4. of evaluations differ in each period. While
4. Observable trends and impacts
of the Cohesion Policy
chapter shows impacts of Cohesion Policy with looking at growths of GDP per
capita and employment rate, out of many, in Member States. Although we cannot
ignore the fact that a number of researches have admitted that the
effectiveness of Cohesion Policy is indecisive, and its evaluation is
encompassing and divergence at multiple levels (Bachtler, et al 2017, European
Commission 2010, Shankar and Shah 2009, Wostner and Šlander 2009), the
following content includes data provided by the European Commission and other
4.1 Methodology and definition
European Commission has published the evaluation of Cohesion Policy for each
project period; however, tools of evaluations differ in each period. While the
2007-2013 period was evaluated with models of Quest III1
the previous period was evaluated with two other models. European Commission
(2016b, p55-56) has pointed out that here are four major difficulties and
complexities in order to evaluate the effect, which we believe should be stated
in this paper as well. Firstly, it is difficult to measure precisely to what
extent the programs under Cohesion Policy were achieved due to a lack in
preciseness of the objectives. Secondly, it is not easy to observe tangible
evidence as the funding is distributed and covered a wide range of projects.
Thirdly, multiple elements have influence on development; hence, it is hardly
capable to conclude that the effects were made by only Cohesion Policy. Lastly,
but not least, the time lag should be considered as it takes certain time that
the projects contribute to the region.
reflecting the evaluations by the European Commission, Member States are
classified into either EU-15 (joined before 2004 enlargement) or EU-12 (joined
in 2004 and 2007). Although the Nomenclature of Territorial Units for Statistic
level is applied to the economic territory of the European Union, due to
different evaluation in each period, the objectives are also differently
the 2000-2006 Cohesion Policy4
1) Objective 1: to promote the development and
structural adjustment of regions whose development is lagged behind, regions
with its GDP per capita of less than 75 percent of the EU average.
2) Objective 2: to support the economic and
social conversion of areas experiencing structural difficulties, development
level is closed to the EU average.
3) Objective 3: to support the adoption and
modernization of education, training and employment policies and systems in
regions not eligible under Objective 1.
the 2007-2013 Cohesion Policy
1) Convergence: regions with its GDP per capita of less than
75 percent of the EU average, and GNI less than 90 percent of the European
2) Transition: regions with its GDP per capita of more than
75 percent but less than the EU average. (Newly created regional category.)
3) Competitiveness: regions with its GDP per capita of more than
the EU average.
4.2 Observable trends during the
implementation period of Cohesion Policy
multiple researches have questioned about contribution of Cohesion Policy,
Ramajo et al. (2008) show that funds under Cohesion Policy influenced
positively in catching-up countries between 1981 and 1996 and the speed of
catching up to the richer Member States was quick during the period.
Beugelsdijk and Eijffinger (2005) also show the same trend between 1995 and
2001. Additionally, according to Leonardi (2005, p102), the disparities in GDP
per capita in less developed regions in Member States had been reduced, while
the levels in other regions were barely changed, thus there was convergence.
Fuente (2002, p 7-8) present that the one period of fund could be substantially
and positively working as “shock, with presenting Spanish case during the
1994-1999 period funds.
the degree of growth differed in respective countries, Table 1 and Table 2 tell
us that poorer regions, which were targeted in the funds, saw relatively higher
growth in GDP per capita and employment rate than more developed regions which
receiving less assistance during 2000-2006. In domestic level, there were some
exceptions, in terms of growth of GDP per capita, in relatively rich countries
such as Belgium and Netherlands. However, majority countries saw higher
economic growth in poor regions. Greece and Ireland, whose development lagged
behind, had seen around 4 percentage of growth, while Italy and Portugal had
seen a little. While significant rises in employment rate were observed in
Greece and Spain, which saw over 8.5 percentage higher employment in 2006,
Belgium, Netherlands and Portugal saw reduction in poor regions, which was due
to the slow economic growth (European Commission 2010, p57).
Table 1. Growth of GDP per capita (%), Objective 1,
Objective 2, and other NUTS 2 regions in EU-15, 2000-2006.
Note: Objective 2 > 45% are regions in which over
45 percent of the population lived in areas receiving funding under Objective 2
Objective 2 20-45% are regions in which 20-45 percent of the population
lived in areas receiving funding under Objective 2
Source: European Commission (2010: p55), reformatted by the author.
