Bulgaria in the EU Cohesion Process

Abstract

Cohesion is a precondition for implementing a number of important
EU internal and external policies, such as functioning of the single market, the Eurozone, Common commercial policy, Environmental policy, etc. Therefore, achieving stronger cohesion is one of the main tasks of the European institutions. But in order to assess the development of the EU cohesion process and thereof the effectiveness of the ongoing cohesion policy, it is necessary to introduce and assess the results of certain cohesion indicators. The article includes nine such indicators: GDP per capita; Research and development expenditure as percentage of GDP; High-tech exports as percentage of total exports; People at risk of poverty or social exclusion; the Gini Coefficient; Life expectancy at birth; Density of motorway network; Share of trains in total inland passenger transport; Population connected to wastewater collection and treatment system. By using the Mean Absolute Deviation (MAD), the study establishes that in the decade of 2004-2014 there was enhanced cohesion in the EU in 8 out of the 9 indicators used. Based on comparison between
Bulgaria’s individual results and those of the EU as a whole, it concludes that Bulgaria has not yet been able to get fully included in the cohesion process: 7 out of the total 9 cohesion indicators
are lower than the average for the EU indicators.

Full text

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Bulgaria in the EU Cohesion Process
Dimitar Hadjinikolov*
Summary:
Cohesion is a precondition for
implementing a number of important
EU internal and external policies, such
as functioning of the single market, the
Eurozone, Common commercial policy,
Environmental policy, etc. Therefore,
achieving stronger cohesion is one
of the main tasks of the European
institutions. But in order to assess the
development of the EU cohesion process
and thereof the effectiveness of the
ongoing cohesion policy, it is necessary
to introduce and assess the results of
certain cohesion indicators. The article
includes nine such indicators: GDP
per capita; Research and development
expenditure as percentage of GDP;
High-tech exports as percentage of
total exports; People at risk of poverty
or social exclusion; the Gini Coefficient;
Life expectancy at birth; Density of
motorway network; Share of trains in total
inland passenger transport; Population
connected to wastewater collection and
treatment system. By using the Mean
Absolute Deviation (MAD), the study
establishes that in the decade of 2004-
2014 there was enhanced cohesion
in the EU in 8 out of the 9 indicators
used. Based on comparison between
Bulgaria’s individual results and those
of the EU as a whole, it concludes that
Bulgaria has not yet been able to get
fully included in the cohesion process:
7 out of the total 9 cohesion indicators
are lower than the average for the EU
indicators.
Key words: cohesion policy, cohesion
indicators, European Union, Bulgaria
JEL Classification: F15, F36, F42, H77
1. Introduction
The EU cohesion is realized in
three different dimensions. They
are mentioned in Article 3 of the Treaty
on European Union where we can read
that the union "…shall promote economic,
social and territorial cohesion, and
solidarity among Member States."1 The
indicators used in this article address all
three of the above-mentioned cohesion
dimensions.
The importance of cohesion in order
to pursue the EU policies can be seen in
table 1.
Bearing in mind the great significance
of cohesion for the implementation of the
EU policies, the author attempts in this
article to measure cohesion in the EU
on national level and also to measure
* Dimitar Hadjinikolov DSc, PhD, is a professor at University of National and World Economy (UNWE), Deputy Head of
International Economic Relations and Business Department, Chairman of Bulgarian European Studies Association (BESA),
www.hadjinikolov.pro; e-mail: dimitar_h@abv.bg
1 European Commission (2012). Consolidated Version of the Treaty on European Union, Official Journal of the European Union,
26.10.2012, C326/17, 26.10.2012.
Economic Alternatives, 2017, Issue 2, pp. 213-225

