The University of Birmingham School of Geography, Earth ...Lise Bourdeau-Lepage2 1. Introduction The...

The University of Birmingham School of Geography, Earth & Environmental

Sciences

Working Papers

No. 17 ADVANCED SERVICES AND REGIONAL

INTEGRATION: THE CASE OF THE CEECs

Lise Bourdeau-Lepage

Université de Bourgogne LEG-CNRS-MSH

BP 26611 21066 Dijon Cedex-France [email protected]

June 2005

ISBN: 07044 25173

Advanced services and regional integration: the case of the CEECs

Working Papers on Services, Space, Society (WPSSS17)

(i)

Advanced services and regional integration: the case of the

CEECs

Contents Page

1. Introduction 1 2. Differentiated Evolution of Employment Structures 4 2.1 Methodological remarks 5 2.2 A slow catching-up process 6 Table 1: CEEC and EU-6 employment structures as percentages 8 (1995 and 2000) 2.3 A twofold evolution of countries 8

3. A Regional Typology 10 3.1 Marked regional specificities 10 3.2 Four classes of regions 13 Table 2: Characteristics of region-classes in 2000 13

4. Conclusion 16

5. References 19

Appendix 22 Table A1: Region –classes in 2000 22 Table A2: Evolution of employment structures (1995-2000) 23 Table A3: Employment structures in the CEEC regions (1995, 2000) 24 Table A4: CEEC regional location quotients in 2000 (employment structures) 25 Table A5: Euclidean distance of the CEECs and of CEEC regions from EU-6 26 Average structure (1995 and 2000)



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Advanced services and regional integration: the case of the CEECs1

by

Lise Bourdeau-Lepage2

1. Introduction

The demise of communism, the ensuing opening up of the Central and Eastern

European Countries (CEECs) and the liberalization of trade triggered the ongoing

process of integration with the European Union and indeed with the world economy.

Integration is a polysemic concept with political, institutional, social, technological and

economic facets. Here, its economic aspects alone are considered. Economic integration

is generally taken to mean decreasing transaction costs or trade costs (Traistaru et al.,

2002). It is understood as trade liberalization (Baldwin, 1995; Traistaru et al., 2002;

Brülhart, 2001), common trade policy, the single market, social and regional

regulations, mutual recognition, harmonization, standardization and competition policy.

Thus, in many studies, the economic integration is analyzed through foreign direct

investments, trade and convergence of GDP per capita (e.g. Altomonte and Resmini,

1999; Landesmann, 1995; Petrakos, 2002; Sapir, 1996). Moreover, research focuses on

change in the location of industries and in their geographic concentration resulting from

the process of integration (Amiti, 1997; Brülhart, 2000; Resmini, 2003; Traistaru et al.,

2002). In this paper, integration is defined as a process by which interactions, especially

complex, high-level interactions, develop among countries, regions and cities. Thus, the

concept of integration is related to the formation of networks. The CEECs will be

successfully integrated into an enlarged Europe if they come to play a significant part in

the post-industrial economy. The existence of intense interactions among EU15 and

1 I would like to thank J.-M. Huriot and the referee for their comments and suggestions. 2 Université de Bourgogne, LEG-CNRS-MSH, BP 26611 21066 Dijon Cedex-France, [email protected]



2

CEEC is not a sufficient condition for successful integration. The nature of the

interactions is crucial.

In the face of European integration, the evolution of each of the CEECs will be an

original combination of two extreme models.

The first model rests on hierarchy. The CEECs might become EU “peripheral

countries” in the economic sense. This could come about through the relocation of labor

intensive activities of some previous EU members to the CEECs, where labor costs are

lower. Or it could also come from the CEEC choice of trade and production

specializations based on labor intensive and raw material intensive activities resulting

from unfavorable initial conditions (ill-adapted economic structures, unstable political

environment, peripheral geographical position with regard to the EU, low-skilled labor

force, lack of entrepreneurship) and structural rigidities which hinder the adjustments of

these countries to international competitive conditions. In this case, the CEECs would

not become strategic decision-makers. This function would be fulfilled by some EU-15

members. This model would constitute only a partial integration of the CEECs with a

form of hierarchy headed by previous EU members. The trade specialization in low-

value-added activities of the CEECs and especially of the Balkan countries underlined

by Petrakos (2002) and by Radosevic (2002) illustrates this situation.

The second model involves the formation of network interactions among all countries in

an enlarged Europe. The CEECs would engage in advanced economic activity just like

any previous EU states, that is, they would be integrated into the processes of creation,

decision making and control at the European scale. This would imply well-developed

advanced services in the CEECs because it is the level of development of such services

which enables, among other things, a country to participate in the global network. Some

research which concludes that the CEECs are not really integrated into the European

innovation system (e.g. Radosevic, 2002) cast doubts on this scenario.



3

Accordingly, this paper examines the level of advanced services in the CEECs. These services are of growing strategic importance across the whole range of production

sectors in developed economies (Bailly and Coffey, 1994; Miles and Wyatt, 1991;

Riddle, 1986; Peneder et al.. 2001; Daniels, 1993). Post-industrial production is more

and more intangible, personalized and global. The resulting complexity of economic

interactions increases the need for coordination (Bourdeau-Lepage and Huriot, 2005),

which gives a decisive role to advanced services. Information becomes the main input

of the globalized economy (Castells, 1996). The rise of advanced services is both the

cause and the consequence of globalization, in a cumulative process (Sassen, 2000).

Thanks to advanced services, large cities, and the regions around them, become closely

interconnected within global networks (Sassen 2000; Taylor 2000). The growth of these

services is a major factor of change in regional disparities, as well as of the emergence

of global cities (Bourdeau-Lepage, 2004; Bourdeau-Lepage and Huriot, 2002 and

2005).

Consequently, the successful integration of the CEECs will depend on their involvement

in the post-industrial production system in which advanced services play a leading part

in driving competitiveness and growth. Therefore, the development of advanced

services will be a key factor in the successful integration of the CEECs into an enlarged

EU. It will also enhance the future performances of these countries permitted by

innovation activities. For decades these countries operated with planned economies. The

priority was given to the “material sphere” and services were considered as

unproductive (Andreff, 1993). Industry was predominant; service supply was deficient

and of poor quality. Furthemorer, the contributions from services to GDP and to total

employment were relatively low (Illeris, 1996; Stare, 2002).

With the deregulation of business, economic coordination has taken on a new form.

Coordination is no longer achieved through central planning; the old regulations and

routines are no longer operative and new economic agents have adopted market

practices and business strategies (Bourdeau-Lepage, 2004). Thus, the institutional and



4

business environment changed and adapted to the market economy. In this context, on

the one hand, some services, in particular, traditional services like transport, provided

by the manufacturing enterprises themselves, were externalized. On the other hand, new

services have emerged (Pietras 2002; Stare, 2005). This has led to a rise in services in

the CEECs, and especially in services related to production (producer services and

finance) concerning the employment and gross value-added structures (Facchini and

Segnana, 2003). This reflects a change in the production structure, the intensity of

which depends on the skill and qualification levels of the labor force. This change will

prepare the success or the failure of CEECs’ integration into the EU.

The aim of this paper is to appraise the potential of CEEC regions for integration into

the EU by examining the most prominent features of their production structures with

special emphasis on the relative importance of advanced services.

The paper is organized as follows: First of all, the main features of employment

structures in the CEECs are stated and compared to those of 6 established European

countries. This evaluation shows that CEEC production potentials fall short of the EU-6

average (section 2). Then, regional production potentials are assessed by calculating

Euclidean distances between the employment structure by sector in each region and the

average employment structure of EU-6 countries (section 3). Large disparities among

the CEECs are observed at regional level. Finally, the analysis reveals that there are

clearly unequal potentials for EU integration explained by three interdependent factors:

the location effect, the historical effect and the metropolitan effect (section 4).

2. Differentiated Evolution of Employment Structures This section aims at characterizing the evolving production structures of the CEECs

relative to the EU average. First, the methodological basis of the analysis is set out

(2.1). Second, the main changes in the CEEC employment structure between 1995 and



5

2000 are identified and comparisons with the EU-6 employment structure are made

(2.2). Third, differences in country structures are revealed (2.3).

2.1 Methodological remarks

The analysis is based on employment data for 17 NACE branches (European

nomenclature of activities) at NUTS1 level in 1995 and 2000. It covers Bulgaria, the

Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania and Slovakia as

well as six established European members: Denmark, France, Greece, Ireland, Italy and

Spain. This data set is provided by the regional data base of Eurostat (REGIO). A more

extended analysis is not possible since the data are deficient at a 17 NACE level for

Slovenia and for the other EU-15 countries. The breakdown of services into 11 branches

is rather limited. The two branches “financial intermediation” and “real estate, renting

and business activities” are not disaggregated. Consequently, they are used as a proxy of

advanced services.

