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National and regional trajectories of convergence and economic integration in Central and Eastern Europe

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This article tests the hypothesis that the geographical location plays a major role in differentiated economic integration of central and eastern regions at different geographical scales. We implement a local measure of Gini index allowing to measure the economic catching-up of regions (across the period 1995-2007 at the NUTS 2-3 level). The findings indicate that a convergence of the CEEC toward the EU-15 seems to take place but at the cost of widening regional inequalities within each state. Our results suggest also the existence of a west-east gradient of regional economic integration.
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Scaling up and convergence are recur-
rent difficulties for countries wishing
to join the European Union. Over the
previous decade, the ten Central and
Eastern European Countries (CEEC)
applying to join have pursued the
same strategy of restructuring and
approach to European standards. To
reach their targets, these countries
have taken different paths, and im-
plemented specific policies that have
affected the rate and extent of their
convergence. While the last two en-
largements of the European Union to
the East (2004 and 2007) both con-
tributed to economic growth in the
new EU Member States (EU NMS), in-
creased economic inequalities at re-
gional level have been observed for
these countries (Petrakos, 1996, 2000;
Henderson, McNab, & Rozsas, 2008;
Aristei & Perugini, 2012).
At stake in transition is the pas-
sage of the former communist coun-
tries and developing countries with
authoritarian regimes towards a more
democratic society and an economy
where markets play a dominant role.
Despite the specific character of na-
tional and regional trajectories, the
manner of transition was similar in all
countries: a drastic reduction in budg-
et subsidies, a substantial drop in pur-
chasing power and a sharp increase in
unemployment, political and demo-
cratic transition, and modifications to
economic structures (Milanovic, 1998,
1999).
Since 1989, three sets of forces
have predominated in Central and
Eastern Europe, conditioning the insti-
tutional change of economies and of
post-socialist countries. There have
been endogenous or internal forces,
determined primarily by organisations
and national stakeholders, European
forces driven by the Member States
and EU institutions, and finally global
forces resulting from international or-
ganisations (IMF, OECD, etc.) and mul-
tinationals. These three force fields
(which can also change) have con-
stantly interfered with each other to
varying degrees depending on the
phase and the country, and made a
significant contribution to the for-
mation of national and regional paths
of change.
In this context, it seems to be of
interest to consider the differentiated
regional integration of Central and
Eastern European countries and re-
gions. Beyond the convergence ob-
served between countries and the di-
vergence observed within each coun-
try (Ezcurra, Pascual, & Rapún, 2007;
Sukiassyan, 2007), the trajectories of
convergence between each Central
and Eastern European country are dif-
ferent. Several recent studies (Sokol,
2001; Melchior, 2009; Gorzelak, Maier,
& Petrakos, 2013) show that there is a
West-East gradient for regional eco-
nomic integration. In this context, we
assume that geographic location plays
a dominant role in the differential
catch-up of countries and regions. In-
troducing location into the analysis of
regional inequalities enables the pres-
ence of spatial effects to be highlight-
ed, characterized by spatial autocorre-
lation and spatial heterogeneity of the
convergence process (Le Gallo &
Dall’Erba, . In recent years, there
have been many studies taking into
account the spatial dimension of the
data in the analysis of convergence
from an empirical point of view
(Fingleton & Lopez-Bazo, 2006; Ertur
& Koch, 2006; Dall'Erba & Le Gallo,
2008), or integrating spatial interde-
pendencies from a theoretical point of
view (Ertur & Le Gallo, 2009). The in-
terest in the geographical nature of
regional economic trajectories is thus
justified. Ordinarilly, the Gini coeffi-
cient is a whole-map, locationally in-
variant measure of inequality (Rey &
Smith, 2013). By instead measuring
this index locally, we determine a ge-
ography of spatial integration of EU
countries and regions. In this context,
we propose a multiscale approach to
the measurement of economic ine-
qualities using the Gini index. Conver-
gence is approached on a local level
and a coefficient is estimated for each
region. By doing this, we deal with the
spatial heterogeneity mentioned in lit-
erature, as our model of local spatial
convergence divides our sample into
cross-sections, by treating each region
and the nearby regions as subsamples.