Table 2. Growth of employment rate (percentage of
population 15-64), Objective 1, Objective 2, and other NUTS 2 regions in EU-15,
Note: see Table 1
Source: European Commission (2010: p57), reformatted by the author.
data shows that GDP per capita in poorer regions in South/ Mediterranean
countries, Ireland and the UK had become more distant from the EU-27 standard
in 2013, and employment rate was declining in majority of countries during the
period. (See Table 3) Greece and Spain had experienced much-lowering GDP per
capita in respective Convergence areas and Transition areas. Although Greek
Convergence regions saw relatively higher GDP per capita compared to the other
Convergence regions in neighbors in 2007, it had drastically fell down to only
over a half of the EU-27 standard. Convergence regions in Portugal had seen
GDP per capita (Purchasing Power Standards) (EU-27 =100 in each year) and
growth employment rate (percentage), Convergence, Transition and
Competitiveness regions in EU-15, 2007-2013.
Note: Convergence: a region with its GDP per
capita less than 75 percent of the EU average,
Transition: a region with its GDP per capita of more than 75 percent but
less than the EU average,
Competitiveness: a region with its GDP per capita more than the EU
Source: European Commission InfoRegio: Evaluation of 2007-2013 programming
period, reformatted by author.
2 shows changes of the convergence rate in terms of regional GDP per capita in
EU-15 for six periods between 1980 and 2011. The period of 1995-2001 saw the
highest convergence rate, which was approximately 2.5 per cent however it has
recently been declining, showing even negative number in the 2007-2011 period,
which means there has been divergence among the EU-15.
Figure 2. Convergence rate (percentage per year) and growth
in GDP per capita (percentage per year), EU-15, 6 periods between 1980 and
Source: European Commission (2016b: p13-14)
4.2.2 EU-10/ EU-12
GDP per capita in Purchasing Power Standards of the Europe Union average
(EU-27) equals to 100, the average of GDP per capita of the EU-12 nations in
2000, not Member States yet, was 44.5, while the average in EU-15 was 2.6 times
bigger. Thus, although there was a tendency that the poorer regions or countries
were catching up and converging to the other richer Member States by the early
2000s, the European Union had to expect that the gaps between Member States
would be widened due to the enlargement.
10 nations joined in the European Union in 2004 have started being included in
the Cohesion Policy since the same year. Although the levels of the GDP per
capita in the EU-10 were substantially lower than ones in EU-15, the growth
rates in the EU-10 was much higher than ones in the EU-15, except Malta (See
Table 4). Most of the country had already seen an increase in their GDP per
capita before enlargement, the Baltic countries increased prominently during
the 2000-2006 period of funds. Additionally, employment rate in those countries
had greatly increased. Some of them observed relatively small or negative
growth in employment rate and those countries saw internal disparities, the
gaps between the capital city/ developed regions and poorer regions, shown in
Table 5. Indeed, inequality was increasing for over ten years. European
Commission (2010, p56) points out “the effect of commuting”, a rise in scale of
commuting from poorer region to the capital city/ developed regions which
development was emphasized in the first period.
Table 4. Growth of GDP per capita
(%) and growth of employment rate (%), Objective 1 and Objective 2 in EU-10,
Note: see Table 1
Source: European Commission (2010: p55 and p57), reformatted by the author.
Table 5. Dispersion of GDP per capita in PPS between NUTS2
regions, EU10 and its selected countries, 1995, 2000
European Commission (2010: p59), reformatted by the author.
the 2007-2013 period of the funds, Bulgaria and Romania have been included.
Majority of regions/ countries was moving upward to the EU-27 level over the
course of the years, whereas negative growth in employment was seen. (See Table
6) European Commission (2014b, p21) reports that “the higher productivity growth
was due to catching up in the use of technology and more efficient methods of
working, …, which in turn led to a reduction in employment.”
Table 6. GDP per capita (Purchasing Power Standards)
(EU-27 =100 in each year) and growth employment rate (percentage), Convergence
and Competitiveness regions in EU-12, 2007-2013.
Note: see Table 3
Source: European Commission InfoRegio: Evaluation of 2007-2013 programming
period, reformatted by author.