In order to evaluate the potentials for integration, CEEC structures of employment are

compared with a European reference by calculating the Euclidean distances between

employment structures.

An employment structure can be represented by the vector of the shares of the different

branches in total employment. The gaps between employment structures can then be

evaluated from the distance between the corresponding vectors.

Distances between numerical vectors, such as the Tchebychev distance, the Klafszky

distance, the logarithmic distance or the angular distance, have statistical drawbacks,

and in particular entail some loss of information. Furthermore, some of them give

greater weights to the largest differences between vectors (Colorni et al., 2001 for a

summary). Euclidean distance is chosen here because all the gaps between vectors are

treated equally, and also because it is easy to calculate and interpret.



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The differences in employment structure between each country, measured by Euclidean

distances, can be used to compare potentials for successful integration into an enlarged

Europe. To facilitate the interpretation, distances are presented on a base of 100. Thus,

this relative Euclidean distance varies from 0 between identical structures to 100

between the most widely differing structures (note in appendix). It must be recalled that

these figures have no absolute meaning because they result from the particular distance

used. But they can be compared with each other and over time. The Euclidean distance

is a simple indicator of dissimilarity but it requires careful interpretation. A large

distance to the EU-6 average may indicate that the country is either lagging behind or

alternatively very far ahead in the conversion process. For the CEECs, the first

possibility is more likely but the second cannot be excluded.

2.2 A slow catching-up process

In the CEECs, from 1995 to 2000, the total number of jobs decreased by 3.8% with the

greatest drops in Estonia, Bulgaria and Romania, where it declined by 9.8%, 9.3% and

9.1% respectively, and the lowest reductions in Poland and Latvia (table A2). The only

exception to this trend was Hungary, with a 5.7% rise in employment, the sign of a

better economic climate than in the other CEECs and of a more advanced adjustment

process. Over this period, in the EU-6, employment increased by 9.3%.

In this context, de-industrialization in the CEECs was noticeable, with 13.7% fewer jobs

in the industry and construction sector. However this sector still accounted for an

average 29.6% of employment in the CEECs compared to 26.5% in the EU-6 in 2000.

Conversely, advanced services expanded with respective employment increases of 9.2%

and 22% in financial intermediation and real estate & business activities over the 1995-

2000 period (table A2). Their expansion was more intensive in the CEECs than in EU-6.

Nevertheless, in the CEECs, these activities occupied about half as many workers on

average as in the EU-6 in 2000 (6.6% versus 12.8%, table 1). Even with the decline of

agricultural employment everywhere except in Romania, agriculture was still dominant



7

in the CEECs and employed 23.3% of workers in comparison with 5.7% in the EU-6

(table 1).

The CEEC employment structure differed from the EU-6 structure in the weight of

traditional market services and non market services too. These services were less

developed in the CEECs than in EU-6 and accounted for 21% and 19.4% respectively

compared with 25.2% and 29.8% in the EU-6 in 2000. Their growth was more

significant in the EU-6 than in the CEEC between 1995 and 2000 (table A2). Thus, the

catching-up of the CEECs on these activities will probably last.

These tendencies in the CEEC employment structure reflect the implementation of the

market economy after the abandonment of real socialism. Economic transformations

take time, especially because of the need for the labor force to adapt in terms of skill

and qualification. Some countries, such as Estonia, are adapting better than the others.

The CEECs still suffer from the domination of industry and from underdeveloped

services compared with the EU-6. Overall, their employment structure is

following the same trend as the EU-6, that is to say de-industrialization, expansion

of services and decline of agriculture (except for Romania and Bulgaria) but these

changes are not intense enough to allow to the CEECs to catch-up with the EU-6

employment structure in the near future. From 1995 to 2000, the Euclidean

distance of employment structure between the CEECs and EU-6 increased from

13.6 to 14.3. This resulted especially from the increasing distance of Bulgaria and

Romania (table A5). These two countries are distinguished by a low level of

transformation compared with the other transition economies perceived from the

analysis of employment structure.



8

Table 1: CEEC and EU-6 employment structures as percentages (1995 and 2000)

Agriculture1 Industry & Construction

Financial intemediation

Real estate &business activities

Traditional market

services2

Non market services3 Total Branches/

Geographical entities 1995 2000 1995 2000 1995 2000 1995 2000 1995 2000 1995 2000 1995 2000

Romania 34.4 41.4 33.6 27.3 0.7 0.9 3.4 3.1 16.3 14.9 11.5 12.4 100 100 Bulgaria 24.4 26.2 32.6 28.3 1.3 1.1 3.1 4.1 19.8 22.0 18.9 18.3 100 100 Poland 27.8 26.3 30.1 26.8 1.3 2.2 4.8 5.6 19.3 20.3 17.3 18.8 100 100 The Czech Republic 6.7 5.2 42.1 39.9 1.9 2.1 5.0 5.6 23.4 24.3 21.0 22.9 100 100 Hungary 8.1 6.6 33.0 33.8 2.3 2.2 3.6 5.4 24.7 25.8 28.2 26.3 100 100 Estonia 10.1 7.1 34.0 33.2 1.1 1.3 4.9 6.9 25.2 27.1 24.6 24.3 100 100 Slovakia 9.0 6.3 36.8 34.0 1.6 1.9 6.7 6.8 24.3 27.0 21.6 24.0 100 100 Latvia 18.5 15.3 25.8 24.4 1.3 1.6 4.8 5.5 25.0 27.4 24.5 25.8 100 100 Lithuania 23.8 19.9 28.2 26.2 1.3 1.0 3.0 3.7 19.8 22.8 24.0 26.5 100 100 CEEC average 23.2 23.3 33.0 29.6 1.5 1.7 3.9 4.9 20.1 21.0 18.3 19.4 100 100 EU-6 average 6.8 5.7 27.8 26.5 2.9 2.8 9.2 10.0 24.8 25.2 28.5 29.8 100 100 1 Agriculture include: agriculture, hunting, forestry; fishing. 2 Traditional market services include: trade and repair; hotels and restaurants; transportation and communication. 3 Non market services include: pub administrat., defense, social security; education; health & social work; other community, social and personal service activities; activities of household. Sources: EUROSTAT (2003); BULSTAT (2003) and CSO (2003).

2.3. A twofold evolution of countries

Bulgaria and above all Romania are still removed from the EU-6 production structure.

Over the period 1995–2000, their distances increased from 15.0 and 21.9 to 16.6 and

27.3 respectively (table A5). This change is partially attributed to the growing

proportion of agriculture in their total employment whereas the EU-6 proportion of

agriculture to the total employment decreased from 6.8% to 5.7% over the same period

(table 1).

In Romania, more than 40% of workers were employed in agriculture in 2000. From

1995 to 2000, this trend intensified. Whereas in 1995 the number of workers employed

in agriculture was 50% higher than the CEEC average, in 2000, it was 80% higher

(table 1). Agricultural employment rose by 9.3% from 1995 to 2000. This was due to

the poor economic situation in those years marked by the 1997–1999 crisis (Andreff,

2003). Economic recession and the land ownership statute (no. 18/1991) led to

migration from urban to rural areas (Parlog and Caracota, 2002). In a context of rising

unemployment, Romanian workers returned to agriculture. That activity seems to be a

downturn activity in Romania. Thus, Romania remained distinct from the EU-6



9

production structure in terms of agricultural employment. In Bulgaria, while the

agricultural sector employed about one in four of the workers, there were more jobs in

industry and construction than in agriculture in 2000. These two Balkan countries are

characterized by a weak service sector and by underdeveloped advanced services.

Although the Polish distance was similar to that of Bulgaria in 2000, Poland is in a

better position than both Bulgaria and Romania. Closer analysis of the employment

structures provides some explanations. First, between 1995 and 2000, the Polish

structural gap with the EU-6 narrowed (from 16.5 to 16.1; table A5). Second, advanced

services, and in particular financial intermediation, are more extended in Poland than in

Romania or Bulgaria. Consequently, the Polish gap is more the result of an

overdeveloped agriculture and/or underdeveloped public and traditional market services

than the effect of underdeveloped advanced services (table 1). Thus, Poland’s

specificity is characterized by a well-developed strategic sector compared with other

CEECs, a predominance of agriculture and a proportion of employment in industry and

construction close to that of EU-6.

Given the small Euclidean distance varying from 6 to 12.2 and the characteristics of

their employment structures, other countries were closer to the EU-6 average structure

than Romania and Bulgaria. However, some differences were noticeable.

Thus, in the Czech Republic, Slovakia, Estonia and Hungary, the proportion of

agricultural employment in 2000 was very close to the EU-6 average of 5.7%, with

5.2%, 6.3%, 6.6% and 7.1% respectively while in Lithuania and Latvia this proportion

was 19.9% and 15.3% respectively (table 1). Industry and construction was the

dominant activity of these four countries. It employed more than one-third of workers in

2000. Moreover, financial intermediation, real estates & business activities were more

developed in Slovakia, Estonia, the Czech Republic and Hungary where they accounted

for more than 7.5% of employment. Furthermore, traditional market sector was equally

as well-developed in these four countries as in the EU-6.



