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Gerard-Daphne-6077589-Economics-Thesis

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Gerard-Daphne-6077589-Economics-Thesis

  1. 1. The effect of the enlargement of the EU in 2004 on Convergence objective regions and the functioning of Structural Funds. Thesis presented by Daphne Gerard Supervisor Florian Mayneris (UCL) Supervisor and Reader Erik de Regt (Maastricht University) Academic year 2014–2015 In order to obtain the Double Degree Master 120 en Sciences économiques, Orientation générale, Finalité spécialisé (UCL/UNamur) and Master of Science in Economic Studies (Maastricht university) Ecole d’économie de Louvain/UCL • Place Montesquieu 3 • 1348 Louvain-la-Neuve / Belgium Département des Sciences économiques/UNamur • Rempart de la Vierge 8 • 5000 Namur / Belgium Maastricht University School of Business and Economics • PO Box 616, 6200 MD Maastricht / The Netherlands
  2. 2. Acknowledgments The dissertation was only possible with the help and support of many different people. I would like to take the opportunity to express my gratitude towards my supervisor Professor Florian Mayneris for his input and useful comments. Moreover, I wish to thank Sebastien Fontenay for his help with Stata. I am also grateful to Professors Erik de Regt, Professor Vincenzo Verardi and Wouter Gelade for their feedback and excellent courses that helped me to develop the necessary skills for this dissertation.
  3. 3. iv
  4. 4. Abstract Structural Funds and the Cohesion Fund are the main instruments of the European Com- mission for regional policy. The objective of Structural Funds is to reduce disparities between regions. The aim of the Convergence objective is to help regions that have a Gross Domestic Product (GDP) per capita in purchasing power parity below 75 percent of the European average, to catch-up with the other regions. Theoretically and empir- ically the results of Structural Funds on regional convergence remain ambiguous. The enlargement of the European Union (EU) with 10 new countries in 2004 can be seen as an exogenous shock affecting the eligibility of regions under the Convergence objective. This information can be used to evaluate if old regions where negatively affected by the enlargement of the EU. Multiple difference in difference regressions for the period 2000- 2011 do not show a divergence in GDP per capita and disposable income between regions that received full funding or transitional funding under the Convergence objective. Keywords. European Structural Funds; Regional disparities; Convergence objective; Enlargement of the EU; Policy Evaluation
  5. 5. vi
  6. 6. Table of contents 1 Introduction 1 2 Historical evolution of regional policy in the European Union 7 3 Theoretical overview 11 3.1 Theoretical perspective: the effect of an integrated market on regional disparities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Exogenous growth theories . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.2 Endogenous growth theories . . . . . . . . . . . . . . . . . . . . . . 12 3.1.3 New Economic Geography . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Theoretical perspective: the effect of Structural Funds on regional conver- gence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Empirical literature review 19 4.1 Empirical overview: evolution of regional disparities in the European Union 19 4.1.1 Beta-convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.2 Sigma-convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1.3 Convergence Clubs . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 Empirical literature: effectiveness of Structural Funds . . . . . . . . . . . . 24 5 Regional policy between 2000 and 2013 27 5.1 Financial instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5.2 Objective 1 and the Convergence objective . . . . . . . . . . . . . . . . . . 29
  7. 7. viii TABLE OF CONTENTS 5.3 The effect of the enlargement of the EU on the Structural Funds . . . . . . 30 6 Data and methodology 35 6.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 6.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 7 Results 43 8 Robustness 49 9 Conclusion 57 Bibliography 59 A Control and treatment group 65 A.1 The control group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A.2 Treatment group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 B Results for disposable income 69
  8. 8. List of Figures 1.1 GDP per capita in PPP terms in 1995 . . . . . . . . . . . . . . . . . . . . 2 1.2 GDP per capita in PPP terms in 2011 . . . . . . . . . . . . . . . . . . . . 5 4.1 Coefficient of variation: evolution of GDP per capita at the NUTS2 level . 23 4.2 Theil index for GDP per capita the NUTS2 level for the EU-27 . . . . . . 23 6.1 Eligibility of the control and treatment group under the Convergence ob- jective (2007-2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 6.2 Evolution of GDP per capita in PPP terms for the control and treatment group between 2000-2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.3 Evolution of disposable income for the control and treatment group between 2000-2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
  9. 9. x LIST OF FIGURES
  10. 10. List of Tables 4.1 Empirical results for absolute Beta-convergence . . . . . . . . . . . . . . . 21 4.2 Empirical results for spatial Beta-convergence . . . . . . . . . . . . . . . . 22 5.1 Absorptive capacity 2007-2013 . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2 Decomposition of the budget for the Convergence Objective between tran- sitional and full support . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.1 Results probit and linear regression: factors that could affect eligibility for the Convergence objective (2007-2013) . . . . . . . . . . . . . . . . . . . . 38 7.1 Difference in Difference regression: growth rate of GDP per capita in PPP terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 7.2 Difference in Difference regression: the level of GDP per capita in PPP terms 45 7.3 Difference in Difference regression: decomposition of time . . . . . . . . . . 47 8.1 Difference in Difference regression: growth rate of GDP per capita in PPP terms, restricted sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 8.2 Difference in Difference regression: growth rate of GDP per capita in PPP terms, including interaction term . . . . . . . . . . . . . . . . . . . . . . . 52 8.3 Difference in Difference regression: the growth rate of GDP per capita in PPP terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 8.4 Triple Difference in Difference . . . . . . . . . . . . . . . . . . . . . . . . . 55 8.5 Difference in Difference regression: growth rate of GDP per capita in PPP terms, adjusted sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 B.1 Difference in Difference regression: the growth rate of disposable income . 70
  11. 11. xii LIST OF TABLES B.2 Difference in Difference regression: levels of disposable income . . . . . . . 71
  12. 12. List of used abbreviations Abbreviations CAP Common Agriculture Policy CO Convergence Objective CRS Constant Returns to Scale EAGGF European Agricultural Guidance and Guarantee Fund EC European Commission EEC European Economic Community EFF European Fisheries Fund EMU European Monetary Union ERDF European Regional Development Fund ESF European Social Fund EU European Union FIFG Financial Instrument for Fisheries Guidance GDP Gross Domestic Product GMM Generalized Method of Moments IRS Increasing Returns to Scale NUTS Nomenclature of Territorial Units for Statistics PPP Purchasing Power Parity
  13. 13. xiv Abbreviations
  14. 14. Chapter 1 Introduction One of the main principles of the European Union (EU) is that all citizens irrespective of where they live should be able to benefit from the economic gains created by the EU. Still, difference between the level of Gross Domestic Product (GDP) per capita in Purchasing Power Parity (PPP) terms can be observed between countries and regions. These differences have always been present since the start of the European Economic Community (EEC). Figure 1.1 shows the situation in 1995. Regions with a low level of GDP per capita in PPP terms were mostly situated in the periphery of the EU. The observed core-periphery pattern was not changed by the enlargement of the EU towards eastern Europe at the beginning of the 21st century (figure 1.2); if anything, it was reinforced by it (figure 1.2). As a consequence, regional policy1 is currently one of the top priorities of the EU. The aim is to create economic and social cohesion between countries, to guarantee harmonious development and to reduce disparities between regions (Fratesi and Rodríguez-pose, 2004). Two financial instruments are used for regional policy, namely the Cohesion Fund and the Structural Funds. The scope of the Cohesion Fund is the national level while the Struc- tural Funds focus on the regional level. Regions are defined following the Nomenclature of Territorial Units for Statistics (NUTS). Different NUTS levels exist, funding from the Structural Funds are allocated to the NUTS2 level. Since the start of the EEC, the importance and budget of regional policy has grown. There were two main drivers behind this evolution. Firstly, the different enlargements of the EEC2 widened the disparities. Therefore, the political awareness and willingness to 1 The term regional policy can be interchangeably used with the term cohesion policy 2 The EEC was replaced by the European Community in 1993. In 2009 the European Community was integrated in the broader framework of the EU. The term EEC will be used for the period before 1993. To avoid confusion, only the concept of the EU will be used for the years after 1993.
