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Real convergence, economic crises and EU cohesion policy
Bartosz Jóźwik
Institute of Economic and Management, The John Paul II Catholic University of Lublin, Poland,
email: bjozwik@kul.pl
Henryk Ponikowski
Institute of Economic and Management, The John Paul II Catholic University of Lublin, Poland,
email: henryk.ponikowski@kul.pl
Abstract
Real convergence is one of the major objectives of the EU cohesion policy in the period 2007-2013. This
objective covers the poorest EU regions, defined as convergence regions. This paper attempts to verify the
hypothesis that the real economic convergence of the European Union is progressing as a result of the
accelerated development of “convergence regions”. Despite the fact that the hypothesis about convergence has
been confirmed by many other researchers, they have only rarely dealt with the convergence that occurred during
the last economic crisis in 2007-2009. We examined the real convergence of the GDP per capita across the EU
regions in 2000-2010 and estimated the convergence of the GDP per capita across European regions following
the classical approach: the absolute beta and sigma convergence model. Additionally, we proposed to use the
three-sigma rule. Our studies confirm the hypothesis that the EU poorer regions known as convergence regions
tend to grow faster than rich ones. In the period 2000-2010, the convergence regions achieved a significantly
higher growth rate than the other strong EU regions. Real convergence is also confirmed by the examination
based on the triple convergence band. The results of our examination show that the process of convergence
within the EU-27 during the economic crises in 2007-2009 occurred as a result of the GDP per capita growth
across the convergence regions and the declined growth rate within the other regions.
JEL: F15, F42
Keywords: convergence, economic crises, economic integration, cohesion policy.
1. Introduction
Real convergence is one of the major objectives of the EU cohesion policy in the period 20072013. This objective covers the poorest EU regions, defined as convergence regions. The key
objective in these regions, eligible for the cohesion policy, involves the stimulation of the
growth potential to maintain and achieve high growth rates there. This objective should be
approached in view of the unprecedented rise in disparities within the enlarged European
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Union, the long-term efforts that will be needed to reduce them and the contribution to the
competitiveness of the whole European Union (Communication from the European
Commission, 2005). The regions targeted by the Convergence objective are the regions whose
per capita gross domestic product (GDP) measured by purchasing power parity is less than
75% of the European Community average. The regions suffering from the statistical effect
due to the reduced European Community average following the European Union enlargement
are to benefit for that reason from substantial transitional aid in order to complete their
convergence process. Additionally, the EU Member States targeted by the Convergence
objective whose per capita gross national income (GNI) is less than 90 % of the European
Community average are to benefit under the Cohesion Fund (Council Regulation, 2006).
Recent studies indicate progressive convergence in the European Union, but they have
only rarely dealt with the convergence that occurred during the last economic crisis in 20072009. Therefore, examining the convergence in the EU should raise some questions: whether
and what implications the economic crisis has had for the convergence across the EU Member
States and regions and whether the economic convergence (or divergence) of the European
Union has been brought on by the high GDP growth rate in the convergence regions or
perhaps the declined GDP in the developed EU members states and regions during the
economic crisis. In this paper, we also attempt to verify the hypothesis that the real economic
convergence of the European Union is progressing as a result of the accelerated development
of convergence regions.
The paper is organised as follows. The introduction is followed by the section that
overviews the studies on economic convergence mainly in the EU member states and regions.
The next section describes the research methods adopted here, i.e. the methods of measuring
convergence and volatility in the GDP growth rate. The fourth and fifth sections present the
research results. The last section concludes the paper.
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2. Literature review
Economic convergence has been examined by many researchers, including: Baumol (1986),
Dowrick & Nguyen (1989), Barro, Mankiw and Sala-i-Martin (1992) or Ben-David (1993).