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The analysis shows a differentiated development of advanced services across the

CEECs and a split between Central and Baltic countries and the two Balkan* countries

in terms of employment structure. Particularly Estonia, Hungary, the Czech Republic

and Slovakia appear to be in a more favorable position than Bulgaria and Romania for

successful integration into the EU.

Indeed, this analysis conducted at the national level reveals the framework in which

regional structures are situated. Within this given framework, regional structures may

differ and be variously endowed in activities which facilitate their integration. The

occurrence of marked economic differences among the CEECs suggests there will be

regional disparities in their potential for integration.

3. A Regional Typology

This section draws attention to similarities and differences in the potentials for

integration of 49 CEEC regions as reflected by their employment structure in 2000. The

same data are used as previously (table A3). The relative size of the main sectors in the

49 regions is evaluated by calculating their location quotients (LQ). For a given sector,

LQ is the ratio of the proportion of that sector in the region’s employment to the average

proportion of that sector in all 49 regions. LQ allows direct comparisons among the

employment structures of the 49 regions. The study is conducted in two stages. First of

all, differences in regional specialization are identified with particular emphasis on

advanced services (3.1). Then, CEEC regions are classified so as to characterize the

overall regional structure of the CEECs (3.2).

3.1 Marked regional specificities

CEEC regions are distinguished by their level of agricultural activity, the proportion of

which varied in 2000 from 1.7% in Közép-Magyarorsz (Hungary) to 51.2% in the

Romanian Sud-Vest and Nord-Est regions. Thus, there is a dramatic regional

differentiation. In 2000, agriculture accounted for less than 12% of employment in the

Czech, Slovakian and Hungarian regions whereas in the eastern regions of Romania and



11

Poland more than 26% of jobs were in agriculture (table A3). Overall, Romania, Poland

and Bulgaria display a significant East-West split. In Poland, this is a well-known

pattern, Poland A and Poland B (Bourdeau-Lepage, 2002 and Chi et al., 2003).

Agriculture is over-represented in eastern regions relative to the CEEC regional

average, with the location quotient varying from 2.2 to 1.6 (table A4) in 2000. These

differences in agricultural activity within CEEC regions intensified from 1995 to 2000

(note that Polish regions are excluded owing to the lack of data for 1995). Thus, the

proportion of agriculture in employment grew in all Romanian and Bulgarian regions

while it declined in the Czech, Slovakian and Hungarian regions. Then, because of their

increasing proportion of agricultural activity, the Romanian and Bulgarian regions

become more and more removed from the European pattern. Indeed, their sectoral

adjustment process has increased their future economic vulnerability with regard to the

post-industrial economy. This remark should be completed by observation of changes in

their industrial employment structures and the development of services.

In terms of location quotients, relative specializations in industry and construction are

less marked than in agriculture. In 2000, the Hungarian, Slovakian and Czech regions

(except for the Budapest and Prague regions) and the Slaskie region (Poland), the

Bulgarian North-Central region and the Romanian Centru region were more specialized

in industry and construction than the CEEC regional average, their LQs varying from

1.1 to 1.5 (table A4). As expected, the greatest location quotients in the industry and

construction sector and in agriculture varied in opposing directions. Thus, the eastern

regions and the remaining regions were not specialized in this sector.

Financial intermediation as well as real estate & business activities sectors are highly

discriminating among CEEC regions. Advanced services are principally concentrated in

metropolitan regions (except for the Bucharest region). In these regions, they accounted

for at least 10% of employment in 2000 as compared with an average of 6.6% in CEEC

regions and 2.2% and 2.7% respectively in the Bulgarian North-West region and the

Romanian Sud-Vest region. It appears that the relative specializations of the five capital



12

regions in advanced services differ. It is more marked in the Warsaw region (LQ: 1.8)

and above all in the Prague and Budapest regions (LQ: 1.9), while the Bratislava and

Sofia regions are less specialized than these three regions (LQ: 1.6). For the Sofia

region, this could stem from the general economic characteristics of Bulgaria, which has

less advanced structures than the other countries (Bourdeau-Lepage, 2004). To a lesser

extent, five Polish regions are also relatively specialized in advanced services. These are

the western border regions and a southern border region (Slaskie). Their share of jobs in

advanced services is from 20% to 60% higher than the average proportion in the 49

CEEC regions (table A4). Poland’s east-west split is again apparent.

The employment structure analysis by region, conducted by using location quotients,

reveals clear relative specialization, especially in activities related to globalization such

as finance and producer services, but also in agricultural activities. CEEC regions in

2000 displayed a simple pattern: an east-west split for agriculture and a center-south

split for advanced services.

In addition, at the regional level, the differences in the Euclidean distances to the EU-6

average ranged from 4.7 to 34.5 in 2000. The Euclidean distance of the Romanian Sud-

Vest region from the EU-6 average was about 7.5 times greater than that of the Prague

region. It should be pointed out that regional data are compared with the EU-6 average,

which masks substantial disparities. Regions follow approximately the same trend as

their respective countries but differences are much more marked. (table A5). Thus, the

Czech, Hungarian and Slovakian regions are close to the EU average, whereas the

Bulgarian and Romanian regions (except the Sofia region) are very far from it. The

Polish regions lie at various distances from the EU average.

These observations may suggest large differences in integration potentials among the 49

CEEC regions. To understand better these complex discrepancies and to identify the

regions with similar employment structures, the 49 CEEC regions were ranked in

ascending order.



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3.2 Four classes of regions

The regions were classified on the basis of their LQ in order to eliminate the size effect

(table A4). Clustering has been obtained by means of the classification in ascendant

hierarchy. Table 2 gives the main characteristics of the classes.

Table 2: Characteristics of region-classes in 2000*

Classes Agiculture1 Industry

& Construction

Advanced services3

Traditional market

services2

Non market services

Total employment

Euclidean distance

from EU-6

GDP per capita EU-15= 100

(in PPS4)

1 8.4 (0.4) 37.1 (1.3) 5.9 (0.9) 24.2 (1.2) 24.3 (1.3) 100 10.0 41 2 45.7 (2.0) 22.1 (0.7) 3.5 (0.5) 14.2 (0.7) 14.5 (0.7) 100 32.2 24 3 27.7 (1.2) 28.5 (1.0) 5.2 (0.8) 19.4 (0.9) 16.9 (0.9) 100 19.4 29 4 11.6 (0.5) 30 (1.0) 10.4 (1.6) 25.6 (1.2) 22.2 (1.1) 100 7.5 50

CEEC average 23.4 (1.0) 29.6 (1.0) 6.6 (1.0) 21 (1.0) 19.4 (1.0) 100 15.7 37 EU-6 average 5.7 26.5 12.8 25.2 29.8 100 - - * The figures in brackets are the employment LQs. 1 Agriculture include: agriculture, hunting, forestry; fishing. 2 Traditional market services include trade, hotels and restaurants, transportation and communication. 3 Advanced services refer to financial intermediation and real estate & business activities. 4 PPS is the abbreviation of Purchasing Power Standard Sources: Calculated from EUROSTAT (2003); BULSTAT (2003); CSO (2003) and Behrens (2003).

In 2000, the 49 CEEC regions formed four clearly distinct and easily characterized

classes.

More than a third of the 49 CEEC regions are predominantly industrial (table A1). Their

industry and construction sector employs on average 37.1% of workers compared with a

CEEC regional average of 29.6% and an EU-6 average of 26.5%. These 17 regions

make up class 1 and comprise the Baltic states (Estonia, Lithuania and Latvia) and all of

Slovakia, the Czech Republic and Hungary but for the capital regions of the latter three

countries. They form an industrial core in a central zone of CEEC regions. This class is

relatively more specialized than the others in the industry and construction sector and in

non market services, with LQs of 1.3 for the two sectors. These regions have well-

developed traditional market services although less so than the EU-6. Financial

intermediation and real estate & business activities are not very developed compared

with the EU-6 even if their level of development is close to the CEEC regional level.

The proportion of employment in advanced services is half that of the EU-6.



14

In contrast with class 1, class 2 consists of an underdeveloped zone on the eastern

border of the CEEC regions. It is composed of nine regions where on average more than

45% of jobs are in agriculture and where the economic performance as measured by per

capita GDP (on a base of 100 for EU-15 in Purchasing Power Standard) amounted only

to 24% of EU per capita GDP in 2000 (table A1 and table 2). The relevant regions are

located along the eastern fringe of the CEECs. They include the eastern regions of

Poland (Lubelskie, Podlaskie, Podkarpackie, Swietokrzyskie), all the Romanian regions

(except the central, western and Bucharest regions) and the Bulgarian North-West

region. In these agricultural regions (LQ: 2), advanced services are not expanding and

employed a mere 3.5% of workers in 2000. The relative under-representation of

traditional market services and non market services completes the picture of this class

(table 2). The employment structure of class 2 is far from that of EU-6 and indeed from

that of the other classes. Its Euclidean distance from the EU-6 average varied from 25.7

for the Bulgarian North-West region to 34.5 for the Sud-vest Romanian region in 2000.