  15. 15. 2 Introduction Figure 1.1: GDP per capita in PPP terms in 1995 88 Açores (P) Madeira (P) Canarias (E) Guyane (F) Guadeloupe (F) Martinique (F) La Réunion (F) 75 up to below 100 100 up to below 125 125 and more Source: Eurostat below 75 GDP by PPS Per Capita 1995 Index: EUR 15 = 100 NUTS 2 Map 1: Gross Domestic Product area of 3.2 million km2 and with an annual gross domestic product (GDP) of 6.8 trillion ECU, the EU is one of the largest and economically strongest regions in the world.3 (10) Nevertheless, the EU shows serious economic imbal- ances impeding the realisation of regionally balanced and sustainable spatial development. The associated imbal- anced distribution of economic potential could be described capita only reaches about 50% to 65% of the EU average. In some regions at the northern periphery of the EU territo- ry - e.g. Northern Finland and the North of the United King- dom - the economic situation is not much better; the regions overseas in most cases reach only a GDP per capita of less than 50% of the EU average5. The ESDP can contribute to achieving, in the medium term, a spatially more balanced development. Source: European Commission (1999a) invest in regional policy increased. Secondly, it was feared that the benefits of market integration would be unevenly spread through the EEC. Different economic theories can be put forward to forecast the effect of market integration on countries and regions. Ex- ogenous and endogenous growth models predict positive effects on economic growth while economic geography models point out that these benefits could be unevenly distributed between regions. Therefore, Structural Funds and its investments should help regions to exploit their full economic potential and reduce disparities between regions. However, theoretically, investments in regions that are lagging behind do not necessarily reduce differences in economic development between regions. Economic geography models like Krugman (1991) and to a certain extent Krugman and Venables (1996) indicate that investments in transport networks and a decrease in trade cost could benefit the bigger regions instead of the targeted regions. Empirical results show that convergence between
  16. 16. 3 countries can be observed. However, disparities within countries have increased. The goals of regional policy have evolved over time. Three main objectives have been put forward for the programming period 2007-2013, similar to the three 2000-2006 objectives: the Convergence objective, the Regional Competitiveness and Employment objective and the European Territorial Cooperation objective. In general, two thirds of the budget is allocated towards the Convergence objective. Regions become eligible for funding un- der the Convergence objective if they have a GDP per capita in PPP terms less than 75% of the EU average. Mostly these goals were reduced to convergence between regions and the effect on economic growth. Many authors have investigated if Structural Funds were meeting their goals, and the results vary from negative, insignificant to highly pos- itive. Different techniques and data have been used. Certain authors used aggregated data (Beugelsdijk and Eijffinger, 2005; Ederveen et al., 2006) while others used data at the NUTS2 level (Becker et al., 2010, 2013; Dall’Erba and Le Gallo, 2004; Fratesi and Rodríguez-pose, 2004; Mohl and Hagen, 2010). The results from different authors are difficult to compare between each other. This is due to the fact that the sample, the time frame and used econometric techniques differ (cross-section, panel data, regression discontinuity design). The aim of this dissertation is to investigate what happens when regions lose funding from the Structural Funds under the Convergence objective. More specifically, what is the effect on GDP per capita in PPP terms and disposable income. This information could be important for further enlargement of the EU and for the transitional funding scheme created by the European Commission (EC). Our focus will solely be on the Structural Funds and the Convergence objective. The enlargement of the EU in 2004 with ten new countries and its effect on eligibility for funding under the Convergence objective, created an interesting quasi-natural experiment. The level of GDP per capita in PPP terms for the new entrants was significantly lower than the average level of GDP per capita in PPP terms from the EU-15. As a consequence, certain regions that were eligible under funding for the programming period 2000-2006 were only eligible for transitional funding for the period 2007-2013. The amount of transitional support is approximately 30 to 35% from the funding they received in the period before. This was due to the fact that no longer the average of the EU-15 but the average of the EU-25 was used to calculate which regions have a GDP per capita lower than 75% of the EU average. From the 57 regions that were eligible for funding under the Convergence objective, only 31 regions remained eligible for the period 2007-2013. The drop in the EU average can be seen as an exogenous shock that created two groups. The first group will be the control group
  17. 17. 4 Introduction and will contain 27 regions3 that received full funding under the Convergence objective. The second group will be named the treatment group and includes 23 regions4 which only received transitional funding between 2007-2013. The framework of difference in difference can be used to investigate if GDP per capita and disposable income between the two groups diverged. It allows us to control for trends that affected both groups in the same way (e.g. the economic crisis). Moreover, control and treatment group do not need to have fully similar characteristics. However, both groups need to have a similar trend in absence of the treatment for the outcome variables. Our tests in this dissertation show that this assumption is met for GDP per capita and for disposable income. Different regressions show no significant difference between the control and treatment group for the level or the growth rate of GDP per capita in PPP terms. Similar results are observed for disposable income5 . Looking at the effect of the funds for every year did not change the results. The results remain robust for different specifications. Potential sources of a bias are: difference between the north and south of Europe during the economic crisis, the absorption capacity, lower level of GDP per capita for the control group and the not strictly applied rule for eligibility. Different robustness test are performed for the growth rate of GDP per capita and they did not show that these potential biases affect the results. The remainder of the dissertation is organized as follows. Chapter 2 will give a brief overview of the historical evolution of Structural Funds. Chapter 3 will discuss different theories and their predictions about the effect of market integration and Structural Funds on regional disparities. Chapter 4 will look at the same questions, but from an empirical point of view. Regional policy in the 21st century will be discussed in chapter 5. Chapter 6 outlines the data and used methodology. Thereafter, in chapter 7, the results will be presented and in chapter 8 the robustness of the results will be tested. The last chapter will be a conclusion that will present the most important findings of this dissertation. 3 The four French overseas regions are not included in the controls. Therefore there are only 27 regions in the control group and not 31 4 The 3 Swedish regions are not included in the treatment group because they did not receive any transitional funding. 5 Results for disposable income should be interpreted with caution as there is a possibility that the crisis and changes in policy have violated the assumption of a common trend for disposable income.
  18. 18. 5 Figure 1.2: GDP per capita in PPP terms in 2011 Source: Eurostat (2014)
  19. 19. 6 Introduction
  20. 20. Chapter 2 Historical evolution of regional policy in the European Union Harmonious regional development and convergence between regions have always been one of the goals of the EC. However, over time the importance of regional policy, its budget and the instruments available to the EC have changed drastically. In the next chapter, an overview of the evolution of regional policy will be given. The goal is not to give a full detailed historical overview, but to highlight the main evolutions and to clarify the context wherein cohesion policy is operating. Economic and social cohesion between different regions was already brought forward in the preamble of the treaty of the EEC that was signed in Rome in 1957. Anxious to strengthen the unity of their economies and to ensure their har- monious development by reducing the differences between the various regions and the backwardness of the less-favored region (The EEC treaty, 1957; European Commission) The instruments available for regional policy were the European Social Fund (ESF), the European Agricultural Guidance and Guarantee Fund (EAGGF) and the European In- vestment Bank (Dall’Erba, 2003). However, these instruments were more focussed on certain specific sectors, like agriculture and transport. Moreover, there was the optimistic view that interregional disparities would disappear with interregional trade (Manzella and Mendez, 2009). The most power was given to the European Investment Bank, while little tools were given to the supranational level namely the EC (Manzella and Mendez, 2009). Regional policy and divergence between regions were mostly seen as an Italian problem at the time. However, this changed due to the economic situation in the seventies. Declining
  21. 21. 8 Historical evolution of regional policy in the European Union industries in many regions and sectoral problems linked to specific territories gave a rationale for regional policy in every country (Manzella and Mendez, 2009). This renewed interest in regional policy was reinforced by the first enlargement of the EEC with Ireland, the United Kingdom and Denmark and the intention to create a monetary union. The Werner report published in 1969 stated that differences in economic structure between regions could threaten a monetary union and the whole functioning of the EEC (Manzella and Mendez, 2009; Werner et al., 1970). Therefore, regional and structural policies were seen as instruments that could help to overcome these problems and to compensate regions for potential distortionary effects created by free competition. Eventually, this led to the creation of the European Regional Development Fund (ERDF) in 1973. The ERDF focussed on regions that were lagging behind and on industrial regions facing economic problems. 4% of the total budget of the EC was allocated to the ERDF. All nine countries were eligible for funding from the ERDF (European Commission, 2015b). Allocation of the budget was done through quotas and not linked to the specific needs of a country. Instead, it was based on political agreements (Manzella and Mendez, 2009). The further enlargement of the EEC in the eighties with Greece, Spain and Portugal and the deepening of economic integration with the Single European act were fertile ground for regional policy (Dall’Erba, 2003). The Single European Act added a new title to the Treaty: Economic and Social Cohesion (European Commission, 2015b). The bud- get of the European Structural Funds increased dramatically after the introduction of the European single market in 1986 (Martin, 2003). This shift came because less devel- oped countries had doubts that the positive effects of the single market would be spread equally through Europe. Therefore, they asked for financial assistance from the more developed European countries (Martin, 2003). Besides an increase in budget, the policy framework was completely redrawn. The different instruments for regional development namely EAGGF, ESF and ERDF were bundled together in national development plans and the EEC shifted away from project financing to multi-annual programming financing (European Commission, 2015b). This meant that countries had to draw up national plans based on the objectives of the EC (Martin, 2003). Five common objectives were formu- lated. The first objective focused on the catching-up process of regions that are lagging behind. The second objective targeted regions or parts of regions that were affected by industrial decline. The third objective aimed at dealing with long-term unemployment, while the fourth objective emphasized on the integration of young people on the labor market. The fifth objective focussed on rural areas (European Commission, 1993). Other key concepts were introduced by the new reform as well. Firstly, partnership: this implied that for the different programs and projects, the relevant actors at the local and national level need to participate (Manzella and Mendez, 2009). Secondly, the EC emphasized
  22. 22. 9 on additionality. They wanted to avoid that Structural Funds would replace national expenditure. Therefore, the increase in available budget for a certain region should be accompanied by a similar increase in public investment of the country (European Com- mission, 1993). 20% of the total budget of the EC for the period 1989-1993 was now allocated to Structural Funds (European Commission, 2015b). Throughout the history of Europe, political and economic reasons have always played a role in the design and development of regional policy. The increasing importance and budget of the Structural Funds and Cohesion Fund were often part of a broader political agreement related to the widening and deepening of the EEC. This pattern was also present during the negotiations of the Treaty on the European Union in 1992. This Treaty was the official start of the 3 phases towards a European Monetary Union (EMU). The Maastricht criteria were created to ensure that countries entering the EMU were sta- ble, had solid public finances and could deliver sustainable economic growth. This implied that countries that were lagging behind needed to catch-up by the use of public invest- ment while on the other hand the Maastricht criteria put restrictions on the level of public debt and budget deficit (Dall’Erba, 2003). To overcome this problem, the Cohesion Fund was created. The European Structural Funds focus on the regional level while the scope of the Cohesion Fund is the national level. Only countries whose gross national income is less than 90% of the EU average are considered as eligible. Most projects financed by the Cohesion Fund are related to infrastructure and the environment (European Commission, 2015a). The widening of the EU with Austria, Finland and Sweden led to the introduction of a sixth objective. This objective focussed on sparsely populated regions (Manzella and Mendez, 2009). A fourth Structural Fund was created and was named the Financial In- strument for Fisheries Guidance (FIFG). Its aim is to contribute to the structural changes in the fishery industry (European Commission, 2004). Every reform was accompanied by an increase in the budget. In 1999, some modest changes were made. First of all, the administrative burden of the complex and detailed programs was decreased by simplifying the required documents (Manzella and Mendez, 2009). The objectives of the Structural Funds and the Cohesion Fund were reduced to three objectives for the period 2000-2006. Objective 1 aims to foster development and structural reforms in regions that have a lower level of GDP per capita. On the opposite, Objective 2 targets regions that are not covered by objective 1 funding, but still face structural difficulties. Objective 3 also covers regions that are not eligible for Objective 1 funding and targets human capital formation like education and training. The budget for the European Structural Funds remained the same between the programming
  23. 23. 10 Historical evolution of regional policy in the European Union period 1994-2000 and 2000-2006 (Manzella and Mendez, 2009). Approximately 30% of the total EU budget went to cohesion policy and almost two thirds of the money for cohesion policy went to Objective 1 (European Commission, 2004). The programming period (2007-2013) again led to small changes to better cope with the enlargement of the EU with 10 new countries in 2004. Given the low level of GDP per capita in PPP terms of the new entrants, this implied that less resources of cohesion policy would be allocated to the EU-15. The three objectives were slightly changed and rede- fined. The first objective was transformed to the Convergence objective. The goal is to help regions that are lagging behind to catch-up by creating an environment that fosters growth and employment (European Commission, 2013). The second objective is named the Regional Competitiveness and Employment objective. Its aim is to promote employ- ment and productivity in regions that are not eligible for funding under the Convergence objective (European Commission, 2013). The third objective is the European Territorial Cooperation objective. The goal of this objective is to strengthen and facilitate cross- border cooperation. EAGGF was incorporated in the Common Agriculture Policy (CAP) while the FIFG was renamed as the European Fisheries Fund (EFF). Therefore, both are no longer considered as a Structural Fund (European Commission, 2015b). The total budget allocated to the 3 objectives for the programming period (2007-2013) is 308.041 billion e and this is approximately 35% of the total budget of the EC.
