Regional Heterogeneity in Labour Markets, InstitutionalQuality and R & D Expenditure, and the Absorption of

European Structural Funds

Preliminary Version

Research Network Macroeconomics and Macroeconomic Policies (FMM),19th Annual Conference, 22-24 October 2015, Berlin

“The Spectre of Stagnation? Europe in the World Economy”

Julia BachtröglerVienna University of Economics and Business, Institute for International

Economics

October 8, 2015

1 Introduction

The developments since the economic and financial crisis and the European debt crisishave revealed the heterogeneity of the member states and regions of the European Union(EU) as a crucial issue. Several aspects, as macroeconomic imbalances and budget sta-bility, have been discussed by various streams of literature and political institutions. Inorder to achieve convergence across the heterogeneous regions, the European Commis-sion’s Directorate-General for Regional and Urban Policy conducts funding programs thatprovide funds transfers with which regional administrative authorities perform projectsaimed at the different objectives. A major part of the funds (Convergence, former Ob-jective 1) is reserved for the relatively weakest regions, i.e., regions with a gross domesticproduct (GDP) per capita below 75 % of the EU average (and exceptions).1 However,evaluations of the Convergence/Objective 1 funds allocation reveal differences in their ef-fectiveness across regions. Firstly, not all of these funds committed to the weakest regionsare actually paid out. There seem to be two main reasons for this gap: i) each projectneeds to be co-financed by the member state or rather the particular region, and ii) the1Up to the EU regional policy programming period (also called multi-annual financial framework) 2000-2006, they have been called Objective 1 regions, thereafter the objectives were restructured and theObjective 1 was renamed to the convergence objective. The funding programs are designed for the budgetperiods of the EU, i.e., 1989-1993, 1994-1999, 2000-2006, 2007-2013, and, now, 2014-2020. The regionsin question in this research are on the NUTS-2 level following the Nomenclature of Territorial Units forStatistics (2010 version).

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administrative effort is high, as detailed and profound operational programs, project andbudget plans need to be provided by the region in order to receive the funds.

Secondly, and that is where we focus on at this stage of research, the effectiveness of thefunds paid out for projects varies. This study investigates the heterogeneity of EuropeanNUTS-2 regions in this respect, i.e., in their ability to take advantage from Europeanstructural funds targeted at convergence, such that the region’s income per capita in-creases over the programming period. The absorptive capacity of regions has been shownto be a relevant factor for the effectiveness of regional transfers like EU structural funds.According to literature on foreign direct investments and other (regional) transfers, theeducation of the labour force, the size of the research & development (R & D) sector aswell as institutional quality are important determinants of a region’s absorptive capacity.Stagnating employment rates of graduates and people with upper secondary educationand steadily increasing numbers of people attaining these levels of education show thathaving attained it is not a guarantee for employment anymore. Thus, this paper inves-tigates how this remarkable issue may affect the absorptive capacity for EU funds and,therefore, the European institutions’ power to actually support convergence of the regions.

The empirical analysis follows Becker et al. (2013), who have developed a regression dis-continuity design to estimate heterogeneous treatment effects of Objective 1 structuralfunds for the regional policy programming periods from 1989-1993 to 2000-2006. Theyhave used the education of the workforce (share of workers with at least upper secondaryeducation) and a quality of government index as indicators for the absorptive capacity of aregion. We use this approach to evaluate the effect of the employment rates of the highlyeducated population of a region, of institutional quality and the size of R & D expenditurein a region on the success of structural funds aimed at convergence.2

In order to assess the administrative system which is responsible for the proposal of fundsand projects as well as their distribution and implementation, we use the corruption per-ception index (CPI) by Transparency International interacted with the European Qualityof Government Index 2013 (EQI 2013) introduced in Charron et al. (2014a). The reasonsfor that are twofold: Firstly, the EQI 2013 is based on regional survey data on the pop-ulation’s perception of the quality of public services. However, for the research questionof this study, corruption is expected to be a major indicator for institutional quality interms of project organization and allocation to different companies. Secondly, the CPI isonly available on the national level but for a time series from 2000 to 2011, whereas theEQI 2013 covers the regional level. Thus, interacting the two indices enables analyzing2The high unemployment rates, even among the population with upper secondary and higher education, inmost European countries are not expected to decrease in the short or medium term, and rising long-termand youth unemployment will deepen the problem further (see, e.g., OECD (2014)). Unemployment hascrucial effects on the budgetary stability and budget formulation of the countries. Therefore, it gets evenmore relevant in the context of the European debt crisis and the implemented policy measures.

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both the time and regional dimension.

In the end, it is companies and regional public institutions receiving the payments andrealizing projects aimed at regional convergence. Therefore, we consider the R & D ex-penditure of companies and entities in all sectors as variable contributing to absorptivecapacity.

At this stage of research, the main results presented in this paper consider the multi-annual financial frameworks (MFFs) 2000-2006 and 2007-2013, and we provide estimationresults taking into account also the programming periods 1989-93 and 1994-99 in the ap-pendix. However, note that the choice of programming periods seems to matter. On theone hand, considering all four MFFs in the estimation reveals a positive and significanteffect of the treatment (allocation with Objective 1/Convergence funds) that appears tobe robust across all models. On the other hand, when including only the periods 2000-06and 2007-13, this effect seems to vanish and the treatment turns insignificant. This phe-nomenon may be due to the decreased GDP per capita growth in the crisis period up from2008 and is a main topic of our ongoing research.

The structure of this paper is as follows. Section 2 introduces the EU’s regional policy, itsinstruments and principles, and why its evaluation may result in mixed empirical evidence.Section 3 treats the concept of absorptive capacity and its relevance for the effectivenessof structural funds. Section 4 motivates the regression design and the model derivation.Section 5 provides the estimation results. Finally, section 6 concludes and indicates futuresteps of this research.

2 EU Regional Policy and its Effectiveness

The EU’s regional policy has become the biggest budget item in EU budgets 2007-2013and 2014-2020.3 Thus, currently, it is the EU’s most important (redistributive) policyinstrument that follows the target of promoting economic growth and the convergence ofEuropean regions.For the multi-annual financial framework 2007-2013, the regional policy budget amountedto about 347 billions of euro, of which approximately 58 % have been assigned to theERDF, 22 % to the ESF and about 20 % to the Cohesion Fund. The budgeted regionalpolicy funds in the following programming period 2014-2020 have decreased to about 336billions of euro, with similar allocation shares for the specific funds. Beyond the three mainobjectives of the EU’s regional policy named above, there is a more detailed prioritization3In the multi-annual financial framework 2007-2013, the expenses for competitiveness for growth and jobs(9.21 %) and for economic social, economic and territorial cohesion (35,73 % of the EU budget) exceededthe expenses for the common agricultural policy (42.31 %). Alike, 13.08 % of the budget for the period2014-2020 are allocated to promoting competitiveness for growth and jobs, and 33.87 % for economicsocial, social and territorial cohesion, while the share of the common agricultural policy diminishes furtherto 38.87 % of the EU budget.

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of policy fields and projects. Figures 1 and 2 show that the majority of the regional policyfunds are allocated to innovation and R & D projects, and that only a relatively smallshare is targeted at labour markets and human capital formation, one of our variables ofinterest.

Data source: European Commission, DG Regional Policy

Figure 1: Priorites 2007-2013

Data source: European Commission, DG Regional Policy. Financial allocations may change during the following months aspartnership agreements are not finalized yet.

