Baumol, 1967).

Other authors rely on trade (Epifani and Gancia, 2008, Thoenig and Verdier, 2003) as a main force to

explain wage inequality. Thoenig and Verdier (2003) investigate a model of defensive skill-biased

innovation, according to it firms have incentives to increase the share of tacit knowledge and non-codified

knowhow embedded in their production processes at the cost of a larger share of skilled labor in their

workforce. Their model incorporate the possibility of endogenous technical change and emphasizes the

emergence of technical bias as an optimal response by firms to the problem of anticipated predation on

their monopoly rents (Thoening M., Verdier T., 2003, p. 21).

Finally in terms of offshoring, Feenstra and Hanson (2003) found that both foreign outsourcing and

expenditures of high-technology equipment can explain a substantial amount of the increase in the wages

of nonproduction (high-skilled) relative to production (low-skilled) workers that occurred during the 1980s.

Blinder and Krueger (2009) measure the “offshorability” of jobs, defined as the ability to perform the work

duties from abroad. They develop multiple measures of offshorability, using both self-reporting and

professional coders. All the measures find that roughly 25% of U.S. jobs are offshorable, on the contrary

routine work is no more offshorable than other work. Offshorability negatively impact on employment

demand.

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In a recent work, Goos, Manning and Salomons (2011) shed light on the changing structure of employment

quantifying direct and indirect effects due to technology, offshoring and institutions. Their main conclusion

is that routinization hypothesis has the most explanatory power for understanding job polarization but still

offshoring plays a role, on the contrary institutional effects are relatively unimportant.

All the literature analyzed till now starts from one common point: the equilibrium framework. The input

prices are based on the demand and supply existing for them; in this sense, there is a strict relation

between employment by skills, tasks and wages. The job polarization thesis is then used to explain the

increasing wage inequality registered in the last years.

Among the authors focusing on role of institutions on shaping inequality trends, Firpo, Fortin, Lemieux

(2011) analyze how occupations contribute to the wage structure trough offshoring and deunionization.

Starting the analysis questioning how much of the change in the wage distribution can be accounted by

occupation-based explanations, they found that technological change and de-unionization played a central

role in the 1980s and 1990s, while offshorability (possibility to perform same jobs abroad) is the main

factor of wage inequality during 2000s. In this sense, the task content framework is partly enriched by

elements such as de-unionization and effect of globalization, totally absent in the first version of the

model. In a wage setting model, the price of skills is influenced by technological change and offshoring,

allowing returns to skill to vary across occupations.

The influence of institutions and labor market factors is clearly depicted in Di Nardo et al. (1996). Applying

an Oaxaca decomposition on the wage density over the period 1979-1988, they found that the declining

minimum wage over the period 1979-1988 had a large impact on the distribution of wages, as well as de-

unionization and demand and supply shocks, concluding that institutions are important as supply and

demand considerations in explaining changes in the U.S. distribution of wages from 1979 to 1988.

Considering both institutional factors and patterns of technological change, Croci Angelini et al. (2009)

analyze the impact of both elements on wage dispersion by industry, particularly criticizing the

“operationalization” of technology as a one-dimensional process. From a neo-Schumpeterian approach, in

industries with a greater innovative efforts in new products, it is likely to experience higher wages for

managers and high-skilled workers due to temporary rents realized by the firm. On the contrary, in those

industries characterized by technological diffusion such as introduction of new processes, low/medium

skilled workers can obtain higher wages in relative terms. Their analysis of wage polarization and wage

dispersion is mainly based on the typology of technologies activities carried out at industrial level. A wage

dispersion effect is associated to the introduction of new products and new markets, on the contrary a

pattern of wage compression is related to the introduction of new machinery and process innovation. They

also control for labor market dynamics.

Recessions matter

Neoclassical perspectives have given limited attention to the impact of business cycles on growth. Real

business cycles approaches and endogenous growth theory have developed diverging explanations of the

sources of fluctuations and growth, paying attention, in very different ways, to the role of technology (Galì,

1999; Gaggl and Steil, 2007). According to endogenous growth studies, downswings can stimulate

productivity and foster long-term growth because inefficient firms are crowded out, raising the rate of

productivity growth of the whole economic system (Caballero and Hammour, 1991, Aghion and Saint-Paul,

1998). On the other hand, a short-term rise in output is able to increase long-run productivity and

employment. By emphasizing the pro-cyclical character of technological change, business cycles can

reinforce growth during an upswing, increasing the stock of capital devoted to training and learning

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(Blackburn and Pelloni, 2004). As described by Stiglitz (1993), upswings can overcome financial constraints

that prevent firms from borrowing resources to innovate. In fact, when firms are credit constrained, they

need to use their own profits to finance R&D and innovation. Others models describe a process of creative

destruction where the continuous improvement of the quality of innovations generates growth by

substituting old technologies and reinforcing the process of diffusion of innovation (Aghion and Howitt,

1992).