  24. 24. Chapter 3 Theoretical overview The historical evolution of cohesion policy in the EU showed that political, ethical and economic reasons have played a role in its creation. Before looking at the theoretical literature related to the effectiveness of the Structural Funds on convergence between European regions and countries, it is informative to look at different theories and their predictions on convergence within the EU in the absence of Structural Funds. Moreover, it could give important insights on the underlying reasons of the European countries and the EC to develop regional policy. Thereafter, a short theoretical evaluation of the use of Structural Funds will be given. 3.1 Theoretical perspective: the effect of an integrated market on regional disparities The traditional approach to look at convergence and divergence of regions is following the concepts and models of growth literature. A more recent trend and new branch within economics that follows a different approach is economic geography. Growth theories and economic geography can help to understand what happens when countries integrate their markets. The theoretical literature related to growth theories can be divided in two groups which both heavily depend on the used model and the related assumptions. On one hand, there is the group of exogenous growth models. On the other hand, there are the endogenous growth models. The former defines technological change as exogenous while the latter says that technological change is the result from the behaviour of firms.
  25. 25. 12 Theoretical overview 3.1.1 Exogenous growth theories The main features of exogenous growth models are perfect competition, constant returns to scale, decreasing marginal returns to capital and labour and free capital mobility. In the steady-state the growth rate of technological change is the only determinant of growth. Neo-classical growth models will forecast convergence between countries or regions regard- less of the initial values of GDP, capital and labour, given they have the same growth rate of technology, savings rate, depreciation of capital and growth rate of population. All those elements are exogenous in the neo-classical framework. In this framework, countries with a low level of GDP will grow faster because they have a lower capital-labour ra- tio. Therefore, they have higher marginal returns to capital. Given diminishing marginal returns to capital, these poorer countries will grow faster (Soukiazis). Eventually, all countries will converge to the same steady state. The latter is called Sigma-convergence while the former, the speed of convergence, is named unconditional Beta-convergence in the literature. Unconditional Beta-convergence is necessary for Sigma-convergence but is not sufficient (Valdés, 1999). In the situation where regions or countries have dif- ferent exogenous variables, the concept of unconditional Beta-convergence is no longer applicable. Instead, if convergence can be found after controlling for these differences, the concept of conditional Beta-convergence is used (Mulquin and Senger, 2007). Neo- classical economists will forecast a positive effect from market integration on the level of output. Increased competition due to free movement of capital will lead to a better use of the available technology. Therefore, countries will shift to a higher steady state level. Growth will be temporarily higher during the transition phase until the new steady-state is reached. Afterwards, growth will go back to the pre-market integration growth rate (Valdés, 1999). 3.1.2 Endogenous growth theories Endogenous growth models no longer take technological change as exogenous. Techno- logical change is initiated by the behaviour of firms, consciously or not. The AK model of Romer (1986), also referred to as the linear in capital model, was one of the first endoge- nous growth models. There is perfect competition, and the production function contains constant return to scale and there are diminishing marginal returns to capital and labour. In this model, positive external spillovers are created due to learning-by-doing (better use of inventions and new inventions due to the use of the current technology in a more optimal way) and the fact that everybody can immediately use the available knowledge without any cost. These positive external spillovers will overcome the diminishing marginal return
  26. 26. 3.1 Theoretical perspective: the effect of an integrated market on regional disparities 13 to capital from the neo-classical framework. Hereby, spillovers will exactly compensate the diminishing marginal returns to capital until marginal returns to capital are constant. Therefore, the effect of market integration will be different across models. While growth will be temporarily higher in exogenous growth models, in the AK model there will be a permanent positive effect on economic growth (Valdés, 1999). Neo-classical growth mod- els predict convergence between countries with the same parameters regardless of their starting position. The same relation is not forecasted by the AK model (Mulquin and Senger, 2007). The AK model does not give any information regarding convergence and divergence between countries as this model is in a permanent steady state (Valdés, 1999). After this model, more endogenous growth models were developed. The AK model could still use the assumption of perfect competition because technology was a positive uncon- sciousness side-effect of the optimisation behaviour of firms. Other endogenous growth models use the assumption of imperfect competition, allowing them to make innovation and technological progress the aim of firms. For example, in the Romer (1990) model, there is imperfect competition and positive external spillovers due to the fact that tech- nology is non-rival and partially excludable (Valdés, 1999). Three sectors are present: the research sector, final goods and the intermediate sector. There are four inputs: capital, human capital, unskilled labour and the level of technology. This specific endogenous growth model does not predict any convergence between countries but indicates potential factors that could explain divergence. For example, different levels of human capital and subsidies for the research sector affect the growth rate of a country. In contrast to exoge- nous growth models, there is room for policy intervention in endogenous growth models. In this specific set-up of Romer (1990), market integration will have a permanent positive effect due to the larger available pool of human capital (Valdés, 1999). In general, en- dogenous growth theories do not predict convergence. Sala-i-Martin (1996) showed that only under certain implausible condition convergence was predicted by endogenous growth models (Mulquin and Senger, 2007). Another pioneering endogenous growth model was developed by Lucas (1988). He incorporates human capital accumulation and learning- by-doing in a two-goods model. These features have interesting consequences for the effect of trade and market integration between two regions. Regions will specialize in the sector where they have a comparative advantage. However, if the positive spillovers of learning-by-doing are different between sectors than growth will be smaller in the regions where the possibilities for learning-by-doing are smaller (Lucas, 1988; Martin, 1997).
