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
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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
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Structure
List of Tables ........................................................................................................... 6
List of Figures.......................................................................................................... 7
List of Annexes........................................................................................................ 7
Glossary of Terms ................................................................................................... 7
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
CEEC Region in % of EU Region 390.1 105.9 97.1 174.0 345.5
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
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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
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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.
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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
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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
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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
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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-
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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
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.
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.
... 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
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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
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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
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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-
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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
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
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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
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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,
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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:
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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
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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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