Evolutionary perspectives have developed a different view. Business cycles have been at the center of the

work of Schumpeter. A large body of literature has explored, building on his approach, the link between

innovation, long economic cycles and employment. For Schumpeter, innovation activity is typically

uncertain and discontinuous and the process of expansion is, in turn, uneven and unbalanced. This

irregularity is transmitted to investments and employment, which expand in response to technological

change (Schumpeter, 1934). While Schumpeter was interested in the unfolding of innovations and their

impact on growth, the notion of long waves has fuelled a debate on the occurrence of clusters of radical

innovations. For Mensch (1979), innovations bunch during depression phases: in upswings, firms do not

have incentives to introduce new products because they can exploit rents from a higher demand for

existing products; in a downswing, expected profits are lower and introducing innovations appears as a

more attractive strategy. Kleinknecht (1982) emphasized the role of depressions in stimulating innovations,

although the evidence is uncertain.

Starting on these considerations, Lucchese and Pianta (2012) explore the way economic cycles influence

the relationship between innovation and employment in manufacturing industries. In particular, their

results show that in upswings employment change is affected by new products, exports and wage growth.

On the contrary, during downswings new processes contribute to restructuring and job losses. Building on

Lucchese and Pianta (2012), we break the assumption of stable relationships in periods of growth and

recession underlying how cycles matters in shaping job losses and new jobs across skills, the role of

demand and, most of all, the links between different types of innovation and jobs.

Technologies matter

A large literature has explored the impact of innovation on employment at the firm and sectoral levels.

Innovation is generally identified as a key factor shaping the dynamics of employment and an important

distinction is usually made between the employment effects of product and process innovation: the first is

able to lead to new jobs, while a negative employment impact is often found for the second.

Starting from the distinction between product and process innovation in Schumpeter (1934), Pianta (2001)

identifies two different types of strategies, technological and cost competitiveness: the former is

associated with the search for product innovation or a better quality of products and a general orientation

towards new markets; the latter is tied to the search for labour-saving process innovation, price

competitiveness and flexibility in production. These categories characterize the main orientation of sectors

in terms of the nature of innovative efforts and can be used in order to appreciate the impact of innovation

on value added, employment and productivity. In Bogliacino and Pianta (2010), the differences in

technological change are studied through the introduction of a Pavitt taxonomy extended to services.

Different mechanisms of job creation are identified for each class, showing that technological

competitiveness has a positive impact on employment in high technology industries, while employment

losses prevail in traditional sectors, more oriented towards a cost competitiveness strategy. Evangelista

and Savona (2003) found similar results on the role of innovation on employment in services in Italy, where

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job creation occurs in small, technology driven firms. Demand expansion emerges as a key factor for

employment growth, while process innovation has mainly led to job losses.

From this perspective, Croci Angelini, Farina, Pianta (2009) analyze the polarizing effects of technological

change on wages underling the impact that different technological strategies have. They found higher

wage polarization in industries with strong product innovation, on the contrary wage compression is

associated to the diffusion of process technologies.

Given these contributions, we aim to investigate polarization in terms of employment focusing on the

different impact played by technology. We also consider value added as a proxy of demand and wages

(labour costs).

As underlined in previous works (Nascia and Pianta, 2009; Lucchese and Pianta, 2011; Bogliacino, 2009),

demand has on average a strong positive impact on employment change. This effect can vary according to

professional categories.

According to the general neoclassical framework, we also expect wages negatively impacting on

employment change. As for the demand, we expect a different impact according to professional

categories. Manual workers could be more affected by change in wages than managers.

Finally, we also expect education having an important weight on polarization. Literature has extensively

focused on returns on education and on average we expect higher education positively influencing

employment change. The positive effect of education is probably different by professional category.

The relationship between technology and job polarization is investigated in this article with a model and an

empirical test where employment polarization in European industries is explained by innovation, demand,

wages and other factors.

The paper is organized as follows: in Section 2 we describe data and we present some descriptive analysis

in order to shed light on polarization dynamics, Section 3 presents the model and the econometric

strategy, in Section 4 we outlined the main results and Section 5 provides a conclusion.

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2. Data and descriptive analysis

The analysis is carried out at two different levels. We present a descriptive analysis based on a POL index

constructed on ISCO classification and the results of a shift share analysis applied on employment variations by

professional category.

Data

In order to identify the role of innovation on employment during the economic cycle and explain polarization trends,

we use industry-level data from the Urbino Sectoral Database (USD) developed at the University of Urbino. It

combines data on innovation, drawn from the second (2000-2002), fourth (2002-2004) and sixth (2006-2008)

Eurostat Community Innovation Survey (CIS) with other international sources of data at the sectoral level of analysis.

The result is a comprehensive dataset that is able to highlight the role of innovation and the dynamics of structural

change in major European economies from 2002 to 2005 and from 2006 to 2011.

The specific dataset used for this work is based on the match of Eurostat CIS data and the 2010 OECD Structural

Analysis (STAN) database. Due to the structural nature of the relationship we study and to the different sources of

data, countries have been selected in terms of the greatest available coverage of sectors and data reliability.

Countries included in the analysis are Germany, France, Italy, Spain and United Kingdom. The dataset covers 21

manufacturing sectors, from 15 to 37 NACE REV.1 and 15 service sectors. The sectors included in the analysis and the

relative NACE Rev.1 codes are presented in Table 1. Due to the period of analysis, we should convert our CIS data for

2009 expressed in NACE REV.2 classification in NACE REV.1 in order to guarantee comparability in our dataset. Data

conversion was realized through a complex procedure based on a conversion matrix weighted on employment share

of each sector. To realize it, we use micro data on Italian firms by sectors at four digit level1.