  27. 27. 14 Theoretical overview 3.1.3 New Economic Geography Economic geography tries to explain the distribution of economic activity between regions. This geographical element is mostly absent in growth models (Combes et al., 2007). Main features of these models are the presence of Increasing Returns to Scale (IRS) and mo- nopolistic competition in the manufacturing sector while there is perfect competition and Constant Returns to Scale (CRS) in the agricultural sector or traditional sector. The core-periphery model of Krugman (1991) is one of the first models that tried to explain theoretically the forces behind agglomeration. In this model, skilled labour is perfectly mobile between regions while unskilled labour is immobile. Skilled workers work in the IRS sector and unskilled labour works for the CRS sector. Both agglomeration and dis- persion forces are present. Agglomeration comes from the fact that firms will locate in the region where demand is the highest. This will be the start of a snowball effect, to attract more workers, real wages will be higher and the price index will be lower due to more varieties. The increase in demand in the bigger region will attract more firms. The dispersion forces are linked to the higher labour cost in the big region due to higher wages, increased competition between firms in the big region in the IRS sector. In addition to this, the immobility of unskilled labour works as a dispersion forces because workers con- sume where they live. Therefore, the immobility of unskilled labours guarantees a certain level of demand for products in every region. The evolution of trade costs will determine which equilibrium will prevail in the Krugman (1991) model. There exist only two types of equilibrium in this model, namely full agglomeration or complete dispersion. If trade costs are high, only dispersion is possible. When trade costs fall below a certain threshold, both equilibriums are possible until trade cost decrease further and only full agglomera- tion is possible. This model forecasts that market integration will lead to more regional disparities. More interesting are the welfare implications: when agglomeration prevails, the indirect utility of the region with the IRS sector will be higher than the utility of the small region (Combes et al., 2007). The smaller region will always prefer dispersion while the bigger region will prefer agglomeration. Charlot et al. (2006) show that there does not exist a scheme were the winners can compensate the losers while still being better off themselves. In this model market integration, the decrease in trade costs and the increase in efficiency comes with the cost of more inequality between the two regions (Charlot et al., 2006; Combes et al., 2007). The Krugman and Venables (1996) model relaxed certain assumptions of the Krugman (1991) model in order to create a more realistic and nuanced view and incorporates ele- ments of economic geography (internal economies of scale) and urban economics (external economies of scale). Firstly, workers are no longer mobile between regions but are mobile
  28. 28. 3.1 Theoretical perspective: the effect of an integrated market on regional disparities 15 between sectors. This adds an extra dispersion force to the model because the supply of labour becomes inelastic. Hereby, wages will rise more in the region where more firms are located. Secondly, the final goods sector uses labour and intermediate goods as an input. The introduction of intermediate goods creates an extra agglomeration force as more intermediate goods will lead to lower prices and this attracts more firms (external economies of scale) (Combes et al., 2007). The welfare implications are similar to Krug- man (1991). Market integration will first lead to an increase in welfare for both regions until a certain threshold is reached. Hereafter, agglomeration will occur in one region. As a consequence, welfare will increase in the specialized region and decrease in other regions as there are now more varieties they have to import. In a third phase, when trade costs fall even more, disparities between regions will gradually disappear. In the case where the demand for the final good is high, reindustrialisation in the small region can occur due to congestion cost in the big region. Labour cost becomes too high because the supply of labour is inelastic. Total welfare will be higher and disparities between regions will disap- pear. These three phases can graphically be represented in a bell-shaped curve (Combes et al., 2007). Where Krugman (1991) proposes a trade-off between efficiency and equity, the bell-shaped curved of Krugman and Venables (1996) shows that both goals could be achieved when trade costs are sufficiently low. Theoretically, different theories and models point in the direction that market integration is positive for the overall economic performance of the EU. Moreover, economic geography predicts that this could lead to a core-periphery pattern. This pattern in itself is not a problem. Empirically, a positive correlation between agglomeration and economic growth can be observed (Ascani et al., 2012). Despite the overall gains, the benefits can be unevenly distributed like in the Krugman (1991) model. In the Krugman and Venables (1996) models, disparities only disappear when trade costs almost become zero. On pure efficiency grounds, there is no clear theoretical answer why Europe needs convergence between its regions. However, based on the core value of the EU that everybody should be able to benefit from an integrated market, interventions by the means of regional policy can be justified. Moreover, politically it is also clear why these disparities could be a threat for the functioning of the EU. Given the unique intergovernmental and supranational structure of the EU, it needs the support of all different member states. Increasing divergence in GDP per capita between member states and regions can undermine this support. Therefore, the aim of cohesion policy is to reduce these disparities between regions because it is an economic and political necessity and part of the solidarity between the different member states (European Commission, 1993). The importance of equity and political
  29. 29. 16 Theoretical overview reasons was again put forward in the Barca (2009) report that reviewed cohesion policy (Ahner, 2009). To give all EU citizens, independently of where they live, a concrete sign that the Union is taking action to ensure that they have an equal chance of benefiting from the opportunities created by the unification of markets and of avoiding the risks. (Barca, 2009) 3.2 Theoretical perspective: the effect of Structural Funds on regional convergence The need for regional policy does not necessarily imply that regional policy as designed by the EC will have the desired effects. Structural Funds can be seen as a way to increase investments in regions. The abstraction is made that funds could be used in a corruptive way or can be invested in non growth-enhancing projects and that funds are always par- tially provisioned by national states (Ederveen et al., 2006). In a neo-classical framework, an increase in investment would create a temporary positive effect that is similar to the effect of market integration. An increase in public infrastructure will lead to capital accu- mulation in endogenous growth models. As a consequence, there is a permanent positive effect on growth (Martin, 1997). Economic geography models give a less clear-cut answer. Moreover, their predictions depend on the nature of investments. Structural Funds that are used to reach the goals of Objective 1 are mostly used for investments in infrastruc- ture. Only a marginal amount of the total budget for Objective 1 was spent on human capital formation (Fratesi and Rodríguez-pose, 2004). Exogenous and endogenous growth models do not take into account the effect of investments in infrastructure on industrial location (Martin, 1997). Increasing the local demand due to transfers has a positive ef- fect on growth in the region but the effect will change once agglomeration forces are in place, especially when investments are mostly focussed on improving the transportation network. Hereby, a periphery region will be better connected to the core. The effect of this investment will be small when the snowball effect that resulted in agglomeration forces is already in place. As a consequence, this small improvement will not change the decision of firms on where to locate. To turn around the snowball effect, a major change in the attractiveness of a region is necessary (Martin, 1997). For example, according to the Krugman and Venables (1996) model, this snowball effect that led to agglomeration could be turned around only if investments decrease trade costs significantly. This is due to the fact that labour cost became too high in the core. As a consequence, partial rein-
  30. 30. 3.2 Theoretical perspective: the effect of Structural Funds on regional convergence 17 dustrialisation will take place in the periphery (Combes et al., 2007). In the analysis of Martin and Rogers (1995), the effect of investment in transport networks between regions can have strong negative effects on the regions in the periphery. It gives firms the option to benefit from the increasing returns to scale in the core and to export their product easily to the periphery (Martin, 1997). The overall theoretical evaluation of Structural Funds becomes less positive once we start looking at predictions of economic geography models.
  31. 31. 18 Theoretical overview
  32. 32. Chapter 4 Empirical literature review Theoretically, the effect of market integration and investments of the Structural Funds on regional disparities is not clear. Instead, the concepts of the different theories can also be used for an empirical investigation. In this chapter, a short overview of different empirical results will be given. Firstly, we will take a closer look at the evolution of disparities in the EU. Secondly, the contribution of Structural Funds towards more convergence between regions will be discussed. 4.1 Empirical overview: evolution of regional dispari- ties in the European Union 4.1.1 Beta-convergence The concept of absolute Beta-convergence is linked to the exogenous growth models. It gives information about the speed of convergence of regions towards their steady state. Ta- ble 4.1 shows an overview of empirical research that looked at absolute Beta-convergence for different samples and time periods. Most researchers found evidence that there is con- vergence but that the convergence rate is very small. For example, the empirical results from Cuadrado-Roura (2001) and López-Bazo (2003) suggest that it will take 35 years before half of the gap between regions and countries will be reduced (Eckey and Türck, 2005). All the empirical results should be interpreted with caution. Moreover, compari- son is difficult due to different samples and time periods (Armstrong and de Kervenoael, 1997). These models have been heavily criticized due to problems with heterogeneity and endogeneity. Moreover, the classic absolute Beta-convergence approach does not incor- porate potential spatial spillovers like technology and migration. More developed regions
  33. 33. 20 Empirical literature review tend to be clustered around each other. Therefore, the existence of spatial heterogeneity can affect the results (Dall’Erba and Le Gallo, 2004). Table 4.2 contains results of re- search that tried to deal with spatial interdependencies in different ways. The speed of convergence estimated by these models is very low (Eckey and Türck, 2005). Empirical results for conditional convergence are very mixed and depend heavily on the included factors and used sample. Monfort (2008) stresses that despite differences in the sample, time, and used model, some common points can be found. Firstly, Beta-convergence between the EU-15 and the EU-27 regions has taken place, but at a slow rate. Abso- lute Beta-convergence models estimate a faster speed of convergence than conditional Beta-convergence models. More convergence is observed between poor regions and rich regions when they are considered separately. This could indicate that there could be convergence clubs. These convergence clubs can be linked to the core-periphery pattern described by economic geography (Monfort, 2008). Armstrong and de Kervenoael (1997) add that from the literature, it can be observed that the seventies are a turning point in the convergence process. Thereafter, convergence decreased in the research of Barro et al. (1991) and Armstrong (1995) or divergence took place as in Dunford (1993). However, these results should be considered with caution as they are very sensitive to the chosen time-frame. During a recession, regional disparities always tend to widen (Armstrong and de Kervenoael, 1997). 4.1.2 Sigma-convergence The concept of Sigma-convergence is closely linked to Beta-convergence. Sigma-convergence takes place when disparities between regions are reducing. Beta-convergence reveals in- formation about the speed of convergence (catching-up process). Beta-convergence is a necessary condition but not sufficient for Sigma-convergence (Monfort, 2008). The goal of the EC is to create Beta-convergence that will lead to Sigma-convergence. A first mea- sure of Sigma-convergence is the coefficient of variation, which looks at the dispersion of the probability distribution (Monfort, 2008). Figure 4.1 shows a downward trend in the coefficient of variation for the EU-15 and the EU-27 regions. This can be interpreted as that disparities between regions were gradually disappearing. Since the nineties, the co- efficient of variation remained stable for the EU-15 regions while it kept on decreasing for the EU-27 regions. This could indicate that convergence between EU-15 regions halted and that the convergence between the EU-27 is mainly driven by the new member states (Monfort, 2008). Another way to look at Sigma-convergence is the Theil index. The Theil index belongs to the category of spatial concentration indexes. It tells how a variable is concentrated given
