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QUAESTIONES GEOGRAPHICAE 37(2) • 2018
REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN
COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY: PATH
DEPENDENCE AND EFFECTS OF EU COHESION POLICY
Wojciech Dyba1, braDley loeWen2, jaan looga3, Pavel Zdražil4
1Institute of Socio-Economic Geography and Spatial Management,
Adam Mickiewicz University in Poznań, Poland
2Department of Institutional, Environmental and Experimental Economics,
University of Economics, Prague, Czechia
3Chair of Public Economics and Policy, School of Economics and Business Administration,
University of Tartu, Estonia
4Institute of Regional and Security Sciences, Faculty of Economics and Administration,
University of Pardubice, Czechia
Manuscript received: September 28, 2017
Revised version: February 16, 2018
Dyba W., loeWen B., looga, J., Zdražil, P., 2018. Regional development in Central-Eastern European Countries at the
beginning of the 21st century: Path dependence and effects of EU Cohesion Policy. Quaestiones Geographicae 37(2), Bo-
gucki Wydawnictwo Naukowe, Poznań, pp. 77–92. 6 tables, 5 gs.
abstract: Cohesion Policy has provided new impulses for development in Central and Eastern European Countries
(CEECs) that continue to be challenged by regional disparities. This paper investigates the effects of the European
Union Cohesion Policy on regional development. After presenting historical development patterns of the investigat-
ed area and opportunities afforded by this policy, its effects on a variety of indicators are analysed for the period
2007–2014. The analysis allowed conrming positive effects of EU Cohesion Policy on the development of CEE regions.
However, these effects differ across the investigated area. Moving forward, it will be crucial to develop institutions and
policies characteristic to each region that are stable and efcient without external funds.
Key WorDs: CEECs, convergence, EU Cohesion Policy, path dependence, regional development
Corresponding author: Wojciech Dyba, Institute of Socio-Economic Geography and Spatial Management, Adam Mickiewicz
University in Poznań, ul. B. Krygowskiego 10, 61-680 Poznań, Poland, e-mail: wojtek@amu.edu.pl
Introduction
Central and Eastern European Countries
(CEECs) – according to the OECD glossary of
statistical terms (2000) – is the group of countries
comprising Albania, Bulgaria, Croatia, Czechia,
Hungary, Poland, Romania, Slovakia, Slovenia
and the three Baltic states: Estonia, Latvia and
Lithuania. The macro-region went the hard eco-
nomic way in the 20th century, suffering vast
destructions from the First and Second World
Wars and afterwards – located east of the Iron
Curtain and dependent on the Soviet Union –
operating ineffectively under centrally-planned
economies. The turn of the previous and current
centuries was a time of socio-economic trans-
formation and integration with the European
and global economy – in particular, with the
European Union (EU). At that time, a new role
was given to regional authorities that gained ad-
ministrative and nancial tools to pursue auton-
omous development policies. It brought many
doi: 10.2478/ quageo-2018-0017
ISSN 0137-477X, eISSN 2081-6383
Wojciech Dyba et al.
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78 WOJCIECH DYBA ET AL.
opportunities, but also new challenges to the
regions of CEECs.
The main aim of the paper is to explain the
development patterns of CEE regions and to ex-
amine the effects of EU Cohesion Policy – the pri-
mary policy tool for regional development – on
CEE regions. To do so, we use the path depend-
ence concept to present the historical legacy of
development in the region. We then look deeper
at Cohesion Policy to analyse its impacts on CEE
regions, investigating two hypotheses: (1) that
Cohesion Policy provides an opportunity for CEE
regions to break from their historical develop-
ment paths and (2) that the impacts of Cohesion
Policy on development potentials differ between
CEE regions. The analysis is conducted on two
levels, country and regional (NUTS II), utilising
data from the 2007–2013 programming period of
the European Union Cohesion Policy (the rst full
period in which CEECs took part). Therefore, we
include the CEE countries covered by the above-
mentioned OECD denition excluding Albania
and Croatia. Moreover, we frame the analysis
with additional background historical data as
well as information for the ongoing period of EU
Cohesion Policy over the years 2014–2020.
Path dependence and the historical
context of regional development in
CEECs
The concept of path dependence is used to de-
scribe regional economic development trajecto-
ries, taking into account historical economic and
political legacies. Rooted in the acknowledge-
ment of historical contingency, path depend-
ence is characterised as the existence of increas-
ing returns, technological ‘lock-in’ and multiple
equilibria resulting from historical decisions in
economic production (Arthur 1994; David 1985,
2001). Moreover, path dependent processes are
believed to shape regional innovation systems
(Isaksen 2001; Tödtling, Trippl 2005), now con-
sidered to be the drivers of economic growth
within endogenous growth or place-based devel-
opment models.
In order to understand the historical basis of
regional development in CEECs, it is useful to
distinguish between the different types of re-
gions in this heterogeneous group of countries.
Isaksen (2001) proposed three types describing
the challenges to regional innovation systems:
old industrial, fragmented and peripheral areas.
Old industrial areas are those primarily affect-
ed by lock-in as the main barrier to innovation,
thus becoming overspecialised in mature indus-
tries experiencing decline (Tödtling, Trippl 2005).
The loss in competitive advantage, nevertheless,
can also be felt in the peripheral (i.e. rural and
remote) areas in terms of their relationships with
the European and national cores, which are char-
acterised by organisational thinness affecting
institutional aspects such as knowledge infra-
structure and absorption capacity (ibid.). In com-
parison, fragmented regions are associated with
metropolitan areas, possibly with clusters, but
lacking networks for innovation activities, coop-
eration and trust (ibid.; Isaksen 2001). Thus, hard
and soft factors affect the regional development
potentials in CEECs, which particularly relate to
the economic and political dimensions of state
socialism that serve today as the institutional leg-
acies affecting regional development.
The old industrial and peripheral types are
highly relevant in CEECs, where, on the one
hand, industrial production based on over-in-
vestment in outdated technologies resulted in
uncompetitive productive structures on the open
market and, on the other hand, peripheral econ-
omies supported by non-market redistributive
structures collapsed following the economic and
political transition (Ehrlich 1991; Berend 2006;
2009; Lux 2009). In CEECs, the fragmented type
of region has hardly been explored, but we can
expect that through different processes of polit-
ical and economic transition between countries,
the institutional possibilities for cooperation and
trust are highly variable between, for example,
the most and least liberalised states.
CEECs are, for the most part, currently con-
sidered to be economically lagging in Europe
(European Commission 2014), with many so-
cialist-industrialised regions being associated
with economic, social and environmental deg-
radation (Lux 2009). Whereas some research has
shown that state socialism led to higher cohesion
in some respects compared to Western Europe
(Noguera-Tur et al. 2009), regional polarisation
has increased in CEECs since the 1990s despite
more than two decades of economic growth and
international convergence (Monastiriotis 2014).
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REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 79
Under the current EU Cohesion Policy regime,
most CEE regions have been designated as con-
vergence zones eligible for investment based on
a relatively low GDP per capita (less than 75%
of the EU average), with the common exception
of capital regions. The aim of Cohesion Policy in
these countries can be stated to reduce econom-
ic, social and territorial disparities appearing
between the capital regions and the peripheries
(European Commission 2014). An understanding
of the historical processes leading to the relative-
ly low economic development of CEECs within
Europe is fundamental to the effective applica-
tion of Cohesion Policy in hopes of further social,
economic and territorial cohesion.