Finally we use as a source of profession and education data the Labour Force Survey (LFS). We aggregate professions

data according to ISCO88COM nomenclature, creating 4 macrogroups, as shown in table 1. As education variables,

we apply ISCED nomenclature (lower secondary education, upper secondary education and university and post

university education). As education variables, we apply ISCED nomenclature (lower secondary education, upper

secondary education and university and post university education).

Table 1. Employment by professional categories

PROFESSIONAL CATEGORIES USED ISCO88COM CLASSIFICATION

MANAGERS LEGISLATORS, SENIOR OFFICIALS AND MANAGERS, PROFESSIONALS, TECHNICIANS AND ASSOCIATE PROFESSIONALS

CLERKS CLERKS, SERVICE WORKERS, SHOP AND MARKET SALES WORKERS

CRAFT WORKERS SKILLED AGRICULTURAL AND FISHERY WORKERS, CRAFT AND RELATED TRADES WORKERS

MANUAL WORKERS PLANT AND MACHINE OPERATORS,ASSEMBLERS ELEMENTARY OCCUPATIONS

CIS data are able to describe the various patterns of technological change through a wide range of measures on the

innovative activities of sectors that overcome the role of traditional indicators such as R&D and patenting. CIS

variables considered in this paper include: the share of turnover due to a new or improved products, the share of

firms for which the sources of innovation come from suppliers of equipment and materials; real expenditures per

employee, which is associated with the acquisition of innovative machinery or equipment. The quality and reliability

of these data have been checked through quality controls on micro data that are described in Pianta, Lucchese and

Supino (2012).

1 The detailed list of sectors is attached in the Appendix.

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Economic variables from OECD STAN include industry-level measures on performance, employment,

competitiveness from 1970 to 2007. In order to explore industry-level dynamics of employment we select industry

data on the number of employees (thousands). All monetary values are expressed in euros at constant prices (2000)

in terms of purchasing power parity.

Descriptive analysis

In the long run job creation has not been uniform across sectors, it has been more concentrated in services.

Manufacturing has experienced a sharp decrease in employment along the period. As reflected in graph 1, we

register two main periods of employment contraction in 2002-2003 and 2009-2011. This is reflected both in services

and manufacturing.

Source: LFS

The different growth of sectors brings about a change in the qualitative composition of composition of jobs offered

as already suggested by Bogliacino et al. (2011). Looking at the composition of employment in 1999 and 2011, we

find a change in employment composition towards upskilling in manufacturing and polarization in services. In

manufacturing we register a sharp increase in managers compared to other professional categories leading to

upskilling in the workforce composition. In services, we register a different pattern basically related to employment

polarization with an increase in managers and manual employment and a decrease in clerks and craft workers.

Overall this seems to confirm the international evidence of employment polarization detected by the literature.

Furthermore, comparing for the same year the workforce composition of manufacturing and services, we detect in

services a higher share of Managers and Clerks while Craft and Manual Workers constitute the greater part of

Manufacturing. This is verified for both periods, 1999 and 2011. Additional evidence on the rates of change of

employment by professional category in the two periods is provided in the Appendix.

-4

-3

-2

-1

0

1

2

3

4

5

6

2000 2002 2003 2005 2006 2009 2011

Fig 1. Average Annual Rates of Growth of Employment from 1999 to 2011. Pool of countries.

POOL TOT

POOL SERV

POOL MANUF

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Source: PED Database

Country differences are significative. In Germany we detect an increase of employment polarization concentrated in

services contrasting upskilling in manufacturing. In Spain and France, we register polarization of employment both in

services and manufacturing, the overall increase in employment is basically concentrated in the manager category.

Finally, in Italy and United Kingdom, employment polarization is basically registered in services. The detailed graphs

by country are provided in the Appendix. Each of them describes the workforce composition of employment in 1999

and 2011 by macro sectors.

The Polarization Index

In order to summarize the main dynamics detected in our employment data and described in the graphics above, we

build a synthetic index. The POL index used is the ratio of the sum of managers and manual workers category minus

crafts workers and clerical workers over managers and manual workers plus crafts workers and clerical workers. In

formula we have:

( ) (1)

where i is the sector at 2 digit (NACE Rev.1) and t corresponds to the year (1999, 2000, 2002, 2003, 2005, 2006,

2009, 2011). It involves all the 4 skill categories and it focuses on the mainly positive weight variation of the more

skilled workers (managers) and the less skilled ones (manual workers) over intermediate skill based workers (clerical

and craft workers). The index has been weighted according to the employment of the base year (1999). In the

Appendix is also available the non-weighted version of the index. As summarized by the graph below, we register an

overall increase in polarization along the period which is basically concentrated in services. As underlined by the

graph, also manufacturing has been polarizing employment till 2006, after then the trend is reverted and

polarization decreases. This is probably due to the decrease in employment registered in manufacturing after 2006

and concentrated in manual workers. Making reference to the employment share of 1999, we are able to detect the

overall movements of skills along the period holding fixed the employment weight at 1999.

