Ida Musiałkowska

Poznań University of Economics, Poland

Chair of European Studies

i.musialkowska@ae.poznan.pl

Conference: Regional Growth Agendas

University of Aalborg, Denmark

28th-31st May 2005

Gateway 8: New developments in regional theory

Regional dynamics, convergence and divergence: The European Union - Poland.

Abstract

The paper will refer to the widely discussed regional convergence and divergence issues. It

will especially focus on the problem of the economies’ fluctuations and present one of the

aspects of the regional dynamics and convergence: business cycles synchronisation. The

context of the situation in the European Union regions before the enlargement will be given,

(based on presentation of some empirical results referring to the regional business cycles

synchronisation in the last 28 years) as well as the identification of main factors influencing

business cycles synchronisation. That part will be followed by the presentation of dynamics of

Polish regions in the transformation period. The key points of the regional development

strategies are going to be a part of the whole image. Main findings, conclusions form the

paper summary.

1

Introduction

Convergence/ divergence processes have been widely discussed over last years. The

neoclassical approach seems to be applied as a standard. Synchronisation of the business

cycles is a part of discussion, in the context of convergence issues. Some authors even use the

term of the “business cycles convergence”. There is relatively large number of research

devoted to national business cycles synchronisation. In integrating Europe, however, regions

have been playing growing role. Their economies, treated as the “mini-economies”, have the

same features as the countries have. They adapt in more flexible way to changing conditions

at the markets, use of new technologies and react faster to the cultural patterns change. They

compete among themselves and adjust their structure to gain better position. Regions:

• react to exogenous shocks;

• their economies are influenced by multipliers and accelerators;

• have growth trajectories subject to shifts in world markets, trade cycles, relative price

changes and competition from outside.[ Klaassen H.L. et al., 1987]

Moreover, local and regional economies are combinations of industries, of which each

is characterised by its own cycles. Cycles duration in some areas dependent on the particular

industry is almost the same as in given industry (i.e. five years cycles in Detroit, Michigan or

Wolfsburg in Germany – areas dependent on the car industry). Such phenomenon is not

present in regions or local areas with highly diversified structure of employment. The more

diversified structure the less sensitive region is to the fluctuations.[2]

Under such conditions the national business cycle cannot be a simple sum of regional

business cycles. The evidence both in the European Union and the United States dismantled

the thesis of uniformity in regional business cycles and response to the stabilisation efforts of

the central governments. Timing and severity of business cycles varies across regions, due to

different factors. Comparing shape of national and regional business cycles one can observe

that some areas do not experience cyclical behaviour during national business cycles. In

general, the timing of the turning points in regional cycles do not correspond to those of

national cycles. In the majority of works the regional business cycles are treated the same way

as it is in case of national economies. Regions have their own smaller economic system. The

morphology of regional business cycles is characterised by: phases (expansion and recession

or upswings and downswings), turning points (upturn and downturn), amplitude of

fluctuations (which determines symmetry of fluctuations) and duration time (the whole cycle

is measured from one turning point to another of the same character i.e.: from one upturn to

the next upturn). [I. Musialkowska; Regional Economies’ Fluctuations in Europe]

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Theories applied in research of national business cycles may be used as well in the researches

of the regional business cycles. In the literature the convergence (or synchronisation) of the

regional business cycles almost has not presented. The conducted researches are rather

“partial” – they refer to the regions of one or only a few chosen countries. The length of the

data series and indexes were differentiated. Comparing those results one cannot evaluate the

process of regional business cycles synchronisation. Therefore, there is necessity to examine

the whole population of the European Union regions.

Data and methodology

The ERECO database served as the main source of calculation. Research comprises regions of

all EU-14 member states.1 The EU regions are classified by EUROSTAT in six categories

(levels) - NUTS. In the article three territorial levels are analysed: countries (NUTS0),

macroregions (NUTS1) and regions (NUTS2). Too short-time series was main reason for non-

including regions of new member states into analysis.2 The analysis covers the whole

population of the NUTS1 and NUTS2 regions – 257,3 and data series – period from 1975 to

2001. The main variable used in the analysis is total GVA (in euro 1995) in particular regions.

The survey based on annual series, due to lack of comparable quarterly or monthly data.

