The Effects of European Regional Policy - An Empirical Evaluation of Objective 1 and Objective 2 Policy

The Eﬀects of European Regional Policy -
An Empirical Evaluation of Objective 1
and Objective 2 Policy
Degree Dissertation for the Master Examination in International Economics
at the Faculty of Economics and Social Sciences of the
Eberhard Karls Universität Tübingen
Examiner:
Prof. Dr. Georg Wamser
Submitted by:
Christoph Schulze
3447835
Date of submission:

Abstract
The European Union provides funds to disadvantaged regions to promote economic
growth and convergence (in terms of per capita income) among regions within Eu-
rope. In this study, I apply Propensity Score Matching on NUTS 3 data for the
operational period of 2007 – 2013 to evaluate European structural policy. I find that
results for Objective 1 policy are not robust to changes within the control group,
leading to both, positive and negative results of structural policy. Findings from the
evaluation of Objective 2 policy suggest success in terms of fighting unemployment
and long term unemployment. Programs aiming at reducing youth unemployment
in turn did not succeed. In fact, treated regions showed significant higher rates in
youth unemployment.
i

Contents
1 Introduction 1
2 Related Literature 2
2.1 Executive Summaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.2 Academic Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 History of structural policy and financial instruments in the European
Union 5
3.1 Treaty of Rome (1957) and European Social Fund . . . . . . . . . . . . . 5
3.2 European Regional Development Fund . . . . . . . . . . . . . . . . . . . 6
3.3 Single European Act and Cohesion Fund . . . . . . . . . . . . . . . . . . 7
3.4 Berlin Summit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.5 Period 2007 - 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4 Theoretical Considerations 9
4.1 Neoclassical Growth Theory . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Endogenous Growth Theory . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3 New Economic Geography . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.4 Other theoretical considerations . . . . . . . . . . . . . . . . . . . . . . . 12
5 Data Sources 12
5.1 Period 2000-2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2 Period 2007-2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.3 Covariates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
6 Descriptive Analysis 14
6.1 Extensive Margin Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.2 Intensive Margin Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7 Econometric motivation to matching 17
7.1 Naive Approach - Average Treatment Effect . . . . . . . . . . . . . . . . 17
7.2 Matching and Average Treatment Effect on the Treated . . . . . . . . . . 17
8 Impact analysis of Structural Funds treatment 19
8.1 Average Treatment Effects . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.2 Average Treatment Effect on the Treated (ATT) . . . . . . . . . . . . . . 21
9 Robustness 24
ii

10 Policy Implications 27
11 Conclusion 28
Appendix 31
List of Tables
1 Deﬁnition of ﬁve thematic priority Objectives . . . . . . . . . . . . . . . 31
2 Thematic Objective of Structural Policy 2000 – 2006 . . . . . . . . . . . 31
3 Thematic Objective of Structural Policy 2007 – 2013 . . . . . . . . . . . 31
4 Total Funds per country per period . . . . . . . . . . . . . . . . . . . . . 32
5 Average amount of funds per treated region in Euro . . . . . . . . . . . . 33
6 Funds per capita per treated region . . . . . . . . . . . . . . . . . . . . . 34
7 Descriptive statistics of complete dataset . . . . . . . . . . . . . . . . . . 38
8 Descriptive statistics for Objective 1 treated regions . . . . . . . . . . . . 38
9 Descriptive statistics for Objective 2 treated regions . . . . . . . . . . . . 39
10 ATE Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11 ATE Objective 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
12 Logit estimation for Objective 1 matching equation . . . . . . . . . . . . 42
13 Logit estimation for Objective 2 matching equation . . . . . . . . . . . . 43
14 ATT Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
15 ATE Objective 2 Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
16 ATT Objective 2 Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
17 ATT Objective 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
18 Germany ATT Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 47
19 Robust ATT Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
20 ATT Objective 2 Control 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 49
List of Figures
1 Treatment status by objective 2007 - 2013 . . . . . . . . . . . . . . . . . 35
2 Treatment status by objective 2007 - 2013 . . . . . . . . . . . . . . . . . 35
3 Intensity of Objective 1 Treatment 2000 - 2006 . . . . . . . . . . . . . . . 36
iii

“Cohesion Policy is the market’s ’visible hand’ which aims at balanced and
sustainable development while fostering economic integration throughout the
EU as a whole.” -Danuta Hübner1
1 Introduction
European structural policy has steadily gained more importance with the enlargement of
the European Union over the last decades. Member states share a single market and often
also one common currency. However, there are still cultural and economical differences
among the states. Cohesion policy promotes programs that foster economic growth and
to help poorer regions to catch up with the more prosperous regions. In the Treaty of
Maastricht of 1992, it states in Article 130:
“[...] In particular, the Union shall aim at reducing disparities between the
levels of development of the various regions and the backwardness of the least
favored regions, including rural areas.”
To do so, cohesion policy seeks to enhance the competitive position of regional economies
by supplying public goods that the market is not able to provide on a European level.
Transport and energy networks, a common European environmental policy and invest-
ments in research and development are all examples of measures that have the potential
to create spillover effects, exceeding the powers of national governments.
These types of policy interventions may affect regions differently, depending on the ini-
tial state of the infrastructure, education or quality of government of a region. Impact
analysis of cohesion policy therefore has to condition for these confounding factors. Pre-
vious studies have applied various sets of econometric approaches in order to evaluate the
treatment of cohesion policy dating to 2006.
This thesis intends to quantify the effects of European Structural Policy by first cal-
culating Average Treatment Effects (ATE) and then in the second step computing the
Average Treatment Effect on the Treated (ATT) by applying Propensity Score Matching.
In contrast to other studies, this analysis includes more recent data from the operational
period 2007-2013 at a more disaggregated regional level. Previous literature focused on
NUTS 22
territories, whereas the study mainly uses NUTS 3 data, which is the smallest
geographical unit of measurement.3
The estimates for the programming period of 2007 - 2013 suggest that Objective 1 treated
regions were subject to lower rates of per capita income growth compared to control re-
gions. These findings were altered by expanding the control group. In changing the focus
1
Current member of the European Commission
2
Nomenclature des Unités Territoriales Statistiques, which is a territorial measurement of Eurostat
identifying different levels of aggregation
3
the smallest geographical unit of measurement where allocation of funds can be identified with
1

of the control group, treated regions showed higher rates of GDP per capita growth.
Concerning Objective 2 treatment, funded regions showed lower rates of unemployment
and long term unemployment, independently of the definition of the control group. How-
ever, looking at the population aged 18 - 25, treated regions had higher rates of youth
unemployment. This indicates the lack of effectiveness of programs that aim to improve
the labor market position for the young population.
This thesis is structured as follows: Section 2 reviews the current state of the literature
concerning the European structural policy and its quantification. Section 3 summarizes
the history of the European Union and its focus of the convergence of regions, empha-
sizing the institutions that were of particular importance in the process of structural
policy. Section 4 in turn provides a general overview on economic growth theory. The
remaining sections analyze the Operational Periods 2000-2006 and 2007-2013, starting
with a descriptive analysis of the distribution of funds. Section 7 discusses the economet-
ric background of the Propensity Score Matching method and sets it into the context of
the treatment evaluation of the European structural policy. Sections 9, 10 and 11 present
robustness checks to the analysis and conclude the results with policy implications from
the underlying study.
2 Related Literature
2.1 Executive Summaries
For the period of 2000-2006, the European engineering consultancy company SWECO has
analysed the ERDF and CF Regional Expenditure. Within their study they established a
database, that distinguishes between the different sources and objectives of EU structural
funds. This has been the primary source of this analysis for the period 2000-2006. In
addition they produced a short report on the geographical distribution of the funds. This
report is only descriptive and does not analyse treatment responses.
A similar analysis of the geography of expenditure of funds has been done by the European
Union for the programming period of 2007 – 2013. The focus in this summary is on NUTS
2 regions, and it considers only descriptive statistics.
2.2 Academic Papers
Besides the executive summaries by the European Commission, there are a wide range
of academic papers assessing the effects of structural funds on regional growth within
the EU. The data researchers used stem from the European Commission4
or Cambridge
4
Regional Database
2

Econometrics5
.
Prior research has focused mainly on the assessment of Objective 1 treatment in the
period of 2000 – 2006 or earlier. In contrast, this thesis aims to evaluate the effect of
Objective 1 and Objective 2 policies for the period of 2007 – 2013. The central research
question extends on the one hand the time frame by considering a more recent program-
ming period and on the other hand considers a wider set of policies implemented by the
European Union.
Becker et al. (2010) used NUTS 2 level data and applied regression discontinuity design
(RDD) to evaluate the treatment effect of Objective 1 Funds on regional growth between
1989 and 2006. They justify their approach by the existence of a sharp selection criterion
for obtaining Objective 1 Funds (75% of average GDP per capita in PPP terms). This
forcing variable is very well defined, but also questionable, as there are other financial
instruments, such as the Cohesion Fund that provide funding to regions which have a
GDP per capita below 90% of the EU average. The treatment of the Cohesion Fund is
relatively similar to the treatment of the ERDF, just on a smaller scale. Therefore one
could question the quality of the forcing variable, as there exist multiple threshold values,
that indicate different sets of treatment. Becker et al. (2010) conclude, that within the
same programming period, Objective 1 treatment raised the real GDP per capita growth
by about 1.6%.
In another paper, Becker et al. (2012) analyse the treatment intensity, measured by struc-
tural funds relative to GDP in the EU. Here they not only analyse Objective 1 Funds,
as in their previous analysis, but also look at structural funds in general. In this setting,
Becker et al. (2012) adopt a matching estimator to compare similar regions and then dis-
entangle the treatment effect in a further step. In particular, they apply the Generalized
Propensity Score matching technique and conclude that between 1994 and 2006 the op-
timal transfer intensity amounted to 0.4% of target region GDP. In this thesis I adapted
the motivation of their matching equations, which states that regions with similar labour
market and population characteristics are also comparable in terms of treatment effect
evaluation.
In a more recent study, Becker et al. (2013) try to evaluate the absorptive capacity of
a region by using a RDD framework. By absorptive capacity the authors interpret the
ability of a region to turn investments in terms of funds into growth. This depends
heavily on the quality of institutions and human capital of the observed units. Becker
et al. (2013) measure this using human capital endowments and the quality of local gov-
ernments taken from the European Union Labor Force Survey. In their analysis they
derive a heterogeneous local average treatment effect (HLATE) and find that the treat-
ment effect is insignificant for regions with low absorptive capacity, whereas regions with
relatively high absorptive capacity also have above average treatment effects. They argue
5
Regional Database see Cambridge Econometrics
3