To address this, the concept of path depend-
ence is used to frame regional development in
CEE since early industrialisation, through the
command economy of the socialist period, to the
restoration of the market economy of the current
era of capitalism and globalisation. Economic
backwardness to the (north-western) European
core has been the case since early conceptualis-
ations of CEE, thus indicating the region’s long-
standing peripherality within Europe (Okey 1992;
Kuus 2004). Nevertheless, the socialist period of
rapid industrialisation can be considered a diver-
gence from the longer term patterns of regional
development from 19th century laissez-faire cap-
italism and continuing through catching-up pro-
cesses of capitalism in the late 20th century.
The effect of the socialist period on regional
development mainly entailed the transformation
of agriculture- to industrial-based regional econ-
omies and widespread provision of social servic-
es. Socialist industrialisation was characterised
by the social appropriation of the means of pro-
duction and the planned, centralised economy
administered by the government (Szczepański
1977). In terms of regional development, the so-
cialist period can be divided into three parts that
corresponded with (1) urban and industrial take-
off, (2) deconcentrated industrial location and (3)
the beginnings of post-industrialisation in the
most developed regions (Enyedi 1990). These de-
velopment trajectories resulted in old industrial
regions and productive structures that could no
longer be supported in the competitive economy,
i.e. state-supported industry, in peripheral areas.
While rapid and heavy industrialisation
has taken a toll on CEE regions, its impact on
spatial structures may have been surprisingly
weak. Development patterns continue to resem-
ble the 19th century imperial legacies that tran-
scend modern borders (e.g. Austro-Hungarian,
Prussian and Russian empires in the territory
of Poland). The perseverance of such patterns
has been noted by researchers who saw more
recent trends (i.e. regional polarisation) as a
continuation of 19th century industrialisation
(Illner, Andrle 1994). The longstanding gradi-
ent of decreasing development levels can still
be seen as one moves eastwards across the re-
gion (European Commission 2014) and within
individual countries such as Poland (Korcelli
1995; Czyż 2001; Stryjakiewicz 2009), across
Czechia and Slovakia (Illner, Andrle 1994) and
in Hungary (Horváth 1998). Moreover, rural are-
as in Czechia, the historical industrial core, have
been found to be economically diversied, while
the remainder of rural areas in CEECs are largely
agrarian (Copus, Noguera 2010).
Estimates of GDP per capita and other prox-
ies of development for the period 1820 to 2002
(Good, Ma 1999; Maddison 2003) showed that
the early periods of socialism posted relative-
ly high growth rates; however, these declined
over the long term, partly due to slower techno-
logical advances. From 1900–1989, for example,
Czechoslovakian and Hungarian per capita GDP
growth rates of 1.8 and 1.6% per year, respec-
tively, were lower than the advanced and mid-
dle income country average growth rates of 2.1
and 2.0% (Maddison 1991). Other comparisons of
GDP per capita ranged from 21% of US levels in
Romania to 27% in Poland, 30% in Bulgaria, 31%
in Hungary, and 42% in Czechoslovakia in 1980,
while the United Kingdom, France and Sweden
were at approximately 58, 64 and 78% of US
levels, respectively (Ehrlich 1991). The average
development of European market economies in
1980 was still half that of the United States, and
the CEE planned economies were developed to
a comparable level with Southern Europe (ibid.).
By the fall of socialism, CEE regions were
more industrialised but still lagging as they
lacked innovation in technology and processes
(Berend 2009; Chojnicki et al. 2009). Moreover,
high specialisation between the countries of the
Council for Mutual Economic Assistance led to
overspecialisation and investment in obsolete
technologies – a hallmark of ‘old industrial’
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80 WOJCIECH DYBA ET AL.
regions – withering CEECs’ competitiveness
amidst globalisation. Berend (2009) has argued
that the oil crises in the 1970s, representing the
creeping globalisation in both East and West,
manifested economic failure in most CEECs. The
impact of industrial collapse had devastating ef-
fects for many regions that are characteristic of
‘old industrial’ regions, not only in CEECs but
also in Western Europe (Lux 2009). Thus, while
the forced industrialisation strategy during so-
cialism produced many of the territorial prob-
lems faced in CEECs, socialist policies are not
sufcient in themselves to explain the creation
of ‘old industrial’ regions, which are also related
to globalisation and widened spheres of compe-
tition. These movements revealed weaknesses in
CEECs’ industrial structures leading up to and
following the collapse of socialism.
The transition period in CEECs concerned
both political and economic reforms, re-estab-
lishing local (and regional, as the case may be)
self-governments through comprehensive ad-
ministrative reforms (Kaczmarek 2016) as well as
the market economy. Thus, territorial decentral-
isation and municipal fragmentation became the
norm, which was potentially detrimental to re-
gional development due to lacking frameworks
for regional planning and cooperation (Illner
1997; Swianiewicz 2010). The re-creation of the
regional level became a pressing need for EU ac-
cession, which was approached with some varia-
tion across CEECs (Brusis 2002; Bruszt 2008). The
countries generally underwent two stages of re-
forms, the rst for re-establishing their democra-
cies and the second for EU compliance and acces-
sion. These opened a new paradigm for regional
development based on private sector actors with
minimal public intervention, through new as-
sistance from the EU by way of pre-accession
instruments and, later, through EU Structural
Funds including Cohesion Policy.
Cohesion Policy itself underwent its own
transformations from a relatively welfare to
competitiveness-based model, the so-called
‘Lisbonisation’, emphasising the place-based ap-
proach (Barca 2006; Barca et al. 2009; Farole et al.
2011; Mendez 2012). By the time of CEECs’ acces-
sion, this policy shift would place many so-called
‘backward’ and uncompetitive regions in a posi-
tion where the promise of regional development
funds would in principle be tied to improving
economic competitiveness over social welfare
and public services. This presents potential prob-
lems for CEE regions, in particular. Evaluations
from the rst full programming period, 2007–
2013, showed lack-lustre performance (European
Commission 2013), partly due to the effects of
the nancial crisis. Indeed, there remains strik-
ing variation in the competitiveness landscape
across Europe, including within CEECs (Annoni,
Dijkstra 2013), which continues to drive the
debate on the objectives and effectiveness of
Cohesion Policy (Avdikos, Chardas 2016). In the
next sections, the overall impact of EU Cohesion
Policy and the potential of the current program-
ming period (2014–2020) are presented.
EU Cohesion Policy as an opportunity
for CEECs
After switching regime in 1989, many CEECs
targeted accession to the EU (Scherpereel 2010),
and succeeded to become full members and
beneciaries of EU Cohesion Policy1. The main
aim of Cohesion Policy is to foster growth and
competitiveness of the EU through investments
in development factors specic to each region.
The name of the policy comes from targeting so-
cial, economic and territorial cohesion at the EU
level in order to reduce disparities between EU
countries and regions (European Commission
2014). In the targeted countries, Cohesion Policy
is realised through the coordination of a range
of nancial instruments including the structur-
al funds and specically earmarked Cohesion
Fund.
As many CEECs have benetted from EU
Cohesion Policy during the past 12 years, it is im-
portant to look at changes in economic growth
that took place after they joined the EU. GDP per
capita in purchasing power standard (PPS) in-
creased both nominally and as percentage of the
EU-28 average in almost all CEECs (Fig. 1 and 2).
The highest relative increases were found in the
Baltic states as well as in Poland, Romania and
Slovakia, while countries that had the highest
nominal levels in 2004 (Czechia and Slovenia)
1 Czechia, Estonia, Hungary, Latvia, Lithuania, Poland,
Slovakia and Slovenia acceded to the EU in 2004, Bul-
garia and Romania in 2007, and Croatia in 2013.