Convergence across countries was extensively investigated by, for example, O’Rourke and
Williamson (1994), Sachs and Warner (1995) and O’Rourke (1999). In the 1990s, there were
many studies on regional convergence. For example, Barro, Sala-i-Martin, Blanchard and Hall
(1991), Barro and Sala-i-Martin (1992) and Armstrong (1995) thoroughly examined the
regional convergence in the European regions, U.S. states, Japanese prefectures, Canadian and
Australian provinces. Their results suggest that convergence occurred in those areas but its
pace varied considerably over various periods.
We, however, intended to draw attention to the research into this period by Neven and
Gouyette (1995). The authors examined the convergence in the output per capita across the
European Community regions in the period 1975-1990. They observed that the pattern of
convergence varied sharply across different sub-periods and regional subsets. In the early
1980s, the south of Europe seemed to catch up with the leading EU countries and stagnated in
the late 1980s. The regions in the north of Europe, by contrast, tended to stagnate or diverge in
the early 1980s and strongly converge later. The authors noticed that this pattern was
consistent with the view that northern European countries adjusted better to the main change
in the policy regime in the mid-1980s, namely the internal market programme as well as the
accession of the Iberian Peninsula countries to the European Community in 1985. As soon as
the internal market programme was implemented, the hypothesis about convergence in the
European Union was confirmed by many other researchers. For example, Eckey, Dreger, and
Türck (2009), who studied the convergence in the 233 regions at the NUTS 2 level in a group
of 23 EU member states in 1995–2003, confirmed the hypothesis about convergence. They
used the human capital augmented Solow model because human capital has a strong influence
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on regional production. They argued that there was a process of economic convergence across
the said regions and even predicted that it would be accelerated by the regions of the new EU
member states.
Contemporary extensive studies on the EU regional economic convergence often
attempt to verify the implementation of the EU (regional) cohesion policy. However, Sala-iMartin (1996) argued that the impact of the government on the process of convergence is
minor by observing that the rates of convergence are surprisingly similar across the U.S.
states. He added that since the degree to which national governments use regional cohesion
policies is very different, the fact that convergence rates are very similar across countries
suggests that a public policy plays a secondary role in an overall process of regional
convergence. Using cross-sectional and panel data analysis, Rodriguez-Pose and Fratesi
(2004) proved that the previous failure of the European development policies to fulfil their
objective of delivering greater economic and social cohesion by examining how the European
Structural Funds support were allocated among different development axes in Objective 1
regions. They discovered that, in spite of concentrating development funds on infrastructure
and business support, the results were negligible. The assistance to agriculture brought about a
short-term positive effect which, however, declined soon. Only investment in education and
human capital had medium-term positive and significant returns.
More optimistic about regional and cohesion policies were the studies by Cappelen,
Castellacci, Fagerberg, and Verspagen (2003) and Esposti and Bussoletti (2008). Cappelan et
al. suggested that the EU regional support had a significant and positive impact on the growth
performance of European regions. The authors stated that the major reform of the structural
funds in 1988 may have succeeded in making the EU regional policy more effective. Their
results also indicate that the economic effects of the regional policy are much stronger in more
developed environments, emphasising the importance of accompanying policies to improve
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the competence of the receiving environments. Esposti and Bussoletti also confirmed a
positive impact of the regional policy, in particular the impact of Structural Funds on
Objective 1 across the entire EU. The authors analysed the data for the 206 regions of the EU15 for the period 1989–2000. They estimated the impact of European Structural Funds on
Objective 1 regions, using a growth convergence model. The estimated conditional
convergence model was derived from the underlying neoclassical growth model where
Structural Funds expenditures were included as a determinant of the regional investment rate.
However, the impact of the Objective 1 policy on growth was generally quite limited and
might have become negligible and even negative in some regions. The authors gave the
examples of regions grouped by country, e.g. Germany, Greece and Spain.
Also, we would like to focus on how an appropriate approach to the role of spatial
effects may shed new light on the European convergence process. Ramajo, Márquez, Hewings
and Salinas (2008), adopting a spatial econometric perspective, estimated the rate of
convergence for a sample of 163 regions of the European Union over the period 1981–1996.