that only about 30% of the eligible regions do have a sufficiently high level of absorptive
capacity to turn the structural funds into economic growth.
Mohl and Hagen (2008) have used similar techniques to evaluate effects of structural
funds. In one paper Mohl and Hagen (2008) also use a generalized propensity score for
the period of 2000 – 2006. In contrast to Becker et al. (2012) they could not find any
statistically significant effect of structural funds on economic growth.
In a second study, Mohl and Hagen (2009) analyse whether or not governments use struc-
tural funds to consolidate their public budget. They estimate policy reaction functions
with a System GMM estimator6
and prove the existence of crowding out of national
spending.
In the most recent analysis, Mohl and Hagen (2010) take NUTS 1 and NUTS 2 data for
the programming period of 2000 – 2006 and apply spatial panel approaches to evaluate
the effects of EU structural funds on economic growth. They find that Objective 1 pay-
ments in particular have a positive significant effect on economic growth. A 1% increase
of structural funds payments translated into a 0.05% increase in GDP per capita growth.
As a key feature of their analysis they include time lags into their econometric specifica-
tion. They argue that they were able to identify growth effects of structural policy after
more than three years after policy measures have been implemented. Moreover, by in-
cluding a spatial weighting matrix they conclude that regional spillovers have significant
effects on regional GDP formation.
Finally, an important share of the literature focuses on the importance of spillover effects
and spatial econometrics, and in particular, the assessment of the spatial heterogeneity
of growth effects caused by structural funds. Dall’Erba and Le Gallo (2008) argue that
spillovers from neighboring regions distort the evaluation of growth effects. Therefore,
Dall’Erba and Le Gallo (2008) develop a spatial weighting matrix, aiming at reducing
the bias of regional spillovers. These techniques have been earlier developed by Anselin
(2003) and are now applied by Dall’Erba (2005) and Dall’Erba and Le Gallo (2008) in the
context of structural funds. Their findings suggest that peripheral regions do not seem
to receive spillover effects to the same extent as core regions do, because investment in
infrastructure happens mainly in core regions.
Ederveen et al. (2006) take a similar approach, but consider also institutional quality in
their analysis. Their conclusion is that structural funds, as an instrument for promoting
economic growth and speeding up convergence within the EU, are ineffective for regions
that lack proper institutional quality. However, regions with good institutions were able
to turn structural funds into Economic Growth.
6
as in Blundell and Bond (1998)
4

3 History of structural policy and financial instruments
in the European Union
Within European structural policy there are several financial instruments that aim at
fostering economic and social cohesion, as well as economic growth in general.
3.1 Treaty of Rome (1957) and European Social Fund
The first mention of cohesion policy can be found in the framework of the European
Union in Article 2 of the EEC Treaty of Rome in 1957:
“The Community shall have as its task, by establishing a common market
and progressively approximating the economic policies of Member States, to
promote throughout the Community a harmonious development of economic
activities, a continuous and balanced expansion, an increase in stability, an
accelerated raising of the standard of living and closer relations between the
States belonging to it.”
Before the treaty was approved, there was no mention of financial instruments in previous
legal documents signed concerning the European Union. As a result of the Treaty of
Rome, the European Social Fund (ESF) was founded with the aim to support employment
within the European Union. It is therefore the first of the EU’s structural funds promoting
social and economic cohesion. At that time, Southern Italy had particularly high rates
of unemployment and should from there on benefit from programmes of the ESF. In
particular, the ESF intended to increase the levels of employment and quality of jobs by
co-funding projects that fostered the inclusiveness of the labor market.
In order to become a member of the European Union, a candidate country has to fulfill
certain membership criteria. These accession criteria are stated in Article 49 of the Treaty
of Maastricht of 1992 and focus on politics, the economy and the legislature.
“Membership requires that candidate country has achieved stability of insti-
tutions guaranteeing democracy, the rule of law, human rights, respect for
and protection of minorities, the existence of a functioning market economy
as well as the capacity to cope with competitive pressure and market forces
within the Union. Membership presupposes the candidate’s ability to take on
the obligations of membership including adherence to the aims of political,
economic and monetary union.”
Countries facing structural difficulties may have also trouble complying these criteria.
It may take a long catching up process to satisfy the membership criteria on the one
hand and to overcome structural difficulties on the other. Once a country has reached
5

political stability and established a state of law, it often still confronts labor market and
infrastructure problems. For this peculiar reason the EU formed the Structural Funds to
help these regions.
3.2 European Regional Development Fund
About 15 years later in 1972, Ireland, Denmark and the United Kingdom joined the Euro-
pean Union. At the same time the Heads of State and Governments debated the objective
of the Economic and Monetary Union an dthe need to create a Regional Development
Fund. The original version of the European Regional Development Fund (ERDF), was
introduced at the Paris summit in 1974 and came into place one year later with the aim
of reducing regional imbalances in terms of per capita income. The policy intended to
implement article 158 of the Treaty of Rome, which states:
“In order to promote its overall harmonious development, the Union shall
develop and pursue its actions leading to the strengthening of its economic,
social and territorial cohesion. In particular, the Union shall aim at reduc-
ing disparities between the levels of development of the various regions and
the backwardness of the least favored regions. Among the regions concerned,
particular attention shall be paid to rural areas, areas affected by industrial
transition, and regions which suffer from severe and permanent natural or
demographic handicaps such as the northernmost regions with very low pop-
ulation density and island, crossborder and mountain regions.”
In order to achieve that goal, specific guidelines for investments consisting of a set of
missions and interventions were defined. The interventions had the objective to finance
projects to improve infrastructure and production. The missions in turn aimed at foster
regions that lagged behind on the one hand (e.g. Italy) and to support regions that were
facing industrial decline on the other hand (e.g. United Kingdom).
The notion of additionality was already present, in that funding for eligible projects could
add up to 50% of public expenditure and were preferably to be carried out in national
state aid areas. This should ensure that cohesion policy does not crowd out national
public spending.
Member States had to apply for ERDF support at the project level. The decisions
whether or not projects were approved were made in a committee of Member States
based on Commission proposals. The focus of investment was on small and medium sized
enterprises (SME) and aimed at technological advances and environmental protection in
regions that particularly presented lags in development.
6

3.3 Single European Act and Cohesion Fund
In 1988 with the Single European Act (SEA), policies regarding the bargaining process
and assigning funds to eligible regions changed significantly. Member countries of the EU
intended to establish a single market within the EU. In order to achieve that, the Treaty
of Rome had to be revised and to some extent updated. Apart from the idea of the single
market, a clear framework regarding the allocation of funds was developed.
In article 130a of SEA, the European Commission now defined four planning principles
for the assignment of funds: programming, concentration, additionality and partnership.
The principle of programming stated that from 1988 on, instead of just directing funds
directly to projects, the assignment of structural funds was subject to multi-year plans
(operational periods). This made it possible to concentrate on long term objectives for
specific regions. Partnership required that national, sub-national and supra-national ac-
tors were involved in the design and implementation of programs. The aim was to ensure
ownership and transparency of the interventions by also including non governmental or-
ganizations and other social partners in the process of assigning funds. The concept of
additionality was intended to guarantee that EU funds were supposed to be complements
and not substitutes for public expenditure. Clearly defined cofinancing rates were intro-
duced to avoid a crowding out effect of national public spending. Lastly, concentration
meant that the funds had to be allocated to the least developed regions, focusing on five
thematic Objectives7
, as in Table 1.
-TABLE 1-
Before the SEA, countries proposed projects in nationally determined areas, which then
were co-financed by the existing structural funds. The funds were distributed after in-
tergovernmental bargaining, based on certain national quotas (Bouvet and Dall’Erba
(2010)). The influence of the European Commission in the allocation process was quite
limited.
After the adoption of the SEA, the apportioning of the funds changed significantly and
became a three stage process. First, the European Commission and member states nego-
tiated over eligible criteria and decided which areas might benefit from structural policy.
In the second stage, eligible regions and states had to formulate plans aimed at support-
ing disadvantaged areas. Lastly, the EU had to adopt the plans (Bachtler and Mendez
(2007)).
One of the major changes at this point was that the member states now had to negoti-
ate with European Commission about the eligible areas on the basis of communitywide
criteria. Furthermore, in accordance with the programming principle, funds were now al-
located within operational periods which lasted six years. This allocation was organized
7
Council Regulation No 2052/88
7

by clearly defined rules, and development plans had to meet coordination and implemen-
tation regulations. The coordination regulations defined the forms of assistance for the
regions, whereas the implementation regulations defined the content of plans and com-
munity support frameworks (csf), coordinated the interventions for a region (Farole et al.
(2011)).
In 1992, the Maastricht Treaty was signed, paving the way for the economic and monetary
union. One consequence of the treaty was the creation of the Euro as the new common
currency within the European Union. Paragraph 177 of the treaty establishes the Co-
hesion Fund as a further financial instrument to help regions whose GDP per capita is
below 90% of EU average. In particular, countries that at that point had recently joined
the EU should benefit, such as Portugal, Spain and Greece. Also, a sixth Objective was
added to the structural policy, concerning the development and structural adjustment of
regions with extremely low population density. This should address Sweden and Finland,
which had shortly before joined the EU. For that programming period, structural and
cohesion policy added up to 168 bn Euro.
3.4 Berlin Summit
During the Berlin Summit in 1999, the European Council decided to adopt a set of
new policies regarding the organization of Structural Funds. The goal was to simplify
the programming steps and to merge selection criteria for regional support. In order to
simplify organization, Objective 2 and Objective 5 criteria were combined. Objective 3
and Objective 4 assignment rules were merged as well. Table 2 shows the criteria for the
operational period of 2000 – 2006.
-TABLE 2-
In order to qualify for the Objective 1 status, a NUTS 2 level region had to have a per
capita GDP of less than 75% of the European Unions average.8
Important indicators
were regional and national prosperity as well as the severity of structural problems in the
particular region.
Regions qualifying for Objective 2 funds were usually undergoing structural difficulties
with respect to employment. The main criteria for Objective 2 regions were high short
and long term unemployment rates and especially a decline in industrial employment.
The funds were therefore intended to create jobs, preferably in small and medium sized
enterprises. Member states of the European Union had to propose a list of areas to the
commission that met the objective criteria.
Finally, Objective 3 funds aimed at improving human capital formation in the selected
regions. Measures of social exclusion, education and training levels and the participation
8
Article 32 European Council meeting Berlin on 24 and 25 March 1999
8

of women in the labor market were important criteria for the assignment of Objective 3
funds. The three criteria were mutually exclusive, meaning a region could only obtain
funds under objective at a time.
Once a region received funds, it had to spend the money for its projects within a certain
time frame. This was defined in the Berlin Summit as the N+2 rule, meaning that
recipients had to spend the funds within two years after the programming period had
completed.
In 2004, Poland, Czech Republic, Hungary, Malta, Cyprus, Slovakia, Slovenia and the
Baltic States9
joined the European Union, leading to a decline in the EU average of per
capita GDP. Regions that had earlier qualified for the Objective 1 criteria now exceeded
the 75% per capita average of GDP. These regions were classified as phasing out regions
and still received Objective 1 funds to a lesser extent, even though they technically no
longer qualified for them.10
In total, the EU dedicated 213 bn Euro for structural and cohesion policy for the EU-15
countries for that programming period, 135 bn (70%) for Objective 1 and 22.5 bn (11.5%)
for Objective 2 treatment.
3.5 Period 2007 - 2013
In the next programming period Objective 2 and Objective 3 were merged together
and the third objective became what was previously the INTERREG initiative, which
aimed to stimulate cross-border, transnational and inter-regional cooperation. The new
objectives of structural policy were:
-TABLE 3-
During this period Bulgaria and Romania joined the European Union, also leading to a
further decline in the eurowide average GDP average. The total of structural and cohesion
funds for this programming period amounted to 347 bn Euro, representing 35,7% of the
entire EU budget.
4 Theoretical Considerations
Making predictions about the effects of structural funds on economic growth depend on
the economic model used as well as the different characteristics of the payments. From
the theory point of view, structural funds can be considered as an income transfer which
enter as public expenditure in the production or expenditure function of the governments
maximization problem.
9
Latvia, Estonia, Lithuania
10
In the analysis here, phasing out regions were still handled as treated regions.
9