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REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 81
experienced smaller changes. The newer mem-
bers, Bulgaria and Romania, still have the lowest
values. Certainly, GDP per capita in PPS is only
a general indicator of development, and there is
a debate on the ‘beyond GDP’ indicators putting
more emphasis on quality of life (Costanza et al.
2009). However, it is a basis for comparison in
the EU, and it is believed that a higher indicator
will lead to a generally higher welfare of people
(Barca 2009).
Despite the above trends of national conver-
gence, CEECs were still faced with one of the high-
est regional inequalities in the EU before entering
the current programming period, 2014–2020.
Therefore, in line with the thematic objectives of
the Europe 2020 Strategy (European Commission
2010a), CEECs changed their Cohesion Policy
driven regional strategies to focus on: sustainable
development (e.g. Czechia, Lithuania, Slovakia,
Poland); living conditions in rural areas (e.g.
Fig. 1. GDP per capita in PPS in CEECs and in EU 28, 2004–2015.
Source: own calculations based on Eurostat (2017).
Fig. 2. GDP per capita in PPS in CEECs as percentage of the EU average, 2004–2015.
Source: calculation based on Eurostat (2017).
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82 WOJCIECH DYBA ET AL.
Latvia, Lithuania, Slovakia); employment and
polycentric development (e.g. Bulgaria, Estonia);
and efciency of public administration (e.g.
Czechia, Latvia). Some countries chose to focus
on improving urban regions, which contribute to
national growth, and others on decreasing region-
al disparities (Davies et al. 2015)2.
Regarding the thematic objectives of Cohesion
Policy in the completed programming period
of 2007–2013 (Table 1), it is possible to analyse
CEECs’ specic priorities believed to have had an
impact on their regional development. Since the
country size is emphasised within the allocation
of the Cohesion Policy budget, Poland gained the
highest amount among the CEECs, while Estonia,
Latvia and Slovenia gained the smallest (in to-
tal numbers). Most of the countries prioritised
thematic objectives like: Transport, Energy & IT,
Environment, Social issues and Innovation and R&D.
Less emphasis was placed on Culture, heritage and
tourism as well as on Urban and territorial dimension.
The evaluation of the 2007–2013 program-
ming period shows that, overall, one million jobs
and one trillion euros additional GDP were creat-
ed in the EU (European Commission 2013). There
is room to increase efciency, by increasing the
possibilities to use nancial instruments, and ef-
fectiveness, according to the specic intervention
2 Further reading about regional policies – strategic ob-
jectives, funding, geographical focus, instruments and
institutional frameworks – can be found in Davies et
al. (2015).
logic, result indicators, project selection accord-
ing to the programme, etc., in delivering the re-
sults (European Commission 2013, 2016).
The 2007–2013 programming period was not
only about positive economic effects, albeit in-
terrupted by the crisis. It was also a time of in-
stitutional learning, and the lessons nevertheless
helped CEECs to prepare for the 2014–2020 peri-
od with fewer regional programmes and higher
thematic concentration. Regarding the thematic
objectives of Cohesion Policy in the 2014–2020
period (Table 2), we can see that the absolute
amount of the Cohesion Policy budget for most of
the countries has increased compared to the pre-
vious programming period, 2007–2013, and there
are some changes in the order of the CEECs on
the basis of the amount of budgets. The highest
sums of money from the Cohesion Policy budget
in CEECs during the 2014–2020 period is devot-
ed to Network Infrastructures in Transport, Energy
& ICT, Climate & Environment and Education &
Employment. The shares of funds devoted to each
sector differ between countries. It is not possible
to compare the changes and relative shares of
thematic objectives across countries and between
the two programming periods because the pro-
grammes do not sufciently correspond.
The strategic measures shown through the
thematic objectives are supposed, in turn, to
have an inuence on the development patterns
in particular countries and regions. However,
as the shares of thematic objectives differ be-
tween the CEE countries, one can expect that
Table 1. The structure of Cohesion policy budget by thematic objectives in CEECs 2007–2013 (% from budget of
Member States allocated to selected aims, based on annual implementation report 2013).
Thematic objectives/Member State PL HU RO CZ SK BG LT LV SI EE CEECs
Transport, Energy & IT 47 36 26 37 37 30 36 33 29 23 38
Environment 10 17 24 11 13 26 15 16 24 22 15
Social issues 10 16 10 10 21 10 14 19 9 21 12
Innovation & RTD 14 6 6 17 11 514 15 19 19 12
Capacity building 3 4 18 4 4 12 4 3 3 2 6
Human capital 7 4 3 8 566353 6
Other SME and business support 510 3 4 3 6 4 2 5 5 5
Culture, heritage and tourism 3 4 2 53 2 3 1 5 5 3
Urban and territorial dimension 2 3 8 4 3 3 58 1 0 3
Total (100%) 100 100 100 100 100 100 100 100 100 100 100
Total (in billion EUR) 63.9 28.6 23.6 21 11.8 7.4 6.7 4.3 4.3 3.3 174.9
Note. Some of the thematic objectives are aggregated:
Transport, Energy & IT = Rail + Road + Other transport + Energy + IT services and infrastructure
Social issues = Labour market + Social Inclusion + Social infrastructure
Source: own calculations based on European Commission (2017a).
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REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 83
the development patterns should differ between
the CEECs and their regions. In the next section,
the links between EU Cohesion Policy perfor-
mance and selected socio-economic indicators
are analysed.
Methods for measuring the impact of
EU Cohesion Policy on development in
CEE regions
The analysis seeks to examine the impacts
of the 2007–2013 programming period of EU
Cohesion Policy on the development of CEE re-
gions. On the one hand, the well-known n+3/n+2
rule allows drawing nancial resources even af-
ter 2013 and it is a known fact that some effects of
Cohesion Policy interventions lagged for years or
even decades (Zdražil, Applová 2017), while on
the other hand, the research is limited by a lack
of recent regional data, as well as by the change
in development interventions since the start of
the next programming period, 2014–2020. Hence,
we examine the period from which the data are
available and that should not be biased by new
interventions, i.e. 2007–20143. The analysis focus-
es on the NUTS-II level of regions, since that is
3 In fact, interventions covered by the 2014–2020 pro-
gramming period did not start in CEE regions before
2015.
the main level at which EU Cohesion Policy per-
forms. Within the 10 CEECs under examination,
we are therefore working with a sample of 53
NUTS-II regions4.
The analysis consists of two parts. First, we
focus on the disparity in economic performance
of CEE regions and its dynamics, since this in-
dicator holds an exclusive position within the
evaluation of EU Cohesion Policy. We apply
the conventional approach of measurement, so-
called Beta-convergence, that is based on works
of Baumol (1986), Barro and Sala-i-Martin (1992;
2004), Mankiw et al. (1992), and many others,
which allows us to evaluate growth patterns si-
multaneously. The Beta-convergence approach is
built on the assumption of the inverse relation-
ship between the level of production and growth,
while generally using estimations through vari-
ous forms of linear, or linearised, regression
models. Even though the Beta-convergence con-
cept is rather adapted for a long turn, it is con-
ventionally used even for examination of shorter
4 The CEECs contain the following number of NUTS-II
regions: Bulgaria, 6; Czechia, 8; Estonia, 1; Hungary,
7; Latvia, 1; Lithuania, 1; Poland, 16; Romania, 8; Slo-
venia, 1; Slovakia, 4. Even though Slovenia contains
two NUTS-II regions, the source database of region-
al policy expenditures maintained by the European
Commission Directorate-General for Regional Policy
(DG REGIO)(2017) provides data only for the whole
country, i.e. 1.