  34. 34. 4.1 Empirical overview: evolution of regional disparities in the European Union 21 Table 4.1: Empirical results for absolute Beta-convergence paper period EU-regions result Cuadrado-Roura (2001) 1977-1994 EU-12 small convergence rate, which is dimin- ishing López-Bazo (2003) 1975-1996 EU-12 small convergence rate Thomas (1996) 1981-1992 EU-12 small convergence rate Martin (2001) 1975-1998 EU-16 small convergence rate, which is dimin- ishing Fagerberg/Verspagen (1996) 1950-1990 EU-6 extreme diminishing convergence rate Yin/Zestos/Michelis (2003) 1960-1995 EU-15 convergence speed is U-shaped Niebuhr/Schlitte (2004) 1950-1998 EU-15 convergence speed is U-shaped Geppert/Happich/Stephan (2005) 1986-2000 EU-15 increase of the conver- gence process Basile/de Nardis/Girardi (2005) 1975-1998 EU-9 increase of the conver- gence process Source: Eckey and Türck (2005)
  35. 35. 22 Empirical literature review Table 4.2: Empirical results for spatial Beta-convergence paper period EU-regions result Baumont/Erthur/Le Gallo (2003) 1980-1995 EU-12 small convergence rate in an absolute convergence model with a spatial error term Fingleton (1999a) 1975-1995 178 NUTS- regions slow convergence process in an conditional convergence model with a spatial lag of the dependent variable Br auninger/Niebuhr (2005) 1980-2002 EU-15 convergence rate below 1% in a spatial lag and a spatial error model Carrington (2003) 1989-1998 10 EU members convergence speed around 1%. Le Gallo/Dall’erba (2003) 1980-1999 EU-12 a very long half life using a spatial SUR model Source: Eckey and Türck (2005) a uniform space. The Theil index makes it possible to distinguish between convergence within a country and between countries. The results are displayed in figure 4.2. It confirms that convergence (T) became stable in the nineties, but slightly decreased at the beginning of the twenty-first century. However, it points out that there was convergence (TC) between countries but that disparities (TR) within countries are increasing (Monfort, 2008). The results displayed by the Theil index confirms that disparities within countries are increasing (Monfort, 2008). 4.1.3 Convergence Clubs The concepts of Beta and Sigma-convergence indicate that there is a unique steady state equilibrium. Conditional Beta-convergence tells us that, after controlling for certain char- acteristics, a unique steady state equilibrium exists. The concept of convergence clubs is different from conditional Beta-convergence as it allows multiple equilibriums that are not based on differences in structural characteristics (Eckey and Türck, 2005). Quah (1996) argued that convergence clubs exist at the top and the bottom of the distribution while the middle class is disappearing (Quah, 1996). These results are in line with the observa- tions of Neven and Gouymtea (1995), who looked at the period 1975-1990. They found
  36. 36. 4.1 Empirical overview: evolution of regional disparities in the European Union 23 Figure 4.1: Coefficient of variation: evolution of GDP per capita at the NUTS2 level 5 s in general highly relevant patial autocorrelation nd tends to reduce the convergence process while convergence is higher for on detecting possible ergence simply refers to a ns in time. The two concepts mally, Beta-convergence is ma-convergence. Intuitively, an converge towards one em apart or because, in the e, economies can converge Beta-convergenceapproach some economists to suggest nce is more revealing of the tribution of income across estimation of a particular mary measures of Sigma- ation or the coefficient of However, other indices exist ee for instance Cowell, 1995 eview of the mathematical mary inequality measures). the coefficient of variation, ex, the Theil index and the For each of these measures, at EU-15 and/or EU-27 levels regions being weighted by s. welfare functions vary across more sensitive to changes at e the coefficient of variation er end of the distribution. e to changes in inequality ese measures may not rank of variability only in relation to the mean value. The following figure shows the evolution of the coefficient of variation calculated for the EU-15 and EU-27 NUTS 2 regions for the period 1980-2005 and 1995-2005 respectively. Figure 1: Coefficient variation: GDP per head, NUTS 2 regions, EU-15 and EU-27 Source: Cambridge Econometrics and EUROSTAT database. DG REGIO’s own calculation. The results are in line with the findings regularly reported in the literature (see for instance Neven and Gouyette 1995, Magrini 2004 or Ertur et al. 2006). Convergence between EU-15 regions was strong up to the mid 90s, but the process since then has lost momentum. From 1980 to 1996, the evolution of disparities among EU-15 regions indeed shows a clear downward trend, the coefficient of variation decreasing from 0.33 to 0.28. On the contrary, since 1996 it has remained in a band of values between 0.28 and 0.29, its fluctuations possibly reflecting some temporary influence of the business cycle on the extent of disparities. On the other hand, disparities continue to decrease rapidly among EU-27 regions, the coefficient of variation falling from 0.43 in 1995 to 0.35 in 2005. This has led many observers to conclude that if convergence is still at work within the EU-27, it is due to the fact that the poorest regions in the new Member States are catching up on the Union’s richest ones, while among EU-15 regions convergence is no longer taking place. The fact that regional disparities decline when considering the EU as a whole does not prevent disparities from increasing within a number of Member States, in particular those that recently joined the Union. The following figure displays the evolution of the coefficient of variation calculated separately for the regions of the new Member States. 0.27 0.28 0.29 0.30 0.31 0.32 0.33 1979 1984 1989 1994 1999 2004 EU-15 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 0.44 EU-27 EU-15 EU-27 Source: Monfort (2008) Figure 4.2: Theil index for GDP per capita the NUTS2 level for the EU-27 7 Formally, if the population is divided into m subgroups (e.g. Member States), if Tri is the Theil index for subgroup i (e.g. reflecting the disparities among regions of Member State i), if si is the share of group i in global income (e.g. the share of Member State i in EU GDP) and Tc the index computed on the basis of the m groups (e.g. reflecting the disparities among Member State), then the Theil index is: T = Σ SiTri + Tc =Tr + Tc While Tc reflects the extent of disparities among the groups, Tr reflects the disparities existing within the groups. Applied to our context, the Theil index can therefore be decomposed into one capturing the extent of disparities among EU Member States and another one capturing the extent of disparities between regions within these Member States. ThefollowingfiguredisplaystheTheilindexanditsdecomposition into its country (Tc) and regional (Tr) components for the EU-27 NUTS 2 regions. Figure 6: Theil index: GDP per head, NUTS 2 regions, EU-27 Source: EUROSTAT database. DG REGIO’s own calculation. The value of the index shows a reduction of disparities among EU regions which is in line with the results obtained above with other measures. However, it clearly appears that this reduction is due to the fact that disparities among Members States are strongly decreasing. On the contrary, disparities among regions within the Member States are slightly increasing. This confirms the observation already mentioned above that the reduction of regional disparities among EU countries and regions may coincide with increasing regional disparities within some countries. The ratio Tr/T corresponds to the share of disparities explained by regional disparities within countries (referred to as intra group inequalities) while its complement Tc/T measures the share explained by disparities among Member States (referred to as inter group inequalities). Table 1 reports these shares for the period considered. being due to dispar By 2005, disparities a of regional disparit disparities within M Mean Logarithmic The mean logarithm generalised entropy to the Generalised E index, the MLD is de within and between responsive to chang The following figure and regional (MLDr Figure 7: MLD: GDP Source: EUROSTAT dat The results are qua Theil index. Globa observation; howev component reflect Member States. W increased. The contribution of to global inequaliti 0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.090 0.100 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Tr Tc T 0.000 0.020 0.040 0.060 0.080 0.100 0.120 1995 1996 MLD m i=1 Source: Monfort (2008) evidence of convergence clubs. Moreover, they observed strong convergence between the northern European regions after the introduction of the single market, while convergence between southern regions happened mostly at the beginning of the eighties (Neven and Gouymtea, 1995). Dall’Erba et al. (2008) estimated convergence clubs in an endogenous model for the period 1991-2003 while incorporating spatial autocorrelations. They found evidence of the existence of four convergence clubs while looking at the growth rate in GDP per capita at the regional level. Fratesi and Rodríguez-pose (2004) also provides evidence of convergence clubs when only taking Objective 1 regions into account (Fratesi and Rodríguez-pose, 2004). Theoretically, the effect of market integration on regional convergence was ambiguous. Empirical literature points in the direction that convergence between countries has taken place. There is also strong evidence that convergence between the EU-15 is slowing down and that regional disparities within countries are increasing. However, the results of empirical studies are not robust and sensitive to sampling, to time periods and also have
  37. 37. 24 Empirical literature review methodological issues. 4.2 Empirical literature: effectiveness of Structural Funds The effectiveness of regional policy has always been under scrutiny, especially due to the increase in budget and importance over time. Cohesion policy is more than only reducing the gap in GDP per capita in PPP terms between regions and member states. It also focuses on the creation of a high level of employment, human capital formation and social well-being (Monfort, 2008). However, in the literature, cohesion policy is mostly reduced to the goals of the Convergence objective (previous Objective 1). In this report, the focus will only be on the Convergence objective as most of the budget is allocated to this objective. Moreover, the EC does not see convergence between regions as a policy goal (Barca, 2009). Despite the fact that the goal of the Convergence objective is to reduce disparities between regions and less-favoured regions, they emphasize on the fact that this goal contains an efficiency side and equity side. Efficiency in the sense that regions should be able to reach their full economic potential and equity implies that all people of the EU should have the same opportunities regardless of where they were born. They argue that convergence of the GDP per capita in PPP terms is not a good proxy for these two policy goals. In their view, a region can reach its full economic potential while the gap with other regions and countries is widening. A potential increase in income or GDP per capita can increase disparities within a region (Barca, 2009). However, most of the evaluations of regional policy used and will continue using convergence of GDP per capita in PPP terms as criteria to measure the effectiveness of the Structural Funds. Boldrin and Canova (2001) conclude from their empirical investigation that Structural Funds have no effect on economic growth for the period 1980-1996. They argue that Structural Funds only serve political goals (Boldrin and Canova, 2001). Ederveen et al. (2006) conclude from a panel data analysis with thirteen European countries for the period 1960-1995 that Structural Funds are ineffective to create growth on the country level. However, when controlling for the quality of institutions, they showed that Structural Funds are creating extra economic growth (Ederveen et al., 2006). These results are in contrast to the empirical investigation of Beugelsdijk and Eijffinger (2005) for the period 1995-2002 for the EU-15. They use a Generalized Method of Moments (GMM) approach and control also for institutional quality. They find positive and significant effects of Structural Funds on growth at the country level. They do not detect any effect of institutions or the level of corruption on the effectiveness of the funds (Beugelsdijk and Eijffinger, 2005).