Characteristically, structural funds are not unconditional transfers. They have to be co-
financed by the government and they are directed to predefined projects. This makes it
somewhat more difficult to formulate a clear hypothesis about the effects of these funds.
The literature on economic growth relevant for this analysis can be divided into three
main categories.
4.1 Neoclassical Growth Theory
The common neoclassic growth model is the Solow Swan model. Its first version was
developed in the 1950’s by Solow and Swan and extended shortly later by Ramsey Cass
Coopman and others (Barro et al. (2004)). It quickly became popular as it attempted to
explain short run, and to some extent, long run economic growth.
One of the key assumptions of the neoclassicists is the decreasing returns to scale. Of
particular interest here are the diminishing returns to scale of the factor capital, which
imply that on the margin, every further unit of capital will yield less additional output
than the unit of input before. Therefore poor regions will grow faster than rich regions
when they are induced with capital in the short run.
A major result of the neoclassical school is convergence. It says that regions with the
same savings rate, depreciation rate, population growth and access to technology will
converge (Barro et al. (2004)). Apart from that, the theory states that economies tend
to reach their long run equilibrium, independent from their starting point.
Combining the concepts of convergence and diminishing returns with respect to struc-
tural funds the theory states that the injection of public expenditure leads only to faster
convergence towards the steady state of the economy. Funds that finance public invest-
ment stimulate only short run growth (Barro et al. (2004)), but will have no effect on long
run growth. Hence, growth through induction of capital is transitory in nature. Long
run growth happens through technological progress, which is not explained within the
framework of these models and assumed to be fixed.
4.2 Endogenous Growth Theory
The major drawback of the neoclassic growth theory is that it does little to explain
how economies will grow the in long run. The endogenous growth theory extends the
neoclassical theory by making explicit assumptions about the composition of technolog-
ical progress. Lucas (1988) and Romer (1994) started out with the idea that knowledge
creation and the formation of human capital play an important part in the technologi-
cal progress. In their model of endogenous growth, technological progress is explained
through investment in human capital. This particular investment is considered to be a
positive externality, which in turn contributes directly to the formation of GDP and also
generates spillover effects.
10

From a Keynesian point of view, fiscal policies such as the structural funds and other
public expenditure are considered as stabilizers that stimulate the economy. In the frame-
work of endogenous growth theory, Keynesians argue that public spending, in particular
investment in human capital, have positive effects on long run growth.
In the context of structural funds, regional policy might have long run growth effects
when funds are used in projects related to human capital formation. Such projects fit
into the category of Objective 2 and Objective 3 areas for the period of 2000 – 2006 and
for regions where the “Regional Competitiveness and Employment” criterion applies for
the planning period of 2007 – 2013.
4.3 New Economic Geography
From the schools of thought above we would assume that economic integration might
cause growth and convergence as long as it increases access to technology. Krugman
(1998) however, argued that this does not have to be the case. He proposed a different
approach to the analysis of regional economics through which he explains the divergence
of countries in the process of increasing European integration.
His main argument is that economic integration may lead to divergence due to the exis-
tence of a spatial core periphery dichotomy, meaning that firms tend to agglomerate in a
so called core region leaving periphery regions around the core, which are characterized
by comparably low economic activity. There are two major forces that argue in favor
of agglomeration and support Krugman’s hypothesis. One is that economic integration
leads to a reduction of transport costs that might stimulate the concentration of economic
activity in core regions. Another is that firms tend to locate to places where a great num-
ber of consumers live. From the perspective of consumers such agglomeration increases
their access to goods and services. In his paper Krugman points to various examples of
his hypothesis, for example: Silicon Valley and big cities such as New York, Chicago or
Hong Kong.
He models his theory by assuming monopolistic competition, which ensures a firms’ mar-
ket power through product differentiation. In the process of economic integration, a firms’
market will increase, which in turn will increase the firms’ profits. The agglomeration of
firms is also characterized by spillover effects in the core regions. These spillover effects
are modeled by assuming increasing returns to scale in the core and constant returns to
scale in the periphery.
Bringing this in the context of structural funds, a considerable portion of the structural
funds does finance transportation projects, which supports his hypothesis. In particular,
25.8% of the programming period of 2000 – 2006 financed projects to improve infrastruc-
ture and transportation. If Krugman’s hypothesis holds, then the evaluation of treatment
effects will be biased due to the presence of core regions within Europe.
11

4.4 Other theoretical considerations
Besides the above-mentioned models, there are numerous other aspects that have to be
considered when modeling or evaluating effectiveness of structural funds. There has been
a lot of research done concerning the role of institutional quality of recipient countries.
According to Nelson (2008), Lundvall and Johnson (1994) and Farole et al. (2011), good
institutions facilitate innovation and technological progress of a country. Acemoglu and
Robinson (2012) argue in the same way saying that there are two different kinds of
institutions: inclusive institutions that allow for innovation and property rights on the
one hand, and exclusive institutions that are mainly characterized by dictorial government
on the other hand. Countries with exclusive institutions usually suffer from high levels of
corruption. In that case the funds often do not reach the projects they were intended for.
In the same vein, political interests and lobby-ism are key determinants of the success of
structural funds.
It is stated in the legislation of European Union11
that countries have to cofinance the
projects in their countries. This idea is referred to as the principle of additionality and
tries to ensure that the structural funds do not crowd out national public spending. There
are, however, different effects that might violate this principle of addionality (Del Bo et al.
(2011)). One being the substitution effect, political authorities using funds to finance
their own planned investments. Ederveen et al. (2006) state that cohesion support may
crowd out private capital especially when the projects the structural funds are invested
in are close substitutes for private capital. The commission tries to counter that effect by
requiring certain cofinancing rates. However, Mohl and Hagen (2010) found that cohesion
support still crowds out private capital to a certain extent.
5 Data Sources
5.1 Period 2000-2006
For the period of 2000-2006, SWECO has analysed ERDF and Cohesion Fund regional
expenditures. For their study they created a database that distinguishes between the
different Structural Funds and Objectives of EU grants. Their data allows for a regional
breakdown of the funds, as the total amounts per operational period are aggregated for
each region at the NUTS 3 level.
The grants for this operational period are classified by funds and by productive environ-
ment. The sources of funding are either the ERDF, the Cohesion Fund or Community
Initiatives. Community initiatives complement to the regional programs such as Objective
2 or Objective 3 treatment and are cofinanced by the structural funds. They also count
11
Article 130a of Single European Act
12

as EU ﬁnancial instruments but may contain projects involving more than one NUTS
3 region. The Dataset contains information on the URBAN II12
and INTERREGIIIA13
Community Initiatives. As for ERDF funds, the database allows for a distinction between
Objective 1 and Objective 2 payments.
The areas of intervention are productive environment, human resources, infrastructure
and other assistance projects; categories have been aggregated to total spending in period.
This dataset has been the primary source for this analysis for the period 2000-2006. In
addition a short report on the geographical distribution of the funds has been produced.
The document provides a short overview of the concentration of productive environments
per region and distinguishes between objectives. In the descriptive part of this thesis I
will go more into detail.
5.2 Period 2007-2013
Data for the operational period of 2007 - 2013 has been obtained directly from the Eu-
rostat Regio Database14
. It contains information on individual projects for each region,
divided up by fund, objectives and diﬀerentiates between expenditures and allocation of
money.
In my analysis I have aggregated expenditures and allocations for each region. For this
operational period I have no information on the sectoral composition of the Structural
Funds. However, I have two comparable datasets, one for 2000-2006 and one for 2007-
2013, each reporting the funds allocated for each NUTS 3 region.
For each period we only know the total amount allocated and not what has been spent
per year per region. As a result I cannot follow Becker et al. (2012) who calculate the
ratio of structural funds to GDP. I have to assume that funds have been spend uniformly
throughout an operational period.
5.3 Covariates
Information on control variables was obtained from Regional Database of the statistics
department of the European Commission, Eurostat15
. The database contains a large
variety of statistics regarding population, the labour market and transport. The major
drawback, however, is that the information on covariates does often not range back far
enough in time in order to perform an econometric analysis of the operational period of
2000 – 2006. For this particular reason I only focus on a descriptive analysis for 2000 –
2006 and a descriptive and econometric analysis for 2007 – 2013. Variables of interest
12
Sustainable economic and social regeneration of troubled towns
13
Strengthen economic and social cohesion by stimulating cross-border, transnational and inter-
regional co-operation
14
http://ec.europa.eu/eurostat
15
http://ec.europa.eu/eurostat/data/database
13

were: population density, GDP absolute, GDP per capita, patents per capita, death and
birth rates and sector employment.
6 Descriptive Analysis
In the descriptive part of the analysis I will focus on the extensive and intensive margin
of treatment. The first looks at, which regions received what kind of treatment; and
the latter focuses on the intensity of treatment, measured in monetary units flowing to
qualified regions.
6.1 Extensive Margin Analysis
As we can see for the operational period of 2000 – 2006, a great range of regions benefited
from Structural Funds.
-FIGURE 1-
From the map, we can distinguish clear patterns of fund distribution for every single
country. Great Britain, for example, obtained mostly funds under the Objective 2 criteria.
This can mainly be explained by the industrial decline in the south and center regions.
The Highlands in the north, on the other hand, received Objective 1 funding. Spain
and Italy show a quite similar pattern of distribution of funds: both received Objective 1
funding in the South and Objective 2 funding in the North. France, Denmark and Austria
in turn received mostly Objective 2 funding, while Greece, Poland, Czech Republic,
Hungary, Ireland, Portugal, Slovakia, Slovenia and the Baltic States16
received almost
entirely Objective 1 funds. Sweden and Finland got both Objective 2 and Objective 1
funds for remote regions in the North. Germany, received Objective 2 funds in the West
and Objective 1 funds in the East, because regions of the former German Democratic
Republic still face structural difficulties compared to the West.
-FIGURE 2-
With the accession of Poland, Czech Republic, Hungary, Malta, Cyprus, Slovakia, Slove-
nia and the Baltic States in 2004 and Romania and Bulgaria in 2007 the allocation of
funds changed significantly. All of these countries had a per capita GDP below the
EU average, qualifying them for Objective 1 funding. The EU per capita GDP average
dropped with the accession. Many regions which received for the Objective 1 funds be-
fore the enlargement no longer met the selection criteria. The regions affected were in
particular Ireland, the North of Spain, Finland, Sweden and the islands of Corsica and
16
Latvia, Estonia, Lithuania
14