They found that the regions in the EU cohesion-fund countries (Ireland, Greece, Portugal and
Spain) converged separately from the remaining EU regions. Their estimations indicated the
faster conditional convergence relative to the income levels in the regions of the Cohesion
countries (5.3%) than in the other EU regions (3.3%).
Other studies on convergence, especially in the enlarged EU after 2004, were done by,
e.g. Smętkowski and Wójcik (2008), Monfort (2009), Petrakos and Artelaris (2009),
Łaźniewska, Górecki and Chmielewski (2011) and Ryszkiewicz (2013). Nevertheless,
previous studies have only rarely dealt with the convergence that occurred during the last
crisis in 2007–2009. However, the Eurostat database on the evolution of the regional GDP per
capita within the EU during and just after this economic crisis enables us to examine this
phenomenon here.
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3. Estimation techniques/analytical framework
This paper employs the Eurostat data which is informative about the regional level. We
analyse the data on the GDP per capita and real GDP in 2000–2010
the NUTS 2 regions
for all of
. The data on the GDP per capita are expressed at current
market prices and purchasing power standards, and the data on the real GDP in purchasing
power standards. The variable t from the period 2000–2010 is 11 years and the variable n is
270 regions. Among the 270 EU regions, there are 84 convergence regions located in 18
countries (see Annex 1). Due to the lack of data, we do not take into account the following
regions: Dresden (DE), Chemnitz (DE), Leipzig (DE), Emilia-Romagna (IT) and Marche (IT).
Most of the studies on regional convergence in GDP per capita apply the concepts of
sigma and beta convergence introduced by Barro and Sala-i-Martin (1991, 1992) (Ezcurra,
Gil, Pascual and Rapu, 2005). Those two concepts of convergence examined interesting
phenomena which are conceptually different: sigma convergence studies focused on the
evolution of income distribution over time and beta convergence studies the mobility of
income within the same distribution (Sala-i-Martin, 1996). Both of them are adopted here.
Sigma convergence occurs when the dispersion of the GDP per capita for a studied
group of territorial units, like countries or regions, decreases over a given period of time. This
means that the standard deviation as a measure of volatility in the level of development
decreases over time, i.e. when
where
is the standard deviation calculated as
follows:
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Accordingly, we arrive at the following vector –
ISBN : 9780974211428
– if the standard
deviation of the natural logarithms of the GDP per capita for all the units in the next years is
calculated according to the formula (1). To show a development trend in the standard
deviation calculated thus, a linear trend equation is formulated:
A decreasing trend of the standard deviation of a natural logarithm of the GDP per
capita can indicate sigma convergence, whereas an increasing trend can indicate divergence.
Convergence can be determined by another method, known as beta convergence1. The
concept of beta convergence is related to the neo-classical growth theory whose one of the
key assumptions is that factors of production are subject to diminishing returns. This growth
process should lead economies to a long-run steady-state characterised by a rate of growth
which depends only on the (exogenous) rates of technological progress and the growth of
labour force. Beta convergence is said to be absolute when all economies are assumed to
converge towards the same steady-state (Monfort, 2009). Absolute beta convergence occurs
when poor economies grow faster than rich ones. If the value of the GDP per capita is
assumed to reflect the level of economic development, countries (or regions) with a lower
GDP per capita should have a faster rate of growth than economies (or regions) with a higher
level of GDP per capita. In the literature, it has often been stressed that a necessary condition
for the existence of sigma convergence is the existence of beta convergence (Sala-i-Martin,
1995).
In order to evaluate whether absolute beta convergence is occurring, the relationship
between the growth rate of the GDP per capita of the studied regions and the level of their
1
The literature distinguishes two types of beta convergence: unconditional (absolute) and conditional. The
calculation in this paper employs absolute convergence only.