Figure 2: Thematic Objectives 2014-2020

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2.1 The Design of EU Regional Policy and its Impact on the Effective-ness of Regional Policy Funds

The main funding instruments are the structural funds, i.e., the European Regional De-velopment Fund (ERDF) and the European Social Fund (ESF), and the Cohesion Fund.Furthermore, the European Commission’s Directorate-General for Regional and UrbanPolicy governs the European Agricultural Guidance and Guarantee Fund (EAGG-F) anda Financial Instrument for Fisheries (FIF). Basically, the main funds are allocated to thefollowing objectives: i) Convergence (former Objective 1), ii) Regional competitivenessand employment (comparable to former Objective 2) and iii) European territorial coop-eration (comparable to former Objective 3). Contrarily to the second and third target,the eligibility of regions for funds aimed at convergence is clearly regulated as this type offunds is available for the relatively weakest regions with a GDP per capita below 75 % ofthe EU average.4

In general, the European Commission and national managing authorities approve op-erational programs with specific priorities, which motivate projects and how they maycontribute to the particular objective. Setting up these programs requires much adminis-trative effort and project planning expertise and makes it very hard to access the funds,especially for new member states. Furthermore, each project needs to be co-financed bypublic funds of the member state and, partly, the recipient itself, which again limits theregions’ ability to actually call transfers (get them paid out) that would be available forthem.5

2.2 The Differing Effectiveness of EU Regional Policy

Various studies have worked on the evaluation of the EU’s regional policy, however, withmixed empirical evidence (see, e.g., Sala-i Martin (1996), Ramajo et al. (2008), Espostiand Bussoletti (2008), Dall’Erba and Le Gallo (2008), Becker et al. (2013) and, for a sur-vey, Hagen and Mohl (2009)). Most studies find a positive, but often very small, effect ofthe regional transfers, while others find none or even a negative impact on convergence.Moreover, statistical significance varies. One reason may be that, on the one hand, theanalyses distinguish from each other by the consideration of different time periods, differ-ent definitions of structural funds receipt and databases on the national or regional level.On the other hand, they use different estimation approaches and methods. The majority ofstudies is based on neoclassical growth theory and estimates the impact of regional trans-fers on (unconditional or conditional) convergence of income levels or economic growth4Furthermore, this type of funds is directed to regions which are geographically isolated or, e.g., face severechallenges, e.g. Northern Ireland. Note that, having a deeper look at actual expenditure data, also fewregions which would not be eligible have received payments.

5The beneficiaries of the funds payments, mostly private companies, need to be reported starting from theprogramming period 2007-13. We are currently setting up a comprehensive database containing micro-level information on structural funds payments, which should be analyzed in more detail in subsequentstudies.

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across regions, with the assumption that the transfers stimulate investment and, by that,enhance growth. Methodologies used reach from econometric studies and macroeconomicsimulations (of the potential of the support of specific regions) to micro-level and casestudies.

In order to estimate the conditional convergence of regions in income levels or economicgrowth, various authors have analyzed (on country level) under which circumstances struc-tural funds and the Cohesion Fund are more effective in promoting convergence. Typically,variables which are expected to influence the consequences of regional transfers are hu-man capital (the share of workers with high education, as, e.g., in Becker et al. (2013)),institutional quality, corruption, the size of the R & D sector (expenditure), the degree ofdecentralization of administration, or openness to trade.

In this study, the effectiveness of structural funds allocated to the convergence objective isexamined. To be more precise, effectiveness is assumed to be given when there is a positiveeffect on the growth of regional GDP per capita across the programming period. We takeinto account the heterogeneity of European regions, and expect the absorptive capacity ofregions to affect the result of regional policy. In order to model absorptive capacity whichwill be explained in more detail in the next subsection, we use not only the share of peoplewith upper secondary and higher education in a region (human capital) but consider ifthey are actually productive, i.e., we use the employment rate of the people with uppersecondary and higher education. Further variables that we assume to determine absorptivecapacity are corruption (institutional quality) and the (size of) R & D expenditure in aregion. We expect that all these determinants of absorptive capacity are positively relatedwith the effectiveness of structural funds, apart from corruption which may lead to worseresults.

2.3 Absorptive Capacity and Employment

The concept of absorptive capacity of a country or region for external funds has beenwidely discussed in literature regarding foreign direct investments (FDI). Nguyen et al.(2009) have emphasized that there are principally two stages of absorption of FDI in aneconomy: the ability and possibility to implement FDI projects and, in a second step, toabsorb the technology or other benefits from FDI into own competencies. Crucial factorsfor the absorption capacity appear to be the technology of the country receiving FDI,the education of the labour force, the extent and quality of the country’s research anddevelopment sector, the development of the financial sector, as well as the institutionalsystem (De Mello Jr, 1997; Blomström and Kokko, 2003; Cohen and Levinthal, 1990; Fu,2008; Hermes and Lensink, 2003; Kalotay, 2000; Durham, 2004). Nguyen et al. (2009)group these characteristics into the absorptive capacity of the recipient country’s firms,comprising the technological development and the education level of workers, and nationalabsorptive capacity. The latter is driven by the technological level, next to human capacity

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(population size) and the financial and institutional system.

There is relatively little literature concerning the absorptive capacity of the EU’s regionaltransfers. Tosun (2014) has referred to the Europeanization literature, e.g., Bachtler et al.(2014), which treats the implementation of EU policies on the national level, as the firststream of literature changing the focus from the allocation of EU structural funds aloneto the way of their usage. Zaman and Georgescu (2009) have analyzed the situation inRomania as regards the structural funds absorption rate, i.e., the share of the structuralfunds assigned to a country or region that is actually retrieved. They indicate that therate is mainly determined by institutional variables: i) the financial capacity to co-financethe structural funds projects across the whole programming period and ii) the nationaland/or regional administrative capacity for preparing efficient project plans and coordi-nating the participants. There are various country studies on absorption rates, e.g., Milio(2007) and Bachtler et al. (2014), likewise, from a multinational perspective, Tosun (2014)has empirically shown that the administrative capacity of a country or region affects theabsorption of European Regional Development Funds (ERDF) for the period 2000-06.

Becker et al. (2013) have used the share of workforce with upper secondary and highereducation as well as a European quality of government index as crucial determinants of theabsorptive capacity of a region. However, employment rates of people with secondary andeven tertiary education have decreased over the last decades (OECD, 2006). Moreover,figures 3 and 4 show that this high level of education does not seem to be a guarantee foremployment anymore.

Figure 3: Share of labour force with ISCED levels 3 and higher

Data source: Eurostat. Considered ISCED levels are level 3 and higher. 248 NUTS-2 regions of EU-25 from 2000 to 2013 (76 of 3472observations missing). Due to data limitations (employment rate per education level, which is one main variable of interest), we skipthe 4 French overseas-departments, two Portuguese regions (Acores and Madeira) and two German regions (Chemnitz and Leipzig).