Table 2. The structure (% from budget of Member State) of the Cohesion policy budget by thematic objectives
in CEECs 2014-2020, based on nances planned).
Thematic objectives/Member state PL CZ RO HU SK HR BG LT LV EE SI CEECs
Network infrastructures in transport, energy & ICT 35 32 31 19 29 19 20 21 31 14 11 29
Climate & Environment 21 23 33 27 25 33 38 27 25 21 24 25
Education & Employment 13 14 14 22 12 13 12 20 14 19 17 14
Research & Innovation 11 14 4 10 17 8 7 10 11 22 15 11
Social inclusion 8 8 8 9 9 898 10 10 7 8
Competitiveness of SMEs 9 5 39312887719 7
Technical assistance 3 3 3 1 4 4 4 3 2 3 4 3
Efcient public administration 0 1 3 3 2 2 3 2 0 3 2 2
Total (100%) 100 100 100 100 100 100 100 100 100 100 100 100
Total (billion EUR) 90.0 28.7 27.4 25.3 17.7 9.8 8.6 7.8 5.1 4.9 3.7 229.1
Note: Some of the thematic objectives are aggregated:
Climate & Environment = Climate Change Adaptation & Risk Prevention + Environment Protection & Resource
Efciency + Low-Carbon Economy,
Education & Employment = Educational & Vocational Training + Sustainable & Quality Employment,
Network Infrastructures in Transport and Energy & ICT = Network Infrastructures in Transport and Energy + Infor-
mation & Communication Technologies.
Source: own calculations based on European Commission (2017b).
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84 WOJCIECH DYBA ET AL.
periods of EU Cohesion Policy (Pike et al. 2006).
This analysis employs the general validation
model as given by (1) (Barro, Sala-i-Martin 2004):
(1)
where (Yi,0) and (Yi,t) refer to the GDP per capita
levels at the interval borders (i); constant term (α)
and convergence coefcient (β) are the parame-
ters to be estimated; and (εi) is the error term.
Another part of this analysis focuses on the
connections between the expenditures of EU
Cohesion Policy and the relevant socio-economic
indicators to examine whether and how the in-
terventions could impact development in CEE
regions. Following endogenous growth theories
(Romer 1986; Lucas 1988; Rebelo 1991) and prin-
ciples of New Economic Geography (Krugman
1991), our endeavour is to examine traditional
economic indicators like capital and labour, as
well as ‘modern’ development indicators such
as human capital, knowledge and innovation
potential. In particular, we examine changes in
selected development indicators in terms of ex-
penditures of EU Cohesion Policy. Considering
that one part of our analysis is based on results
of the cluster analysis (see below), we examine
only development indicators that we have found
to be uncorrelated. The list of both selected and
unselected indicators is captured in Table 3. The
source data have been linked from datasets pro-
vided by DG REGIO (2017) and Eurostat (2017).
Since we seek to reveal whether there are
universal or specic patterns in impacts of EU
funding, we provide an intentional analysis of
different groups of regions, classied by similar-
ities in development over the examined period.
The groups of regions resulted from the k-means
clustering procedure, which has been applied on
standardised data of changes in selected devel-
opment indicators (Table 3). The optimal number
of clusters (three) has been determined empiri-
cally to obtain large enough and relatively bal-
anced groups of the examined samples. This ty-
pology of similarly developing regions allow us
to deduce possible differences in Cohesion Policy
impacts on the actual forms of development. For
tracking the connections between EU Cohesion
Policy funds and selected development indica-
tors, we employ the non-parametrical approach
based on Spearman’s rank-order correlation (ρ),
which can be computed as (2):
(2)
where (ri) is the ranking position of the analysed
region (i) within the rst variable, (si) is that with-
in the second variable and (n) refer to the number
of observations.
Finally, we found the development of some
regions to be far different. Hence, we identied
and eliminated outliers to increase the accuracy
of results and decrease the probability of errors
in our research. In particular, we applied the con-
ventional non-parametrical approach of Tukey’s
fence (3) (Tukey 1977):
[Q1−k(Q3−Q1); Q3+k(Q3−Q1)] (3)
where (Q1) and (Q3) are the lower and upper
quartiles, (k) is a degree of outlaying. We apply
the measure for ‘standard outliers’, i.e. (k) = 1.5 as
Tukey suggests (1977).
Table 3. Development indicators under examination
Selected (mutually uncorrelated) Unselected (correlated)
gross xed capital formation (GFCF); employment
(EMP); participation in education and training (PET);
gross expenditure on research and development of the
business enterprise sector (GERB); gross expenditure on
research and development of the non-business enterprise
sector (GERN)*
gross domestic product; productivity of labour; compen-
sation of employees; population size; age dependency
ration; median age of population; education levels at-
tained; employment in R&D; human resources in science
and technology
Notes: All the indicators were weighted by the size of population; all monetary terms were expressed in EUR.
* where the vast majority is allocated from the government sources.
Source: own elaboration.
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REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 85
Effects of Cohesion Policy on CEE
regions
Let us briey describe the position of regions
of CEECs within the EU, especially through com-
parison with the ‘old’ EU countries (EU-15). As
shown in Fig. 3, the levels of GDP per capita (in
EUR) were lower in CEE regions before the 2007–
2013 programming period. However, the aver-
age annual growth rates of CEE regions between
2007 and 2014 were generally much higher. The
performance of all CEE regions rose, while many
regions reached the average annual growth rate
of between 4 and 7%. At the same time, the re-
gions of the EU-15 rose slower or even declined
(especially the regions of Greece, Spain, Italy and
the United Kingdom). These simple facts show
that the gap between the economic performance
of EU-15 regions and CEE regions decreased. In
particular, these facts play an important role in
the convergence process across the EU-27 regions
that result from a negative slope. We have to point
out that, based on the coefcient of determina-
tion, this model has been found to be signicant
at the 0.01 (t-test has been applied to resolve the
statistical signicance). In addition, the model es-
timates the average ‘speed of convergence’ across
the EU at 1.83% per year. In general, the ndings
of catching-up process of CEECs towards the
EU-15 are in one line with other recent studies
(Matkowski et al. 2016; Dobrinsky, Havlik 2014),
and help to accept the rst hypothesis.
Furthermore, based on Fig. 4, which focuses
exclusively on the CEE regions, we can conclude
again that the Beta-convergence process has been
conrmed at the signicance level of 0.01. In ad-
dition, the average convergence rate of 1.96% per
year is even slightly higher than in the case of the
all EU regions. As can be seen in Fig. 4, the least
developed and most growing regions refer to re-
gions of Bulgaria and Romania, while the more
developed regions of Czechia and Hungary grew
slower. Again, we can say that these conclusions
follow and extend the previous ndings of many
recent studies (Zdražil, Applová 2016; Gligoric
2014; Spruk 2013).
As the results of the disparity measurement
have shown, CEE regions converged towards
the EU-15 regions, as well as to each other. We
must note, however, that the convergence across
the investigated years showed some uctuations
concerning GDP growth. As recent studies con-
rmed (Matkowski et al. 2016; Stanisic 2016), re-
gional disparities inside some CEECs decreased
during the years of crisis and have since returned
Fig. 3. Beta-convergence of EU regions (2007–2014).