Consequently, the differences in employment structures are too sizeable for the gap to

be narrowed in the immediate future.

The third class is an intermediate zone between West and East, characterized by a

balance between agriculture and industry and by a weak level of economic performance

(41% of EU per capita GDP). This class includes the six central regions of Poland, three

Romanian regions (the Bucharest, western and central regions) and the easternmost

Bulgarian regions (table A1). The balanced distribution of activities between the

industry and construction sector and the agricultural sector, and the low proportion of

employment in advanced services are the main features of class 3. Thus, the agricultural

sector and the industry and construction sector provide 27.7% and 28.5% of

employment respectively while advanced services account for only 5.2% of

employment. The employment structure of this class is still far removed from the EU-6

structure. The Euclidean distance varied from 15 for the Polish region of Warminsko-

Mazurskie to 23.7 for the Romanian Vest region in 2000, with an average of 19.4. The

Bucharest region deserves a comment. This region is included in class 3 rather than



15

class 4 because of its agricultural over-representation and the underdevelopment of

advanced services in the vicinity of the Romanian capital.

Finally, urbanized regions, and to a lesser extent the Polish western borders, are

classified as advanced services centers in the CEECs and have the highest economic

performance of CEEC regions. Class 4 is composed of the capital regions (except

Bucharest) and five western regions of Poland (table A1). Its outstanding feature is its

high relative level of advanced services compared with the CEEC regional average.

Class 4 easily ranks first for advanced services sector, which is largely more represented

(LQ: 1.6) than the CEEC average. Advanced services account for more than 10% of

employment, which is close to the EU-6 average. Among the regions in this class,

advanced services are expanding more in the capital regions than in the Polish western

borders. That is hardly surprising. It is well-known that these activities are concentrated

in large cities. It is through advanced services that large cities, and the surrounding

regions, become closely interconnected within global networks. Thus, large cities seem

to take on the same role in Central and Eastern Europe as in Western Europe. This class

has as well-developed traditional market services sector as EU-6. However, the industry

and construction sector is dominant, employing 30% of workers. This characteristic

means this class does not fully match the EU-6 employment structure. Nevertheless, it is

the closest to the EU-6 with an average Euclidean distance of 7.5 (table 2). Within this

class, the Warsaw region lies at the greatest distance from the EU-6 structure. This does

not reflect underdevelopment of the advanced services but is due to the considerable

weight of the agricultural sector in the vicinity of the capital city. Thus, the Polish

capital region is in a better position than implied by the indicator of dissimilarity.

Region class 4 (urbanized class) and region class 1 (industrialized class) appear to be

the classes that are the most advanced in the transition process, based on their Euclidean

distance, the characteristics of their employment structure and their per capita GDP.



16

4. Conclusion Examining employment structures and economic performances reveals significant

differences between Central and Eastern Europe and Western Europe, as well as serious

disparities between regions in the CEECs. This could lead to critically unequal

potentials for EU integration, especially in terms of global coordination. Consequently,

the overall process of integration will prove more complex than the two models

described in the introduction. CEEC regions differ and will continue to differ in the way

they interact with each other and with the EU regions. Thus, it is very difficult to predict

the future role of these regions in the EU. This role will depend notably on three series

of interdependent factors resulting from the present analysis: the location effect, the

historical effect and the metropolitan effect.

The location effect conveys the handicap of the geographical periphery. Developing

regions of classes 2 and 3 are still a long way short of the European level of economic

performance and employment structure. These regions do not possess well-developed

advanced services compared with the EU-6. Consequently, they do not display the

requisite features to participate in the processes of decision-making, creation and

control. Coordination functions seem to be fulfilled by other regions such as the capital

regions of the CEECs. Thus, the Bulgarian, Romanian and Polish regions and especially

the eastern border regions have little chance of full integration into the EU network of

economic coordination and power. In addition, these regions do not enjoy a favorable

geographical location. They have a peripheral position with respect to the EU and are

located near weakly developed countries like the Ukraine whereas the Western regions

benefit from the proximity of EU (Resmini, 2003). In this context, the Eastern border

regions, might become the “peripheral regions” of the enlarged Europe. This

peripherality refers not only to geography but also takes on an economic meaning as

underlined by Copus (2001). Thus, these regions may well be only partially integrated

into the enlarged Europe. This situation is likely to continue after enlargement if nothing

is done for developing infrastructures and education in the immediate future. Actually,

some commentators, notably Tondl & Vuksic (2003), recommend investing in transport



17

infrastructures to overcome the location disadvantages of these easternmost regions, but

such a measure may be counter-productive. Indeed, in accordance with agglomeration

economics, it could reinforce the attractiveness of the most developed regions rather

than of the least developed ones. Thus, one can hardly conceive any efficient economic

measure in favour of the latter regions.

Not surprisingly, each region’s past affects its current potential for integration, so that a

historical effect must also be considered. As expected, the Czech, Hungarian, Slovakian

and Western Polish regions are in a more favorable position than regions of classes 2

and 3. In this respect, they form the historical industrial core of Central and Eastern

Europe. In the early 19th century, the Czech Republic and the regions around the

Hungarian cities of Miskolc, Gyor and Pecs were as industrialized as France was

(Bairoch, 1997). Thus, long-term history matters. The past differences in development

between the CEEC regions and countries seem to have affected the transition process of

CEEC regions. In combination with their historical assets, these regions enjoy location

advantages relative to their proximity to the EU, especially in terms of markets and

investments (Tondl and Viskav, 2003). Consequently, the Hungarian, Czech, Slovakian

and Western Polish regions, together with Estonia, Latvia and Lithuania, are the most

advanced in the catching-up process.

Even these geographically and historically advantaged regions are highly differentiated

depending on their level of urbanization.

Among the Central and Western regions, the urbanized regions (class 4) and especially

the capital regions have the employment structures that most resemble that of the EU-6.

Services are more developed and productive, and efficient firms have developed in

these regions. In addition, these regions attract foreign direct investments and are

relatively specialized in advanced services. Agglomeration economies and spatial

spillovers play a significant part in this concentration. These regions benefit from the

advantages in terms of political, social and economic factors enjoyed by the capital



18

cities, that is to say from the proximity of political power and economic decisions-

makers (Bourdeau-Lepage and Huriot, 2002). These regions have a more diversified

and skilled labor force than the others. Most of the foreign direct investment in their

respective countries is concentrated in these areas. Thus, between 1996 and 2000 the

Polish, Hungarian, Slovakian and Czech capital regions accumulated foreign direct

investment stocks, evaluated as a percentage of GVA (growth of value-added) at twice

the national average (Tondl and Vuksic, 2003). As a whole, their economic performance

in terms of progress in the transition process is better than that of other regions. This is

confirmed by several studies which report that in the regions where capital cities are

located, services are more extensive and growth outperforms other regions (in

particular, Erkut and Özgen, 2003; Resmini 2003; Tondl and Vuksic, 2003 and Jeney,

2003). This kind of regional effect in capital regions may be called the “metropolitan

effect” by reference to the concept of the metropolis. Consequently, the capital regions

will probably fully integrate with the European Union, that is, interact with the other EU

regions and take part in the decision-making processes. These regions may well become

the gateway to the global network for all CEEC regions and the economic core of the

Central and Eastern Europe.

These three effects are mutually reinforcing and generate cumulative processes which

can hardly be halted. A number of other factors which may influence the integration

process have been omitted here because this paper seeks to shed light on production

structures. Accordingly, further analysis will be necessary for a fuller understanding of

the process of integration, in particular the role of foreign direct investments and of

social and regional regulations.



19

5. References

Altomonte, C. and Resmini, L. (1999) Specialization and (Geographic) Concentration of European Manufacturing, Enterprise DG Working paper 1, Background paper for ‘The competitiveness of European Industry: 1999 Report’, Brussels.

Amiti, M. (1997) Specialisation patterns in Europe, CEP Discussion Paper, 363,

London School of Economics. Andreff, W. (1993) La crise des économies post-socialistes : la rupture d’un système,

Grenoble: Presses Universitaires de Grenoble. Bailly, A. and Coffey, W.J. (1994) Localisation des services à la production et

restructurations économiques, L’espace géographique, 1, 224-230. Bairoch, P. (1997) Victoires et déboires. Histoire économique et sociale du monde du

XVIe siècle à nos jours, Tome I, Paris: Gallimard (folio, histoire). Baldwin, R.E. (1995) The Eastern Enlargement of the European Union, European

Economic Review, 39, 474-481. Behrens, A. (2003) Produit Intérieur Brut régional des pays candidats 2000,

Communauté européennes EUROSTAT, série: Statistiques en bref, Table. 2, www.europa.gov.eu.