We will thus be able to observe
whether this local convergence ap-
proach brings a spatial structuring of
the convergence process to the fore-
National and regional trajectories of convergence and eco-
nomic integration in Central and Eastern Europe
Sebastien Bourdin
Territorial Development Institute (France), Normandy Business School. Address com-
ments to sbourdin@em-normandie.fr.
Submitted 22 March 2014. Accepted 26 August 2015.
© Canadian Regional Science Association / Association canadienne des sciences régio-
nales 2015.
Bourdin, S. 2015. National and regional trajectories of convergence and economic inte-
gration in Central and Eastern Europe. Canadian Journal of Regional Science / Revue ca-
nadienne des sciences régionales 38(1/3), 55-63.
This article tests the hypothesis that the geographical location plays a major role in dif-
ferentiated economic integration of central and eastern regions at different geograph-
ical scales. We implement a local measure of Gini index allowing to measure the eco-
nomic catching-up of regions (across the period 1995-2007 at the NUTS 2-3 level). The
findings indicate that a convergence of the CEEC toward the EU-15 seems to take place
but at the cost of widening regional inequalities within each state. Our results suggest
also the existence of a west-east gradient of regional economic integration.
54 Bourdin Convergence and economic integration
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ground. According to the works by
Rey (2003, 2004), we hypothesize that
the emphasis placed on a global
measurement of regional inequality
may hide different regional trajecto-
ries that may have explicit spatial rep-
resentations reflecting pockets of
poverty, convergence clubs and other
forms of geographical grouping.
The first part of this article pre-
sents the theoretical foundations and
conceptual framework of our analysis.
We then present the methodology
and data used. The third part of the ar-
ticle sets out the results of this ap-
proach which aims at demonstrating
that the process of convergence and
regional integration in Central and
Eastern Europe is geographical by na-
ture.
Theoretical foundations and concep-
tual framework
The neoclassical growth theory devel-
oped by Solow (1956) stating that
economic convergence between
countries with uneven development is
possible, is at the origin of work on
economic convergence. He works on
the assumption that there are regional
disparities at the beginning and that
these disparities tend to decrease over
time. Each region converges towards a
long-run income per capita growth
rate called steady-state rate1. This
explains how a country’s economy
tends to converge more rapidly if it is
below its steady state. The beta and
sigma convergence econometric and
statistical tests performed on a wide
sample of countries, over a period of
nearly thirty years, lead us to reject
the catch-up hypothesis.2 In other
words, the growth of the countries
that are initially the least developed is
not systematically quicker than that of
developed countries.
Subsequent to these studies, new
theories on economic convergence
were developed. They are based on
Myrdal’s research  which states
that growth is a spatially cumulative
process likely to increase inequalities.
This saw the emergence of new en-
dogenous growth theories resulting
from the introductory work of Romer
(1986) and Lucas (1988). These models
underline the lack of diminishing re-
turns to capital, which is related to
the endogenous nature of produc-
tion technology. The level of human
capital, the amount of investment in
R&D or even knowledge spillovers
are explanations for divergent growth
trends. Another theoretical movement
developed following the lead by Paul
Krugman (1991): the New Geographic
Economy (NGE). It acknowledges that
economic activities are focused on
clusters and this polarization modifies
the spatial distribution of wealth be-
tween regions (Fujita & Thisse, 1996).
Williamson (1965) is at the origin of
the emergence of this economic trend
as the author was already highlighting
the role of space to explain regional
growth at that time. The role of space
in this economic theory is essential, as
it helps explain the phenomena of
economic growth. The geographical
distribution of regional growth phe-
nomena is hardly ever random: on the
contrary, the economic performance
of adjacent regions is often similar.
Studies on convergence resulting
from the NGE show that some coun-
tries manage to take more advantage
of growth, while others fail to do so.
We can thus observe two phenomena:
economic convergence for some
countries, and divergence for others in
a synchronous movement. The result
of this process is not predetermined,
and the new theoretical models that
highlight the role of geographic space
show that there can be both economic
convergence and divergence. As a re-
sult, the effects of economic conver-
gence on regional integration can be
both positive and negative. It there-
fore depends a lot on the initial situa-
tion, on the ability of the regions to
adapt and on the neighbourhood ef-
fects. This article proposes the meas-
urement of local convergence in this
framework, to assess the simultane-
ous phenomena of economic conver-
gence and divergence, using the
breakdown of inequalities on a local
scale.