Thus, this study amplifies the human capital definition by Becker et al. (2013) by consider-ing employment, and uses the employment rates of the labour force with upper secondary

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Figure 4: Employment rates of labour force with at least upper secondary education (ISCEDlevel 3 and higher)

Data source: Eurostat. Considered ISCED levels are level 3 and higher. 248 NUTS-2 regions of EU-25 from 2000 to 2013 (101 of3472 observations missing). Due to data limitations (employment rate per education level, which is one main variable of interest),we skip the 4 French overseas-departments, two Portuguese regions (Acores and Madeira) and two German regions (Chemnitz andLeipzig).

and higher education (ISCED levels 3 and higher) as variable interacting with the effective-ness of convergence funds.6 Moreover, the corruption perception index by TransparencyInternational and the EQI 2013 by Charron et al. (2014a) are applied to proxy institutionalquality, as well as R & D expenditure in the European NUTS-2 regions is considered inorder to represent the priority put on innovation and higher education.

3 The Assignment of Convergence Funds and the Regres-sion Discontinuity Design

Our empirical analysis follows Becker et al. (2010) who have introduced a regression dis-continuity design (RDD) for the analysis of the effectiveness of structural funds aimedat convergence in European regions. The model allows for heterogeneity of the regionsand, thus, measures a heterogeneous local average treatment effect (HLATE). The RDDidentification is based on a non-random treatment assignment in a way that the eligibilityof entities for treatment depends on some threshold in one (or more) forcing variable(s).In the following, we outline the assignment procedure of convergence funds, which callsfor a regression discontinuity design. The estimation of the heterogeneous local averagetreatment effects are described in section 3.3.6For the measurement of the education level, the International Standard Classification of Education(ISCED 2011) by UNESCO Institute for Statistics (UIS) is used. Level 0: Early childhood educationaldevelopmen, pre-primary education; Level 1: Primary education; Level 2: Lower secondary education(general, vocational); Level 3: Upper secondary education (general, vocational); Level 4: Post-secondarynon-tertiary education (general, vocational); Level 5: Short-cycle tertiary education (general, vocational);Level 6: Bachelor’s or equivalent level; Level 7: Master’s or equivalent level; Level 8: Doctor or equivalentlevel.

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3.1 The Assignment Procedure as Forcing Variable

The convergence target (former Objective 1) captures the least developed regions, namelythose with a GDP per capita below the 75 % EU average (the relevant number for theprogramming period 2007-2013 is the EU-25 average for the years 2000 to 2002, for theperiod 2000-06 the EU-25 average for the years 1994-1996, for the period 1994-99 the EU-15 average for 1988-90, and for the MFF 1989-93 the EU-12 average for 1984-86) as wellas those that succeed the threshold due to the decreasing effect of the EU enlargement onthe EU average GDP per capita. Moreover, as in the previous programming period from2000-06, the geographically remote regions receive special funding, for instance the fourFrench overseas regions Guadeloupe, Martinique, Guyane and Reunion.

Becker et al. (2013) have analyzed the issue that the discontinuity in the eligibility criterion(forcing variable), i.e., the 75 % threshold of the average GDP per capita, does not matchexactly with the actually treated regions per programming period.7 They use a fuzzyRDD so that they are able to keep regions, which did not comply with the treatment rulein one or more of the three programming periods, in their sample and calculate treatmentprobabilities. In this study, we analyze the effectiveness of convergence funds assigned forthe programming periods 2000-06 and 2007-13, by means of increasing average GDP percapita growth. At this stage of research, we use a sharp regression discontinuity design,with GDP per capita below 75 % of the EU 25-average from 1994 to 1996 or rather from2000 to 2002 (and the EU-15 average from 1988-90 and EU-12 average from 1984-86, re-spectively) as instrument for actual treatment. In other words, regional GDP per capitais the forcing variable.8

7The following amendments to the basic eligibility criterion defined in Council Regulations 1260/1999point into this direction: Firstly, regions with a remote spatial location, relatively very low populationdensity, Northern Ireland due to societal challenges, as well as transition from previous Objective 1regions are supported. Secondly, the Commission decision in European Commission (1999) does notinclude the Eastern European countries that were to access the EU in 2004. However, data providedin the final regional expenditure study in SWECO (2008), which is provided on the EU regional policywebsite, shows that all of the Estonian, Latvian, Lithuanian, Maltese, Czech, Hungarian, Polish, Slovenianand Slovakian NUTS-2 regions have received Objective 1 structural funds from the European RegionalDevelopment Fund (ERDF) and European Social Fund (ESF). Therefore, the ten countries that havebecome member states of the EU in 2004 are included in the sample.

8To be more precise, we use GDP per capita per NUTS-2 regions by Cambridge Econometrics (transformedto NUTS-2010 classification). There are no regions in our sample with a GDP per capita exactly at thethreshold.

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Figure 5: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita 1994-96) and the actual assignment of Convergence funds in 2000-06

Data source: DG Regional Policy, Eurostat. Data for EU-25 on NUTS-2 level (248 regions), excludingRomania, Bulgaria and Croatia. The four French overseas departments Guadeloupe, Martinique, Guyaneand Reunion, two Portuguese regions Acores and Madeira, as well as two German regions are excluded

due to a lack of data on employment rates per education level.

Figure 6: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita 2000-02) and the actual assignment of Convergence funds in 2007-13

Data source: DG Regional Policy, Eurostat. Data for EU-25 on NUTS-2 level (248 regions), excludingRomania, Bulgaria and Croatia. The four French overseas departments Guadeloupe, Martinique, Guyaneand Reunion, two Portuguese regions Acores and Madeira, as well as two German regions are excluded

due to a lack of data on employment rates per education level.

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For the programming period from 2007 to 2013, we find 66 regions (in total we consider248 regions in EU-25 countries), whose GDP per capita averaged over the years 2000 to2002 is lower than the EU-25 average for the same period (see Figure 6). 63 of these 66regions, on which the forcing variable assigns treatment, actually receive structural fundspayment (Malta, and one region each in Hungary and Slovakia are the exceptions). How-ever, note that 15 regions, including the Austrian region Burgenland, have been treatedalthough they do not meet the treatment rule, i.e., the forcing variable does not lie below75 % of the EU-25 average.

In the multi-annual financial framework 2000 to 2006, 67 regions (of 248) have a GDP percapita below the crucial value of 75 % of the EU-25 average from 1994 to 1996 (forcingvariable). 66 of them are treated, while Bratislava, a Slovak region, contrarily to its GDPper capita value, was not eligible for Convergence funds. Note that 37 regions should nothave received funds according to the treatment rule but did.

For the sharp RDD, we use the forcing variable as “instrument” for treatment, which isonly indirectly correlated with the outcome variable of rising average GDP in the actualtreatment period. Thus, e.g., for 2007-13, we eliminate 18 regions from our sample andjust keep the regions complying with the treatment rule.9 For the multi-financial frame-woork 2000 to 2006, we exclude 38 regions from the analysis, from which 37 succeed thethreshold of the forcing variable but receive funds and one Slovak region should have re-ceived convergence funds according to the treatment rule (see Figure 5. This leads us tothe treatment rule used for the estimation of the local average treatment effect (LATE)which is a deterministic function of the forcing variable in the sharp design (Brand andThomas, 2013).

Ti = 1(xi ≤ x0) (1)

where Ti is a binary variable indicating treatment or non-treatment of a region i and x0

is the EU-25 average of GDP per capita in the years 1994 to 1996 or rather 2000 to 2002.In our case, the treatment status depends on only one forcing variable, while it is possibleto extend it to a vector of forcing variables (see Becker et al., 2013 for the general case).