Bourdeau-Lepage, L. (2002) Marchés du travail et disparités régionales en Pologne,

Région et Développement, 15, 74-104. Bourdeau-Lepage, L. (2004) Metropolization in Central & Eastern European. Unequal

Chance, GaWC Research Bulletin 141(A), Loughborough University: Global and World Cities Study Group and Network, http://www.lboro.ac.uk/gawc/rb/rb141.html

Bourdeau-Lepage, L. and Huriot, J.-M. (2002) Metropolization in Warsaw. Economic Change and Urban Growth, Canadian Journal of Regional Science, vol. XXV: 3, 423-446.

Bourdeau-Lepage, L. and Huriot, J.-M. (2005) The Metroplis in Retrospect. From the

Trading Metropolis to the Global Metropolis, Louvain Economic Review, Forthcoming.

Brülhart, M. (2000) Evolving Geographical Specialization of European Manufacturing

Industry, Cahier de Recherche Economiques du Département d’Econométrie et d’Economie Appliquée, 00-08.



20

Bulgarian Central Statistical Office, BULSTAT (2003), Employees by branches, data non-available.

Castells, M. (1996) The Rise of the Network Society, Oxford: Blackwell. Chi, J. Mercier, G. and Samson, I. (2003) Short term and long term assessment of

regional potential and vulnerability in accessing countries, preliminary report, Grenoble.

Colorni, A. Laniado, E. and Muratori, S. (2001) The distance between vectors in

sensitivity and conflict analysis for multicriteria decision making, in Colorni A., M. Paruccini, and Roy, B. Aide multi-critère à la décision, Brussels: European Communities.

Copus, K. A. (2001) From Core-Periphery to Polycentric Development Concepts of Spatial and Aspatial Peripherility, European Planning Studies, vol. 9, 4.

Czech Statistical Office, CSO (2003) Labour Force Survey, data non-available. Daniels, P. W. (1993) Service Industries in the World Economy, Oxford: Blackwell. Erkut, G. and Özgen, C. (2003) The Economic and Spatial Peripherality of Border

Regions in Southeastern Europe, The 43rd European Congress of the Regional Science Association, 27th-30th August 2003, Finland.

EUROSTAT, (2003) REGIO. Economic Accounts. ESA95, Brussels, data non-available. Facchini, G. and Segnana, M. L. (2003) Growth at the EU periphery: the Next

Enlargement, The Quartely Review of Economics and Finance, vol. 43, 5, 827-862. Fujita, M. and Thisse, J.-F. (2002) Economics of Agglomeration. Cities, Industrial

Location and Regional Growth, Cambridge: Cambridge University Press. Illeris, S. (1996), The Service Economy - A Geographical Approach, Wiley. Jeney, L. (2003) The Role of Urban Development in European Regional Inequalities, in:

Jakoli, A. (ed.) Frontiers of Geography, Department of Regional Geography Eotvos Lorand University, Budapest.

Krugman, P. (1996) The Self-Organizing Economy, Cambridge: Blackwell. Ladesmann, M. (1995) The Pattern of East-West European Integration: Catching-up or

Falling Behind? in Dobrinski, R. and Landesmann, M. (Ed.) Transforming Economies and European Integration Aldershot:Edward Elgar, 116-140.



21

Miles, I. and Wyatt, S. (1991) Technological Change in Services and International Trade Competitiveness, Geneva: UNCTAD.

Parlog, C. and Caracota, D. (2002) Dezechilibre regionale in ocuparea fortei de munca,

A.S.E. Congress, Romania. Peneder, M. Kaniovski, S. and Dachs, B. (2001) External services, Structural Change

and Industrial Performance, Enterprise Papers, 3, European Communities. Petrakos, G. (2002) Peripheral European Transitions: Performance, Structure and Trade

Relations in the Balkan Region, Discussion Paper Series, 8 (1), 1-18. Pietras, J. (2002) Business Services in Transitional Economy of Poland, in E. Kigossy-

Schmidt (Ed.) Business Related Services in Central and Eastern Europe: A Cross-Country Approach, Deutscher Universitats-Verlarg, 131-167.

Radosevic, S. (2002) , Regional Policy, National and Regional Foresight in Central and

East European Candidate Countries, STRATA-ETAN Expert Group Action on ‘Mobilising the Regional Foresight Potential for an Enlarged European Union’, July, Brussels: EC-Research DG-Directorate K.

Resmini, L. (2003) Economic integration, industry location and frontier economies in

transition countries, Economic system, 27, 205-221. Riddle, I. D. (1986) Services-Led Growth: The Role of the Service Sector in World

Development, New-York: Praeger. Sassen, S. (2000) Cities in a World Economy, Thousand Oaks: Pine Forge Press. Sapir, A. (1996) The Effects of Europe’s Internal Market Program on Production and

Trade: A First Assessment, Weltwirtschaftliches Archiv, vol. 132, 3, 457-475. Stare, M. (2005) Service Development in transition Countries: Achievements and

Missing Links, in Bryson, J. and Daniels, P.W. (Ed.) The Handbook of Services Industries, Cheltenham: Edward Elgar, Forthcoming.

Taylor, P.J. (2000) World cities and territorial states under conditions of contemporary globalization, Political Geography, 19, 5-32

Tondl, G. and Vuksic, G. (2003) What makes regions in Eastern Europe catching-up? The role of foreign investment, human resources and geography, IEF Working paper, 51.

Traistaru, I. Nijkamp, P. and Longhi, S. (2002) Regional Specialization and Concentration of Industrial Activity in Accession Countries, ZEI Working Paper, B16.



22

Appendix

Note: Calculating distances between employment structures

Let us consider a number of spatial units j (j = 1, … J) in the CEECs, and let eu-6 designates Europe of 6. The units set can be defined in terms of regions, groups of regions, or countries. Let j

ie be the share of sector i employment, in the spatial unit j of the CEECs, with

0 100 and 100j ji i

ie e< < =∑ ,

6euie the average share of sector i employment in eu-6, with the same properties.

The Euclidean distance between the employment structure of unit j and the employment structure of eu-6 is given by:

( )26( , 6) j euii

iD j eu e e= −∑

The maximum value of this distance is 141. By dividing D by 1.41, we obtain an indicator that varies between 0 and 100:

( , 6)( , 6)1.41

D j eud j eu = .

Table A1: Region-classes in 2000

Class 1 Class 2 Class 3 Class 4

Jihozápad (cz) North-West region (bg) North Central region (bg) South-West region (bg) Severozápad (cz) Lubelskie (pl) South central region (bg) Zachodniopomorskie (pl) Nyugat-Dunántúl (hu) Podlaskie (pl) Vest (ro) Dolnoslaskie (pl) Moravskoslezko (cz) Podkarpackie (pl) Centru (ro) Lubuskie (pl) Severovýchod (cz) Swietokrzyskie (pl) North-East region (bg) Pomorskie (pl) Strední Morava (cz) Nord-Est (ro) South-East region (bg) Slaskie (pl) Közép-Dunántúl (hu) Sud-Vest (ro) Kujawsko-Pomorskie (pl) Mazowieckie (pl) Jihovýchod (cz) Sud-Est (ro) Wielkopolskie (pl) Praha & Strední Cechy (cz) Dél-Dunántúl (hu) Nord-Vest (ro) Warminsko-Mazurskie (pl) Bratislavský & Západné Slovensko (sk) Észak-Alföld (hu) Lódzkie (pl) Közép-Magyarország (hu) Észak-Magyarország (hu) Opolskie (pl) Dél-Alföld (hu) Malopolskie (pl) Stredné Slovensko (sk) Sud & Bucuresti (ro) Východné Slovensko (sk) Estonia Lithuania Latvia



23

Table A2: Evolution of employment structures (1995–2000)

1995 2000 Percentage change in employment structures between 1995 and 2000 1995 2000

Branches CEEC average

Bul

gari

a*

Est

onia

The

Cze

ch

Rep

ublic

Hun

gary

Lith

uani

a

Lat

via

Pola

nd

Rom

ania

Slov

akia

EU-6 average

Total 100 100 -3.8 -9.3 -9.8 -4.6 5.7 -3.4 -0.7 -0.7 -9.1 -7.4 9.3 100 100

Agriculture1 23.2 23.4 -3.0 -2.3 -37.0 -26.2 -14.7 -19.1 -17.8 -6.0 9.3 -35.2 -9.0 6.8 5.7 Mining & quarrying 2.0 1.3 -35.6 -37.3 -21.7 -27.4 -43.8 -10.3 -35.5 -33.0 -43.9 -8.5 -27.8 0.3 0.2 Manufacturing 23.1 20.5 -14.6 -21.3 -17.9 -9.7 9.6 -7.3 -13.1 -13.3 -26.3 -16.4 2.0 19.7 18.4 Electricity, gas, water supply 1.9 1.8 -8.5 4.7 -3.9 -23.7 -17.0 -15.4 4.1 -10.3 1.3 0.7 -10.2 0.8 0.7