The neoclassical theory implies
that uneven development between
countries tends to decrease over time.
Having said this, it does not give a sat-
isfactory explanation for the long-run
factors of economic growth. This is
why we have favoured an approach
based on the arguments of the en-
dogenous growth theory (Romer,
1986; Lucas, 1988) and geographic
economy in our article, as they show
that the economic situation of a re-
gion depends on its interrelations with
its neighbours.
Measuring economic convergence
at local level is of interest as it enables
us to understand the origin of the per-
sistence or reduction in regional dis-
parities in some parts of the EU. Local
convergence can be defined as a situa-
tion in which convergence rates in
terms of economic growth are similar
for observations located nearby in
space Ertur, Gallo, & LeSage, . )n
other words, there could be a spatial
clustering of regions with similar sig-
ma convergence coefficients, thus
confirming the geographical nature of
the regional catch-up process. We can
therefore estimate a local conver-
gence index for each region in our
sample, then investigate whether
these estimations provide empirical
confirmation of our concept of local
convergence. The interest is therefore
to consider both economic conditions
and geographic proximity as being po-
tential influences on economic con-
vergence. For instance, the lack of
catch-up even divergence of a lag-
ging region could be explained by the
fact that it is surrounded by other lag-
ging regions with similar convergence
rates.
Local convergence measures the
reduction (or increase) in economic
disparities within a group of adjacent
regions. In other words, there is local
convergence when the GDP per capita
of the regions included in the deline-
ated area tend towards the average
level of the GDP per capita of the area
in question.
Methodology and data
During the last decade, the empirical
research on economic convergence
has rapidly developed and often pro-
duces contradictory results (Quah,
1996; Islam, 2003; Abreu, de Groot, &
Florax, ; Dall’erba & Le Gallo,
2008; Le Pen, 2011). The origin of the
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sensitivity of these results can be
found in the differences in ideas about
convergence and therefore in the
methodology inherent to each ap-
proach.
One of these approaches consists
in cross-sectional convergence tests
introduced by Baumol (1986), and de-
veloped by Barro & Sala-i-Martin (1991,
1992). There are two tests (beta and
sigma convergence. β-convergence
attempts to highlight the fact that
poor countries can catch up with rich
ones (negative relationship between
the variation rate and the initial level),
while σ-convergence tries to measure
the change in economic disparities
(reduction in the difference of GDP
per capita between two dates).
In order to highlight the effects of
geographic location on the differential
catch-up of regions, we propose the
development of a local convergence
index based on spatialization of the
Gini index. Local convergence is an in-
dicator measuring the reduction (or
increase) in economic disparities with-
in a group of adjacent regions. In oth-
er words, there is local convergence
when the GDP per capita of the re-
gions included in the delineated area
tend towards the average level of the
GDP per capita of the area in question.
Our article proposes a multiscale
analysis of convergence thanks to the
calculation of a standard Gini index for
each country, and the calculation of a
local Gini index on regional subsam-
ples. We have chosen the following
Gini index see equation (1)where
Xk corresponds to the cumulative per-
centages of the number of regions
and Yk the cumulative percentages of
the GDP per capita of these regions.
We have developed a formalisa-
tion of local convergence in order to
calculate the local Gini index. We have
a spatial measurement X = (x1, x2, ... xn)
(for this case study, GDP is in PPS3)
over a population I = {1, 2, ..., n} con-
sisting of regions, and over which a
spatial measurement is defined (the
total population of a region) denoted
by Z = (z1, z2, ..., zn). The following nota-
tions can be usedsee equation (2)
wherein si is the density of Q com-
pared to P and is also the density of X
compared to Z. The order of values is
fundamental because the shape of the
Lorenz curve depends directly on
them. Individuals are thus ordered by
increasing density such that s1s2≤...≤sn
(referred to as the Lorenz order). We
can define totals using these Lorenz
order conditions on individuals: see
equations (3) and (4).