3.2 The Outcome Variable and the Heterogeneity of the Treatment Ef-fect

The outcome variable is derived, like the forcing variable, from regional GDP per capita.Following Becker et al. (2013), we calculate the average growth per programming period,9The 18 regions eliminated are Burgenland (Austria), Province Hainaut (Belgium), Cyprus (Cyprus),Brandenburg, Mecklenburg-Vorpommern, Lueneburg, Sachsen-Anhalt and Thueringen (Germany), StereaEllada, Attiki and Notio Aigaio (Greece), Basilicata (Italy), Cornwall and Isles of Scilly, West Wales andThe Valleys, Highlands and Islands (UK) and, finally, Bratislava (SK), Malta and Koezep-Magyarorszag(HU).

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i.e., the mean of GDP growth rates from 2000 to 2006 and 2007 to 2012.10

As discussed in section 2, we use the employment rate of the population aged between 25and 64 years with at least upper secondary education as one of three interaction variables.The Spearman correlation between this measure in 2000 and 2013 turns out to be signif-icant with a coefficient of 0.97, which shows it to be quite stable over time. Therefore,following Becker et al. (2013), we consider the employment rate as a time-invariant as wellas -variant interaction variable. Both are measured as deviations from the sample mean.

The second interaction variable suggested by literature is the quality of administration ina region. We apply the corruption perception index (CPI) by Transparency Internationalthat is created based on perceptions of the degree of corruption in the public administra-tion of a country and interact it with the EQI 2013. CPI data is collected by different(also international) institutions, which is intended to increase objectivity (TransparencyInternational (2012)). This indicator offers the advantage that it is provided on a yearlybasis since 2000 for most countries, whereas it is only available on the national level.However, especially corruption may influence the effectiveness of structural funds due tothe usage of the funds by the local (national) administrative authorities and there maybe drawn interesting conclusions from only considering this characteristic of the nationalpublic service. To be able to consider the regional level, we interact it with the EQI 2013.We also calculate a time-invariant (regional average over time) and time-variant interac-tion variable that is expressed as deviation from the sample mean.

Moreover, R & D expenditure in a region serves as a third interaction variable. We expectthat more R & D expenditure increases the absorptive capacity and, thus, the positiveeffect of structural funds on regional GDP per capita growth. As the data is not availableon a regular annual basis for all reigons, we do not provide a time-variant measure for thisvariable.

Table 2 shows descriptive statistics of the forcing and interaction variables used in theregression. Apparently, there is heterogeneity in the employment rate of people with uppersecondary and higher education (ISCED level >= 3), the institutional quality measure andR & D expenditure. E.g., the employment rate varies from the minimum of 54.76 % in aregion to a maximum value of about 87 %.102013 data on GDP per capita is not available from Cambridge Econometrics.

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Table 1: Descriptive Statistics

Mean Std Dev Min Max Obs.Regional avg. GDP pc relative to 1.00 0.47 0.15 3.58 496EU-25 avg. 1994-96/2000-02Regional avg. employment rate of ISCED >= 3 (/%) 76.14 5.35 54.76 86.97 992Regional avg. employment rate of ISCED 0-2 (%) 53.85 9.58 20.37 86.66 992Regional avg. CPI x EQI 2013 3.98 5.78 -10.72 26.44 980Country avg. Corruption Perception Index (CPI) 6.85 1.67 4.17 9.51 992Regional avg. EQI 2013 0.41 0.80 -2.26 2.14 980Regional avg. R & D expenditure relative to GDP (%) 1.52 1.26 0.10 7.50 984

Data: Cambridge Econometrics (transformed to NUTS-2010 classification), Eurostat, Transparency International, Charron et al.(2014b). Sample 2000-06 and 2007-13: Averages of interaction variables are calculated for EU-25 (248 NUTS-2 regions), from2000-2013 (employment rates per education level), 2000-2011 (CPI) or rather 2000-2013 (R & D expenditure relative to GDP). Thefour French overseas departments, two Portuguese regions Acores and Madeira, as well as two German regions are not included inthe dataset. EQI2013: Data is missing for two Spanish regions (Ciudades Autónomas de Ceuta y Mellila, ES63, ES64) and oneFinnish region (Helsinki-Uusimaa, FI1B). R & D expenditure: Data for two German regions (in Bavaria, DE22, DE23) is missing.

3.3 The Heterogeneous Local Average Treatment Effects (HLATE) Model

The treatment rule discussed indicates heterogeneity of the units before treatment, i.e.,the assignment of convergence funds is not random but depends on the regions’ averageGDP per capita during a certain period (see Brand and Thomas (2013) for a discussion ofthe different types of heterogeneity in the treatment effects estimation framework). Beckeret al. (2013) have applied and introduced the heterogeneous local average treatment effectsapproach (HLATE) for their research purpose. The HLATE considers heterogeneity in theresponse to treatment. This type of heterogeneity across the regions is modeled by meansof absorptive capacity and employment rates, the interaction variables in our model. Theexpected outcome (in our case, the average GDP per capita growth over the programmingperiods 2000-06 and 2007-13) depends on the (difference between) the regional forcingvariable and its EU-25 average in the years 1994 to 1996 and 2000 to 2002 (as instrumentfor the treatment status) and vectors of interaction variables, measured as differencesbetween the regional values and the sample mean. At this stage of research, a sharpRDD is used, i.e., the treatment rule is applied strictly and we eliminate non-compliantregions from the sample. The following equation describes the HLATE model (notationis according to Becker et al. (2013), p. 35):

yi = α+ f0(x̃i) + h0(z̄i) + Ti[β + f∗1 (x̃i) + h∗

1(z̄i)] + εi (2)

where yi denotes the average GDP growth of a region in 2007 to 2011 (outcome), Ti =1(xi ≤ x0) (sharp RDD) and subscript 1 stands for treatment, 0 for non-treatment. f0(x̃i),h0(z̄i), f∗

1 (x̃i) and h∗1(z̄i) are sufficiently smooth polynomial functions in terms of the

deviation from the 75 % threshold of the forcing variable xi or rather the sample mean ofthe interaction variables zi and f∗

1 (·) ≡ f1(·) − f0(·) and h∗1(·) ≡ h1(·) − h0(·).

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4 Data

For the treatment eligibility, i.e., the forcing variable GDP per capita in the specific thresh-old years 2000-02 (EU-25; for MFF 2007-13), 1994-96 (EU-25; for MFF 2000-06), 1988-90(EU-15; for MFF 1994-99) or rather 1984-86 (EU-12; for MFF 1989-93), we use data onGDP and population by Cambridge Econometrics.11 The same data source determinesthe outcome variable, namely, regional average GDP per capita growth over each MFF(2007-12 for the latest, as data is not available yet for 2013).

In order to see which regions are compliers with the treatment rule, that is, regions whoseGDP per capita lies above 75 % of the EU-average and that do not receive Objective1/Convergence funds and vice versa, we use structural funds expenditure data which areavailable for 2007-13 and 2000-06, and, for the two previous MFFs 1989-93 and 1994-99,lists of eligible regions in EU regulations (Council Regulation (EEC) No. 2081/93 of 20July 1993 and Council Regulation (EEC) No. 2052/88 of 24 June 1988). Regarding theMFF 2007-13, we use data on the projects that have actually been financed per region,available on the website of the European Commission’s DG Regional Policy, and took thisas indicator for the treatment in a region. For 2000-06, the DG Regional Policy providesa regional expenditure study (SWECO, 2008) and we consider a region as treated if thereis a corresponding expenditure reported under Objective 1. We do so because examiningthese data sources shows that there are regions receiving payments that are not eligiblefollowing the relevant EU regulations (Council Regulation (EC) No. 1083/2006 of 11 July2006 or rather, for 2000-06, Commission Decision 1999/502/EC of 1 July 1999), and wewant to capture the actual treatment or non-treatment.