Sardinia. These regions are referred to as “phasing out” regions in the literature. This
term is used to indicate their transitional status. These territories now obtain a more
limited amount of funds, still from the Objective 1 funds. To be more precise, some of
the above mentioned regions, the North of Spain, Sardinia, Cyprus and East Finland
are part of the “phasing in” regions, meaning they switched from being Objective 1 to
Objective 2 funds.
6.2 Intensive Margin Analysis
In evaluating the intensive margin of the distribution of Structural Funds, I look at the
regional and the country level. For the operational period 2000 – 2006, treated regions in
Spain, Italy and Portugal benefited mostly from Objective 1 funds, receiving on average
EUR 441m in Portugal to EUR 667m in Spain per treated NUTS 3 region.
-FIGURE 3-
However, countries vary significantly both in size of their regions and in the total number
of regions within the country. Germany, for example, has by far the highest number of
NUTS 3 regions, amounting to 412. There are far fewer regions in other countries, such
as Portugal which contains only 30 regions. Looking at the aggregate country level we
see that Germany and Italy received substantially high amounts of Objective 1 funds.
Spain, however, received most of Objective 1 funds (25.4 bn Euro), followed by Italy
(EUR 15.9bn) and Greece (EUR 15.1bn).17
-FIGURE 4-
Examining the distribution of Objective 2 funds, we can see that regions in Spain, the
Czech Republic and France received the highest amounts of Objective 2 funds, ranging
on average from EUR 63m in France to EUR 182m in Spain per treated region.
-FIGURE 5-
Again, looking at the aggregate country level we see that France received the most Objec-
tive 2 funds (EUR 5.6bn), followed by the United Kingdom (EUR 4.2bn) and Germany
(EUR 3.2bn).
The second operational period 2007 – 2013 shows quite a different pattern in the distri-
bution of funds.
-FIGURE 6-
17
see Table 4 in Appendix for more Information
15

A ranking of countries by whose regions obtained on average the most Objective 1 funds
shows that regions in countries that had recently joined the European Union benefited
substantially. In particular, at the regional level, Czech Republic, Italy and Spain bene-
fited most. The amount per treated region ranges on average from EUR 458m in Spain
to EUR 643m in Czech Republic.
Looking at national aggregate figures, Poland (EUR 22bn), Italy (EUR 12bn) and Spain
(EUR 10bn) received the most Objective 1 funding for this period.
As for Objective 2 funds, regions in Hungary, Czech Republic and Portugal received on
average most of the funding. Funds on average range from EUR 171m to EUR 743m per
treated region.
Summing up, of all treated regions Spain (EUR 4.3bn), France (EUR 3.7bn), Germany
(EUR 3.7bn) and the UK (EUR 2.9bn) obtained most of the Objective 2 funds.
Another perspective for the descriptive analysis is to look at funds per capita,because
regions differ substantially in population size.
In the first operational period of 2000 – 2006, an individual who lived in a treated region
in Greece obtained the most funds with about 1780 Euro on average per person. Portugal
follows with around 1714 Euro. Spain is in third position with 1300 Euro for a qualified
Objective 1 region. These amounts appear so large, because these countries are rather
sparsely populated in comparison with Germany or France. Looking at the distribution
of Objective 2 funds for the same period, we see that Spain obtained most funds with
about 203 Euro per person in a treated area. Austria is in second position with 156
Euro per person on average. The United Kingdom follows with on average 150 Euro per
person.
In the second operational period, Hungary received most Objective 1 funds per treated
individual amounting to 924 Euro/person on average, followed by Greece with 890 Euro
per person. In third position is Slovenia with 886 Euro/person. This reflects the fact
that all regions in Hungary received Objective 1 funding.
Looking at Objective 2 funding, treated regions in Hungary received on average the most,
508 Euro/Person. Portugal received the second most funds with 408 Euro/person per
treated region. These numbers seem comparably high. However, only two regions in
Hungary and three regions in Portugal received Objective 2 funds. There is not much
variation in the observations of treated regions. Spain comes in third position with 304
Euro/person.
Comparing the two operational periods it becomes clear that the enlargement of the EU
towards East caused a shift in the distribution of structural funds. The accession of the
Eastern countries reallocated Objective 1 funds towards these territories, as they suffered
under labor market and infrastructure difficulties.
From a theoretical point of view this completely makes sense. As mentioned before,
countries that recently fulfilled membership criteria might have caught up in terms of po-
16

litical variables, such as institutional quality. Economic criteria, however, are not clearly
defined in the treaty. New member states lag behind in labor market characteristics and
transportation systems. Therefore directing funds to these regions makes sense.
The real question of interest is, whether or not structural funds had significant positive
economic effects, with regard to the labor market and economic growth.
7 Econometric motivation to matching
7.1 Naive Approach - Average Treatment Effect
The naive approach in assessing the treatment effect of structural policy is that we simply
compare the group means of treated and control regions. This method is also called the
Average Treatment Effect (ATE), as we only look at the average over treatment and non
treated groups.
τATE = E(τ) = E[Y (1) − Y (0)] (1)
Y (1) indicating the outcome of treated units and Y (0) being the control outcome. This
approach answers the following questions: "How does the program change the outcome of
participants compared to what they would have experienced if they had not participated?
What is the expected outcome if individuals in the population were randomly assigned to
treatment?" (Heckman (1997)). Applying this method in this particular case, however, is
controversal due to the presence of selection bias. The underlying data cannot considered
to be results of a randomized experiment, because there are specific selection criteria for
the treatment assignment.
E[Y (1)|T = 1] − E[Y (0)|T = 0] = τATT + E[Y (0)|T = 1] − E[Y (0)|T = 0]
SelectionBias
(2)
From the formula above we note that the ATE is only identified in the absence of selection
bias. This occurs in randomized trials, where treatment assignment T and outcome Y
are conditionally independent given the covariates X. In non-randomized experiments,
however, this does not happen.
7.2 Matching and Average Treatment Effect on the Treated
In order to overcome this problem is to condition on the treatment decision and consider
treated and non-treated observations which participated in the program. This method of
17

evaluation is called the Average Treatment Effect on the Treated (ATT):
τATT = E(τ|T = 1) = E[Y (1)|T = 1] − E[Y (0)|T = 1] (3)
where Y (1)|T = 1 denotes the treated outcome of observations who actually participated
in the program and Y (0)|T = 1 identifies observations that would qualify for treatment
but did not participate in the program. In the Econometrics literature the last term is
often referred to as the counterfactual mean, which is not observable and therefor only
a theoretical construct. In matching we try to approximate this counterfactual mean
by finding a large group of nonparticipants who are similar to the group of participants
with respect to all relevant pretreatment characteristics. Optimally, each observation
finds a statistical twin with the only difference that one participated and the other did
not. Therefore we can say, that matching with respect to pretreatment characteristics
X intends to mimic the effect of randomization, as the outcome Y becomes independent
of the treatment decision T. Rosenbaum and Rubin (1983) and Rubin (1980)18
proved
that adjusting to a set of relevant covariates is sufficient to eliminate confounding due to
selection bias.
Propensity Score Matching
When there are many confounding factors to control for, matching with respect to every
covariate X becomes problematic . Rosenbaum and Rubin (1983) have proposed a suit-
able method to pair treated program participants to their counterfactual mean and so to
overcome the problem of high dimensionality. They introduce the term balancing scores
b(X), which are functions of all relevant characteristics X, such that the distribution of
X, given b(X), is independent of the treatment decision T.
T⊥X|b(X) (4)
The balancing score applied in this analysis is the propensity score p(X), which is the
conditional probability of receiving treatment given all relevant covariates X.
p(X) = Pr(T = 1|X) = E(T|X) (5)
This propensity score reduces the number of dimensions to a single scalar variable.
Conditions
In order for propensity score matching to work, the following conditions have to hold:
18
First mentioning of selection bias in Rubin (1974). Matching as a solution to bias proposed in
Rosenbaum and Rubin (1983)
18

Condition 1 Unconfoundedness19
:
Y (0), Y (1)⊥D|X (6)
Y (0), Y (1)⊥D|p(X) (7)
As mentioned, controlling for X becomes hard as the number of covariates is large.
Therefore we only condition on the propensity score, proposed by Rubin (1974) as this is
also sufficient to reduce the selection bias20
and to mimic randomization21
. This condition
basically states that systematic differences in outcomes between the treated participants
and the matched counterfactual mean with the same values of X or p(X), are attributable
to the treatment.
Condition 2 Overlapping:
0 < P(T = 1|X) < 1 (8)
The idea is that observations with the same X values must have a positive probability of
being treated or non-treated.
8 Impact analysis of Structural Funds treatment
In the following analysis, two methods will be applied to quantify the impact of treat-
ment: the Average Treatment Effect and the Average Treatment Effect on the Treated.
The difference in the two methods shall approximate the Selection Bias mentioned in (2).
The concentration in the analysis will be on the Objective 1 and Objective 2 treatments.
It will examine effects on GDP growth for Objective 1 treatments and labor market char-
acteristics, such as unemployment rates will be used to evaluate Objective 2 treatment.
Specifically, the focus will be turned on yearly, bi-yearly, three-yearly and four-yearly
GDP growth rates. Objective 1 cohesion policy aims to support regions that have a per
capita GDP below 75% of the European average. Regions that are receiving structural
funds, therefore, are expected to have higher GDP growth compared to their matched
control groups.
The Objective 2 treatment impact measures are unemployment, long-term unemploy-
ment and youth unemployment rates. Structural funds aim to foster human capital and
labor market competitiveness. Objective 2 treated regions are expected to have lower
unemployment rates (or higher employment rates).
19
in treatment literature also referred to as selection on observables or conditional independence as-
sumption
20
see Imbens (2004) for further proof
21
see Rubin (1974) for further proof
19