We can use these notations to de-
fine Gδ(i) as the measurement of the
local Gini index of the distribution of
the GDP per capita for a region i and
its neighbours j. This index depends on
a neighbourhood Vδ(i) of the region i
defined by a spatial weight matrix that
may take different forms (inverse of
the distance, k-nearest neighbours or
contiguity).
It can be shown that this local in-
dex around the region i is calculated
using equation (5):
We have chosen to use a nearest
neighbour matrix defined as follows,
for our studysee equation (6)where
w*ij(k) is an element in the non-
standardised weight matrix, wij(k), is
an element in the standardised row
matrix. Also, dij(k) is the threshold val-
ue defined for each region i it is the
shortest distance of order k between
regions i and j such that region i has
exactly k adjacent regions. In order to
make sure that our results are robust,
we have implemented the estimations
of the local Gini index for k = 15.
As regards the territorial unit cho-
sen, the NUTS 2 suffers from the
MAUP (Modifiable Areal Unit Prob-
lem) due to the variability in the size of
the European regions. Grasland &
Madelin (2006) recommend using a
unit in between levels NUTS 2 and
NUTS 3: level NUTS 2/3. The data we
have used in our article are those from
Equations


(1)
(2)
which is the sum of the smallest population values i.
(3)
and we also say P0 = 0 and Q0 = 0.
(4)
(5)






and


(6)
n
i
i
zp
1
p
z
pi
i
n
i
i
xq
1
p
x
qi
i
i
i
ip
q
s
i
j
ji pP
1
i
j
ji qQ
1
56 Bourdin Convergence and economic integration
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Eurostat on the GDP per capita in PPS
between 1995 and 2007 at level NUTS
2/3 for all the EU-27 countries. We
chose to not use data after 2008 for
two reasons; firstly, the economic cri-
sis would have made it more difficult
to interpret our results, and secondly,
because Eurostat data are incomplete
for the chosen unit for the years after
2011 (especially for some Eastern Eu-
ropean countries and Italy).
Results: from global analysis to local
analysis of the Gini index
Figure 1 shows the trajectory (overall
trend) for each country and the extent
of its internal inequalities. The direc-
tion and inclination of the arrows (Ta-
ble 1) make it possible to assess
whether there is a situation of conver-
gence or divergence in the European
Union between 1995 and 2007.
Figure 1 helps answer the question
of whether what we see is a double
catch-up internal (between regions
of the same country) and external (be-
tween the EU countries) or if one of
the two conditions is not met. First of
all, there is a fairly clear-cut difference
between the EU-15 countries and the
CEECs. Indeed, in 2007, all of the EU-15
countries, except for Portugal and
Greece, were above the EU average in
terms of GDP per capita, while those
of the CEECs were all below. Moreo-
ver, the evolution of the regional dis-
tribution of GDP per capita for a given
country is more even in the EU-15
countries than in the CEECs. Reading
this graph, it is possible to conclude
that convergence between the EU-27
countries began between 1995 and
2007. Convergence in the CEECs seems
to be present but at the cost of widen-
ing regional inequalities within each
state confirming former work (Sokol,
2001; Egger, Huber, & Pfaffermayr,
2005; Perugini & Martino, 2008; Sme,
Tkowski, & Wójcik, 2012; Gorzelak,
Maier, & Petrakos, 2013). The length of
the straight lines on the graph for the
CEECs shows how these inequalities
have consistently widened.
When the communist period end-
ed, market liberalisation in these
countries created severe inequalities
with, on the one hand, the regions
connected to the European or even
global system (this is particularly true
of capitals) and on the other hand,
remote regions where the conversion
process is currently underway but re-
mains largely incomplete. The end of
this system firstly signalled the rapid
disengagement of the government, to
which were added budgetary and fis-
cal crises, significantly reducing the re-
sources to be redistributed, while
support for growth centres was fa-
voured over regional planning policies
(Bogalska-Martin, 2005; Prchniak,
2011). In this context, regional dispari-
ties will widen even faster if the old
system is rapidly left behind, and these
differences are driven by the strong
growth achieved by the capital city re-
gions.