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development calculated by the GDP per capita needs to be specified. The mean growth rate of
the GDP per capita in the i-th region between years
and
is specified by the formula (3):
where:
– the logarithm for the mean growth rate of the GDP per capita in the i-th region
between years
and
,
– the GDP per capita in the i-th region in the first year of the examination,
– the GDP per capita in the i-th region in the last year of the examination,
– the first year of the examination,
– the last year of the examination,
– the number of years between the first and the last year of the examination.
The regression equation is applied to evaluate the parameters (Sala-i-Martin, 1995):
,
which enables the verification of the hypothesis about the occurrence of absolute beta
convergence.
Absolute beta convergence will occur if parameter
is a negative number, which
means a higher growth rate of the GDP per capita in regions with a lower GDP per capita in
an initial stage than in regions with a higher GDP per capita in an initial stage.
Additionally, to study the convergence, we use the triple convergence band. The triple
convergence band is the relation between the arithmetic mean
. The ranges of variability –
,
and
and the standard deviation
– can be properly known
as single, double and triple bands. The triple convergence band, which takes into account the
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range of the dispersion of the GDP per capita for almost all of the regions under study, can be
regarded as a good measure of convergence.2 Obviously, bands contract during less volatile
per capita GDP periods and widen during volatile GDP per capita periods. However,
convergence can be better understood if the developing trends of the upper and lower triple
convergence band edges are investigated. Actually, convergence occurs when a convergence
band is contracting. Although an empirical distribution of the GDP per capita has positive
asymmetry, the idea of a convergence band confirms the EU’s development tendencies, which
occur between the mean level of the GDP per capita and its standard deviation. Therefore, this
method can be a useful tool to measure economic convergence.
4. Regional economic convergence across the EU member states
Sigma convergence occurs when a long-term development trend in disparities in an economic
development level is decreasing. This means, in fact, a downward trend in the natural
logarithms of the GDP per capita. The following functions of the trend were achieved for the
said regions in 2000–2010:
for the convergence regions and
for the EU-27,
for the non-convergence regions; see
Figure 1.
2
A two-standard deviation range is known in stock market investigation as the Bollinger band.
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Figure 1. Regional sigma convergence across the EU-27 member states, the convergence regions
and non-convergence regions (GDP per capita at current prices)
Source: self-developed based on the Eurostat data.
This means that economic convergence is occurring in all NUTS 2 regions in the EU-27.
Convergence is also occurring across convergence and non-convergence regions. The
downward trend in the standard deviation of the natural logarithms of the GDP per capita for
all the EU-27 regions (the slope of the trend is -0.0245) is similar to that for convergence
regions (the slope of the trend is -0.0262). Figure 1 also shows the divergence which occurred
in 2008, just during the economic crisis.
Investigations into the regional GDP per capita inequalities and convergence in the
European Union also need to capture disparities in prices and their effects on purchasing
power. Therefore, our next investigation on sigma convergence employs GDP per capita in
purchasing power standards that captures price disparities at the national level. These
disparities chiefly refer to the prices of non-tradable goods which do not converge towards the
common international price level. In particular, the price level in the new EU Central and
Eastern Europe member states is lower than in the EU-15 and, thus, their purchasing power is
higher than the one indicated by GDP per capita measured in the Euro (Niebuhr and Schlitte,
2004) p. 172. Figure 2 illustrates the regional sigma convergence across the EU-27 member
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states as well as the convergence regions and non-convergence regions based on GDP per
capita in purchasing power standards.
Figure 2. Regional sigma convergence across the EU27 member states, convergence regions
and non-convergence regions (GDP in purchasing power standards)
Source: self-developed based on the Eurostat data.
Regarding these price disparities, different results have been achieved. The UE-27 regions
show a significantly low convergence rate, i.e. 1% compared to a nearly 2,45% rate of GDP
per capita at current prices. The same applies to the convergence within the convergence
regions although the convergence rate remains there at a higher rate of 1,1%.