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Bulgaria’s progress in the EU cohesion
process.3
2. Review of Literature
Concerning the progress toward the
achievement of the Europa 2020 Strategic
Goals Rappai (2015) states that although
Eurostat annually publishes the values of a
number of indicators on both national and
cross-national levels, not many studies have
been conducted on the methodology of how
the progress can be quantified4. Therefore,
he decides to construct a quite sophisticated
complex index to measure the progress both
on national and on regional level. Although
the complex Rappai index contributes
Cohesion type Impacts Affected EU policies
Economic
Lower costs to comply with uniform
standards and minimum safety
requirements
Single market, Environment policy,
Competition policy, Common agricultural
policy, Common transport policy
Greater convergence of the
economic cycle Eurozone2
Greater similarity in export
specialization
Customs union, Common commercial policy,
Development policy
Better energy efficiency
Common energy policy, Climate change
policy, Environment policy, Common foreign
policy, Development policy
Social
Convergence of national social
models and gradual establishment
of a single EU social model
Social policy, Education policy, Health care
policy, Fiscal policy, Eurozone
Bridging the gap between Western
and Eastern Europe
Common foreign policy, Common security
and defense policy, Neighborhood policy,
Development policy, Single area of freedom,
security and justice
Territorial
Lower logistic and transport costs Single market, Tourism, Customs union,
Common commercial policy
Lower costs for transmission of
electricity and natural gas
Common energy policy, Climate change
policy, Common foreign policy, Neighborhood
policy
Better internet and communications Single market, Single information area,
Education policy, Innovation policy
Lower investment costs Industrial policy, Single market, Fiscal policy,
Eurozone, Innovation policy
Table 1. Cohesion impact on different EU policies
2 See Baldwin, R., Wyplosz, Ch. (2009) The Economics of European Integration, 3rd Edition, London: McGraw-Hill, pp.
326-331 about the relationship between homogeneity (strong cohesion) of the Eurozone and the capabilities to pursue
single monetary policy.
3 It is important to stress that cohesion is recognized as a significant factor for growth and sustainability not only on regional,
national and supranational level (the EU), but also on global level – see: OECD. (2011). Perspectives on Global Development
2012: Social Cohesion in a Shifting World. OECD, Paris, p. 17.
4 Rappai, G. (2015). Europe En Route to 2020: A New Way of Evaluating the Overall Fulfillment of the Europe 2020 Strategic
Goals, Social Indicators Research, 129(1), pp. 77-93.

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towards assessing the implementation
results of the EU 2020 Strategic Goals,
the cohesion (approximation) among the
Member States is in fact not measured.
Pasimeni (2013)5 makes a successful
attempt to decrease the negative impact of
heterogeneity which exists with regard to the
indicators under examination and creates
three complex indexes for each of the
examined spheres of activity in the Europa
2020 strategy. These indexes he indicates as:
Smart Growth Index, the Sustainable Growth
Index and the Inclusive Growth Index. By
calculating the geometric average of these
three indices he constructs the so-called
Europe 2020 Index. The Pasimeni index has
the same purpose as the index of Rappai,
showing the progress towards the objectives
of the Europa 2020 strategy, but not the rate of
approximation of regions or Member States.
Nevertheless, we can state that the Smart
Growth Index shows to a certain extent the
progress in the field of economic cohesion,
the Sustainable Growth Index in territorial
cohesion and Inclusive Growth Index in social
cohesion. But this does not take place at
national level, only at regional level.
Çolak and Ege (2013) put emphasis
on measuring the differences in achieving
the objectives of "Europa 2020 strategy".
That is why this model also gives some
information with regard to the development
of the cohesion process in the EU, although
it does not affect some of its significant
dimensions. Moreover, the analysis of these
authors is also only at regional level.6 Bal-
Domańska, Sobczak (2016), analyse the
relationship between the implementation of
the smart growth indicators in the Europa
2020 strategy and the growth in the GDP
per capita of the population in the regions
receiving assistance.7 Mohl (2016) makes
extensive research on the macroeconomic
effects of the EU Cohesion policy. His
attention is focused on measuring the
effectiveness of the policy8. His findings
indicate that EU Cohesion Policy has some
positive impact on economic growth in the
poorest regions but not evidence can be
given that EU funds significantly increase
public investment, which is a very important
precondition for sustainable growth.
On behalf of the European commission
Jerzy Pieńkowski and Peter Berkowitz
(2015) analyse most of the existing
models on measuring the impact of the
cohesion policy (a total of 22 models). All
these models, however, are on regional
level. After identifying the progress
made in analytical methods, they rightly
noted some shortcomings that should
be addressed in future. These include
the limited scope of the studied regions
which are found mainly in the old Member
States; lack of analyses that show the
effects of individual Member States, and
in particular for newly Member States,
which receive the bulk of the resources
of cohesion funds. In addition, the models
analyzed by them are concentrated on
the impact of cohesion measures on
increasing the GDP per capita, which is
not the only indication for the availability
or lack of cohesion. As a substantial
disadvantage, the two authors point out
the use of "very technical language" which
hinders interpretation and use of models
in taking political decisions.9
5 Pasimeni, P. (2013). The Europe 2020 index. Social Indicators Research, 110(3), pp. 613–635.
6 Çolak, M. S., & Ege, A. (2013). An assessment of EU 2020 strategy: Too far to reach? Social Indicators Research, 110(3),
pp. 659–680.
7 Bal-Domańska, B., Sobczak, E., (2016), On the Relationships between Smart Growth and Cohesion Indicators in the EU
Countries. Statistics in Transition, Vol. 17, No. 2, Wrozlaw, pp. 249-264.
8 Mohl, Ph. (2016) Empirical Evidence on the Macroeconomic Effects of EU Cohesion Policy, Springer Gabler, pp. 12-16.
9 Pieńkowski, J., Berkowitz, P. Econometric assessments of Cohesion Policy growth effects: How to make them more relevant
for policy makers?, Regional Working Paper 2015, European Commission, WP 02/2015, Brussels, 2015, p. 12

