The impact of EU enlargement on cohesion · Table 4.1.3: Trade data of the EU 15, 1998 Table 4.1.4:...

German Institute for Economic Research and

The Impact of EU Enlargement on Cohesion

European Commission Tender No. PO/00-1/RegioA4

The authors gratefully acknowledge financial support from the European Commission, in the context of

the Second Cohesion Report and Second Cohesion Forum. Nevertheless the opinions remain those

of the authors, and do not necessarily represent Commission views

Christian Weise, John Bachtler, Ruth Downes,

Irene McMaster, Kathleen Toepel

FINAL REPORT

Berlin and Glasgow, March 2001

EPRC EUROPEAN POLICIES

RESEARCH CENTRE

"Enlargement and Cohesion" – Background Study for the 2nd Cohesion Report

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Structure

List of Tables ........................................................................................................... 6

List of Figures.......................................................................................................... 7

List of Annexes........................................................................................................ 7

Glossary of Terms ................................................................................................... 7

EXECUTIVE SUMMARY ............................................................................................ 9

1 Enlargement and Cohesion: Concepts and Definitions...................................... 15

1.1 Introduction .................................................................................................... 15

1.2 EU Enlargement and Cohesion...................................................................... 16

1.3 Policy Objectives............................................................................................ 17

1.4 Indicators of Cohesion ................................................................................... 18

1.5 Aims and Approach of the Study.................................................................... 21

2 Economic Characteristics of Enlargement ......................................................... 24

2.1 Enlargement: Just a Political Project or Deepening of Economic Integration?24

2.2 Possible Impact of Enlargement..................................................................... 26

2.3 Alternatives to Enlargement? ......................................................................... 28

3 Economic and Social Cohesion in the Candidate Countries and European Union:

Analysis of Disparities........................................................................................ 31

3.1 Socio-Economic Context at the National Level .............................................. 31

3.1.1 Macro-economic Developments.............................................................. 31

3.1.2 Labour Market......................................................................................... 35

3.1.3 Social Inequality...................................................................................... 40

3.1.4 Environment............................................................................................ 45

3.2 Patterns of Regional Disparity in CEE and EU Countries .............................. 46

3.2.1 General Overview ................................................................................... 47

3.2.2 Population Density .................................................................................. 50

3.2.3 GDP per Head ........................................................................................ 50


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3.2.4 Unemployment........................................................................................ 51

3.2.5 Employment Structure............................................................................. 53

3.3 Types of Regional Problems in the CEECs.................................................... 54

3.3.1 Capital Cities and Major Urban Agglomerations ..................................... 55

3.3.2 CEE: EU Border Regions........................................................................ 56

3.3.3 Peripheral Regions: Eastern Border and Rural ....................................... 61

3.3.4 Old Industrial Regions............................................................................. 63

3.4 Types of Regions in Applicant Countries and the EU: Cluster Analysis ......... 65

3.5 Island Economies........................................................................................... 72

3.5.1 Malta ....................................................................................................... 73

3.5.2 Cyprus..................................................................................................... 75

4 Effects of Enlargement: Critical Issues .............................................................. 78

4.1 Trade.............................................................................................................. 79

4.1.1 Trade Volume ......................................................................................... 79

4.1.2 Trade Structure ....................................................................................... 85

4.1.3 Regional and Social Impact .................................................................... 90

4.1.4 Outlook and Conclusion.......................................................................... 94

4.2 Investment...................................................................................................... 96

4.2.1 Volume of FDI ......................................................................................... 96

4.2.2 Allocation of FDI.................................................................................... 101

4.2.3 Role of FDI............................................................................................ 103

4.2.4 Impact of Increased FDI to CEECs ....................................................... 105

4.2.5 Conclusion ............................................................................................ 107

4.3 Migration ...................................................................................................... 108

4.3.1 Migration Flows..................................................................................... 108

4.3.2 Regional and Social Impact .................................................................. 111

4.3.3 Types of Migration ................................................................................ 113


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4.3.4 The Cases of Germany and Austria...................................................... 114

4.4 Border Regions ............................................................................................ 117

5 Projected Patterns of Disparity ........................................................................ 122

5.1 Objectives .................................................................................................... 122

5.2 Approach...................................................................................................... 122

5.2.1 Determinants and Alternative Development Paths of National GDP..... 122

5.2.2 Patterns for Regional Convergence in the Applicant Countries ............ 123

5.3 Results ......................................................................................................... 125

6 Summary and Perspectives............................................................................. 128

6.1 Introduction .................................................................................................. 128

6.2 Economic Characteristics of Enlargement ................................................... 128

6.3 Economic and Social Cohesion in the Candidate Countries and European

Union: Analysis of Disparities....................................................................... 129

6.4 Effects of Enlargement: Critical Issues......................................................... 132

6.5 Projected Patterns of Disparity..................................................................... 133

6.6 Prospects and policy issues......................................................................... 134

References ............................................................................................................. 137


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List of Tables

Table 3.1.1: GDP growth in Applicants and selected EU Members (annual %)

Table 3.1.2: GDP in Applicants and selected EU Members, 1989 = 100

Table 3.1.3: Gross domestic investment in Applicants and selected EU Members (% of GDP)

Table 3.1.4: Inflation in Applicants and selected EU Members, consumer prices (annual %)

Table 3.1.5: Labour force in Applicants and selected EU Members, total, 1990 = 100

Table 3.1.6: Unemployment in Applicants and selected EU Members, total (% of total labour force)

Table 3.1.7: Female Unemployment in Applicants and sel. EU Members (% of female labour force)

Table 3.1.8: Male Unemployment in Applicants and sel. EU Members (% of male labour force)

Table 3.2.1: Inner-National Disparities in Applicant Countries (GDP)

Table 3.2.2: Inner-National Disparities in Applicant Countries (Unemployment)

Table 3.3.1: Regional Disparities at the EU - CEEC - Border

Table 4.1.1: EU Member States� Exports, Imports and Net-Exports with AC 12

Table 4.1.2: EU 15 Exports, Imports and Net-Exports with individual Applicants

Table 4.1.3: Trade data of the EU 15, 1998

Table 4.1.4: EU exports and imports with selected Applicants by selected product group

Table 4.1.5: EU 15 Imports from selected Applicants, sum of product group No. 84=100

Table 4.1.6: Regional Trade Data for Poland

Table 4.2.1: FDI Stocks of EU members in CEECs, 1997, Assets

Table 4.2.2: FDI Stocks of EU members in CEECs, 1997, Assets, EU 15 = 100

Table 4.2.3: FDI Flows from EU members to CEECs, 1998, EU 15=100

Table 4.2.4: FDI Flows from CEECs to EU, 1998, in Mio Euro

Table 4.2.5: FDI Stocks of EU members in CEECs, 1997, Assets, World = 100

Table 4.2.6: FDI Flows from EU to CEECs, 1998

Table 4.3.1: Distribution of Immigrants from Central and East European Countries among the Member

States of the EU

Table 4.3.2: Projections for the Growth of the Population of Citizens of the Central and East European

Candidate Countries Resident in the EU

Table 4.3.3: Projections for the Stock Population of Citizens of the Central and East European Candi-

date Countries Resident in the EU

Table 4.4.1: Shares of Exports of the New German Bundesländer as a percentage of German Exports

Table 5.1: Scenarios of Regional Convergence: Summary of Main Results for 2030


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List of Figures Figure 4.1.1: Germany: Regional Structure of Trade with the CEECs 1997

Figure 4.3.1: Germany: Regional Distribution of Employees from the CEECs

List of Annexes

Annex 1: Maps on Regional Disparities in the EU and the Applicant Countries

Annex 2: Summary Table of Regional Indicators in the EU and the Applicant Countries (NUTS II level)

Annex 3: Clusters of Regions of the EU and the Applicants

Annex 4: Scenarios of long-run National GDP Growth in the Applicant Countries

Annex 5: Scenarios of long-run Regional GDP Growth in the Applicant Countries

Glossary of Terms

AC Applicant Countries

CEE Central Eastern Europe

CMEA Council for Mutual Economic Assistance

FTA Free Trade Area

IIT Intra-Industry Trade

NUTS Nomenclature des Unités Territoriales Statistiques

PPP Purchasing Power Parities

SME Small and Medium Sized Enterprise

SMP Single Market Programme


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The Impact of EU Enlargement on Cohesion

Preparation of the Second Report on Economic and Social Cohesion, Study Area 11

commissioned by the European Commission, DG Regio

FINAL REPORT

DIW, German Institute for Economic Research, and

EPRC, European Policies Research Centre

EXECUTIVE SUMMARY

Berlin and Glasgow, March 2001

It is a common view that enlargement poses a severe challenge for EU structural and

cohesion policies. Far less clear and uncontroversial, however, is the empirical and

analytical basis for that statement. Three broad questions need to be addressed:

(1) What is the current state of economic and social cohesion in the applicant coun-

tries and how will, as a consequence, the situation in a future EU 27 differ from that

in the current EU 15? (2) How will enlargement itself affect cohesion via the expected

intensification of economic integration? (3) How long will EU structural policy have to

deal with the challenges of enlargement? A detailed analysis of these questions is

the purpose of the present study.

Cohesion can be interpreted in various ways: a level of stability or a process of con-

vergence; specifically in terms of income or unemployment levels, or elastically to

encompass employment opportunities and living standards. EU structural and cohe-

sion policies give primacy to two measures: GDP per capita and unemployment

rates. This report interprets �national and regional cohesion� as meaning the degree

of disparity in GDP per capita in PPP. Social cohesion refers to the exclu-


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sion/inclusion of sections of the population from/in the labour market as well as to

poverty.

Enlargement could result in differing scenarios for economic and social cohesion. A

decisive factor seems to be the preparedness for structural change in all members of

the enlarged EU. A delay of enlargement due to pessimistic expectations would

probably contribute decisively to the realisation of these expectations and would

harm rather than protect less competitive sectors of EU economies.

Analysis of existing disparities

Despite recent growth rates above the EU 15 average in the CEECs, economic con-

vergence remains limited. Poland, Slovenia, Hungary and the Czech and Slovak Re-

publics overall display the most positive macro-economic indicators. Considerable

labour market changes have occurred associated with the processes of economic

restructuring, privatisation and liberalisation. These include a sharp fall in industrial

employment and a considerable rise in service sector employment, but there remain

substantial differences with the employment structure of the EU Member States. Un-

employment has risen in all CEE countries to varying extents. Income levels and

standards of living have declined and poverty has spread considerably (with variation

between countries and a disproportional effect on certain social groups). Rapid

industrialisation, inefficient raw material extraction, obsolete technology and a lack of

environmental controls have left a legacy of environmental degradation with signifi-

cant spatial differentiation. While reduction in pollution levels is evident, the costs of

clean-up are still extremely high.

The spread of sub-national disparities (in GDP and unemployment) in the CEECs is

smaller than in some EU Member States. Disparity patterns (at NUTS II level) include

the following:

• GDP per capita in CEE regions is considerably less than the EU average � only

Prague and Bratislava lie above this level;

• regional unemployment is relatively low in CEE in comparison to the EU, with

considerable sub-national variation (but again less than in EU Member States);

• CEE regions are in general more sparsely populated than in the EU; and

• agriculture dominates regional employment structures in some CEECs (eg. Ro-

mania and Poland) to a much greater extent than in the EU.


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The types of regional problems in CEE reflect both the unique process of transition,

as well as structural changes already undertaken in Western countries but delayed in

CEE by geo-political factors. Overall groupings include:

• Capital cities/major urban agglomerations which demonstrate the most favourable

economic indicators, benefiting from high investment, skilled labour and training

facilities, better infrastructure, business services and access to decision-makers;

• Western border regions which benefited from proximity to the EU, encouraging

investment, trade, tourism and educational/technological initiatives;

• Peripheral eastern and rural regions which are among the most economically dis-

advantaged in CEE. Geographical location, poor infrastructure, low investment,

declining agriculture and rural out-migration are all contributory factors. These re-

gions have particularly high rates of unemployment;

• Old industrial regions, the drivers of economic activity under socialism, which

have been particularly negatively affected by privatisation, enterprise restructur-

ing/closures, subsidy loss and market re-orientation. Problems include unem-

ployment, lack of entrepreneurship and environmental decline.

A cluster analysis was conducted to classify all ca. 260 EU and AC regions simulta-

neously in types of regions according to their employment structure and population

density. This produced six clusters: Agglomerations; Service dominated; Service bi-

ased; Industry; Agriculture biased; Agriculture dominated. The distribution of the re-

gions among the clusters shows the very poor development of the service sector and

the importance of agriculture in the transition countries compared to the EU 15. In-

dustry plays a dominant role for employment only in a small number of the AC re-

gions. This economic structure of the AC regions is noteworthy because, in general,

regions with above-average GDP per head are more likely to be found in the ag-

glomeration or service clusters than in the industry cluster. An agricultural bias is

clearly associated with a low per capita GDP. Labour market problems, however, are

not obviously associated with specific clusters.

Effects of Enlargement

During the 1990�s, the CEECs have managed to re-direct their exports away from the

former COMECON members towards the European Union. The trade volume has

increased significantly, and the EU has become the most important trading partner of


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the CEECs. From the point of view of the EU, the AC are much less important part-

ners. Geographical proximity seems to play a key role in determining bilateral trade

flows. Regional trade data available indicate that this pattern also applies at the re-

gional level. CEECs have been able to change the commodity structure of their ex-

ports from inter-industry to intra-industry trade but still export mainly products with

comparatively low unit values. There is no indication that the CEECs constitute a se-

vere competition for the EU cohesion countries or other EU members.

As in the case of trade, recent years have seen a marked increase in FDI flows from

the EU to the AC, dominated by the main trading countries but also by France and

the Netherlands. Contrary to the situation with regard to trade, there are practically

no FDI flows from the AC to the EU.

Migration is often cited as the most important post-enlargement effect with automati-

cally associated negative consequences for EU members. But more diligent analyses

do not expect a massive influx of migrants after enlargement and see only minor

- and by no means necessarily negative - effects on wage and employment in the

EU. Migration flows will be directed mostly into Germany and Austria. Inside these

countries, they will be directed to centres of economic activity, not necessarily to the

border regions. However, the inflow will not be as excessively high as sometimes

expected and it will slow down over time.

Border regions are potentially most affected by enlargement, accentuating internal

disparities inside these regions with both, positive and negative, possible effects.

Competitive enterprises, sectors and areas will gain from the proximity of new mar-

kets and the supply of a wider selection of inputs. Less competitive ones will suffer

from increased competition. Along the EU:CEEC border the impact will most likely be

concentrated on the eastern Austrian regions. The impact is not necessarily negative

on balance but the adjustment pressure will be highest here.

Challenges for support policies

The analysis of the challenges to support policies has to start with an assessment of

the long-run perspectives for convergence of GDP per head in an EU 27. First, a

plausible range for convergence of national GDP per head was derived from the ex-

perience of post-war developments in 21 European market economies. A conver-

gence rate of 2 %, i.e. reducing the income gap by 50 % in 35 years, seems to be


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most plausible. The ten applicants from CEE would then achieve a per capita GDP of

approx. 65 % of the EU 15 average in 2037 (starting from 38 % in 1997).

This analysis was complemented by the calculation of eight different scenarios for

convergence of regional GDP per head � not to identify the most plausible one but

rather to cover a wide range of reasonably possible developments. Even in the most

optimistic scenario, only 14 of the 51 regions of the applicants would achieve a per

capita GDP of more than 75 % of the EU 15 average in 2030.

The empirical work in this study has shown that enlargement will not pose a severe

challenge for today's EU regions. Development problems of the applicants, however,

are severe and will persist. Given the severity of the regional development problems

in an enlarged EU and the duration of very low standards of living in most of the ap-

plicants (which are to be expected under reasonable assumptions), the options for a

reform of EU structural policies have to be discussed.


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1 Enlargement and Cohesion: Concepts and Definitions

1.1 Introduction

The invitation of the EU to the Central and Eastern Europe countries (CEECs) to join

the European Union � upon compliance with certain conditions � has been part of EU

policy since the European Council of Copenhagen in June 1993. At the time of the

publication of the First Cohesion Report the transition countries had formally applied

for membership but substantial negotiations had not begun yet. Since then, signifi-

cant progress has taken place. The EU has offered the formal status of a �candidate

country� to all applicant countries from Central and Eastern Europe (CEE) as well as

to Cyprus, Malta and Turkey in two steps in Luxembourg (December 1997) and in

Helsinki (December 1999). With the notable exception of the case of Turkey, formal

accession negotiations with all applicant countries (AC) have now begun � first with

the so-called Luxembourg Group (Cyprus, Czech Republic, Estonia, Hungary, Po-

land and Slovenia), then with the Helsinki Group (Bulgaria, Latvia, Lithuania, Malta,

Romania and Slovak Republic). While negotiations with the individual countries have

made varied degrees of progress, some of the applicants had reached an advanced

stage by Summer 2000.

Parallel to the enlargement negotiations, the EU has had to begin preparing its own

policies for the inclusion of new Member States almost all of which have considerably

lower figures for per capita GDP (relative to the EU average) than any other new or

old EU Member State. Hitherto, the main response of the EU to this challenge has

included the reform of the EU structural and agricultural policies in March 1999

(Agenda 2000) and the development of specific support programmes for the appli-

cant countries (ISPA, SAPARD etc.). However, the more real the prospect of en-

largement becomes, the more important and concrete becomes the question of the

impact of enlargement on cohesion in an enlarged EU.

Work on this report was carried out by Christian Weise and Kathleen Toepel (DIW

Berlin, German Institute for Economic Research) and John Bachtler, Ruth Downes

and Irene McMaster (EPRC, European Policies Research Centre, Strath-

clyde/Glasgow); Annex 4 was provided by Herbert Brücker (DIW Berlin). The authors

are grateful to the Commission officials at DG Regio, Eurostat and other EU offices

for their support during the preparation of this study. In addition, thanks are due to


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research assistants and secretarial staff at DIW and EPRC. This report was funded

by the European Commission in the context of the Second Cohesion Report. How-

ever, the opinions expressed are those of the authors and do not necessarily repre-

sent the Commission's views.

1.2 EU Enlargement and Cohesion

At the heart of the debate over the challenges of EU enlargement is economic and

social cohesion in a wider Union. Cohesion is an important pillar of the European so-

cial market economy, it underpins EU action in the field of regional development and

it will take on greater political, economic and social significance in an enlarged EU

given the relative underdevelopment of the CEE accession countries. The impor-

tance accorded to cohesion derives from the belief that �solidarity and mutual support

are an equally important basis for progress [as market forces], not only for social rea-

sons but also for optimising overall economic benefits since there is ample evidence

of detrimental effects of inequality of growth� (CEC, 1996). This commitment to terri-

torial and social justice provides the rationale for the EU Structural Funds and the

Cohesion Fund as well as the pre-accession instruments, ISPA and SAPARD.

While there is a clear political commitment to economic and social cohesion at EU

and national levels, the architecture of future policies is not clear. Several issues

need to be taken into account. First, the impact of enlargement on cohesion is still

speculative, in particular because reliable sub-national data for the CEECs are only

now starting to become available. Second, the size and diversity of an enlarged EU

requires a fundamental reappraisal of the rationale and objectives of policies to ad-

dress economic and social cohesion. Third, the scope for EU intervention will be in-

fluenced by the willingness of the EU 15 to commit financial resources (the size of

the structural policy budget) and their preparedness to forego the aid provided to cur-

rent recipient regions (the criteria for allocating the budget). Fourth, the relationship

between EU and national policies in the field of regional development is changing,

affecting the scope for current and future Member States to implement their own re-

gional policies. Greater coherence is driven partly by regulation (Structural Fund re-

form, EC regional aid guidelines) and partly by a convergence in thinking about

strategies for economic and social cohesion, but the relationship is still uneasy.

Lastly, it is becoming increasingly recognised that effective delivery of both EU and


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national policy intervention in regional development requires significant investment in

institutional capacity at national, regional and local levels.

Cohesion is not a straightforward concept and can be interpreted in various ways.

For some, it implies a level of stability in territorial and social relations; for others, it

involves a process of convergence in disparities between regions and social groups.

In some cases, it is defined specifically in terms of income levels or unemployment

rates; it is also used more elastically to encompass access to employment opportuni-

ties and desirable living standards. Further, the use of the term is associated with

very different policy choices. In some territories, cohesion is addressed through pol-

icy objectives of equalising regional and social differences through an explicit redis-

tribution of growth, employment etc.; in others, policy is oriented towards maximising

the contribution of regions and social groups to national economic efficiency.

1.3 Policy Objectives

The rationale for regional policy is the key determinant of the choice and use of indi-

cators, and the timescales and spatial scales used for analysing cohesion. The

objectives of cohesion differ greatly between countries and between Member States

and the EU. As is well known, regional policies have been introduced for a mix of

economic motives (e.g. utilisation of production factors, congestion costs), social fac-

tors (commitment to full employment, welfare considerations), environmental argu-

ments (e.g. over-crowding, pollution in congested areas) and political reasons (con-

sequences of disparities for voting patterns) (Vanhove, 1999). The objectives of re-

gional policy are often discussed as a trade-off between: aggregate national effi-

ciency, involving a more efficient allocation of regional resources to maximise net

national benefit; and inter-regional equity, involving a more equal distribution of in-

come, employment or infrastructure over space (Bachtler, 2000).

Over the long term, EU countries tended to introduce regional policies for reasons of

equity but have progressively given greater priority to efficiency since the mid-1970s.

Regional policy goals are increasingly concerned with optimising the contribution of

regional resources to the creation of economic growth by promoting competitiveness

and reducing unemployment (Prud�homme, 1994). This is true of smaller EU coun-

tries where regional differences are comparatively small (Austria, Denmark, Nether-

lands, Switzerland), as well as larger Member states (UK, France) which have suf-

fered relatively high, nation-wide unemployment for much of the past 20 years and


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which have extensive areas experiencing deep-seated industrial decline and social

problems. However, the equity goals of regional policy still exist: the historic concern

with spatial equality as an objective of regional development remains to some degree

in the Nordic countries, France (in part) and in Germany, where the aims of regional

policy continue to advocate the reduction of inter-regional disparities in relation to

income generation and employment opportunities (Yuill/Bachtler/Wishlade, 1999).

The efficiency-equity trade-off is particularly difficult for the Cohesion countries which

have to avoid jeopardising national efficiency in the interests of economic growth

while at the same time channelling resources to less-favoured regions.

From the perspective of CEECs, these issues are highly pertinent. While policies to

address economic and social cohesion were not a priority for CEECs in the early

years of transition, the challenge of rising territorial and social inequality is forcing

regional policy onto the agenda of CEE governments, encouraged by the pre-

accession aid requirements of the EU. However, the CEECs face difficult questions:

when the performance of a country is relatively poor, should resources be focused on

particular regions? What is the balance between the need for national economic

growth and the social exclusion of sections of the population? At what level should

cohesion be addressed? What is the balance between economic and political con-

siderations? In short, CEE policymakers have to strike a balance between internal

cohesion within national boundaries and external cohesion from a European

perspective, i.e. how to reduce the economic gap with the EU while addressing

internal disparities and social inequality.

1.4 Indicators of Cohesion

Across the EU, there are different approaches to identifying areas for cohesion poli-

cies. This reflects the perceptions of cohesion, approaches to selecting areas for pol-

icy intervention, methodological preferences and political perspectives.

At Member State level, the types of indicator for measuring cohesion fall into three

main groups (Wishlade/Yuill, 1997). First, there are physical indicators (those associ-

ated with geographical or natural conditions) which are particularly relevant in the

Nordic Member States where peripherality and sparsely populated areas are a cen-

tral preoccupation of regional policy. In addition, the Cohesion countries, and to some

extent Italy, have problems associated with peripherality. Regional policies in France,

Germany, the UK and Austria also give some recognition to physical factors. Second,


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concern with economic disparities is most evident in the four Cohesion countries, as

well as in Germany and Italy. Belgium, Finland and the Netherlands all utilise GDP

per head as a measure of regional prosperity. Most of these countries, plus Ger-

many, France, Sweden and the UK are also concerned with regional differences in

employment structure. Germany and Portugal take account of the level of amenity or

infrastructure. Many countries consider the potential impact of demographic trends.

Third, there are measures of cohesion relating to social inequalities, notably differ-

ences in living conditions and income. In Germany and Spain, there is an explicit

constitutional commitment to equalising standards of living. Elsewhere, social dispari-

ties are primarily concerned with (un)employment, especially in the UK. Apart from

Greece, Portugal and Sweden, all Member States use unemployment rates for

measuring cohesion to varying extents. Other measures include future employment

trends, the economically active population, levels of personal income and quality of

the labour force.

At the EU level, the choices of cohesion indicators are more restricted. Methodologi-

cal difficulties are compounded by the technical problems of comparative analysis

across countries and regions. For EU policy purposes, economic development

(measured in terms of per capita GDP) largely determines access to the Structural

Funds (most notably through Objective 1) and is a key consideration in EC competi-

tion policy reviews of regional aid maps. Also, EU regional policy and competition

policy both use unemployment rates as an important measure of cohesion as well as

employment trends (for the Structural Funds).

The features of EU regional socio-economic patterns are well-known: regional dis-

parities across the EU are wide by international standards, and there is a significant

core-periphery disparity in regional GDP per head and (partly) in regional unemploy-

ment. According to EU data, the gaps in GDP per head between the cohesion coun-

tries and the rest of the EU tended to narrow over the 1986-96 period, with an im-

provement in the GDP per head of the four countries from 65 % of the EU average in

1986 to almost 77 % in 1996 and forecast to rise to 78 % in 1999 (CEC, 1996).

Such conclusions about cohesion are, however, sensitive to the time period and spa-

tial scale used for analysis. Over the long term, several studies have found a narrow-

ing of regional disparities at different levels up to 1973 but thereafter a freezing (or

even widening) of disparities from the 1970s to early 1990s (Dunford/Smith, 1998;


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Pettenati/Canullo, 1994). Other research on trends in disparities (Armstrong/Vicker-

man, 1995; Dignan, 1995) suggests some convergence in EU regional differences

from the mid-1980s to the early 1990s, but with a convergence trend that was weak,

halting and mainly due to convergence between some peripheral countries and the

rest of the EU. Analyses of trends in regional unemployment also find limited evi-

dence of any widespread convergence of regional unemployment rates over the pe-

riod from the early 1980s to early 1990s. Indeed, it has been argued that European

regional unemployment disparities are in fact characterised by an equilibrium state of

persistent high disparities in regional unemployment (Baddely/Martin/Tyler, 1998).

EC research shows that the 25 regions with the lowest unemployment rates were

much the same in the late 1990s as ten years previously, while rates in the most af-

fected regions increased from 20 % to almost 24 % (CEC, 1996). At a different spa-

tial scale, regional differences within countries appear to have been diverging, al-

though there is great variation between Member States and little consensus among

the research undertaken.

Research on EU disparities is, of course, affected by spatial scale. For example, the

choice and size of spatial units alters the measurement of GDP. At high levels of spa-

tial disaggregation, disparities in levels of GDP per head increase, while conversely,

high levels of aggregation lead to differences between areas being averaged out.

The spatial unit used is also relevant, since the use of �non-functional units� can lead

to a separation of centres of economic activity from their prosperous commuter belts.

The NUTS classification used by Eurostat is associated with all of these problems,

since it is based on administrative boundaries and involves areas of greatly differing

size, population and population density, influencing the genuine comparability of sta-

tistical indicators across the EU (Wishlade/Yuill, 1997). There is still scope for im-

provement in comparability, a fact recognised by the European Commission which is

preparing a regulation on NUTS to set standards.

With respect to the indicators themselves, measuring regional GDP per capita is as-

sociated with several important methodological challenges, including the difficulties in

assigning output where activities cross regional boundaries or where the income ac-

crues from off-shore natural resources, and the effects of government transfers and

the size of the black economy on the composition of regional GDP. Using purchasing

power parities (PPP) to take account of the cost of living in the measurement of re-

gional GDP per head can make a substantial difference to the position of regions


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across the EU, making some regions appear poorer and others richer, but overall

reducing the disparities between regions. The use of population as the denominator

may also give a partial view of economic development compared, for example, to the

size of the regional labour force. Methodological difficulties are also associated with

the use of regional unemployment rates, notably the limitations on comparability of

regional unemployment between countries due to the differences between national

labour markets and the varied determinants of unemployment rates (Wishlade/Yuill,

1997).

Bearing in mind these caveats, this report interprets national and regional cohesion

as meaning the degree of disparity in GDP per capita (measured in purchasing

power parities). �Social cohesion� refers to the exclusion/inclusion of sections of the

population in the labour market, captured principally by various unemployment

indicators (official rate of unemployment, long-term and youth unemployment etc.).

Social cohesion, as proposed, mainly depends on the overall growth and the

development of national and regional cohesion.

�National� cohesion refers to the 15 Member States of the current EU, the ten appli-

cants from Central and Eastern Europe, and the island economies of Malta and Cy-

prus. �Regional� cohesion refers to the NUTS II level wherever applicable.

1.5 Aims and Approach of the Study

The aim of this study is to provide a detailed examination of the possible conse-

quences of enlargement for cohesion in the current EU Member States and in the

candidate countries to support the preparation of the Second Report on Economic

and Social Cohesion in the EU. The study will clarify the main channels through

which enlargement will affect national, regional and social cohesion in Europe, iden-

tify the main similarities and contrasts between (relevant groupings of) regions and

regional developments in Western and Eastern Europe, and produce a picture of the

possible consequences of enlargement for regional disparities and social cohesion,

e.g. employment opportunities and unemployment risks.

The study is structured as follows. The first step has been to clarify the terms and

concepts used. As regards �cohesion�, this was done in Chapter 1. Discussions on

the possible impact of enlargement in East and West are often confused and over-

burdened if each and any development in the transition countries or in East-West


DIW 22 EPRC

economic and political relations is attributed to the envisaged inclusion of these coun-

tries in the EU. Chapter 2, therefore, discusses the specific aspects that distinguish

enlargement from transition and economic integration of the CEECs in the interna-

tional division of labour that might be expected without the prospect of becoming an

EU Member State. In addition, different scenarios of the impact enlargement might

have on the economic and social prospects of the (enlarged) EU are developed.

The topic �impact of enlargement on cohesion� can be interpreted in at least three

different ways. First, it has to be shown how different intra-EU disparities will be in an

enlarged EU as compared to the present EU 15. Chapter 3 provides a detailed

analysis of the disparities in the applicant countries and of the range of new devel-

opment problems associated with the inclusion of these countries in the EU. Second,

in addition to this �static� approach, it has to be asked whether enlargement will ame-

liorate or worsen inner-EU disparities in a dynamic sense. Chapter 4 tackles this

question by an analysis of trade, investment flows and migration between the EU and

the applicants and develops a judgement of the possible regional implications of an

intensified economic integration. Third, the challenges of enlargement for cohesion

policy deserve attention. Chapter 5 develops � as a background to this question � a

plausible upper and lower boundary for the development of national convergence of

the CEECs (to the EU average) and presents some plausible scenarios for regional

convergence of AC regions. This demonstrates how much time is needed to achieve

substantial cohesion. Chapter 6 gives a summary and concludes the study.