The methodology referring to the links between regional business cycles fluctuations is based

on the methodology used in surveys of international business cycles synchronisation. It is

embedded in the theories of real business cycles and assumes that business cycles fluctuations

can be identified by decomposition of time series on a trend and deviation from a trend.

Oscillations around the trend represent the path of business cycles fluctuations, while

correlation coefficients between deviation form the trends in regions reflect the convergence

level of regional business cycles.

In order to identify trend in time series Hodrick-Prescott filter was applied. That methodology

aims at time series decomposition to growth element, which represents a trend and to cyclical

element. It is often presented as;

yt = gt + ct for t=1,…, T

where:

yt – empirical time series

gt – growth element

ct – cyclical element

1 Luxembourg treated as a country and one region at the same time was excluded from the research. 2 The data series are annual and data for 10 new members cover the period since 1990, while for the rest the data are available since 1975. 3 In the database there are 262 regions, but for the 5 French outseas territories (DOMs) the data is not available.

3

Growth and cyclical series are reduced to:

( ) ( ) ( )[ ] }∑∑−

++−

−−−+⎩⎨⎧

−1

2

211

1

2minT

tttt

T

tttg

gggggyt

λ

where λ is a smoothing parameter. In the analysis, as the most frequently suggested, λ=100

for the annual data. After eliminating a trend from the time series, the first differences form

cyclical fluctuations. The next step was creation of Pearson correlation matrix, representing

the correlation coefficients ( r) for pairs of analysed regions.

257,2572,2571,257

2,21,2

1,1

rrrrr

r

The analysis was mainly based on the level of r≥0,75 (high correlation).

The research will serve as a base for further deepened analysis.

3. Results

The main purpose of the research was verification whether synchronisation of regional

business cycles do exist or not and to what extent. Two kinds of synchronisation was defined:

• internal

• external.

Synchronisation of regional business cycles within each country, at three following levels,

form internal synchronisation:

1. NUTS1 and NUTS2 regions’ cycles synchronisation with the NUTS0 (country) cycle

2. NUTS1 and NUTS2 regional business cycles

3. NUTS2 regional cycles.

External synchronisation comprises cross-country synchronisation of regional business cycles

at the NUTS1 and NUTS2 regional level.

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Scheme 1. Level of analysis of regional business cycles synchronisation in the EU-14.

macroregion NUTS1

region NUTS2

region NUTS2

region NUTS2

macroregion NUTS1

other NUTS2regions in the country

Other EU regions: macrooregion NUTS1

region NUTS2

country NUTS0

macroregion NUTS1

region NUTS2

Internal synchronisation

External synchronisation

Own elaboration

Observation of the internal synchronisation let formulate the following conclusions:

1) In the majority of the EU countries relatively often regional and national business cycles

are synchronised. 186 of 257 (72,37%) of regions have the cycles correlated with the

national one. At the same time a part of regions does not show correlation with the

national business cycles. Ireland and Portugal have the maximum percentage of the

regional cycles correlated with the national one, while Belgium, Greece and Austria – the

minimum. (Table 1)

2) Within the country not all NUTS2 regions have their cycles synchronised with the

business cycles of the NUTS1 regions that they belong to i.e.: in the United Kingdom,

Belgium, the Netherlands, Denmark, Greece and Italy. About 10% of all NUTS2 regions

do not synchronise their cycles with the NUTS1 regions’ cycles in a particular country

(this fact may reflect biases within particular countries). Strong internal correlation of

business cycles at those levels is in Spain, Portugal, Finland, Western Germany, Austria

and partly - in France.

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Table 1. Synchronisation of the regional business cycles with the national business cycle in the EU countries.