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The only econometric model which uses
the EU Cohesion policy on national level is
the model Tomova, M., et al. (2013). But it
has a limited task "to test empirically the link
between the soundness of national fiscal
and economic policies and the achievement
of the European Union objectives regarding
socio-economic development"10. To this
intent an index was constructed based on
several indicators on infrastructure, health,
education, employment opportunities,
environmental sustainability and welfare.
Then the authors compared the volume
of the cohesion expenditure and progress
with regard to their constructed index. This
approach indicates the effectiveness of
the used funds, but does not analyze the
cohesion state in itself (convergence or
divergence).
3. Methodology
The first step is to select the most suitable
indicators for measuring the EU cohesion. It
is important to bear in mind that for this study
the cohesion indicators have to be measured
at national and not at regional level and
have to include the three dimensions of EU
cohesion - economic, social and territorial.
Then based on these selected cohesion
indicators, we have to establish how far the
EU has gone into the cohesion process and
what the dynamics in this process has been
over the last decade. The third step is to
compare the development of the cohesion
process as a median value for the EU and of
Bulgaria, as an individual achievement.
The most synthesized cohesion indicator
at national level is without doubt the GDP
per capita indicator. The more similar the
results of different Member States are,
the stronger the cohesion is, and vice
versa, the greater the deviations are from
the average, the weaker the cohesion is.
That is why we can use the mean average
deviation formula.
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࢔෍ሾ࢞࢏
࢔
࢏ୀ૚െࣆሿ
where: n = 28 (the number of EU Member States),
хi is the GDP per capita in the member state i, while is
the mean size of GDP per capita in the EU.
The GDP per capita indicator is very
important, however, it is not sufficient to
measure the cohesion and we have to go in
details in order to find out different factors
that determine the state of the cohesion.
Eurostat uses 26 so-called cohesion
indicators, grouped in 4 groups: Smart growth,
Sustainable growth, Inclusive growth and
Context.11 These indicators, however, cannot
be directly applied in the current research,
because on the one hand, they are too many
in number and there is no sufficient updated
statistical data on all of them. On the other
hand, these indicators are at regional and not
at national level. Moreover, these Eurostat
indicators are selected in such a way as to
be in compliance with the objectives of the
Europe 2020 strategy12. Thus they serve
primarily to assess the achievements in
implementing this strategy which do not fully
coincide with the achievements of the three
dimensions of the EU cohesion – economic,
social and territorial. That is why for the
purpose of this study are used only 4 of the
indicated indicators of Eurostat,13 and the
remainder is selected by the author.
10 Tomova, M. et al., (2013) EU Governance and EU Funds – Testing the Effectiveness of EU Funds in a Sound Macroeconomic
Framework, European Commission, DG ECFIN, European Economy, Economic Papers No 510, 2013, Brussels, p. 7.
11 See: Eurostat (2016) Cohesion Indicators, http://ec.europa.eu/eurostat/web/cohesion-policy-indicators/cohesion-indicators.
12 European Commission, (2010) Europa 2020: A Strategy for Smart, Sustainable and Inclusive Growth, COM(2010) 2020 final,
Brussels.
13 These are: “Research and development expenditure as % of GDP”; People at risk of poverty or social exclusion (Percentage
of total population); “Life expectancy at birth” and “Population connected to wastewater collection and treatment system”.