Note: 272 regions, i.e. without the outliers of Inner London - West and Luxembourg.
Source: own calculations based on Eurostat (2017).
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86 WOJCIECH DYBA ET AL.
to pre-crisis levels. The decrease in regional dis-
parities occurred because the crisis affected the
export- and construction-oriented regions, while
self-reinforcing agglomeration effects took the re-
gional disparities to the same level as before the
crisis (Davies et al. 2015).
Nevertheless, our ndings suggest that one
of the main goals of EU Cohesion Policy for the
2007–2013 programming period, i.e. economic
development while reducing inequalities be-
tween the EU countries and regions, was fullled
to a certain extent. However, we may ask wheth-
er and how these actual gures were inuenced
by the real interventions of EU Cohesion Policy.
In fact, there are many other relevant factors af-
fecting the development of CEE regions (e.g. the
economic integration of EU membership, busi-
ness cycles, etc.). Hence, we seek to examine
relationships between the expenditures of EU
Cohesion Policy and the relevant socio-economic
indicators below to deduce general conclusions
about its efciency.
Territorial differences in the effects of
Cohesion Policy in CEE regions
In this section, we have excluded the outlaid
regions of Bratislava, Bucuresti – Ilfov, Prague,
Slovenia and Southeast (in Czechia); hence, the
following analysis contains 48 regions. The cap-
ital regions (including Slovenia) are outliers in
terms of GFCF and, for the most part, GERB and/
or GERN. However, we can conclude that such
a result seems logical, since the headquarters of
important companies and institutions respon-
sible for large shares of investments are usually
located in capital cities. Also, the reason for ex-
cluding the Czech region of Southeast is very
similar – extremely high (positive) changes in
GERB and GERN. We suppose this is due to the
city of Brno, which recently grew up into one of
the most important IT research and innovation
centres in Europe with many headquarters and
large branch ofces of global companies.
Let us start with the overall view on connec-
tions between the expenditures of EU Cohesion
Policy per capita (EEUCP) and development in-
dicators across the CEE regions, the results of
which are presented in Table 4. One should inter-
pret the positive signicant correlation between
the EEUCP and GERB as a result in favour of EU
Cohesion Policy since EU funding aims at invok-
ing investments in knowledge and innovation
in the business sector. On the other hand, con-
nections to the other relevant indicators have not
been approved.
Focusing on particular groups of regions, the
k-means procedure allows us to identify three
different types (groups) of development among
Fig. 4. Beta-convergence of CEE regions (2007–2014).
Note: 51 regions, i.e. without outliers of Bratislava and Bucuresti – Ilfov.
Source: own calculations based on Eurostat (2017).
Table 4. Correlations between the expenditures of EU
Cohesion policy and development indicators across
CEE regions.
GFCF EMP PET GERB GERN
EEUCP 0.008 0.227 0.001 0.544** 0.224
Notes: ** signicant at the 0.01.
Source: own calculations based on DG REGIO (2017) and
Eurostat (2017).
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REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 87
CEE regions. In general, the regions of the larg-
est cluster – Group 1 experienced average (i.e. ±
10%) increases in GFCF, PET and GERN, and be-
low-average increases in EMP and low increase
in GERB. Moreover, regions of this group usual-
ly consumed less European funding – the whole
group shows about 80% of the CEE average. The
gures of Group 2 show above-average increas-
es in all indicators except PET, which is very
low and the least favourable across the CEECs.
On the other hand, dominance in traditional
economic factors, GFCF and EMP, is very high.
The consumption of Cohesion Policy funding
of regions classied in Group 2 usually reached
average values. Finally, the regions of Group 3
share major increases in PET and GERB while be-
low-average increases in GERN. However, these
regions experienced a very unfavourable devel-
opment in the traditional factors of economic
growth and development, since only 4 of 14 re-
gions experienced (a slight) increase in GFCF and
the least favourable increase in EMP. In addition,
Table 5. Mean values of clustered groups in relation to the CEE average (in %; CEE = 100).
No. of regions GFCF EMP PET GERB GERN EEUCP
Group 1 21 108.8 73.9 93.1 57.2 97.6 80.0
Group 2 13 237.8 182.9 13.7 113.7 123.8 101.9
Group 3 14 –41.2 62.2 190.5 151.4 81.4 128.3
Source: own calculations based on DC REGIO (2017) and Eurostat (2017).
Fig. 5. K-means clustering of CEE regions.
Source: own calculations based on Eurostat (2017).
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88 WOJCIECH DYBA ET AL.
the regions of Group 3 consumed above-average
amounts of EU funding. All details can be found
in Table 5.
As can be seen in Fig. 5, many regions of
particular countries are classied together (e.g.
5 Romanian in Group 1; 5 Hungarian and all
Slovak in Group 3; Polish regions are equally di-
vided between Group 1 and 2). We can consider
that this is due to an inuence of the above-dis-
cussed path-dependent development trajectories
that are shaped by traditions, national strategies,
legal frameworks, etc. However, one can see that
the k-means procedure helped to identify some
cross-border geographical regions with similar
development patterns – in particular, Group 1
(Bulgarian and Romanian regions) and Group
3 (Hungarian and Slovak regions). In fact, from
the long term perspectives and historic facts, the
similarities in the development of Hungarian
and Slovak regions are interesting. However, in
this analysis, we can only speculate whether this
is due to long term effects of path-dependence
processes, randomness or another reason.
The results of correlation analysis per groups
are summarised in Table 6. In general, the num-
ber of signicant correlations between EU fund-
ing and development indicators is low. There is a
signicantly positive connection to GERB within
Groups 1 and 2 and to GERN within Group 1.
All of those can be seen as desirable results and
the possible products of EU Cohesion Policy.
However, one could consider the number of con-
rmed relationships between EU funding and
development indicators to be rather low.
Considering the results above, we can suppose
that regions of CEECs experienced variegated
types of development over the examined period,
and the EU funding patterns were variegated as
well. We can suggest the resources of Cohesion
Policy might be ineffective in ‘traditional’ areas
of regional growth and development, i.e. capi-
tal and labour, while there are possible connec-
tions to the ‘modern’ areas, especially the R&D
investments. In particular, it seems that R&D
investments of the business sector might be in-
voked by EU funding in a large number of CEE
regions. Secondly, the innovation potential of re-
gions with rather lower dynamics in most of the
development indicators (Group 1) might be large-
ly inuenced by the amount of Cohesion Policy
resources. Indeed, this potential might even rise
with an increase in EU funding or a drop in the
case of a decrease in resources. Unfortunately,
the amount of EU support that these regions con-
sumed over the 2007–2013 programming period
was low in comparison to the other CEE regions.
Similarly, the potential of R&D business sector
in regions that experienced the largest develop-
ment in the traditional areas, as well as solid de-
velopment in R&D investments (Group 2), might
be positively inuenced by additional funding.
Finally, the results also suggest that the regions
with the worst development of ‘traditional’ are-
as but highest dynamics of ‘modern’ areas of re-
gional growth and development are the highest
consumers of EU resources (Group 3). However,
no connection between the Cohesion Policy re-
sources and education with R&D investments
has been measured in this case. We can interpret
this conclusion as a lower dependency of regions
on Cohesion Policy support or the existence of
other strong determinants that inuence the de-
velopment, respectively.