Empirical work for this study has mostly had to rely on data available from the Euro-

pean Commission. Regional data were provided mainly by DG Regio and Eurostat;

data on trade and FDI came from the COMEXT database and Eurostat's EU Direct

Investment Yearbook, respectively. The World Development Indicators of the World

Bank have been used for the macroeconomic analysis. In addition, selected data

came from the UN-ECE Economic Survey of Europe, the World Development Report,

the UNDP Human Development Report, the Transition Report of the EBRD and Eu-

rostat's Statistical Yearbook on Candidate and South-East European Countries. Mi-

gration figures are from the OECD and national sources.

A systematic review of the home-pages of the applicant countries� statistical offices

brought mixed results. While some countries provide interesting data, others do not.

As the empirical work needed to use comparable and reliable data, regional data


DIW 23 EPRC

from national sources was only used for anecdotal support of the analysis of Eurostat

data. However, special emphasis was placed on obtaining regional trade figures for

German Federal States and Polish Provinces (see section 4.1). Data analysis had to

work with data at NUTS II level. On the one hand, this is due to availability of data

and to practical reasons of work efficiency. On the other hand, EU structural policy is

directed (in its relevant elements) at the NUTS II level. However, as there are only

approximately 50 NUTS II regions in the CEECs (and six applicant countries consist

of just a single NUTS II region), these data necessarily sometimes lack detail. There-

fore, the analysis on regional disparities in the CEECs (chapter 3) draws on pub-

lished and unpublished studies that are more selective (and, necessarily, less com-

parable) but make use of more detailed data from various sources.

An extensive literature search was undertaken in support of the data analysis on

economic and social cohesion in the CEECs and the effects of enlargement. This

involved a review of official publications, academic studies and the grey literature de-

rived from a range of sources including the EC, European Parliament, OECD, EBRD,

IMF, World Bank, UNECE, German Institute for Economic Research (DIW), Vienna

Institute for International Economic Studies (WIIW), NEI, EastWest Institute, Centre

for Economic Policy Research (CEPR), Bank of Finland and Rhine-Westphalia Insti-

tute for Economic Research (RWI). The literature review covered the following issues

in particular � European enlargement, trade and FDI, labour market and structural

change, EU/CEE migration and regional development. The references cited are

drawn mainly from the last 3-4 years given that the situation is highly dynamic and

many texts are outdated relatively quickly. The literature review aims to provide a

review of the main points raised in relation to economic and social cohesion in the

CEECs and an extensive bibliographic reference list.


DIW 24 EPRC

2 Economic Characteristics of Enlargement

The last decade has seen dramatic changes in the political and economic systems of

the applicant countries from CEE. The process of transforming socialist to market

economies was associated by a liberalisation of the integration in the international

division of labour, in general, and by developing closer economic and political rela-

tions with the EU, specifically. This process has resulted in more political freedom

and much better opportunities for pursuing individual concepts of life for the people of

the transition countries as well as in enhanced economic welfare for, at least, a sub-

stantial part of the population. For many, in the EU as well as in the AC, enlargement

is a logical and almost inevitable next step to ensure smooth progress of the eco-

nomic and political development in the AC and economic gains for all European

economies. Not surprisingly, however, transition has not been without negative con-

sequences for selected social groups and economic sectors in the AC. Accordingly,

fears that EU membership might be associated with even more liberalisation and re-

form pressures leading to a worsening of cohesion in the new member states are a

political reality. At the same time, the argument is raised in the EU that economic

benefits of integration with the CEECs are already fully exploited and enlargement

would only lead to limited political gains but immense economic burdens for the cur-

rent members of the EU.

To discuss these fundamental positions of enlargement supporters and sceptics with

more clarity it seems to be decisive (1) to develop a clear understanding of how

- from an economic point of view - membership of the Union differs from membership

in a Free Trade Association as it is, by and large, already achieved between the EU

and the applicants; (2) to evaluate how enlargement could either benefit or worsen

cohesion in Europe and who might be affected; and (3) to check whether there are

viable alternatives to enlargement of the EU. The aim of this section is to develop the

main lines of arguments without trying to give a prognosis of the most likely outcome

or presenting detailed empirical evidence.

2.1 Enlargement: Just a Political Project or Deepening of Economic Integration?

According to integration theory, different levels of economic integration have to be

distinguished. Creating a Free Trade Area (FTA) while maintaining individual tariffs is


DIW 25 EPRC

the least intensive form of integration. The next step is the establishment of a Tariff

Union that applies the same tariffs to all third countries and no restrictions to internal

trade. Current EU members have achieved a much higher degree of integration with

the Single Market and, for most of them, the introduction of the Euro. The Europe

Agreements between the EU and those transition countries that apply for EU mem-

bership basically ensure a Free Trade Area (with some significant exceptions). EU

membership, however, is � in economic terms � equivalent with inclusion in the Sin-

gle Market and other Common Policies. This obviously has severe economic implica-

tions, the most important of them being, most probably:

• liberalisation of agricultural trade,

• a ban on any threat to use anti-dumping measures,

• a potential for more intensive trade with current EU members (today an EU mem-

bership dummy contributes significantly to the explanation of trade flows between

industrial countries),

• a change in conditions for investments both from domestic or foreign sources

(cancellation of a still existing risk premium due to uncertainty about future

developments),

• free movement of labour,

• inclusion in transfer programmes of the EU, most notably the Structural Funds,

• enhanced political and economic stability and insurance against consequences of

possible negative shocks in the future,

• applicability of the acquis communautaire, most notably in competition policy, so-

cial policy and environmental matters, and

• possible participation in the European Monetary Union (although the new member

states do not have to take part necessarily).

It has to be noted, however, that all these implications of membership (as opposed to

'mere' FTA) will take effect at different points of time that are not always easy to indi-

cate. As in the case of Southern enlargement or SMP '92 it is highly likely that enter-

prises will take enlargement for granted, at the latest, when the negotiations are de

facto concluded. First round effects will therefore begin to materialise prior to the offi-

cial date of enlargement, i.e. they have, most probably, already begun to materialise.


DIW 26 EPRC

2.2 Possible Impact of Enlargement

The actual consequences of enlargement cannot be determined ex ante with cer-

tainty. The empirical evidence available today and the probability of some aspects of

possible scenarios are analysed in later chapters of this study. Here, the objective is

to outline how the various economic aspects of enlargement can either contribute to

a favourable development or result in a more problematic situation regarding cohe-

sion in the enlarged EU.

Scenario 1: The yellow brick road to an integrated Europe

An optimistic scenario would, most importantly, have to rely on the willingness and

ability of all EU member states to accept the need for structural change. With regard

to the four freedoms in the Single Market (free flows of goods, services, capital and

persons) this would imply, among other things, a further liberalisation of the Common

Agricultural Policy (rising share of income support payments � according to social

policy criteria -, rising share of co-financing by national governments, gradual reduc-

tion of support) accompanied by support for the re-structuring of rural areas (whether

from national or Community sources). In general, trade volumes would rise signifi-

cantly confirming past experience that trade between EU members is higher than can

be explained by factors like GDP per head or distance alone. Enterprises from the

AC would prove able to withstand competition from EU firms and develop market

niches in EU core markets. The commodity structure of the applicants exports would

become more and more similar to that of the EU members indicating much smaller

and less concentrated import competition pressure in the EU.

EU membership would signal a sustainable legal, political and economic framework

for investors (domestic and foreign). Rising investment shares in GDP would contrib-

ute to higher growth rates and create new employment opportunities. This favourable

economic situation would ameliorate internal cohesion in the applicants: On the one

hand, economic growth opportunities would trickle down also to regions that are not

immediately affected from enhanced investment and, on the other hand, intra-

national migration would become easier because of ameliorated labour market and

housing conditions. International migration would not be a major topic as potential

migrants see positive prospects in their home countries.


DIW 27 EPRC

The applicability of the acquis communautaire would improve the framework condi-

tions for competitive enterprises (competition policy) as well as for employees and

citizens in general (social and environmental policy). Where the implementation of

the acquis would be too costly for either the enterprises or the government, there is

either Community support available or a transition rule applies.

This scenario would require the allocation of sufficient structural funds transfers to

the new members and their efficient implementation, i.e. the funds and national de-

velopment measures are focused on increasing competitiveness of enterprises, hu-

man capital and attractiveness of regions for investment.

In this scenario, the over-all implications for the current EU would be positive. The

CEECs would constitute stable and growing neighbours that provide an interesting

market for EU products and services as well as promising investment opportunities.

This might also lead to a significant inflow of foreign capital into EU financial markets

and rising indices at EU stock exchanges. The CEECs would become more and

more competitive but � because of the high diversification and the modernisation of

their economies � this competition pressure would not focus on specific sectors or

regions of the EU. Net migration flows from East to West would be quite low.

Scenario 2: Why (and how) things may go wrong

Obviously, the consequences of enlargement might also be less positive.

On the AC side, the political will to pursue reforms and structural change may slow

down as soon as EU membership is achieved. Under the pressure of highly competi-

tive enterprises from 'old' EU members, combined with an excessive burden implied

by the EU acquis, slowly developing modern sectors in the CEE economies would

not survive. CEECs would have to rely on current comparative advantages in re-

source- and labour-intensive production. These, too, would be hampered in their de-

velopment by social and environmental standards of the EU. The trade volume would

not rise significantly: The CEECs would not be a promising, growing market for

EU 15 exports nor would CEECs exports be able to compete on EU 15 markets.

Convergence of the CEE economies to the EU average as well as internal cohesion

would slow down. Public funds would be used almost exclusively for subsidies and

social cushioning. Structural funds and (limited) national resources would not be

used efficiently � either due to misguiding incentives or to a lack of monitoring and


DIW 28 EPRC

evaluation. Governments would only pay lip service to the implementation of the ac-

quis (especially in competition policy) and to the development of an efficient admini-

stration. Foreign investment would concentrate on selected regions and on the ex-

ploitation of labour costs advantages; the contribution to development would be

meagre. There would not be any efforts to achieve a balanced regional development

and, consequently, no trickle down to poorer regions. Due to unsatisfactory prospects

for young (and especially well-educated) persons migration to the EU 15 would be

substantial.

On the EU side, the potential strength of the CEECs in agricultural products would be

answered by de facto-discriminating special support for EU 15 farmers (which,

among other things, leads to welfare losses for the EU 15 due to delayed WTO nego-

tiations). There would be no growth impulses from CEE. Adjustment pressures would

be concentrated on low-qualified employees or job seekers. They would suffer from

import competition and from competition on the labour market resulting from a mas-

sive inflow of cheap labour from the CEE.

In general, protectionist tendencies would gain ground at the expense of support for

European integration. The EU would not only be weakened economically � resulting,

among other things, in an outflow of capital to, say, the USA and South East Asia �

but also politically.

In this scenario, the repercussions on the EU would be negative. The more advanced

EU regions would miss the opportunity to trade with a growing market in the East

while the lagging regions of the EU and some rural areas would possibly come under

competition pressure from CEEC suppliers. In addition, fierce battles on the distribu-

tion of structural funds would evolve.

2.3 Alternatives to Enlargement?

As the possible outcomes of enlargement seem to cover a wide range, including

some unpleasant developments, it might be seen as sensible to adopt a cautious

'wait and see'-approach and to monitor possible early indicators of enlargement suc-

cess such as political stability and administrative capacity, trade volumes and com-

modity structure, investment motives etc. over a longer time horizon. Alternatives to a

firm enlargement policy include a strategy of low EU commitment to enlargement with

an intentional delay of accession negotiations or an open termination of negotiations


DIW 29 EPRC

that might be re-orientated towards a membership in the European Economic Area.

The political plausibility and potential costs of such strategies cannot be evaluated

here. However, it is interesting to look at the economic impact of such an anti-monde

on cohesion in the EU and the AC.

The main immediate impact would most probably be a severe setback to economic

growth in the applicants. Several studies have shown the potential gains from the

implementation of the Single Market (see, e.g., Baldwin, 1989) but also that the pe-

riphery might be disadvantaged, at least temporarily, during the process of integra-

tion (see, e.g., Krugman/Venables, 1990). Excluding the AC from further integration

or offer them integration without any support to develop their competitiveness would

diminish their development prospects (under the assumption of an efficient use of EU

transfers). Perhaps even more important seems to be the loss of credibility of the re-

form process in the CEE (see Piazolo, 1999). Analyses of the state of reform in the

CEE suggest that, although much has already been achieved, there still is a distinc-

tion between the political systems of the transition countries and that of developed

democracies and market economies (see, e.g., the ratings of the EBRD). Kaufmann

et al. (1999a and b) have developed a method to aggregate various governance indi-

cators in order to rank a sample of 160 countries according to their state of "voice

and accountability", "political instability and violence", "government effectiveness",

"regulatory burden", "rule of law" and "graft". The method is, according to the authors,

still rather imprecise and should, therefore, only be used with caution when analysing

the relative position of a specific country. However, it is instructive to note that almost

all CEECs lay below the EU members (except Greece) in practically all categories.

Only Hungary and Slovenia are close to Italy and Belgium, the worst-performing EU

members in this analysis; they are followed by Poland and the Czech Republic.

There are differing views on whether FDI in the CEECs will grow after enlargement or

whether it might already have achieved its long-term level. However, it is quite clear

that delayed or even cancelled enlargement would hamper investment conditions. It

would result in potential FDI not being realised and/or FDI stocks being scaled down

and/or FDI being directed to less intensive forms of co-operation like outward-

processing (governed by low wages) instead of developing own R & D-capacities

abroad.


DIW 30 EPRC

As structural change will, in the anti-monde, be more difficult in the AC, import com-

petition in the EU would concentrate on goods that have a comparatively low content

of R & D and human-capital. Protectionist measures against these imports would not

be an option because of the Europe Agreements (and because of the WTO member-

ship of all transition countries in CEE). In addition, it has to be noticed that imports

from the CEE in traditionally problematic sectors like textiles are often much smaller

than those from newly industrial countries e.g. from South East Asia.

Infrastructure as well as social policy and environmental issues would become worse

in the AC because the acquis would not apply and there would be less resources

available for necessary investments (see Garvey/Hager, 2000).

Legal migration would be restricted. However, as push-factors for emigration would

be much more pressing there would be a rising inflow of illegal migrants to the EU.


DIW 31 EPRC

3 Economic and Social Cohesion in the Candidate Countries and European Union: Analysis of Disparities

The emergence of spatial and social disparities occurs within a broader socio-

economic context and is influenced by macro-economic and wider social and envi-

ronmental factors. The economic and social framework for both the individual citizen

and the development prospects of specific regions are often dominated by decisions

and developments at the national level. The CEE region, despite a common socialist

heritage, is not economically or socially homogenous and, at national as well as re-

gional levels, differences have become increasingly apparent during transformation

as individual countries have made reform choices against unique historical, political

and institutional backgrounds. While the focus of this section (and report) is on dis-

parity and cohesion, it is important to highlight the context within which these spatial

and social aspects are emerging.

This chapter starts with an overview of key aspects of the socio-economic framework

which forms the background to the spatial disparities subsequently dealt with in more

detail. These sections are brief, as many of the issues are dealt with in more detail in

other parts of the Second Cohesion Report and they are the context for the main fo-

cus of this study. The data analysis (unless otherwise stated) was carried out by the

project team for the report and is presented in detail in the tables. The chapter then

goes on to analyse in more detail the patterns of regional development in CEE and

the EU 15 to provide a picture of cohesion within an enlarged Europe. Types of re-

gional problems currently facing CEECs are then discussed further before clusters of

similar regions from the EU and the AC are identified. The chapter concludes with a

review of the two island economies of Malta and Cyprus which are in a different

situation to the ten candidate countries of CEE.

3.1 Socio-Economic Context at the National Level

3.1.1 Macro-economic Developments

Growth rates in most of the transition countries have exceeded those of the majority

of the EU 15 in the second half of the 1990s, but substantial convergence is still lim-

ited (see table 3.1.1). Poland and Slovenia are the only two candidate countries

which, in 1998, had exceeded their pre-transition GDP level (see table 3.1.2). The


DIW 32 EPRC

GDP of the Czech Republic, Hungary and the Slovak Republic had almost reached

the level of 1989, while the other CEE candidate countries remain between 70-75 %

of the 1989 level. Latvia had achieved only 57 % of the 1989 level while in Bulgaria

and Romania, sustainable recovery is yet to begin.

Table 3.1.1 GDP growth in Applicants and selected EU Members (annual %)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998

Bulgaria -9 -8 -7 -1 2 3 -10 -7 4Czech Republic -1 -12 -1 0 2 6 4 0 -2Estonia -7 -8 -21 -9 -2 4 4 11 4Hungary -3 -12 -3 -1 3 1 1 5 5Latvia -1 -10 -35 -15 1 -1 3 9 4Lithuania 9 -6 -21 -16 -10 3 5 7 5Poland -5 -5 3 4 5 7 6 7 5Romania -6 -13 -9 2 4 7 4 -7 -8Slovak Republic -3 -15 -7 -4 5 7 7 7 4Slovenia -9 -5 3 5 4 4 5 4Cyprus 7 1 9 1 6 6 3 2 5Malta 6 6 5 4 6 6

France 3 1 1 -1 3 2 2 2 3Germany 2 -1 3 1 1 2 3Italy 2 1 1 -1 2 3 1 2 1United Kingdom 0 -2 -1 2 4 3 2 3 2

Greece 0 3 1 -2 2 2 2 3 4Ireland 8 3 5 4 8 12 8 11 10Portugal 4 2 3 -1 2 3 3 4 4Spain 4 2 1 -1 2 3 2 4 4

Source: World Bank, World Development Indicators 2000.


DIW 33 EPRC

All major economic theories concur that the level and development of investment pat-

terns is decisive for persistent economic growth. The CEECs should have investment

rates clearly above those of the EU. Among the five more successful candidate coun-

tries, Poland and Slovenia had (domestic) investment rates comparable to those of

the four major EU economies in the 1990s while Hungary and, in particular, the

Czech and Slovak Republics, had notably high rates (see Table 3.1.3). Bulgaria had

the lowest rates of the CEECs. However, high rates of investment are not sufficient in

themselves: Romania had quite high rates until 1996 although Romanian growth is

meagre. Even more important appears to be whether or not domestic investment is

rising. On this basis, the rates in Bulgaria, Latvia and Romania are currently much

lower than in the early 1990s, while the Czech and Slovak Republics, Hungary and

Slovenia invest a higher share of their GDP. Foreign direct investment is most impor-

tant in Hungary and Estonia � FDI levels of around five percent of GDP or above

Table 3.1.2 GDP in Applicants and selected EU Members, 1989 = 100

Country Name 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

Bulgaria 100 91 83 77 76 77 80 72 67 69Czech Republic 100 99 87 87 87 89 94 98 98 96Estonia 100 93 86 67 62 61 63 66 73 76Hungary 100 97 85 82 82 84 86 87 91 95Latvia 100 99 88 58 49 49 49 51 55 57Lithuania 100 109 103 81 68 61 63 66 71 75Poland 100 95 90 93 97 102 109 115 123 129Romania 100 94 82 75 76 79 85 88 82 76Slovak Republic 100 97 83 78 75 78 84 89 95 99Slovenia 100 91 86 89 93 97 101 105 109Cyprus 100 107 108 118 119 126 134 138 140 147Malta 100 106 113 118 124 131 139

France 100 103 103 105 103 106 108 110 112 116Germany 100 102 101 104 105 106 109 112Italy 100 102 103 104 103 105 108 109 110 112United Kingdom 100 100 98 98 100 104 107 110 113 116

Greece 100 100 103 104 102 104 106 109 112 116Ireland 100 108 111 117 121 131 146 158 175 193Portugal 100 104 107 110 108 111 114 117 122 127Spain 100 104 106 107 106 108 111 114 118 122



DIW 34 EPRC

were reached in only a few applicant countries in selected years. More analysis and

discussion on the specific issues of investment and trade are provided in Chapter 4.

In the early 1990s, all the transition countries experienced very high inflation rates

(see table 3.1.4) and, while many were able to achieve one-digit rates in 1997 and

1998, inflation remained relatively high in Poland (16 and 12 % respectively) and

Hungary (18 and 14 %). Romania and Bulgaria were again the most problematic

cases with extremely high inflation throughout the 1990s. Inflation peaked in 1997

(1,087 % in Bulgaria and 155 % in Romania) and, although falling dramatically in

1998, still stood at 22 and 59 % respectively.

Table 3.1.3 Gross domestic investment in Applicants and selected EU Members (% of GDP)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998

Bulgaria 26 23 20 15 9 16 8 11 15Czech Republic 25 23 26 27 30 34 35 33 30Estonia 30 26 27 27 29 27 28 32 29Hungary 25 20 16 20 22 24 27 28 31Latvia 40 34 41 9 19 18 19 23 23Lithuania 33 24 16 19 18 25 24 27 24Poland 25 19 14 15 18 20 22 25 26Romania 30 28 31 29 25 24 25 21 18Slovak Republic 33 31 28 27 23 28 39 43 39Slovenia 17 18 19 21 23 23 24 25Cyprus 27 26 29 24 25 25 25Malta 33 32 28 30 31 32 29 26 23

France 22 22 20 17 18 18 17 17Germany 23 23 22 22 22 21 21Italy 21 21 20 17 17 18 17 18United Kingdom 19 16 15 15 16 16 16 16

Greece 23 23 21 20 19 19 19 20Ireland 21 19 16 15 16 17 19 20Portugal 29 27 26 23 24 24 24 26Spain 25 25 23 20 20 21 21 21



DIW 35 EPRC

3.1.2 Labour Market

Labour market changes are a key element in the transformation process of the

CEECs. With the fall of socialism, new labour market models have evolved, charac-

terised by liberalisation, increased private sector employment (including self-

employment), an extension of deregulatory functions of the state in the use of man-

power and the introduction of new systems of labour relations and negotiation (Bil-

sen/Konings, 1998; Franz, 1995).

The labour market changes in individual CEE countries vary depending on a range of

factors including the nature of reform and the existing sectoral and employment

structure. In some cases, the growth of the private sector has been sufficient to com-

pensate, to a considerable degree, for the decline and restructuring of the state sec-

tor. In other cases, the decline has been more dramatic, and, combined with a weak

private sector development, has fuelled employment decline and unemployment. Pol-

Table 3.1.4 Inflation in Applicants and selected EU Members, consumer prices (annual %)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998

Bulgaria 24 338 91 73 96 62 123 1082 22Czech Republic 10 9 9 8 11Estonia 90 48 29 23 11 8Hungary 29 34 23 22 19 28 23 18 14Latvia 243 109 36 25 18 8 5Lithuania 410 72 40 25 9 5Poland 555 77 45 37 33 27 20 16 12Romania 231 211 255 137 32 39 155 59Slovak Republic 61 10 23 13 10 6 6 7Slovenia 32 20 13 10 9 9Cyprus 5 5 7 5 5 3 3 4 2Malta 3 3 2 4 4 4 2 3 2

France 3 3 2 2 2 2 2 1 1Germany 3 2 5 4 3 2 1 2 1Italy 6 6 5 4 4 5 4 2 2United Kingdom 9 6 4 2 2 3 2 3 3

Greece 20 19 16 14 11 9 8 6 5Ireland 3 3 3 1 2 3 2 1 2Portugal 13 11 9 7 5 4 3 2 3Spain 7 6 6 5 5 5 4 2 2



DIW 36 EPRC

icy choices in areas such as privatisation affected the labour market situation, result-

ing in both employment decline but also private sector employment growth. The dis-

mantling of factors which previously maintained excess employment, such as soft

budget constraints and inefficient management, also had, and continue to have, im-

pacts on the CEE labour markets.

Wider changes in the economic structure of the CEE economies have also impacted

on the labour market. First, significant sectoral change has occurred since the start of

the reform process. The pre-reform sectoral structure of the CEE countries was

characterised principally by a large industrial sector complemented by an equally

significant agricultural sector (in some cases, four times higher than the EU average)

and a relatively small share of services. The main post-reform sectoral changes in-

clude the marked rise in the share of the service sector and a corresponding fall in

the role of manufacturing industry and agriculture (EBRD, 1998; OECD, 1997).

Industrial employment (defined in this case as mining, manufacturing and utilities) is

estimated to have fallen by between 25-50 % in different CEECs (UNECE, 2000).

However, the decline in most transition countries reflects more previous high levels of

over-employment and a deterioration of the industrial and manufacturing base than a

positive process of restructuring.

A comparison with the employment structure of the EU (see, e.g., Annex 3) shows

that the share of the service sector in the CEECs is still significantly below that of the

EU. Employment is, compared to the EU 15, still concentrated on industry and agri-

culture (see section 3.4). Intensified restructuring in 1999 accelerated the rate of em-

ployment decline in mining, manufacturing and the utilities in countries including Po-

land and the Czech Republic, while Hungary was the only CEEC where employment

in these sectors has been increasing since 1997 (UNECE, 2000). Industrial produc-

tion (defined in this case as non-agricultural or service related production) as a share

of GDP still exceeds the EU average in a number of transition economies (Martzanis

and Petrakos, 1998). Specifically, manufacturing employment has declined to a

lesser extent than in the larger category of industry in the majority of the CEECs and

has been a motor of economic recovery in some countries. In Latvia and Lithuania,

however, manufacturing has fallen much more than industry (more than 40 %), re-

flecting the breakdown of former close production ties and the loss of key markets in

the former Soviet Union.


DIW 37 EPRC

In the services sector, there has been an overall increase in the share of total em-

ployment in all CEE countries, although the countries had different starting points.

The Czech Republic and Slovakia, for example, had service sectors which accounted

for over 40 % of employment in 1990 while the corresponding figure in Romania was

only 27 % (Employment Observatory, various years) and � according to UNECE �

only in Poland and in the Czech Republic, the service sector was significantly grow-

ing in terms of absolute employment figures. Certain sub-sectors such as finance,

hotels and trade have displayed particularly notable increases. The development of

the private sector (see below), as well as a proportionally lower fall in employment in

public services such as health, education and other social services, are important

contributors to service sector growth in most candidate countries but there is still a

long way to go.

Employment trends in agriculture are mixed. While employment in this sector has

fallen in most acceding countries, there are important exceptions. In Poland and Ro-

mania, agriculture plays a key role and, at the start of the transition period, accounted

for over 25 % of the total working population. In the Polish case, this is largely related

to the fact that full collectivisation was never achieved. Agricultural employment actu-

ally increased in Romania (as well as in Bulgaria, Lithuania and Latvia) during the

early 1990s, acting partly as a reservoir for job losses in other sectors. Agricultural

smallholdings also became a necessary source of additional income and insurance

against unemployment. Romania remains the extreme case and, in 1999, agriculture

still accounted for 42 % of total employment.

The second trend with ramifications for the labour market is the shift in ownership

structures and the growth of the private sector. This has emerged partly as a result of

the increase in private entrepreneurial enterprise (Johnson/Loveman, 1995; Welter,

1997, 1995; Zemplinerova, 1997; Bilsen, 1997) and partly through the process of pri-

vatisation (Pohl, 1997; Iankova, 1998; Ostojic/Scott, 1996; Smith/Cin/Vodopivec,

1997). Privatisation has involved various approaches in different countries, starting in

some states with the restitution of property, and then generally including a small pri-

vatisation programme bringing small-scale enterprises in the retail, construction and

commercial service sectors into the private sector. This was then followed by a more

extensive transfer of state-owned industrial assets or the privatisation of large state-

owned enterprises. Business start-ups have increased, encouraged by packages of

measures liberalising the business environment. This trend has been particularly no-


DIW 38 EPRC

table in the service sector. The growth of new firm formation and share ownership

suggests that considerable entrepreneurship exists in the CEE countries (Jackson,

1996) � although a high turnover of businesses is associated with difficult overall

economic conditions.

Data calculations undertaken for this report show the current labour markets of the

candidate countries to be relatively homogenous (see table 3.1.5). After initial falls in

overall employment in all the CEECs, the size of the labour force in 1998 in most of

the transition countries was between 94 and 108 % of the 1990 level � Latvia was the

exception with a figure of 90 %. Poland, the Czech and Slovak Republics were the

most successful while Hungary, Slovenia, Lithuania and Romania managed to stabi-

lise employment figures.

Unemployment is one of the most obvious phenomena to have emerged as a result

of economic reform, both from a political and social perspective. There is much less

Table 3.1.5 Labour force in Applicants and selected EU Members, total, 1990 = 100

Country Name 1990 1991 1992 1993 1994 1995 1996 1997 1998

Bulgaria 100 99 98 97 97 96 96 95 95Czech Republic 100 99 100 102 102 103 103 103 105Estonia 100 100 100 98 97 96 95 95 94Hungary 100 100 100 99 101 101 100 100 102Latvia 100 98 97 95 94 92 92 91 90Lithuania 100 101 99 98 98 98 100 100 99Poland 100 100 101 103 103 103 103 106 106Romania 100 100 98 98 100 100 100 99 99Slovak Republic 100 102 102 103 103 105 105 108 108Slovenia 100 102 102 100 102 102 102 101 101Cyprus 100 102 104 105 107 108 109 110 111Malta 100 104 105 106 107 108 108 112 112

France 100 101 101 102 102 105 105 106 106Germany 100 101 101 102 103 103 103 103 103Italy 100 100 100 103 103 103 104 104 104United Kingdom 100 100 101 101 101 102 102 103 103

Greece 100 101 104 105 105 105 106 108 109Ireland 100 103 104 107 107 111 112 116 117Portugal 100 100 100 100 102 102 102 103 103Spain 100 100 103 103 103 106 106 106 109



DIW 39 EPRC

homogeneity in this area of the CEE labour markets. Table 3.1.6 indicates that, in

1997, Bulgaria and Latvia had the highest rates of unemployment (14 %) with Poland

(12 %), Slovak Republic (11 %) and Hungary and Estonia (10 %) all also in double

figures. The lowest rates were recorded in the Czech Republic (5 %) and Romania

(6 %), with Lithuania and Slovenia also low (7 %). The under-registration of hidden

unemployment and ineligibility for benefits among those out of work for prolonged

periods are thought to raise the real levels of unemployment above these rates in a

number of countries. It is notable, however, that the CEE unemployment levels are

comparable to, and in many cases lower than, those in EU countries. However, it

must also be considered that the low rates in some CEECs (and Romania especially)

are due to delayed economic restructuring. The spatial and social dimensions of un-

employment are discussed further in following sections.

Table 3.1.6 Unemployment in Applicants and selected EU Members, total (% of total labour force)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997

Bulgaria 2 11 15 21 20 17 14 14Czech Republic 4 4 4 4 5Estonia 1 2 5 8 8 10 10Hungary 2 9 10 12 11 10 10 9Latvia 2 6 7 19 18 14Lithuania 0 4 4 5 7 6 7Poland 13 14 14 13 12 11Romania 3 8 10 8 8 7 6Slovak Republic 7 11 13 14 13 11 12Slovenia 5 8 12 9 9 7 7 7Cyprus 2 3 2 3 3 3 3Malta 4 4 4 5 4 4 5


Greece 7 8 8 9 9 9 10 10Ireland 13 15 15 16 15 12 12 10Portugal 5 4 4 6 7 7 8Spain 16 16 18 23 24 23 22 21



DIW 40 EPRC

3.1.3 Social Inequality

The official policy of communist regimes in CEE was to eradicate inequalities be-

tween different social groups and, to some extent, policies were implemented to limit

the extent of income disparities. Nevertheless, inequalities in incomes and standards

of living persisted during the communist period and, in the post-communist period,

have generally deepened while new socio-economic divisions have also developed.