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In the field of economic cohesion, we
can, for example, successfully use as
indicators: High-tech exports as % of total
exports and Research and development
expenditure as % of GDP. Both indicators
reflect well the structure of the respective
economy and the more a given member
state is closer to the average EU indicators,
the closer it is to the average structure
of the EU economy. Correspondingly, the
sum of the differences is an indicator of
the state of the economic cohesion. The
smaller this sum is (MAD), the greater the
economic cohesion is.
With regard to social cohesion, both the
distribution of the gross domestic product
and the provision of the population with
basic services are of importance. A key
aspect in social cohesion is the social
homogeneity within a community i.e. how
fair the distribution of income is and what
the conditions are in order to include all
segments of society in social life.14 To
this end, three social indicators can be
used: Gini coefficient15, People at risk of
poverty or social exclusion (Percentage
of total population)16 and Life expectancy
at birth17 As is the case in measuring
economic cohesion, social cohesion as
well is reversely proportional to MAD, i.e.
the greater the total deviation is, the less
the cohesion is and vice versa, the more
the deviation decreases, the greater the
cohesion is.
With regard to territorial cohesion,
applying the same method, we can
use the following indicators: Density
of motorway network (km per 1000 sq.
km per area); Modal split of passenger
transport - percentage share of trains
in % in total inland passenger-km and
Population connected to wastewater
collection and treatment system. The
latter indicator characterizes well the rate
of similarity (or the extent of differences)
in the environmental infrastructure and
respectively has strong relevance towards
both the territorial and social cohesion.
After we have examined the changes in
EU cohesion as a whole, we shall compare
the average results of all Member States and
those of Bulgaria. Thus we will be able to make
some conclusions and recommendations
affecting not only some common phenomena
in the EU, but also Bulgaria’s specific place in
the cohesion process.
4. Findings
Firstly, we will examine the synthesized
cohesion indicator of the GDP per capita
and establish MAD (in percentage points) in
2004 and in 2015, i.e. at the moment of the
Eastern enlargement of the EU and eleven
years later.
As we can see in table 2, in 2004,
MAD was 33.1 – indicating a rather weak
cohesion. Ten years later in 2015, we have
a result of 26.4, indicating an increase in
cohesion.
14 The concept “social cohesion” emerged in the 20th century. It is a force that unites (keeps together) the social groups
in society, regardless of ethnic, racial or gender differences. See: Stanley, D., (2003), What Do We Know about Social
Cohesion: The Research Perspective of the Federal Government’s Social Cohesion Research Network. The Canadian Journal
of Sociology, Vol. 28, No. 1, Special Issue on Social Cohesion in Canada (Winter, 2003), Montréal, pp. 5-17 and Chan. J.,
Chan. E., To, H.-P. (2006). Reconsidering Social Cohesion, Developing a Definition and Analytical Framework for Empirical
Research. Social Indicators Research, Vol. 75, No.2 273-302.
15 The Gini coefficient is defined as the relationship of cumulative shares of the population arranged according to the level of
equalized disposable income, to the cumulative share of the equalized total disposable income received by them. For more
information see: http://ec.europa.eu/eurostat/.
16 This indicator corresponds to the sum of persons who are: at risk of poverty or severely materially deprived or living in
households with very low work intensity. For more information see: http://ec.europa.eu/eurostat/.
17 See also http://ec.europa.eu/eurostat/.

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 2004 2015
 Xi Xi - μ [Xi - μ] Xi Xi - μ [Xi - μ]
EU 100.0   100  
Belgium 12 1. 0 2 1. 0 2 1. 0 119.0 19.0 19.0
Bulgaria 34.0 -66.0 66.0 47.0 -53.0 53.0
Czech Republic 78.0 -22.0 22.0 87. 0 -13.0 13.0
Denmark 124.0 24.0 24.0 127.0 27.0 27.0
Germany 120.0 20.0 20.0 124.0 24.0 24.0
Estonia 54.0 -46.0 46.0 75.0 -25.0 25.0
Ireland 145.0 45.0 45.0 137.0 37.0 -37.0
Greece 96.0 -4.0 4.0 68.0 -32.0 32.0
Spain 98.0 -2.0 -2.0 90.0 -10.0 10.0
France 110.0 10.0 10.0 106.0 6.0 6.0
Croatia 55.0 -45.0 45.0 58.0 -42.0 42.0
Italy 110.0 10.0 10.0 96.0 -4.0 4.0
Cyprus 97. 0 -3.0 3.0 82.0 -18.0 18.0
Latvia 46.0 -54.0 54.0 64.0 -36.0 36.0
Lithuania 49.0 -51.0 5 1. 0 75.0 -25.0 25.0
Luxembourg 238.0 138.0 138.0 264.0 164.0 164.0
Hungary 61. 0 -39.0 39.0 68.0 -32.0 32.0
Malta 80.0 -20.0 20.0 88.0 -12.0 12.0
Netherlands 133.0 33.0 33.0 128.0 28.0 28.0
Austria 126.0 26.0 26.0 128.0 28.0 28.0
Poland 50.0 -50.0 50.0 69.0 - 31. 0 3 1. 0
Portugal 8 1. 0 -19.0 19.0 7 7. 0 -23.0 23.0
Romania 34.0 -66.0 66.0 57. 0 -43,0 43,0
Slovenia 88,0 -12,0 12,0 83,0 -17,0 17,0
Slovakia 57,0 -43,0 43,0 77,0 -23,0 23,0
Finland 117,0 17,0 17,0 109.0 9.0 9.0
Sweden 126.0 26.0 26.0 124.0 24.0 24.0
UK 119.0 19.0 19.0 108.0 8.0 8.0
  MAD 2004 33.1 MAD 2015 26.4
Table 2. Deviation in GDP per capita (as % of EU average)
Note: * хi is the GDP per capita in the member state i in percent of the average GDP
** xi – μ is the deviation of the individual result to the mean size of the GDP per capita in the EU (μ)
*** [xi - μ] is the absolute size of the deviation.
Source: Estimated by the author on figures from Eurostat