With all of the above in mind, we can con-
clude that EU resources tend to behave differ-
ently in different types of regions. This analysis
revealed that regions with higher absorption of
EU funding show higher performance in knowl-
edge and innovation potential. Rather curiously,
we found some connections between those indi-
cators only among the regions with lower shares
of funding. However, as we found only positive
relationships between EU funding and develop-
ment indicators, EU Cohesion Policy seems to
be an important determinant that should help
in the long process of transformation of obsolete
Table 6. Correlations between EU funding and development indicators in groups of CEE regions
GFCF EMP PET GERB GERN
Group 1 EEUCP 0.165 –0.009 –0.051 0.664** 0.471*
Group 2 EEUCP 0.418 0.319 0.204 0.692** 0.275*
Group 3 EEUCP 0.218 0.490 –0.057 0.103** –0.064*
Notes: * signicant at the 0.05; ** signicant at the 0.01
Source: own calculations based on DC REGIO (2017) and Eurostat (2017).
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REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 89
economic structures of CEE regions. In particular,
as the Cohesion Policy effects seem to be mostly
positive in terms of R&D, CEECs should try to
catch this opportunity to re-shape the ongoing
development trajectories by looking for strong
economic, competitive advantages. Hence, if the
CEECs make an effort to empower their com-
petitiveness on the global markets and improve
living conditions of inhabitants, they should
endeavour to use as much of the resources as
possible that have been allocated for the current
2014–2020 programming period of EU Cohesion
Policy. Finally, as the allocation of European re-
sources will most likely signicantly decrease for
many CEECs after the current programming pe-
riod, they have to explore new ways to continue
in their established development trajectories in
the future.
Conclusions and challenges for the
future development of CEE regions
In this paper, we showed the historical and
economic conditions for the development of CEE
regions at the beginning of the 21st century and
changes that took place there as a result of the
European Union Cohesion Policy. The study
showed that since their entrance to the EU, GDP
per capita in PPS has been systematically increas-
ing in CEECs, both nominally and as percentage
of the EU average, and therefore countries were
catching up in this eld to the ‘old’ EU coun-
tries (EU-15). Moreover, the beta-convergence
analysis conducted for all regions in the EU-28
countries as well as exclusively for CEE regions
suggests that there is a diminishing gap between
the investigated area and the more developed
European countries.
We acknowledge that other factors such as
globalisation, opening of the European markets,
results of other national and European policies,
as well as simply starting from the initial lower
starting point, could be as important in develop-
ment and convergence processes as EU Cohesion
Policy. However, EU Cohesion Policy seems to
be an important determinant in the long process
of transforming CEE economies; it played an im-
portant role in regional development in CEECs
by opening more nancial opportunities and
providing new thematic objectives. This policy is
responsive to path dependence in regional econ-
omies, as the recent developments in CEE regions
have been shown to follow from historical devel-
opment patterns stemming as far back as the 19th
century. Contrary to the notion that the socialist
period would produce a more equitable develop-
ment across CEECs that could disrupt capitalist
patterns of development, it rather tended to re-
sult in inefcient spatial development, leaving
environmental damage as well as social and eco-
nomic problems to be re-addressed in the capital-
ist period. Entry to the EU at the beginning of the
21st century created a chance for CEE regions to
benet from the use of EU funds under Cohesion
Policy to seek new economic opportunities and
break from their historical development paths.
Therefore, we suppose that Hypothesis 1 can be
accepted.
At the same time, the study proved different
performance patterns of EU Cohesion Policy in
various types of regions. In general, for the three
groups of regions with different levels of initial-
ly uncorrelated socio-economic indicators (such
as employment, participation in education and
trainings, expenditures on research and devel-
opment), different impacts of EU funding under
Cohesion Policy on these indicators were ob-
served. For example, regions with higher absorp-
tion of EU funding showed no correlation with
development indicators, while the performance
in knowledge and innovation potential was high-
er. On the other hand, regions with rather aver-
age and lower absorption of funding showed
some positive correlations between EU resources
and development indicators. However, unam-
biguously arguing about more statistical patterns
of the effects of structural and investment funds
on the groups of regions seems to be difcult.
This allows us to conrm Hypothesis 2, that the
impacts of Cohesion Policy on development po-
tentials differ among CEE regions. It seems that
positive impacts of EU Cohesion Policy could be
observed by looking at each CEE region and type
of indicator separately.
Positive, although territorially diversied, re-
sults of Cohesion Policy on regional development
in CEECs are even more visible on the regional
and local scale (other examples are presented
by Kozak 2011; Bienias, Gapski 2014; Churski,
Stryjakiewicz 2016; Zdražil, Applová 2017). At the
same time, public development administration
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90 WOJCIECH DYBA ET AL.
units and policies in CEECs were totally refor-
mulated to absorb and optimally use EU struc-
tural and investment funds. However, obtaining
and spending EU money for regional develop-
ment must not be perceived as the optimal and
nal model of regional policy in the investigated
countries (Churski, Ratajczak 2010; Gorzelak et
al. 2010). There are also potential traps in using
external support: over-investing in infrastructure,
attributing the same support for all kinds of re-
gions, seeking cohesion at all costs, and nancing
projects that play a social rather than develop-
mental role (Gorzelak 2010; Molle 2012).
Among the future challenges for CEECs, we
see the need to develop and implement country
level comprehensive models of regional policies
that would operate without large external sup-
port from the EU budget, as the EU funds will
eventually decrease or nish (Avdikos, Chardas
2016). The model should be based on integrated
territorial (spatial) and socio-economic develop-
ment and should include: efcient institutions
(organisations and regulations), alternative
sources of funding for new development projects
(such as public-private partnerships, nancial in-
stitutions etc.) as well as considered strategies for
maintaining the infrastructure and facilities built
and capacities gained since the integration with
the EU.
CEE regions should continue to follow the
recent paradigm of regional policy, which is: a)
place-based, context-specic and geared to dif-
ferent types of regions rather than ‘one-size-ts-
all’; b) multi-level and including various actors
(public, private, NGOs) rather than centralised;
as well as c) proactive for potential – focusing on
endogenous local assets and knowledge rather
than reactive to problems – based on exogenous
investments and transfers (Bachtler, Yuill 2001;
Tödtling, Trippl 2005; Barca et al. 2012; Vanthillo,
Verhetsel 2012). Such a model would continue
to respond to the widely discussed need for re-
newed territorialisation and regionalisation of
development policies while looking for compet-
itive advantages (Porter 1998; OECD 2010, 2012;
Capello, Fratesi 2011; Szlachta 2011; Asheim et al.
2011). One of the most promising tools for shap-
ing regional competitiveness in economic terms
is smart specialisation, whereby sectors having
the highest future growth potentials are discov-
ered and adopted by each country and region
(Foray 2009; Karo, Kattel 2015). The strategy, hav-
ing been widely implemented in the 2014–2020
programming period, cannot yet be evaluated
for CEE regions. Nevertheless, our ndings from
the 2007–2013 programming period support its
underlying principles. The regional economic
development processes through smart specialisa-
tions should be supported by higher R&D spend-
ing (as a percentage of GDP), deliberate innova-
tion strategies, and better cooperation between
business and research or education institutions.
Acknowledgements
We are grateful to two anonymous reviewers
as well as to prof. Tadeusz Stryjakiewicz from
Adam Mickiewicz University in Poznań for help-
ful comments on an earlier version of this paper.