Construction 6.0 5.9 -4.7 -23.1 14.6 -2.4 22.9 -16.0 15.6 3.7 -26.3 -11.3 12.1 7 7.2 Trade and repair 11.8 12.7 4.0 9.6 -0.9 -0.6 17.7 10.3 18.6 5.8 -10.3 4.9 10.9 14.7 14.9 Hotels and restaurants 1.8 1.9 3.4 11.4 15.7 1.5 14.4 44.3 11.3 8.7 -24.7 -4.0 20.1 4.2 4.6 Transport, storage, communication 6.6 6.3 -7.3 -12.9 -10.9 -2.9 -2.5 5.8 -7.2 -0.7 -24.6 0.6 6.1 5.9 5.7

Financial intemediation 1.5 1.7 9.2 -24.0 11.6 7.7 1.5 -25.2 18.0 21.3 4.2 6.3 5.6 2.9 2.8 Real estate & business activities 3.9 4.9 22.0 18.5 28.2 8.7 56.8 17.7 13.5 50.6 -16.3 -5.8 19.2 9.2 10.0

Pub administrat., defense, social security 3.2 3.9 14.4 25.1 -1.9 14.5 6.4 14.9 11.2 22.5 12.3 5.4 8.1 8.1 8.0

Education 6.4 6.6 -1.3 -14.7 -17.1 -3.3 -5.2 14.2 -3.4 2.7 -3.4 6.9 5.7 6.9 6.6 Health & social work 5.7 5.8 -1.9 -20.9 -20.4 2.6 4.4 7.7 -9.0 -3.1 2.5 4.4 9.2 7.3 7.3 Other community, social and personal service activities

2.9 3.1 4.1 -17.3 1.0 3.1 -12.8 -17.4 31.1 39.2 -18.3 -12.2 59.0 3.2 4.7

Activities of household 0.0 0.0 38.1 - - 150.0 -45.1 - - - - - 15.0 3.0 3.2

Agriculture1 23.2 23.3 -3.0 -2.3 -37.0 -26.2 -14.7 -19.1 -17.8 -6.0 9.3 -35.2 -9.0 6.8 5.7 Industry & construction 33.0 29.6 -13.7 -21.2 -11.9 -9.7 8.3 -10.2 -6.3 -11.6 -26.2 -14.3 3.9 27.9 26.5

Financial intemediation 1.5 1.7 9.2 -24.0 11.6 7.7 1.5 -25.2 18.0 21.3 4.2 6.3 5.6 2.9 2.8 Real estate & business activities 3.9 4.9 22.0 18.5 28.2 8.7 56.8 17.7 13.5 50.6 -16.3 -5.8 19.2 9.2 10.0

Traditional market services2 20.1 21.0 0.2 1.1 -3.1 -1.1 10.1 11.0 8.9 4.1 -16.6 2.9 11.3 24.7 25.2

Non market services3 18.3 19.4 2.2 -12.3 -10.8 4.1 -1.6 6.8 4.4 8.0 -2.4 2.9 14.4 28.4 29.8 Total 100 100 -3.8 -9.3 -9.8 -4.6 5.7 -3.4 -0.7 -0.7 -9.1 -7.4 9.3 100.0 100.0* 1996-2000. 1 Agriculture include: agriculture, hunting, forestry; fishing. 2 Traditional market services include trade and repair; hotels and restaurants; transportation and communication. 3 Non market services include pub administrat., defense, social security; education; health & social work; other community, social and personal service activities; activities of household. Sources: EUROSTAT (2003); BULSTAT (2003) and CSO (2003).



24

Table A3: Employment structures in the CEEC regions (1995*, 2000)

Agriculture1 Industry & construction

Traditional market

services2

Financial intermediation

Real estate & business activities

Non market services3 Geographical Entities /

Branches Total

1995 2000 1995 2000 1995 2000 1995 2000 1995 2000 1995 2000 EU-6 average 100 6.8 5.7 27.8 26.5 24.8 25.2 2.9 2.8 9.2 10.0 28.5 29.8 North-West region 100 30.6 38.6 31.2 25.2 16.1 15.7 1.0 0.7 1.2 1.5 19.9 18.3 North Central region 100 25.3 27.2 36.1 32.5 18.5 20.4 1.0 0.7 1.5 2.1 17.6 17.1 North-East region 100 31.8 33.3 27.2 23.4 20.1 22.2 1.1 0.8 1.7 2.5 18.0 17.9 South-West region 100 12.6 13.3 33.2 29.1 23.8 26.0 2.1 2.0 7.0 8.5 21.3 21.1 South central region 100 27.4 30.5 35.1 30.5 16.5 19.5 0.9 0.7 1.8 2.3 18.3 16.4 South-East region 100 31.3 32.8 28.9 24.1 20.7 23.3 1.0 0.9 1.6 2.2 16.4 16.7 Praha & Strední Cechy 100 4.0 2.9 33.1 30.6 27.6 29.1 2.7 3.2 8.1 9.6 24.5 24.7 Jihozápad 100 10.3 7.4 41.8 42.1 23.2 24.3 2.0 2.0 3.6 4.1 19.1 20.3 Severozápad 100 5.1 3.9 44.8 41.7 26.1 24.4 1.6 1.6 3.3 4.8 19.1 23.6 Severovýchod 100 7.8 6.1 46.4 43.8 21.8 22.0 1.8 1.8 3.6 4.2 18.6 22.1 Jihovýchod 100 8.7 7.7 42.2 40.9 20.0 20.5 1.5 1.7 6.0 5.4 21.6 23.7 Strední Morava 100 8.4 6.0 46.5 45.3 20.7 21.9 1.6 1.7 2.8 3.5 20.0 21.5 Moravskoslezko 100 4.0 3.3 48.2 44.4 22.1 24.6 1.1 1.8 4.0 3.8 20.4 22.1 Közép-Magyarország 100 2.3 1.7 27.9 28.0 29.3 30.1 3.5 3.4 6.6 9.3 30.3 27.4 Közép-Dunántúl 100 8.3 6.9 42.1 43.9 20.4 21.3 1.8 1.6 2.4 3.5 25.0 22.8 Nyugat-Dunántúl 100 7.7 6.1 42.0 41.8 21.9 23.5 2.0 1.6 2.3 3.7 24.1 23.2 Dél-Dunántúl 100 12.2 10.0 31.3 32.5 24.4 24.9 1.2 1.6 2.1 4.1 28.8 26.8 Észak-Magyarország 100 6.7 5.6 37.6 37.2 21.3 23.0 2.1 2.0 2.8 3.7 29.5 28.5 Észak-Alföld 100 11.0 8.9 30.3 33.3 23.3 24.5 1.6 1.2 2.2 3.5 31.5 28.6 Dél-Alföld 100 17.9 14.9 29.9 31.7 24.1 24.8 1.5 1.7 1.7 2.8 24.8 24.1 Dolnoslaskie 100 - 15.6 - 30.7 - 22.7 - 2.4 - 6.9 - 21.7 Kujawsko-Pomorskie 100 - 24.2 - 29.4 - 20.6 - 2.0 - 4.7 - 19.2 Lubelskie 100 - 47.9 - 16.4 - 14.5 - 1.6 - 2.6 - 16.9 Lubuskie 100 - 17.0 - 29.3 - 23.8 - 2.3 - 6.1 - 21.5 Lódzkie 100 - 29.7 - 27.2 - 18.5 - 2.0 - 4.7 - 17.9 Malopolskie 100 - 33.8 - 23.5 - 18.2 - 1.6 - 5.0 - 17.8 Mazowieckie 100 - 22.4 - 22.6 - 22.4 - 3.5 - 8.8 - 20.3 Opolskie 100 - 27.8 - 28.9 - 17.9 - 1.9 - 4.6 - 18.9 Podkarpackie 100 - 44.8 - 21.9 - 13.9 - 1.3 - 3.1 - 15.0 Podlaskie 100 - 42.1 - 18.3 - 16.1 - 1.9 - 3.0 - 18.6 Pomorskie 100 - 14.3 - 30.2 - 25.0 - 2.5 - 6.5 - 21.7 Slaskie 100 - 12.2 - 38.3 - 22.8 - 1.9 - 6.4 - 18.3 Swietokrzyskie 100 - 44.9 - 20.4 - 15.4 - 1.4 - 2.7 - 15.3 Warminsko-Mazurskie 100 - 24.7 - 26.6 - 20.5 - 2.0 - 4.9 - 21.3 Wielkopolskie 100 - 24.4 - 30.3 - 20.9 - 2.1 - 5.2 - 17.2 Zachodniopomorskie 100 - 15.1 - 27.8 - 27.1 - 2.3 - 5.8 - 21.9 Nord-Est 100 43.0 51.2 29.0 22.5 13.9 11.8 0.6 0.5 2.2 2.2 11.3 11.8 Sud-Est 100 37.5 44.4 30.1 25.3 18.8 15.5 0.7 0.7 2.1 2.4 10.8 11.8 Sud & Bucuresti 100 25.9 32.2 36.9 29.5 17.9 17.8 0.9 1.3 6.0 5.6 12.4 13.6 Sud-Vest 100 43.3 51.2 30.0 23.4 14.2 11.9 0.6 0.6 2.1 2.1 9.7 10.8 Vest 100 30.1 35.9 35.0 30.7 18.7 16.6 0.8 0.8 3.3 2.9 12.2 13.1 Nord-Vest 100 38.3 45.9 31.9 25.6 13.9 13.2 0.8 0.8 3.1 2.3 12.0 12.2 Centru 100 29.1 34.0 40.4 34.3 15.8 16.0 0.7 0.9 2.5 2.3 11.5 12.5 Bratislava & Záp. Slov. 100 8.2 5.8 35.9 33.7 25.5 26.6 1.9 2.2 8.1 8.5 20.4 23.2 Stredné Slovensko 100 9.8 7.3 40.2 36.9 22.6 25.8 1.4 1.6 4.7 4.2 21.4 24.2 Východné Slovensko 100 9.9 6.4 35.1 32.0 23.6 28.9 1.3 1.4 5.8 5.7 24.3 25.6 Estonia 100 10.1 7.1 34.0 33.2 25.2 27.1 1.1 1.3 4.9 6.9 24.6 24.3 Lithuania 100 23.8 19.9 28.2 26.2 19.8 22.8 1.3 1.0 3.0 3.7 24.0 26.3 Latvia 100 18.5 15.3 25.8 24.4 25.0 27.4 1.3 1.6 4.8 5.5 24.5 25.8 CEEC average 100 20.7 23.4 34.6 29.6 20.6 21.0 1.4 1.7 4.0 4.9 18.8 19.4 * 1996 for Bulgarian regions. 1 Agriculture includes: agriculture, hunting, forestry; fishing. 2 Traditional market services include trade and repair; hotels and restaurants; transportation and communication. 3 Non market services include public administration, defense, social security; education; health & social work; other community, social and personal service activities; activities of household. Sources: calculated from EUROSTAT (2003); BULSTAT (2003) and CSO (2003).