Regarding the sample size, there are 1452 NUTS 3 regions within the European Union22
,
465 observations of which qualified for Objective 1 and 811 for Objective 2 treatment.
This leaves about 183 observations in the control group. From Figures 1 - 6 we see that
these control regions lie within Switzerland, Estonia, Iceland, Latvia, Macedonia, Nether-
lands, Norway, Turkey and the UK. Control regions are not necessarily located within
the European Union, but nearby. The selection of control region should therefore not be
a problem.
8.1 Average Treatment Effects
Calculating the Average Treatment Effect, we compare only group averages sorted by
treatment status. Surprisingly, none of the calculated ATE appeared to be statistially
positively significant (see column 1 of Table 10). The only statistically significant differ-
ences in GDP growth occured in between 2009-2010, 2010-2011, 2008-2010, 2009-2011 and
2008-2011 and turned out to be all negative. Concerning the yearly growth rates, treated
regions were facing a more than 2% lower GDP growth on average than non-treated
regions, when taking only statistically signficant results into account. Extending the
time frame, the difference even increases up to 5.1% lower GDP growth from 2009-2011.
For the remaining statistically significant result of 2007-2011, the difference amounted
to 4.7% lower GDP growth in treated regions on average. This stands in sharp contrast
with the Neoclassical Growth Theory, which would predict a comparably higher growth
in treated regions. Krugmann’s New Economic Geography in turn would predict these re-
sults, given treated regions are rural areas. Ederveen et al. (2006) came to similar results
in their analysis and pointed out, that institutions play a crucial role in the effectiveness
of structural funds.
-TABLE 10-
Turning the focus to Objective 2 treatment, the magnitude of the impact seems to vary
over the years and time horizons. The effects on all three variables of interest however,
unemployment, long term unemployment and youth unemployment always move in the
same directions. With respect to the year 2012, the difference in unemployment between
treated and control regions happened to be positively significant for all three outcome
variables, when analysing yearly, bi-yearly or three yearly changes. Comparing the un-
employment figures to the year 2014, the treatment of Objective 2 funds turned out the
be negatively significant for all three unemployment variables, regardless of the base year.
In other words, changes in unemployment such as 2010-2014, 2011-2014, 2012-2014 and
2013-2014 resulted to be negatively significant, indicating a success of structural policy
22
under NUTS 3 2010 classification
20

for this operational period. Most of the other combinations were not statistically sig-
nificant. The significant findings go in line with what would have been expected from
economic theory.
-TABLE 11-
8.2 Average Treatment Effect on the Treated (ATT)
In looking at the Average Treatment Effect on the Treated, not all regions will be included
in the analysis. Only regions that qualified for treatment or regions that are especially
similar to them are considered in the following analysis.
Matching Equations
The first step in Propensity Score Matching is to formulate matching equations to pair
a treated region to a non-treated characteristically similar region. A set of different
matching equations has been set up for evaluating Objective 1 and Objective 2 treatment.
For the case of Objective 1 funds, the approach of Becker et al. (2012) has been followed
and a natural logarithm of variables such as population density, unemployment rates and
quantity of sector employment was used to determine whether two regions are comparable
or not. The idea of this identification strategy is that regions that are alike in terms
of labor market and population characteristics are also similar with respect to other
unobserved variables such as institutional or human capital quality. Hence, the variables
included in the matching equations shall be sufficient to pair similar regions Becker et al.
(2012).
-TABLE 12-
GDP per capita figures have been excluded on purpose, as this is the selection criteria for
obtaining Objective 1 funding. Including this variable in the matching equation would
therefore violate the unconfoundedness condition (Caliendo and Kopeinig (2008)) stating
that independent of treatment, variables must follow the same distribution. Regarding
GDP per capita, treated and non-treated values differ systematically and cannot be
included.
-TABLE 13-
For the Objective 2 treatment, similar matching equations have been formulated in order
to evaluate the treatment effects. The key variables in this case were higher order polyno-
mials of the GDP per capita and natural logarithms of population density and the total
number of workers per specific sector. Here, employment measures have been excluded
in order to not violate the unconfoundedness condition.
21

Matching Algorithms
In the second step of the Propensity Score Approach, the matching algorithm has to be
defined. Within this analysis, the Nearest Neighbor Algorithm and the Kernel Matching
Algorithm have been applied. There exists a variety of other matching algorithms, such
as Radius or Stratification Matching that require additional computational capacity.
Nearest Neighbor Matching
The Nearest Neighbor control match j for a treated region i is denoted by
C(i) = min
j
||pi − pj|| (9)
whereby
τM
=
1
NT
i∈T
Y T
i −
1
NT
j∈C
Y C
j (10)
denotes the Nearest Neighbor estimator that pairs to every treated unit one (or multiple
in case of same propensity scores) control unit which is most similar in terms of the
propensity score (NT
being the number of treated units; Y T
i treated outcome; Y C
j the
control outcome). The advantage of that algorithm is that it is fairly simple to implement
and fast to compute. This advantage might also turn into a disadvantage in cases of bad
matching. Other algorithms compare each treated unit with several control group regions
and are therefore not as prone to bias as the Nearest Neighbor algorithm.
When applying this algorithm to Objective 1 treatment, hardly any results turn out to
be significant, considering the control group as regions that did not have any treatment
at all. One major problem is the availability of data for the control variables, which leads
to a relatively low number of treated and non-treated regions, 458 and 57 respectively.
-TABLE 14-
The Average Treatment Effect on the Treated appears to be negative in terms of Objective
1 treatment and significant only with respect to the year 2011. Looking at the yearly
growth rates, it amounts to -0.057, meaning the GDP growth rate of treated regions was
on average 5.7 percentage points lower than control regions going from 2010 to 2011.
Considering a two year period growth rate from 2009 to 2011, the ATT results to be
-0.073, indicating a difference of 7.3 percentage points of GDP growth. Similar effects
appear for the periods of 2008-2011, 2007-2011 and 2006 to 2011 with -0.055, -0.078 and
-0.094 respectively. All other combinations of growth rates turned out to be insignificant.
-TABLE 16-
22

Regarding Objective 2 treatment, applying Nearest Neighbor Matching did not lead to
significant differences in the unemployment rates of the treated and non-treated regions.
The signs of the coefficients tend to move in the expected directions. Still, the variation
between the regions is too high and not filtered out by the matching equations.
-TABLE 17-
More important are the changes in the unemployment statistics. Treated regions may
have higher unemployment rates, which is why they received funds in first place. The
treatment effect, however, should be captured by measuring the change in these figures.
As for unemployment rates in general, yearly changes from 2009 to 2010 and 2012 to
2013 resulted to be significantly negative, indicating slower increase (or faster decrease)
unemployment compared to the control regions. Extending the time frame of analysis it
becomes apparent that treated regions were more successful in combating unemployment
in the aftermath of the financial crisis compared to their control observations. Taking
2008 as the base year and calculating two year, three year and four year changes, findings
suggest that treated regions had significant differences in unemployment statistics com-
pared to control regions. Specifically, the differences appeared to be negative, indicating
a success of structural policy. Similar observations can me made for changes in youth
and long term unemployment rates.
Kernel Matching Algorithm
The Kernel Matching Algorithm differs from the Nearest Neighbor Matching as it takes
all control units into account and assigns specific weights to every single one of them.
This improves the consistency of the estimator as it averages over all control units. The
formula of the Kernel Matching estimator is
τK
=
1
NT
i∈T

Y T
i −
i∈C Y C
j G
pj−pi
hn
i∈C G pk−pi
hn

 (11)
Again, NT
being the number of treated units, Y T
i treated and Y C
j the control outcome.
Furthermore, G(·) for the Kernel function and hn representing a bandwidth parameter. It
takes longer to compute this estimator as it calculates the treatment effect for all possible
pairs for each individual treated region and weights them respective to their propensity
score. Using this Kernel estimator to evaluate Objective 1 treatment, the results change
significantly. The signs of the results remained the same, whereas the magnitude differed.
However, using the Kernel matching algorithm, the standard errors are smaller in con-
trast to the Nearest Neighbor matching and therefore lead to significant results. Apart
from 2011, the ATE of 2007 and 2008 also results to be significantly negative. Extending
the time frame of the growth rates to longer periods of time, more results turn out to
23

be significant. For the two year period, the growth rates of 2009 to 2011, 2006 to 2008
and 2005 to 2007 turned out to be negatively significant, amounting to -0.081, -0.094 and
-0.063 respectively. Similar significant negative ATE results for the long run growth rates
of three, four and five year periods.
Turning again to Objective 2 treatment, similar results regarding the rates have been
found. However, for youth and long term unemployment, treated regions faced higher
rates compared to their control regions for the year 2008. Youth unemployment in Ob-
jective 2 qualified territories exceeded the rates of the control regions by 6.15 percentage
points. Long term unemployment rates were 0.81 percentage points higher as compared
to the non-treated regions. All other years did not turn out to be significant.
Concentrating on the change in unemployment rates, results from Kernel Matching seem
to show the same picture as for the Nearest Neighbor Matching. Unemployment, youth
unemployment and long term unemployment follow the same pattern: treated regions
suffered severely under the financial crisis, as shown in a significantly positive change in
unemployment rates. Nonetheless, analyzing the time period after of the financial crisis
of 2008, treated regions had significantly negative changes in unemployment rates com-
pared to the control observations. This indicates effectiveness of Objective 2 funding as
it improved the labor market position of these regions in times of economic difficulties.
Both, Nearest Neighbor and Kernel Matching, contradict the Neoclassical Growth The-
ory and Endogenous Growth Theory as the estimates predict slower economic growth
in treated regions. Furthermore, the findings of this analysis disagree with results from
other executed studies. The findings on Objective 2 policy confirm the findings of Farrell
(2004), who could not find positive employment effects for Spain for earlier operational
periods.
9 Robustness
Country specific treatment evaluation
One major obstacle in the analysis of a treatment is finding the correct variables to
construct the propensity score for the different regions. In this case here, the number of
variables is limited to labor market and population indicators. There is no data available
on institutional quality or for evaluating political structure, such as the rule-of-law. These
variables are particularly important in the sense that they define the "absorptive capacity"
(Becker et al. (2013)) of a region. The political variables could be assumed to be constant
within a country. Regions within a certain country share the same government and the
same constitution. Therefore the same laws apply to these regions. The idea here is
to apply an exact matching, meaning matching regions only within the boundaries of a
particular country, as for example, Germany. This should match regions that have similar
24

values of political variables (assuming that values of these indicators are similar between
regions of a country).
-TABLE 18-
As a robustness check, I take Germany and compare regional GDP growth as the treat-
ment outcome. The sample size and coverage of covariates is particularly good for Ger-
many. Other countries such as Spain, Italy and the UK have also a high number of NUTS
3 regions, but a comparably poor coverage of covariates. In Germany we have overall 412
NUTS 3 regions, including 97 Objective 1 regions and 313 Objective 2 regions. Looking
again at map 5 we can see that there is a clear concentration of Objective 1 regions in
the East and Objective 2 regions in the West.
The only problem is that every region in Germany received either Objective 1 or Objec-
tive 2 payments. Therefore I have to assume that Objective 2 payments affect only labor
market indicators, such as unemployment or sector employment composition and leave
GDP growth unchanged.
The results are significantly different from the overall findings. For the years 2007 and
2010, treated regions had a significantly higher growth compared to the control regions.
However, in 2009 the growth rate of treated regions was significantly lower than in the
control regions. We can interpret this again as reflecting the fact that the regions in the
East were more affected by the financial crisis than the regions in the West. As for the
other years it is hard to assign the growth effects to the structural funds, as we do not
know for sure at what point of time the funding was used. To make a clear statement
we would have to know exactly when the money was assigned to which particular region.
Or in other terms, we need to know at what point of time the project with the funding
was realized. Also, data on GDP per capita is only available until 2011. For the period
of 2007-2013 I am not able to make any statements on long run growth due to a lack of
data.
A major drawback from this check is that regions in the East obtained funds from the
Solidaritätszuschlag, which was paid by the Western regions. These additionally received
funds are likely to disturb the findings. Growth effects might be overestimated, as this
additional source of funding has not been considered in this analysis. Treatment effects
might have to be attributed to these sources instead of the structural funds of the EU.
Expanding the control group
For analyzing the Impact of the Structural Funds, two approaches can be considered
determining the control group. First, as in the previous analysis, the control group can
consist of regions that did not receive treatment at all (Control 1 in Tables). A more
relaxed approach includes observations that did not receive the particular treatment of
25