0

20

40

60

80

100

MANUFACTURING MANUFACTURING SERVICES SERVICES

1999 2011 1999 2011

31,93 25,11 16,91 18,49

30,55 27,32

7,62 5,88

13,11

13,42

45,32 41,68

24,31 34,15 30,15 33,96

Fig.2 Employment composition by professions and sectors. Pool of countries.

MANAGERS

CLERKS

CRAFT

MANUALS

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Source: PED database

Structural change or Upskilling? A decomposition analysis

From the analysis above, we detect an increase in polarization which is basically concentrated in services along the

entire period. In this section, we aim to analyze how much of the shift in high and low skills is due to intersectoral

movements of empoyment between sectors or intrasectoral change toward upskilling or downskilling in the sector.

The total variation of employment for each professional category

in the period 1999-2011 has been decomposed

applying a standard shift share analysis as decribed by the following formula (Guarini, Tassinari, 2000, p. 237) :

∑

∑

where the first element is the growth rate of employment at macro level (in our case pool of European countries and

professional categories), the second term is the structural component ( and the

third term is the sectoral component.

Table 1. Shift Share macro sectors

TREND COMPONENT

STRUCTURAL COMPONENT

SECTORAL COMPONENT

TOTAL CHANGE IN SKILLS

MANAGERS 9,63% 1,97% 22,36% 33,96%

CLERKS 9,63% 9,25% -8,21% 10,67%

CRAFT 9,63% -12,89% -14,48% -17,74%

MANUALS 9,63% -6,17% -4,94% -1,48%

In the first table, we consider two macro sectors (manufacturing and services) and four professional categories. The

aim of the analysis is to detect how much of the employment growth of each category can be explained by sectoral

components or structural movements of employment from one macro sector to another.

Managers’ job growth is basically explained by sectoral movements. The overall variation of managers in the whole

economy registered in the period 1999-2011 (33,96%) is explained for 22,36% by an intrasectoral increase in

managers leading to an upskilling trend at sectoral level. Clerks’ job growth is basically explained by structural

1,03 1,08

1,20 1,22 1,30 1,34 1,36

1,32

0,51 0,52 0,54 0,55 0,56 0,58 0,53 0,50 0,54 0,57

0,67 0,69 0,76 0,78

0,85 0,83

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1999 2000 2002 2003 2005 2006 2009 2011 1999 2000 2002 2003 2005 2006 2009 2011 1999 2000 2002 2003 2005 2006 2009 2011

Fig. 3 Weighted Polarization index (1999). Pool of countries. Years 1999-2011.

TOTAL

MANUFACTURING

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movements of employment meaning increase in employment basically in sectors with high proportion of clerks. This

trend is counterbalanced by negative clerks employment growth at sectoral level. The final effect is a positive

variation of clerks employment due to job growth concentrated in “high-clerks” sectors.

The negative performance of craft workers is due to a sectoral and a structural effect. Both movements are negative

and equally sized.

We also register a decline in manual workers at sectoral and structural level. The positive growth of manual workers

in services does not counterbalance the negative performance of manual jobs both at sectoral and structural level.

In order to provide a more precise picture of employment change by skills and sectors, we apply the simple

decomposition algorithm used by Berman, Bound and Machine (1998) and Landesmann et al. (2009) described in

formula 2:

∑ ̅

∑ ̅

∑

where the aggregate change in skill composition as proportion of the skill in the economy as a whole in the period

1999-2011 has been decomposed in a between component capturing employment shift towards sectors with

an high initial level of the analyzed skill and a within effect due to skill upgrading or downgrading in the same sector

. In table 2, we report the results for each sector and professional category.