  38. 38. 4.2 Empirical literature: effectiveness of Structural Funds 25 Fratesi and Rodríguez-pose (2004) found from their panel data, a small positive or no effect at all on regional growth for the period 1989-1999. They argue that a possible explanation for the lack of effect on regional growth is that Structural Funds are used for the wrong projects. As a consequence, they may foster short-term effects, but do not impact the medium and long-term growth. They divided the use of Structural Funds for Objective 1 along four axes. The first is support to agriculture. The second and third axes are businesses & tourism support and investment in human capital formation. The fourth axe, which accounts for almost half of the total budget, is investment in infrastructure and transport. Only 23% of the total budget goes to the second objective, with the remaining 20% equally divided among the other two objectives. A short-term positive effect of the support for agriculture on regional growth was observed. More interesting, no significant effect was observed for the second and fourth axes while they account for almost 75% of the total budget. Only investments in human capital formation have medium-term positive effects. This research indicates that the allocation of the budget towards infrastructure could be one of the reasons why disparities between regions are not reducing (Fratesi and Rodríguez-pose, 2004). Mohl and Hagen (2010) use a panel dataset of 124 NUTS1 and NUTS2 regions. They investigated the period 1995-2005 and distinguish between payments for the different objectives. They only find positive effects on growth for the first objective. Moreover, they observe a time lag of four years before the effects on growth are noticeable (Mohl and Hagen, 2010). Becker et al. (2010) follow a different approach. They use a regression discontinuity design to explore the effects of being eligible for funding under the Convergence objective at the NUTS2 level. They find positive effects on growth in GDP per capita but no effect on the growth rate of employment (Becker et al., 2010). In further research they used the same approach, but added the concept of the absorptive capacity to their dataset. They used institutional quality and level of human capital as a proxy for the absorption capacity. They argued that regions need a certain level of institutional quality and human capital before they can effectively absorb funding and only then Structural Funds have positive effects (Becker et al., 2013). Becker et al. (2013) found that many regions do not have the necessary level to absorb funding. This could imply that the effectiveness of the funds could be increased by reallocating funding towards regions with certain minimum levels regarding human capital and institutional quality. Small significant positive effects were also found by Dall’Erba and Le Gallo (2004) for the period 1989-1999 for 145 NUTS2 regions when including spatial spilloves. However, positive spatial spillover effects are small and even smaller for the southern regions in Europe (Dall’Erba and Le Gallo, 2004).
  39. 39. 26 Empirical literature review
  40. 40. Chapter 5 Regional policy between 2000 and 2013 Despite the lack of theoretical and empirical results on the effectiveness of funds, their importance kept on increasing especially since the ratification of the treaty of Maastricht. The introduction of the euro in 1999 and the enlargement of the EU with ten new member states in 2004 were two important milestones that drastically changed the political and economic equilibrium in the EU. It was hoped that the introduction of the euro would be an extra push for countries with a lower level of GDP per capita in PPP terms to catch-up. A higher degree of financial integration would imply that countries with excessive domestic investments could borrow from the rest of Europe. They would no longer be constrained by national savings (Jaumotte and Sodsriwiboon, 2010). However, the restrictions imposed by the Maastricht criteria on the public deficit and debt, the uncertain outcome of the introduction of the euro and political negotiations led to the creation of the Cohesion Fund and the expansion of the Structural Funds. Regional disparities widened within the EU with the enlargement of the EU in 2004. That year, 90% of the population of the new entrants lived in regions that had a GDP per capita in PPP terms lower than 75% of the EU average. On the contrary, only 13% of the population of the EU-15 lived in regions below the 75% threshold before the enlargement in 2004 (European Commission, 2005). Another important consequence of the enlargement of the EU besides the increased regional disparities was the reallocation of the budget for regional policy from the south of Europe towards the east of Europe. The effect of receiving funding from the Structural Funds has been extensively studied. As discussed in chapter 4, the results on their effectiveness are mixed. However, the effect of losing funding from Structural Funds on the growth rate of GDP per capita has not yet been investigated. The expansion of the EU in 2004 and its effect on the EU GDP per capita can be used as an exogenous shock in empirical investigations. It gives the opportunity to look at how regions that receive funding are affected by the expansion of
  41. 41. 28 Regional policy between 2000 and 2013 the EU, with new regions that have a low level of GDP per capita in PPP terms. In the next section, we will clarify how the enlargement of the EU can be used as an exogenous shock. Firstly, the allocation of the budget for regional policy towards the different objectives will be discussed. Secondly, the funds and objectives that will be investigated will be defined. Thereafter, the effect of the enlargement of the EU on the Structural Funds will be described in more detail. 5.1 Financial instruments As mentioned earlier, the two main instruments for regional policy are the Cohesion Fund and the Structural Funds. Since the reforms in 2006, there are only two Structural Funds namely the ERDF and the ESF. As discussed in chapter 2, Structural Funds were organised around 3 objectives for the programming period 2000-2006. These objectives were renamed and slightly redefined for the programming period 2007-2013 and became: 1. The Convergence objective 2. Regional Competitiveness and Employment objective 3. European Territorial Cooperation objective A total of 213 billion e was allocated to regional policy for the programming period 2000- 2006. 122.8 billion e came from the ERDF and 81% of the funding from the ERDF went to Objective 1. In total, 146 billion e was allocated by the Structural Funds to Objective 1 regions in the EU-15. An extra 22 billion e was made available for the new entrants that were eligible for funding between 2004 and 2006 (Ward and Wolleb, 2010). The budget for regional policy increased to 308 billion e for the programming period 2007- 2013. Objective 1 was renamed to the Convergence objective. 81.54% from the total budget for regional policy was allocated to the Convergence objective. 70% of the money for the Convergence objective came from the ERDF and the ESF; the rest came from the Cohesion Fund. 15.95% was allocated to the employment objective and 2.52% was given to the European Territorial Cooperation objective (European Commission, 2013). These numbers show that most of the budget was allocated to the Convergence objective during the two programming periods that will be under scrutiny in this research. More than two thirds of the money came from the ERDF. As stipulated before, the focus of this dissertation is the Convergence objective and the effect on regional disparities. Therefore, in the empirical evaluation that follows, only the Structural Funds and its contribution to
  42. 42. 5.2 Objective 1 and the Convergence objective 29 the Convergence objective will be taken into account. However, the Cohesion Fund also contributes to a smaller extent to the budget for the Convergence objective, but targets countries instead of regions. 5.2 Objective 1 and the Convergence objective A region qualifies as a Convergence Objective (CO) region when the GDP per capita in purchasing power parity terms is lower than 75% of the EU average. A region is defined at the NUTS2 level. The EU has three different classifications of regions in Europe, namely NUTS1, NUTS2 and NUTS3. When the NUTS1 classification is followed, Europe is divided in regions of more than 3 million inhabitants while the NUTS2 definition creates regions with 0.8 to 3 million inhabitants. The NUTS3 classification divides regions into regions with less than 0.8 million inhabitants (Becker et al., 2010). A NUTS3 region is embedded in a corresponding NUTS2 and NUTS1 region. The NUTS2 classification is used for the allocation of Structural Funds. For some countries, by way of derogation, the NUTS3 classification is used. Moreover, over time countries could adapt the borders of the NUTS classification in their country (ESPON, 2015). The rule to be considered as a CO region is very clear. As a consequence, this should imply that during previous programming periods, only regions at the NUTS2 level with a GDP per capita in PPP terms lower than 75% of the EU average should have received funding. However, this was not always the case. Different factors have played a role. Firstly for example, to determine the eligible regions for the programming period 2007- 2013, the average GDP per capita in PPP terms for the period 2000-2002 was used. This was due to the fact that at the time, when the list with eligible regions was drawn up in 2006, this was the most recent available data. Regions around the 75% threshold could have experienced significant growth in their GDP per capita during 2002-2006, making them no longer eligible for funding in the next programming period. Moreover, the calculation of the GDP per capita in PPP terms at the NUTS2 level could have been subject to corrections later when more accurate or revised information would be available. In addition, political agreements and negotiations could have influenced which regions are considered as a CO region. Becker et al. (2010) calculated that, between 1989 and 2006, the rule was not followed correctly in 7% of the cases. On average, eligible regions received transfers around 1.8% of their GDP per capita for the period 1994-1999 and 1.1% of their GDP per capita for 2000-2006 (Becker et al., 2010). Structural Funds use the principle of additionality. Projects are never fully financed by the ERDF. A maximum of 75% will be financed by the ERDF; the rest comes from the national
  43. 43. 30 Regional policy between 2000 and 2013 government. Projects financed by the ERDF have to be part of a national strategic reference framework. Therefore, receiving funding from the ERDF is conditional on a national plan and on the ability of the government to fund the project. As a consequence, it could be the case that regions are not able to fully use the budget allocated to them. Table 5.1 shows the absorptive capacity of a country for the programming period 2007- 2013. The absorptive capacity of a country is the percentage of funding that was available for the eligible countries in a region that was actually taken up by the country. On average countries absorbed 72% of the total funding for the programming period 2007-2013. The main reasons for this deviation were the lack of national funding and issues with the strict European administrative procedure. 5.3 The effect of the enlargement of the EU on the Structural Funds In 2004 the EU enlarged with 10 new countries. These countries had a lower GDP per capita in PPP terms than the EU average. The total population increased by 20% while the GDP of the EU only increased by 5% (European Commission, 2014b). Hereby, the EU average of GDP per capita in PPP terms dropped significantly. As a consequence, it influenced the eligibility of regions for funding under the Convergence objective. There- fore, certain regions lost EU funding due to the drop in the average of GDP per capita. Suddenly, their GDP per capita was above 75% of the average of the EU. However, they lost their funding due to a drop in the EU average and not because they started perform- ing better. The EU tried to compensate these regions by allocating them smaller funding for the next programming period. In practice, this meant that the enlargement of the EU became noticeable for the programming period 2007-2013. The new entering regions received extra assistance in the period 2004-2006. This did not affect the older member states of the EU. However, for the programming period 2007-2013, the EU-25 average was used and no longer the EU-15 average to calculate which regions are eligible for funding under the Convergence objective. As a consequence, from the 57 regions1 that received funding in 2000, only 31 remained eligible for funding in the next period while 26 regions lost funding (European Commission, 1999b, 2006c). The EC was aware of this problem. Therefore, 1 The list of eligible regions in 2000 contained 60 regions. Due to mergers between NUTS2 regions in Germany, there are 57 eligible regions in 2000 if we follow the most updated NUTS2 classification.