Economic transformation in particular has been associated with emerging social

problems and widening inequalities within CEE societies (Cox/Mason, 1999;

Mare�/Mo�ný, 1995). The emergence of opportunities for the ownership of capital, a

growth of employment opportunities in the private sector and a general liberalisation

of CEE economies have all contributed to a growth of inequalities in the distribution

of incomes and wealth.

A commonly used method of measuring income distribution is the Gini coefficient

which is scaled between 0 and 100. The coefficient equals 0 in the hypothetical case

where all units (usually households) in a society have the same income, and it equals

100 when one unit receives all of the society�s income. According to Wyzan�s (1996)

calculations, measured inequality generally rose in CEE, especially in Bulgaria, Esto-

nia and Lithuania. In comparison with some countries in the West, income disparities

in Visegrad countries and Slovenia were not large: all had Gini Coefficients of around

30 in 1993 which is roughly on a par with the more egalitarian EU members states

(Wyzan, 1996). Nevertheless, when compared with the situation under communist

rule, the present levels of inequality in CEE appear to represent a significant widen-

ing of differentials - although problems inherent in using communist statistics as a

guide to inequality need to be taken into account.

New, privileged, wealthy groups have generally benefited from processes of trans-

formation which have created conditions enabling private ownership and entrepre-

neurship and the conversion of the managers of state-owned enterprises into man-

agers and owners of new independent privatised firms (Cox/Mason, 1999; Eyal et al.

1997). At the same time, income levels and standards of living have declined and

poverty has spread considerably. According to Deacon (1993), living standards

dropped by up to one third in the early 1990s, increasing the number of people living

on or below the official poverty line. The low levels of economic development in gen-

eral, and the specific problem of poverty, are undoubtedly factors influencing the


DIW 41 EPRC

wide discrepancies between East and West Europe in key social indicators such as

life expectancy. In 1993, male life-expectancy in Hungary was behind the EU aver-

age by eight years, and the figures were six years for Romania, 4.6 for Poland and

4.3 in Bulgaria (Amato/Batt, 1999). Poverty is also inexorably linked to social prob-

lems such as increased crime rates and discrimination against minority groups.

Analysing the true extent of poverty in CEE can be difficult, as definitions and indices

of poverty vary. There is consensus in the literature that levels of poverty in CEE are

considerable, although significant variation appears to exist between countries. Ac-

cording to the World Bank, which compares actual incomes against a common pov-

erty line set for all CEECs at $120 per person per month, relatively small numbers of

people were below the poverty line in the Czech Republic and Hungary in the early

1990s (Cox/Mason, 1999). In Estonia roughly one third of the population lives below

the nationally defined poverty level (European Parliament, 1999a). According to

Amato/Batt (1999), the number of people living below the subsistence minimum in

Hungary reached three million in 1995, over one third of the population, while in Bul-

garia, over 70 % were living below the poverty line in 1998.

The incidence of poverty also varies across social groups. Evidence suggests that

particular sections of CEE society are more adversely affected than others by dra-

matic socio-economic reform, e.g. the elderly, specific ethnic groups, single-parent

families, unemployed, low-paid employees and women. There are a number of rea-

sons for the increase in poverty generally and for the particular vulnerability of these

social groups.

Loss of income through unemployment is a major contributory factor. Unskilled,

poorly educated workers in vulnerable sectors of the economy have been particularly

affected by unemployment and youth unemployment has also reached high levels

(see Map). New entrants into the job market have found gaining an initial foothold in

labour markets particularly difficult when employment was generally falling

(Cox/Mason, 1999). Women have tended to be concentrated in low-skilled and poorly

paid jobs, and have been particularly vulnerable to redundancies brought about by

economic liberalisation and enterprise restructuring. In 1997, female unemployment

(as a percentage of the female labour force) was higher than the male equivalent in

the Czech Republic, Poland, Latvia and the Slovak Republic (see tables 3.1.7 and

3.1.8). This is similar to the situation in the majority of EU countries. Hungary and


DIW 42 EPRC

Estonia were the only countries where these figures were reversed, unemployment

evels among men and women being equal in all other cases. According to Bretherton

(1999), women once unemployed were significantly less likely than men to be re-

employed. In 1997, the share of long-term unemployed women (as a percentage of

all unemployed women) was higher than total long-term unemployment in all CEECs

except the Czech Republic and Slovenia (no data for Hungary).

Table 3.1.7 Female Unemployment in Applicants and selected EU Members (% of female labour force)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997Bulgaria 22 20 17 14Czech Republic 5 4 5 5 6Estonia 1 2 4 8 8 9 9Hungary 1 8 9 10 9 9 9 8Latvia 3 6 7 18 18 15Lithuania 3 3 5 8 7 7Poland 15 16 16 15 14 13Romania 4 11 13 9 9 7 6Slovak Republic 7 12 13 13 13 13 13Slovenia 5 8 11 8 8 7 7 7Cyprus 3 4 3 4 4 4 4Malta 2 3 3 3 2 3 3





DIW 43 EPRC

Bretherton (1999), Subhan (1996) and Watson (1993) have also noted the emer-

gence of some discriminatory practices in recruitment. Minority groups, and in par-

ticular the Roma population of CEE, experience particularly high unemployment lev-

els and discrimination. In 1995, the unemployment rate for the Roma population of

Hungary was 45 % compared to 10.6 % for the rest of the population (Szamuely,

1996). The Bulgarian Roma minority has similarly high unemployment rates (Euro-

pean Parliament, 1999a). In many CEECs, specific measures to assist minority

groups, women, youth and long-term unemployed back into employment are limited

(European Parliament, 1999a), although some exceptions exist. In Slovakia, for ex-

ample, programmes are in place to target the problems of specific groups, e.g. youth,

elderly and the long-term unemployed. Further, a large system of social transfers in

the country produces a reduction in measured poverty rates � at least in relation to a

very basic living standard � and there are apparently few people well below the pov-

erty line (OECD, 1996).

Another factor affecting poverty levels has been the decline in real wages, and in par-

ticular minimum wage levels, following the introduction of price liberalisation and

Table 3.1.8 Male Unemployment in Applicants and selected EU Members (% of male labour force)

Country Name 1990 1991 1992 1993 1994 1995 1996 1997

Bulgaria 21 20 16 14Czech Republic 3 3 4 3 4Estonia 1 2 5 8 7 11 11Hungary 2 9 11 13 12 11 11 10Latvia 2 5 6 20 19 14Lithuania 4 4 4 7 6 7Poland 12 13 13 12 11 10Romania 2 6 8 8 8 6 6Slovak Republic 6 11 13 14 14 10 11Slovenia 5 9 12 10 10 8 8 7Cyprus 1 2 2 2 2 2 2Malta 4 4 4 5 4 5 6





DIW 44 EPRC

strict income policies by CEE governments. As Standing/Vaughan-Whitehead (1995)

have noted, while the minimum wage helped to provide a barrier to severe poverty in

the past, in the new market-oriented economy it very often became the means of in-

tensifying poverty. Moreover, the decline in minimum wage levels has also had impli-

cations not only for the lowest paid workers, but in many countries for a wider range

of workers whose wage rates are set with reference to the minimum wage. Only

44 % of respondents in a study by Rose/Haerpfer (1998) claimed their regular job

paid enough to cover their needs and in Bulgaria the figure was as low as 17 %. Ac-

cording to this and other studies (e.g. Amato/Batt, 1999) individuals often have to

turn to the second economy (unregistered economic activity) in order to supplement

their incomes. However, the poorest and least skilled groups tend to be excluded

from the second economy through lack of skills, resources and personal connections.

The social situation is critical for those who have insufficient incomes, especially in

countries such as Romania and Bulgaria. Economic difficulties, and the delay by

many governments in implementing major social reform, has particularly impacted on

certain groups e.g. the unemployed, pensioners and farmers, and made them vul-

nerable to poverty (European Parliament, 1999a).

Vulnerable social groups have also been affected by cuts in service provision and

governments� difficulties in establishing new institutional structures to deal with social

problems (Elster/Offe/Press, 1998). In the Czech Republic, for example, poverty has

recently increased because spending on the social security system was the first area

to be cut as part of a response to the recent economic crisis and increase in unem-

ployment in the country (European Parliament, 1999a). Formerly, social protection

systems were broad and universal, but guaranteed only a relatively low standard of

living. In the post-communist period, there has been a general decline in the level of

social services provided by the state-owned enterprises as enterprises striving to

meet new profit criteria close créches, clinics, subsidised canteens and other �fringe�

benefits. Groups such as single parent families and the elderly have been particularly

adversely affected. The greatly reduced availability, and increased cost, of child-care

provision has been a particular impediment to single-parent families and low-income

groups (Ferge, 1997). Both groups have also been affected by the failure of some

benefits, e.g. child support and pensions, to keep up with the price increases of the

early 1990s. The European Briefing paper �The Social Aspects of Enlargement of the

European Union� (1999) highlights some of the problems. In Bulgaria, for example,


DIW 45 EPRC

the level of social benefits offered provide inadequate protection. In Romania, pen-

sion systems are facing major difficulties: due to an ageing population, pre-pension

schemes and redundancies in state owned enterprises, the number of pensioners in

1998 was bigger than the number of contributors. Further, the sharp decrease in the

purchasing power of pensions has exposed the retired population to an increased

risk of poverty.

3.1.4 Environment

Heavy industrialisation in the pre-transition period caused extensive deterioration in

environmental quality, affecting parts of every CEE country (Manser, 1993). Rapid

industrial development combined with the inefficient extraction of raw materials,

obsolete technology and a lack of environmental regulatory controls created a legacy

of environmental degradation. The risks of old technology in areas such as nuclear

power generation are particularly serious, with important nuclear installations in coun-

tries such as Lithuania, Bulgaria and Slovakia. Substantial improvements have, how-

ever, been made in the last decade in the reduction of environmental pollution. This

has been influenced by a range of factors including policy changes, the introduction

of newer technologies, the fall in industrial production and the elimination of subsi-

dies, and structural changes in energy supply.

Considerable spatial differentiation in environmental problems is evident in CEE,

largely as a consequence of concentrated environmentally damaging production

close to the centres of raw materials and energy resources. Karl et al. (2000) high-

light the spatial concentration of pollution in Poland where Upper Silesia produces

around 25 % of all industrial dusts, 30 % of all gases, 25 % of all non-cleaned sew-

age and around 60 % of all industrial solid waste. Other heavily polluted areas are

located in the south and south-west border regions � the Legnica-Katowice copper

basin and the Glogow-Ryback coal basin � as well as Gdansk and Szczecin on the

Baltic coast. Similarly in the Czech Republic, polluted areas are concentrated in the

capital city region and Northern Bohemia - the area of the Ore mountains, together

with the adjacent border districts in Germany (Saxony), and Poland (Silesia), is

sometimes referred to as the �Black Triangle�. The spatial dimension can change the

overall national picture � in Hungary, for example, while overall levels of environ-

mental degradation are relatively lower than other CEECs, the problems in the so-


DIW 46 EPRC

called �industrial crescent�, stretching from Ajka, Gyor and Tatabanya in the west to

Miskolc and Ozd in the east, are more comparable (Karl et al., 2000).

Overall, the CEE countries face the same environmental challenges as other indus-

trialised European countries but from a base of more accentuated difficulties. The

cost of environmental clean-up and the conflict with other policy priorities negatively

affected the progress made in this area. Shifting the costs onto private industry has

perceived risks of slowing the process of industrial restructuring, while concern has

been voiced that the strict application of environmental regulations could dissuade

foreign investors from taking on privatised enterprises or making greenfield invest-

ment. On the other hand, foreign companies have been the source of new environ-

mental practices in many cases, and legislation against pollution has been acceler-

ated in part by the need to conform to the EU acquis. From a governmental perspec-

tive, the problems have been compounded by the lack of reliable data (on various

pollution variables) and limited monitoring and enforcement mechanisms available to

policy-makers (OECD, 1996). This has led to political difficulties in determining where

the priorities in environmental expenditure should be, or indeed what the most ap-

propriate policy instruments are to encourage compliance with regulations (Ambler

and Marrow, 1998; OECD, 1994b). The Sixth Periodic Report notes that the legisla-

tion being introduced in the CEE still generally imposes requirements which are less

than those in the EU and that there are further problems with implementation. The

costs both of reducing pollution and dealing with the legacy of the past are unlikely to

be able to be borne by the CEECs alone (CEC, 1999).

3.2 Patterns of Regional Disparity in CEE and EU Countries

Within the socio-economic context outlined above, regional and social disparities in

the CEECs are becoming increasingly evident. The following section, based on data

analysis carried out by the project team and using principally 1997/98 figures, pro-

vides more detail on the patterns of disparity in key economic areas both within

CEECs and in comparison to the EU. Annex 1 includes a list of all AC regions (at

NUTS II level) as well as an overview map to locate them and maps for each of the

indicators discussed below for AC regions and for EU and AC regions combined. Full

Tables are provided in Annex 2.


DIW 47 EPRC

3.2.1 General Overview

A summary of data on sub-national disparities in the applicant countries is provided

in tables 3.2.1 and 3.2.2 and show a differentiated picture for individual countries. A

country-specific analysis is hampered at NUTS II level due to the small overall size of

many applicant countries. Six CEECs comprise a single NUTS II region and Bulgaria

is divided into only three regions which gives little space for distinctive regional pro-

files. The Tables include, in their first column, all 6 applicants with more than one re-

gion with their national average in per capita GDP and employment. As a point of

reference, values are provided for Germany and Italy, the two EU members with the

most severe internal regional disparities.

In each of the six candidate countries with more than one NUTS II region, the capital

region is the wealthiest in the country. The general pattern throughout CEE is a sig-

nificant concentration of economic activity in the political centre of the country (see

Summary Table in Annex 2 as well as section 3.3.1). However, the very high relative

values of some of the capitals are influenced by commuter flows. Nyugat-Dunantul in

Hungary, Slaskie and Wielkopolskie in Poland and Centru in Romania are the only

regions, apart from the capital city regions, which are clearly above their respective

national averages. The poorest regions in Poland, the Slovak Republic and Hungary

are located at the eastern border, and the very poor region of Nord-Est in Romania

also fits this pattern. The regions of Bulgaria and the Czech Republic are relatively

homogenous.

The national spread, or difference between the richest and poorest regions, is often

distorted by artificially high GDP values in the wealthiest regions resulting from com-

muter flows. A better measure for sub-national disparity levels is the standard devia-

tion, calculated from the weighted distance between the regional per capita GDP and

the national average. Compared to the most extreme case of the EU 15, sub-national

disparities in the candidate countries are not high. Only Hungary and Poland pose a

possible problem in this respect while the situation in the Czech Republic is domi-

nated by the exceptional case of Prague.


DIW 48 EPRC

Applicant Country (in brackets: GDP p.c. EU15 = 100)

Wealthiest Region Poorest region

GDP p. c. 1997,

National Average

=100

GDP p. c. 1997,

EU15=100

National Spread (1)

Standard Deviation, National

Average = 100

BULGARIA (22.6) SOFIA STOLITSA 103.4 23.4 4.1 1.4SEVERNA BALGARIJA 99.3 22.5

CZECH REP. (63.2) PRAHA 189.6 119.7 111.6 32.9STREDOCESKY 78.0 49.3

HUNGARY (47.5) KOZEP-MAGYARORSZAG 149.1 70.9 81.8 32.9ESZAK-MAGYARORSZAG 67.3 32.0

POLAND (35.4) MAZOWIECKIE 150.3 53.2 80.1 23.6SWIETOKRZYSKIE 70.2 24.8

ROMANIA (30.4) BUCURESTI 141.6 43.0 60.1 13.0NORD-EST 80.5 24.4

SLOVAKIA (45.4) BRATISLAVSKÝ KRAJ 225.3 102.2 147.2 16.9

VÝCHODNÉ SLOVENSKO 78.1 35.4

Memo Items:GERMANY (108.4) HAMBURG 181.9 197.1 126.4 28.6

DESSAU 55.5 60.1

ITALY (101.7) EMILIA-ROMAGNA 129.2 131.4 71.4 26.5CALABRIA 57.8 58.8

(1) Difference between wealthiest and poorest region (in percentage points of national average).

Source: Eurostat.

Table 3.2.1: Inner-National Disparities in Applicant Countries (GDP)


DIW 49 EPRC

Applicant Country (in brackets: total unemployment rate 1998)

Region with: Lowest Unemployment Highest Unemployment

Unemployment Rate 1998

National Spread (1)

Standard Deviation

BULGARIA (16.0) SOFIA STOLITSA 9.3 10.2 3.4SEVERNA BALGARIJA 19.5

CZECH REP. (5.9) PRAHA 3.1 6.5 2.1SEVEROZAPAD 9.6

HUNGARY (8.9) NYUGAT-DUNANTUL 6.4 6.6 2.2ESZAK-MAGYARORSZAG 13.0

POLAND (9.9) LUBELSKIE 7.3 8.3 2.1WARMINSKO-MAZURSKIE 15.6

ROMANIA (5.6) SUD-VEST 4.6 2.6 0.9NORD-EST 7.2

SLOVAKIA (14.4) BRATISLAVSKÝ KRAJ 6.4 15.2 4.9VÝCHODNÉ SLOVENSKO 21.6

Memo Items:GERMANY (9.8) OBERBAYERN 4.7 17.6 4.6

DESSAU 22.3

ITALY (12.3) TRENTINO-ALTO ADIGE 3.3 23.7 7.6CALABRIA 27.0

Source: Eurostat.

(1) Difference between regions with highest and lowest unemployment in percentage points of national unemployment rate

Table 3.2.2: Inner-National Disparities in Applicant Countries (Unemployment)


DIW 50 EPRC

In Bulgaria and the Slovak Republic, the wealthiest and poorest regions are also

those with the lowest and highest rates of unemployment. Sub-national disparities

with regard to unemployment are less pronounced in the CEECs than the EU 15.

With the exception of Bucharest, unemployment in the capital regions is � sometimes

clearly � below the national average. There are only few regions with especially high

unemployment (compared to the respective national average) and these include

Severna Balgarija in Bulgaria, Ostravsky and Severozapad in the Czech Republic,

Eszak-Magyarorszag in Hungary, Warminsko-Mazurskie and Zachodnio-Pomorskie

in Poland and Vychodne Slovensko in the Slovak Republic.

3.2.2 Population Density

Most of the CEEC regions are sparsely populated (see Map). This holds true in par-

ticular for Estonia and Latvia, where population density is only 34-38 inhabitants per

km2. Overall, most of the CEE regions have an average population density below 100

inhabitants per km2, fulfilling the characteristics of a rural region. In addition to capital

cities, large agglomerations and their hinterlands, industrial areas are more densely

populated e.g. the industrial belt in the south of Poland (Slaskie, Malopolskie) and the

north-east of the Czech Republic (Ostravsky).

The EU is generally more densely populated (see Map) with an average population

density above 100 inhabitants per km2. The main exceptions are the Nordic countries

of Finland and Sweden which are very sparsely populated and have considerably

fewer inhabitants per km2 than any CEE region. The more densely populated regions

in CEE can be compared with EU regions such as Muenster, Lombardia and West

Vlaanderen. The CEE capital cities, apart from Prague, are in general less densely

populated than the large EU equivalents.

3.2.3 GDP per Head

The general picture of disparities between the CEECs and the EU 15 is well known.

In general, the GDP per head of the CEE regions is considerably less than the EU

average (see Map). Only Prague (120 %) and Bratislava (102 %) are above the av-

erage GDP per head in the EU while Budapest (Kozep Magyarorszag in the Tables),

Slovenia and Cyprus approach two-thirds of the EU average figure. The next group

of regions has GDP per head levels of around 50 % of the EU average and includes

the whole of the Czech Republic, the region around Warsaw in Poland and a Hungar-


DIW 51 EPRC

ian region close to the Austrian border. The remainder of the CEE regions are all well

below 50 % of the EU average figure, with the most underdeveloped regions located

at the eastern borders with Russia, Belarus, Ukraine, Moldova and to the south bor-

dering the former Yugoslavia and Greece. The poorest regions are found in Bulgaria

with only 22-23 % of the EU average. Below NUTS II level, even greater differentia-

tion is found with the poorest CEE region identified in the Sixth Periodic Report as

Latgale in Latvia with a GDP per capita of 16 % of the EU average (CEC, 1999a).

On the basis of the current Structural Fund regulations, the majority of the CEE re-

gions would qualify for Objective 1 support. Only Prague and Bratislava are above

the 75 % threshold. Even the poorest EU region (Ipeiros in Greece) reaches 43 % of

the EU average and therefore has a comparable GDP per head with the Czech Re-

public, one of the most developed CEECs. This indicator provides one of the sharp-

est disparity levels between CEE and the EU 15.

3.2.4 Unemployment

Disparities in regional unemployment figures must always be interpreted with caution

as official figures depend strongly on the labour market rules and methods of meas-

urement within individual countries. Levels of officially registered unemployment can

be seriously affected by the nature of the social benefit system, including ineligibility

for those unemployed for long periods, which can hide the true level of unemploy-

ment.

In contrast to the situation with GDP per head, official unemployment figures for

many CEE countries and regions are relatively low in comparison with the EU. In

1998, the Czech Republic (5.9 %), Romania (5.6 %) and Slovenia (7.4 %), as well as

Cyprus (3.3 %) and Malta (5.1 %), all had (official) unemployment rates considerably

below the EU average (10.1 %). Indeed, the official statistics appear to suggest al-

most full employment in four regions � North West and South West Romania and

Central and South West Bohemia in the Czech Republic. Hungary (8.9 %), Estonia

(9.6 %) and Poland (9.9 %) also had national unemployment rates below the EU av-

erage. Bulgaria, the Slovak Republic, Latvia and Lithuania had unemployment rates

exceeding the EU average, the highest in 1998 recorded in Bulgaria at 16 %.

As in the EU, the variation at sub-national level is considerably higher. Even in coun-

tries with relatively low national rates, such as the Czech Republic and Romania, re-


DIW 52 EPRC

gional differences are considerable. In the Czech Republic, for example, the lowest

rate in 1998 was recorded in Prague (3.1 %) while the industrialised region of

Severozapad in the north-west had an unemployment rate of 9.6 %. In Hungary, the

regions close to the eastern border of the country have higher rates of unemployment

(Eszag-Magyarorszag, 13 %, and Eszak Alfold, 11.6 %) � although still not markedly

over the EU average. Rural regions in Bulgaria, Slovakia and northern Poland have

among the highest unemployment rates in CEE, with the eastern region of Slovakia

(near the Ukrainian border) recording the highest level of 21.6 %.

Unemployment levels in some EU regions are considerably higher than in CEE coun-

terparts and this is particularly true in the south of Italy and Spain where unemploy-

ment rates of up to 29 % are recorded (see Map). The highest unemployment rates

in the CEE regions correspond better to rates in regions such as Dessau, Halle,

Magdeburg in Eastern Germany and Sardegna in Italy. Only some Dutch, Austrian

and southern British regions have low unemployment figures comparable with those

in Czech and Romanian regions.

While the overall employment rate in the applicant countries is slightly lower than in

the EU 15, the female employment rate is higher. Female unemployment follows by

and large the same regional pattern as total unemployment. In Latvia and Bulgaria as

well as Lubuskie/PL, Central Hungary, Central and South West Romania female un-

employment reaches the same level as the overall unemployment. In the other Baltic

states, the Eastern part of Romania and the Western part of Hungary, female unem-

ployment is lower than the overall unemployment rate. Especially the Czech Repub-

lic, Cyprus and the western part of Poland suffer from highest female unemployment

compared to the overall unemployment rate. In these regions female unemployment

is between 20 to 50 per cent higher than general unemployment. It seems that the

regions with a greater share of agriculture in employment are suffering less from fe-

male unemployment.

However, in absolute terms, the highest female unemployment can be found in East-

ern Slovakia (22.8 %), Zachodnio-Pomorskie/PL (19.2 %) and Northern Bulgaria

(19.6 %). In Slovakia � having the highest unemployment rates in the CEEC � men

and women are suffering almost equally from unemployment. Compared to the

EU15, female unemployment of the CEEC regions in absolute terms lies close to the


DIW 53 EPRC

EU average, but it is considerably lower than female unemployment in Spain, the

south of Italy, Eastern Germany and France.

Regarding the overall labour market problems and the future strategies, it is neces-

sary to note that youth unemployment is in all CEEC regions remarkably higher than

female unemployment. The regions with high overall unemployment (by national

standards) are consistently those regions with high youth unemployment. In addition,

Sud-Est and Bucuresti in Romania and Podkarpackie in Poland show high youth un-

employment, although overall unemployment is less significant. The problem of youth

unemployment is most severe in Bulgaria, the Slovak Republic and the labour market

problem regions of Poland.

3.2.5 Employment Structure

An analysis of regional level employment structure reveals a clear dominance of ag-

riculture in many CEE regions. This is especially true in Romania where, outside Bu-

charest, the lowest share of agriculture in total employment is 32 % rising to a maxi-

mum of 57 % in Nord-Est. These findings are confirmed in other literature sources.

Croft et al. (1998), for example, state that, in the mid-1990s, CEE countries employed

proportionally four times as many people in agriculture as the EU (22.5 % of the total

workforce in comparison with 5.3 %).

The capital cities are, unsurprisingly, dominated by services, with figures ranging

from 64 % in Sofia to over 70 % in Bratislava and Prague. In the case of Budapest

and Sofia, both cities also have a considerable share of industry (40 and 33 % re-

spectively). Regions dominated by industry include the northern border regions of the

Czech republic, the industrial belt in the south of Poland and the north-east of Hun-

gary � all with shares in excess of 40 %. The Czech, Slovakian and Slovenian re-

gions also have considerable shares of industrial employment, as well as agricultural

labour.

The EU regions tend to be much more diversified in terms of employment structure. It

is rare to find a dependency on agriculture in EU regions which is similar to that

found in CEE, and shares in agricultural employment are much lower even in regions

where agricultural employment is dominant e.g. in Spain and southern Italy. Only two

Greek regions (Anatoliki Makedonia and Peloponnisius) come close to the Romanian

levels.


DIW 54 EPRC

3.3 Types of Regional Problems in the CEECs

The type of regional development problem to emerge in the CEECs is a product of a

wide range of factors. To some degree, the transformation of the 1990s in the

CEECs resembles the start of structural change which took place in more developed

economies a few decades previously but which was prevented in the CEE case by

the geo-political changes following the Second World War. Regional economic de-

velopment problems and disparity patterns associated with similar structural change

in Western Europe can, therefore, be anticipated in CEE. While this may be the case,

the process of economic and political transition in CEE is also quite distinct and, in

spatial terms, faces the particular legacy both of internal socialist development pat-

terns and a re-orientation away from the formerly imposed economic integration links

with eastern, rather than western or global, markets. The result is a complex mix of

regional development disparities and problems which have common elements both

between the individual CEECs and with Western equivalents but also influenced by

deep-seated historical and cultural factors, ethnicity, and specific national character-

istics.

The ability of individual regions to adapt to the fundamental changes in economic

environment rests on a range of issues including their socio-economic structure, level

of initial development and proximity to capital and innovation as well as the way in

which they are affected by national policy decisions. An early conceptual framework

classifies regions on the basis of their relative position at the start of transition and

their likely response of the process of economic change (Gorzelak, 1994) and identi-

fied certain groups of relatively advantaged or disadvantaged regions in the CEECs.

The regional out-working of this framework has not altered significantly, and a similar

group of four inter-related types of regional disparity is currently identifiable: the con-

trast between urban and rural areas; a core/periphery disparity, especially in coun-

tries with a mono-centric urban structure; a west/east difference, particularly evident

in border areas; and concentrations of restructuring problems in old-industrial areas

(Bachtler/Downes, 1999). Clearly, the detailed patterns of regional disparity are more

complex but the overall groupings hold broadly true and are useful in providing an

overview of the type of regional difficulties which have emerged in the CEECs. The

analysis in this section has been drawn principally from research by the project team

and the wider review of available literature.


DIW 55 EPRC

3.3.1 Capital Cities and Major Urban Agglomerations

Throughout the available literature, major CEE agglomerations and urban areas are

consistently identified as leaders in the transformation process (Bacht-

ler/Downes/Gorzelak, 2000; Boeri/Brücker et al., 2000; Aru, 1998; Vaidere, 1998;

Gorzelak, 1994). Most prominent is the dominant role of core and capital city regions;

indeed in countries such as Hungary, the Czech Republic, Estonia and Latvia there is

no centre which rivals the capital city. The impact of mono-centric settlement struc-

tures is formidable: the Tallinn area (Estonia) has 80-90 % of foreign investment and

tourism and 40 % of all registered enterprises; Riga (Latvia) has 30 % of the national

population and almost half of the FDI stock; in Prague (Czech Republic), GDP per

capita is approaching a level twice the national average, and wage levels are almost

one third higher than the Czech average; and Budapest (Hungary) accounts for 40 %

of the total urban population, 35 % of service sector employment, and nearly two-

thirds of all FDI flowing into Hungary, contributing to a GDP per capita level three

times that of the worst-placed county in Hungary (Szabolcs-Szatmár-Bereg) (Bacht-

ler/Downes/Gorzelak, 2000). The Bratislava region (Slovakia) was found to be simi-

larly dominant in studies by Buček (1999) and Smith/Fernečíková (1998): in 1997,

the region accounted for 62 % of inflows of FDI, 92 % of banking and insurance sec-

tor employees and 41 % of R&D and business service sector employees.

Boeri/Brücker et al. (2000) found that, in comparison to the situation in EU countries,

CEE capitals were relatively small in terms of population share (except Hungary) but

had significantly higher shares of overall GDP. The absence of major secondary cen-

tres in many of these CEECs means that, outside the capital cities, spatial disparities

in growth are more limited.

In the CEE countries with more multi-polar urban structures, while the capitals still

demonstrated more favourable economic indicators (especially for foreign invest-

ment), other important centres for economic development can be identified. These

include Poznań, Kraków, Wrocław, Gdańsk, Katowice, Szczecin and Łódź in Poland,

Kaunas and Klaipeda in Lithuania and Varna and Plovdiv in Bulgaria (Kozak, 1998;

Bachtler/Downes/Gorzelak, 2000). In Slovenia, the topography of the country means

that 75 % of the population live on one-fifth of the territory. Central Slovenia, which

includes the capital Ljubljana, together with the north-western regions, were found to

display generally better economic indicators (for 1997) than the rest of the country,


DIW 56 EPRC

but the disparities are not as marked as in other CEECs (Bachtler/Downes, 1999;

Piry, 1998).

As established centres of economic development, major cities benefit from a high

level of investment, a skilled labour force, more developed infrastructure, business

services, access to key decision-makers, a higher standard of accommodation and

retail facilities. These conditions are recognised as supporting accelerated progress

in key areas of economic transformation areas such as privatisation and restructur-

ing. More specifically, urban agglomerations demonstrated advanced rates of service

sector growth, FDI inflows, and new business development. On the whole, capital city

regions have lower unemployment levels than surrounding regions. According to the

European Commission�s Sixth Periodic Report (CEC, 1999a), regional unemploy-

ment in Hungary is lowest in Central Hungary (containing Budapest) and urban cen-

tres in Poland, such as Warsaw, Poznań, and Katowice, have the lowest unemploy-

ment levels in the country. Extremely low rates of unemployment in Prague (Czech

Republic) have even given rise to concerns about worker shortages.