Table 2. Decomposition details MANAGERS CLERKS CRAFT MANUALS

Sectors Between industry

Within industry

Between industry

Within industry

Between industry

Within industry

Between industry

Within industry

FOOD PRODUCTS -0,03% 0,21% -0,04% 0,13% -0,04% -0,22% -0,06% -0,12%

TEXTILES -0,18% 0,13% -0,11% -0,02% -0,22% -0,08% -0,43% -0,02%

WEARING APPAREL, DRESSING AND DYEING OF FUR -0,12% 0,11% -0,10% 0,05% -0,23% -0,14% -0,25% -0,02%

LEATHER AND LEATHER PRODUCTS AND FOOTWEAR -0,04% 0,04% -0,04% 0,00% -0,15% -0,07% -0,10% 0,03%

WOOD AND PRODUCTS OF WOOD AND CORK -0,04% 0,05% -0,04% 0,03% -0,14% -0,04% -0,10% -0,03%

PULP, PAPER AND PAPER PRODUCTS -0,06% 0,03% -0,03% 0,00% -0,05% 0,03% -0,15% -0,07%

PRINTING AND PUBLISHING -0,18% 0,32% -0,07% -0,01% -0,10% -0,14% -0,07% -0,18%

COKE, REFINED PETROLEUM PRODUCTS AND NUCLEAR FUEL

-0,04% 0,03% -0,01% -0,02% -0,01% -0,01% -0,02% 0,00%

CHEMICALS AND CHEMICAL PRODUCTS -0,35% 0,32% -0,10% -0,03% -0,05% -0,06% -0,21% -0,22%

RUBBER AND PLASTICS PRODUCTS -0,09% 0,16% -0,04% -0,04% -0,05% 0,03% -0,19% -0,15%

OTHER NON-METALLIC MINERAL PRODUCTS -0,08% 0,13% -0,05% -0,03% -0,12% -0,04% -0,15% -0,06%

BASIC METALS -0,13% 0,10% -0,05% -0,01% -0,19% -0,02% -0,22% -0,07%

FABRICATED METAL PRODUCTS, -0,06% 0,40% -0,03% 0,01% -0,13% 0,08% -0,07% -0,48%

MACHINERY AND EQUIPMENT, N.E.C. -0,30% 0,38% -0,11% -0,01% -0,31% -0,16% -0,16% -0,21%

OFFICE, ACCOUNTING AND COMPUTING MACHINERY -0,27% 0,00% -0,06% -0,01% -0,07% 0,01% -0,06% 0,00%

ELECTRICAL MACHINERY AND APPARATUS, NEC -0,13% 0,10% -0,04% -0,02% -0,09% 0,03% -0,09% -0,12%

RADIO, TELEVISION AND COMMUNICATION EQUIPMENT

-0,13% 0,12% -0,03% -0,01% -0,05% -0,03% -0,05% -0,08%

MEDICAL, PRECISION AND OPTICAL INSTRUMENTS 0,03% 0,10% 0,01% 0,01% 0,01% -0,07% 0,01% -0,04%

MOTOR VEHICLES, TRAILERS AND SEMI-TRAILERS -0,14% 0,35% -0,04% -0,02% -0,16% -0,07% -0,14% -0,26%

OTHER TRANSPORT EQUIPMENT -0,02% 0,14% 0,00% -0,03% -0,02% -0,08% -0,01% -0,03%

MANUFACTURING NEC -0,12% 0,26% -0,08% 0,03% -0,22% -0,20% -0,12% -0,08%

12

TRADE AND REPAIR OF MOTOR VEHICLES 0,02% 0,21% 0,04% 0,11% 0,05% -0,34% 0,01% 0,02%

WHOLESALE TRADE 0,01% 0,03% 0,01% -0,06% 0,00% 0,02% 0,00% 0,01%

RETAIL TRADE 0,10% -0,26% 0,33% 0,24% 0,03% -0,41% 0,05% 0,43%

HOTELS AND RESTAURANTS 0,24% 0,31% 1,50% -0,82% 0,04% -0,02% 0,40% 0,52%

LAND TRANSPORT 0,01% 0,10% 0,01% -0,06% 0,00% -0,13% 0,04% 0,08%

SEA TRANSPORT -0,01% 0,04% -0,01% -0,02% 0,00% -0,01% -0,01% -0,01%

AIR TRANSPORT -0,01% 0,02% -0,02% 0,03% 0,00% -0,03% 0,00% -0,01%

TRAVEL AND TRANSPORT 0,09% 0,03% 0,16% -0,09% 0,02% 0,01% 0,16% 0,06%

POST AND TELECOMMUNICATIONS -0,29% 0,12% -0,43% -0,02% -0,05% -0,19% -0,09% 0,09%

FINANCIAL INTERMEDIATION -0,23% 0,80% -0,26% -0,74% 0,00% -0,01% -0,01% -0,05%

INSURANCE AND PENSION FUNDING -0,03% -0,19% -0,02% 0,20% 0,00% 0,00% 0,00% -0,01%

ACTIVITIES RELATED TO FINANCIAL INTERMEDIATION 0,13% 0,01% 0,09% 0,01% 0,00% 0,00% 0,00% -0,01%

REAL ESTATE ACTIVITIES -0,06% 0,06% -0,04% 0,05% -0,01% 0,02% -0,02% -0,14%

RENTING OF MACHINERY AND EQUIPMENT -0,01% 0,02% -0,01% -0,01% 0,00% -0,01% 0,00% 0,00%

COMPUTER AND RELATED ACTIVITIES 0,85% 0,08% 0,14% -0,04% 0,03% -0,05% 0,02% 0,01%

RESEARCH AND DEVELOPMENT 0,09% 0,09% 0,01% -0,05% 0,00% -0,01% 0,00% -0,03%

BUSINESS SERVICES 2,39% 0,44% 1,55% -0,40% 0,32% 0,19% 1,26% -0,23%

Total 0,80% 5,38% 1,98% -1,67% -1,94% -2,24% -0,85% -1,47%

If we analyze the Manager group, we can detect an overall process of upskilling explained by within sector

movements, this is verified both for manufacturing and services. Even if in manufacturing, we register a “negative

structural change” basically explained by employment change from manufacturing to services, we register for all

sectors with very few exceptions a positive variation of managers. The overall change in managers composition of

6,18% is explained for 5,38% by intrasectoral movements.

For the other professional categories, we are not able to detect a clear pattern. The overall positive variation of

clerks (0,31) is basically concentrated in an intersectoral movement, on the contrary we register a decline in clerks’

employment at intrasectoral level. Finally for craft and manual workers, both at sectoral and structural level there is

a contraction of low-skill employment. At aggregate level the modest increase in manual workers registered in figure

2 is concentrated in services and it is basically explained by intrasectoral variation (retail and business services).