Number of regions, whose cycle is synchronised with the national business cycle

Country Total number of regions High

correlation r≥0,75

Quite high correlation 0,7≤r<0,75

Medium correlati

on 0,5≤r<0,7

Low correlation

r<0,5

Belgium 13 - - 3 10 Denmark 3 2 - 1 - Germany 48 37 - - 11 Greece 16 7 3 5 1 Spain 23 21 1 1 - France 284 18 1 7 2 Ireland 2 2 - - - Italy 25 22 1 1 1 Netherlands 16 11 1 1 3 Austria 12 7 4 1 - Portugal 8 8 - - - Finland 7 6 1 - - Sweden 8 8 - - - United Kingdom 48 37 4 4 3

Source: Own elaboration

3. Business cycles of the NUTS2 regions are apparently synchronised. Strong correlation is

shown in a half of analysed countries (Spain, Portugal, Finland, Sweden, Ireland, Germany,

Austria and France). In Germany and Austria cycles are correlated in two groups: Eastern and

Western part.

Business cycles synchronisation of many regions within a country does not mean existence of

one “single” cycle in all those regions. The level of synchronisation is differentiated with the

values of correlation coefficients. The highest values (over 0,90) are present in Spain,

Germany and Portugal. In the rest of the countries, apart form the strong correlation between

regional cycles, there are also regions that are characterised by different fluctuations than the

others. Those regions are “specific”, especially due to different structure (i.e. with a high

percentage of employed in agriculture) or specialised functions (i.e. manufacturing). The

character of regional economy is often conditioned by geographic location. All these features

influence non-numerous correlation of their business cycles i.e.: mountainous regions of

Scotland, Western Austria or the Northern Ireland.

External synchronisation analysis gave the following results:

1. 228 of 257 (88,72%) of NUTS1 and NUTS2 regions have the synchronised cycles.

Values of the correlation coefficients are varied, however (from 0,75 to 0,98).

4 Ther are total number of 33 NUTS regions, but for 5 of them the data is not available.

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Table 2. The number of regions, the cycle of whose is correlated with the other regional business

cycles Correlation

coefficient r≥0,75 r≥0,95 0,95>r≥0,90 0,90>r≥0,85 0,85>r≥0,80 0,80>r≥0,75

Number (%) of EU

regions

(257=100)

46 (17,9%) 135 (52,35%) 180 (70,04%) 205 (79,77%) 225 (87,55%)

Source: Own elaboration

The correlation coefficients were divided into classes upon their values. In the first class of

correlation coefficient r≥0,95 there are 46 regions of 9 European countries (17,9%). The most

represented are Spanish and German regions, whose business cycles’ correlation is also very

high.

In the second class 0,95>r≥0,90 there are 135 (52,35%) regions of all EU-14 member states,

except for Ireland. There are also relatively few regions of Finland, Greece and United

Kingdom. In the next three classes there are regional cycles of all analysed countries.

2. The maximum number of cycles synchronized with one regional cycle is 98, while the

average is 32,37 – and above average correlation is shown by 119 regions (46,3 %).

3. Synchronisation of the regional business cycles was analysed separately in the groups of

NUTS1 and NUTS2 regions. Tendencies in both groups are similar. The most open

regions (with the highest number of correlated cycles) are in Spain, Western Germany,

Italy and Portugal. There are also single French, Swedish, Belgian and Austrian regions.

As far as the number of correlated cycles is concerned, at the bottom are regions from the

Netherlands, United Kingdom, Greece and Italy.

4. In the research two groups of regions, where the cycles are synchronised in each group,

were identified.

The first one – the northern area – comprises regions from the countries of Anglo-Saxon

model of their economies. Those are regions of southern and middle parts of the United

Kingdom, Ireland and Scandinavia – Sweden, Denmark and Finland (Fig. 1.).

The single regions from the remaining EU member states have their cycles correlated with

those enumerated peripheral ones. All regions are characterised by high level of

urbanisation and high percentage of employed in services. There are also very often

clusters (and new technologies industries) located on their territories. The latter may

explain high correlation of their cycles. At the same time those regions are also very well

developed.