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Additional information about the pace
of convergence of GDP per capita in
Bulgaria and the EU average is given in
the figure below:
An indicator which reflects well the
structure of the economy in the different
Member States and is useful in determining
the economic cohesion is R&D expenditure
as % of GDP. In this indicator, the dynamics
of MAD is shown in table 3.
With regard to the High-tech exports
as % of total exports indicator the results
obtained in the same way as those indicated
in the above table are as follows. In 2007,
MAD was equal to 51.0 percentage points,
and in 2014 it was 38.8 points.
In social cohesion, the following results were
established: With regard to the People at risk
of poverty or social exclusion as % of GDP
indicator: Mean Absolute Deviation (MAD) in
2007 = 28.4 percentage points and in 2014 was
equal to 23.7 percentage points. This shows
that in this important indicator for measuring EU
social cohesion, deviations have gone down, i.e.
we have increase in cohesion. At the same time,
it can be pointed out that during that period in
17 of a total of 27 Member States we have a
growth in the share of people at risk of poverty
or social exclusion as % of GDP and only in
10 a decrease of this share, which shows that
the problem of poverty has not been resolved.
Progress is mainly due to the decrease in the
index of most of the New Member States:
Bulgaria, Romania, Poland, Slovakia, etc.
As regards the Gini Coefficient, the
situation is improving. While in 2007 MAD
was equal to 12.7 percentage points, in
2014, it went down to 11.1 percentage points.
This speaks about a certain improvement of
social cohesion in the EU and also in terms
of disparities in income distribution within
the different Member States.
With the third indicator of the level of social
cohesion – Life expectancy at birth (total for
the whole population), the situation again is
similar to the results obtained for the other two
Fig. 1 Dynamics of GDP per capita in Bulgaria as percentage of EU average (EU = 100)
Source: Estimated by the author on figures from Eurostat
34 37 38 41 43 44 45 45 46 46 46 47
0
10
20
30
40
50
60
70
80
90
100
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