Given the important differences existing between countries, we apply the Berman, Bound and Machine (1998)’s

decomposition at macro sectors for each country. Results are shown in the tables below.

Table 3. Decomposition by country

GERMANY between industry component within industry component Total variation

MANAGERS 0,02% 2,15% 2,17%

CLERKS 1,81% 0,33% 2,13%

CRAFT -1,51% -2,89% -4,40%

MANUALS -0,31% 0,41% 0,10%

SPAIN

between industry component within industry component total variation

MANAGERS 0,21% 5,87% 6,08%

CLERKS 4,78% 0,06% 4,85%

CRAFT -3,36% -1,80% -5,16%

MANUALS -1,63% -4,14% -5,77%

13

FRANCE


MANAGERS 0,22% 12,06% 12,27%

CLERKS 2,00% -2,36% -0,36%

CRAFT -0,91% -3,95% -4,86%

MANUALS -1,31% -5,75% -7,05%

ITALY


MANAGERS 0,59% 6,12% 6,70%

CLERKS 3,59% -2,67% 0,92%

CRAFT -2,71% -1,71% -4,42%

MANUALS -1,47% -1,44% -2,91%

UNITED KINGDOM


MANAGERS -0,04% 9,00% 8,96%

CLERKS 3,32% -8,00% -4,68%

CRAFT -1,99% -1,82% -3,81%

MANUALS -1,29% 0,82% -0,47%

Considering each country, the overall variation of managers over the period 1999-2011 is basically concentrated

within the same industry or sector compared to clerks, craft and manuals whose variation is equally dived in inter

and intra sectoral movements. An interesting pattern of skills change is related to United Kingdom and Germany. In

both countries, we register a small increase in managers explained by within industry movements, leading to

downskilling. For Spain, France and Italy, it is more evident an overall trend of upskilling both explained by skill

upgrading in the sector (Managers) and technical change (Clerks and Craft workers) from manufacturing to services

(table 2).

3. The model and the econometric strategy

In this section we aim to investigate the determinants of polarization trends following the theoretical approach

outlined in the first section. As already verified in previous works (Bogliacino, 2009; Lucchese and Pianta, 2011;

Pianta, 2001; Pianta, 2003), we expect a different impact of technology on job growth. Technology cannot be treated

as un undifferentiated process, at least two kind of strategies should be detected and their impact is different in

terms of employment growth.

In the model we explain variation in polarization trends in terms of different typology of technology introduced,

variation in wages, aggregate demand and education:

(3)

where POL is the normalized POL index presented above, tc and cc are proxies for technological and cost

competitiveness strategies, VA is value added, a proxy for demand2, w is the compound annual rate of change of

labor compensation (changes in labor cost), is the share of workers with education3, and ε is the error term, for

industry i. The model introduces specific country effects in order to account for differences in country characteristics

and sector specificities. From a theoretical point of view controlling for country characteristics is important in terms

of national system of industrial relations and welfare institutions, as well as economic and employment structures.

2 We estimate model (2) both in terms of Value Added at sectoral level and Labor Productivity as a share of Value Added for

employees by sector. 3 According to the professional category, we will use secondary education or university education. At sectoral level, we will use

share of workers with University degree.

14

The baseline model can be estimated consistently with OLS, it is adjusted for heteroschedasticity and intra-group

correlation at the industry level, checking for intra-sectoral heterogeneity. In order to weight observations, we use

the average employment level in each sub-period and for the entire time span (1999-2011) with the aim to assure

stability over time. We also control for the possibility of multicollinearity between regressors through a VIF test

(Variance Inflation Factors).

Building on the literature review, we expect that technological and cost competitiveness strategies have a

contrasting effect on employment: employment growth emerges in product innovation oriented sectors, while new

processes generally result in employment losses.

The model specification (3) is estimated for three different periods: 1999-2011, 2002-2005 and 2006-2011.

According to recent findings in Lucchese and Pianta (2012), we expect different relations in upswings and

downswings. In upswings, employment change should be affected by new products, value added/productivity and

wage growth, while during downswings new processes contribute to restructuring and job losses.

We estimate a model that allows coefficients to differ in upswings (2002-2005) and downswings (2006-2011). The

differences in β coefficients describe the impact of innovation on employment in different phases of cycle. In Table 4

we report the variables and the periods.

Table 4. Key variables and periods

Variables Source Period 1 Period 2

Share of firms introducing innovation to reduce labour costs CIS 2004 2008 Share of firms innovating to open new markets CIS 2004 2008 Average Firm size – Employees per firm Share of firms introducing innovation at sectoral level Total expenditure in innovation

CIS CIS CIS

2004 1999 1999

2008 2011 2011

Percentage change of Value Added STAN 2002-2005 2006-2009 Percentage change of Labor Compensation per employee STAN 2002-2005 2006-2009 Percentage change of Labor Productivity STAN 2002-2005 2006-2009

4. Results

4.1 Baseline regression

We firstly estimate a general regression with the baseline model for the entire period 1999-2011 using as a

dependent variable the change in the weighted POL index during the entire period 1999-2011. Coefficients are

corrected by heteroscedasticity. As first model we use a single proxy for innovation (Total Innovative expenditure or

share of firms performing innovation) capturing both process and product innovation performed at sectoral level.