The Authors started collaboration through
the Regional Studies Association. Wojciech Dyba
was a scholarship holder of the Adam Mickiewicz
Foundation. Bradley Loewen is a doctoral stu-
dent at the University of Economics, Prague and
an Early Stage Researcher in the Marie Curie
Initial Training Network RegPol2 (REA grant
agreement no. 607022). Junior research fellow
Jaan Looga is supported by the University of
Tartu ASTRA Project PER ASPERA, nanced by
the European Regional Development Fund.
References
Annoni P., Dijkstra L., 2013. EU Regional Competitiveness
Index 2013. Publications Ofce of the European Union,
Luxembourg.
Arthur W.B., 1994. Increasing returns and path dependence in the
economy. University of Michigan Press, Ann Arbor.
Asheim B.T., Boschma R., Cooke P., 2011. Constructing re-
gional advantage: Platform policies based on related va-
riety and differentiated knowledge bases. Regional Stud-
ies 45(7): 893–904.
Avdikos V., Chardas V., 2016. European Union Cohesion
Policy Post 2014: More (place-based and conditional)
growth – Less redistribution and cohesion. Territory, Pol-
itics, Governance 4(1): 97–117.
Bachtler J., Yuill D., 2001. Policies and strategies for regional
development: A shift in paradigm? Regional and Industrial
Policy Research Paper 46. University of Strathclyde, Glas-
gow.
Barca F., 2006. An agenda for a reformed Cohesion Policy: A
place-based approach to meeting European Union challenges
and expectations. European Commission, Brussels.
Barca F., McCann P., Rodríguez-Pose A., 2009. The case for
regional development intervention: Place-based ver-
Unauthenticated
Download Date | 5/7/18 9:34 PM
REGIONAL DEVELOPMENT IN CENTRAL-EASTERN EUROPEAN COUNTRIES AT THE BEGINNING OF THE 21ST CENTURY 91
sus place-neutral approaches. Journal of Regional Science
52(1): 134–152.
Barro R., Sala-i-Martin X., 1992. Convergence. Journal of Polit-
ical Economy 100(2): 223–251.
Barro R., Sala-i-Martin X., 2004. Economic Growth. The MIT
Press, Cambridge.
Baumol W., 1986. Productivity growth, convergence, and
welfare: What the long-run data show. American Econom-
ic Review 76(5): 1072–1085.
Berend I.T., 2006. An economic history of twentieth-century
Europe: Economic regimes from laissez-faire to globalization.
University Press, Cambridge.
Berend I.T., 2009. From the soviet bloc to the European Union:
The economic and social transformation of Central and Eastern
European since 1973. University Press. Cambridge.
Bienias S., Gapski T., 2014. Effects of Cohesion Policy in re-
gions in Poland. In: Ambroziak A. (ed.), New Cohesion
Policy of the European Union in Poland. Contributions to eco-
nomics. Springer, Cham.
Brusis M., 2002. Between EU requirements, competitive pol-
itics, and national traditions: Re–creating regions in the
accession countries of Central and Eastern Europe. Gov-
ernance 15(4): 531–559.
Bruszt L., 2008. Multi-level governance – the Eastern ver-
sions: Emerging patterns of regional developmental
governance in the new member states. Regional & Federal
Studies 18(5): 607–627.
Capello R., Fratesi U., 2011. Globalization and regional growth
in Europe: Past trends and future scenarios. Springer, Berlin.
Chojnicki Z., Czyż T., Parysek J., 1999. Transformations and
dilemmas of the Polish economy. In: Carter W.F., Maik
W. (eds), Shock-shift in an enlarged Europe: The geography
of socio-economic changes in East-Central Europe after 1989.
Aldershot, Ashgate: 7–26.
Churski P., Ratajczak W., 2010. Regional development and re-
gional policy in Poland: First experiences and new challeng-
es of the European Union membership, Part I and II. Polish
Academy of Sciences, Warsaw.
Churski P., Stryjakiewicz T., 2016. Poznań – an attempt to as-
sess changes during 10 years of membership in the European
Union. Bogucki Wydawnictwo Naukowe, Poznań.
Copus A., Noguera J., 2010. A typology of intermediate and
predominantly rural NUTS 3 regions. EDORA Scientif-
ic Working Paper No. 24. EDORA Final Report, Annex 1
(Part C: Scientic Working Papers). ESPON 2013 Pro-
gramme, Luxembourg.
Costanza R., Hart M., Posner S., Talberth J., 2009. Beyond
GDP: The need for new measures of progress. Pardee Pa-
per 4, Pardee Center for the Study of the Longer-Range
Future, Boston.
Czyż T., 2001. Zróżnicowanie regionalne Polski w układzie
nowych województw (Poland’s regional diversity in the
new voivodeships). Biuletyn KPZK PAN 197: 7–35.
David P.A., 1985. Clio and the economics of QWERTY. Amer-
ican Economic Review 75(2): 332–337.
David P.A., 2001. Path dependence, its critics and the quest
for ‘historical economics’. In: Garrouste P., Ioannides S.
(eds), Evolution and path dependence in economic ideas: Past
and present. Edward Elgar, Cheltenham: 15–40.
Davies S., Ferry M., Gross F., 2015. Policy reform under chal-
lenging conditions: Annual review of regional policy in
Europe. European Policy Research Paper 14(1): 1–63.
Directorate-General for Internal Policies, 2014. Key Enabling
Technologies (KETs) – Booster for European Leadership
in the Manufacturing Sector. European Union, Brussels.
Directorate-General for Regional Policy (DG REGIO), 2017. Data
for research – Regional Policy Online: ec.europa.eu/
regional_policy/en/ policy/evaluations/data-for-re-
search (accessed 22 July 2017).
Dobrinsky R., Havlik, P., 2014. Economic convergence and
structural change: The role of transition and EU accession.
WIIW, Vienna.
Ehrlich E., 1991. Contest between countries: 1937–1986. Soviet
Studies 43(5): 875–896.
Enyedi G., 1990. New basis for regional and urban policies in
East-Central Europe. RKK Discussion Paper 9. Hungarian
Academy of Sciences, Centre for Regional Studies, Pécs.
European Commission, 2010a. Europe 2020: EU Strategy for
smart, sustainable and inclusive growth. European Union,
Brussels.
European Commission, 2010b. Regional Policy contributing to
smart growth in Europe 2020. European Union, Brussels.
European Commission, 2013. Expert evaluation network on the
performance of Cohesion Policy 2007–2013: Synthesis of Na-
tional Reports 2013. European Union, Brussels.
European Commission, 2014. Investment for jobs and growth:
Promoting development and good governance in EU regions
and cities. Sixth report on economic, social and territorial co-
hesion. Publications Ofce of the European Union, Lux-
embourg.
European Commission, 2016. Commission staff working doc-
ument, Ex post evaluation of the ERDF and Cohesion Fund
2007–13. Online: ec.europa.eu/regional_policy/en/
policy/evaluations/ec/2007-2013/ (accessed 29 August
2017).
European Commission, 2017a. Data for research, period 2007–
2013, ERDF/ESF/CF Priority theme overview 2007–2013.
Online: ec.europa.eu/regional_policy/en/policy/eval-
uations/data-for-research/ (accessed 29 August 2017).
European Commission, 2017b. European Structural and In-
vestment Funds. Online: cohesiondata.ec.europa.eu/data-
set/ESIF-2014-2020-FINANCES-PLANNED-DETAILS/
e4v6-qrrq (accessed 1 August 2017).
Eurostat, 2017. Database. Online: ec.europa.eu/eurostat/
data/database (accessed 22 July 2017).