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 2004 2014
 xi* xi – μ** [xi - μ] xi* xi – μ** [xi - μ]
EU (28) 1.76   2.03   
Belgium 1. 81 102.8 2.8 2.8 2.46 12 1. 2 2 1. 2 2 1. 2
Bulgaria 0.47 26.7 -73.3 73.3 0.80 39.4 -60.6 60.6
Czech Republic 1. 15 65.3 -34.7 34.7 2.00 98.5 -1. 5 1. 5
Denmark 2.42 137.5 37.5 37.5 3.05 150.2 50.2 50.2
Germany 2.42 137.5 37.5 37.5 3.05 150.2 50.2 50.2
Estonia 0.85 48.3 -51.7 5 1. 7 2.87 1 41. 4 4 1. 4 4 1. 4
Ireland 1. 18 6 7. 0 -33.0 33.0 1.52 74.9 -25.1 25.1
Greece 0.53 30.1 -69.9 69.9 0.84 41. 4 -58.6 58.6
Spain 1.04 59.1 -40.9 40.9 1.23 60.6 -39.4 39.4
France 2.09 118.8 18.8 18.8 2.26 111.3 11.3 11.3
Croatia 1.03 58.5 -41.5 4 1. 5 0.79 38.9 -61.1 6 1. 1
Italy 1.05 59.7 -40.3 40.3 1.29 63.5 -36.5 36.5
Cyprus 0.34 19.3 -80.7 80.7 0.48 23.6 -76.4 76.4
Latvia 0.40 22.7 -7 7. 3 77. 3 0.69 34.0 -66.0 66.0
Lithuania 0.75 42.6 -57.4 57. 4 1.01 49.8 -50.2 50.2
Luxembourg 1.62 92.0 -8.0 8.0 1.26 62.1 -37.9 37.9
Hungary 0.86 48.9 -51.1 5 1. 1 1.37 6 7. 5 -32.5 32.5
Malta 0.49 27.8 -72.2 72.2 0.83 40.9 -59.1 59.1
Netherlands 1. 81 102.8 2.8 2.8 1. 9 7 97. 0 -3.0 3.0
Austria 2.17 123.3 23.3 23.3 2.99 147.3 47.3 47.3
Poland 0.56 31. 8 -68.2 68.2 0.94 46.3 -53.7 53.7
Portugal 0.73 4 1. 5 -58.5 58.5 1.29 63.5 -36.5 36.5
Romania 0.38 2 1. 6 -78.4 78.4 0.38 18.7 -81.3 8 1. 3
Slovenia 1.37 7 7. 8 -22.2 22.2 2.39 117.7 17. 7 17. 7
Slovakia 0.50 28.4 -7 1. 6 7 1. 6 0.89 43.8 -56.2 56.2
Finland 3.31 188.1 88.1 88.1 3.17 156.2 56.2 56.2
Sweden 3.39 192.6 92.6 92.6 3.16 155.7 55.7 55.7
UK 1. 61 91.5 -8.5 8.5 1. 7 0 83.7 -1 6 . 3 16.3
MAD 2004 47.3 MAD 2014 42.4
Table 3. Deviation by Member States in research and development expenditure as % of GDP (compared
to EU average)
Note: * хi is the GDP per capita in the member state i in percent of the average GDP
** xi – μ is the deviation of the individual result to the mean size of the GDP per capita in the EU (μ)
*** [xi - μ] is the absolute size of the deviation.
Source: Estimated by the author on figures from Eurostat

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indicators of this group: MAD decreases from
3.3 percentage points in 2007 to 2.8 in 2014.
As a whole, it can be said that with regard
to this indicator there has been the greatest
approximation of Member States’ results which
shows that medical and related to them social
services are at a high level, typical of the
developed economies. Nevertheless, some
substantial differences have remained been the
economically developed countries and the less
developed ones. Differences in life expectancy
between Bulgaria and Romania, on the one
hand, and counties such as Spain, Italy, and
Finland remain as much as 8-9 years.18
In territorial cohesion, the situation is as
follows: Based on Eurostat data and other
sources19 it can be seen that with regard to
the indicator Density of motorway network
(km per 1000 sq. km per area), there has
been a decrease in disparities, with MAD for
this indicator being 94.6 points in 2014, and
falling to 75.1 points in 2014.
As for the Share (%) of railway transport
(trains) in total inland passenger transport
(passenger-km) indicator, the situation
however is different: in 2004 MAD was 37.1
percentage points, and ten years later, in 2014
it was 38.0 points. This slight increase in MAD
shows a certain decrease in territorial cohesion
for this indicator. If we look deeper at the
results (see table 4) we will find that disparities
could have been greater if there had not been
a substantial decrease of the share of railway
transport for passengers in some of the
Member States that lag behind economically,
such as Poland, Romania, Hungary and the
Baltic republics. This negative trend in the
mentioned Member States neutralizes the
rise in the share of the more advanced ones
such as the Scandinavian Member States,
Austria, and the UK. The opposing trends in
these groups of countries to a certain extent
neutralize themselves and are due to the
increasing technological gap between the
railway transport in the developed part of the
EU and the part that lags behind economically.
That is why, it can be expected that when this
compensatory moment disappears and if
the technological disparities remain, MAD in
the field of the railway transport will start to
increase even more.
18 http://ec.europa.eu/eurostat
19 http://www.nationmaster.com/country-info/stats/Transport/Road/Motorway-length
Table 4. Deviation by Member States in share (%) of railway transport (trains) in total inland passenger
transport (passenger-km) (compared to EU average)
2004 2014
xi* xi – μ** [xi - μ] xi* xi – μ** [xi - μ]
EU (26) 6.8 7. 6
Belgium 7. 1 104.4 4.4 4.4 7.7 101.3 1. 3 1. 3
Bulgaria 5.1 75.0 -25.0 25.0 2.5 32.9 -67.1 6 7. 1
Czech Republic 7. 5 110.3 10.3 10.3 8.4 110.5 10.5 10.5
Denmark 9.3 136.8 36.8 36.8 10.1 132.9 32.9 32.9
Germany 7. 5 110.3 10.3 10.3 8.5 111.8 11.8 11.8
Estonia 1. 8 26.5 -73.5 73.5 1. 9 25.0 -75.0 75.0
Ireland 3.0 44.1 -55.9 55.9 2.9 38.2 -61.8 6 1. 8
Greece 1. 6 23.5 -76.5 76.5 0.9 11.8 -88.2 88.2
Spain 5.0 73.5 -26.5 26.5 6.5 85.5 -14.5 14.5
France 8.7 127.9 27.9 27.9 9.3 122.4 22.4 22.4