the treatment group, but may have received other treatments (Control 2 in Tables). This
increases the size of the control group and affects the asymptotics of the results.
Average Treatment Effect
By including more observations in the control group, all computed ATE’s regarding Ob-
jective 1 policy turn out to be statistically significant. Results that were statistically
significant before, did not change the sign, but only in magnitude. Looking at funding
for projects in regions that lag behind, it turns out that in the short run treated regions
had higher GDP growth rates for the years 2007 and 2008. However, from 2009 on the
yearly GDP growth rates were significantly lower, compared to non-treated territories.
Looking at the longer run (three, four and five-yearly GDP growth rates in turn), it
emerges that treated regions had higher growth rates in comparison to the control group.
This indicates that regions that qualified for treatment indeed lagged behind control re-
gions in terms of GDP per capita before the operational period 2007 - 2013, but were able
to catch up to the control group. Looking at the yearly growth rates it becomes apparent
that the financial crisis had more severe effects on the regions that suffered already under
structural difficulties.
Inference from Objective 2 treatment however, looks quite different. Concerning un-
employment and long term unemployment, the signs of statistically significant positive
effects turned in fact completely into statistically significant negative effects, indicating
a success of funds in terms of fighting unemployment. Youth unemployment however did
not show the same pattern. Statistically significant positive results remained positive and
statistically significant negative results often turned into statistically significant positive
results, indicating an increase in youth unemployment in treated regions compared to
control regions.
Nearest Neighbor ATT Matching
The results are altered by expanding the control group, taking now all regions apart from
those with the particular treatment into account. The clear advantage here is the increase
of observations for the control group from 57 to 449 for Objective 1 treatment. However,
the control group now contains regions that received money which may have fostered
growth even though it was intended for other purposes. This might bias the ATE, as
members of the control group may also have received a certain type of other treatment.
-TABLE 19-
The Nearest Neighbor Matching of the total population changed results significantly
compared to the more restricted sample. Growth rates on the yearly basis resulted to
be negatively significant for the time period of 2008-2009 with a coefficient of -0.019,
26

indicating a difference of -1.9% in GDP growth. However, for 2007-2008 the Average
Treatment Effect on the Treated turned out to be positively significant, amounting to
0.048. Extending the time frame of the growth rates to two, three and four years, more
ATTs appeared to be positively significant. From 2007 to 2009, GDP growth rates in
treated regions exceeded the rates of control units by 2.11 percentage points. Broadening
the time span further to three years, the differences of GDP growth rates of 2007 to 2010
and 2006 to 2009 amounted to 3.43% and 9.20% respectively. Lastly, the difference in
the GDP growth rate from 2007 to 2011 added up to 6.2 percentage points. With the
exception of the growth rate of 2008 to 2009, all growth rates that turned out to be
significant and positive.
Kernel Matching
Applying the Kernel matching algorithm, similar effects can be observed. On average,
treated regions seemed to have a steady and higher GDP growth up until 2008, compared
to control regions. In the aftermath of the financial crisis of 2008, control regions recov-
ered on average better in terms of GDP growth, compared to treated regions.
Regarding Objective 2 policy, Nearest Neighbor and Kernel matching seem to be alike.
However, earlier obtained results are altered by changing the definition of the control
group. Concerning yearly unemployment statistics, all three variables suggest that be-
tween 2008 and 2009, treated regions increased their number of unemployed compared
to control regions in the aftermath of the global economic recession. A similar picture
appears when analyzing bi-yearly growth rates between 2007 and 2009. Apart from that,
results appear to be robust to changes in the control group.
-TABLE 20-
In contrast to the first findings, these results do not contradict the Neoclassical Growth
Theory. Treated regions had comparably higher GDP growth in the short and long
run, supporting the Neoclassical and Endogenous Growth Theory. In addition, after
expanding the focus of the control group, the findings confirm the results from Becker
et al. (2010), Becker et al. (2012) and Mohl and Hagen (2010).
10 Policy Implications
The policy implications from this analysis are not straight forward. Applying different
sets of robustness checks changes the results and therefore the policy implications. The
applied econometric analysis have not been of a marginal character. The Propensity
Score Matching allows us to make statements about whether or not a program is effective
or not. However, it is not possible to formulate clear statements on how much one extra
27

unit of money in structural funds will translate into economic growth or an increase in
the rate of employment.
Looking at the results from the econometric approach (including findings of robustness
checks), it turns out that some of programmes financed by the European structural funds
have been effective, whereas others did not show statistical significant effects. This im-
plies that the European Union should continue financing the effective programmes to
support qualified regions.
To be precise, the Commissions policies fighting unemployment and long term unemploy-
ment seemed to be effective by the evaluation of both, ATE and ATT analysis. Youth
unemployment programmes in turn, appeared to be effective up until 2011. Towards the
end of the operational period however, youth unemployment seemed to increase faster in
treated regions compared to control regions, indicating inefficiency of the programmes.
A clear policy recommendation at that point would be to restructure programmes that
aim to fight youth unemployment.
In order to make more precise policy recommendations, a complete dataset would be
necessary, including covariates that characterize similar regions covering the whole Euro-
pean Union. Apart from the covariates, more recent figures on GDP dating at least to
the end of the programming period would be necessary. Having this data, a Generalized
Propensity Score Approach might indicate which treatment intensities might be optimal.
11 Conclusion
So far, researchers have evaluated European structural policy up until 2006, mostly using
data on the NUTS 2 level. This study analysed the most recent program period dating
to 2013, looking at regional statistics disaggregated at the NUTS 3 level. The findings
indicate to a significant difference in unemployment and long term unemployment in
Objective 2 treated regions, compared to the control regions. Treated regions performed
better in terms of fighting these particular unemployment figures. However, regarding
youth unemployment, treated regions appeared no do worse than control regions. These
findings are backed by Farrell (2004) who ended up with similar results for Spain, which
suffered severely under youth unemployment during the last years (Blanchard and Jimeno
(1995), Bermeo (2014)). Programs aiming at eliminating youth unemployment still seem
to have room for improvement. By expanding the focus of the control group, the results of
the analysis changed and appeared to be in line with predictions from economic theories.
In addition, they now confirmed findings from other studies realised by Becker et al. or
Mohl and Hagen.
For this study, the geographical coverage of data is often limited to only a certain number
of countries or it is not disaggregated to the desired NUTS 3 level. This eliminates
valuable variation in the data, which in turn leads to biased results. Control variables,
28

such as unemployment rates, life expectancies or other population statistics are often to be
found only for Germany, France or the UK. After the enlargement of the European Union,
it became of particular interest to include new member states in the analysis, as they have
not received any structural funds before. However, the set of control variables was rather
limited, which may have altered the analysis. The robustness check on Germany proved
that including more control variables in the matching equation does change the results
significantly, concluding positive and significant effects of European structural policy to
some extent.
Leaving the context of European structural policy aside, the importance of distinguishing
the Average Treatment Effect and the Average Treatment Effect on the Treated has
become clear in the descriptive and statistical part of the analysis. In this particular
case, there are significant differences in the two approaches, which indicates the existence
of selection bias. During the process of Nearest Neighbor matching, the sample size has
been reduced, due to the lack of statistical twins for every treated region.
Concluding from this study, several caveats have to be mentioned by applying Propensity
Score Matching or any other econometric approach on this particular data set. First, we
do not know exactly when a specific region received funds or implemented programs.
We only know the total amount per operational period that a region received. However,
knowing the exact moment of program realization would make it easier to attribute
growth effects to structural funds. Furthermore, knowing the exact timing of funding
would allow for the computation of a funding to GDP quotient. Becker et al. (2012)
followed that approach and computed with that the optimal treatment intensity.
Second, the data used in this analysis date to 2014, or even earlier for some variables. As
a rule of conduct, local governments have to spend its allocated funds by two years after
the operational period has finished. This is known as the N+2 rule. Taking this into
account, policy makers have until 2015 to invest the funds in programs for the operational
period of 2007 - 2013. As it takes time for these programs to affect the local economy,
more recent data is necessary to evaluate the impact of these policies. The data set used
in this study included GDP figures ranging to 2011. To make a clearer statement on
growth effects, more recent data on GDP need to be made available.
Lastly, the results of the Objective 1 impact assessment were not robust to changes in
sample size or matching equations. Considering that, one might rely on other econometric
approaches for quantifying structural policy of that kind. As there exists a sharp threshold
of 75% of per capita GDP for Objective 1 treatment, a researcher may want to consider
Regression Discontinuity Design as applied in Becker et al. (2010).
Future research might control for the duration of the treatment. Regions would then be
treated differently when they obtained funds in previous operational periods. To make
statements about long term growth, researches could differentiate regions by the number
of operational periods they obtained funds by the EU. Another aspect would be to look
29

at regions that received funds in the first operational periods, but not anymore today.
Furthermore, analysing the growth effects of different operational periods would allow to
verify if some operational periods were more effective than other periods. Assignment
procedures for funds changed during the years. By looking at the complete history of EU
structural policy we could make statements on the optimal allocation rules for funds.
30

Appendix
Table 1: Definition of five thematic priority Objectives
Objective 1 Promoting the development and structural adjustment of
regions whose development is lagging behind23
Objective 2 Converting regions seriously affected by industrial decline24
Objective 3 Combating long term unemployment25 26
Objective 4 Facilitating the occupational integration of young people
Objective 5 Speeding up the adjustment of agricultural structures
and promoting the development of rural areas
Table 2: Thematic Objective of Structural Policy 2000 – 2006
Objective 1 Promoting the development and structural adjustment of regions
whose development is lagging behind
Objective 2 Supporting the economic and social conversion of areas facing
structural difficulties
Objective 3 Supporting the adaptation and modernization of policies
and systems of education, training and employment
Table 3: Thematic Objective of Structural Policy 2007 – 2013
Convergence Speeding up convergence of the least developed
(former Objective 1) member states and regions defined by GDP per capita
of less than 75% of EU average
Regional Competitiveness Covers all other EU regions with the aim of
(former Objective 2 & Objective 3) strengthening regions competitiveness and attractive-
ness and employment
European Territorial Cooperation Based on Interreg Initiative; cross border, trans-
(former INTERREG) national and interregional cooperation
23
Article 8 of the Council Regulation No 2052/88
24
25
26
Defined by European Commission as unemployment that lasted longer than 12 month
31