For the interaction variables, we get data on the regional distribution of the highest at-tained level of education (2000-2014) and regional employment rates per education level(2000-2013) from Eurostat. As already indicated above, we measure education accordingto the International Standard Classification of Education (ISCED 2011) by UNESCO In-stitute for Statistics (UIS).12 Moreover, data on regional R & D expenditure in all sectorsas a percentage of GDP (2000-2013) is taken from Eurostat.13

Finally, the variable measuring institutional quality is based on the corruption perceptionindex (CPI) by Transparency International which is available on the national level (2000-11In order to be comparable with further data sources, we transform it to the NUTS-2010 classification ofEuropean regions.

12We refer to upper and secondary and higher education as ISCED levels 3-8. Level 0: Early childhoodeducational developmen, pre-primary education; Level 1: Primary education; Level 2: Lower secondaryeducation (general, vocational); Level 3: Upper secondary education (general, vocational); Level 4: Post-secondary non-tertiary education (general, vocational); Level 5: Short-cycle tertiary education (general,vocational); Level 6: Bachelor’s or equivalent level; Level 7: Master’s or equivalent level; Level 8: Doctoror equivalent level.

13Note that there are missing data points for different years across regions. That is why, as mentionedabove, we do not calculate a time-variant measure for the R & D expenditure interaction.

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2013) and the European Quality of Government Index 2013 (EQI2013) by Charron et al.(2014a). The latter is provided on the regional level, for one point in time. Combiningboth data sources allows capturing both the time and the regional dimension.

5 Results

Tables 2, 3 and 4 indicate the estimation results according to the HLATE specification inEquation 2 for the programming periods 2000-06 and 2007-13. We use second- to fourth-order polynomials of the vectors of the forcing variable in terms of deviation from the 75 %EU-25 average and of the interaction variables as deviations from their sample mean. Thetime-invariant measures of the employment rate of the labour force with upper secondaryand higher education and institutional quality are calculated by taking the difference be-tween the regional average over the years 2000 to 2013 (employment rates) and 2000 to2011 (CPI multiplied by EQI 2013), while the time-variant measure considers the actualvalues across the years. As already indicated above, we estimate a sharp RDD takinginto account only regions that are compliant with the treatment rule for the assignmentof convergence funds in the programming periods 2000-2006 and 2007-2013.

Firstly, for the estimation of the HLATE considering the employment rate of the higheducated labour force, describing the heterogeneous absorptive capacity of the regions,Table 2 shows that we do not find a significant effect on average GDP per capita growth2000-06 or rather 2007-13 in most specifications. However, robustness checks consideringall programming periods (including 1989-93 and 1994-99) suggest a significantly positiveeffect of the treatment in all specifications. For the estimation with time-variant inter-action variables, we even find negative effects of the treatment. This may be due to thefact that GDP per capita growth in the programming period 2007-13 has decreased sig-nificantly with the crisis. Therefore, in a next step, we will examine this issue and let thetreatment effect vary over programming periods.

Having a look at the significant interaction effect with the employment rate, a higher rateamplifies the positive effect of the treatment. This holds true for all specifications and pe-riods considered. For the 3rd-order polynomial, the coefficient can be interpreted in thatway that raising the employment rate of the higher educated population by one standarddeviation above the average would cause the HLATE to be 0.90 percentage points higherthan the average HLATE. The coefficient of the treatment’s interaction effect appearsto be higher for the employment rate of the highly educated people than their share inpopulation in most model variants, pointing to the fact that creating jobs beside fosteringhigher education is crucial.

Secondly, though, for the estimation of the interaction effect with the institutional qualitymeasure (corruption perception index (CPI) multiplied by the EQI 2013), the results do

15

not reveal a clear picture. For only one specification, the coefficient is positive, pointing tohigher GDP per capita growth when corruption is lower.14 Contrarily, robustness checksconsidering all four programming periods reveal a different picture with a positive signifi-cant effect of both the treatment and its interaction effect with institutional quality. Thisresult emphasizes the need for further research on the behaviour and organization of thepublic sector relating to the administration of structural funds.

Thirdly, the interaction of structural funds assignment with R & D expenditure seems tofollow a clear pattern, namely, that higher R & D expenditure strengthens the positiveeffect of structural funds transfers in a region. However, this positive effect only turnssignificant when considering all four programming periods since 1989. Note that theseare preliminary results and especially data on R & D expenditure may be exposed tomeasurement issues. That is, deeper analysis of this issue is necessary (and ongoing).

6 Conclusion and Future Research

In this study, we aim at combining the concept of absorptive capacity for regional transfers,which can be described by human capital, institutional quality and the priority put on theR & D sector, with employment rates of the labour force with different education levels.To be more precise, we have a deeper look at the development of employment rates of theavailable workforce with upper secondary and higher education, which have stagnated inthe last few years. This measure, together with the corruption perception index interactedwith the European quality of government index 2013 and R & D expenditure is used todepict heterogeneity of European NUTS-2 regions in the context of the effectiveness of EUstructural funds.

The results of the empirical analysis in section 5 indicate that the average heterogeneouslocal average treatment effect of an assignment of convergence funds to a region is not sig-nificant for increasing regional average GDP per capita growth. We have been interestedwhether the heterogeneity of NUTS-2 regions is reflected in the response to treatmentwith structural funds aimed at convergence. We show that a high employment rate of thelabour force with upper secondary and higher education (with an interaction effect thatis significant across all specifications), and more R & D expenditure seem to strengthenthe treatment effect of converge funds, and thereby are associated with higher GDP percapita growth. For (perceived) corruption and public administration, the result is notthat clear. The majority of specifications estimated suggests that lower corruption woulddecrease the outcome variable.

This study hopes to provide first insights, with various issues remaining for future research:Firstly, a fuzzy regression discontinuity design will be estimated and we will take into14Remember that a higher value of the CPI means lower perceived corruption.

16

account the share of allocated funds to eligible regions that has been actually paid out.Secondly, we will let the treatment effect vary over programming periods as the first resultsshow negative effects of the treatment in the crisis programming period 2007-13. Thirdly,the model will be applied to a micro-level database on structural funds transfers includinginformation on the company receiving the funds and the project implemented with it.Finally, it is likely that there is spatial correlation in the treatment status of the regionswhich should be taken into account.