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5. Conclusions and recommendations
Overall EU cohesion is increasing. This
can be seen from data on deviation in GDP
per capita presented in table 2. This result
is confirmed by the results in table 5, where
cohesion is examined in its three dimensions
– economic, social and territorial. The EU
Cohesion process has developed despite
the crisis in 2008-2009. As we can see in
table 5, in all selected cohesion indicators,
except Share of trains in total inland
passenger transport, there is decrease in
MAD, which comes to suggest that there is
increase in cohesion.20 Nevertheless, the
differences between the Member States
As a third indicator, characterizing
similarities (disparities) in the infrastructure
we can use the Population connected to
wastewater collection and treatment system
indicator. This indicator characterizes well
the degree of similarity (or disparities) in the
ecological infrastructure and it also affects
social cohesion. The table below shows that
in 2007, MAD was 49.7 percentage points
and went down to 33.3 points in 2014.
Generalized dynamics of MAD by individual
cohesion indicators is shown in table 5.
Comparison between the average
indicators for the EU (MAD) and the
individual cohesion indicators for Bulgaria
by the above-mentioned indicators is shown
in table 6.
Note: * хi is the GDP per capita in the member state i in percent of the average GDP
** xi – μ is the deviation of the individual result to the mean size of the GDP per capita in the EU (μ)
*** [xi - μ] is the absolute size of the deviation.
Source: Estimated by the author on figures from Eurostat
Croatia 4.2 61. 8 -38.2 38.2 3.0 39.5 -60.5 60.5
Italy 5.5 80.9 -19.1 19.1 6.3 82.9 -17. 1 17. 1
Latvia 5.4 79.4 -20.6 20.6 4.1 53.9 -46.1 46.1
Lithuania 1. 5 22.1 -77. 9 7 7. 9 1. 0 13.2 -86.8 86.8
Luxembourg 3.6 52.9 -47.1 47.1 4.3 56.6 -43.4 43.4
Hungary 13.4 1 97. 1 9 7. 1 97. 1 9.9 130.3 30.3 30.3
Netherlands 8.4 123.5 23.5 23.5 9.7 127.6 27.6 27.6
Austria 9.4 138.2 38.2 38.2 12.1 159.2 59.2 59.2
Poland 8.5 125.0 25.0 25.0 5.8 76.3 -23.7 23.7
Portugal 3.8 55.9 -44.1 44.1 4.2 55.3 -44.7 44.7
Romania 11.4 1 6 7. 6 6 7. 6 67. 6 4.8 63.2 -36.8 36.8
Slovenia 2.7 39.7 -60.3 60.3 2.1 27.6 -72.4 72.4
Slovakia 6.0 88.2 -11.8 11.8 7. 3 96.1 -3.9 3.9
Finland 4.7 69.1 -30.9 30.9 5.0 65.8 -34.2 34.2
Sweden 7. 5 110.3 10.3 10.3 8.9 117.1 17. 1 17. 1
UK 5.7 83.8 -1 6 . 2 16.2 8.5 111.8 11.8 11.8
MAD 2004 37.1 MAD 2014 38.0
20 TThese data do not correspond to the rather pessimistic picture, presented in the analysis of Stratfor (Stratfor (2015). The
Controversial EU Cohesion Policy Falls Short, https://www.stratfor.com/analysis/controversial-eu-cohesion-policy-falls-short