25

Table A4: CEEC regional location quotients in 2000 (employment structures)

Geographical Entities/Branches

Agr

icul

ture

1

Min

ing

& q

uarr

ying

Man

ufac

turi

ng

Ele

ctri

city

, gas

, wat

er

supp

ly

Con

stru

ctio

n

Tra

de a

nd r

epai

r

Hot

els a

nd

rest

aura

nts

Tra

nspo

rt, s

tora

ge,

com

mun

icat

ion

Fina

ncia

l in

term

edia

tion

Rea

l est

ate

&

busi

ness

act

iviti

es

Pub

adm

inis

trat

., de

fens

e, so

cial

se

curi

ty

Edu

catio

n

Hea

lth &

soci

al w

ork

Pers

onal

&

hous

ehol

d se

rvic

es 2

Indu

stry

&

cons

truc

tion

Adv

ance

d se

rvic

es3

Tra

ditio

nal m

arke

t se

rvic

es4

Non

mar

ket s

ervi

ces5

North-West region 1.6 0.3 0.8 2.7 0.6 0.6 0.9 1.0 0.4 0.3 0.8 1.1 1.0 0.8 0.9 0.3 0.7 0.9 North Central region 1.2 0.4 1.3 1.0 0.6 0.9 1.2 1.1 0.4 0.4 0.7 1.1 0.8 0.8 1.1 0.4 1.0 0.9 North-East region 1.4 0.3 0.8 1.0 0.7 0.9 1.6 1.2 0.5 0.5 0.7 1.2 0.9 0.8 0.8 0.5 1.1 0.9 South-West region 0.6 1.4 1.0 0.9 0.9 1.1 1.5 1.4 1.2 1.7 1.1 1.1 1.0 1.3 1.0 1.6 1.2 1.1 South central region 1.3 1.7 1.1 1.1 0.7 0.8 1.6 0.9 0.4 0.5 0.6 1.1 0.8 0.6 1.0 0.5 0.9 0.8 South-East region 1.4 0.7 0.9 0.8 0.7 0.9 1.7 1.3 0.5 0.5 0.7 1.0 0.8 0.8 0.8 0.5 1.1 0.9 Praha & Strední Cechy 0.1 0.3 0.9 0.8 1.7 1.2 2.0 1.5 1.8 1.9 1.8 0.9 1.1 1.8 1.0 1.9 1.4 1.3 Jihozápad 0.3 0.4 1.5 1.1 1.6 1.0 2.2 1.1 1.1 0.8 1.8 0.8 1.0 0.8 1.4 0.9 1.2 1.0 Severozápad 0.2 3.2 1.2 1.4 1.7 0.9 1.7 1.5 1.0 1.0 1.9 0.9 1.2 1.1 1.4 1.0 1.2 1.2 Severovýchod 0.3 0.4 1.6 0.6 1.4 0.9 1.6 1.1 1.0 0.9 1.6 1.0 1.0 1.1 1.5 0.9 1.0 1.1 Jihovýchod 0.3 0.4 1.4 0.9 1.6 0.9 1.4 0.9 1.0 1.1 1.8 1.1 1.1 1.0 1.4 1.1 1.0 1.2 Strední Morava 0.3 0.3 1.7 0.8 1.6 1.0 1.7 1.0 1.0 0.7 1.4 1.1 1.0 1.0 1.5 0.8 1.0 1.1 Moravskoslezko 0.1 4.6 1.4 0.8 1.4 1.1 1.3 1.3 1.0 0.8 1.4 1.1 1.1 1.0 1.5 0.8 1.2 1.1 Közép-Magyarország 0.1 0.1 0.9 0.8 1.4 1.3 1.8 1.6 2.0 1.9 1.9 1.2 1.0 2.0 0.9 1.9 1.4 1.4 Közép-Dunántúl 0.3 0.9 1.6 1.6 1.2 0.9 1.6 1.1 0.9 0.7 1.8 1.1 1.0 0.9 1.5 0.8 1.0 1.2 Nyugat-Dunántúl 0.3 0.5 1.6 1.1 1.1 1.0 2.3 1.1 0.9 0.7 1.6 1.2 1.0 1.1 1.4 0.8 1.1 1.2 Dél-Dunántúl 0.4 0.3 1.1 1.6 1.0 1.1 2.1 1.1 0.9 0.8 1.9 1.4 1.2 1.1 1.1 0.8 1.2 1.4 Észak-Magyarország 0.2 0.9 1.3 1.7 1.0 1.0 1.7 1.2 1.1 0.7 2.3 1.4 1.3 1.0 1.3 0.8 1.1 1.5 Észak-Alföld 0.4 0.3 1.3 1.0 0.9 1.0 1.4 1.3 0.7 0.7 1.9 1.6 1.3 1.1 1.1 0.7 1.2 1.5 Dél-Alföld 0.6 0.2 1.1 1.0 1.1 1.1 1.7 1.1 1.0 0.6 1.9 1.1 1.0 1.1 1.1 0.7 1.2 1.2 Dolnoslaskie 0.7 1.8 1.0 1.1 1.1 1.1 0.9 1.0 1.4 1.4 0.9 1.0 1.4 1.0 1.0 1.4 1.1 1.1 Kujawsko-Pomorskie 1.0 0.1 1.1 0.8 0.9 1.1 0.5 0.9 1.1 1.0 0.9 1.0 1.1 0.9 1.0 1.0 1.0 1.0 Lubelskie 2.0 0.3 0.5 0.6 0.6 0.7 0.4 0.7 1.0 0.5 0.7 1.0 1.1 0.5 0.6 0.6 0.7 0.9 Lubuskie 0.7 0.2 1.1 0.9 0.9 1.2 0.7 1.1 1.3 1.2 1.1 1.0 1.3 1.0 1.0 1.3 1.1 1.1 Lódzkie 1.3 0.5 1.0 1.0 0.7 1.0 0.6 0.7 1.2 1.0 0.8 0.9 1.2 0.8 0.9 1.0 0.9 0.9 Malopolskie 1.4 0.8 0.8 0.6 1.0 0.9 0.9 0.7 0.9 1.0 0.7 0.9 1.1 0.9 0.8 1.0 0.9 0.9 Mazowieckie 1.0 0.0 0.7 0.7 1.0 1.2 0.7 0.9 2.0 1.8 0.9 0.9 1.0 1.7 0.8 1.8 1.1 1.0 Opolskie 1.2 0.3 1.0 1.1 1.1 0.9 0.7 0.8 1.1 0.9 0.8 0.9 1.3 0.7 1.0 1.0 0.9 1.0 Podkarpackie 1.9 0.5 0.8 0.7 0.7 0.7 0.4 0.6 0.8 0.6 0.7 0.8 0.9 0.5 0.7 0.7 0.7 0.8 Podlaskie 1.8 0.1 0.6 0.7 0.7 0.8 0.5 0.7 1.1 0.6 0.8 1.0 1.2 0.7 0.6 0.7 0.8 1.0 Pomorskie 0.6 0.1 1.1 0.9 1.1 1.2 1.2 1.2 1.4 1.3 0.9 1.1 1.3 1.1 1.0 1.3 1.2 1.1 Slaskie 0.5 6.7 1.0 1.3 1.2 1.2 0.7 1.0 1.1 1.3 0.7 0.9 1.2 0.9 1.3 1.3 1.1 0.9 Swietokrzyskie 1.9 0.5 0.7 0.9 0.8 0.8 0.4 0.7 0.8 0.5 0.6 0.8 1.0 0.6 0.7 0.6 0.7 0.8 Warminsko-Mazurskie 1.1 0.1 0.9 0.9 0.9 1.0 0.8 0.9 1.2 1.0 1.0 1.1 1.3 0.9 0.9 1.0 1.0 1.1 Wielkopolskie 1.0 0.6 1.1 0.7 1.0 1.1 0.6 0.8 1.2 1.1 0.7 0.9 1.0 0.9 1.0 1.1 1.0 0.9 Zachodniopomorskie 0.6 0.2 0.9 1.2 1.1 1.3 1.4 1.3 1.3 1.2 1.0 1.0 1.3 1.1 0.9 1.2 1.3 1.1 Nord-Est 2.2 0.6 0.8 0.9 0.6 0.6 0.4 0.5 0.3 0.4 0.3 0.8 0.7 0.5 0.8 0.4 0.6 0.6 Sud-Est 1.9 0.6 0.9 1.0 0.8 0.6 0.6 1.0 0.4 0.5 0.4 0.7 0.7 0.6 0.9 0.5 0.7 0.6 Sud &Bucuresti 1.4 1.0 1.0 1.2 0.8 0.8 0.6 1.0 0.8 1.1 0.6 0.7 0.7 0.8 1.0 1.0 0.9 0.7 Sud-Vest 2.2 2.4 0.7 1.4 0.7 0.6 0.5 0.6 0.3 0.4 0.4 0.7 0.6 0.4 0.8 0.4 0.6 0.6 Vest 1.5 2.7 1.0 1.2 0.7 0.8 0.7 0.7 0.5 0.6 0.5 0.7 0.8 0.6 1.0 0.6 0.8 0.7 Nord-Vest 2.0 1.1 0.9 0.8 0.6 0.6 0.5 0.7 0.5 0.5 0.4 0.8 0.7 0.5 0.9 0.5 0.6 0.6 Centru 1.4 0.9 1.3 1.1 0.6 0.8 0.7 0.8 0.5 0.5 0.4 0.8 0.7 0.5 1.2 0.5 0.8 0.6 Bratisl. & Záp. Slov. 0.2 0.9 1.1 1.5 1.2 1.3 1.0 1.2 1.3 1.7 1.2 1.4 1.0 1.1 1.1 1.6 1.3 1.2 Stredné Slovensko 0.3 0.3 1.4 0.7 1.2 1.3 0.9 1.2 0.9 0.9 1.2 1.6 1.3 0.6 1.2 0.9 1.2 1.3 Východné Slovensko 0.3 0.6 1.1 0.8 1.2 1.4 1.0 1.4 0.8 1.2 1.1 1.5 1.3 1.2 1.1 1.1 1.4 1.3 Estonia 0.3 0.9 1.1 1.4 1.2 1.1 1.8 1.6 0.8 1.4 1.7 1.2 0.9 1.6 1.1 1.2 1.3 1.3 Lithuania 0.8 0.2 0.9 1.2 1.0 1.2 0.9 1.0 0.6 0.7 1.3 1.5 1.2 1.4 0.9 0.7 1.1 1.4 Latvia 0.7 0.1 0.8 0.9 1.1 1.3 1.3 1.3 0.9 1.1 1.6 1.3 1.0 1.8 0.8 1.1 1.3 1.3 CEEC average 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1 Agriculture includes: agriculture, hunting, forestry; fishing. 2 Personal & household services include other community, social and personal service activities; activities of household. 3 Advanced services include financial intermediation; real estate & business acivities. 4 Traditional market services include trade and repair; hotels and restaurants; transportation and communication. 5 Non market services include pub administration, defense, social security; education; health & social work; other community, social and personal service activities; activities of household. Sources: calculated from EUROSTAT (2003); BULSTAT (2003) and CSO (2003).