Table 2: Economic Growth, Structural Funds and (Employment Rates of) Labour Forcewith ISCED >= 3 [Preliminary results]

Second-order polynomial Third-order polynomial Fourth-order polynomialEMPL USEC EMPL USEC EMPL USEC

Employment rate of labour force with education level (ISCED) >= 3 (Time-invariant and -variant)

Time-invariant - 2000-2006, 2007-13Treatment 0.008 0.008 0.026∗ 0.004 0.022 0.002

(0.007) (0.007) (0.013) (0.014) (0.016) (0.015)Treat x EMPL/USEC 0.103∗∗∗ 0.130∗∗∗ 0.169∗∗∗ 0.129∗∗∗ 0.237∗∗∗ 0.232∗∗∗

(0.022) (0.020) (0.045) (0.042) (0.055) (0.068)EMPL/USEC 0.015 0.047∗∗∗ 0.030 0.061∗∗∗ 0.020 0.065∗∗∗

(0.018) (0.005) (0.030) (0.008) (0.028) (0.007)Constant 0.008∗∗∗ 0.012∗∗∗ 0.008∗∗ 0.014∗∗∗ 0.002 0.010∗∗

(0.002) (0.001) (0.003) (0.003) (0.004) (0.004)Observations 440 440 440 440 440 440R2 0.222 0.303 0.238 0.305 0.253 0.325Robustness Check: Time-variant - 2000-2006, 2007-2013Treatment −0.005 −0.002 0.009 0.003 0.016∗∗ 0.013

(0.005) (0.007) (0.006) (0.012) (0.008) (0.014)Treat x EMPL/USEC 0.070∗∗ 0.079∗∗∗ 0.106∗∗∗ 0.069∗∗ 0.064∗∗∗ 0.232∗∗∗

(0.030) (0.018) (0.028) (0.029) (0.037) (0.047)EMPL/USEC −0.019 0.019∗∗∗ −0.032∗∗ 0.024∗∗ −0.041∗∗ 0.019

(0.013) (0.006) (0.015) (0.011) (0.016) (0.011)Constant 0.008∗∗∗ 0.009∗∗∗ 0.006 0.010∗∗∗ 0.000 0.002

(0.002) (0.002) (0.003) (0.003) (0.005) (0.005)Observations 2997 3013 2997 3013 2997 3013R2 0.191 0.220 0.215 0.228 0.223 0.285Notes: Pooled OLS estimation. *** denotes significance at the 1% level, ** at the 5% level, * at the10% level. Standard errors are clustered at regional level. Dependent outcome variable: regionalaverage GDP per capita growth per programming period, i.e., 2000-06 and 2007-12 (data for 2013are not available by Cambridge Econometrics). Treatment: forcing variable x̃, i.e., regional averageGDP per capita 1994-96 or rather 2000-02 as deviation from the 75 % EU-25 average 1994-96 /2000-02 (Cambridge Econometrics). EMPL: employment rate of share of labour force (people agedbetween 25 and 64 years) with upper secondary and higher education (ISCED levels 3 and higher)as deviation from the sample mean over years 2000 to 2013 (Eurostat). USEC: share of labourforce with upper secondary and higher education as deviation from the sample mean. The sampleis based on EU-25, i.e., the EU member states without Bulgaria, Croatia and Romania, due tolimited data availability. Furthermore, 56 regions have been excluded as they do not comply withthe treatment rule (sharp RDD). Suffering from further data limitations, we also do not considerthe four French overseas departments, two Portuguese and two German regions. Thus, the resultingsharp RDD sample comprises 440 NUTS-2 regions.

17

Table 3: Economic Growth, Structural Funds and Institutional Quality [Preliminary re-sults]

Second-order polynomial Third-order polynomial Fourth-order polynomialCorruption Perception Index x EQI 2013 (Time-invariant and time-variant)

Time-invariant - 2000-2006, 2007-13Treatment −0.003 0.007 0.015∗

(0.006) (0.007) (0.008)Treatment x EQICPI 0.001 −0.001 −0.001

(0.002) (0.001) (0.001)EQICPI 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗

(0.000) (0.000) (0.000)Constant 0.009∗∗∗ 0.010∗∗∗ 0.006

(0.001) (0.003) (0.004)Observations 434 434 434R2 0.200 0.218 0.224Robustness Check: Time-variant - 2000-2006, 2007-2013Treatment 0.007 0.015∗ 0.008

(0.007) (0.008) (0.010)Treatment x EQICPI −0.001 −0.001 0.000

(0.001) (0.001) (0.002)EQICPI 0.001∗∗∗ 0.001∗∗∗ 0.001∗∗∗

(0.000) (0.000) (0.000)Constant 0.010∗∗∗ 0.006 0.009

(0.003) (0.004) (0.006)Observations 3038 3038 3038R2 0.218 0.224 0.229Notes: Pooled OLS estimation. *** denotes significance at the 1% level, ** at the 5% level, * at the10% level. Standard errors are clustered at regional level. Dependent outcome variable: regionalaverage GDP per capita growth per programming period, i.e., 2000-06 and 2007-12 (data for 2013are not available by Cambridge Econometrics). Treatment: forcing variable x̃, i.e., regional averageGDP per capita 1994-96 or rather 2000-02 as deviation from the 75 % EU-25 average 1994-96 /2000-02 (logs) (Cambridge Econometrics). EQICPI: corruption perception index multiplied withEQI 2013 as deviation from the sample mean over years 2000 to 2011 (CPI)15 (TransparencyInternational). The sample is based on EU-25, i.e., the EU member states without Bulgaria,Croatia and Romania, due to limited data availability. Furthermore, 56 regions have been excludedas they do not comply with the treatment rule (sharp RDD). Suffering from further data limitations,we also do not consider the four French overseas departments and two German regions. Thus, theresulting sharp RDD sample comprises 440 NUTS-2 regions.

18

Table 4: Economic Growth, Structural Funds and R & D Expenditure [Preliminary results]

Second-order polynomial Third-order polynomial Fourth-order polynomialR & D expenditure as % of GDP

Time-invariant - 2000-2006, 2007-13Treatment 0.000 0.013 0.018

(0.006) (0.010) (0.013)Treatment x R & D 0.010 0.022 0.008

(0.006) (0.014) (0.031)R & D 0.002∗∗∗ 0.003∗∗∗ 0.003∗∗∗

(0.001) (0.001) (0.001)Constant 0.009∗∗∗ 0.010∗∗∗ 0.002

(0.002) (0.003) (0.005)Observations 436 436 436R2 0.190 0.208 0.214Notes: Pooled OLS estimation. *** denotes significance at the 1% level, ** at the 5% level, * at the10% level. Standard errors are clustered at regional level. Dependent outcome variable: regionalaverage GDP per capita growth per programming period, i.e., 2000-06 and 2007-12 (data for 2013are not available by Cambridge Econometrics). Treatment: forcing variable x̃, i.e., regional averageGDP per capita 1994-96 or rather 2000-02 as deviation from the 75 % EU-25 average 1994-96 /2000-02 (logs) (Cambridge Econometrics). R & D: R & D expenditure in % of GDP as deviationfrom the sample mean over years 2000 to 2013 (Eurostat). R & D expenditure data suffer fromNA’s that need to be examined further. The sample is based on EU-25, i.e., the EU member stateswithout Bulgaria, Croatia and Romania, due to limited data availability. Furthermore, 56 regionshave been excluded as they do not comply with the treatment rule (sharp RDD). Suffering fromfurther data limitations, we also do not consider the four French overseas departments and twoGerman regions. Thus, the resulting sharp RDD sample comprises 440 NUTS-2 regions.

19

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21

7 Appendix

7.1 HLATE Assumptions

Becker et al. (2013) name three assumptions for the estimation of the HLATE (see Beckeret al. (2013), p. 35):

Firstly, the main assumption for identification of a RDD is that the expected outcome inregions with treatment or non-treatment status is continuous at the vector of the forcingvariable.