Table 4: Total Funds per country per period
NUTS 0 2000-2006 2007-2013
Objective 1 Objective 2 Objective 1 Objective 2
AT 175 678 96 371
BE 420 391 318 397
BG - - 2132 -
CY - 28 - -
CZ 903 71 8997 186
DE 12000 3220 9436 3655
DK - 127 - 216
EE 233 - - -
EL 15100 - 7002 -
ES 25400 2550 10540 4352
FI 497 376 - 866
FR 2430 5590 1334 3771
HR - - 153 -
HU 1240 - 7338 1486
IE 1950 - - 289
IT 15900 2720 12151 2392
LT 584 - - -
LU - 43 - 21
LV 382 - 707
MT 46.7 - - -
NL 81.7 695 - 741
PL 4970 - 22606 -
PT 13200 - 5308 515
RO - - 3618 -
SE 486 381 - 814
SI 137 - 1554 -
SK 566 36.2 2578 60
UK 3810 4160 1341 2919
Note: Amounts are reported in millions of Euro per country (NUTS
0)
32

Table 5: Average amount of funds per treated region in Euro
NUTS 0 2000-2006 2007-2013
AT 58.436 2.261 32.138 11.581
BE 59.962 17.005 45.478 10.747
BG - - 76.152 -
CY - 28.023 - -
CZ 64.524 71.262 642.700 186.400
DE 137.600 20.773 97.279 11.677
DK - 18.078 - 19.645
EE 46.564 - - -
EL 295.600 - 159.100 -
ES 667.300 182.400 458.200 120.900
FI 62.097 26.885 - 45.592
FR 347.100 63.541 333.400 38.879
HR - - 7.265 -
HU 61.969 - 366.900 743.100
IE 243.300 - - 36.160
IT 497.400 40.015 467.300 29.527
LT 58.394 - - 20.939
LV 63.674 - 117.800 -
MT 23.349 43.794 - -
NL 81.660 34.739 - 24.717
PL 110.500 - 342.500 -
PT 441.200 - 189.600 171.600
RO - - 86.134 -
SE 53.949 22.434 - 38.762
SI 11.377 - 129.500 -
SK 80.918 36.167 322.300 59.948
UK 146.500 57.774 89.423 24.741
Note: Amounts are reported in millions of Euro per treated NUTS
3 region per period
33

Table 6: Funds per capita per treated region
NUTS 0 2000-2006 2007-2013
AT 602 157 375 63
BE 314 71 235 53
BG - - 310 -
CY - 33 - -
CZ 91 57 875 150
DE 892 133 712 53
DK - 55 - 45
EE 194 - - -
EL 1780 - 891 -
ES 1301 204 701 304
FI 360 138 - 231
FR 742 130 697 72
HR - - 42 -
HU 132 - 924 508
IE 438 - - 79
IT 927 91 778 78
LT 158 - - -
LV 186 - 351 -
LU - 80 - 38
MT 169 - - -
NL 204 124 - 62
PL 193 - 581 -
PT 1715 - 782 409
RO - - 175 -
SE 241 86 - 148
SI 75 - 886 -
SK 120 58 471 97
UK 488 150 479 67
Note: Amounts are reported in Euro per individual that lived in a
treated NUTS 3 region of that country per period
34

Figure 1: Treatment status by objective 2007 - 2013
(1,2]
(0,1]
[0,0]
Figure 2: Treatment status by objective 2007 - 2013
(1,2]
(0,1]
[0,0]
35

Figure 3: Intensity of Objective 1 Treatment 2000 - 2006
(3.61e+08,4.55e+09]
(1.81e+08,3.61e+08]
(1.13e+08,1.81e+08]
(7.19e+07,1.13e+08]
(4.14e+07,7.19e+07]
(0,4.14e+07]
[0,0]
Note: Absolute number of funds assigned to a region in legend
(7.16e+07,7.90e+08]
(4.50e+07,7.16e+07]
(3.11e+07,4.50e+07]
(2.16e+07,3.11e+07]
(1.33e+07,2.16e+07]
(5701059,1.33e+07]
(595403,5701059]
(0,595403]
[0,0]
36

(386559264.00,2038860931.49]
(216530656.00,386559264.00]
(124244192.00,216530656.00]
(76896136.00,124244192.00]
(51765764.00,76896136.00]
(18586632.00,51765764.00]
(0.00,18586632.00]
[0.00,0.00]
(49175524.00,814988281.77]
(25267790.00,49175524.00]
(17595016.00,25267790.00]
(11885170.00,17595016.00]
(7833960.50,11885170.00]
(3682503.25,7833960.50]
(1331816.63,3682503.25]
(0.00,1331816.63]
[0.00,0.00]
37

Table 7: Descriptive statistics of complete dataset
Variable Obs Mean Std. Dev. Min Max
(gdp_2007)2 1323 6.94e+08 1.16e+09 1960000 3.35e+10
(gdp_2007)3 1323 2.80e+13 1.75e+14 2.74e+09 6.14e+15
(gdp_2007)4 1323 1.83e+18 3.11e+19 3.84e+12 1.12e+21
(gdp_2007)5 1323 2.05e+23 5.66e+24 5.38e+15 2.06e+26
∆ gdp_2007 1323 0.068 0.064 -0.071 0.442
density_2007 1316 5.099 1.381 0.095 9.947
unemployed_2007 1450 1.890 0.474 0.642 3.552
employed_2010 964 4.675 0.885 1.435 8.091
employed_agriculture_2010 961 1.162 1.582 -3.912 5.268
employed_construction_2010 964 1.984 0.915 -0.693 5.350
employed_ﬁnance_2010 865 2.372 1.073 -0.916 6.528
employed_building_2010 865 3.211 0.924 0 7.014
employed_industry_2010 964 2.915 0.953 -2.303 6.026
employed_production_2010 868 2.759 0.972 -2.303 5.958
employed_public_2010 868 3.325 0.857 0.588 6.905
Note: Polynomials of absolute GDP per capita. GDP growth from 2006 – 2007. Re-
maining variables are reported in natural logarithm of variables. Density in people
per km2. Unemployed in rates per NUTS 2 region. Employed in absolute number of
workers.
Table 8: Descriptive statistics for Objective 1 treated regions
density_2007 506 4.628 1.256 0.095 9.047
unemployed_2007 640 2.069 0.547 0.642 3.552
employed_2010 356 4.706 0.827 2.345 7.020
employed_building_2010 353 3.263 0.883 1.206 5.889
Note: Variables are reported in natural logarithm of variables. Density in
people per km2. Unemployed in rates per NUTS 2 region. Employed in
absolute number of workers.
38

Table 9: Descriptive statistics for Objective 2 treated regions
(gdp_2007)2 859 9.48e+08 1.37e+09 1960000 3.35e+10
(gdp_2007)3 859 4.08e+13 2.16e+14 2.74e+09 6.14e+15
(gdp_2007)4 859 2.77e+18 3.85e+19 3.84e+12 1.12e+21
(gdp_2007)5 859 3.14e+23 7.03e+24 5.38e+15 2.06e+26
∆ gdp_2007 859 0.049 0.039 -0.071 0.267
density_2007 906 5.308 1.434 0.095 9.947
employed_2010 643 4.633 0.898 1.435 8.091
employed_ﬁnance_2010 544 2.474 1.084 -0.916 6.528
employed_building_2010 544 3.158 0.923 0 7.014
employed_public_2010 547 3.318 0.893 0.588 6.905
Note: Polynomials of absolute GDP per capita. GDP growth from 2006 – 2007.
Remaining variables are reported in natural logarithm of variables. Density in people
per km2. Employed in absolute number of workers.
39

Table 10: ATE Objective 1
Variable ATE
Control 1 Control 2
∆ GDP 2011-2010 -0.022∗∗
-0.008∗∗∗
(0.009) (0.003)
∆ GDP 2010-2009 -0.026∗∗∗
-0.008∗∗
(0.009) (0.004)
∆ GDP 2009-2008 0.001 -0.012∗∗∗
(0.009) (0.004)
∆ GDP 2008-2007 0.013 0.073∗∗∗
(0.015) (0.005)
∆ GDP 2011-2009 -0.051∗∗∗
-0.017∗∗∗
(0.015) (0.005)
∆ GDP 2010-2008 -0.025∗∗
-0.021∗∗∗
(0.012) (0.004)
∆ GDP 2009-2007 0.019 0.051∗∗∗
(0.017) (0.005)
∆ GDP 2008-2006 0.012 0.136∗∗∗
(0.024) (0.008)
∆ GDP 2011-2008 -0.047∗∗∗
-0.030∗∗∗
(0.015) (0.005)
∆ GDP 2010-2007 0.019 0.049∗∗∗
(0.020) (0.006)
∆ GDP 2009-2006 0.018 0.107∗∗∗
(0.021) (0.007)
∆ GDP 2008-2005 0.017 0.190∗∗∗
(0.033) (0.012)
∆ GDP 2011-2007 -0.002 0.043∗∗∗
(0.026) (0.007)
∆ GDP 2010-2006 0.017 0.107∗∗∗
(0.026) (0.008)
∆ GDP 2009-2005 0.024 0.154∗∗∗
(0.027) (0.009)
∆ GDP 2008-2004 0.048 0.286∗∗∗
(0.047) (0.018)
Note: The estimates above project the
ATE on the differences in GDP per capita.
Standard Errors appear in paranthesis. ∗∗∗
1% level of significance, ∗∗ 5% level of sig-
nificance, ∗ 10% level of significance. Com-
mon support option has been ommitted
due to reduction in sample size. Num-
ber of observations (control 1 approach):
512. Number of observations (control 2
approach): 1323
40