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The second group – continental Western European area – consists of regions from

Germany, France, Spain and Portugal (Fig. 2.). A part of Spanish and Portuguese regions

has an agricultural character, while German and French are dominated by the service

sector. The factor of the level f economic development does not explain their cycles’

correlation. Synchronisation can be explained, however, by neighbouring of countries:

Spain and Portugal, Spain and France, France and Germany, and trade between those

countries and regions.5

There are also regions that do not have the cycles correlated with the others i.e.: Scotland,

Northern Ireland, parts of Belgium, the Netherlands, Denmark, Italy, Greece, Western

Austria and Eastern Germany. Very high percentage of employed works in agriculture

sector or manufacture. Regions are of the islander or mountainous character. Both, rich

and poor regions are between them. Those regions do not show correlation within the

country they belong to, as well.

The hypothesis on the regional business cycles synchronisation was also verified in the

analysis of total synchronisation (external and internal), excluding synchronisation with the

national business cycle. Two levels of the correlation coefficient were compared:

1. for the correlation coefficient r≥0,75 results that at this level of correlation:

- correlation is shown by 249 (96,87%) of 257 regional business cycles

- the average number of cycles correlated with one regional business cycle is 45,8

- above average correlation of their business cycles is shown by 119 regions (46,3%).

2. At the level of correlation coefficient r≥0,50

- there is no region with non-synchronised business cycle

- the average number of the business cycles correlated with one regional business cycles is

121

- above average correlation is shown by 152 regions (59,14%).

The regions with the highest and most numerous synchronisation belong to the following

countries: Spain, Germany and Italy (mainly its northern and southern parts). At the opposite

side are regions from: northern part of the United Kingdom, Eastern Germany, central Italy,

western Austria, part of Greece and single regions from Belgium, the Netherlands, Denmark

and Sweden.

The explanation of such a situation can be found in acting of the factors, described in the

literature.6 The most significant for the regional business cycles correlation were:

5 Based on the calculation made by S. Barrios and J.J. de Lucia. 6 In the article no econometric calculation estimating influence of the factors was made, due to lack of

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- geographic location

- structure of economy and regional specialisation

- neighbouring of countries (while smaller importance had regional neighbourhood)

- urbanisation (the metropolitan regions’ business cycles are generally synchronised, as well

as those of the capital ones)

- trade and historic linkages (observation on the basis of literature)

Influence of European integration was not clearly visible7. The strength of correlation of the

regional business cycles not always is bigger in case of the pairs of regions from the “core”

countries in comparison to the pairs of regions from the “core” and peripheries.

The level of economic development seems to be not very important for the process of

synchronisation. Regions that are better developed show the similar number of the business

cycles correlated within the group of 30 of the richest regions, as – in 30 of the poorest ones. 8

(Table 3.)

Table 3. Regions with the highest and the lowest level of GVA per capita in PPS and the number of

the business cycles synchronised within each group, 2002 Regions of the highest GVA Regions of the lowest GVA

r≥0,75 1. IT3 9 ES43 10 2. IT32 9 PT12 9 3. IT6 8 ES42 9 4. UKI1 7 PT2 8 5. IT31 7 PT11 8 6. DE5 7 ES61 8 7. UKJ1 7 ES6 8 8. IT1 7 PT15 8 9. UKH1 7 GR41 7

10. UKI 7 GR14 7 11. DE6 6 GR1 7 12. FR1 6 GR2 7 13. DE71 6 ES11 7 14. BE1 5 GR13 7 15. DE21 5 GR11 6 16. IT11 5 GR25 6 17. IT33 4 PT14 4 18. AT13 3 GR22 4 19. FI16 3 PT3 3 20. IE02 3 DEE1 3 21. IT51 3 DE4 3 22. IT13 2 DED1 3 23. IT4 1 DE8 3 24. IT12 1 GR23 2 25. SE01 1 GR21 2

comparable regional data. The analysis has descriptive character. 7 The influence of integration is understood as the period of the EU membership. 8 The ranking of 30 richest and poorest regions bases on the values of GVA per capita, compared to the rankings by the European Commission and clusters analysis.