Secondly, one assumption for the appropriate application of the RDD is that the distribu-tions of the so-called compliers (regions with a forcing variable below or above the 75 %threshold) and the interaction variables, which picture the heterogeneity of the treatmenteffect, are not systematically correlated. Indeed, we find that the correlation coefficientbetween the compliers with the treatment rule for 2007-13 and the (time-invariant) inter-action variables (employment rate of the labour force with upper secondary and highereducation, EQI2013 interacted with CPI and R & D expenditure) shows to be significantbut very low (the correlation coefficient with the time-invariant measure of the employ-ment rate amounts to -0.02, that of the measure for institutional quality and corruption is-0.09, and that of R & D expenditure 0.08, respectively). Furthermore, see the followingtables that do not show any sign of a discontinuity in the relationship between actualtreatment assignment and the interaction variables.

Thirdly, the error term in equation 2 needs to be uncorrelated with the vectors of inter-action variables z̄, which is controlled for by estimating with fixed effects.

We use the HLATE model for testing whether the treatment with convergence funds in-creases the average GDP per capita growth of a region during the programming period,and whether the interaction variables, assumed to depict the absorptive capacity of theregions, influence this local average treatment effect.

22

Figure 7: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita from 1994 to 1996) and the regional average employment rates of people

with upper secondary and higher education

Figure considering 248 regions 2000-2013 (EU-25). Education data missing in 14 regions (76 obs):Denmark (5: 2000-2006), Slovenia (2: 2000), Germany (4: 2000,2001; 1: 2004), Italy (2: 2000),Luxembourg (1: 2000), Malta (1: 2000), Finland (1: 2000-2004; 1: 2000-2001, 2003-2005, 2006-2013),France (1: 2000-2003, 2008), Slovenia (2: 2000) UK (2: 2000, 2001). Additionally, we miss 25 obs of theemployment rate: Italy (2: 2001-2004) Finland (2: 2000-2004), France (1: 2004), UK (2: 2002-2004).Data: Cambridge Econometrics, Eurostat.

Figure 8: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita from 2000 to 2002) and the regional average employment rates of people

with upper secondary and higher education

Figure considering 248 regions 2000-2013 (EU-25). Education data missing in 14 regions (76 obs):Denmark (5: 2000-2006), Slovenia (2: 2000), Germany (4: 2000,2001; 1: 2004), Italy (2: 2000),Luxembourg (1: 2000), Malta (1: 2000), Finland (1: 2000-2004; 1: 2000-2001, 2003-2005, 2006-2013),France (1: 2000-2003, 2008), Slovenia (2: 2000) UK (2: 2000, 2001). Additionally, we miss 25 obs of theemployment rate: Italy (2: 2001-2004) Finland (2: 2000-2004), France (1: 2004), UK (2: 2002-2004).Data: Cambridge Econometrics, Eurostat.

23

Figure 9: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita from 1994 to 1996) and the regional average Corruption Perception Index

(CPI)

Figure considering 248 regions from 2000 to 2011 (EU-25; CPI not available for Malta in the years 2000to 2003 and for Cyprus from 2000 to 2002; EQI2013 not available for two Spanish regions and oneFinnish region). Data: Eurostat, Transparency International.

Figure 10: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita from 2000 to 2002) and the regional average Corruption Perception Index

(CPI)

Figure considering 248 regions from 2000 to 2011 (EU-25; CPI not available for Malta in the years 2000to 2003 and for Cyprus from 2000 to 2002; EQI2013 not available for two Spanish regions and oneFinnish region). Data: Eurostat, Transparency International.

24

Figure 11: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita from 1994 to 1996) and the regional average Corruption Perception Index

(CPI)

Figure considering 248 regions (EU-25; not annually; not available for two German regions in Bavaria).Data: Eurostat.

Figure 12: Relationship between forcing variable (75 % of the EU-25 average of GDP percapita from 2000 to 2002) and the regional average Corruption Perception Index

(CPI)

Figure considering 248 regions (EU-25; not annually; not available for two German regions in Bavaria).Data: Eurostat.

25

7.2

Rob

ustnessChe

cks

Tab

le5:

GDP

percapita

grow

th,E

mploymentan

dEd

ucation-t

ime-invaria

ntan

dtim

e-varia

nt

(Employmentrate

of)workforce

with

educationlevel(ISCED

)>=

3(T

ime-invaria

ntan

d-variant)

2nd-orderpo

lyno

mial

3rd-orderpo

lyno

mial

4th-orderpo

lyno

mial

EM

PL

USE

CE

MP

LUSE

CE

MP

LUSE

CT

ime-

inva

rian

t-

2000

-200

6,20

07-1

3Tr

eatm

ent

0.00

80.

008

0.02

6∗0.

004

0.02

20.

002

(0.0

07)

(0.0

07)

(0.0

13)

(0.0

14)

(0.0

16)

(0.0

15)

Tre

atx

Em

ploy

ed/

ISC

ED

>=

30.

103∗∗

∗0.

130∗∗

∗0.

169∗∗

∗0.

129∗∗

∗0.

237∗∗

∗0.

232∗∗

∗

(0.0

22)

(0.0

20)

(0.0

45)

(0.0

42)

(0.0

55)

(0.0

68)

Employed

/ISCED

>=3

0.01

50.

047∗∗

∗0.

030

0.06

1∗∗∗

0.02

00.

065∗∗

∗

(0.0

18)

(0.0

05)

(0.0

30)

(0.0

08)

(0.0

28)

(0.0

07)

Con

stan

t0.

008∗∗

∗0.

012∗∗

∗0.

008∗∗

0.01

4∗∗∗

0.00

20.

010∗∗

(0.0

02)

(0.0

01)

(0.0

03)

(0.0

03)

(0.0

04)

(0.0

04)

Observatio

ns440

440

440

440

440

440

R2

0.22

20.

303

0.23

80.

305

0.25

30.

325

Robu

stne

ssC

heck

:T

ime-

vari

ant

-20

00-2

006,

2007

-13

Treatm

ent

−0.

005

−0.

002

0.00

90.

003

0.01

6∗∗0.

013

(0.0

05)

(0.0

07)

(0.0

06)

(0.0

12)

(0.0

08)

(0.0

14)

Tre

atx

Em

plR

ate

ISC

ED

>=

30.

070∗∗

0.07

9∗∗∗

0.10

6∗∗∗

0.06

9∗∗0.

064∗∗

0.23

2∗∗∗

(0.0

30)

(0.0

18)

(0.0

28)

(0.0

29)

(0.0

37)

(0.0

47)

EmplRateISCED

>=3

−0.

019

0.01

9∗∗∗

−0.

032∗∗

0.02

4∗∗−

0.04

1∗∗0.

019

(0.0

13)

(0.0

06)

(0.0

15)

(0.0

11)

(0.0

16)

(0.0

11)

Con

stan

t0.

008∗∗

∗0.

009∗

∗∗

0.00

60.

010∗∗

∗0.

000

0.00

2(0.0

02)

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

05)

(0.0

05)

Observatio

ns2997

3013

2997

3013

2997

3013

R2

0.19

10.

220

0.21

50.

228

0.22

30.

285

Note:

Pooled

OLS

,followingBeckere

tal.(2013),sha

rpdesig

n,2000-06an

d2007-13;

***significan

tat

1%,*

*5%,*

10%.Stan

dard

errors

areclusteredat

region

allevel.