As rapid development in the capital cities has advanced, specific urban-related prob-

lems have started to emerge including, for example, pollution, congestion and hous-

ing shortages. Fassman (1998) and Duke/Grime (1997) have also identified the

emergence of intra-urban social inequality as the larger urban areas become increas-

ingly stratified by social group and polarised along Western European lines. Recent

research on housing privatisation (Grime et al., 2000) suggests that social inequali-

ties are developing rapidly, with unemployed residents particularly disadvantaged.

The study concluded that societal distribution patterns of post-communist cities are

moving towards Western models of polarisation.

3.3.2 CEE: EU Border Regions

The western border regions of CEEC during the socialist era were in an unfavour-

able, peripheral position because of the geo-political orientation of the CEECs. Post-

1989, these regions started to benefit from their position within a new European po-

litical and economic geography. Proximity to the EU, relatively developed infrastruc-

ture and low labour costs combined with labour force skills all contributed to stimulate

markets and encourage investment into western border regions (Boeri/Brücker et al.,

2000; Gorzelak/Zanycki, 1995; Bachtler/Downes/Gorzelak, 2000). Other advantages,

such as relatively developed infrastructure highlighted by Boeri/Brücker et al. (2000),


DIW 57 EPRC

also compound their advantage. Western Polish border regions, for example, have a

more diversified industrial structure and a higher number of SMEs. In Hungary, west-

ern regions have witnessed falling unemployment in recent years and a positive in-

flow of investment, building on existing industrial structures, proximity to western

markets and cultural factors which make them attractive for investment from neigh-

bouring countries (Lorentzen, 2000; Bozóki, 1997).

In addition to this type of economic development, other benefits experienced by

western border regions include increased tourism (Turnock, 1997), cross-border re-

tail and educational/technological initiatives such as the international university in

Frankfurt/Oder-Słubice and the ACCESS cross-border technology and business park

on the Austrian-Czech border. Western European centres that lie relatively close to

CEE borders � Vienna, for example, is only 65 km from Bratislava and Berlin lies only

110 km from the Polish border � have been found to exert a strong influence on

neighbouring border regions (Gorzelak/Zanycki, 1995). Boeri/Brücker et al. (2000)

note the particular growth of regions along the main east-west transport axes and the

western border areas in Hungary, Slovakia and Poland.

A study of Euro-Regions in Poland (Bojar, 1996) found that local, economically moti-

vated cross-border co-operation could promote population stability, stimulate pros-

perity and revive company co-operation in border regions. The Győr-Moson-Sopron

region in north-western Hungary has benefited from Austria and Hungary�s inter-

regional plans to develop road and train links and upgrade the region�s basic infra-

structure. Local firms in Hungary�s western Transdanubian region are also profiting

from cross-border business with Austria. Bozóki (1997) suggests Austria�s entry into

the EU further encouraged cross-border co-operation and the INTERREG II �

PHARE CBC programme offers Győr-Moson-Sopron, Vas and Zala counties new,

co-ordinated developmental opportunities. However, in some documented cases,

differing economic, social and political conditions in EU and CEE border regions have

caused difficulties for cross-border developments, e.g. in the case of German-Polish

Euro-Region of Viadrina (Bertram, 1998).

It is often thought that enlargement will have the greatest impact on regions on the

border between the current EU and the applicant countries. The above section high-

lights the impact of economic transition on CEE western border regions and sec-

tion 4.4 will analyse the potential effect of enlargement in more detail. It is, however,


DIW 58 EPRC

instructive at this point to review selected data for neighbouring regions at the

EU:CEE border in a little more detail to provide an indication of the starting position

along the current external border of the EU 15.

Table 3.3.1 shows regional indicators for every pair of regions along the current EU

external border, as well as the relative value for the CEEC region as a percentage of

the value of its western neighbour. Per capita GDP and productivity (excluding com-

muters) is lower in the eastern border region than in its western counterpart in all the

border pairs. The only exception is Bratislava in comparison to its neighbouring Aus-

trian regions in Niederösterreich and Burgenland. This is most probably related to the

inability to take commuters into account, as well as the fact that Bratislava is the capi-

tal region in the Slovak Republic. It has to be noted that per capita GDP and produc-

tivity levels are well below the EU 27 average not only for all the CEEC border re-

gions but also for the new German Länder, Burgenland (the Austrian Objective 1 re-

gion) and the Greek border regions. These EU border regions also have a very low

population density, indicating a relatively low levels of economic activity on the west-

ern side of the border.

In the German border regions, total unemployment is higher than in the neighbouring

Polish and Czech regions, while youth unemployment is almost always clearly higher

in the CEECs. In the Austrian, Italian and Greek border regions, both selected unem-

ployment indicators show better labour market conditions on the EU side of the bor-

der than in the neighbouring CEE regions.


DIW 59 EPRC

Neighbouring Regions Density pop/km²

GDP p.c. 1997 (EU27 = 100)

Productivity (excluding

commuters) 1997 (EU 27 = 100)

Unemploy-ment Rate 1998, total

Unemploy-ment Rate

1998, youth

EUR 27 112.3 16542 40003EU 15 117.3 19163 46287 10.1 19.3

MECKLENBURG-VORPOMMERN (D) 78.7 76.4 73.4 19.6 11.8ZACHODNIOPOMORSKIE (PL) 75.1 39.5 43.8 15.6 35.6

CEEC Region in % of EU Region 95.4 51.6 59.6 79.4 301.6

BRANDENBURG (D) 86.6 86.2 80.7 17.6 12.9ZACHODNIOPOMORSKIE (PL) 75.1 39.5 43.8 15.6 35.6


BRANDENBURG (D) 86.6 86.2 80.7 17.6 12.9LUBUSKIE (PL) 72.9 35.1 40.9 11.5 22.7


DRESDEN (D) 222.1 82.1 77.8 17.9 12.2DOLNOSLASKIE (PL) 149.6 38.7 41.2 12.1 22.3


DRESDEN (D) 222.1 82.1 77.8 17.9 12.2SEVEROVYCHOD (CZ) 119.9 63.9 57.4 5.3 10.3


CHEMNITZ (D) 277.2 69.7 66.1 17.9 12.2SEVEROZAPAD (CZ) 130.7 64.2 62.1 9.6 14.3


OBERFRANKEN (D) 154.1 123.2 109.6 7.1 7.7SEVEROZAPAD (CZ) 130.7 64.2 62.1 9.6 14.3


OBERPFALZ (D) 109.3 112.0 97.6 6.3 6.1JIHOZAPAD (CZ) 67.0 69.6 62.1 4.4 7.8


NIEDERBAYERN (D) 111.3 117.3 101.6 5.3 5.4JIHOZAPAD (CZ) 67.0 69.6 62.1 4.4 7.8


OBERÖSTERREICH (AT) 115.0 118.7 106.1 3.2 4.9JIHOZAPAD (CZ) 67.0 69.6 62.1 4.4 7.8


Table 3.3.1: Regional Disparities at the EU - CEEC - Border


DIW 60 EPRC

Neighbouring Regions Density pop/km²

GDP p.c. 1997 (EU27 = 100)

Productivity (excluding

commuters) 1997 (EU 27 = 100)

Unemploy-ment Rate 1998, total

Unemploy-ment Rate

1998, youth

NIEDERÖSTERREICH (AT) 79.8 111.8 100.8 3.7 4.6JIHOZAPAD (CZ) 67.0 69.6 62.1 4.4 7.8

CEEC Region in % of EU Region 84.0 62.3 61.7 118.4 168.7NIEDERÖSTERREICH (AT) 79.8 111.8 100.8 3.7 4.6JIHOVYCHOD (CZ) 118.8 64.8 60.3 5.0 9.1


NIEDERÖSTERREICH (AT) 79.8 111.8 100.8 3.7 4.6ZÁPADNÉ SLOVENSKO (SK) 123.9 46.2 45.9 12.0 26.4

CEEC Region in % of EU Region 155.3 41.4 45.5 323.2 573.6NIEDERÖSTERREICH (AT) 79.8 111.8 100.8 3.7 4.6BRATISLAVSKÝ KRAJ (SK) 311.2 118.3 97.9 6.4 15.9


BURGENLAND (AT) 69.6 83.6 76.7 4.1 5.7BRATISLAVSKÝ KRAJ (SK) 311.2 118.3 97.9 6.4 15.9


BURGENLAND (AT) 69.6 83.6 76.7 4.1 5.7NYUGAT-DUNANTUL (HU) 88.8 57.9 55.7 6.4 9.3


STEIERMARK (AT) 73.6 105.6 96.5 5 7.6SLOVENIJA (SI) 98.1 78.0 74.2 7.4 17.6


KÄRNTEN (AT) 59.1 103.0 97.6 5.6 9.6SLOVENIJA (SI) 98.1 78.0 74.2 7.4 17.6

CEEC Region in % of EU Region 165.9 75.7 76.0 131.9 183.0FRIULI-VENEZIA GIULIA (IT) 151.1 144.9 147.1 5.8 16.9SLOVENIJA (SI) 98.1 78.0 74.2 7.4 17.6


KENTRIKI MAKEDONIA (GR) 95.0 75.8 82.2 10.4 29.6YUZHNA BALGARIJA (BG) 63.5 26.1 29.5 15.5 35.7


ANATOLIKI MAKEDONIA, THRAKI (GR) 39.7 69.0 69.5 8.9 16YUZHNA BALGARIJA (BG) 63.5 26.1 29.5 15.5 35.7


Source: Eurostat.

Table 3.3.1 continued: Regional Disparities at the EU - CEEC - Border


DIW 61 EPRC

Cross-border economic relations between neighbouring regions are likely to intensify

after enlargement where there are concentrations of economic activity on both sides

of the border and if labour market conditions and income levels are better on the cur-

rent EU side. On the basis of this measure, EU border regions that might be most

affected by enlargement could include those in Bavaria and Austria, as well as Friaul.

However, well-founded conclusions require a much more detailed analysis than that

possible on the basis of NUTS II data (see also section 4.4).

3.3.3 Peripheral Regions: Eastern Border and Rural

The majority of the poorest or most economically disadvantaged regions in Western

Europe are located on the periphery of the EU 15 and a similar pattern can be identi-

fied in the CEE � with implications for the eastern periphery of an enlarged EU. Two

important contributory factors can be identified for the weak position of CEE periph-

eral areas. The first is geographical location. While western CEE border regions are

generally winners in the transformation process, the situation is consistently found to

be reversed in the case of the eastern border regions, e.g. regions bordering Russia,

the Ukraine and Belarus. Boeri/Brücker et al. (2000) and Gorzelak (1996) identified a

belt of backward depressed regions which passes along the former Soviet border

from north-eastern Poland to the Moldavian districts of Romania. Sometimes termed

the �eastern wall�, the eastern border regions tend to have relatively poor infrastruc-

ture, little investment and unfavourable economic structures characterised by a pre-

dominance of agrarian activity, high unemployment, low educational attainment and

qualifications in the labour force, limited labour mobility, unsatisfactory environmental

conditions and unfavourable demographic structures (Gorzelak, 1996; Buček, 1999).

The infrastructure endowment of the eastern peripheral regions in CEE is much

poorer than that of peripheral regions in the EU 15 (CEC, 2000).

There are a number of sources of potential disadvantage for eastern border regions.

Communication and transportation routes are generally oriented towards former mar-

kets in the East and the re-orientation towards western markets has had a corre-

spondingly negative impact. The current poor economic condition of the post-Soviet

republics limits opportunities for trans-border co-operation and joint economic initia-

tives, although some links are slowly being encouraged (Bachtler/Downes, 1999).

Border regions between less developed CEECs e.g. Bulgaria and Romania, have

also been found to experience similar difficulties to regions on the external eastern


DIW 62 EPRC

border. Lukas (1999), for example, identified Slovak regions close to the Hungarian,

Polish and Ukrainian borders as lagging behind central and western regions in terms

of new employment opportunities while Buček�s (1999) analysis of Slovak regional

disparities found that the east-central region of Presov had an average unemploy-

ment 3.5 times higher than that of Bratislava (4.95 and 18.4 % respectively in 1997)

and a per capita GDP of only a sixth of that of the capital. Eastern Hungarian regions,

similarly, have been worse affected by rising unemployment than their western coun-

terparts (CEC, 1999a).

The second factor affecting the economic position of peripheral areas is their rural

structure. In many cases, these two factors come together as seen in the strongly

agrarian structure of many eastern border regions. In addition, rural regions some-

times known as the �internal periphery� suffer similar economic development prob-

lems. These CEE regions inherited a poor economic structure and position from the

socialist period, which has generally worsened through the process of economic

transformation and reform. This is characterised particularly by increasing unem-

ployment and declining employment opportunities, both within and out of agriculture.

The privatisation of agriculture has, in some CEE countries, left farmers in a very un-

certain position. There has been a partial return to subsistence farming on small, un-

economic plots, lacking fertilisers and other costly resource inputs, and not viable for

mechanised farming. In some cases, such small-scale farmers have to compete

against state-owned farms still receiving preferential government support or rely on

agricultural supply or marketing enterprises still under state control. Overall, agricul-

ture in the CEECs is in need of urgent structural reform to meet the current and fu-

ture competitive environment. Although agrarian price levels are low, product quality

(also in hygiene terms) is often barely competitive on Western markets. Overall,

many analysts concur that predominantly rural regions on eastern peripheries in par-

ticular, e.g. eastern regions of Poland, Latvia and Estonia continue to witness little

improvement in their socio-economic position (Boeri/Brücker et al., 2000; Bacht-

ler/Downes/Gorzelak, 2000; Gorzelak, 1996, 1999; Fassmann, 1998; Hajdú/Horváth,

1994; CEC, 1996).

Such employment and unemployment problems, largely due to the declining agricul-

tural sector and lack of alternative opportunities, are evident in regions including

South-Transdanubia and the Northern Great Plain of Hungary, rural, mountain re-

gions of Bulgaria (see Spiridonova/Grigorov, 2000), eastern Estonia and northern


DIW 63 EPRC

Poland. Notable exceptions are the southern and eastern regions of Poland where

high proportions of workers were still employed in agricultural sectors and unem-

ployment is low (CEC, 1999a). As noted earlier, the share of primary sector employ-

ment also remains high in Lithuania and Latvia (ca. 21 % to total employment in

1999) and particularly in Romania (over 40 %), with even higher regional specific

rates (see section 3.2.5). Lower unemployment rates are partly attributable to high

out-migration, of some rural and underdeveloped agricultural regions (Bacht-

ler/Downes, 1999). As in Western Europe, migrants are often younger or better quali-

fied and there are low rates of return to the origin region. This exacerbates already

imbalanced age-sex structures and impacts on the ability of rural regions to under-

take successful, long-term economic regeneration. The problem of an ageing popula-

tion in a number of countries (Slovenia, Czech Republic, Hungary) is concentrated in

rural and peripheral areas on the eastern borders of CEE.

3.3.4 Old Industrial Regions

Old-industrial regions can be counted among regions which have been most ad-

versely affected by the process of economic transition. During the socialist period,

the heavy industrial regions were the focus of planned development and were the

drivers of economic activity (Michalski/Saraceno, 2000). During the transformation

period, old industrial regions were severely affected by privatisation, enterprise re-

structuring and closures, the reorientation of trade from formerly secure markets and

the loss of subsidies. The decline in socialist-style heavy industry in particular has

played a significant part in widening regional disparities in CEECs (Bacht-

ler/Downes/Gorzelak, 2000). Many old industrial regions have high rates of unem-

ployment with a large proportion of groups which are more difficult to re-integrate into

the labour market e.g. low qualified and long-term unemployed.

The Slovak Republic inherited an industrial legacy from the former Czechoslovakia,

and the subsequent reduction in military production in particular (in some cases by

almost 90 %) has created very high unemployment and persistent socio-economic

problems around the industrial towns of Dubnica, Martin, Povazska Bystrica, and

Detva (Buček, 1999). North-eastern Estonia, which is heavily dependent upon power

production, heavy industry and the former Soviet military-industrial complex, faces

similar problems. Piry (1998) highlights the industrial-related problems of Slovenian

regions including Gorenjska, Savinjska, Spodnej Posavska and Koro�ka which are


DIW 64 EPRC

burdened with heavy industry including metallurgy in the lead and steel branches. In

Poland, regions in the industrial north and west of the country have also experienced

large-scale decline. Gorzelak/Jalowiecki (1997) identified Upper Silesia as a region

with one of the most difficult restructuring challenges of any CEE industrial region,

with an estimated 800,000 people, of a total of four million, employed in endangered

sectors.

Old-industrial regions commonly face severe degeneration of the environment, in

terms of air, water and ground pollution, often associated with the scale and poor

technology of former industrial production. Economic restructuring, as well as the re-

quirement to meet environmental components of the acquis, have, in some cases,

led to the modernisation or closure of polluting industrial sites (CEC, 2000). Major

environmental problems still persist, however, which will add to the cost of further

restructuring and may act as a disincentive to progress and investment (CEC, 2000)

(see section 3.1.4).

While many old-industrial regions have already experienced sharp economic and so-

cial transformation costs, others have yet to undergo a full process of restructuring.

According to the EBRD�s 1999 Transition Report, the restructuring of existing enter-

prises remains one of the greatest tasks of economic transformation in CEE. Old,

mono-structural areas face infrastructural, technological, skill-based and environ-

mental challenges, exacerbated by the increased requirement for the adoption of

new technology, managerial and entrepreneurial skills required to remain competi-

tive. Economic reform has not universally led to quality investment and the develop-

ment of new products and production methods. Large numbers of enterprises have

not fully embarked upon restructuring programmes and inefficient enterprises have

been allowed to remain open, e.g. in Upper Silesia (Poland) and the Ostrave Karviná

region (Czech Republic). Lack of restructuring has largely been due to weak political

commitment, often associated with fears about the political and social consequences

of massive job losses. Eser (1998) identifies, for example, the case of the coal and

steel-producing region of Ostrave Karviná. Inefficient and outdated plants in vulner-

able industrial sectors were able to postpone restructuring by competing on the inter-

national market, undercutting prices of CEE competitors (e.g. from Poland and Bul-

garia) on the basis of the devaluation of the Czech koruna.


DIW 65 EPRC

3.4 Types of Regions in Applicant Countries and the EU: Cluster Analysis

The preceding sections showed the severe challenges for cohesion in the EU that

result from the specific regional development problems of the applicant countries. In

this section, the starting point of the analysis is not the specific situation in the

CEECs (and therefore the difference between the EU and the CEEC regions) but the

search for similarities of the regions in Eastern and Western Europe. The tasks were

the development of a typology of regions and the classification of all approx. 260

European regions into this various types of regions.

To achieve these tasks it was, first of all, necessary to select adequate indicators. It

has to be distinguished between indicators that measure basic socio-economic

framework conditions for the development of a region ("structural indicators") and

performance indicators. Performance indicators are not useful for the typology of re-

gions because they either may change rapidly (e.g. GDP growth) or they might sepa-

rate Western and Eastern regions too strictly in different groups (e.g. GDP level).

Most importantly, they are better used in analysing the situation inside the various

groups of regions instead of defining these groups (e.g. GDP levels or unemployment

indicators). A structure indicator often used for identifying rural areas and/or agglom-

erations is population density. Other promising (and available) structural indicators

were the shares of agriculture, industry and services in total employment. These four

indicators were included in the attempt to classify the regions of the EU and the AC

into well-defined groups of regions.

There was no viable way to postulate, in a first step, specific values for all the indica-

tors by discretion that would, then, have to serve as thresholds in order to distinguish,

say, an industry-dominated rural area from a service-driven agglomeration. First, this

would have included too high a degree of arbitrariness. Second, there are not always

easy and unambiguous solutions if there is more than one indicator involved.

An established methodology to solve the tasks is the use of a cluster analysis. The

main objective of this method is to build clusters of � in this case � regions that are

as homogenous as possible with regard to the elements inside each cluster and as

heterogeneous as possible as regards the profiles of the individual clusters. Cluster

analyses allow the inclusion of an indefinite number of indicators without having to

define critical values for these indicators by discretion. However, some discretion is


DIW 66 EPRC

also always necessary in a cluster analysis (see Box for technical aspects; see also

Backhaus et al. 2000).

Box: Technical Aspects of the Cluster Analysis

There is no uniform way to conduct a specific cluster analysis. At the various steps of the analysis several questions need to be answered (choice of indicators, choice of proximity measure, choice of clustering algorithm, depth of clustering, etc.). The dif-ferent approaches lead to different solutions and not all of them deliver useful results. In particular, wrong or inconsistent specifications of individual regions are unavoid-able to a certain extent. Therefore, some discretion is necessarily also associated with each cluster analysis. This box summarises some technical aspects of the clus-ter analysis conducted to classify the NUTS II regions of the EU and the AC into dif-ferent types of regions.

We checked various combinations of the selected variables. Because the three em-ployment shares are not independent of each other we only included two of them in each clustering. (The three shares should add up to 100; however, due to data prob-lems this is not always the case in practice.) As the indicators are measured in differ-ent units they had to be standardised. A so-called "z-transformation" was used ensur-ing an average of zero and a variance of 1 for each variable.

There are also different ways to measure the proximity or similarity of elements in the analysis. We compared results for all so-called Minkowski-Measures (City-Block, Euclidean Distance, Squared Euclidean Distance). As regards the method used to combine the various elements to clusters, the Ward-Procedure has been proven to produce the most consistent results; this procedure requires the prior elimination of exceptional cases using the Single Linkage Procedure. However, we also checked other approaches (Complete Linkage). We conducted separate cluster analyses for the 210 EU regions and for 51 AC regions as well as an analysis that combined all regions. Malta could not be included because comparable Eurostat data on employ-ment shares of economic sectors were lacking.

The most consistent simultaneous classification of all NUTS II regions of the EU and the AC was produced by using the Ward-Procedure and the Squared Euclidean Dis-tance. (One exceptional case had to be eliminated but could easily be assigned to a cluster afterwards.) In this approach, we used the shares of agriculture and services in total employment as well as the population density. The computer analysis (using SPSS 9.0) lead to eight different clusters that were re-assigned to six clusters. Only 13 regions, i.e. 5 %, had to be assigned to new clusters by discretion. Detailed re-sults are reported in the text and in Annex 3.


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The cluster analysis that produced the most consistent results (see Tables in An-

nex 3) led to eight clusters that were re-assigned by discretion to the following six:

1. Cluster 1 "Agglomerations": Membership in this cluster was clearly determined

by exceptional high values for population density. The cluster analysis produced

two groups of agglomerations with different levels of population density. However,

to simplify the classification it seemed sensible to combine both of them to a sin-

gle cluster. This new cluster also includes now the exceptional case of the analy-

sis which was "Inner London" with its extremely high population density.

2. Cluster 2 "Service dominated": All regions in this cluster show remarkably high

shares in service employment (69 % and more compared to the EU 27 average of

62 %). Industrial and agricultural employment is below the average (8 % and

30 % respectively).

3. Cluster 3 "Service biased": This cluster combines regions with an above-

average share in services that are not dominated by services, i.e. they have also

a significant share (close to average) of employment in either agriculture or indus-

try.

4. Cluster 4 "Industry": These regions all have above average shares of industrial

employment and no specific strength in either agriculture or services.

5. Cluster 5 "Agriculture biased": These regions show above average shares of

agricultural employment. However, the values are less pronounced than in Clus-

ter 6 and all regions in this cluster also have comparatively high shares in either

industrial or service employment.

6. Cluster 6 "Agriculture dominated": This cluster combines two clusters that

were produced by the computer analysis. Both were characterised by very high

shares of agricultural employment. As one of these only consisted of three Ro-

manian regions with extremely high values for agricultural employment (50 % and

more) it seemed sensible to combine all agriculture dominated regions.

First of all, it is interesting to note how Eastern and Western regions are distributed

among the different clusters; this is shown in a map in Annex 1.

Prague is the only Eastern region in the "Agglomeration"-Cluster that includes eleven

Western regions (among them London, Wien, Berlin, Brussels and, admittedly, Ceuta

y Mellila). There is also only one Eastern region, Bratislava, where employment is


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dominated by services. Western regions in the service dominated cluster include

tourist destinations like Islas Baleares and modern economic centres like Luxem-

bourg; 16 of the 48 EU regions in this cluster come from the UK. (According to na-

tional Maltese data, Malta would probably be included here.) The service biased clus-

ter shows also only very few AC regions, i.e. Sofia and Kozep-Magyarorszag (incl.

Budapest). Among the 55 EU regions in this cluster are approx. half of the French

and the Dutch as well as a quarter of the German regions, Denmark and almost all of

the remaining UK regions.

With eleven out of 66 regions the applicants have roughly a proportionate share in

industrial regions. These comprise most of the remaining Czech as well as three

Hungarian regions, Lubuskie and Slaskie in Poland and Bucharest. More than half of

the industrial EU regions are from Germany. In addition, there are mainly some Ital-

ian and French regions in this cluster.

The important role of agriculture in the AC is well-known and becomes evident also in

the results of the cluster analysis. 19 of 50 agriculture biased regions and 17 of 27

agriculture dominated regions in the sample come from the applicants. Agriculture

biased EU regions, i.e. with an above average share of agricultural employment but

also some notable signs of industry and/or services, are mostly Spanish and Portu-

guese regions as well as Ireland and some Greek, Austrian and Finnish regions.

From the AC come seven of the 16 Polish regions, all of Slovakia (apart from Brati-

slava), Cyprus, Estonia, Lithuania and Slovenia. Agriculture dominated is defined

here as a share of agriculture in total employment of at least 20 % and a clearly un-

derdeveloped service sector; some of the regions do show, however, an above aver-

age share of industrial employment (e.g. Centru/ROM and Dytiki Makedonia/GR). In

this cluster are all Romanian and Bulgarian regions (apart from Bucharest and Sofia)

as well as Latvia and seven Polish regions. EU regions in this cluster include Galicia

in Spain, Centro in Portugal and eight Greek regions.

This overall picture already hints at one severe development problem associated with

the economic structure of the AC economies. Services are often a key to economic

growth and a favourable employment situation. The service sector, however, is

clearly underdeveloped in the applicant countries while it is quite important for the EU

regions. Consequently, one half of all EU regions have an economic structure that is

similar to only four of the 51 AC regions. In this part of the spectrum, the cluster


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analysis accentuates differences between East and West rather than identifying simi-

larities of specific regions. Services are, of course, not the only option for obtaining a

satisfactory economic situation as the industrial orientation of the German economy

demonstrates. Unfortunately, though, a clear sign of an "industrial tradition" that

might serve as a basis for future economic development is only evident in a fifth of all

AC regions. Two thirds of the AC regions even show an above average share of ag-

ricultural employment which is the case for only one fifth of the EU regions.

Annex 3 comprises all regional data available for the six clusters constructed in this

analysis; the table "national averages" serves for comparison purposes. The follow-

ing text cannot aim to give a complete overview of all the information included in this

annex but presents � cluster by cluster � some instructive results derived from a

comparison of the AC region(s) included with its Western counterparts in the same

cluster.

With a view to a comparative analysis of AC and EU regions the first three clusters

only have to be discussed briefly. Six of the 106 million inhabitants of the AC live in

agglomerations (Prague) or in regions with a notable share of services in employ-

ment (Bratislava, Sofia, Budapest-Region). Mostly, the regions in these three clusters

have a per capita GDP of at least the EU 15 average. In the case of the agglomera-

tions, the sometimes quite high values are obviously distorted by commuter flows.

The service dominated cluster comprises also some of the capital regions of the EU

members and other economic centres with distinct above average GDP per capita.

While there are some quite wealthy regions in the service biased cluster (Antwerpen,

Oberbayern, Darmstadt, Groningen) the GDP per head tends to be somewhat lower

than in the service dominated cluster and six EU regions in the cluster would qualify

as Objective 1 region in 1997.

Prague and Bratislava were the only AC regions with a GDP per capita above the

EU 15 average in 1997 and were by no means the poorest members of their cluster,

either then or in 1994. Both regions significantly improved their position relative to the

EU 15 average from 1994 to 1997 (by 13.4 and 15.3 percentage points). This distin-

guishes them clearly from the EU regions in their clusters; only very few of them

managed to improve their GDP per capita relative to the EU average significantly and

only two, Noord-Holland and Uusimaa/SF, gained more than ten percentage points.

The situation for Sofia and Budapest-Region was different. Both were poor by the


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standards of their cluster in 1997 � Sofia exceptionally so and Budapest-Region at a

level with some Greek and Eastern German regions and Sardegna. Worse still, Sofia

had a significantly lower GDP per head (relative to the EU 15 average) in 1997 than

in 1994; Budapest's improvement was only meagre. The employment rate was com-

paratively high in Prague (and, consequently, productivity relatively low) as well as in

Bratislava while GDP per head and productivity were roughly in line in Sofia and Bu-

dapest-Region.

The labour market situation in Prague was notably better than in the other agglom-

erations by all available measures (total, female, youth and long-term unemploy-

ment). In Bratislava, the labour market situation was also quite favourable in 1998 but

most of the Dutch and the UK regions in the service dominated cluster had a lower

total unemployment rate than the Slovak capital (6.4 % in 1998). This holds also true

for female and youth unemployment. However, long-term unemployment was less

pressing in Bratislava than in almost all other service-dominated regions. The various

unemployment measures for Sofia and the central Hungarian region are worse than

for Prague and Bratislava but still below the EU 15 average (with the exception of

youth unemployment in Sofia). They fit well with the rates of other regions in the

"Service Biased"-cluster. The overall picture in this cluster is heterogeneous (and

seems to be determined by country specific factors) but total unemployment rarely

exceeds the EU 15 average.

In the industry cluster, only some German and Italian regions as well as North East-

ern Scotland have a GDP per head clearly above average. The values for the AC

regions are without exception below those of their EU counterparts in this cluster with

the richest AC region (Ostravsky/CZ, 59.3 %) just matching the poorest EU region

(Dessau, 60.1 %). The AC regions managed to improve their relative GDP position

slightly from 1994 to 1997 (with the exception of Bucharest and the Czech Severoza-

pad) while quite a few of the EU regions in the cluster experienced a severe setback.

Brandenburg, Staffordshire and Karlsruhe were most successful in this. Judged by

their GDP per head there are clear intra-CEEC differences in this cluster between, on

the one hand, the Czech regions and Nyugat-Dunantul/HU with 50 % or more of the

EU 15 average and, on the other extreme, Eszak-Magyarorszak/HU and Lubuskie in

Poland with less than a third of the average.


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In general, differences in productivity were less pronounced among the AC industrial

regions than differences in GDP per head of population, i.e. the employment rate in

the Czech regions was comparably high. However, the EU regions tend to show a

higher employment rate than the AC regions in this cluster. The industrial cluster

comprises some of the well-known labour market problem regions in Spain, Eastern

Germany and other regions. Apart from these, unemployment was below average for

the EU regions. Unemployment in the Eastern industrial regions was notable in the

two very poor regions but also in the relative rich Czech regions Ostravsky and

Severozapad (ranging from almost 10 to 13 %). Labour market problems among the

Eastern regions concentrated in these regions (plus high youth unemployment in Bu-

charest) but were not as pronounced than, for example, youth unemployment in

some of the Spanish, French and Italian regions in the cluster.