  44. 44. 5.3 The effect of the enlargement of the EU on the Structural Funds 31 Table 5.1: Absorptive capacity 2007-2013 Member State Year Absorption (%) Estonia 2015 94.9% Portugal 2015 94.3% Lithuania 2015 93.7% Finland 2015 93.1% Sweden 2015 92.5% Poland 2015 91.4% Luxembourg 2015 90.6% Denmark 2015 90.3% Greece 2015 90.2% Ireland 2015 89.2% Slovenia 2015 88.4% Netherlands 2015 87.9% Latvia 2015 87.9% Belgium 2015 87.8% Germany 2015 87.0% Austria 2015 86.8% Cyprus 2015 85.1% France 2015 83.4% EU28 2015 81.9% United Kingdom 2015 78.9% Hungary 2015 78.7% Malta 2015 78.4% Spain 2015 77.4% Bulgaria 2015 76.3% Czech republic 2015 72.8% Italy 2015 71.9% Slovakia 2015 63.2% Romania 2015 61.4% Croatia 2015 48.1% Source: European Commission (2014a)
  45. 45. 32 Regional policy between 2000 and 2013 23 regions that lost funding received transitional support2 . The 3 Swedish regions that were partially eligible for funding under the Convergence objective did not receive any transitional support (European Commission, 2006c,d). Table 5.2 reports the amount of transitional funding and convergence funding received for the old member states3 . The amount received by member states alternates due to differences in size and number of eligible regions. Therefore, these numbers should not be used to compare different countries. However, it gives an indication on the difference between transitional support and full support. The amount of transitional support is roughly 34% of the total support for the Convergence objective. However it should be taken into consideration that transitional support is used for 24 regions4 while it is ded- icated to 31 regions for the Convergence objective. The difference between receiving full funding and transitional funding is significant enough to have an effect on the regions. 2 Belgium also receives transitional support while it already received transitional support in the period before. Normally regions can only receive transitional support for one programming period. Therefore the Belgian region Hainaut will not be taken into account or reported in table 5.2. 3 We do not report the budget allocation towards the new member states. However it should be noted that Cyprus and Hungary also received transitional support. These numbers will not be reported in table 5.2 nor will this regions be taken into account. 4 24 regions instead of 23 regions receives transitional support this is due to the fact that Lüneburger never received full funding in the period 2000-2006, therefore we do not consider this as a region that lost funding due to the enlargement of the EU.
  46. 46. 5.3 The effect of the enlargement of the EU on the Structural Funds 33 Table 5.2: Decomposition of the budget for the Convergence Objective between transitional and full support Available budget from the Structural Funds for the programming period 2007- 2013 for transitional support and convergence Country Transitional support Convergence objective Germany 3 703 187 217.00 e 10 360 473 669.00 e Greece 6 347 127 476.00 e 8 358 352 296.00 e Spain 4 931 001 421.00 e 17 283 774 067.00 e Italy 902 514 861.00 e 17 283 774 067.00 e Austria 158 159 247.00 e - Portugal 578 020 351.00 e 15 143 387 819.00 e United Kingdom 1 038 198 261.00 e 2 429 762 895.00 e Ireland 418 744 086.00 e - Finland 324 544 537.00 e - Total 18 401 497 457.00 e 70 859 524 813.00 e Average budget per region 766 729 060.71 e 2 285 791 123.00 e Transitional support as a % of the total budget for the Convergence objective 34% 2004 prices, transitional support 24 regions, convergence 31 regions Source: European Commission (2006a,b)
  47. 47. 34 Regional policy between 2000 and 2013
  48. 48. Chapter 6 Data and methodology The enlargement of the EU in 2004 and its effect on eligibility for funding from Structural Funds created an interesting quasi-natural experiment. The drop in the EU average can be seen as an exogenous shock. It divided the regions eligible before 2006 into a control group, and a treatment group after 2006. It gives the opportunity to investigate what happened to the growth rate of GDP per capita and disposable income in regions that only received transitional support. In the next section, more clarifications concerning the treatment group, the control group and the used data will be given. Afterwards, the used methodology will be explained and in chapter 7 the results will be discussed. 6.1 Data The database created for this empirical investigation will contain data for the period 2000-2011 and are all available on Eurostat. Given the fact that Structural Funds focus on the NUTS2 level, all data will be at the NUTS2 level. We will not aggregate the data as it was common in earlier research (Beugelsdijk and Eijffinger, 2005; Ederveen et al., 2006). Instead we will follow the approach used by Fratesi and Rodríguez-pose (2004), Becker et al. (2010) and Dall’Erba and Le Gallo (2004) where data at the NUTS2 level were used. As pointed out before, the enlargement of the EU in 2004, created a situation where certain regions lost funding due to a drop in the EU-average for the programming period 2007-2013. Hereby, two groups are created. On one hand, a group of regions that received funding between 2000 and 2013. On the other hand, a group that received funding for the programming period 2000-2006 but only received transitional funding for the period 2007- 2013. The latter will become the treatment group while the former will be the control
  49. 49. 36 Data and methodology group. 31 regions remained eligible for funding from the 57 eligible regions in the period 2000-2006. The control group will only contain 27 regions because the 4 French overseas regions will not be considered given that the focus is on the mainland of Europe. The regions in the control group are spread over six different countries: Germany, Greece, Spain, Portugal, Italy and the United Kingdom. The treatment group contains 23 regions spread over 9 countries: Germany, Greece, Spain, Portugal, Italy, the United Kingdom, Austria, Finland and Ireland. The 3 Swedish regions that received partial funding and afterwards did not receiving transitional funding are not included in the treatment group. Therefore, from the 26 regions that lost funding only 23 will be in the treatment group1 . Regions that never received Objective 1 funding or the new entrants are not taken into consideration as they were not affected by the drop in the EU average. A detailed list of the different regions can be found in Annex A. In a next step, this control and treatment group can be used for an empirical investigation using differences in differences. The creation of the control and treatment group is based on the assumption that only the eligibility rule played a role2 . Figure 6.1 shows that for 8 out of 50 regions, the rule for eligibility for funding is not followed correctly3 . Revision of the data and political agreements4 can explain why in 16% of our sample there is a deviation from the rule. However, it is remarkable that most of the mistakes are made (6 times) with regions that lost funding while they should have been eligible for full funding from the Structural Funds. This could be an indication that political agreements where not that important as politicians would try to get the most funding possible for their region. Therefore, the deviations from the rule are not problematic for the construction of the control and treatment group. A more rigorous way to examine if differences in the level of GDP per capita were the only factor that affected eligibility, is by looking at a linear and a probit regression. In table 6.1, the results of a linear and probit regression for the year 2006 are displayed. Different factors that could potentially affect the probability of regions losing funding are included. In column 1 and 2 GDP per capita is included, while in column 3 and 4 GDP per capita as a percentage of the EU average is used in the different regressions. The different 1 Regions that received transitional funding for the period 2000-2006 and 2007-2013 are not included in our sample. 2 The average GDP per capita in PPP terms between 2000-2002 is compared with the EU-25-average between 2000-2002 3 Certain dots can represent multiple regions 4 The current NUTS2 classification is used here, however sometimes borders were slightly redrawn without changing the code of the NUTS2 region. Therefore, this could also lead to the deviation which can be observed in figure 6.1 as this figure is based on the current classification.