26

Tab

le6:

GDP

percapita

grow

th,E

mploymentan

dEd

ucationover

four

programmingpe

riods

(Employmentrate

of)workforce

with

educ

ationlevel(ISCED

)>=

3(T

ime-invaria

nt,a

llpe

riods)

2nd-orde

rpo

lyno

mial

3rd-orde

rpo

lyno

mial

4th-orde

rpo

lyno

mial

EM

PL

USE

CE

MP

LUSE

CE

MP

LUSE

CTr

eatm

ent

0.00

8∗∗∗

0.00

6∗∗∗

0.00

9∗∗∗

0.00

6∗∗0.

010∗∗

∗0.

005

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

03)

(0.0

03)

(0.0

03)

Tre

atx

Em

ploy

ed/

ISC

ED

>=

30.

061∗∗

∗0.

057∗∗

∗0.

090∗∗

∗0.

064∗∗

∗0.

068∗∗

∗0.

038∗

(0.0

11)

(0.0

09)

(0.0

12)

(0.0

10)

(0.0

25)

(0.0

19)

Employed

/ISCED

>=3

0.04

9∗∗∗

0.03

5∗∗∗

0.05

8∗∗∗

0.04

3∗∗∗

0.05

9∗∗∗

0.05

0∗∗∗

(0.0

06)

(0.0

05)

(0.0

09)

(0.0

09)

(0.0

09)

(0.0

08)

Con

stan

t0.

014∗∗

∗0.

014∗∗

∗0.

017∗∗

∗0.

016∗∗

∗0.

016∗∗

∗0.

016∗∗

∗

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

02)

Observatio

ns79

079

079

079

079

079

0R

20.

205

0.20

00.

222

0.20

10.

214

0.20

7

Note:

Four

prog

rammingpe

riods

1989

-93,

1994

-99,

2000

-06,

2007

-13.

Pooled

OLS

,fol-

lowingBeckeret

al.(201

3),s

harp

desig

n;**

*signific

antat

1%,*

*5%,*

10%.Stan

dard

errors

areclusteredat

region

allevel.

27

Tab

le7:

GDP

percapita

grow

than

dInstitu

tiona

lQua

lity-t

ime-invaria

ntan

dtim

e-varia

nt

Europe

anQua

lityof

Governm

entInde

x20

13xCorruptionPe

rcep

tionInde

x2n

d-orde

rpo

lyno

mial

3rd-orde

rpo

lyno

mial

4th-orde

rpo

lyno

mial

Tim

e-in

vari

ant

-20

00-0

6,20

07-1

3Tr

eatm

ent

−0.

003

0.00

70.

015∗

(0.0

06)

(0.0

07)

(0.0

09)

Tre

atm

ent

xE

QIC

PI

0.00

1−

0.00

1−

0.00

1(0.0

02)

(0.0

01)

(0.0

01)

EQIC

PI0.

001∗∗

∗0.

001∗∗

∗0.

001∗∗

∗

(0.0

00)

(0.0

00)

(0.0

00)

Con

stan

t0.

009∗∗

∗0.

010∗∗

∗0.

006

(0.0

01)

(0.0

03)

(0.0

04)

Observatio

ns43

443

443

4R

20.

199

0.21

80.

224

Robu

stne

ssC

heck

:T

ime-

vari

ant

-20

00-2

006,

2007

-13

Treatm

ent

−0.

003

0.00

70.

015∗

(0.0

06)

(0.0

07)

(0.0

08)

Tre

atm

ent

xE

QIC

PI

0.00

1−

0.00

1−

0.00

1(0.0

02)

(0.0

01)

(0.0

01)

EQIC

PI0.

001∗∗

∗0.

001∗∗

∗0.

001∗∗

∗

(0.0

00)

(0.0

00)

(0.0

00)

Con

stan

t0.

009∗∗

∗0.

010∗∗

∗0.

006

(0.0

01)

(0.0

03)

(0.0

04)

Observatio

ns30

3830

3830

38R

20.

199

0.21

80.

224

Note:

Pooled

OLS

,followingBeckeret

al.(201

3),sha

rpde

sign,

2000

-06,

2007

-13;

***sig-

nific

antat

1%,*

*5%,*

10%.Stan

dard

errors

areclusteredat

region

allevel.

28

Tab

le8:

GDP

percapita

grow

than

dInstitu

tiona

lQua

lityover

four

programmingpe

riods

Europe

anQua

lityof

Governm

entInde

x20

13xCorruptionPe

rcep

tionInde

x2n

d-orde

rpo

lyno

mial

3rd-orde

rpo

lyno

m.

4th-orde

rpo

lyno

m.

5th-orde

rpo

lyno

m.

Tim

e-in

vari

ant

-19

89-9

3,19

94-9

9,20

00-0

6,20

07-1

3Tr

eatm

ent

0.01

1∗∗∗

0.00

9∗∗∗

0.01

0∗∗∗

0.00

8∗∗∗

(0.0

02)

(0.0

02)

(0.0

03)

(0.0

03)

Tre

atm

ent

xE

QIC

PI

0.00

1∗∗∗

0.00

1∗∗∗

0.00

1∗∗∗

0.00

1∗∗

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

EQIC

PI0.

001∗∗

∗0.

001∗∗

∗0.

001∗∗

∗0.

001∗∗

∗

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Con

stan

t0.

012∗∗

∗0.

014∗∗

∗0.

014∗∗

∗0.

016∗∗

∗

(0.0

01)

(0.0

02)

(0.0

02)

(0.0

03)

Observatio

ns77

977

977

977

9R

20.

204

0.20

60.

211

0.21

3

Note:

Pooled

OLS

,followingBeckeret

al.(201

3),sha

rpde

sign,

four

prog

ammingpe

riods

1989

-93,

1994

-99,

2000

-06,

2007

-13;

***signific

antat

1%,**

5%,*1

0%.Stan

dard

errors

areclusteredat

theregion

allevel.

29

Tab

le9:

GDP

percapita

grow

than

dR

&D

expe

nditu

reover

four

programmingpe

riods

R&

Dexpe

nditu

rerelativ

eto

GDP

2nd-orde

rpo

lyno

mial

3rd-orde

rpo

lyno

m.

4th-orde

rpo

lyno

m.

5th-orde

rpo

lyno

m.

Tim

e-in

vari

ant

-19

89-9

3,19

94-9

9,20

00-0

6,20

07-1

3Tr

eatm

ent

0.01

1∗∗∗

0.00

8∗∗∗

0.00

8∗∗∗

0.00

7∗∗

(0.0

02)

(0.0

02)

(0.0

03)

(0.0

03)

Tre

atm

ent

xR

&D

0.00

9∗∗∗

0.00

8∗∗∗

0.00

7∗∗∗

0.00

9∗∗∗

(0.0

02)

(0.0

02)

(0.0

02)

(0.0

03)

R&

D0.

002∗∗

∗0.

003∗∗

∗0.

003∗∗

∗0.

003∗

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

Con

stan

t0.

012∗∗

∗0.

014∗∗

∗0.

013∗∗

∗0.

015∗∗

∗

(0.0

01)

(0.0

02)

(0.0

03)

(0.0

03)

Observatio

ns78

278

278

278

2R

20.

172

0.17

90.

187

0.18

9

Note:

Pooled

OLS

,followingBeckeret

al.(201

3),sha

rpde

sign,

four

prog

ammingpe

riods

1989

-93,

1994

-99,

2000

-06,

2007

-13;

***signific

antat

1%,**

5%,*1

0%.Stan

dard

errors

areclusteredat

theregion

allevel.

30