4.3 Impacts of Cohesion Policy
previously observed, the economic and finance crisis in the late 2000s and
onwards has made economic climate change in majority of Member States, and
governments had to face severe conditions. Public investment was severely
squeezed, which is especially the case in the strictly instructed Eurozone
countries. Indeed, public investment with respect to the total amount in all
Member States, has been continuously falling down after 2009, when the
aggregate spending was increasing, thanks to the funds. (See Figure 3) In the year
of 2013, approximately one-fifth of the total expenditure on public investment
in the 27 Member States was accounted by the funds. We could find, from Figure
4, that the ratio in the new Member States is prominently high, which shows
three to over eight times bigger than the EU-27 average. Over 80 percent of the
spending in Slovakia was supported by the funding from Cohesion policy,
followed by Lithuania, Hungary, Bulgaria and Latvia. The funds also covered
more than 60 percent and 20 percent of the investment in Portugal and Greece,
respectively. Nations without convergence areas saw very small contribution by the
funds, on the other hand.
Figure 3. Contribution of Cohesin Policy on Public
Investment (EUR billion), EU-28, 2007-2013.
Source: European Commission (2014b: p155)
Figure 4. Average share of ERDF, ESF, Cohesion Fund
and national co-financing* in total public investment, EU-27, 2011-2013.
States have to commit in order to avoid replacing national funding with the
European Commission (2014b: p156)
According to Monfort, Piculescu, et al. (2017), the
2007-2013 Cohesion Policy has hugely influenced to growth of the EU-12. Their
GDP would be growing 4 percentage higher in average by the year of 2015, when
the projects were executed, and the effect will last for a long term (Monfort,
Piculescu, et al. 2017, p15) (Figure 5). Although the impact to the EU-15 is
expected to be positive but much weaker, it is estimated that the funds will
contribute to growths of GDP in Greece and Portugal, which are 2.8 percentage
and 2.6 percentage in 2023 respectively (Monfort, Piculescu, et al. 2017, p15).
Figure 5. Impact of 2007-2013 Cohesion Policy to GDP (percentage),
EU-12 EU-15 and EU-27, during 2007 and 2023.
Source: Monfort, Piculescu, et al. (2017: p15)
Piculescu, et al. (2017, p17) have also argued that the impact would depend on
patterns of distribution of the projects. Nearly 57 per cent of the funds were
distributed to the EU-12 nations during the period. Figure 6 shows the funds
allocation in the EU-15 and EU-12 on average. In the EU-12, approximately a
half of allocated funds were used in Infrastructure sector, followed by support
to private sector (25.52 percent) and human capita (13.74 percent). The distribution
trend is quite similar among the nations. While the EU-15 countries, on
average, allocated to 43.0 per cent of the funds to private sector, followed by
human capital (25.65 percent) and infrastructure (18.95 percent). The biggest
allocation in Greece, Portugal and Spain was to infrastructure, and one in
Belgium and Netherlands was human capital.
Figure 6. The allocation of the funds
(percentage), EU-15 and EU-12, during the 2007-2013 Cohesion Policy.
Note: Infrastructure includes investments in
transport, telecommunications, energy, environmental and social infrastructure.
Research and Development includes investments in research, technological
development and innovation.
Human capital includes investments in education, vocational training and
labor market interventions.
Aid to Private Sector includes investments in small and medium-sized
enterprise, facilitation to credit, tourism services and culture.
Technical Assistance includes investments in building administrative
capacity, monitoring, evaluation, and various compensations for specific
Source: Author’s calculation based on Monfort, et al. (2016: p10)
we could observe from Figure 7 that shows each sectors contribution to the GDP,
each sector hugely influences to economic growths, especially for EU-12 that is
supported more by the funds in total. Investment in infrastructure and private
sector, which consist of over 60 per cent in the EU-15 and over 70 per cent in
the EU-12, would see strong short effect. The EU-12 would be hugely benefited
from investment in infrastructure with steady growth for short-term and fixed
growth for long-term, which is due to both demand and supply-side effect for
short run, and supply side effect for long run. (Monfort, Piculescu, et al.,
2017 p11) Impact of supporting private sector would be observed during the
implementation period, hence it is also considered as short run effect. With
reallocation of high skilled workers away from production sectors, Research and
Development saw negative impact at first. However, economy will appreciate the
investment for long term with improving productivity. Human capital has
persistent and long-run effect and contributes to productivity in Research and
Development. However, it would become weaker with reduction of working age
population (Monfort, et al. 2016, p12).
Figure 7. Impact of four sectors on GDP, EU-27, EU-15
and EU-12, 2007-2023
Source: Monfort, et al. (2016: p12-14), reformatted by
4.4 Reported results of the policy
during the 2007-2013 period, it is estimated that one million jobs were newly
created, and 121,400 start-ups as well as nearly 100,000 research projects were
supported in the all regions5.