9

26. FI2 1 ES63 1 27. AT32 1 BE32 1 28. DK01 1 UKK3 1 29. IT2 0 UKM4 0 30. NL31 0 UKL1 0

Średnia 4,4 Średnia 5,06 Source: Own calculations 4. Polish case

As a supplement to the research of the whole population of the EU-14 regions, short

description of the Polish regions’ situations will be given. Even in the era of non-market

economy in Poland the first sights of cyclical regional development was perceived.

[Domański R., 1989] However questions about regions’ growth trajectory and fluctuations

have arisen after 1989, starting point of economic transformation in Central and Eastern

European countries. The structural changes and economic fluctuations are imposed on each

other. Moreover, short term since transformation beginning and lack of regional statistics in

current prices make dynamics observation impossible. However, there are some initial

researches on the issue of regional fluctuations in Poland.9

As far as the enterprise profitability index in Poland in 1992-1997 is concerned we obtain

the following stages [Domański R., 2002]:

1. basic structural changes (1994);

2. expansion in which the boom in the national and European economies was superimposed

on the results of the initial impact of the reforms (1994-95)

3. inflection and contraction of the economy (1995-97)

4. recession (1997-98)

In analysed period economic fluctuations were superimposed on the structural changes.

Poland nowadays is divided into sixteen regions (voivodships) that correspond to NUTS2

classification and in all of them GDP is below 75% of the EU average. The best-developed

regions are: mazowieckie, wielkopolskie, dolnośląskie, zachodniopomorskie. At the bottom

are: podlaskie, podkarpackie, świętokrzyskie, warmińsko-mazurskie. As far as differentiation

of the value added per capita in the regions one may distinguish the following groups:

- the biggest dynamics was visible in: mazowieckie, wielkopolskie, małopolskie

- the lowest - in: lubelskie, lubuskie, kujawsko-pomorskie, śląskie, opolskie.

The majority of regions are concentrated around the national average level, however.

Such differentiation allows for indicating the cyclicity in regions’ development. In the given

example, the statistic population was limited to four regions: two from Western (poznańskie

and wrocławskie) and two from Eastern Poland (białostockie and lubelskie). In the research

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the former names of the regions are used, due to the fact that new administrative division of

the country has been introduced recently [Domański R., 2002].

Comparison of cyclicity between four voivodships:

1. Employment per inhabitant in 1990-1998 (1990=100)

In Poland there were seen the following stages:

• drop in the number of employed in 1990-93

• increase in 1993-1997

• decline since 1997.

The most severe employment index decrease was in the wrocławskie. The białostockie gained

its position faster than the others due to regional economy structure: traditional branches:

agriculture, forestry, FCMG industry. While in the wrocławskie many industrial branches

were deeply affected by transformation processes.

2. Industrial output per inhabitant in 1990-1998 (1990=100)

The biggest dynamics the poznańskie noticed, the lowest - the białostockie, which resulted

from the lower labour productivity. In the poznańskie during the first phase the index

fluctuated because of deeper industry transformation, which caused better market adjustment

and higher dynamics in the next years.

3. Retail sale of goods per inhabitant in 1990-1998 (1990=100)

High dynamics is observed in the białostockie and lubelskie, which stemmed mainly from

trade relations with the inhabitants from Belarus and Ukraine. After visa restrictions imposed

on those countries, the dynamics fell. Low sale index in the wrocłwskie is correlated with the

fall in the number of employed.

9 Research bases on Alonso, Lösch and Weidlich theoretical and methodological approach.

11

4. Investment outlays per inhabitant in 1990-1998 (1990=100)

Dynamics in the Western regions is higher than in the Eastern ones. High indexes of

employment and retail sale did not correspond to investment dynamics. Short-term economic

growth in 1994-1997 was caused by temporary impacts and did not transfer to other growth

factors: i.e.: industry factors. Increase in investment outlays in the Western regions was due to

foreign direct investment and capital inflow from Germany and the USA. It brought new

technologies, management and marketing systems.