The "agriculture biased" cluster consists of regions that have an above average

share of agriculture in total employment (average: 8 %; in the cluster: between 8 and

20 %) but do also show strength in either industry or services. In the case of the AC

regions in this cluster, this means almost always a significant share of industry. Just

Cyprus had a distinctly above average share of services, few of the other regions

showed a balanced structure (Mazowieckie/PL, Lithuania). The regions of the EU

members in this cluster were more heterogeneous in this respect. The Austrian re-

gions combined their agriculture bias with high values for industrial employment while

the Greek and the Italian regions tend to show a higher share of employment in ser-

vices; the employment structure of the regions from other EU members was mixed.

Only three of the regions in this cluster, Ireland, Oberösterreich and Navarra, had at

least an average GDP per head (compared to EU 15). The rest of the cluster in-

cludes, among others, some of the poorest regions of the EU and the AC. With Cy-

prus, Slovenia, Jihozapad/CZ, Jihovychod/CZ and Mazowieckie/PL the cluster in-

cludes some AC regions that are comparatively well-off. From 1994 to 1997 most of

the regions in this cluster improved their relative GDP per head (most of all Ireland,

Alentejo and Mazowieckie). This applied for all AC regions in the cluster (apart from

the three Hungarian regions).

In the comparatively well-off AC regions the employment rates were above average.

This was also the case for the Austrian and Portuguese regions while they were quite

low in some of the Spanish and Italian regions. With only few exceptions (mostly in


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Austria and Portugal) the agriculture biased regions had dissatisfying unemployment

rates. Female and youth unemployment was also high, especially in Spain and Italy.

Among the AC regions in this cluster, labour market problems were concentrated in a

few regions, mostly Warminsko-Mazurskie and Zachodniopomorskie in Poland as

well as Zapadne and Vychodne Slovensko in the Slovak Republic.

The composition of the agriculture dominated cluster differs from that of the other

clusters because only a few countries have regions in the cluster and these mostly

form large parts of the affected countries. The cluster mainly consists of Romania

and Bulgaria (complete except the capital regions), Latvia, eight of 13 Greek and

seven of 16 Polish regions. All these regions had a very low per capita GDP, the dis-

tinctly richest region being Crete with 70 % of the EU 15 average. However, there still

is a clear East-West-difference in this cluster. The poorest EU region (Ipeiros, 42 %)

still was well above the richest AC region in the cluster (Centru/ROM, 34 %). None of

the regions in the cluster managed to grow above average between 1994 and 1997

(with the exception of Centro/PT and Galicia/ESP).

Only very few regions in the cluster had to cope with distinctly above average total

unemployment (Bulgaria and Galicia) but the unemployment rates of the EU regions

were markedly higher in 1998 than they have been in 1993. Female and youth un-

employment was quite high, however, in most of the EU and some of the AC regions.

3.5 Island Economies

The island economies of Cyprus and Malta, together with Slovenia, are the most

prosperous of the 12 accession countries. Cyprus and Malta are differentiated from

the CEE candidate countries in two critical areas. First, they are both island econo-

mies with very small populations � in 1997, Cyprus had 741,000 and Malta 374,000

inhabitants. Second, neither country is undergoing economic, political, social or envi-

ronmental upheaval on the scale of the CEE countries. Both islands gained inde-

pendence from the United Kingdom in the early 1960s and are strongly dependent on

foreign trade and FDI as economic growth drivers in light of their small domestic

markets. The following sections outline the main characteristics of the two island

economies in more detail and, where they are evident, highlight relevant issues relat-

ing to regional disparities and socio-economic cohesion.


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3.5.1 Malta

The Maltese economy is characterized by a protected domestic-oriented sector and

an open export-oriented sector (UNECE, 2000). The former sector comprises both

state and non-state owned monopolies, particularly in utilities, as well as small manu-

facturing firms producing goods and services for the domestic market, most of which

have been broadly protected from external competition. The export-oriented sector is

built up of manufacturing firms, established with FDI, and a tourism sector, which is

an important source of growth and is dominated by Maltese capital. Overall, the pro-

tection of the former sector has diverted resources from the latter and limited eco-

nomic growth. The application for EU membership has highlighted the need for re-

structuring in the protected manufacturing industries and, from October 1999, Malta

has begun to remove remaining trade barriers with a target completion date of 2003.

Malta�s level of per capita GDP has increased by approximately 20 per cent in real

terms since 1993, but slowed towards the end of the 1990s and remains relatively

low in comparison to the EU average. While Maltese statistics do not allow for direct

international comparison in purchasing power parities, figures from the UNECE

(2000) show that Malta had a comparative per capita GDP level in current prices of

42.7 (assuming EU=100) in 1998. Private consumption was the main component of

growth in 1999 and the rate of fixed investment was weak.

In terms of economic structure, the share of agriculture and fisheries in GDP is small.

Agriculture accounted for 2.8 % of GDP in 1998 and 1.6 % of total employment in

1997. The fishing industry accounts for three percent of GDP and provides direct

employment for about 2,500 people. 95 % of the production is exported to EU Mem-

ber States (mainly Italy). Industrial production accounts for less than one third of

GDP (27.4 % in 1998) (Government of Malta, 1999). Shipbuilding and repair continue

to be the largest industrial sub-sector � although, despite restructuring attempts, it

makes a negative contribution to GDP. Indeed, shipbuilding and repair have had a

high profile in EC reports on Maltese membership, particularly in light of the high

level of state aid received by this sector which conflicts with EU competition rules.

Special derogations are likely to be requested by the Maltese government for a tran-

sitional period after membership (UNECE, 2000). Electrical machinery production is

an important contributor in terms of exports, investment and employment and its


DIW 74 EPRC

share of total manufacturing value added increased from 15.2 % in 1993 to 27.9 % in

1997.

The role of services has strengthened over time (accounting in 1998 for nearly 70 %

of GDP) and private market services now account for around 33 % of total employ-

ment (UNECE, 2000). This reflects growth both in traditional service industries (e.g.

tourism and market services) and expansion in new sectors such as financial ser-

vices, transhipment activities from the Malta Free Port and ship registration. Tourism

remains the most important services sub-sector - more than one million tourists visit

Malta each year, four-fifths of whom are from the EU (Government of Malta, 1999).

Tourism income accounted in 1998 for 21.3 % of the value of exports of all goods

and services and 6.3 % of total employment. It also has an important multiplier effect

on the remainder of the economy.

Malta's economy overall is dominated by SMEs, mainly involved in the manufacturing

industry and services. The most important SME sectors in Malta are the construction

industry, transport and motor vehicle repair, clothing, food and beverages and tour-

ism. SMEs in all sectors share a number of structural weaknesses associated with

their size including distance from export markets, machinery operating below capac-

ity, a tight labour market and lack of expertise in management and sales promotion,

and limited access to finance (CEC, 1999b).

The EU continues to be Malta's principal export market, absorbing just over half of

Malta's exports in 1999 and supplying nearly 70 % of its imports (UNECE, 2000). The

level of exports to the EU has declined in the 1990s, from a starting level in 1990 of

nearly 85 %, due in part to a sharp drop in exports to Italy (falling from 37 % in 1990

to five percent in 1999). Clothing was the dominant Maltese export to the EU until the

start of the 1980s after which the share of electronic goods increased to become the

leading export. Change in export composition has also contributed to an amended

trade direction with North American and Asian markets increasing in importance in

the 1990s � exports to America have increased from 3.9 to 22.2 % over the period

1990-99 while the Asian export share rose from 5.2 to 20.5 % over the same time

period (UNECE, 2000).

A recent Maltese government report (1999) highlights the susceptibility of the island�s

trade performance to sudden shifts in international demand as a result of the high

degree of concentration of merchandise exports in a single sector product (one semi-


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conductor firm accounts for approximately 50 % of Malta's total merchandise ex-

ports). The ability of the Maltese economy to cope with the competitive pressures

within the Union remains a challenge. This is particularly true for small enterprises in

sectors such as agro-industry, services, handicraft and furniture.

Unemployment has historically been low but has increased recently from an average

rate of 3.5-4 % to 5.1 % in June 1999, reflecting the slowdown in the economy (CEC,

1999b). The increase in the unemployment rate primarily reflects a fall in total em-

ployment, although the increased size of the labour force is also a factor (European

Parliament, 1998). One challenge for the Maltese labour market is a reduction in pub-

lic sector employment. Further, the processes of restructuring and privatisation, likely

to be accelerated by the prospect of EU membership, will have negative implications

for the labour market in the short- to medium-term in the absence of compensatory

economic growth.

3.5.2 Cyprus

Cyprus is a slightly larger island economy which, according to UNECE figures, had a

comparative per capita GDP level in 1998 (in current purchasing power parities) of

78.5, assuming that EU=100 (Discussion in this section generally does not include

data for the Turkish occupied part of the island). The Cypriot economy has grown

steadily over the past three decades, driven in the 1970s and 1980s mainly by manu-

facturing and, in the last two decades, by tourism and other services, including finan-

cial and business services. Inflation is very low (1.7 % in 1999) and unemployment,

although rising slightly in recent years, has remained below four percent since the

mid-1990s and stood at 3.7 % in 1999 (UNECE, 2000).

Agricultural production accounts for around five per cent of GDP. Manufacturing in-

dustry accounted for about 12 % of GDP (a decline from the position at the end of the

1980s) and 16 % of employment in 1998 (Government of Cyprus Press and Informa-

tion Office). The most important sectors in terms of value added are food and bever-

ages, apparel and footwear, and metal products. The development of manufacturing

was initially geared to supplying the local market but, as opportunities for import-

substitution became exhausted, the emphasis switched to exports. More recently,

manufacturing industry has experienced periods of decline in production, exports and

employment. This is related to an erosion of competitiveness, both abroad and in the

local market, due to rising production costs and insufficient productivity gains, at a


DIW 76 EPRC

time of intensified international competition (Government of Cyprus Press and Infor-

mation Office, 1999). According to official estimates, the per capita GDP of people

employed in manufacturing is just 30-40 % of the level of Spain (CEC, 1999a). Over-

all, Cypriot industry faces structural weaknesses such as labour shortages, lack of

domestic raw materials and a small domestic market, leading to competitiveness

problems in the face of low cost competitors or high wage-producers competing on

quality (Bachtler/Downes/Helinska-Hughes/Macquarrie, 1999). Accession is likely to

increase these pressures, and the Cypriot government has recently introduced

measures to promote the development of high-tech industries, attract FDI and im-

prove export competitiveness of domestic firms.

In terms of services, Cyprus has become an important off-shore centre for many

(mainly European) companies providing financial, legal, accounting, maritime and

other services which have been attracted by favourable tax treatment (UNECE,

2000). A number of the tax implications of EU accession and the adoption of the ac-

quis could have important negative ramifications for the island. As in Malta, tourism

has also grown considerably and, including indirect impact on other sectors such as

construction and retail/wholesale trade, accounted in 1998 for 20 % of GDP and

15 % of total employment. The EU accounts for over 60 per cent of income from tour-

ism (CEC, 1999a). An upgrading of tourist services is underway as part of an attempt

to compete within the Mediterranean market.

The service sector is an important growth area, though there are some concerns

about over dependence upon tourism. In 1995, jobs in services accounted for over

63 % of the total following a significant shift away from agriculture and, to a lesser

extent industry (CEC, 1999a). Tourist arrivals in Cyprus reached the level of 1.95 mil-

lion in 1996, 2.1 million in 1997 and 2.2 million in 1998 (Government of Cyprus,

Press and Information Office, 1999). Revenues form tourism also increased. During

1999, an acceleration of foreign demand for services was mainly due to favourable

development in the tourist sector (Central Bank of Cyprus, 1999). A more recent par-

ticular growth area has been in the field of international business and offshore bank-

ing (Bachtler/Downes/Helinska-Hughes/Mcquarie 1999). Registered unemployment

has risen only slightly from 3.1 % in 1996 to 3.4 per cent in 1999. Unemployment re-

mains low even among young people and women, the only group for whom the rate

is high are those over 50 (CEC, 1999a). Labour shortages have materialised, espe-


DIW 77 EPRC

cially in some activities demanding high skills and may constrain growth in the next

few years (CEC, 1999a).

Cyprus has a relatively open economy and is strongly dependent upon foreign trade

(Ayres, 1999). Over the period 1993-1998, the value of foreign trade increased by a

moderate annual average growth rate of seven per cent (Government of Cyprus

Press and Information Office, 1999). The composition of trade generally reflects the

structure of the economy. The main exports include clothing, footwear, potatoes and

citrus fruits (CEC, 1999a). EU countries accounted for 54.7 % of total Cypriot imports

and 40 % of exports in 1998. The United Kingdom and Greece are the main trading

partners of Cyprus in terms of both exports and imports. The transition countries of

CEE, and Russia in particular, have become increasingly important trading partners

for Cypriot exports, with their share rising from 8.5 % in 1992 to 25.9 % in 1998 (UN-

ECE, 2000).


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4 Effects of Enlargement: Critical Issues

The process of economic change and liberalisation in the CEECs has had important

spatial ramifications, with patterns of increasing regional disparity emerging (see

chapter 3). The enlargement of the European Union will also not be a spatially ho-

mogenous process, but will have differing impacts both on the regions of the current

EU 15 and those of the CEECs.

Much of the literature written on the effects of enlargement has focussed on the na-

tional level, with the term �regional� often denoting either the �CEE region� as a whole

or national level breakdowns within it. Much less empirical research has been done

on the effects of enlargement on sub-national units within the CEECs or the EU 15.

The uncertainties of the enlargement process, including a shifting timetable, differing

patterns and speeds of economic reform and development in the CEECs and eco-

nomic change in the EU 15, and continually evolving links between CEECs and

EU 15 countries in terms of trade, investment and labour flow make the assessment

of effects of enlargement difficult even at national level. A recent analysis of various

research studies looking at the costs and benefits of enlargement to include the

Czech Republic, Hungary, Poland and Slovenia over the period 1994-98 concludes

that while several questions have been clarified, there still is a high degree of uncer-

tainty in important aspects of eastern enlargement. This exacerbates the constraints

of applying model-based methodologies and makes robust assessments of acces-

sion-related costs and benefits extremely difficult to produce (Mortensen/Richter,

2000). The challenge of assessing the effects of enlargement at regional level is

even greater - although it is likely that some of the greatest effects of enlargement

will be felt at this level.

In this context, the following section analyses the critical issues of enlargement, fo-

cusing on trade, investment and migration. The following discussion will tackle these

issues separately. However, they are also interconnected issues. For instance, capi-

tal movements to the CEECs can increase trade and, thus be neutral or beneficial for

wages and employment in the affiliated sector. The spatial implications of these is-

sues are often, by necessity, given at national level although, wherever possible, the

regional impact is drawn out. The chapter also includes a short analysis of the par-

ticular issue of border regions in the eastern enlargement of the EU.


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4.1 Trade

Trade patterns have been fundamentally realigned since the initiation of economic

and political reform at the start of the 1990s. The major shift has been the re-

orientation of economic links from the former CMEA countries to the EU 15. Meas-

ures to liberalise domestic trading conditions and support international trade were

made early on in the transition process and included, for example, the removal of

national trade restrictions and barriers of the benefit of domestic companies and the

introduction of currency convertibility at international rates (Brada, 1998; Desai, 1998;

Drabek and Brada, 1998). The initial Interim or Free Trade Agreements, and the sub-

sequent Europe Agreements also encouraged trade with the EU 15 through the dis-

mantling of tariff barriers for many products. While these Agreements were asymmet-

rical, leaving the CEECs more time for adjustment, restrictions were maintained on

EU �sensitive� products which continued to protect important sections of the EU mar-

ket from CEE exports.

The EU is now clearly the main trading partner of the CEECs, built on factors such as

a freer trade regime, geographical proximity, the rapid exploitation of new market op-

portunities by some EU countries, government policy and the advantages also to the

CEECs of lower transport and information costs associated with market entry. As

early as 1993, EU countries accounted for around half of Polish, Czech and Hungar-

ian imports and, by the late 1990s, the EU market accounted for 50-60 percent of all

CEE exports � approximately the importance of the EU market for EU nations them-

selves. Despite high growth rates of trade, the CEE market is much less important to

EU exports and imports, taking only around 4-5 percent of the EU 15 exports in 1997

� although significant country variation is evident, with the CEECs being more signifi-

cant for the main border countries of Germany, Greece, Finland, Italy and Austria

(Baldwin et al., 1997; Boeri/Brücker et al., 2000; Grabbe/Hughes, 1997; Richter,

1998).

4.1.1 Trade Volume

The beginning of the 1990s was marked by a significant rise in the volume of trade

between the CEECs and the EU. In 1998, EU exports to the CEECs were on average

7 times higher than in 1990 and imports were 5 times higher (see table 4.1.1).


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Table 4.1.1: EU Member States' Exports, Imports and Net-Exports with AC-12

Exports in Mio Euro Imports in Mio Euro1990 1994 1998 98/'90 98/'95 1990 1994 1998 98/'90 98/'95

FRANCE 1151 3630 7930 6.9 1.8 1262 2833 4809 3.8 1.4 BEL/LUX 447 1734 3957 8.9 1.9 471 1248 2570 5.5 1.6 NETHERLANDS 761 2502 4886 6.4 1.7 780 1964 3625 4.6 1.5 GERMANY 5239 19421 39759 7.6 1.7 5023 17339 32542 6.5 1.5 ITALY 2220 6953 12338 5.6 1.5 1900 4840 7953 4.2 1.4 UNITED KINGDOM 1135 2743 5399 4.8 1.6 1083 2485 4275 3.9 1.5 IRELAND 75 193 629 8.3 1.8 80 111 271 3.4 2.2 DENMARK 317 1049 1889 6.0 1.6 327 841 1432 4.4 1.5 GREECE 320 926 1267 4.0 1.3 288 577 901 3.1 1.2 PORTUGAL 36 76 207 5.8 1.8 21 88 238 11.4 1.4 SPAIN 186 930 2213 11.9 1.9 272 616 1409 5.2 1.4 SWEDEN - - 3072 - 1.8 - - 2073 - 1.6 FINLAND - - 3106 - 1.8 - - 1143 - 1.5 AUSTRIA - - 7665 - 1.5 - - 5937 - 1.8

EUR (of reported data) 11887 40158 94316 6.9 1.6 11507 32942 69178 5.1 1.5

EU Members Net-Exports in Mio Euro1990 1994 1998

FRANCE -111 797 3121 BEL/LUX -24 486 1387 NETHERLANDS -19 538 1261 GERMANY 216 2083 7217 ITALY 319 2113 4385 UNITED KINGDOM 52 258 1124 IRELAND -5 82 358 DENMARK -10 208 457 GREECE 32 349 367 PORTUGAL 15 -12 -31 SPAIN -86 314 804 SWEDEN - - 999 FINLAND - - 1962 AUSTRIA - - 1728

EUR (of reported data) 379 7216 25139

Exports, EU 15=100 Imports, EU 15=1001990 1994 1998 1990 1994 1998

FRANCE 9.7 9.0 8.4 11.0 8.6 7.0 BEL/LUX 3.8 4.3 4.2 4.1 3.8 3.7 NETHERLANDS 6.4 6.2 5.2 6.8 6.0 5.2 GERMANY 44.1 48.4 42.2 43.7 52.6 47.0 ITALY 18.7 17.3 13.1 16.5 14.7 11.5 UNITED KINGDOM 9.5 6.8 5.7 9.4 7.5 6.2 IRELAND 0.6 0.5 0.7 0.7 0.3 0.4 DENMARK 2.7 2.6 2.0 2.8 2.6 2.1 GREECE 2.7 2.3 1.3 2.5 1.8 1.3 PORTUGAL 0.3 0.2 0.2 0.2 0.3 0.3 SPAIN 1.6 2.3 2.3 2.4 1.9 2.0 SWEDEN - - 3.3 - - 3.0 FINLAND - - 3.3 - - 1.7 AUSTRIA - - 8.1 - - 8.6

EUR (of reported data) 100 100 100 100 100 100

Source: Eurostat Comext.


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The trade surplus of the EU in 1998 over the CEECs was 25 billion Euro. The biggest

net-exporters to the CEECs among the EU 15 were Germany (7.2 billion Euro), Italy

(4.3 billion Euro), France, Finland and Austria (see table 4.1.1). For 1999, preliminary

figures show a decreased surplus due to sharply increased imports from the CEECs

(Statistics in focus � external trade, 10/2000). Among the CEECs, Poland has the

highest trade deficit with the EU followed by the Czech Republic and Hungary (see

table 4.1.2).

Germany is by far the most important trading partner of the CEECs. In 1998, 42 % of

all EU exports to the CEECs and 47 % of all EU imports from the CEECs were con-

ducted with Germany. Italy followed with 13 % of all EU exports to the CEECs and

11.5 % of all imports (see table 4.1.3). Austria has also gained from rapidly expand-

ing economic relations with CEECs, according to Richter (1998). Palme (1999) esti-

mates that approximately 200,000 production workers in Austria have worked on ad-

ditional exports to key CEE markets between 1989 and 1996. In the same period,

Austria�s manufacturing jobs are estimated to have grown by an additional 0.2 to

0.5 % points per annum owing to additional foreign trade with CEECs. On the basis

of this type of evidence, Mayhew (1998) and Egger (1999) anticipate that enlarge-

ment in combination with continued economic growth in CEE countries, is expected

to further increase the importance of CEE markets to EU exporters.

Malta has strong trading relations with the EU. In 1998, 70 % of all imports came

from the EU and 50 % of the Maltese exports went into the EU with France and Italy

being the most important trading partners (Statistics in focus � external trade,

7/2000). Cyprus on the other hand has stronger trade relations to countries outside

the EU than most other candidate countries. In 1998, only 38 % of their exports went

into EU member states and 55 % of their imports came from the EU with the UK and

Greece being the most important trading partner among the EU member states.

Malta and Cyprus both accounted for 2 % of all exports from the candidate countries

to the EU and for 1 % of all their imports from the EU in 1998 (see table 4.1.2).

Geographical proximity plays a key role in trade between the EU and the CEECs.

The border countries Germany, Greece, Finland, Austria as well as Italy account for

two-thirds of the trade with the CEECs. Their share of EU exports to and imports

from the CEECs is considerably higher than in overall EU trade, whereas the share

of France and the UK is rather small (see table 4.1.3).


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Table 4.1.2: EU 15 Exports, Imports and Net-Exports with individual Applicants

Exports in Mio Euro Imports in Mio Euro1990 1994 1998 98/'90 98/'93 1990 1994 1998 98/'90 98/'93

MALTA 1210 1867 1977 1.6 1.1 641 1015 735 1.1 1.0 ESTONIA - 309 2695 - 12.8 - 262 1743 - 9.8 LATVIA - 487 1817 - 5.8 - 732 1375 - 2.2 LITHUANIA - 726 2390 - 5.0 - 719 1350 - 2.3 POLAND 4393 10975 28146 6.4 2.8 4993 8913 15741 3.2 2.1 CZECH REP. - 7928 17168 - 2.8 - 6202 14296 - 3.0 SLOVAKIA - 1794 5708 - 4.7 - 1820 5246 - 4.7 HUNGARY 2876 6153 16837 5.9 3.4 2845 4751 14221 5.0 3.7 ROMANIA 1227 2647 6278 5.1 2.7 1477 2365 4888 3.3 3.1 BULGARIA 904 1597 2436 2.7 1.8 471 1032 1765 3.7 2.3 SLOVENIA - 3675 6757 - 2.2 - 3372 5138 - 1.8 CYPRUS 1277 1999 2108 1.7 1.1 524 571 376 0.7 0.7

- -

CEEC (of reported data) 11887 40158 94316 7.9 2.8 10950 31754 66873 6.1 2.7

EU-15 Net-Exports in Mio Euro1990 1994 1998

MALTA 569 852 1242 ESTONIA - 47 952 LATVIA - -244 442 LITHUANIA - 7 1040 POLAND -600 2062 12404 CZECH REP. - 1725 2872 SLOVAKIA - -26 463 HUNGARY 31 1402 2616 ROMANIA -250 282 1390 BULGARIA 433 566 671 SLOVENIA - 302 1619 CYPRUS 753 1428 1732

CEEC (of reported data) 937 8403 27443

Exports, CEEC=100 Imports, CEEC=1001990 1994 1998 1990 1994 1998

MALTA 10 5 2 6 3 1 ESTONIA - 1 3 - 1 3 LATVIA - 1 2 - 2 2 LITHUANIA - 2 3 - 2 2 POLAND 37 27 30 46 28 24 CZECH REP. - 20 18 - 20 21 SLOVAKIA - 4 6 - 6 8 HUNGARY 24 15 18 26 15 21 ROMANIA 10 7 7 13 7 7 BULGARIA 8 4 3 4 3 3 SLOVENIA - 9 7 - 11 8 CYPRUS 11 5 2 5 2 1

CEEC (of reported data) 100 100 100 100 100 100



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Table 4.1.3: Trade data of the EU 15, 1998

Exports Imports Exports Imports FRANCE 7930 4809 286000 274500 BELGIUM/LUXEMBOURG 3957 2570 159500 148800 NETHERLANDS 4886 3625 186600 175200 GERMANY 39759 32542 482500 413400 ITALY 12338 7953 215600 192500 UNITED KINGDOM 5399 4275 243900 285800 IRELAND 629 271 58900 38200 DENMARK 1889 1432 43700 41800 GREECE 1267 901 9500 24300 PORTUGAL 207 238 21600 32900 SPAIN 2213 1409 93300 111600 SWEDEN 3072 2073 75500 60800 FINLAND 3106 1143 39000 29400 AUSTRIA 7665 5937 55200 61100

EUR 94316 69178 1970800 1890300

Exports Imports Exports Imports FRANCE 8.4 7.0 14.5 14.5 BELGIUM/LUXEMBOURG 4.2 3.7 8.1 7.9 NETHERLANDS 5.2 5.2 9.5 9.3 GERMANY 42.2 47.0 24.5 21.9 ITALY 13.1 11.5 10.9 10.2 UNITED KINGDOM 5.7 6.2 12.4 15.1 IRELAND 0.7 0.4 3.0 2.0 DENMARK 2.0 2.1 2.2 2.2 GREECE 1.3 1.3 0.5 1.3 PORTUGAL 0.2 0.3 1.1 1.7 SPAIN 2.3 2.0 4.7 5.9 SWEDEN 3.3 3.0 3.8 3.2 FINLAND 3.3 1.7 2.0 1.6 AUSTRIA 8.1 8.6 2.8 3.2

EUR 100 100 100 100

Share of trade with AC-12 in total foreign tradeExports Imports

FRANCE 3 2 BELGIUM/LUXEMBOURG 2 2 NETHERLANDS 3 2 GERMANY 8 8 ITALY 6 4 UNITED KINGDOM 2 1 IRELAND 1 1 DENMARK 4 3 GREECE 13 4 PORTUGAL 1 1 SPAIN 2 1 SWEDEN 4 3 FINLAND 8 4 AUSTRIA 14 10

EUR 5 4


Trade of EU 15, in Mio Euro

Trade of EU 15, EU 15 = 100 Trade with AC-12 Total foreign trade

Trade with AC-12 Total foreign trade


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This country variation is also true for the CEECs. In 1998, Poland, the Czech Repub-

lic and Hungary accounted for two-thirds of CEECs exports and imports with the EU

(see table 4.1.2). These three countries now belong to the ten most important trading

partners of the EU.

According to calculations by Schumacher/Trübswetter (2000), EU trade with the

CEECs reached in 1997 on average only about 50 % of exports and 60 % of imports

to be expected from �normal� trade relations estimated on the basis of distance and

GDP. This indicates that overall trade between the EU and the CEECs will most

probably rise significantly during the next years. However, this high growth potential

does not apply to all EU member states. Estimates from Boeri/Brücker et al. (2000)

show that Austria, Germany and other countries bordering the CEECs have reached

or even surpassed the �normal� volume of trade with the CEECs in 1997. Neverthe-

less, there are no indications that the growth of trade between the neighbouring

countries of the EU 15 and the CEECs is slowing down.

4.1.2 Trade Structure

According to international trade theory such as the Heckscher-Ohlin-theory, com-

parative advantage arises from differences in national factor endowments. Conse-

quently, the CEECs should have a comparative advantage in the production of la-

bour-intensive and resource-intensive products, whereas the EU member states

should have a comparative advantage in the production of (human-) capital-intensive

and R&D-intensive products. By and large, this expectation was confirmed by data

on the commodity structure of trade for the early 1990s between the EU and the

CEECs with CEE exports to the EU leaning towards resource-based and labour-

intensive products and EU exports to the CEECs leaning more towards capital-

intensive goods and higher labour and R&D skill products (Raines/Bachtler, 1997).

During the 1990s, the development of trade between the EU and the CEECs was

characterised by a decline in inter-industry trade and a strong increase in intra-

industry trade (IIT). Recent studies emphasise the future role of intra-industry trade

and vertical differentiation of products in trade between the EU and the CEECs

(Boeri/Brücker et al., 2000; Aturupane/Djankov/Hoekman, 1997; Landesmann/Burg-

staller, 1997). The increase of intra-industry trade might seem to imply that CEECs

managed to increase their exports of highly specialised products. However, an


DIW 86 EPRC

analysis of the unit values does not confirm this view. Intra-industry trade can be dis-

tinguished between horizontal (two-way trade in products of similar quality, but differ-

ent characteristics or attributes) and vertical IIT (trade of similar products of varying

qualities and therefore varying unit values). Theory suggests that the more similar

countries are in terms of their factor endowments, their technological development

and other production conditions, the greater is the share of horizontal IIT (Aturupane

et al., 1999). The trade analysis in Boeri/Brücker et al. (2000) shows that unit values

of goods produced in the EU and goods produced in the CEECs differ strongly within

the same commodity group. The EU members specialise in the higher price and

quality segments of markets with a high content of human capital and R&D, whereas

the CEECs specialise in the lower price and quality segments of markets with a lower

content of human capital and R&D.

The absolute levels of unit values in 1996 in EU imports from the CEECs were more

or less comparable to those of developing countries (Boeri/Brücker et al., 2000).

Burgstaller/Landesmann (1999) identify Hungary as the only exception as it is mov-

ing towards more technology- and skill-intensive engineering industries. Other

CEECs such as Poland, Bulgaria and Romania tend to concentrate more on labour-

and less skill-intensive (variations of) products. The overwhelming share of intra-

industry trade, in fact around 80 to 90 %, between the EU and the CEECs is trade in

vertically differentiated goods, which indicates great differences in factor endow-

ments (Aturupane et al., 1999). In 1995, horizontal IIT accounted for much less than

10 % of total trade with the EU. It was comparably high for the Czech Republic and

Slovenia with around 8 % and comparably low for Bulgaria with about 2 %. These

figures also imply that due to the large differences in unit values CEECs do not yet

compete with Southern EU 15 member states in the same market segments. Thus

the fear of these EU members that the Eastern Enlargement would bring a displace-

ment of their exports cannot be verified.

It was not possible within the framework of this report to calculate and to analyse bi-

lateral trade flows between the EU and the CEECs in an extensive way. Neverthe-

less, some results can be drawn by regarding trade data from selected bilateral trade

flows between the EU and three CEECs (Poland, the Czech Republic and Hungary).