The poorest regions have achieved 2.2-percentage growth of GDP per capita by
2010. In addition, environment and living condition were improved across
regions. Poland, as the biggest fund receiver, saw significant development in
many sectors over the course of years. (See Annex 1)
on European Commission (2014b and 2016a), this section gives some examples of
projects supported by the funds during the 2007-2013 periods.
The projects created around 4,900km of new roads
across Member States, over 70 per cent of it were in the EU-12, and improved
nearly 28,000km of already existing roads. (European Commission, 2016a: p
27-28) Majority of projects have contributed to timesaving, facilitating to
access the city and to the neighbor countries. A number of the new and improved
roads could resulted in reducing traffic, thus pollution eventually, for some
transport systems were also supported, which was especially the case in the
EU-15 with 90 per cent of the total brand new railways. (European Commission,
2016 p 28) The Sofia metro system in Bulgaria was expanded with more than
double lengths from 18km of railways and 2.5 times greater number of stations
by 2015. This resulted in changing a pattern of commuting, increasing use of
public transport, thus reducing use of vehicle and congestion in the city,
which could eventually reduce carbon emissions. (European Commission, 2016 p
Spain, approximately 3.3 million people can access to improved supply of
drinking water, especially 1.7 million people in convergence regions. (European
Commission, 2014b p217)
public facility, museum and the forth are newly opened or renovated, which
contributes to tourist, cultural, social and educational projects as well. They
were implemented in Convergence regions of both the EU-15 and the EU-12. It was
observed that the number of overnight stays in multiple cities/ towns in
Slovenia, although a causal link is not necessary. (European Commission, 2014b,
funds support projects modernizing of education and training for 25.9 million
participants in 13 Member States. Nearly 696,000 participants progressed
further education and approximately 300,000 participants entered labor market.
Five Member States including Portugal and Italy have strengthened programs for
young people after the crisis. Additionally, in eight Member States, whose
young unemployment level is high level, Youth Employment Action Teams by the
European Commission were newly established in 2012, and it is expected that
over one million of young people could benefit from job opportunity made
thought the projects. (European Commission 2014b, p219-220)
to Private Sector and Research Development
to European Union (2014b, p214-215), the huge amount of funds was allocated to
job creation and approximately 78,000 start-up firms were financed among Member
States. In Portugal, for instance, financial help for new businesses,
especially high-tech and knowledge intensive sectors, was applied. In Greece
and Bulgaria, against the limited borrowing from the market, financial
instrument scheme with the form of low-interest loans was implemented to around
3,000 small and medium-sized enterprises.
only small and medium-sized enterprises, but also large enterprises were also
targeted, which consisted of roughly 20 percent of the funds. (European
Commission, 2016a p22). Supporting the large enterprises does not only mean
increasing private investment, productivity or employment. It possesses
spillover effect to small and medium-sized enterprises, so the local economy
could appreciate it. (European Commission, 2016a p22-23)
supporting enterprises was covering Research and Development. (European
Commission, 2016a p20) Although some countries saw short and limited effect in
investing in the sector of research and development, most of projects have
shown positively with an increase in employment and activities, hence there is
a great potential (European Commission, 2014b p226).
1 According to Monfort et al (2016,
p10-12), QUEST III, developed by DG Economic and Financial Affairs of the
European Commission, assess the impact of the structural reforms. See more
details on European Commission (2016): Ex post evaluation of Cohesion Policy
programmes 2007-2013, focusing on the European Regional Development Fund (ERDF)
and the Cohesion Fund (CF), WP1: Synthesis report, Luxembourg: Publications
Office of the European Union. ISBN 978-92-79-61655-6.
2 RHOMOLO is the newly developed dynamic
spatial general equilibrium model the European Commission. See more details on
Brandsma, A., Kancs, D., Monfort, P., and Rillaers, A. (2013): RHOMOLO: A
Dynamic Spatial General Equilibrium Model for Assessing the Impact of Cohesion
Policy, WP 01/2013, Luxembourg: Publications Office of the European Union.
3 According to European statistic on cities
(2016), “Nomenclature of Territorial Unites for Statistics (NUTS) is a
hierarchical classification with three levels, NUTS1: major socioeconomic
regions; NUTS2: basic regions for the application of regional policies; NUTS3:
small regions for specific analyses
4 Based on EUR-Lex
Commission, InfoRegio, Key achievements of Regional Policy