Table 5. Change in POL index in 1999-2011 (European industries). Pool of manufacturing and services industries. Dependent variable: Change in POL index 1999-2011

Value Added (rate of growth) 0.00449 (0.00217)***

0.00505 (0.00191)***

Labour compensation per employee (rate of growth) -0.00304 (0.00155)***

-0.00321 (0.00144)***

Share of University Education 0.002126 (0.00813)***

0.00266 (0.00102)***

Total innovative expenditure -0.00315 (0.00161)***

Share of firms performing innovation -0.00167

15

(0.00102)*

Country dummies Yes* Yes*

Manufacturing -0.0186 (0.0093)**

-0.00464 (0.0140)

N obs

177 184

R2 0.5449 0.5454

Weighted Least Squares regression with robust standard errors. Standard Errors in parentheses: * significant at 10% ** significant at 5% *** significant at 1% level

As we can see from the results, in the long run the positive change in value added seems to have a positive impact

on polarization of employment, this seems to justify the polarizing trend detected in services over the period. As

expected, an increase in labor compensation negatively impact on polarization of employment, on the contrary

education significantly increases job polarization. Innovation negatively impacts on job polarization, but this is a

generic result including both process and product innovations having different impacts on skills. Finally, as described

in section 2, sectoral and country differences contribute in shaping different patterns of employment polarization.

4.2 Controlling for cycles and patterns of innovation

As a further step we decide to split the sample in two periods in order to account for cycles and to differentiate

innovation according to the main purpose behind it (cost competitiveness or technological innovations). To check if

the estimated model differs across the two different time periods we apply the Chow test after estimating a pooled

cross section by interacting all explanatory variables with time dummies and performing an F-test that the

interactions are jointly insignificant. We detect a structural break in the sample related to two different periods:

2002-2005 (upswing) and 2006-2011 (downswing). In this sense, we estimate different coefficients for the upswings

and downswings, due to the existence of major differences in the way different innovative strategies, demand and

wages affect overall changes in employment. In table 5 and 6, we report the main results.

Table 5. Change in POL index in UPSWING PERIOD (European industries). Pool of manufacturing and services industries in 2002-2005 for DE, FR, IT, SP, UK. Dependent variable: Change in POL index

Value Added (rate of growth) 0.2609 (0.2354)

Labour Productivity (rate of growth) 0.2374 (0.1676)

Labour compensation per employee (rate of growth) 0.0812 (0.2060)

-0.1642 (0.2004)


0.1642 (0.0515)***

Share of firms introducing innovation to reduce labour costs 0.2286 (0.0894)***

0.1607 (0.0809)***

Share of firms introducing innovation to open new markets -0.1749 (0.0739)***

-0.1624 (0.0759)***

Country dummies Yes Yes

N obs 165 167 R2 0.3993 0.3979


During the upswing, we detect a positive impact of education on the POL index, which is confirmed also in

downswing with an even greater elasticity. This result seems to be in line with previous findings on human capital

and its impact on wages and employment. Neither change in value added nor change in wages are significant

16

variables in the small sample (2002-2005) to determine POL variation, this could be explained by the positive impact

of demand on employment of all professional categories nullifying the general impact on polarization in the short

run.

On the contrary, both technological variables are significant at 1% of confidence level and they have the expected

signs. Firms innovating to reduce labour costs (process innovators) have a positive impact on the POL index, as

expected. During upswings, introducing process innovations proxied by share of firms introducing innovation to

reduce labour costs could lead to expansion in manual workers employment. On the contrary, product innovations

may have a decreasing impact on polarization leading to reduction of middle professional groups employment.

As described in the previous section, country dummy need to be considered in the analysis due to the specific

country characteristics.

In the downswing results change, the recession impacts on employment leading to a strongly restructuring

processes performed by firms and in general job losses associated with a restructuring of industries (Lucchese,

Pianta, 2012, p. 14). In this framework, the job creating potential of new products is lost. All innovation activity is

captured by process innovations leading to job losses mostly concentrated in the manual workers category.

Education is still significative and a positive impact on polarization.

Both models are estimated including change in Value Added and Labour Productivity, coefficients have in both cases

same signs and magnitude confirming stability of results.

Table 6. Change in POL index in DOWNSWING PERIOD (European industries). Pool of manufacturing and services industries in 2006-2011 for DE, FR, IT, SP, UK. Dependent variable: Change in POL index

Value Added (rate of growth) 0.1997 (0.1757)

Labour Productivity (rate of growth) 0.1212 (0.1839)

Labour compensation per employee (rate of growth) -0.0943 (0.2060)

-0.0930 (0.1320)


0.2646 (0.1034)***

Share of firms introducing innovation to reduce labour costs -0.5240 (0.3019)**

-0.6595 (0.2928)***

Share of firms introducing innovation to open new markets -0.1011 (0.1773)

-0.0784 (0.1522)

Country dummies Yes* Yes*

N obs 125 130

R2 0.3461 0.3394


5. Conclusions

A large macroeconomic literature, generally with a Keynesian perspective, has investigated employment dynamics in

their relationship to the cyclical patterns of economic growth. The ups and downs of aggregate demand have been

shown to affect changes in production and demand for labor. Along the cycle, however, the same production

function is generally assumed to operate and the relationship between output and labor demand has rarely been

17

questioned. Distinctions have been made between the determinants of employment in short-term business cycles

and in long-term growth, with the consideration of specific capital-labor complementarities, the evolution of labor

supply and, more recently, the diversity of labor skills. In this piece of work, we investigate polarization both through

a descriptive analysis and an econometric estimation.