Table 4. Cyclical symptoms in the Polish voivodships in 1990-1998

Poznańskie Wrocławskie Białostockie Lubelskie

Index

Employment

fluctuations and downswing upswing

downswing stagnation upswing

2 downswings 2 upswings

2 downswings 2 upswings

Industrial output

fluctuations: upswing and downswing gradual growth

gradual growth

gradual growth

upswing downswing

Retail sale growth growth growth growth

Investment outlays gradual growth

fluctuations: upswing and downswing gradual growth

fluctuations slight growth

growth stagnation

Source: Elaborated on basis of R. Domański; Zróżnicowanie i wahania gospodarki regionów. Suplement do dyskusji nt. teorii Augusta Loscha. “Przegląd Geograficzny” 2002, 74, 2, p. 157-174

The research and observation of other of Polish regions10 confirm that fluctuations

depend on the economies’ structure (mainly industrial one). The presence of sensitive

industrial branches is reflected in slightly appearing cyclical patterns. In general, regions

better developed (as in this case the Western regions: poznańskie and wrocławskie) develop

faster and in more stable way. They attract investors and create conditions for

competitiveness.

Another research conducted for all new Polish voivodships based on the industrial

sales and unemployment time series in 1999-2003. Four regional business cycles groups of

similar course were identified. The main feature differentiating the course of two time series,

is higher volatility of seasonality in industrial sales growth rates than the unemployment rates.

10 ERECO Database, Cambridge Econometrics, 2003

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There was different variation coefficients in both cases: in case of unemployment rates the

average was 0,15 and changed from 0,08 to 0,22 while for the industrial sales rates, the

average was 0,44 and changed form 0,27 to 0,84). The unemployment in the nature is more

stable, which explains the situation. [Wyżnikiewicz et al., 2004] The above mentioned groups

of regions were identified on comparison of those two time series business cycles’ course. As

resulted, those groups are the following: (Map 1.)

1. southern and eastern Poland – industrial (małopolskie and podkarpackie)

2. northern and eastern Poland (warmińsko-mazurskie, podlaskie, lubelskie and

świętokrzyskie) - industrial

3. northern and central Poland (zachodniopomorskie, pomorskie, kujawsko-pomosrskie,

mazowieckie and łódzkie) - unmeployment

4. south-western Poland (wielkopolskie, lubuskie, dolnośląskie, opolskie and śląśkie) -

unemployment

Source: Wyżnikiewicz et. al.; Regional Differntiation of Business Cycles in Poland 1999-2003

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The regions in the groups are in close neighbourhood – they can create particular

“macroregions”. The explanation of such “grouping” needs further analysis. The main

features influencing the process are: economic structure, historical traditions, economic

relations and geographic location and trade with the i.e. Germany (as in case of the fourth

group).

Patterns of behaviour: convergence of industrial or unemployment time series and

transmissions of business cycles impulses are typical for transformation period in the

economies. Both research confirm the specific of the period.

Summary

In the former European Union (EU-15) the regional business cycles are synchronised. The

majority of them have their cycles synchronised with the others. Many factors influence the

process of such a convergence. Those are i.e. geographic location, economic structure,

regional specialisation, trade and historic links. However, in case of new member states there

are still research comparing the development and regional dynamics in new and old EU

countries. The EU-25 is much more diversified than EU-15. There is still question arising:

what is the optimum of economic growth and optimal amplitude of fluctuations. Those doubts

are especially important in the stage of Economic and Monetary Union, where the monetary

policy is pursued from the European level. The countries lost one of the fluctuations’

stabilisation instruments. The role of fiscal policy has increased in importance, as well as the

redistribution of resources within i.e. the structural policy.

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Figure 1. The northern area – regions that have cycle correlated with many other regional business

cycles within the area.

Europe

Own elaboration Figure 2. The continental-Western European area – regions that have cycle correlated with many other regional business cycles within the area.

Europe

Own elaboration

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