Beta convergence is the second method we employ to examine convergence. Beta
convergence occurs when regions with a low level of development in a base year can achieve
a higher GDP per capita growth rate in the next years than territorial units with higher levels
of development. There are at least three reasons for such convergence. Firstly, the
development of any economy tends to converge with the path to sustainable growth, known as
steady-state, so poorer economies can be expected to approach richer ones. Secondly, the rate
of growth on capital is slower in economies with higher capital per worker, which can
stimulate the capital flow from rich to poor economies and thus favour convergence among
countries. Finally, the spread of knowledge and technology is delayed, which generates
income discrepancies because there will always be countries that still do not apply the best
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solutions. These discrepancies may disappear as soon as poorer countries gain access to up-todate knowledge and technology (Romer, 2000). Consequently, there are certain phenomena
typical of the process of imitation, which are well known in the literature.
The research into the correlation between the mean GDP per capita in purchasing
power standards growth rate in 2000–2010 and the GDP per capita in 2000 for all the EU-27
regions has confirmed that beta convergence is occurring in the European Union (Figure 3).
 - triangles represent the convergence regions,  - crosses represent non-convergence regions (developed
regions)
Figure 3. Regional beta convergence across the EU-27 at the NUTS 2 level
Source: self-developed based on the Eurostat data.
Actually, the underdeveloped convergence regions achieved a much higher growth
rate over the period under study than the other developed EU regions. In the long run, this
situation must result in balancing the levels of development of all the regions.
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Our third examination of economic convergence is based on the triple convergence
band. The clearly contracting convergence band for all the EU-27 regions also indicates the
ongoing process of convergence. The smallest width was reported for all the EU-27 in 2008.
One can also notice that the lower triple band edge for all the EU-27 regions tends to increase,
whereas the upper one tends to decrease (Figure 4).
Figure 4. Triple convergence bands for the EU-27 regions
Source: self-developed based on the Eurostat data.
The decreasing trend of the upper triple band edge for the EU-27 can indicate that the EU’s
economy growth is slowing. Simultaneously, the increasing trend of the lower band edge
indicates that convergence is progressing due to the accelerated development of weaker
regions. It should be emphasised that the upper band edge represents the regions with the
highest level of development such as Luxembourg (LU) and Inner London (UK), whereas the
lower band edge, the weakest regions among the convergence regions such as Severozapaden
(BG), Severen tsentralen (BG), Severoiztochen (BG) Yugoiztochen (BG), Yugozapaden
(BG), Yuzhen tsentralen (BG), Nord-Vest (RO), Centru (RO), Nord-Est (RO), Sud-Est (RO),
Sud – Muntenia (RO), Sud-Vest Oltenia (RO) and Vest (RO).
5. Convergence and economic crises
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The fluctuations in the convergence regions GDP growth rate examined over the period 20002010 are increasingly converging with the fluctuations in the GDP growth rate within the nonconvergence regions. These fluctuations, however, differed greatly in the early 1990s. Those
differences were mainly due to the economic transformation across the Central and Eastern
European EU member states. Figure 5 depicts the regional chain-weight GDP growth rate for
the convergence regions, the EU-27 regions and non-convergence regions in 2000-2010.
Figure 5. Regional chain-weight GDP growth rate for the convergence regions, the EU-27 regions
and the non-convergence regions
Source: self-developed based on the Eurostat data.
As seen, the regional GDP growth rate in the convergence regions is higher than in the EU-27
and logically, in the non-convergence regions. Accordingly, the development disparities in the
EU are undoubtedly reduced, which triggers economic convergence. It should be pointed out
that the chain-weight GDP growth volatility (until the previous year) is decreasing for both
groups of regions (convergence and non-convergence regions) mainly due to the 2000
economic downturn and the much stronger crisis that began in 2007. Actually, the economic
crisis of 2007–2009, as confirmed by research, affected the convergence regions to a lesser
extent, which must have been obviously reflected in the way in which convergence was
proceeding. These figures confirm the fact that the process of convergence in the European
Union during the crisis resulted from both the regional GDP growth in the convergence
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regions and slower growth in the remaining regions. A high growth rate mainly in poor
regions can reduce development disparities, or economic convergence increases. Our study on
sigma convergence also confirms the impact of the crisis 2007-2009 on convergence. In the
years of the crisis, the convergence trend clearly collapsed (see Figure 1 and 2).