Data for EU imports from Poland, the Czech Republic and Hungary and exports to

these countries on CN2 basis (2-digits of the Combined Nomenclature) seem to


DIW 87 EPRC

show a rather similar commodity structure. EU exports and imports are mostly con-

centrated on three product groups: vehicles, machinery and mechanical appliances

as well as electrical machinery and equipment (see table 4.1.4). Thus it seems that

the commodity compositions of these CEECs are increasingly reaching proportions

characteristic of intra-EU trade (Korhonen/Randveer, 2000; Richter, 1998). Still, sig-

nificant differentiations can be distinguished.

The share of �Electrical machinery and equipment� and �vehicles and parts thereof�

in Polish and Czech exports to the EU was in 1998 with 20 % and 30 % relatively

high but still significantly lower than the export share of these product groups from

the EU to these two countries. The Hungarian export structure is the most similar to

the EU export structure. In 1998, the three product groups �vehicles and parts

thereof�, �machinery and mechanical appliances� and �electrical machinery and

equipment� accounted for 58 % of all EU imports from Hungary (see table 4.1.4) and

for 56 % of all EU exports to Hungary. This corresponds to other findings, which

claim that Hungary�s exports to the EU, are based on more human capital intensive,

medium-to high technology products (Kaminski, 2000) while those of other CEECs

such as the Czech Republic and Poland are still relatively more labour intensive (Lu-

kas, 1999).

A closer look on CN4 digits, i.e. a more disaggregated commodity structure, for the

product group 84 �machinery and mechanical appliances and parts thereof�, shows

even further difference between import and export goods of Poland and the Czech

Republic and the EU. Polish and Czech exports among the CN2 digit 84 are concen-

trated on parts for engines and machinery (8409, 8431, 8466) and appliances for

pipes and pumps (8481, 8413), which are not distinguished from finished products

on CN2 basis and which do not require a very high level of R&D or human capital

(see table 4.1.5). Again, the export structure of Hungary looks the most similar to the

structure of EU exports with the product groups of �automatic data-processing ma-

chines and units thereof� (8471) and �parts for machines� (8473) accounting for 29 %

of all Hungarian exports into the EU among the CN2 group 84. These two product

groups also accounted for 25 % of EU exports to Hungary in this CN2 group. Thus

these results support the findings of Boeri/Brücker et al. (2000) stated earlier that the

trade between the CEECs and the EU is still dominated by vertical IIT.


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Table 4.1.4: EU exports and imports with selected Applicants by selected product group

PolandEU-Exports = 100 EU-Imports = 100NACE categories 1990 1994 1998 1990 1994 199827 Mineral fuels, oils 3.1 3.1 1.9 10.0 8.0 5.830 Pharmaceutical products 2.2 2.9 2.9 0.0 0.1 0.139 Plastics and articles thereof 3.2 6.0 6.0 2.0 1.3 1.844 Wood and articles of wood 0.1 0.3 0.7 4.6 5.9 5.248 Paper & paperboard 1.2 2.4 3.6 0.8 1.3 1.862 Art of apparel & clothing 1.1 0.9 0.8 8.5 13.4 9.472 Iron and steel 2.6 1.4 1.9 5.5 3.9 3.473 Articles of iron or steel 1.8 2.2 3.2 3.9 5.0 5.184 machinery/mechanical applian. 25.0 17.8 20.7 4.2 3.8 5.685 Electrical mchy equip/ parts 7.4 8.3 10.9 4.1 5.0 11.187 Vehicles, parts thereof 5.8 8.9 11.8 2.3 6.4 9.690 Optical, photo, cine, meas 3.3 2.6 2.1 0.4 0.4 0.594 Furniture; bedding 0.6 1.1 1.6 3.7 7.3 9.1

Sum of chosen products 57 58 68 50 62 68Sum of all exports 100 100 100 100 100 100

Czech RepublicEU-Exports = 100 EU-Imports = 100NACE categories 1990 1994 1998 1990 1994 199827 Mineral fuels, oils - 0.8 1.3 - 2.9 2.530 Pharmaceutical products - 2.2 2.4 - 0.2 0.239 Plastics and articles thereof - 4.5 6.0 - 3.7 3.144 Wood and articles of wood - 0.5 0.9 - 4.3 3.648 Paper & paperboard - 1.6 2.5 - 1.6 1.262 Art of apparel & clothing - 1.1 0.8 - 4.3 2.272 Iron and steel - 1.5 2.7 - 6.8 3.973 Articles of iron or steel - 2.9 3.1 - 5.9 6.184 machinery/mechanical applian. - 21.5 19.2 - 8.7 13.185 Electrical mchy equip/ parts - 13.2 16.1 - 9.0 12.987 Vehicles, parts thereof - 9.8 10.1 - 7.0 17.090 Optical, photo, cine, meas - 3.8 2.9 - 0.0 0.094 Furniture; bedding - 1.9 1.9 - 4.9 4.6

Sum of chosen products - 65 70 - 59 70Sum of all exports 100 100 100 100 100 100

HungaryEU-Exports = 100 EU-Imports = 100NACE categories 1990 1994 1998 1990 1994 199827 Mineral fuels, oils 0.2 0.6 0.7 2.9 1.2 1.730 Pharmaceutical products 1.5 2.9 2.1 0.2 0.3 0.239 Plastics and articles thereof 3.7 3.9 4.2 3.4 4.1 2.644 Wood and articles of wood 0.7 0.3 0.5 2.9 2.1 1.748 Paper & paperboard 2.1 2.0 2.8 0.6 0.7 0.862 Art of apparel & clothing 1.5 1.6 0.9 9.4 9.4 4.472 Iron and steel 2.1 1.3 1.5 5.4 4.2 1.973 Articles of iron or steel 2.1 2.4 2.5 3.1 2.8 1.984 machinery/mechanical applian. 24.4 17.4 21.4 8.2 12.4 29.185 Electrical mchy equip/ parts 8.4 10.9 19.2 6.0 12.9 21.887 Vehicles, parts thereof 6.5 12.4 15.8 0.8 3.5 6.890 Optical, photo, cine, meas 3.7 3.0 2.3 0.5 0.9 1.194 Furniture; bedding 0.6 2.1 1.4 2.4 3.0 2.3

Sum of chosen products 58 61 75 46 58 76Sum of all exports 100 100 100 100 100 100



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In general the trade composition of CEECs is in line with expectations based on their

expected comparative advantages. The CEECs trade composition is currently more

labour- and resource-intensive. It has to be noted, though, that trade patterns among

the CEECs are very diverse. Romania, Poland and the Slovak Republic have a dis-

tinct comparative advantage in labour intensive goods, the Baltic countries and Bul-

garia have a distinct comparative advantage in resource intensive goods (Freuden-

berg/Lemoine, 1999). Slovenia, Hungary and the Czech Republic, which have a sig-

nificantly higher share of intra-industry trade with the EU (Unguru, 1999) reveal a

much lower comparative disadvantage in human capital intensive goods vis-à-vis the

EU. The EU member states in general concentrate on (human-)capital-intensive and

technology-intensive products. Nevertheless, the comparative advantage of the EU

Table 4.1.5: EU 15 Imports from selected Applicants, sum of product group No. 84=100

Poland Czech RepublicNACE categories 1990 1994 1998 1990 1994 19988409 Parts for engines (mostly aircraft and marine engines) 9 12 10 - 2 38413 Pumps for Liquids 1 1 2 - 4 78418 Refrigerators. Freezers 4 3 3 - 2 48431 Parts for machinery (mostly lifting or loading machinery) 5 10 9 - 10 88433 Harvesting machinery 1 2 2 - 3 28466 Parts for machine-tools 4 3 4 - 7 58471 Automatic data-processing machines 0 1 2 - 2 58473 Parts for offices machines 0 1 2 - 6 48481 Aplliances for pipes 5 6 7 - 3 4

sum of chosen products 30 40 40 - 40 42sum of product group No. 84 100 100 100 100 100 100

Hungary NACE categories 1990 1994 19988409 Parts for engines (mostly aircraft and marine engines) 0 1 38413 Pumps for Liquids 2 4 18418 Refrigerators. Freezers 18 12 38431 Parts for machinery (mostly lifting or loading machinery) 12 9 28433 Harvesting machinery 13 5 28466 Parts for machine-tools 3 1 08471 Automatic data-processing machines 0 4 208473 Parts for offices machines 1 2 98481 Aplliances for pipes 5 2 1

sum of chosen products 54 41 41sum of product group No. 84 100 100 100



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vis-à-vis the CEECs in human capital and technology intensive goods can be seen to

decline over time (Quaisser, 1999). Goldin (1998) argues that advanced CEECs

have a relatively highly educated workforce and their long run comparative advan-

tage is anticipated to lie in more high skilled, high-tech sectors than at present. May-

hew (1998) further claims that as restructuring progresses and as domestic CEE

producers and foreign investors improve the quality of locally produced consumer

goods, the potential for CEE exports to the current EU members will increase and the

amount of horizontal IIT will expand.

Trade in agricultural products is the area where most potential post-accession impact

may occur. However, even in this area, existing barriers to agricultural trade will start

to be dismantled now to help prevent subsequent enlargement overly disrupting mar-

kets on either side. This is expected to bring significant pre-accession increases in

trade. There are considerable questions over the competitiveness of this sector in the

CEECs, particularly in light of its current protection from strong competition and the

structure and productivity of the industry. In the short-term, adjustment strains from

exposure of the CEE countries to competition could be considerable, not least in the

shedding of surplus labour (Avery and Cameron, 1998). The dismantling of remaining

trade barriers in this area, together with parallel developments such as the integration

of new CEE Member States into the Common Agricultural Policy (in whatever form)

and the adoption of western production techniques, is expected to improve the com-

petitiveness of the CEE agricultural sector in the medium- to long-term. In sectoral

terms (Huber and Pichelmann, 1998; Illés, 1997), the product structure in agriculture

in the CEE countries was found to correspond extensively to that of the rural regions

of Western Europe, with production in Mediterranean countries viewed as comple-

mentary to CEE. If the CEECs succeeded in increasing their currently low rates of

productivity in comparison to EU levels, the peripheral rural regions of the western

EU Member States could eventually come under particular adaptation pressure.

Within the CEE countries, this would require continued privatisation as well as land

reform, capital intensification and infrastructure renewal.

4.1.3 Regional and Social Impact

As was stated earlier there are immense differences in trade volume and structure

between the individual CEECs, but there are also significant differences between

regions within a country. So after the overview over national trade data, it is important


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to have a closer look at the regional impact as well as at the social impact of an east-

ern enlargement. Unfortunately, data on the regional level is less reliable because of

different accounting standards and also less available and accessible. For these rea-

sons, this report had to concentrate its regional analysis on Germany and Poland,

both the most important participants in East-West trade among the EU and the

CEECs respectively. For Germany, the regional data is more or less complete. The

search for regional trade data of Poland was more difficult. All 16 statistical offices of

the Polish Provinces were contacted but only four of them provided useable trade

data.

The key role of geographical proximity in the national distribution of trade between

the EU and the CEECs suggests that EU regions bordering on the CEECs should

trade at more than proportional levels (Boeri/Brücker et al., 2000). The following data

from Germany and Poland supports this view:

The shares of the CEECs in the trade of the German Länder neighbouring the candi-

date countries are twice as high as in the trade of the Länder in the west of Germany.

In fact, in Eastern Germany not only high levels but also strong dynamics in trade

with candidate countries can be observed. On average the share of CEECs in total

trade turnover in the Länder in Eastern Germany in 1997 was over 15 %, whereas

this share in the Länder in Western Germany was only around 6 % (see figure 4.1.1).

The case for Poland seems to be similar, although the data basis is much smaller

here. Data was available for the Provinces Opole, Zielona Gora, Jelenia Gora and

Mazowieckie. In 1998 there was a restructuring of all Provinces in Poland and the

number of 49 Provinces earlier to 1998 was reduced to 16 Provinces. The data for

the first three Provinces Opole, Zielona Gora, Jelenia Gora is from the old structure

of Provinces, whereas the data for the fourth Province is from the new Province Ma-

zowieckie.


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Figure 4.1.1 Germany: Regional Structure of Trade with the CEECs 1997

BELGIUM

LUXEMB.

NETHERLANDS

POLAND

SWITZERLAND

CZECH REPUBLIC

DENMARK

FRANCE

AUSTRIA

LIECHTENSTEIN

SCHLESWIG-HOLSTEIN

5.2MECKLENBURG-

WESTERN POMERANIA

16.6

BRANDENBURG

14.2

SAXONY-ANHALT

14.7

SAXONY

20.2

BRE-MEN

(STATE)

3.2

HAM-BURG

(STATE)

3.5LOWER

SAXONY

8.4

THURINGIA

11.0

BAVARIA

8.9

NORTHRHINE-WESTPHALIA

6.5

HESSEN

5.5RHINELAND-PALATINATE

6.7

SAAR-LAND

4.6

BADEN-WURTTEMBERG

6.5

BERLIN(STATE)

9.4

BRANDENBURG

14.2

Share of CEEC-10 in total trade turnover in %:

< 5 5 to 8 > 8 to 12 > 12

Source: Unpublished data of the German Federal Statistical Office, Boeri/Brücker et al. (2000).


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Even though the economic activity in Western Poland is much lower compared to the

economic centre around Warsaw, the two Provinces Zielona Gora and Jelenia Gora

in Western Poland as well as the Province Opole trade with the EU at more than av-

erage level (see table 4.1.6). Between 80 and 82 % of all exports from these three

Provinces go into the EU and between 73 and 81 % of all imports come from the EU.

In contrast the national average level is with 64 % for both exports and imports much

lower. Nevertheless, the economic impact of the three Provinces Zielona Gora, Jele-

nia Gora and Opole is rather limited. These three Provinces together account only for

5 % of Poland�s exports, whereas the Province Mazowieckie alone accounts for

around 20 % of Poland�s exports.

A European Parliament Briefing Paper (1999b) reports that accession will generate

sectoral and regional winners and losers, notwithstanding the overall positive balance

for the European Union. Large expansions of industrial production are anticipated for

the Länder of Vorarlberg, the Tyrol, Vienna, Lower Austria, Upper Austria and Styria

(Palme, 1999). Palme views industries around major cities and in the highly central-

ised areas of the Länder as well positioned to exploit increasing returns to scale

brought about by eastwards enlargement of the EU.

Table 4.1.6: Regional Trade Data for Poland

in Mio $ in % in Mio $ in % in Mio $ in % in Mio $ in % in Mio $ in %

ExportsTotal 504.1 100 258.3 100 275.9 100 4983.1 - 25751.3 100Share of EU 404.0 80.1 213.7 82.7 226.8 82.2 - - 16526.5 64.2

ImportsTotal 563.0 100 332.5 100 300.7 100 19358.6 - 42307.5 100Share of EU 419.4 74.5 269.1 80.9 218.4 72.6 - - 26998.2 63.8

Exports in % of reg. GDP 18.15 28.99 1) 19.31 19.57 23.12

Imports in % of reg. GDP 20.27 37.32 1) 21.05 76.04 37.98

Regional GDP p.c. in % of nat. GDP p.c. 94 87 94 150 -

Source: National and Provincial Statistical Offices.

Poland (1997)Vojevoidship Opole (1997)

Vojevoidship Zielona Gora

(1998)

Vojevoidship Jelena Gora

(1997)

Vojevoidship Mazowieckie

(1999)


DIW 94 EPRC

Conversely, EU producers of labour intensive goods (e.g. textiles, footwear and

leather products) and of capital and scale intensive goods with a low degree of tech-

nological sophistication (printing, chemicals, plastics and rubber product) will con-

tinue to lose out through increasing competition (European Parliament, 1999b).

Bachtler, Downes, Helinska-Hughes, Macquarie (1999) predict that enlargement

would create competitive pressures particularly for those EU countries (and regions)

which depend on:

• agriculture, with a typical production range for central Europe (grain, vegetables,

fruit, cattle and pig farming);

• industrial operations with labour intensive but not particularly human resource in-

tensive production;

• primary chemicals plants or shipyards with relatively low level product ranges;

and

• assembly plants.

It is likely that there will be localised instances where increased competition from

CEE producers will have a detrimental effect. For instance, labour and energy inten-

sive production sectors, e.g. in eastern Austria, will come under increased competi-

tion from low cost producers in CEECs. Southern and Eastern border regions of Aus-

tria (e.g. Styria and Burgenland) have larger concentrations of these industries and

consequently these regions would be likely to suffer. Conversely, energy-intensive

branches of industry in CEECs, e.g. in Slovakia, remain highly competitive, as long

as energy prices in the country remain subsidised and low. Abraham and Koning

(1999) concur with the predicted employment losses for unskilled workers in EU

countries, especially eastern border regions.

4.1.4 Outlook and Conclusion

The immediate trade effects of enlargement are not likely to be very significant, given

the already existing levels of trade liberalisation with the EU. However, Baldwin et al.

(1997), Avery/Cameron (1998) and Boeri/Brücker et al. (2000) predict that accession

will generate gains for EU 15 countries. They also find that these gains are likely to

be unevenly distributed. According to Breuss/Schebeck (1999), the extent to which

Eastern enlargement will affect EU countries will not least depend on their existing

trade relations and interdependencies with CEE countries (see also RWI/EPRC,


DIW 95 EPRC

2000). Gains from EU enlargement are also likely to benefit particular sectors over

others. For instance, Mayhew (1998) anticipates that EU exports in investment goods

are expected to remain high as CEE industry undergo restructuring processes and

businesses consequently �re-tool�. EU business and financial services are also ex-

pected to retain a clear competitive advantage (RWI/EPRC, 2000). High productivity

industries are also likely to benefit.

Over the medium- to longer-term, the adoption of the acquis, including standardisa-

tion, certification and product liability rules, will reduce transaction costs for trade in

goods and services. As has already been discussed, it is also possible that the com-

modity composition of CEECs may shift. In the short-term, however, accession is ex-

pected to increase production costs in the CEECs at the same time as firms are fac-

ing increased competition from a fully integrated market. Polish and Romanian manu-

facturing firms, surveyed as part of a study of enterprise �readiness� for EU accession

(Carlin/Estrin/Schaffer, 1999), expressed particular concerns about the likely costs of

compliance with EU regulations, competition from the EU and the loss of skilled la-

bour to the EU. Costs and benefits of increased levels of trade will vary between CEE

countries.

Costs and benefits are also likely to vary between sectors. Based on current condi-

tions, Avery/Cameron (1998) anticipate accession to increase pressure on large sec-

tors of CEE industry, including small and medium sized enterprises (SMEs) and ser-

vices. Rollo (1998) also anticipated that CEE producers of high value added goods,

of traded services, and in the finance industry could suffer from increased competi-

tion. Further within CEECs, sectors with a high R&D intensity which substituted for

imports from the world market have already been most affected by the removal of

trade barriers according to Boeri/Brücker et al. (2000). However, CEE regions are

also in a position to become the lowest cost producers inside the EU in textiles steel

and bulk chemicals (Rollo,1998).

Bröcker/Jäger-Roschko (1996) suggest that manufacturing industries in advanced

CEECs are likely to diversify away from any areas of competition between CEE and

EU peripheries. Urban (1998) speculates that as ongoing specialisation of the

CEECs takes place in the direction of the more sophisticated engineering branches

rather than towards low-skill, labour intensive industries, some advanced CEECs

may be taking an intermediate position between the industrially advanced EU-North


DIW 96 EPRC

and the less advanced EU-South countries. Taking everything together, concerns

that peripheral EU regions will be replaced by the CEECs as trading partners of other

EU countries are not necessarily well founded (Avery and Cameron, 1998). Some

countries, e.g. Greece and Italy, may even gain from proximity.

However, the precise nature of the economic costs and benefits of enlargement will

depend upon the conditions in which the single market is enlarged, which in turn de-

pends upon the progress which the associated countries are able to make prior to

accession in aligning their laws and practices with those of the EU (see also

RWI/EPRC, 2000). Economic gains will also depend upon the adequacy of transport,

telecommunication and energy infrastructures and networks in acceding countries,

which are necessary to support increased trade and economic activity.

4.2 Investment

Foreign capital flows into CEECs largely take the form of foreign direct investments.

Cumulative net inflows of FDI into the CEE region amounted to USD 50 billion be-

tween 1991 and 1997 (Boeri/Brücker et al., 2000). In terms of FDI, a similar re-

alignment towards the EU has taken place as was the case for trade flows. Foreign

investment in the CEECs has been encouraged since the start of the political and

economic reform process by expanding domestic markets, low cost labour and a

range of other incentives and by the privatisation process (Hughes/Helinska-Hughes,

1998; OECD, 1997; Zemplinerova, 1997; Stankovsky, 1995). FDI data for the CEECs

has been variable and often displays inconsistencies in the amount of investment

recorded both for individual countries and the region as a whole. Undoubtedly, FDI

levels have increased considerably over the 1990s, with Hungary, Poland and the

Czech Republic taking the majority of the total (Estrin et al., 1997, Mayhew, 1998;

Grabbe/Hughes 1997; Estrin/Hughes/Todd 1997).

4.2.1 Volume of FDI

Boeri/Brücker et al. (2000) observe that from the perspective of EU members�, capital

flows to CEE countries are negligible: an annual net capital flow of around USD 15

billion corresponds to a share of 0.15 per cent of GDP and 0.8 per cent of gross fixed

investment in the EU 1998. However, from the stand point of candidate countries,

those annual capital inflows amount to 5 per cent of GDP and more than 20 per cent

of gross fixed investment and thus have contributed significantly to capital formation,


DIW 97 EPRC

relieving domestic capital markets in CEE countries and having a substantial impact

on growth, interest rates and wages (Boeri/Brücker et al., 2000). It is widely assumed

that accession will lead to increased FDI flow into CEE countries. Experience from

past enlargements demonstrate that accession to the EU can considerably increase

capital inflows, at least for a transitional period. Boeri/Brücker et al. (2000) predict

that capital flows to the CEE countries may double in the wake of accession and the

inflow of portfolio capital will pick-up as the harmonisation of the regulation of finan-

cial markets gains momentum.

The main countries receiving FDI among the CEECs are Hungary (30 % of total EU-

FDI stocks in CEECs in 1997), the Czech Republic (28 %) and Poland (26 %). Al-

most 90 % of total FDI stocks in the CEECs were located in the candidate countries

of the Luxembourg-group in 1997 (see table 4.2.1). Countries with sales strategies in

privatisation (e.g. Hungary) reached significantly higher shares of FDI than countries

with voucher strategies (e.g. Slovakia). The sale of public utilities and telecommuni-

cations had an important impact on the size of FDI inflows (Boeri/Brücker et al.,

2000).

Table 4.2.1: FDI Stocks of EU members in CEECs, 1997, Assets

Applicant Countries

in Mio €

in % of reported CEEC

data

Poland 7165 26Estonia 399 1Lithuania 390 1Latvia 177 1Czech Republic 7669 28Slovakia 1290 5Hungary 8120 30Romania 748 3Bulgaria 347 1Slovenia 809 3

Sum of reported CEECs data 27114 100

Source: Eurostat, European Union direct investment, Yearbook 1999.

FDI Stocks fromEU Members in CEECs


DIW 98 EPRC

Analogous to trade and migration, geographical proximity seems to play an important

role in determining bilateral FDI flows. The main investing countries of the EU are

Germany, France, Austria and the Netherlands (see table 4.2.2). Germany alone ac-

counts for about 38 % of all reported FDI stocks of the EU 1997 in the CEECs. All

these main investing countries account for approximately two thirds of all EU FDI

stocks in the CEECs. In 1998, EU-FDI flows to Slovakia were dominated by Germany

and Austria, EU-FDI flows to Poland mainly came from Germany and almost all FDI

flows to the three Baltic states originated in Sweden or Finland (see table 4.2.3). This

evidence suggests that bordering regions in CEECs might be affected by FDI much

more strongly than the average. Unfortunately, FDI figures are not reported at the

regional level in a concise and overall picture so far. However, selected data for Po-

land and Hungary show that the capitals and its surrounding regions as well as the

industrialised regions bordering to the EU profited most from FDI inflows (see also

section 4.2.4).

Applicant Countries EU Germany France UK Austria Finland Netherlands Portugal Sweden

Poland 100 39.1 7.7 4.9 4.3 0.7 17.0 - 4.8Estonia 100 1.0 - - 0.0 - 6.0 - -Lithuania 100 4.1 - - 1.8 - - - -Latvia 100 32.2 - - 0.6 - - - -Czech Republic 100 42.9 8.6 5.1 12.7 0.1 15.5 0.01 -Slovakia 100 40.3 3.5 1.0 27.9 - 7.4 - -Hungary 100 38.4 7.7 - 16.6 0.1 11.3 0.1 0Romania 100 20.7 45.6 5.2 5.6 - 2.8 - -Bulgaria 100 9.5 0.0 4.3 5.2 - 15.9 0.3 -Slovenia 100 25.3 12.9 5.7 39.2 - 4.8 - -

Sum of reported CEECs data 100 37.6 8.6 3.2 12.5 0.3 13.1 0.0 1.3EU FDI-Stocks in the World 100 18.9 12.7 25.0 0.9 1.4 14.1 0.4 5.0


Table 4.2.2: FDI Stocks of EU members in CEECs, 1997, Assets, EU 15 = 100


DIW 99 EPRC

Applicant Countries EU Germany France UK Italy Austria Finland Bel/Lux

Poland 100 47.2 13.8 6.4 0.7 4.2 -0.2 1.8Estonia 100 1.7 - - 0.3 0.0 16.3 -0.3Lithuania 100 4.4 - - 0.3 0.3 3.5 3.2Latvia 100 17.4 - - 0.0 2.2 17.4 0.0Czech Republic 100 54.7 6.5 - 1.1 16.9 -0.2 4.2Slovakia 100 50.9 -2.6 2.2 1.3 51.7 - 8.6Hungary 100 54.7 -0.9 -7.5 0.9 13.1 1.1 29.0Romania 100 29.4 28.0 -1.0 8.7 15.4 - 2.2Bulgaria 100 23.7 33.5 11.0 0.6 3.5 - 5.8Slovenia 100 14.0 56.7 7.6 1.9 41.4 - -12.7

Sum of reported CEECs data 100 44.6 10.4 2.0 1.2 10.2 0.9 6.9Sum of FDI flows from EU members to the World 100 23.4 10.8 24.8 3.4 0.6 6.2 -

Applicant Countries Denmark Spain Netherlands Portugal Sweden

Poland 2.8 0.7 15.4 0.9 6.4Estonia 4.5 0 - - 97.6Lithuania 12.6 0 - - 99.7Latvia - 0 - - 60.9Czech Republic 1.8 0.8 15.0 0.1 1.5Slovakia - -9.1 12.5 - -0.9Hungary -10.3 0.2 28.5 0.1 -0.1Romania - 0.2 8.7 - 1.9Bulgaria - 22.5 - 0 1.2Slovenia - 0 - - 0.6

Sum of reported CEECs data 0.5 0.7 15.4 0.4 10.8Sum of FDI flows from EU members to the World 1.1 5.2 11.0 0.8 4.7


Table 4.2.3: FDI Flows from EU members to CEECs, 1998, EU 15=100


DIW 100 EPRC

Applicant Countries EU Germany France UK Italy Austria Finland Bel/LuxEquity+ Equity+ Equity+ Total Equity+ Total Total Equity+

other other other capital other capital capital other

Poland -20 10 6 - 0 17 - 9Estonia 7 -1 - - - 0 - 4Lithuania 2 1 - - - 0 - 0Latvia 0 -2 - - - -1 - 0Czech Republic 30 -34 2 - 0 1 - 22Slovakia -3 -4 -1 0 - 1 - -1Hungary 261 143 1 - 0 65 - 1Romania -2 -3 -1 - 0 0 - 0Bulgaria 1 0 0 - 0 1 - 0Slovenia -3 -2 1 0 0 1 - 0

Sum of reported CEECs data 273 108 8 0 0 85 - 35Sum of FDI flows from the world to EU members 193409 17766 24577 48930 2332 3996 8743 -

Applicant Countries Denmark Spain Netherlands Portugal SwedenEquity+ Equity+ Equity+ Equity+ Equity+

other other other other other

Poland - 1 - 1 -Estonia - 0 - - 1Lithuania - 1 - - -Latvia 0 1 - - -Czech Republic - 2 - - 25Slovakia - 1 - - -Hungary - 3 - - 0Romania - 1 - - -Bulgaria - 0 0 - 0Slovenia - 0 - 0 -

Sum of reported CEECs data 0 10 0 1 26Sum of FDI flows from the world to EU members 5761 10104 29134 1029 14377


Table 4.2.4: FDI Flows from CEECs to EU, 1998, in Mio Euro


DIW 101 EPRC

So far there are almost no capital movements from the CEECs to the EU. In 1998,

Hungary, which accounted FDI flows in the EU of 261 Mio Euro, was the main invest-

ing country among the CEECs. The Czech Republic followed at a distance with 30

Mio Euro (see table 4.2.4).

If capital movements to the CEECs can make themselves felt at all in the present EU,

then few sectors in few countries and regions may be affected by FDI. Overall, the

share of the CEECs in world-wide FDI stocks 1997 of the EU members was only

2 %. Only in the case of Austria this share exceeds the EU average significantly:

28 % of all Austrian FDI goes to the CEECs (see table 4.2.5).

4.2.2 Allocation of FDI

CEECs already differ according to the importance of FDI in their economies. Publica-

tions, based on IMF and World Bank figures, show the accumulation of FDI over the

period 1989-1996 to be US$ 12.9 billion in Hungary, US$ 12.8 billion in Poland, and

US$ 5.5 billion the Czech Republic, then falling to US$ 1.2 billion for the next highest

recipient, Romania. At the bottom of the scale, Lithuania received US$ 286 million

and Bulgaria US$ 467 million (Pavlínek/Smith, 1998). Hungary has been the most

successful economy in terms of attracting FDI (Kaminski, 2000). Foreign affiliates

accounted for two thirds of the equity capital in manufacturing and produced more

Applicant Countries EU Germany France UK Austria Finland Netherlands Portugal Sweden

Poland 0.53 1.10 0.32 0.10 2.55 0.28 0.64 - 0.51Estonia 0.03 0 - - 0 - 0.01 - -Lithuania 0.03 0.01 - - 0.06 - - - -Latvia 0.01 0.02 - - 0.01 - - - -Czech Republic 0.57 1.29 0.38 0.12 8.04 0.03 0.63 0.02 -Slovakia 0.10 0.20 0.03 0 2.98 - 0.05 - -Hungary 0.60 1.22 0.37 - 11.15 0.07 0.48 0.09 0Romania 0.06 0.06 0.20 0.01 0.35 - 0.01 - -Bulgaria 0.03 0.01 0.00 0.00 0.15 - 0.03 0.02 -Slovenia 0.06 0.08 0.06 0.01 2.62 - 0.02 - -

Sum of reported CEECs data 2.01 4.00 1.36 0.25 27.91 0.38 1.87 0.13 0.51EU FDI-Stocks in the World 100 100 100 100 100 100 100 100 100


Table 4.2.5: FDI Stocks of EU members in CEECs, 1997, Assets, World = 100


DIW 102 EPRC

than 60 % of the Hungary�s manufacturing output in 1996 (Hunya, 1998). The re-

structuring of Hungarian industry seems to have been largely FDI led. Short-term

problems have emerged due to the rapid restructuring, capacity destruction and lay-

offs (Kaminski, 2000). Resultant enterprise closures and unemployment have exac-

erbated social and regional disparities in the country. More generally however, the

high level of FDI and the presence of multi-nationals in the economy contributed to-

wards the increased international competitiveness of a growing number of Hungarian

enterprises (Agh, 1999). Lorentzen (1998) identifies similar experiences in Poland

and Hungary: manufacturing firms with foreign ownership outperformed domestic

firms in terms of export growth, the technology intensity of exports and labour

productivity.