These inequalities in regional inte-
gration between metropolitan regions
and rural regions farther to the East
question the effectiveness of the dif-
ferent policies pursued by the EU (no-
tably via the cohesion policy) in the
opening up of the Central-Eastern ter-
ritories and their convergence.
The analysis of local convergence
(change in the spatialized Gini index
Gδ between  and  shows the
formation of a (multi-)polarisation of
the convergence process (Figure 2).
There is indeed a geographical distri-
bution of convergence phenomena
which takes shape in space as a trend
toward a grouping of regions in a situ-
ation of either local convergence or of
local divergence.
The mapping of the variation of
the Gini index between 1995 and 2007
highlights the presence of a spatial
concentration of regions along the
former iron curtain, characterised by
local convergence. This wide area is
made up of more urbanised regions,
with great economic dynamics offer-
ing better infrastructures than the rest
of Central and Eastern Europe. Ex-
changes are possible thanks to the ex-
istence of differentials (in cost, supply,
structure according to age, etc.) be-
tween the regions either side of the
former iron curtain. In the words of
Szűcs , this buffer zone can be
characterised as an in-between
space. It can generally be said that the
model of development with a West-
East gradient seems appropriate to
account for disparities both at a su-
pranational level and on a regional
scale. This local convergence area is a
privileged space for economic integra-
tion with the emergence of cross-
border cooperation between the EU
and East-Central Europe as well as be-
tween East-Central European states.
The rapid development of trade rela-
tions between Western and Eastern
Europe thanks to the entry of the
CEECs into the EU has led to strong
integration between the old blocks
(Western capitalist and Eastern com-
munist), so much so that it now seems
appropriate to wonder whether the
convergence process is not complete,
at least for some countries (Festoc-
Louis & Roudaut, 2012).
Table 1. Analysis of state trajectories.
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On the other hand, we have found
a spatial concentration of regions with
high, or even very high, local diver-
gence in Romania and Bulgaria. These
considerations refute the results of
the empirical research into conver-
gence based on the neoclassical
growth theory which assumes that
there is a negative correlation be-
tween the GDP per capita and the
economic growth of regions. Indeed,
most of these regions have the lowest
GDP per capita in the EU, and despite
this, have not recorded the expected
growth rates. Other explanations
therefore need to be found, and we
can find them in the endogenous
growth theory and geographic econ-
omy. The spectacular economic devel-
opment of Bucharest may be at the
origin of an increase in local inequali-
ties. The capital of Romania has devel-
oped, leaving its adjacent regions to
one side, at the origin of this sustaina-
ble local divergence process. This de-
velopment in Bucharest at the ex-
pense of other regions is explained in
part by the considerable polarisation
of foreign direct investment generally
in the capital city regions of Central
and Eastern European countries. This
concentration of investment4 helps
explain the heterogeneity of Romani-
an regional development (Serbu Mas-
ca, 2007; Petrakos, 2008; Danciu,
Goschin, & Gruiescu, 2010). This pock-
et of local divergence marked by sharp
discontinuities provides a concrete
example of what Krugman (1996) calls
the agglomeration shadow effect.
This occurs when there is significant
domination by an economic centre
over the rest of its territory. Given
centripetal forces, activities and indi-
viduals are attracted by the Romanian
capital at the expense of surrounding
regions, following the predictions of
the gravity model. Therefore, the sec-
ondary poles can appear only at a dis-
tance sufficient enough to avoid the
phenomenon of attraction, leaving the
intermediate spaces relatively empty.
From this point of view, the European
cohesion policy is confronted with en-
dogenous factors specific to Romania
such as administrative inertia, low
human capital accumulation, low in-
vestment in R&D and its location on
the edge of Europe.
A final example is Poland, marked
by economic disparity dividing the ter-
ritory between a local convergence
area and a local divergence area (Fig-
ure 2). This structural division puts a
richer, more urbanised5 Poland,
marked by the domination of Royal
Prussia since the 13th century, then by
Germany until after the Second World
War, on one side. Besides, the majority
of direct foreign investments are lo-
cated in the western regions of Poland
(Chidlow, Salciuviene, & Young, 2009).
In addition, the price differential be-
tween Germany and Poland due to the
relatively low standard of living of
Poles and the low tax on products
benefitted Polish border territories
Figure 1. The trajectories of convergence.