Table 11: ATE Objective 2
Variable Unemployment Long term UE Youth UE
Control 1 Control 2 Control 1 Control 2 Control 1 Control 2
∆ % 2014-2013 -0.095∗∗∗
-0.008 -0.101∗∗∗
0.018∗
-0.064∗∗∗
0.011
(0.015) (0.007) (0.024) (0.21) (0.011) (0.010)
∆ % 2013-2012 -0.026 0.018 -0.054 -0.008 -0.073∗∗
0.026∗∗
(0.018) (0.008) (0.034) (0.013) (0.030) (0.012)
∆ % 2012-2011 0.060∗∗∗
-0.006∗∗
0.117∗∗∗
-0.038∗∗∗
0.075∗∗∗
0.032∗∗∗
(0.015) (0.009) (0.022) (0.013) (0.017) (0.011)
∆ % 2011-2010 0.027∗∗
-0.054∗∗∗
0.004 -0.089∗∗∗
0.031∗∗
0.012
(0.012) (0.008) (0.019) (0.012) (0.015) (0.010)
∆ % 2010-2009 -0.029∗
-0.077∗∗∗
-0.013 -0.011 -0.022 -0.054∗∗∗
(0.016) (0.009) (0.039) (0.020) (0.016) (0.011)
∆ % 2009-2008 -0.076∗∗
0.023 -0.090∗∗
0.050∗∗
-0.035 0.048∗∗∗
(0.030) (0.014) (0.036) (0.020) (0.028) (0.015)
∆ % 2008-2007 -0.100∗∗∗
0.007 -0.039∗
0.038∗∗∗
-0.141∗∗∗
0.007
(0.015) (0.009) (0.021) (0.012) (0.020) (0.011)
∆ % 2014-2012 -0.122∗∗∗
0.008 -0.164∗∗∗
0.011 -0.137∗∗∗
0.033∗∗
(0.024) (0.011) (0.048) (0.019) (0.040 ) (0.016)
∆ % 2013-2011 0.034 0.011 0.084∗∗
-0.045∗∗
0.013 0.058∗∗∗
(0.024) (0.013) (0.036) (0.019) (0.028 ) (0.015)
∆ % 2012-2010 0.080∗∗∗
-0.066∗∗∗
0.107∗∗∗
-0.123∗∗∗
0.089∗∗∗
0.026∗
(0.021) (0.015) (0.033) (0.022) (0.023 ) (0.015)
∆ % 2011-2009 0.000 -0.134∗∗∗
-0.013 -0.126∗∗∗
0.013 -0.040∗∗∗
(0.021) (0.015) (0.051) (0.029) (0.023 ) (0.015)
∆ % 2010-2008 -0.128∗∗∗
-0.082∗∗∗
-0.154 0.038 -0.070∗
-0.034
(0.046) (0.022) (0.102) (0.050) (0.037 ) (0.022)
∆ % 2009-2007 -0.233∗∗∗
0.018 -0.084∗∗
0.098∗∗∗
-0.254∗∗∗
0.030
(0.050) (0.024) (0.042) (0.029) (0.054) (0.024)
∆ % 2014-2011 -0.057∗
0.008 0.018 -0.013 -0.049 0.069∗∗∗
(0.029) (0.015) (0.044) (0.024) (0.038) (0.018)
∆ % 2013-2010 0.064∗∗
-0.047∗∗∗
0.084∗
-0.147∗∗∗
0.035 0.053∗∗∗
(0.028) (0.018) (0.049) (0.029) (0.030) (0.017)
∆ % 2012-2009 0.056∗∗
-0.150∗∗∗
0.119∗
-0.161∗∗∗
0.077∗∗∗
-0.025
(0.028) (0.021) (0.063) (0.041) (0.029) (0.021)
∆ % 2011-2008 -0.059 -0.141∗∗∗
-0.099 -0.050 -0.009 -0.004
(0.043) (0.026) (0.113) (0.060) (0.037) (0.024)
∆ % 2010-2007 -0.278∗∗∗
-0.069∗∗
-0.057 0.139∗∗
-0.286∗∗∗
-0.044
(0.066) (0.031) (0.093) (0.059) (0.064) (0.030)
∆ % 2014-2010 -0.015 -0.045∗∗
0.018 -0.106∗∗∗
-0.021 0.060∗∗∗
(0.031) (0.019) (0.058) (0.033) (0.036) (0.019)
∆ % 2013-2009 0.038 -0.138∗∗∗
0.130 -0.185∗∗∗
0.013 -0.014
(0.037) (0.025) (0.084) (0.051) (0.037) (0.023)
∆ % 2012-2008 0.040 -0.152∗∗∗
0.157 -0.049 0.090∗∗
0.021
(0.044) (0.034) (0.110) (0.073) (0.041) (0.030)
∆ % 2011-2007 -0.167∗∗∗
-0.103∗∗∗
0.056 0.117 -0.162∗∗∗
0.018
(0.058) (0.032) (0.105) (0.071) (0.056) (0.030)
Note: The estimates above project the ATE on the changes of unemployment rates.
Standard Errors appear in paranthesis. ∗∗∗ 1% level of significance, ∗∗ 5% level of
significance, ∗ 10% level of significance. Common support option has been ommitted
due to reduction in sample size. Number of observations (control 1 approach): 932.
Number of observations (control 2 approach): 1383
41

Table 12: Logit estimation for Objective 1 matching equation
Variable Coefficient
(Std. Err.)
density_2007 -0.705∗∗
(0.280)
unemployed_2007 0.741∗
(0.430)
employed_2010 2.853∗∗
(1.378)
employed_agriculture_2010 -0.511∗∗
(0.251)
employed_construction_2010 0.734
(0.626)
employed_building_2010 -2.696∗∗∗
(1.031)
employed_industry_2010 0.960
(1.993)
employed_production_2010 -0.711
(1.826)
_cons -1.926
(2.538)
N 307
R2 0.147
Note: Standard Errors appear in paranthe-
sis. ∗∗∗ 1% level of significance, ∗∗ 5% level of
significance, ∗ 10% level of significance. Com-
mon support option has been ommitted due
to reduction in sample size.
42

Table 13: Logit estimation for Objective 2 matching equation
Variable Coefficient
(Std. Err.)
(gdp_2007)2 1.46e-07∗∗∗
(4.14e-08)
(gdp_2007)3 -9.42e-12∗∗∗
(2.62e-12)
(gdp_2007)4 2.04e-16∗∗∗
(5.60e-17)
(gdp_2007)5 -1.34e-21∗∗∗
(3.67e-22)
∆ gdp_2007 -54.456∗∗∗
(14.659)
density_2007 -2.887∗∗∗
(0.746)
employed_2010 0.395
(9.627)
employed_agriculture_2010 -1.255∗∗∗
(0.463)
employed_construction_2010 -1.059
(1.653)
employed_finance_2010 0.346
(2.005)
employed_building_2010 -3.401
(3.767)
employed_industry_2010 0.681
(4.353)
employed_production_2010 2.256
(3.835)
employed_public_2010 2.988
(3.496)
_cons 6.967
(18.044)
N 541
R2 0.748
Note: Standard Errors appear in paranthesis.
∗∗∗ 1% level of significance, ∗∗ 5% level of sig-
nificance, ∗ 10% level of significance. Common
support option has been ommitted due to re-
duction in sample size.
43

Table 14: ATT Objective 1
Variable ATT
NN Kernel
∆ GDP 2011-2010 -0.058∗∗∗
-0.064∗∗∗
(0.022) (0.019)
∆ GDP 2010-2009 -0.008 -0.012
(0.012) (0.017)
∆ GDP 2009-2008 0.008 0.014
(0.015) (0.022)
∆ GDP 2008-2007 -0.024 -0.052∗∗
(0.024) (0.024)
∆ GDP 2011-2009 0.073∗∗∗
-0.082∗∗∗
(0.026) (0.029)
∆ GDP 2010-2008 -0.001 0.001
(0.018) (0.029)
∆ GDP 2009-2007 -0.009 -0.032
(0.023) (0.022)
∆ GDP 2008-2006 -0.041 -0.095∗∗∗
(0.025) (0.029)
∆ GDP 2011-2008 -0.055∗∗
-0.058∗
(0.028) (0.032)
∆ GDP 2010-2007 -0.012 -0.048
(0.025) (0.035)
∆ GDP 2009-2006 -0.022 -0.070∗∗∗
(0.019) (0.026)
∆ GDP 2008-2005 -0.069∗∗
-0.130∗∗∗
(0.034) (0.034)
∆ GDP 2011-2007 -0.078 -0.119∗∗∗
(0.051) (0.038)
∆ GDP 2010-2006 -0.023 -0.092∗∗∗
(0.024) (0.035)
∆ GDP 2009-2005 -0.043 -0.099∗∗∗
(0.028) (0.025)
∆ GDP 2008-2004 -0.056 -0.163∗∗∗
(0.051) (0.049)
Note: The estimates above project the
ATT on the differences in GDP per
capita. Standard Errors appear in paran-
thesis. ∗∗∗ 1% level of significance, ∗∗ 5%
level of significance, ∗ 10% level of sig-
nificance. Common support option has
been ommitted due to reduction in sam-
ple size. Number of treated observations:
458. Number of control observations: 183
44

Table 15: ATE Objective 2 Rates
Year Unemployment Long term UE Youth UE
Control 1 Control 2 Control 1 Control 2 Control 1 Control 2
2014 -1.320∗∗∗
-3.396∗∗∗
-0.010 -2.195∗∗∗
-0.423 -2.781∗∗
(0.486) (0.320) (1.885) (0.221) (1.782) (1.422)
2013 -0.691 -3.519∗∗∗
0.016 -2.400∗∗∗
0.643 -3.178∗∗
(0.479) (0.328) (0.377) (0.216) (1.628) (1.476)
2012 -0.711 -3.583∗∗∗
-0.206 -2.285∗∗∗
1.901 -3.185∗∗
(0.489) (0.309) (0.863) (0.194) ( 1.582) (1.454)
2011 -1.405∗∗∗
-3.291∗∗∗
-0.557 -1.994∗∗∗
0.672 -2.685∗∗
(0.484) (0.256) (0.401) (0.165) (1.551) (1.344)
2010 -2.061∗∗∗
-3.121∗∗∗
-0.676∗
-1.743∗∗∗
-1.724 -3.953∗∗∗
(0.507) (0.241) (0.405) (0.156) (1.692) (1.314)
2009 -2.362∗∗∗
-2.754∗∗∗
-0.828∗∗
-1.688∗∗∗
-2.367 -2.491∗∗
(0.504) (0.223) (0.390) (0.144) (1.825) (1.232)
2008 -1.880∗∗∗
-2.699∗∗∗
-0.883∗∗
-1.820∗∗∗
-2.294 -3.226∗∗∗
(0.502) (0.205) (0.443) (0.158) (1.471) (0.994)
Note: The estimates above project the ATT on the unemployment rates.
Standard Errors appear in paranthesis. ∗∗∗ 1% level of significance, ∗∗ 5%
level of significance, ∗ 10% level of significance. Common support option
has been ommitted due to reduction in sample size. Number of treated
observations: 805. Number of control 1 observations: 183. Number of
control 2 observations:
Table 16: ATT Objective 2 Rates
Year Unemployment Long term UE Youth UE
NN Kernel NN Kernel NN Kernel
2014 -9.438∗∗
-3.884 -3.892∗∗
-1.696 6.428 2.717
(4.428) (3.634) (1.885) (1.689) (6.936) (4.331)
2013 -9.684∗∗
-4.446 -3.358∗
-1.502 5.474 0.084
(4.357) (3.291) (1.785) (1.586) (8.122) (5.763)
2012 -6.080∗∗
-2.742 -1.043 -0.804 6.830 2.338
(3.094) (2.386) (0.863) (0.864) (5.100) (3.694)
2011 -5.978∗∗
-3.356 -0.291 -0.842 6.784 2.287
(2.915) (2.688) (0.925) (0.883) (4.568) (4.510)
2010 -5.353∗
-3.422 0.539 -0.474 6.868 1.732
(2.880) (2.856) (0.718) (1.013) (5.146) (3.428)
2009 -3.749 -1.568 0.589 0.328 7.807∗
4.513
(2.891) (1.903) (0.507) (0.611) (4.198) (3.299)
2008 -1.742 0.308 0.969∗∗∗
0.812∗∗∗
7.273∗∗∗
6.148∗∗∗
(1.552) (1.123) (0.277) (0.315) (2.789) (2.108)
Note: The estimates above project the ATT on the unemployment
rates. Standard Errors appear in paranthesis. ∗∗∗ 1% level of
significance, ∗∗ 5% level of significance, ∗ 10% level of significance.
Common support option has been ommitted due to reduction in
sample size. Number of treated observations: 805. Number of
control observations: 183
45

The Effects of European Regional Policy - An Empirical Evaluation of Objective 1 and Objective 2 Policy

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