Huge differences in the allocation of FDI between the candidate countries indicate

that the credibility of institutional and economic reforms plays a key role in the loca-

tion of foreign capital flows. Not surprisingly, countries with a stable institutional and

economic environment benefit more significantly from capital inflows than other coun-

tries. However, while the favourable and stable economic and institutional environ-

ment seems to play a key role for the selection of a particular destination country it

has less influence on the choice of the particular location inside a country as the ba-

sic institutional framework is often determined at the national level.

The concentration of around 85 % of EU-net capital flows into the CEECs in the Lux-

embourg Group candidates in 1998 may be interpreted as an indication that the

prospects for accession contribute considerably to the credibility of the CEECs (see

table 4.2.6). Causality works here in both directions: stability may have contributed to

the inclusion of countries in the Luxembourg Group, while this inclusion may have

contributed to the credibility of the new institutions and economic policies and hence

triggered FDI (Boeri/Brücker et al., 2000).

The distribution of FDI is expected to continue to be uneven between CEECs and

between regions in countries. FDI is likely to be heavily concentrated in the more ad-

vanced CEE countries. Further, accession may even widen disparities according to

some studies. For instance, CEECs which remain outside the EU lose out as FDI

diverts to the new member states. Zuleeg (2000) estimates the costs of delayed ac-

cession to be high for the CEECs furthest from EU membership (Romania and Bul-

garia).


DIW 103 EPRC

4.2.3 Role of FDI

Two main arguments exist in the literature regarding the longer-term role of FDI in

the national and regional economies of the CEECs. The first proposes that the

CEECs, in order to restructure their legacy of inefficient industries, use FDI as a

means to position themselves within flows of global capital, often resulting in restruc-

turing based on low-cost competitiveness and an erosion of wage relations and

worker flexibility (Dunning, 1993; Grabher, 1997; Pavlínek, 1998, Smith/Swain,

1998). The second argument supports the view that FDI can, under certain condi-

tions, lead to a progressive upgrading of a national/regional economy � through the

creation, for example, of good local supplier networks, upgrading of technology, in-

creased efficiency and increased competition (Dicken et al., 1994; Lorentzen, 1998;

Malmberg et al., 1996; Amin/Thrift, 1994).

According to the latest survey of the Institut der deutschen Wirtschaft (Institute of the

German Economy) in which 480 German companies were questioned about their

motives for FDI, the most important reasons named were market access, production

for home market and exploitation of country specific factor endowments such as

lower labour costs and lesser taxation and regulations. According to this survey mar-

ket access was of less than average importance for European companies to invest in

Table 4.2.6: FDI Flows from EU to CEECs, 1998

Applicant Countries

Equity+ other, in Mio €

in % of reported CEEC data

Poland 3799 46Estonia 288 3Lithuania 317 4Latvia 46 1Czech Republic 1487 18Slovakia 232 3Hungary 1430 17Romania 415 5Bulgaria 173 2Slovenia 157 2

Sum of reported CEEC data 8344 100


FDI Flows fromEU Members to CEECs


DIW 104 EPRC

the CEECs, whereas production for home markets was the most important aspect

named (Beyfuß/Eggert, 2000).

Most studies of actual FDI flows in CEE arrived at different conclusions, though. The

results of the analysis of Boeri/Brücker et al. (2000) highlight the fact that foreign di-

rect investments to the CEECs are not motivated exclusively by low labour costs.

The branch structure of investment indicate that market access was the primary mo-

tive for investment. Only around one fifth of foreign investment was found to be lo-

cated where low labour costs play a significant role and the share of unskilled labour

is relatively high. Boeri/Brücker et al. argue that quite often an important investment

motive is to supply the markets of the FDI-host country and the exploitation of first-

mover advantages in markets with no or limited competition. Nearly half of FDI to

CEECs are made in non-tradable sectors such as public utilities (electricity, transport

and communication), construction and service sectors. According to Boeri/Brücker et

al. (2000), market access already seems to be an important investment motive even

for investments in tradable sectors and is likely to gain in relative importance with

growing incomes in the CEECs. These investments have no or only negligible effects

on wages and employment in the EU. Other recent in depth studies of motives for

investment in CEE (Altzinger/Winklhofer, 1998; Lankes/Venables, 1996), also found

that factor-cost considerations are less important. CEE based production places EU

firms within growth markets, which implies that products will be sold in the local mar-

kets and consequently would not imported back (Hardy 1998; Pavlínek/Smith 1998).

Furthermore, in the long run, revenues from FDI may outstrip initial capital exports

and stabilise parent companies in the EU. In the short run, employment in the parent

companies may increase by the so-called home-office effects, i.e. by the establish-

ment of necessary back offices in the headquarters. Finally, the investments in public

utilities contribute to the improvement of infrastructure for further business activities

in the CEECs. There is still a considerable demand for investment in infrastructure in

the CEECs at the moment but it may be estimated that investment in the non-

tradable sectors will decline in importance in the long run. The share of primary sec-

tors in FDI is negligible. Within manufacturing, the investments are concentrated in

the production of motor vehicles, food stuffs, petroleum, chemicals, rubber and plas-

tics.


DIW 105 EPRC

Insofar as foreign investment in the tradable sectors is motivated by market access

FDI in the CEECs complement rather than substitute trade, i.e. it raises the value

added of parent companies in home countries relative to a case without foreign in-

vestment. However, the high labour intensity of FDI in the CEECs is an indication

that the exploitation of the wage differential may be an important investment motive

for at least some tradable sectors (e.g. textiles, clothing, electrical machinery, rubber

and plastics, motor vehicles). If a negative impact of FDI on employment and wages

can be felt at all in the present EU, then on unskilled labour in the above mentioned

sectors in countries and regions bordering the CEECs. The picture regarding labour

intensity and skill and wage levels is ambiguous. For instance, the shares of low

tech, low wage and unskilled industries in German FDI in the CEECs is higher than in

German FDI in the rest of the world, but this is not the case for Austria. Capital inten-

sive industries play the crucial role in this case (Boeri/Brücker et al., 2000).

Thus, Boeri/Brücker et al. (2000) concluded overall that only a minor part of FDI is

driven by low-wage costs in CEE and replaces home production. The major part of

FDI is aimed at market access. Accordingly, many of the companies which have in-

vested in CEE and those which will do so in the future are not necessarily investing in

CEE at the cost of investment in EU member states. In this case, investment is cre-

ated rather than diverted from elsewhere in the EU.

4.2.4 Impact of Increased FDI to CEECs

Within CEECs, major urban agglomerations have traditionally attracted the highest

levels of FDI and will continue to do so. Western border regions can also expect to

be foci for FDI investment, though they have not proved to be as uniformly attractive

as urban agglomerations. Pavlínek (1998) found western border regions of the Czech

Republic to be particular foci for cross border export-oriented investments. These

types of investment, according to Pavlínek, merely exploit low labour costs in the

service of external markets and are not likely to contribute to durable regional

economies. Conversely, western border regions of Poland were found not to be at-

tractive for German investors. High wage levels relative to the rest of the country, out

migration of skilled labour, mainly attracted by high wages in the Berlin urban ag-

glomeration and the adjustment shock faced by East German enterprise have limited

cross-border investments, according to Bertram (1998).


DIW 106 EPRC

Empirical research on investment patterns also indicates that foreign investments are

motivated by market proximity (Döhrn 1996; Markusen, 1995; Boeri/Brücker et al.,

2000). Thus, border countries and regions are particularly well placed to exploit the

new investment opportunities offered as enlargement opens up new markets and

investment opportunities. Austria already ranks among the top investing countries

(Richter, 1998). Austrian firms have more than 11,000 affiliates and joint ventures in

the CEECs and are among the most significant foreign investors in the region (Rich-

ter 1998). German enterprises have also expanded into CEE regions (Mayhew,

1998). Penetrating eastern markets and outsourcing part of the production to lower

cost locations may strengthen the international competitive position of EU firms. This

is especially the case for small and medium sized companies in German and Austria,

for which proximity for the new markets and production locations matter more than

for globally oriented corporations.

Anticipated gains for CEE countries and regions have sparked concerns about pos-

sible job and investment losses in current EU countries and regions. There are par-

ticular concerns in some member states concerning the �delocalisation� of employ-

ment to CEE (i.e. EU companies may close factories at home and move operations

to lower cost CEE sites) and the impact of competition from some CEE producers.

Concerns appear to be particularly pronounced in border regions of countries neigh-

bouring CEECs. As EU enlargement becomes more of a certainty and as unit costs

remain high in some EU countries, it might be expected that enterprises, especially in

Austria and Germany, would be attracted to new production locations in nearby CEE

regions, particularly western border regions. FDI investment in CEECs in order to cut

costs and exploit cheaper labour costs are also likely to be concentrated within par-

ticular industrial sectors. Djankov/Hoekman (1998) and Boeri/Brücker et al. (2000)

predict that job losses due to delocalisation will be concentrated in construction, food

and beverages, leather and footwear, textiles and clothing, electrical machinery, pre-

cision instruments and furniture. Pavlínek (1998) and Smith et al. (2000) suggest that

even within these sectors job losses may be confined to outsourcing of more labour-

intensive tasks. Moreover, nearly half of FDI from the EU to CEE is directed at non-

tradable sectors (i.e. public utilities and communication, financial inter-mediation and

other services) (Boeri/Brücker et al., 2000). Additionally, some FDI may tend to foster

the specialisation of production in human capital intensive processes in the EU and

labour intensive production in CEE. This outcome could conceivably hurt wages and


DIW 107 EPRC

could conceivably hurt wages and employment of unskilled workers in specific sec-

tors and branches. However in these branches, large trade surpluses vis-à-vis the

CEE countries can be observed, so that undesirable effects of FDI on wages and

employment of unskilled workers could be compensated by increasing exports

(Boeri/Brücker et al., 2000). Thus, fears of large-scale job and investment losses in

the EU appear to be less well founded.

FDI is highly correlated to increasing shares of intra industry trade, which is an indi-

cation that production processes might become increasingly segmented in human

capital intensive activities on the side of the EU and labour intensive activities in the

CEE countries (Boeri/Brücker et al., 2000). Investment attracted to CEECs by poten-

tial reductions in production costs implies that enlargement could encourage restruc-

turing based on low-cost competitiveness (Dunning, 1993; Grabher, 1997;

Smith/Swain, 1998). However, as economic restructuring continues, it may also be

possible that FDI could focus on technology and skill intensive production, at least in

the more advanced CEECs. These types of investment are more likely to capitalise

on skilled labour and improve local supplier networks, upgrade of technology, in-

crease efficiency and increase competition in CEECs (Dicken et al., 1994; Lorentzen,

1998; Malmberg et al., 1996; Amin/Thrift, 1994).

4.2.5 Conclusion

FDI notably increases the capital endowment of the CEECs and, furthermore, en-

forces growth through the transfer of technologies, knowledge and human capital. In

the short and medium term it is not expected that all regions will benefit evenly from

FDI inflows. In the best positions are the regions immediately bordering the present

EU member states and the capital regions. This trend will succeed in future because

these regions offer the best conditions regarding the main influence factors for FDI:

(1) Proximity to the donor country reduces the transaction costs; (2) the infrastructure

of almost all CEECs is directed towards the capital cities - although CEEC-wide con-

cise figures about the regional differentiation of infrastructure are not available, se-

lected non-standard data show that the quality and availability of infrastructure is bet-

ter in the capital cities as well as along the main transport corridors; (3) even more

important, large shares of the population in the CEECs are concentrated in the capi-

tal regions (e.g. 28 % of the whole Hungarian population live in Budapest and its sur-


DIW 108 EPRC

roundings), and as market access is a decisive investment motive, the population

share to be reached will play a crucial role for location decisions in the future.

Without accession and related transfers from the EU, the CEECs� economic �catch-

ing-up� process will be slower (wage gaps will take longer to close), and the incentive

to utilise the wage gap will persist much longer. Further, accession will be conditional

to the introduction of all EU regulation in environmental protection, technical and so-

cial standards in the new member countries. This will necessarily imply considerable

increases in the cost of production in CEECs, thus the low-cost advantages of CEE

based production are gradually eroded.

4.3 Migration

The anticipated post-eastern enlargement migration flows, and their subsequent im-

pact on EU labour markets in particular, are one of the most sensitive issues in the

EU-internal debate on the accession of CEECs to the European Union. The wage

differential between the CEECs and the EU Member States (much larger than in any

previous enlargement round), the relative economic underdevelopment of the candi-

date countries, and the more highly integrated EU market have all led to fears of

massive post-enlargement migration flows into the EU.

A key component of current, as well as future post-enlargement, migration is its geo-

graphical concentration, primarily in the neighbouring countries of Germany and Aus-

tria. Of current CEE residents in the European Union, 73 % of the working age popu-

lation and 80 % of the employees are in these two countries. The share of current

CEE nationals in total employment was 1.1 % in Austria and 0.5 % in Germany in

1995 � in Greece and Sweden the figure was 0.2 % while in the remainder of the EU

countries, the total was negligible (Boeri/Brücker et al., 2000, see table 4.3.1).

4.3.1 Migration Flows

While some estimates, reported in the media, came to possible inflows of 20-40 mil-

lion migrants from the CEECs, recent empirical studies have been much more con-

servative in their estimates of numbers of potential migrants. They range, for exam-

ple, from a net immigration of ca. 335,000 residents with an assumed removal of bar-

riers to migration in 2002 (falling below 150,000 within a decade) (see table 4.3.2,

Boeri/Brücker et al., 2000) to at least three percent of the eastern population within

15 years i.e. an immigration flow of ca. 3 million people or 0.81 % of the 1995 EU


DIW 109 EPRC

population (Bauer/Zimmerman, 1999). Individual country estimates also range widely

- estimates in the Austrian case, for example, range from immigration of ca. 150,000

people to an annual inflow of ca. 18,000 and 23,800 commuting into border regions

and large urban areas (Freihsl, 1998). In dealing with any figure for migration flows,

care must be taken to ensure that inclusions and exclusions are clarified, as well as

the underlying assumptions regarding time scales and the nature of the future en-

largement process. For example as Mayhew (1998) notes, levels of migration will be

influenced by how CEE and EU economies perform in the future and whether capital

moves to the sources of labour or the reverse.

Persons Persons Persons

Austria n.a. n.a. 103000 (6) 1.3 37989 1.1Belgium 4225 0.0 10773 0.1 1584 0.0Denmark 5918 0.1 8863 0.2 1571 0.1Finland 10931 0.2 11985 0.2 487 0.0France n.a. n.a. 22000 (6) 0.0 7965 0.0Germany 585417 0.7 554869 0.7 160852 0.5Greece n.a. n.a. 20131 0.2 8324 0.2Ireland n.a. n.a. 200 (6) n.a. n.a. n.a.Italy n.a. n.a. 34490 (4) 0.1 1595 0.0Luxembourg 1224 0.4 700 (6) 0.2 212 0.2Netherlands 14410 0.1 9606 (5) 0.1 2579 0.0Portugal n.a. n.a. 781 0.0 200 (7) 0.0Spain n.a. n.a. 10539 0.0 2256 0.0Sweden 18723 0.2 26191 0.3 7271 (7) 0.2United Kingdom 1257 0.0 39000 (4) 0.1 15146 0.1

total 3) 642105 0.3 727228 0.2 240558 0.2

EU-15 (estimate) .. .. 853128 0.2 248029 0.2

1) Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia.-2) Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic.- 3) Non-estimated countries only.-4) 1996.- 5) 1997.- 6) Figure is estimated on basis of national records of labour participation of foreigners.-7) Figures estimated on the basis of national statistics on foreign residents.

Sources: EUROSTAT, Hönekopp (2000), Brücker/Trübswetter/Weise (2000).

Table 4.3.1: Distribution of Immigrants from Central and East European Countries among the Member States of the EU

in % of employees

Stock of Citizens of CEEC-6 (2) working in Host Countries (2)

% of the Population

% of the Population

1990-1997

Cumulative Net Migration from the

CEEC-10 (1)

Stock of Citizens of CEEC-10 residing in Host

Countries (1)

January 1, 1998 1995


DIW 110 EPRC

A recent OECD study (OECD, 1998) concludes that, for a number of reasons, large-

scale migration flows from east to west are not likely to occur and should not be

overemphasised in the enlargement agenda. First, it is anticipated that within the

process of membership, there will be delays in the introduction of the free movement

of people beyond the accession date � as was the case when Portugal, Greece and

Spain joined the EU. Second, such temporary restrictions on the movement of people

are likely to be accompanied by bilateral agreements between current EU Member

States and the candidate countries. Austria and Germany, the two countries most

affected by migration flows, have already signed such agreements with a number of

CEECs. Third, the membership requirements may, in themselves, reduce the incen-

tives to migrate if a move towards convergence in economic and social indicators

and the harmonisation of labour standards reduces existing differences in living and

Residents from the CEEC-10, absolute

Share of Country in

%

2002 2005 2010 2015 2020 2025 2030

Austria (1) 103000 12.1 40547 30020 17739 9972 5107 2101 286Belgium 10773 1.3 4241 3140 1855 1043 534 220 30Denmark 8863 1.0 3489 2583 1526 858 439 181 25Finland 11985 1.4 4718 3493 2064 1160 594 245 33France (1) 22000 2.6 8661 6412 3789 2130 1091 449 61Germany 554869 65.0 218430 161720 95560 53721 27510 11320 1539Greece 20131 2.4 7925 5867 3467 1949 998 411 56Ireland (1) 200 0.0 79 58 34 19 10 4 1Italy 34490 4.0 13577 10052 5940 3339 1710 704 96Luxembourg (1) 700 0.1 276 204 121 68 35 14 2Netherlands 9606 1.1 3782 2800 1654 930 476 196 27Portugal 781 0.1 307 228 135 76 39 16 2Spain 10539 1.2 4149 3072 1815 1020 523 215 29Sweden 26191 3.1 10310 7634 4511 2536 1299 534 73UK 39000 4.6 15353 11367 6717 3776 1934 796 108

Total EU-15 853128 100.0 335843 248649 146926 82598 42297 17405 2366

The extrapolation of the projection for Germany on the EU-15 countries relies on the assumption that countryshares in the number of foreign residents from the CEEC-10 remains constant.

1) The number of foreign residents is estimated for a number of countries on basis of employment figures.

Sources: EUROSTAT, Brücker/Trübswetter/Weise (2000).

Table 4.3.2: Projections for the Growth of the Population of Citizens of the Central and East European Candidate Countries Resident in the EU

Increase in the Number of Residents, absolute

1998


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working conditions (Amato/Batt, 1999). The study also points to the possibility that,

following enlargement, migration flows may be directed towards the relatively more

developed CEE Member States, rather than any of the current EU 15.

4.3.2 Regional and Social Impact

Many existing recent migration studies suggest that the effects on wages and labour

markets in the EU as a whole will be negligible. Bauer/Zimmerman (1999), for exam-

ple, working on the assumption of immigration levels of ca. 200,000 a year over the

next 15 years, estimate that the fall in the wages of workers in the current EU Mem-

ber States would, at most, be 0.8 % of current wage levels. Further, they conclude

that while the empirical evidence on the employment effects of immigration is more

contradictory than that on wages, the majority of the evidence further points to rela-

tively small effects. An important factor influencing any migration impact is the skill

level of the migrants. The inflow of low-qualified workers is likely to result in an in-

crease in the unemployment of domestic low qualified workers and a decrease in the

unemployment of high-qualified workers and vice versa in the case of an inflow of

high qualified workers. The time element also affects the impact of migration, with

initial high levels following the introduction of the freedom of movement, likely to fall

over time and overall (potentially significant) increases in the foreign population in the

most affected countries (Germany and Austria) occurring over a longer time period

(see tables 4.3.2 and 4.3.3). The annual increase of the foreign population from the

ten CEE candidate countries in Germany, for example, is expected to be negligible

30 years after the introduction of freedom of movement (Boeri/Brücker et al., 2000).

While migration may benefit EU 15 states, the economies of the applicant countries

may suffer from the negative impacts of brain drain and loss of a valuable part of

young mobile workforce. European labour market mobility can be hindered by the

importance of formal educational degrees for specific jobs that differ between coun-

tries, by different languages and cultural traditions, by rigid housing and labour mar-

kets (Abraham/Konings, 1999). The same factors may also apply to countries in Cen-

tral and Eastern Europe. Past enlargement experiences have already demonstrated

that only a relatively small fraction of the workforce may be expected to migrate, even

given large differences in incomes and wages (Boeri/Brücker et al., 2000). Further,

recent evidence from Hungary and Bulgaria shows a strong regional divergence in


DIW 112 EPRC

unemployment, suggesting that unemployed Hungarian and Bulgarian workers do

not even move within their own country to find a job (Burda, 1998).

Much attention has been paid to East-West migration but, since 1989, CEECs have

rapidly become hosts to migrants from the former Soviet Union and Romania. Hun-

gary has received large influx of Romanian citizens. Immigration to Poland, from

Russia, the Ukraine and Belarus also increased. Polish Authorities estimate that be-

Residents from the

CEEC-10 in Persons

Share of Country in

%

2002 2005 2010 2015 2020 2025 2030

Austria 103000 12.1 140026 239982 351013 414974 449318 465246 469931Belgium 10773 1.3 14646 25100 36713 43403 46995 48661 49151Denmark 8863 1.0 12049 20650 30204 35708 38663 40034 40437Finland 11985 1.4 16293 27924 40844 48286 52282 54136 54681France 22000 2.6 29908 51258 74974 88635 95971 99373 100374Germany 554869 65.0 754329 1292799 1890933 2235498 2420513 2506319 2531556Greece 20131 2.4 27368 46904 68604 81105 87818 90931 91846Ireland 200 0.0 272 466 682 806 872 903 912Italy 34490 4.0 46888 80359 117538 138956 150456 155790 157359Luxembourg 700 0.1 952 1631 2386 2820 3054 3162 3194Netherlands 9606 1.1 13059 22381 32736 38701 41904 43390 43827Portugal 781 0.1 1062 1820 2662 3147 3407 3528 3563Spain 10539 1.2 14327 24555 35916 42460 45974 47604 48084Sweden 26191 3.1 35606 61023 89256 105520 114253 118304 119495UK 39000 4.6 53019 90867 132908 157126 170130 176161 177935

Total EU-15 (1) 853128 100.0 1159804 1987718 2907367 3437146 3721613 3853542 3892345

memo items:

... the Resident Population of the Country of Origin

0.8 1.1 1.9 2.8 3.4 3.7 3.8 3.9

... the population in the EU-15 0.2 0.3 0.5 0.8 0.9 1.0 1.1 1.1

The extrapolation of the projection for Germany on the EU-15 countries relies on the assumption that country shares in the number of foreign residents from the CEEC-10 remains constant.1) The number of foreign residents is estimated for a number of countries on basis of employment figures.Sources: EUROSTAT, Brücker/Trübswetter/Weise (2000).

Table 4.3.3: Projections for the Stock Population of Citizens of the Central and East European Candidate Countries Resident in the EU

Resident Population from the CEEC-10

Resident population of the CEEC-10 as a % of ...

1998


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tween 100,000 and 200,000 illegal migrant workers were in the country (Koslowski,

1998). The prospect of EU membership is identified as a factor which contributes to

increasing migration to some advanced CEE countries. The criteria these state must

fulfil in order to achieve membership include major policy changes in the areas of

immigration and law enforcement. However, enlargement would eliminated the buffer

zone that key CEE countries have formed between EU frontier and the likes of Rus-

sia, Croatia, and Romania - potentially very large migrant and refugee sending coun-

tries (Koslowski, 1998).

One aspect to arise from recent studies on the impact of migration relates to the age-

ing of the west European labour force (Bauer/Zimmermann, 1999; Freihsl, 1998;

Mayhew, 1998; Amato/Batt, 1999). The anticipated ageing of the west European

population and corresponding decline in working age population will contribute to la-

bour market tensions which can attract migrant workers. The predicted ageing proc-

ess is lowest in the UK, with the size of the over-65 group expected to increase from

15.7 % to 19.4 % between 1990-2025, and highest in Greece, with a rise from 13.8 to

22.2 % in the same group over the same period. With the exception of Ireland, the

working age (15-64) population share is expected to decline in all EU countries by

2-5 percentage points over the same time period. In the CEECs, Bulgaria and Hun-

gary display similar demographic developments but all the other countries are char-

acterised by relatively smaller age groups beyond 65 and larger ones for the 0-14

age range (Bauer/Zimmermann, 1999). This difference suggests a migration potential

for young people in the East, due to labour shortages in the West, particularly in oc-

cupational areas usually taken by younger age groups. An inflow of working migrants

could not only combat labour market shortages but also make an important contribu-

tion to the alleviation of falling tax income and the difficulty of adequately financing

pension and social security systems.

4.3.3 Types of Migration

Migration between the CEECs and the EU Member States is characterised by a rela-

tively high gap in per capita incomes over a short geographical distance. This

changes the potential for different types of migration and significantly increases the

options for short-term, temporary migration as well as cross-border commuting

- which almost exclusively affects Germany and Austria. Temporary migration is al-

ready very significant, and has risen at the expense of permanent migration, and full


DIW 114 EPRC

post-enlargement integration is likely to increase the potential for this type of activity

even further. The regional impact of different forms of migration varies. The choice of

destination for daily commuters from the CEECs is strongly influenced by spatial

proximity and accessibility factors, overtly favouring border areas and accessible lar-

ger urban centres. Non-daily commuters, even if still short-term migrants, are likely to

include job possibilities, wage levels and living conditions to a much greater extent in

their decision and operate within a much wider spatial area.

4.3.4 The Cases of Germany and Austria

The patterns of sub-national distribution and impact of migration in Germany and

Austria also differ. In the German case, the share of migrants in the east German

Länder is well below average � even in the direct border regions. The share of mi-

grant employees in total employment along the German-Polish border is negligible

whereas, in the rest of Germany, migration from the CEECs is concentrated at the

border with the Land of Bavaria and the Czech Republic (see figure 4.3.1,

Boeri/Brücker et al., 2000). The migration patterns into Germany, therefore, do not

just follow immediate geographical proximity but also agglomeration of prosperous

industries. The number of border commuters and guest workers in Germany is cur-

rently very small, with the much more significant group of temporary migrants largely

confined to the agriculture and construction sectors. In 1996, for example, 80 % of

the temporary workers were seasonal (maximum of three months employment per

year) and 90 % of these were employed in agriculture. The employment of agricul-

tural seasonal workers has had broadly complementary effects on the incomes of

native farm workers whereas the wages and employment of native workers in con-

struction was negatively affected in some cases through the subcontracting of CEE

firms.

The Austrian situation differs from the German case, principally because of its com-

mon borders with four CEECs and the close proximity of centres of population and

economic activity to these borders). The Austrian capital, Vienna, two Land capitals,

Graz and Linz, and a number of other important regional centres such as Villach,

Klagenfurt, Wolfsburg, Wiener Neustadt and Baden are all within a 90 minute drive

from the border regions of the neighbouring CEECs. The case of Vienna is also par-

ticular given the close proximity of the Slovakian capital of Bratislava, only 65 kilome-

tres away. An influx of migrants from eastern Europe and other parts of the region


DIW 115 EPRC

into Austria has stoked up fears of job losses among blue-collar workers who, how-

ever flimsy the evidence, perceived their jobs to be threatened (Caplen, 2000).


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Figure 4.3.1 Germany: Regional Distribution of Employees from the CEECs

Source: Federal employment services, Boeri/Brücker et al. (2000). DIW


DIW 117 EPRC

A recent study on the migration potential from CEECs, of both daily commuting and

longer-term labour migration, highlighted a number of key conclusions (Birner et al.,

1999): Assuming the free movement of people from 2004, a potential additional

250,000 people could enter the Austrian labour market from the Czech Republic,

Hungary, Slovenia and Poland (40,000 daily commuters, 85,000 non-daily commut-

ers and 120,000 migrants) as well as 50,000 just from Slovakia (20,000, 10,000 and

20,000 respectively).

• Over a third of this commuter and migration potential would be concentrated on

Vienna, including a third of the total of both non-daily commuters and migrants

and 50-60 % of the daily commuters. The inclusion of Slovakia in the free move-

ment of people would place particular pressure on the situation in Vienna.

• The western Länder of Salzburg, Tirol and Vorarlberg would be barely affected (6,

4 and 2 %), principally because, with the exception of Salzburg, they lie outside

the commuter range.

• 73 % of the anticipated commuter and migration potential from the CEECs would

be absorbed in border regions (215,000 with and 177,000 without Slovakia). Of

this total, three-quarters will be concentrated in the central areas of the border re-

gions and only a quarter in the peripheral parts.

• Assuming a delay in the free movement of people until 2010, the migration poten-

tial would fall given the probable diminished migration push factors. Within 10

years, between 270,000 (including Slovakia) and 230,000 (excluding Slovakia)

additional CEE workers could come onto the Austrian labour market.

4.4 Border Regions

The ten candidate CEECs have land borders with four current EU Member States:

Germany (Poland and Czech Republic); Austria (Czech Republic, Slovakia, Hungary

and Slovenia); Italy (Slovenia) and Greece (Bulgaria). Estonia also has a very close

sea border with Finland. With the exception of the Greek/Bulgarian border, the land

borders with Germany, Austria and Italy are with the more advanced CEECs, four

being members of the Luxembourg Group and Slovakia showing rapid economic re-

covery and improved political stability since the elections in September 1998. The EU

Member State regions along the border show greater disparity, ranging from the

more economically disadvantaged Länder in eastern Germany to the Vienna ag-


DIW 118 EPRC

glomeration in Austria. However, within national contexts, the eastern border regions

of Germany and Austria display economic and structural weaknesses and both the

new German Länder and Burgenland in Austria are Objective 1 regions in the 2000-

06 Structural Funds programming period. Further, the immediate border area in the

remaining regions in Germany and Austria is almost entirely designated under Objec-

tive 2. The weak economic structure of, for example, the new German Länder be-

comes evident in regional trade data (see table 4.4.1). While the share of the five

new Länder has been growing from 2.6 to 3.8 % in total German exports from 1991

to 1998 this is still distinctly below their share in German GDP (10.8 %) or population

(17 %). In addition, eastern Germany's exports consist to a comparatively high de-

gree of raw materials, semi-finished and intermediate products.

The preceding discussions of the impacts of Eastern enlargement suggest that the

volume and nature of EU-CEE trade, investment and migration are such that en-

Year Exports Brandenburg Mecklenburg- Sachsen Sachsen- Thüringen TotalVorpommern Anhalt

1991 Total 0.4 0.2 0.8 0.8 0.4 2.6

Raw Materials 1.2 0.3 1.1 0.7 0.6 3.9Semi-finished Goods 1.1 0.5 0.6 1.0 1.0 4.2Intermediate Goods 0.9 0.1 0.6 1.1 0.3 3.0Final Products 0.2 0.2 0.8 0.7 0.4 2.3

1993 Total 0.4 0.5 0.9 0.6 0.5 2.9


1996 Total 0.5 0.3 0.9 0.5 0.6 2.8


1998 Total 0.7 0.2 1.6 0.6 0.7 3.8


memo item:

Share in Population, 1997 3.1 2.2 5.6 3.3 3.0 17.2Share in GDP (in PPS), 1997 2.1 1.4 3.5 2.0 1.8 10.8

Sources: Eurostat, Statistical Yearbooks of the Bundesrepublik Deutschland, various years.