26

Table A5: Euclidean distance* of the CEECs and of CEEC regions from EU-6 average structure (1995 and 2000)

Eucl. distance in 1995 Eucl. distance in 2000 Geographical Entities Base

EU-6 1995 Base

EU-6 2000 Base

EU-6 2000 Base

EU-6 1995

GDP per capita EU-15= 100 (in PPS2, in 2000)

CEEC average 13.6 14.9 14.3 13.1 37 Bulgaria1 15.0 16.4 16.6 15.4 26

North-West region1 19.4 20.7 25.7 24.7 24 North Central region1 17.7 19.1 18.8 17.5 23

North-East region1 20.0 21.1 21.5 20.5 23 South-West region1 7.0 8.2 7.5 6.3 34

South central region1 17.8 19.1 20.2 18.9 21 South-East region1 19.9 21.0 21.2 20.1 26

Estonia 7.1 8.6 6.0 4.8 40 The Czech Republic 8.4 9.8 8.4 7.1 56

Praha & Strední Cechy 4.1 4.8 4.7 4.9 85 Jihozápad 9.2 10.7 10.7 9.3 52

Severozápad 9.1 10.2 8.8 7.8 46 Severovýchod 12.3 13.7 12.5 11.2 48

Jihovýchod 9.5 10.9 9.8 8.3 49 Strední Morava 13.6 14.9 13.1 11.8 45 Moravskoslezko 11.2 12.3 10.7 9.7 47

Hungary 6.1 7.5 6.6 5.1 50 Közép-Magyarország 5.7 5.6 4.9 5.4 76

Közép-Dunántúl 10.2 11.7 12.2 10.8 50 Nyugat-Dunántúl 11.2 12.5 11.9 10.6 57

Dél-Dunántúl 7.4 8.7 7.6 6.0 37 Észak-Magyarország 8.7 10.1 9.2 7.8 32

Észak-Alföld 8.6 9.9 9.1 7.6 32 Dél-Alföld 10.1 11.5 9.8 8.2 36

Lithuania 13.6 14.7 12.2 11.1 36 Latvia 9.8 11.0 8.7 7.9 31 Poland 16.5 17.7 16.1 15.0 39

Dolnoslaskie - - 9.2 7.9 40 Kujawsko-Pomorskie - - 15.1 13.8 35

Lubelskie - - 31.9 31.0 27 Lubuskie - - 10.3 8.9 35

Lódzkie - - 18.6 17.5 34 Malopolskie - - 21.3 20.4 35

Mazowieckie - - 13.0 12.3 59 Opolskie - - 17.4 16.2 33

Podkarpackie - - 29.4 28.5 28 Podlaskie - - 27.5 26.6 29

Pomorskie - - 8.6 7.2 39 Slaskie - - 10.1 9.1 43

Swietokrzyskie - - 29.5 28.6 30 Warminsko-Mazurskie - - 15.0 13.9 29

Wielkopolskie - - 15.2 13.9 41 Zachodniopomorskie - - 8.8 7.7 38

Romania 21.9 23.1 27.3 26.2 23 Nord-Est 27.6 28.7 34.2 33.3 16

Sud-Est 23.9 25.0 29.4 28.4 21 Sud & Bucuresti 16.5 17.8 20.6 19.5 31

Sud-Vest 28.2 29.2 34.5 33.6 20 Vest 19.2 20.3 23.7 22.5 24

Nord-Vest 24.6 25.8 30.6 29.5 22 Centru 21.0 22.4 23.4 22.1 25

Slovakia 7.1 8.6 7.3 6.1 46 Bratisl. & Záp. Slov. 6.1 7.4 6.4 5.3 56

Stredné Slovensko 10.2 11.7 10.4 9.0 39 Východné Slovensko 7.4 8.9 7.4 6.3 35

* Euclidean distance are presented on a base of 100. 1 in 1996. 2 PPS is the abbreviation of Purchasing Power Standard. Sources: calculated from EUROSTAT (2003); BULSTAT (2003); CSO (2003) and Behrens (2003).

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