58 Bourdin Convergence and economic integration
Reproduced with permission of the copyright holder. Further reproduction prohibited.
which captured German consumers. In
contrast, the Eastern Polish regions
hardly benefit from this return to Eu-
rope. This part of the country was
once within the Russian sphere of in-
fluence (Gorzelak, 2006; Bański, 2010).
The pocket of local divergence is char-
acterised by a lack of infrastructure,
poor urban fabric, few industries and
fragmented agriculture which is not
very competitive. It should be noted
that between 1950 and 1985, 18 re-
gions (out of 49) in the Eastern part of
Poland were entitled to only 0.1% of
industrial investments made by the
former USSR.
Thanks to the examples of East-
Central Europe, Romania and Poland,
we have found a process of regional
integration that is spatially very differ-
ential and highlighted by our local
convergence index. We can thus con-
firm our two original hypotheses. First
of all, geographic location (both
neighbourhood effects and effects of
absolute location in Europe) play a
dominant role in the differential catch-
up of regions. Secondly, the various
regional trajectories observed at a lo-
cal level are characterised by explicit
spatial representations, revealing
pockets of poverty or, on the contrary,
grouping of regions with strong eco-
nomic and territorial dynamics.
Conclusion
The aim of this article was to question
the existence of a geography of re-
gional integration with reference to
the endogenous growth theory and
contribution from the geographic
economy. It was also to analyse the
process of economic catch-up of
states and regions with respect to the
EU. Thus, the convergence observed
between EU states sometimes con-
ceals an increase in intra-state regional
inequalities (global analysis of the Gini
index). Moreover, the convergence
phenomena observed at a global level
produce either convergence or diver-
gence at a local level (local analysis of
the Gini index), thus confirming the
empirical research stemming from the
endogenous growth theory and the
NGE. The mapping of the results ena-
bles us to account for differential and
territorialised regional integration. A
large number of regions located either
side of the former iron curtain are
characterised by a situation of local
economic convergence despite very
different levels of GDP per capita
along the borders (see map 1). In fact,
the wall of money Grasland, 2004)
that replaced the iron curtain seems
to be slowly cracking too. These re-
gions recorded high local convergence
between the two periods of analysis,
and may form an economic conver-
gence club creating long-run growth
dynamics, thanks to their geographical
grouping.
This differential economic catch-up
and these trajectories of local conver-
gence or divergence are based on spa-
tial development considerations of
various scales, as well as on the histo-
ry, both recent and more distant, of
the country concerned. At the begin-
ning of the systemic transformation
process, certain regions benefitted
from initial advantages linked to
their faster growth, thanks to their
past and their infrastructure and
equipment endowment. The hetero-
geneity of geographic space thus ap-
pears as an explanation for regional
inequalities in development, highlight-
Figure 2. A multipolarization of local convergence.
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ed in particular by the opening up of
the country to the West. From this
point of view, the 90s were a water-
shed and resulted in profound chang-
es for the CEECs, which had to manage
the question of the socialist past,
while also turning towards the EU.
Spatial clustering of the local con-
vergence process highlighted in this
article calls for differentiated political
responses. They must take into ac-
count the various factors at the origin
of regional economic convergence,
which include the effects of heritage
and context, effects of proximity and
European Structural Funds.
In this regard, the latest survey re-
ports about the cohesion policy re-
vealed two points of view that oppose
discussion about regional policy (Pike,
Rodríguez-Pose, & Tomaney, 2010;
Barca, McCann, & Rodríguez-Pose.
2012). On the one hand, the idea of a
space neutral regional development
policy, emphasising the advantages of
urban areas and spillovers from geo-
graphical concentration (World Bank,
2009; Gill, 2010). On the other hand, a
territorial or place-based approach
that assumes that the territorial/local
context must be taken into account
especially the role of institutions, the
importance of local knowledge, the
socioeconomic characteristics (Garci-
lazo, Martins, & Tompson, 2010; ES-
PON, 2010 and 2013). In this context,
the new architecture of the cohesion
policy breaks tradition with the pro-
jects supported by the European
Funds beforehand6 (European Com-
mission, 2014). For the period 2014-
2020, the European Commission has
invited each region to present its
strengths and to establish a Smart
Specialisation Strategy, known as
RIS3. Our article shows that local di-
vergence observed in many regions of
Central and Eastern Europe questions
their ability to implement such strate-
gies due to their structural lag (low
capacity of innovation, declining de-
mographics, low level of training, low
capacity to unleash European Funds,
etc.). Besides, our results question the
EU’s capacity to pursue its objectives
of cohesion and competitiveness at
the same time.