Table 4.4.1: Shares of Exports of the New German Bundesländer as a percentage of German Exports


DIW 119 EPRC

enlargement is unlikely to have negative impact upon the EU economy

(Boeri/Brücker et al., 2000). However, border regions may be disproportionately vul-

nerable to the enlargement effects of migration and labour market change, industrial

re-location, and cross-border trading but also have the potential to exploit geographi-

cal proximity and market knowledge to their advantage. The impact of migration on

the border regions, in Austria in particular, is outlined in section 4.3.4. The economic

and labour market structure of the western and corresponding eastern border re-

gions, as well as the nature and timetable of enlargement (critically including the type

of transition periods put in place for the free movement of labour) are among the fac-

tors which will affect the individual adaptability of border regions within an enlarged

EU.

In light of the particular geographical location of Austria, and the relative vulnerability

of their border regions, a larger volume of regional-specific research is available on

the impact of enlargement on Austria. A recent publication by ÖROK, the co-

ordinating body for regional policy in Austria, outlines the differing impact on three

particular types of region near the Austrian-CEE border (ÖROK, 1999):

• Large cities

The Austrian cities located near the external border with CEE (Vienna, Linz, Graz)

are likely to benefit particularly in the early integration phase. The specialisation of

these urban economies is in the human capital-intensive branches of both goods

production and service sector. These areas are likely to experience relatively low

post-enlargement import competition but will most profit from export expansion in

the CEECs (e.g. in the technology sector and transit trade). These advantages

will decrease in later stages of integration once the neighbouring CEECs catch-up

in human capital-intensive production areas. Once this occurs, if any associated

market loss coincides with the ending of transitional labour market periods, labour

market related problems could emerge in the larger cities as it is to be expected

that they will be the destination of most of the foreign workers coming into the

country.

• Central areas and suburbs

The central areas and the suburbs of large cities are likely to experience favour-

able effects from enlargement in the medium-term once an enlarged internal mar-

ket has been created with the CEECs. Among the most important effects of the


DIW 120 EPRC

wider market are increasing returns to scale which can be best exploited in the

more populated areas outside the large cities. These areas offer technologies with

increasing returns to scale, the locational advantages of a good transport and

communications infrastructure and relatively available cheap land. The competi-

tive pressure is somewhat higher than in the larger cities because goods produc-

tion in these areas involve a relatively higher share of energy intensive raw mate-

rials, which in some cases can be obtained more cheaply in the CEECs. The la-

bour market is likely to be put less under pressure although these areas are likely

still to be of some interest to incoming migrants.

• Rural border areas

Rural border areas will face greater competitive pressure in the initial integration

phases than later in the process. Since the liberalisation of the CEECs, these re-

gions have lost their locational advantage in goods production as low wage cost

areas for labour-intensive production (particularly in the textiles sector). The rela-

tively good employment development in these areas in the first half of the 1990s

is misleading in the sense that it was based principally on the mobility and low

wage demands of foreign workers (particularly from CEECs) and not on sustain-

able factors linked to the local location. Regional markets play a key role in border

areas because of their locational disadvantages on international goods markets.

The eastern enlargement of the EU will remove current barriers to the longer-term

formation of cross-border regional markets. High wage and price differences with

the neighbouring CEECs are likely to place demand shocks in regional goods and

supply shocks in regional labour markets in Austrian border regions.

The smaller settlement centres of the Austrian border regions have already come

under considerable pressure in terms of retail trade and other commercial services

with immovable locations of service (e.g. hairdressers etc.). The areas which will face

increased post-enlargement pressure are the commercial areas in which the supplier

brings his/her services to the source of the demand (e.g. construction and ancillary

support). As increased standardisation with EU and Austrian requirements occurs

after enlargement, the potential for cross-border service provision increases.

If there is an early liberalisation of the labour market and the nominal wage difference

with the CEECs (in comparison to exchange rates) stays relatively high, this could

result in a high level of commuting of foreign labour into the Austrian border areas,


DIW 121 EPRC

although it is likely that relatively few would choose to live there. In the longer-term,

the labour market pressure in the border regions is likely to be lower than in the more

urbanised areas. An indirect labour market pressure, particularly in the case of Vi-

enna and its wide commuter belt, would be the increased competition in the city be-

tween foreign labour and commuters from the Austrian border regions.


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5 Projected Patterns of Disparity

5.1 Objectives

Chapters 3 and 4 have developed a detailed picture of the specific problems of the

AC regions and of the challenges they face. In this chapter, we follow a different ap-

proach to assess the magnitude of the development task and focus on the GDP per

head (in purchasing power parities) as the single most decisive development indica-

tor that is also most important for eligibility for support from the Structural Funds.

Based on orders of magnitude and development patterns that have been observed in

the past we aim to identify a plausible range for future development of disparities of

CEECs vis-à-vis the EU and within CEECs in the long run. This includes analysing

the spectrum for national developments and studying various patterns of regional

developments.

5.2 Approach

We analysed the development of the relative GDP per head (in PPP) of the applicant

countries and their regions, measured as a percentage of EU 15 GDP per head. We

concentrated on convergence rates and did not deal with probable growth rates, i.e.

we identified possible relative, not absolute, levels for GDP per head. The analysis

was based on lessons from past convergence processes and proceeded in two main

steps: Firstly, we identified a plausible range for national convergence of the appli-

cants to the EU 15 average; secondly, we calculated various scenarios for conver-

gence of the AC regions that fall into the plausible range for national convergence.

5.2.1 Determinants and Alternative Development Paths of National GDP

As a starting point for the assessment of long run development of relative per capita

GDP of the AC we took the experience of post-war European market economies as

yardstick for what is most probably a plausible order of magnitude. In a first step, we

derived the speed of convergence of GDP per head (β in a Barro-type regression) for

a sample of 21 European market economies from 1950 to 1990 (including all EU 15

member states) using Penn World Table data. We then tested the forecasting quality

of the estimated growth equation within the sample. In order to forecast convergence

of the CEECs to the EU average we followed, lastly, three different scenarios for na-

tional convergence from 1997 until 2037:


DIW 123 EPRC

1. In the first scenario, we only included the present income levels of the CEECs

(unconditional convergence);

2. In the second scenario, we included, in addition, investment rates and population

growth. We assumed that, in the CEECs, these will equal the mean values of the

European market economies in the post-war period;

3. In the third scenario, we assumed instead that future investment rates and popu-

lation growth in the CEECs will be equal to current CEE values.

Results are discussed in section 5.3; methodological details, regression results and

graphs are provided in Annex 4. Our main conclusion is that a convergence rate β of

2 % seems to be most plausible. However, these calculations are always associated

with a certain degree of uncertainty. Therefore, we calculated confidence intervals for

the forecasted income levels in the year 2037 at the 95 % level and derived implied

β's. In the most plausible scenario, these were never below 1 % and in only two

cases above 3 %. This was the basis for our development of regional convergence

scenarios.

5.2.2 Patterns for Regional Convergence in the Applicant Countries

Theoretical and empirical analyses in regional economics might be used to support

either expectations of divergence of the AC regions or of convergence. In this sec-

tion, we do not aim at taking sides in this discussion. Rather, we calculated eight dif-

ferent scenarios for convergence of GDP per capita of the AC regions from 1997 to

2030. These should, in our view, cover a wide range of the possible developments.

Tables and graphs for each scenario are presented in Annex 5.

Scenarios 1 to 5 concentrate directly on the (possible) closure of the income gap be-

tween AC regions and the EU 15 average. Figures 5.1 to 5.5 in Annex 5 present the

results for these scenarios. They show the development of the per capita GDP of the

1st, 11th, 21st, 31st, 41st and 51st AC region (ranked according to their initial GDP per

head). Scenarios 6 to 8 imply a given convergence rate of national income levels of

the AC to the EU average and three different patterns for intra-national convergence.

As four CEECs only consist of one region this is a relevant exercise for six of the ten

AC only. Figures 5.6 to 5.8 show the development of per capita GDP of the richest

and the poorest region of each of these countries. The results are throughout ex-

pressed in regional GDP per capita (PPP) as a percentage of EU 15 GDP per capita.


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In scenarios 1 and 2, all AC regions converge with the same rate to the EU average.

In order to start the analysis with two distinct options � a pessimistic and an optimistic

one � we assumed in:

1. scenario 1 ("slow overall convergence"): A convergence rate of 1 % for all AC

regions from 1997 to 2030;

2. scenario 2 ("quick overall convergence"): A convergence rate of 3 % for all

AC regions from 1997 to 2030.

In scenarios 3 to 5, we studied different possible patterns of regional convergence

during the period until 2030. We ranked the AC regions according to their GDP per

head and cumulated their shares of total AC GDP. We then separated the 'rich', the

'average' and the 'poor' third of regions (GDP weighted). The 'rich' third consists of

seven of the eight Czech regions, Bratislava, Cyprus, the Budapest and the Warsaw

region and Slovenia. The 'poor' third includes Bulgaria, six of the eight Romanian re-

gions, Latvia, Lithuania, nine Polish and one Hungarian region. The 'average' regions

converged with 2 % during the whole period. In addition, we assumed in:

3. scenario 3 ("divergence in CEE"): A convergence rate of 3 % for the 'rich' re-

gions and of 1 % for the 'poor' regions until 2010; thereafter the convergence

rate was 2 % for all regions;

4. scenario 4 ("convergence in CEE"): A convergence rate of 1 % for the 'rich'

regions and of 3 % for the 'poor' regions until 2010; thereafter the convergence

rate was 2 % for all regions;

5. scenario 5 ("divergence, followed by convergence in CEE"): A convergence

rate of 3 % for the 'rich' regions and of 1 % for the 'poor' regions until 2010 fol-

lowed by the opposite pattern, i.e. a β of 1 % for the 'rich' and 3 % for the

'poor'.

In scenarios 6 to 8, we were interested in intra-national convergence (or lack thereof)

in the AC economies. We assumed a rate of 2 % for the convergence of national

GDP per capita to EU 15 levels. We then assumed in:

6. scenario 6 ("slow intra-national convergence in the individual AC"): A rate of

1 % for convergence of regional GDP per capita to their national average dur-

ing the period from 1997 to 2030;


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7. scenario 7 ("quick intra-national convergence in the individual AC"): A rate of

3 % for convergence of regional GDP per capita to their national average dur-

ing the period from 1997 to 2030;

8. scenario 8 ("intra-national divergence, followed by convergence in the individ-

ual AC"): A rate of -1 % for convergence, i.e. divergence, of regional GDP per

capita to their national average during the period from 1997 to 2010 and of

2 % thereafter.

5.3 Results

Detailed results for the individual scenarios are provided in annexes 4 (national con-

vergence) and 5 (regional convergence).

As regards the three scenarios on national convergence, the point estimates for na-

tional relative GDP per capita in 2037 were lowest in the first scenario (unconditional

convergence; resulting in a range from 41 % of the EU average for Bulgaria to 79 %

for Slovenia) and highest in the third one (current investment rates and population

growth are maintained until 2037; results between 44 % for Bulgaria and 103 % for

the Czech Republic). The disadvantage of the first one is the exclusion of other influ-

ence factors than initial income levels which lowers the quality of the regression. The

disadvantage of the third scenario is that the investment rates of the CEECs are on

average extremely high and will probably not prove to be sustainable. This applies in

particular for the Czech and the Slovak Republic. Hence, we expect that actual

growth will lie between the first and the second scenario. In the second scenario, the

CEECs would achieve between 50 % for Bulgaria (the only country faring better in

the second scenario than in the third) and 83 % for Slovenia. The upper bands of the

confidence interval for the first and the second scenario are very close to each other.

As regards the eight scenarios on regional convergence, it should be noted first that

convergence implies a worsening of the relative position for Prague and Bratislava,

the only AC regions above the EU average in 1997. Among scenarios 1 to 5 the sec-

ond produces the most positive results. 14 of 51 regions would achieve more than

75 % of the EU 15 average GDP per capita in 2030. The 15th region, Kozep-

Dunantul/HU would get 74.7 % in this scenario but only 57 % in the slow conver-

gence scenario (scenario 1) that proved to be the worst scenario. The poorest region


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in the sample, Severna Balgarija/BG, would achieve 57.4 % and 34.2 %, respectively

(see Table 5.1 for a summary of results).

The third scenario was almost as good as the second for the 'rich' AC regions.

Twelve regions would end up with more than 75 % of the average EU 15 GDP in

2030. However, the 'poor' regions would fare worse (41 and 51 % instead of 57 and

66 % for Severna Balgarija/BG and Vest/RO, the least-poor in the 'poor' third). Inter-

estingly, the results of scenarios 4 ("convergence in CEE") and 5 ("divergence, fol-

lowed by convergence in CEE") for 2030 are rather similar for all regions. A tentative

interpretation might be that - in a long term perspective � initially more rapid growth

of already 'rich' regions does not necessarily harm cohesion if a catch-up of the

poorer ones is achieved in a later stage.

Among the last three scenarios the sixth and the eighth show a very similar pattern.

The poorer regions would be less well-off in the sixth and the eighth than in the sev-

enth scenario; only the growth poles would fare worse in scenario 7. The "quick in-

Table 5.1Scenarios of Regional Convergence: Summary of Main Results for 2030

Number of AC Regions GDP p.c., PPP, of

above below Richest Poorest75% 55%

AC Regionof EU15 GDP p.c., PPP (EU15=100)

Scenario 1 5 36 113.8 34.2

Scenario 2 14 0 106.9 57.4

Scenario 3 12 21 108.5 41.5

Scenario 4 5 8 111.2 50.8

Scenario 5 6 13 110.5 48.7

Scenario 6 8 21 124.9 45.3

Scenario 7 10 16 100.0 46.3

Scenario 8 8 21 128.5 44.6

Source: Eurostat; calculations of DIW; method: see text and annex 5.


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ternal cohesion"-scenario 7 would lead more AC regions above the 75 %-threshold

than both of the other.


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6 Summary and Perspectives

6.1 Introduction

Cohesion can be interpreted in various ways: a level of stability or a process of con-

vergence; specifically in terms of income or unemployment levels, or elastically to

encompass employment opportunities and living standards. Cohesion is associated

with different policy choices. These include objectives of equalising regional and so-

cial differences through redistribution of growth, employment etc. (equity), but also

policies oriented towards maximising the contribution of regions and social groups to

national efficiency (efficiency). Indicators for measuring cohesion fall into three

groups: physical indicators associated with geographical or natural conditions; eco-

nomic indicators such as GDP for measuring regional prosperity; and social indica-

tors such as unemployment rates and quality of the labour force. EU analysis of re-

gional disparities, and EU structural and cohesion policies, give primacy to two

measures: GDP per capita and unemployment rates. Both are associated with meth-

odological difficulties that limit their use, but alternatives are limited. This report inter-

prets �national cohesion� as meaning the degree of disparity in GDP per capita in

PPP. Social cohesion refers to the exclusion/inclusion of sections of the population in

the labour market as captured by various unemployment indicators (unemployment

rates, long-term and youth unemployment), as well as to poverty.

6.2 Economic Characteristics of Enlargement

Enlargement will lead to a much higher degree of economic integration than has al-

ready been achieved by the Europe Agreements. Key words are completion of trade

liberalisation, signalling effects for investors, free movement of labour and applicabil-

ity of the full acquis communautaire (incl. transfer programmes). Enlargement could

result in quite differing scenarios for economic and social cohesion. A decisive factor

seems to be the preparedness for structural change in all members of the enlarged

EU. Integration combined with well-targeted support could lead to a win-win situation.

Protectionist measures must not be allowed to gain ground. A delay of enlargement

due to pessimistic expectations would, most probably, contribute decisively to the

realisation of these expectations (key words: lower growth in applicants, less inflow of

FDI, less technological up-grading, more inter-industry trade between EU and

CEECs with applicants exerting more and more pressure on low-tech EU producers,


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more illegal migration). It would harm rather than protect less competitive sectors of

EU economies.

6.3 Economic and Social Cohesion in the Candidate Countries and European Union: Analysis of Disparities

Overview: Despite recent above EU 15 average growth rates in the CEECs, eco-

nomic convergence remains limited. Poland, Slovenia, Hungary and the Czech and

Slovak Republics overall show the most positive macro-economic indicators. Consid-

erable labour market changes have occurred associated with the processes of eco-

nomic restructuring, privatisation and liberalisation. Broad sectoral change includes a

sharp fall in industrial and a considerable rise in service sector employment but dif-

ferences to the employment structure of the EU members remain substantial. Agricul-

tural employment has generally declined but with important exceptions (e.g. Roma-

nia). Unemployment has risen in all countries to varying extents but remains at levels

which are comparable to EU Member States. Economic transformation has been as-

sociated with emerging social problems and widening inequalities within CEE socie-

ties. Income levels and standards of living have declined and poverty has spread

considerably (with variation between countries). Poverty has a disproportional effect

on certain social groups e.g. the elderly, specific ethnic groups, single-parent fami-

lies, unemployed, low paid employees and women. This is affected by unemploy-

ment, discrimination and changes to social protection systems. Rapid industrialisa-

tion, inefficient raw material extraction, obsolete technology and a lack of environ-

mental control has left a legacy of environmental degradation. Considerable spatial

differentiation is evident. While reduction in pollution levels is evident, the costs of

clean-up are still extremely high.

Patterns of Regional Disparity in CEE and EU Countries: The spread of sub-

national disparities (in GDP and unemployment) in the CEECs is smaller than in

some EU Member States. CEE regions (at NUTS II level) are more sparsely popu-

lated than in the EU (except the Nordic countries). GDP per capita in CEE regions is

considerably less than the EU average � only Prague and Bratislava lie above this

level. The poorest regions are in Bulgaria (ca. 23 % of the EU average). The poorest

EU region (Ipeiros in Greece) is 43 % of the EU average, comparable with Hungary.

Regional unemployment is relatively low in CEE in comparison to the EU, with con-

siderable sub-national variation (but again less than in EU Member States). The low-


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est rate (1998) was in Prague (3.1 %) and highest in eastern Slovakia (21.6 %). Agri-

culture dominates regional employment structures in some CEECs (e.g. Romania

and Poland) to a much greater extent than in the EU. Capital cities have the highest

levels of service sector employment. EU regions tend to have more diversified

employment structures.

Types of Regional Problems in the CEECs: The types of regional problems in CEE

reflect both the unique process of transition, as well as structural changes already

undertaken in Western countries but delayed in CEE by geo-political factors. Overall

groupings include: (1) Capital cities/major urban agglomerations which demonstrate

the most favourable economic indicators, benefiting from e.g. high investment, skilled

labour force and training facilities, more developed infrastructure, business services

and access to decision-makers. Some capitals (e.g. Budapest, Prague, Tallinn, Brati-

slava) are highly dominant in the national economic structure. (2) Western border

regions which benefited from proximity to the EU, encouraging investment, trade,

tourism and cross-border retail and educational/technological initiatives. At the

EU:CEE border, per capita GDP and productivity (excluding commuters) is lower in

all the CEE border regions than their EU neighbours (except for the case of Brati-

slava and neighbouring Austrian regions of Niederösterreich and Burgenland). Total

unemployment is higher in German border regions than neighbouring Polish and

Czech ones but the situation is reversed on the Austrian, Greek and Italian border

with CEE. (3) Peripheral eastern and rural regions which are among the most eco-

nomically disadvantaged in CEE. Geographical location, poor infrastructure, low in-

vestment, declining agriculture and rural out-migration are all contributory factors.

These regions have particularly high rates of unemployment. (4) Old industrial re-

gions, the drivers of economic activity under socialism, which have been particularly

negatively affected by privatisation, enterprise restructuring/closures, subsidy loss

and market re-orientation. Problems include unemployment, lack of entrepreneurship

and environmental decline. A full process of restructuring still has to be undertaken in

some old, mono-structural areas.

Cluster Analysis - Types of Regions in the Applicants and the EU: A cluster

analysis was conducted to classify all ca. 260 EU and AC regions simultaneously in

types of regions according to their employment structure and population density. This

led to six clusters: Agglomerations; Service dominated; Service biased; Industry; Ag-

riculture biased; Agriculture dominated. The distribution of the regions among the


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clusters shows the very poor development of the service sector and the importance

of agriculture in the transition countries compared to the EU 15. Industry plays a

dominant role for employment only in a minor part of the AC regions. This economic

structure of the AC regions is noteworthy because, in general, regions with above

average GDP per head are more likely to be found in the agglomeration or service

clusters than in the industry cluster (although some of the industry regions, mostly

from Germany or Italy, are quite well-off). An agriculture bias is clearly associated

with a low per capita GDP. Worse still, with only few exceptions, the AC regions were

clearly the poorest regions in their respective cluster. While the cluster analysis pro-

duced quite homogenous groups of regions with regard to their overall structural

characteristics, there is no uniform socio-economic situation among the regions of a

specific cluster. Labour market problems tend to be concentrated on selective re-

gions in the EU as well as in the AC. They are not obviously related to the GDP level

of a region and national characteristics seem to play a dominant role.

Island Economies: The island economies of Malta and Cyprus (with Slovenia) are

the most prosperous of the 12 candidate countries. Both are small island economies

strongly dependent on foreign trade and FDI as economic drivers. Malta has an

economy characterised by an open, export-oriented sector and a protected domestic-

oriented sector (utilities and manufacturing) which has come under restructuring

pressure following application for EU membership. Services account for 70 % of

GDP, with tourism the most important sub-sector. The EU accounts for 50 % of Mal-

tese exports and 70 % of imports. Exports to the EU have declined in the 1990s,

while trade with North America and Asia has increased. Unemployment, historically

low, rose in 1999 to 5.1 %. Restructuring and privatisation, likely to be accelerated by

EU accession, could have a negative labour market impact. Cyprus is larger than

Malta and has a per capita GDP (in current PPP) of just over 75 % of the EU aver-

age. The economy has experienced steady growth over the past three decades, has

very low inflation and unemployment of less than four percent. A recent decline in

manufacturing competitiveness is associated with rising production costs, insufficient

productivity gains and intensified international competition. Accession is likely to

highlight the structural weaknesses faced by Cypriot industry. The service sector is

important but quite dependent on tourism. Cyprus is also an important off-shore cen-

tre for (mainly European) companies and the tax implications of EU accession could

have important ramifications for this area. EU countries account for 55 % of Cypriot


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imports and 40 % of exports. Recently, the CEECs and Russia have become more

important trading partners.

6.4 Effects of Enlargement: Critical Issues

Trade: During the 1990s, the CEECs have managed to redirect their exports away from the former CMEA members towards the European Union. The trade volume has increased significantly and the EU has become the most important trading partner of the CEECs. From the point of view of the EU, the AC are much less important part-ners. Geographical proximity seems to play a key role in determining bilateral trade flows. Main trading partners are Germany and Austria, as well as Finland, Italy and Greece on the EU side and Poland, the Czech Republic and Hungary on the CEE side. Regional trade data available indicate that this pattern also applies at the re-gional level. However, eastern German, as well as western Polish regions do not ac-count for significant shares in total trade of their respective countries. CEECs have been able to change the commodity structure of their exports from inter-industry to intra-industry trade, i.e. their export structure is now more similar to that of the EU as in the early 1990s. However, it is important to note that bilateral exchange is over-whelmingly trade in vertical differentiated products with the CEECs being exporters of product variations with lower unit values. Only Hungary seems to be an exception. There is no indication that the CEECs constitute a severe competition for the EU co-hesion countries or other EU members.

FDI: As in the case of trade, recent years have seen a marked increase in FDI flows from the EU to the AC, dominated by the main trading countries but also by France and the Netherlands. While FDI flows are important for the receiving countries (most notably Hungary, the Czech Republic and Poland), Austria is the only EU member where CEE plays a prominent role as a destination for FDI flows. Other than being the case with trade, there are practically no FDI flows from the AC to the EU. The choice of destination seems to be influenced, in general, by proximity and political stability. The motives for investment are not entirely clear. While surveys show a slightly above average importance of wage costs advantages for FDI in CEE (com-pared with overall FDI outflows from the EU), there are also indications that this is not the dominating influence factor. Market access and first-mover advantages also play a decisive role.

Migration is often cited as the most important post-enlargement effect with automati-cally associated negative consequences for EU members. High estimates of future


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migration are introduced in the debate apparently based on crude measures and without econometric and economic-modelling background. More diligent analyses do not expect a massive influx of migrants after enlargement and see only minor � and by no means necessarily negative - effects on wage and employment in the EU. Mi-gration flows will be directed mostly into Germany and Austria as these countries are already home to the largest shares of CEEC citizens in the EU. Inside these coun-tries, they will be directed to centres of economic activity, not necessarily to the bor-der regions. Actual migration flows depend on the income gap, the labour market situation in the destination country and the stock of migrants. The share of citizens of the country of origin that are already living abroad determines, on the one hand, the destination choice of new migrants. More importantly, on the other hand, it dampens the potential for further emigration from a specific country because the propensity to migrate is not distributed evenly among the population. It is, therefore, to be expected that migration flows will rise after enlargement (there are only comparatively few CEECs citizens already living in the EU). However, the inflow will not be as exces-sively high as sometimes expected and it will slow down over time. The actual labour market effects do not just depend on the number of migrants but also on their qualifi-cation. Highly qualified migrants can have positive effects for low qualified domestic workers.

Border regions are potentially most affected by enlargement accentuating internal disparities inside these regions. Competitive enterprises, sectors and areas will gain from the proximity of new markets and the supply of a wider selection of inputs. Less competitive ones will suffer from increased competition. Along the EU:CEEC border, the impact will most likely be concentrated on the eastern Austrian regions. The im-pact is not necessarily negative on balance but the adjustment pressure will be high-est here.

6.5 Projected Patterns of Disparity

Based on orders of magnitude and development patterns that have been observed in

the past, a plausible range for future development of disparities of CEECs vis-à-vis

the EU and within CEECs in the long run was identified. Firstly, the analysis was

concentrated on national convergence of the applicants to the EU 15 average; sec-

ondly, various scenarios for convergence of the AC regions were calculated that fall

into the plausible range for national convergence.


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As a starting point for the assessment of long run development of relative per capita

GDP of the AC, the convergence experience of post-war European market econo-

mies was taken as a yardstick. Of the various scenarios used to forecast conver-

gence of the CEECs to the EU average, the one where investment rates and popula-

tion growth in the CEECs equalled the mean values of the European market econo-

mies in the post-war period seemed to produce the most plausible results. In this

scenario, the CEECs would achieve a per capita GDP between 50 and 83 % of the

EU 15 average in the year 2037.

Based on a rate of convergence of 2 % for the convergence of the applicants� na-

tional GDP per head to the EU 15 average, several scenarios for regional conver-

gence were calculated. While results, not surprisingly, also depended on the underly-

ing assumptions, it became clear that internal disparities in the CEECs will remain to

be a task for (community and national) regional development policies for the next few

decades. However, one tentative interpretation of the regional scenarios was that - in

a long term perspective � initially more rapid growth of already 'rich' regions does not

necessarily harm cohesion if a catch-up of the poorer ones is achieved at a later

stage.

6.6 Prospects and policy issues

Enlargement of the European Union to include up to 27 Member States is the most

significant project in the history of the Union. As discussed in this report, enlargement

could result in quite different scenarios for economic and social cohesion. On the one

hand, there are, overall, positive scenarios with economic, political and social gains

for both the EU and CEE countries; on the other hand, under some pessimistic sce-

narios, there could be negative repercussions for both EU and CEE economies. A

decisive factor seems to be the preparedness for structural change in all members of

the enlarged EU. Integration combined with well-targeted support, avoiding protec-

tionist measures, could lead to a win-win situation for the EU and CEE.

Enlargement has often been discussed in a negative manner such as �threats of

competition�, an �influx of migrants� and �cost burdens�. This report has shown that it

is important to keep these issues in perspective. The EU 15 currently have a €25 bil-

lion trade surplus with CEE countries, particularly in investment goods, and there is

no indication that the CEECs constitute severe trade competition for the EU cohesion

countries or other EU members. Similarly, the CEE economies host a stock of €27


DIW 135 EPRC

billion of foreign direct investment from EU countries, only a small part of which ap-

pears to be driven by low-wage costs in CEE. The major part of FDI is motivated by

market access; investment in CEE is created rather than diverted from elsewhere in

the EU. Lastly, high estimates of future migration appear to be based on crude calcu-

lations. More detailed analyses do not suggest massive out-migration from CEE

countries after enlargement and foresee only minor, and by no means necessarily

negative, effects on wage and employment in the EU.

Nevertheless, as noted above, the critical factor for a positive enlargement scenario

is the preparedness for structural change. Along with the economic, industrial and

social policies of the EU and national governments, enlargement presents formidable

challenges for EU structural policies. Widening the EU to include 27 Member States

would increase the territory of the Union by 34 % and its population by 28 %,

whereas the average GDP per capita would decline by approx. 15 %. Accession of

the ten Central and Eastern European countries would radically alter the EU maps of

regional problems and disparities. Agriculture dominates regional employment struc-

tures in the transition countries to a much greater extent than in the EU 15, while the

service sector remains relatively under-developed, especially outside the capital cit-

ies. The agriculture bias is associated with low per capita GDP; the poorest CEE re-

gions (Bulgaria, Latvia, Lithuania and parts of Poland and Romania) have a GDP per

capita of below 30 % of the (current) EU average.

EU enlargement will, therefore, require a reorientation of the Structural and Cohesion

Funds. Under the current budgetary parameters, for whatever objectives and criteria

are used for allocating funding, there would need to be a substantial shift away from

current recipients to the transition countries. However, there is also scope for new

thinking about the way in which the EU and Member States work together on regional

policy.

The EU Treaty commitment to economic and social cohesion is an important pillar of

the European social market economy and it underpins intervention through EU struc-

tural policies. However, the economic development logic of EU action is undermined

by a perception of the Structural and Cohesion Funds as a �side payment� to enable

agreement in other policy areas and by the political bargaining associated with the

allocation of funding. EU enlargement presents an opportunity to improve the alloca-


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tional logic of EU regional policy and maximise its impact on economic and social

cohesion.

The principles of the 1988 reform of the Structural Funds � concentration, multi-

annual programming, partnership and additionality - have proved to be a good basis

for regional development policy. However, their impact outside the Cohesion Coun-

tries has been obscured by the dissipation of aid over small areas, the bureaucracy

of programming, the wide range of interventions and the short programming periods

(in Objective 2 areas). For EU structural policy in CEE, it will be important to develop

medium-to-long-term priorities and consistent objectives for policy measures. In par-

ticular, there is a need to concentrate on a limited number of key priorities. Assis-

tance should be concentrated geographically (growth poles) and export-oriented to

promote the �motors� of development.

The major lesson of the past 15 years of Structural and Cohesion Fund implementa-

tion in the EU is the critical role of institutional capacity. There are major differences

in the mode of implementation among Member States, but the common experience is

that there is a long �learning curve� relating to all aspects of programming. Given the

historical institutional legacy in many CEECs, the �vacuum� of regional self-

government and the slow process of territorial administrative reform, it will be impor-

tant to recognise, for the time being, the primary role of national governments in the

implementation of the Funds and to respect the institutional differences between

countries.


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