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remain substantial. For example, if we
look at the differences in GDP per capita,
although MAD has decreased from 33.1 to
26.4, in the case of Bulgaria, extrapolating
the 2004-2015 trend of approximation of
about 3% per year, we can estimate that the
country will need 53 years more to catch with
the EU average. The recommendation could
be – to preserve, and if possible to increase
the priority assistance of the EU Member
States that are economically lagging behind
the EU average, by using cohesion financial
instruments as differences continue to be
substantial.
Indicator
Initial result Final result
Year MAD Year MAD
GDP per capita 2004 33.1 2015 26.4
Research and development expenditure as % of GDP 2004 47.3 2014 42.4
High-tech exports as % of total exports 2007 5 1. 0 2014 38.8
People at risk of poverty or social exclusion (EU 27) 2007 28.4 2014 23.7
Gini Coefficient (EU 27) 2007 12.7 2014 11.1
Life expectancy at birth 2007 3.3 2014 2.8
Density of motorway network 2002 94.6 2014 75.1
Share of trains in total inland passenger transport 2004 37.1 2014 38.0
Population connected to wastewater collection and treatment
system 2007 49.7 2014 33.3
Table 5. EU MAD dynamics in selected cohesion indicatorstors
Source: Estimated by the author
Indicator
Period Change in deviation
(%)
Initial result Final result EU MAD BG to EU
average
GDP per capita 2004 2015 -20 -19
Research and development expenditure as % of GDP 2004 2014 -10 -17
High-tech exports as % of total exports 2004 2014 -24 -8
People at risk of poverty or social exclusion (EU 27) 2007 2014 -16 -56
Gini Coefficient (EU 27) 2007 2014 -12 +5
Life expectancy at birth 2007 2014 -14 +2
Density of motorway network 2002 2014 -21 -14
Share of trains in total inland passenger transport 2004 2014 +3 +268
Population connected to wastewater collection and treatment
system 2007 2014 -33 -17
Table 6. Comparison of EU MAD dynamics and Bulgaria approximation dynamics in chosen cohesion indicators
Source: Estimated by the author

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From the viewpoint of economic
cohesion the EU has shown progress
with regard to the Research and
development expenditure as % of GDP
index, as well as in the High-tech exports
as % of total exports index. Bulgaria
has made up for the differences in the
average indicators for the EU along
these cohesion indicators. The rate of
convergence, however, is different. As we
can see in table 6, it is more successful
in R&D expenditure, while as regards
high-tech exports, Bulgaria lags behind
the average rate of convergence in the
EU. What causes it? We can assume that
the better result in R&D expenditures is
due to the subsidies from the EU budget
in the period 2007-2013 through the EU
Competitiveness operational program.
The growth in expenditures, however, has
not yet shown the expected impact on
the structure of revenues from research
and development. Revenues from
professional, scientific and technical
activities in Bulgaria are 20% less than
the average indicator for the EU. The
recommendation should be to improve
the effectiveness in the research and
development expenditure. For this
purpose, reporting could be done not by
the rate of funds utilization but through
the incoming revenues from implemented
projects.
With respect to social cohesion, the
EU has also made progress in the three
indicators under examination. Comparing
its progress with Bulgaria’s progress
(Table 6), it is evident that considerably
better results than the EU average have
been achieved in the People at risk of
poverty or social exclusion indicator.
However, this can hardly be the reason
for complacency, bearing in mind that
in 2014 the share of people at risk of
poverty or social exclusion in Bulgaria
remained extremely high - 40% of the
total population. It is true that according
to Eurostat in 2007, it was even as high
as 60% and there is a trend towards the
EU average value. However, this is not
due to a sharp rise in income as it can
be seen in the GDP per capita data. It is
also not due to fairer income distribution
as we can see from the Gini coefficient.
It seems this is due to overcoming the
initial absolute poverty, owing to having
reached a basic level of provision, typical
of the development of countries with
emerging economies. This conclusion
is also confirmed by the results of
the indicator Life expectancy at birth,
where Bulgaria is not in the general
process of convergence. One of the
recommendations would be to work more
actively towards a single social model
in the EU, which could decrease the
large differences in the Gini coefficient.
Another recommendation is during the
following program period to pay greater
attention to the social dimension of
the EU cohesion process, particularly
with respect to countries which are
economically least developed, such as
Bulgaria and particularly in the field of
medicine.
With respect to territorial cohesion, the
greatest disparities are by the Density of
motorway network indicators. Hence a
recommendation can be made for a more
active use of cohesion funds to build a
single EU motorway network. With regard
to Bulgaria’s results, it can be noted that
as seen in Table 7 for the two Density
of motorway network and Population
connected to wastewater collection and
treatment system indicators, convergence
to the average EU indicators has been going
on at a slower rate than the EU average,
but on the whole satisfactory. However,
the third indicator, Share of trains in total

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inland passenger transport marks a serious
situation. Not only is there no convergence,
but conversely, Bulgaria’s results indicate a
divergence from the EU average. Bulgaria’s
railway network does not facilitate the
development of speedy communications
and this makes railway transport for
passengers uncompetitive. This exposes
the need to pay special attention to railway
transport when developing EU’s cohesion
policy as regards Bulgaria.
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