This study must be considered as
the first step in the analysis of local
convergence as an operative concept
to understand the geographical nature
of the process to reduce regional ine-
qualities. We have studied conver-
gence in a relatively stable economic
context. Future studies could explore
the territorial dimension of the Euro-
pean economic crisis (ESPON, 2014)
and its impact on the results, and ana-
lyse the extent to which spatial clus-
tering of local convergence or diver-
gence could vary in such a context.
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1 A steady state is an important
phenomenon for two reasons: an economy
that has reached this state no longer
changes, and an economy that has not
reached it tends towards it. A steady state
represents the long-run balance of an
economy.
2 In order to evaluate the economic
convergence of countries/regions, it is
possible to rely on the beta or sigma-
convergence test.
While beta-convergence focuses on
detecting possible catching-up processes,
sigma-convergence refers to a reduction of
disparities among countries/regions over
time. As Quah (1993) shows, the concept of
sigma-convergence is more revealing of the
reality because it directly describes the
distribution of income across
countries/regions without relying on the
estimation of a particular model. The Gini
index is one of the measure to quantify the
evolution of dispersion of the GDP over
time (sigma-convergence).
3 Eurostat database (1995-2007)
4 Although the election of Constantinescu
in 1996 led to a significant influx of FDI
characterised by economic growth, FDI has
not been evenly distributed over the
territory (Goschin, Danciu, & Gruiescu,
2008; Raluca and Mihaela, 2011). Bucharest
accounts for 85% of national GDP, it holds
more than 20% of national export volume
and almost 40% of imports, it hoards 55% of
national GDP spending on R&D and has an
unemployment rate lower than half the
national average (3.4% against 7.2% for
Romania).
5 The urbanisation rate is over 65% in the
West, while it is under 45% in the East.
CJRS/RCSR 38(1/3) 2015 6161
Reproduced with permission of the copyright holder. Further reproduction prohibited.
6 Following the integration of ten CEECs
into the EU, a debate about the
development of the most lagging regions
was initiated. The European Commission
(2008) wanted massive investment in
resources so that these regions could
develop at a quicker pace. Grzegorz
Gorzelak (2010) nevertheless asserts that
lagging Central-Eastern regions (where
agriculture is dominant, there is hardly any
industry or services, and even less capital,
including social and cultural capital) have
never managed to develop thanks to a
large influx of European funds.
... This can be partially explained by the fact that trade is more intense due to the existence of differentials (in cost, offering, structure by age, etc.) and associated regional growth distribution phenomena. From this point of view therefore, there is a shift from the Iron Curtain to the Golden Curtain (Bourdin 2015). Not all regions enjoy the same benefits depending on their location. ...
... Moreover, if the scope is changed, the convergence observed between EU Member States sometimes actually masks an increase in regional intra-state inequalities. Convergence phenomena observed at NUTS 3 level produce either convergence or divergence locally (Bourdin, 2015;Butkus et al., 2018). Some studies have particularly highlighted a quick growth recorded in metropolitan regions due to a concentration of service activities, direct foreign investment, and a significant number of start-up launches. ...
... Ils utilisent des indicateurs classique de concentration tels que l'indice de Gini. Or, ce dernier ne prend pas en compte la dimension spatiale alors qu'elle peut présenter un intérêt (Bourdin, 2015). Il ne considère pas la façon dont les régions sont définies (Amara, Au regard des travaux antérieurs de Catin et Van Huffel (2003), on pourrait expliquer les évolutions des phénomènes de concentration/dispersion du fait de l'entrée de la Tunisie dans une nouvelle phase de développement (Bechir, 2018), caractérisée par le développement des industries à fort contenu technologique. ...
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