Farole T., Rodríguez-Pose A., Storper M., 2011. Cohesion Pol-
icy in the European Union: Growth, geography, institu-
tions. Journal of Common Market Studies 49(5): 1089–1111.
Foray D., 2009. Understanding smart specialisation. In: Pon-
tikakis D., Kyriakou D., Van Bavel R. (eds), The questions
of R&D specialisation: Perspectives and policy implications.
European Commission/Joint Research Centre, Se-
ville: 14–24.
Gligoric M., 2014. Paths of income convergence between
country pairs within Europe. Economic Annals 59(201):
123–155.
Good D.F., Ma T., 1999. The economic growth of Central and
Eastern Europe in comparative perspective, 1870–1989.
European Review of Economic History 3(2): 103–137.
Gorzelak G., 2010. Facts and myths of regional development.
Studia Regionalne i Lokalne SI: 5–28.
Gorzelak G., Bachtler J., Smętkowski M., 2010. Regional devel-
opment in Central and Eastern Europe. Development process-
es and policy challenges. Routledge, New York .
Horváth G., 1998. Regional and Cohesion Policy in Hungary.
RKK Discussion Paper 23. Hungarian Academy of Scienc-
es, Centre for Regional Studies, Pécs.
Illner M., 1997. Territorial decentralization – a stumbling
block of democratic reforms in East-Central Europe? Pol-
ish Sociological Review 117: 23–45.
Unauthenticated
Download Date | 5/7/18 9:34 PM
92 WOJCIECH DYBA ET AL.
Illner M., Andrle A., 1994. The regional aspect of post-com-
munist transformation in the Czech Republic. Czech Soci-
ological Review 2(1): 107–127.
Isaksen A., 2001. Building regional innovation systems: Is
endogenous industrial development possible in the glob-
al economy? Canadian Journal of Regional Science 24(1):
101–120.
Karo E., Kattel R., 2015. Economic development and evolv-
ing state capacities in Central and Eastern Europe: Can
“smart specialization” make a difference? Journal of Eco-
nomic Policy Reform 18(2): 172–187.
Kaczmarek T., 2016. Administrative division of Poland – 25
years of experience during the systemic transformation.
EchoGéo 35.
Korcelli P., 1995. Regional patterns in Poland’s transforma-
tion: The rst ve years. Zeszyty IGiPZ PAN 34, Warsaw.
Kozak M., 2011. Impact of Cohesion Policy on Poland. Studia
regionalia 33: 29–42.
Krugman P., 1991. Increasing returns and economic geogra-
phy. Journal of Political Economy 99(3): 483–499.
Lucas R., 1988. On the mechanics of economic development.
Journal of Monetary Economics 22(1): 3–42.
Lux G., 2009. Divergent patterns of adaptation among Cen-
tral European old industrial regions. European Spatial Re-
search and Policy 16(1): 145–157.
Maddison A., 1991. Dynamic forces in capitalist development: A
long-run comparative view. Oxford University Press, New
York.
Maddison A., 2003. The world economy: Historical statistics.
OECD Publishing, Paris.
Mankiw N., Romer D., Weil D., 1992. A contribution to the
empirics of economic growth. Quarterly Journal of Eco-
nomics 107(2): 407–437.
Matkowski Z., Prochniak M., Rapacki R., 2016. Real income
convergence between Central Eastern and Western Eu-
rope: Past, present, and prospects. Ekonomista 24: 853–
892.
Mendez C., 2012. The Lisbonization of EU Cohesion Policy:
A successful case of experimentalist governance? Europe-
an Planning Studies 19(3): 519–537.
Molle W., 2012. Competitiveness, EMU and cohesion. Condi-
tions for an effective post crisis EU policy. In: Latoszek E.
et al. (eds), European integration process in the new regional
and global setting. University Press, Warsaw: 233–267.
Monastiriotis V., 2014. Regional growth and national devel-
opment: Transition in Central and Eastern Europe and
the regional Kuznets Curve in the East and the West. Spa-
tial Economic Analysis 9(2): 142–161.
Noguera-Tur J., del Mar García-García M., Veloso-Pérez
E., Lückenkötter J., 2009. Review of current situation and
trends: Access to services of general interest. EDORA Ap-
plied Research Project 2013/1/2. ESPON 2013 Pro-
gramme, Luxembourg.
OECD, 2000. Glossary of statistical terms. Online: stats.oecd.
org/glossary/detail.asp?ID=303 (accessed 20 July 2017).
OECD, 2010. Regional development policies in OECD countries.
OECD, Paris.
OECD, 2012. Draft synthesis report on innovation driven-growth
in regions: The role of smart specialisation. OECD, Paris.
Okey R., 1992. Central Europe / Eastern Europe: Behind the
denitions. Past & Present 137(1): 102–133.
Pike A., Rodriguez-Pose A., Tomaney J., 2006. Local and re-
gional development. Routledge, London.
Porter M.E., 1998. Competitive advantage of nations: Creating
and sustaining superior performance. The Free Press, New
York.
Rebelo S., 1991. Long-run policy analysis and long-run
growth. Journal of Political Economy 99(3): 500–521.
Romer P., 1986. Increasing returns and long-run growth.
Journal of Political Economy 94(5): 1002–1037.
Scherpereel J.A., 2010. EU Cohesion Policy and the Europe-
anization of Central and East European regions. Regional
and Federal Studies 20(1): 45–62.
Spruk R., 2013. Economic growth and income convergence in
transition: Evidence from central Europe. Journal of Global
Economics 1(104): 1–9.
Stanisic N., 2016. Income convergence in the process of the
Western Balkan states’ accession to the European Union.
Economic Horizons 18(1): 3–15.
Stryjakiewicz T., 2009. The old and the new in the geograph-
ical pattern of the Polish transition. AUPO Geographica
40(1): 5–24.
Swianiewicz P., 2010. If territorial fragmentation is a prob-
lem, is amalgamation a solution? An East European per-
spective. Local Government Studies 36(2): 183–203.
Szczepański J., 1977. Early stages of socialist industrializa-
tion and changes in social class structure. International
Journal of Sociology 7(3–4): 11–36.
Szlachta J., 2011. Strategic programming of regional develop-
ment. Towards the national system of startegic thought
in the eld of regional policy. In: Kolczyński M., Żuber P.
(eds). New Paradigm in Action. Ministry of Regional De-
velopment, Warszaw.
Tödtling F., Trippl M., 2005. One size ts all? Towards a dif-
ferentiated regional innovation policy approach. Research
Policy 34: 1203–1219.
Tukey J., 1977. Exploratory data analysis. Addison–Wesley,
Reading.
Valdaliso J.M., Magro E., Navarro M., Aranguren M.J., Wil-
son J.R., 2014. Path dependence in policies supporting
smart specialisation strategies: Insights from the Basque
case. European Journal of Innovation Management 17(4):
390–408.
Vanthillo T., Verhetsel A., 2012. Paradigm change in region-
al policy: Towards smart specialisation? Lessons from
Flanders (Belgium). Belgeo – Belgian Journal of Geography
2012(1–2).
Zdražil P., Applová P., 2016. Growth disparities among re-
gions of the Visegrad group countries: An evidence of
their extent and nature. E & M Ekonomie a Management
19(2): 37–54.
Zdražil P., Applová P., 2017. The impacts of the 2007–2013
programming period of EU regional policy on develop-
ment of Czech micro-regions. In: Klimová V., Žitek V.
(eds), 20th International Colloquium on Regional Sciences.
Masaryk University, Brno: 75–82.
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