Given our research questions, we provided evidence on two major points: polarization trends and the relation

innovation-employment/polarization in different phases of the cycles.

In terms of job polarization, we detected in the first empirical analysis a declining trend during the period 2006-2011

due to job losses registered by manual workers. In this sense, the job polarizing trend detected by the literature is

more evident during the upswings when job growth is concentrated in the upper and lower professional categories.

We assessed an increasing job polarization for 1999-2005 which is reverted in 2006-2011 due to job losses

concentrated in the manual workers’ category instead of middle tasks jobs. Job polarization is more evident during

upswings than downswings. This is even more evident if we consider manufacturing and services. In manufacturing,

we register an upskilling trend mostly related to employment contraction, in this sense structural change as

employment movement toward services still plays an important role. In services, we detect polarization. In some

countries as Germany and United Kingdom, an increase in manual workers is basically explained by a downskilling

process (within sector component) registered in retail trade and hotels and restaurants.

Secondly, we show that polarization is affected by the nature of technological change but also by cycles, following

this direction we combined macroeconomic analysis of business cycles with a special focus on the nature of

technology in manufacturing and service industry. We found evidence that innovation could have a negative impact

on employment during downswings when product-oriented strategies may also lead to job losses due to a

restructuring effect. These results are similar to a previous analysis (Lucchese and Pianta, 2011). During upswings

aggregate industry grows and it is supported by both new products and new process (total innovation expenditure),

even if specifically new processes could lead to job contraction on a sector-based level. During downswings

innovation has a negative impact on job growth, new processes associated to restructuring appear significant in

supporting the increase in value added or in containing its fall, but new products lose their relevance.

From this perspective, further research is needed. Firstly, it will be helpful consider both employment and wage data

by professional category in order to capture both the job and wage polarization phenomenon detected by literature.

Furthermore, even if we control for country specificities, institutional elements should be introduced in the analysis

in order to account for the political and institutional framework influencing employment growth and polarization.

A more comprehensive database including employment for temporary workers will be helpful in order to capture

the increase in low skill jobs detected in services.

18

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22

Appendix

Descriptive graphs

2,47

0,85

-1,61

-0,12

-2

-1

0

1

2

3

Managers Clerks Craft W. Manual W.

Average rate of growth. 1999-2011. Pool of countries and sectors.

1,58

-1,13

-2,17

-3,21 -4

-3

-2

-1

0

1

2

3


Average rate of growth. 1999-2011. Pool of countries. Manufacturing.

2,90

1,18

-0,30

2,65

-1

0

1

2

3

4


Average rate og growth. 1999-2011. Pool of countries. Services.

23

0%10%20%30%40%50%60%70%80%90%

100%


1999 2011 1999 2011

26,42 32,25 29,78 29,37

14,13 15,35

44,77 44,15 35,17

30,46

8,73 7,10

24,28 22,13 16,72 19,37

Germany. Composition of employment by professions and sectors

MANUALS

CRAFT

CLERKS

MANAGERS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


1999 2011 1999 2011

16,09 27,27 21,56 24,80

8,47

11,55

47,38 46,13 34,22

30,29

7,29 6,23 41,23

33,56 23,77 22,85

Spain. Composition of employment by professions and sectors

MANUALS

CRAFT

CLERKS

MANAGERS

24

0

10

20

30

40

50

60

70

80

90

100


1999 2011 1999 2011

27,16

44,50 34,34

43,33

11,20

10,46 39,07

35,65

21,49

15,33

7,78 5,04 40,15

31,46 18,81 16,93

France. Composition of employment by professions and sectors

MANUALS

CRAFT

CLERKS

MANAGERS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


1999 2011 1999 2011

13,79 25,18 23,90 25,78

16,52

14,22

51,00 47,99 35,34 32,96

9,65 7,91 34,36 28,72

15,45 17,88

Italy Composition of employment by professions and sectors

MANUALS

CRAFT

CLERKS

MANAGERS

25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


1999 2011 1999 2011

31,83 44,16

33,74 40,52

12,92

10,93

47,14 37,31 23,86 21,62

5,57 4,03

31,39 26,66 13,54 19,59

United Kingdom Composition of employment by professions and sectors

MANUALS

CRAFT

CLERKS

MANAGERS

1,03 1,05 1,14 1,16

1,21 1,23 1,24 1,20

1,29 1,31 1,39 1,42

1,51 1,56

1,51 1,45

0,89 0,91

1,01 1,04 1,08 1,09

1,14 1,10

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

1,60

1999 2000 2002 2003 2005 2006 2009 2011 1999 2000 2002 2003 2005 2006 2009 2011 1999 2000 2002 2003 2005 2006 2009 2011

Polarization index. Total employment of Germany, France, Italy, Spain and United Kingdom. Years 1999-2011

TOTAL MANUFACTURING SERVICES

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