6. Conclusions
The attempt to answer the questions formulated and to verify the hypothesis posed here can
conclude that convergence is obviously occurring across both the EU-27 regions and the
convergence regions. The regional GDP growth rate among the convergence regions is higher
than in the entire EU-27. Undoubtedly, this phenomenon reduces growth disparities within the
EU. This study also confirms the hypothesis that beta convergence is occurring within the
EU-27 if regions with poor growth in a base year have a higher per capita GDP growth rate in
the next years than more developed regions. In the period 2000–2010, the underdeveloped
convergence regions achieved a significantly higher growth rate than the other strong EU
regions. As a result, the level of development of all the regions is balanced in the long run.
Economic convergence is also confirmed by the examination based on the concept of a triple
convergence band. The clearly contracting band for all of the EU-27 regions indicates the
ongoing process of convergence. The decreasing trend of the upper triple band edge for the
EU-27 indicates that the EU’s economic growth is slower. Furthermore, this phenomenon
means that the convergence progresses are due to accelerated development in poorer regions.
Thus, the results of this examination show that the process of convergence within the EU-27
during economic crises, especially in 2007–2009, occurred as a result of the faster GDP
growth within the convergence regions and the slower growth rate within the other regions.
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Annex 1. Convergence regions
Country
Number of regions
convergence
NUTS 2
region
Bulgaria
6
6
Czech Republic
8
7
Germany
38
6
Estonia
1
1
Greece
13
8
Spain
France
Italy
Latvia
Lithuania
19
26
21
1
1
4
4
4
1
1
Hungary
7
6
Malta
1
1
Poland
16
16
Portugal
7
4
Romania
8
8
Slovenia
2
2
Slovakia
4
3
United Kingdom
37
2
Convergence regions
Severozapaden, Severen tsentralen, Severoiztochen,
Yugoiztochen, Yugozapaden, Yuzhen tsentralen
Strední Cechy, Jihozápad, Severozápad, Severovýchod,
Jihovýchod, Strední Morava, Moravskoslezsko
Brandenburg, Mecklenburg-Vorpommern, Dresden,
Chemnitz, Leipzig, Thüringen
Estonia
Anatoliki Makedonia, Thraki, Thessalia, Ipeiros, Ionia
Nisia, Dytiki Ellada, Peloponnisos, Voreio Aigaio, Kriti
Galicia, Castilla-la Mancha, Extremadura, Andalucía
Guadeloupe, Martinique, Guyane, Réunion
Campania, Puglia, Calabria, Sicilia
Latvia
Lithuania
Közép-Dunántúl,
Nyugat-Dunántúl,
Dél-Dunántúl,
Észak-Magyarország, Észak-Alföld, Dél-Alföld
Malta
Łódzkie, Mazowieckie, Małopolskie, Śląskie, Lubelskie,
Podkarpackie, Świętokrzyskie, Podlaskie, Wielkopolskie,
Zachodniopomorskie, Lubuskie, Dolnośląskie, Opolskie,
Kujawsko-Pomorskie, Warmińsko-Mazurskie, Pomorskie
Norte, Centro, Alentejo, Região Autónoma dos Açores
Nord-Vest, Centru, Nord-Est, Sud-Est, Sud – Muntenia,
Bucuresti – Ilfov, Sud-Vest Oltenia, Vest
Vzhodna Slovenija, Zahodna Slovenija
Západné Slovensko, Stredné Slovensko, Východné
Slovensko
Cornwall and Isles of Scilly, West Wales and The
Valleys
Data source: Eurostat.
July 1-2, 2014
Cambridge, UK
18