  50. 50. 6.2 Methodology 37 Figure 6.1: Eligibility of the control and treatment group under the Convergence objective (2007-2013) 0.2.4.6.81 treatment 60 70 80 90 100 GDP per capita in PPP terms for 2000-2002 as a percentage of the EU-25 average Note: treatment is equal to 1 for regions that only receive transitional funding after 2006 and 0 if regions receive full funding since 2000 under the Convergence objective; GDP per capita in PPP terms per NUTS2 region is based on the average for the period 2000-2002 and compared to the EU-25 average for the period 2000-2002; A region is eligible for funding if their GDP per capita in PPP terms is below 75% of the EU average. specifications show that the only variable that can explain why certain regions lost funding and others did not is GDP per capita. These results indicate that the main factor that determined that certain regions where no longer eligible under the Convergence objective is GDP per capita. More specifically, these regions lost funding after the enlargement of the EU as it affected the rule to determine eligibility for funding under the Convergence objective5 . 6.2 Methodology In this paper, our aim is to investigate the effect of losing funding from the Structural Funds on GDP per capita and disposable income. Both can be seen as a proxy for regional convergence. Only comparing GDP growth for regions that lost funding before and after 2006 would not give an answer to the question if losing funding affects GDP per capita growth. It 5 Instead of using the EU-15 average of GDP per capita, the EU-25 average of GDP per was used for the programming period 2007-2013
  51. 51. 38 Data and methodology Table 6.1: Results probit and linear regression: factors that could affect eligibility for the Con- vergence objective (2007-2013) linear probit linear probit Dependent variable: treat treat treat treat Variables lncapitalformation -0.0234 0.0502 -0.0225 0.0489 (0.0486) (0.174) (0.0485) (0.174) lnunemprate 0.0994 -0.0857 0.107 -0.0450 (0.255) (0.908) (0.255) (0.902) lnearly -0.122 -1.292 -0.121 -1.295 (0.184) (0.909) (0.186) (0.904) lnneet 0.120 1.596 0.117 1.577 (0.243) (1.365) (0.242) (1.346) lnseconatt 0.146 0.589 0.144 0.580 (0.161) (0.563) (0.163) (0.562) lnGDPpppcapita 2.382*** 11.37*** (0.354) (3.712) lnGDPpppeuaverage 2.381*** 11.38*** (0.351) (3.668) Constant -23.55*** -114.9*** -10.53*** -52.83*** (3.729) (37.64) (1.865) (17.24) Observations 45 45 45 45 R-squared 0.444 0.444 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: data for the year 2006 at the NUTS2 level is used; treat is a dummy equal to 1 if a region received transitional funding between 2007-2013 and equal to 0 if it received full funding under the Convergence objective between 2000 and 2013; lncapitalformation: logarithm of gross fixed capital formation; lnunemprate: logarithm of the unemployment rate; lnearly: logarithm of the percentage of the population aged 18 to 24 having attained at most lower secondary education attainment; lnneet: logarithm of the percentage of people between 15-24 not in education or employment; lnseconatt: logarithm percentage of people between 25-64 with lower secondary education attainment compared to total population; lnGDPpppcapita: logarithm of GDP per capita in PPP terms; lnGDPpppeuaverage: logarithm of GDP per capita as a percentage of the EU average
  52. 52. 6.2 Methodology 39 would only show the evolution within regions and neglect other shocks that could have taken place that affected changes in GDP per capita. This creates endogeneity due to an omitted variable bias. For example, the fall of Lehman brothers at the end of 2008 and the following financial and bank crisis negatively affected GDP per capita. As a consequence, neglecting this time-varying trend would lead to a biased estimator. Instead, comparing the GDP per capita of the control and treatment group in 2011 would only be possible if the following assumption can be made: the control and treatment group are completely similar. This assumption is only credible if it is a fully randomized experiment. In this cross-section approach, time-invariant unobserved factors that can create a wedge between the control and treatment group are not taken into account. However, this cross-section approach can be combined with elements of time-series in the framework of difference in difference. The approach behind differences in differences is to compare a control and treatment group before and after the treatment (or shock). The difference between the control and treatment group before a shock are compared to the situation after the shock. The logic of differences in differences can be summarized in equation 6.1. Two time periods, one before a shock and one after the shock and two groups are assumed. The difference before and after the shock for group 1 are represented by (y11 − y10). (y21 − y20) contains the same information but for the second group. (y11 − y10) − (y21 − y20) (6.1) Hereby, it is not necessary to make the assumption that control and treatment group need to be similar. Instead, the before and after approach allows to control for unob- served characteristics that do not vary over time between control and treatment group while the comparison between a control and treatment group controls for time trends. As a consequence, factors like the economic crisis that affected both the control and treat- ment group are taken into account. However, shocks that took place after the treatment that affected the outcome variable for both groups in a different way can still bias the results. An important assumption that needs to be satisfied in the difference in differ- ences framework, is the assumption of the common time trend. The control and treatment group need to have a similar evolution over time for the outcome variables. The difference in difference approach can be applied to the case presented here and can be written down in a more formal way: yist = β0 + β1Post2006t + β2Treateds + β3Post2006t ∗ Treatedst + β4Xit + µi + ist (6.2)
  53. 53. 40 Data and methodology • The subscript i refers to the NUTS2 level while s refers to the group level and t to time. • yist is the outcome variable • Post2006t a time dummy equal to 1 for all the years after the treatment • Treateds a dummy equal to 1 for all the regions that lost funding • Post2006t ∗ Treatedst is an interaction term • Xit represents the different time-variant covariates that can be added at the regional level • µi stands for the regional fixed effects that can be included in the regression • ist is the error term • β3 is the coefficient of interest as it will reveal if there is a difference between the control and treatment group after comparing the difference between both groups before and after treatment The inclusion of covariates and fixed effects at the regional level is not necessary, but they can increase the precision of the estimates (Pischke, 2014). They should not significantly change the results. Difference in difference does not control for time trends that affect the outcome variable differently for the control and treatment group. However, it is possible to overcome this problem if the differences arise from observables characteristics by interacting the control variable with the time dummy. Violations of the common time trend could be overcome when it is possible to condition on observables (Roberts and Whited, 2012). Two outcome variables will be used in this paper namely: GDP per capita in PPP terms and disposable income. Disposable income is closely related to GDP per capita. However, the main difference between both variables is that disposable income incorporates transfers from the governments to household. Therefore, changes in policy at the regional or the national level can affect the outcome. This should be taken into account as it can potentially affect the assumption that treatment and control group have a similar trend in the absence of the treatment. This assumption cannot be tested in a rigorous way. Still, looking at the time period before the treatment can give an indication if both groups follow a similar pattern. Figure 6.2 shows the evolution for GDP per capita between 2000 and 2006. This figure demonstrates a common trend between both groups while the levels differ. However, the
  54. 54. 6.2 Methodology 41 differences in levels are not important for difference in difference. The results displayed for disposable income in figure 6.3 seem also to indicate a similar evolution. However, a small divergence between the groups at the end of the period can be observed. This could be an indication that certain factors affected treatment and control group differently. If this is true, the difference in difference regression will be biased. Therefore, the results for disposable income should be interpreted with caution. Figure 6.2: Evolution of GDP per capita in PPP terms for the control and treatment group between 2000-2006 9.59.69.79.89.910 lnGDPpppcapita 2000 2002 2004 2006 year treatment group control group Note: the control group consist of 27 regions which received full fund- ing under the Convergence objective between 2000 and 2013. The treatment group contains 23 regions which received transitional fund- ing for the period 2007-2013; lnGDPpppcapita: the average of the logarithm of gross domestic product per capita in PPP terms.
  55. 55. 42 Data and methodology Figure 6.3: Evolution of disposable income for the control and treatment group between 2000- 2006 9.29.39.49.5 lndisposableincome 2000 2002 2004 2006 year treatment group control group Note: the control group consist of 27 regions which received full fund- ing under the Convergence objective between 2000 and 2013. The treatment group contains 23 regions which received transitional fund- ing for the period 2007-2013; lndisposableincome: the average of the logarithm of disposable income.
  56. 56. Chapter 7 Results In this chapter the results obtained after putting equation 6.2 in practice will be displayed. GDP per capita in PPP terms and disposable income will be used as outcome variables. The change in levels and growth rate will be investigated for both variables. Looking at the growth rate of GDP per capita helps to answer the question if Structural Funds can speed up growth. Looking at levels instead of growth rates reveals information about changes in the levels of GDP per capita in PPP terms. This gives the opportunity to answer the question if Structural Funds can generate growth. Table 7.1 and 7.2 show the results obtained for the growth rate and levels of GDP per capita in PPP terms. Column 1 shows the difference in difference regression without the inclusion on any regional covariates. Firstly, in column 2, regional fixed effects are added. In a second step, the unemployment rate is added and in column 4 capital formation and the variable neet (the percentage of people between 15-24 years old not in education or employment) are included. The variable posttreat is the difference in difference estimator and is insignificant over the different specifications for both the growth rate and levels. The time dummy post2006 shows that the crisis negatively affected the growth rate, but remains positive for the levels regression. Thus, despite the crisis, the level of GDP per capita rose after 2006 compared to the period 2000-2006. Capital formation can be linked to treatment, but is added at the regional level and not at the group level. Still, the inclusion of this covariate does not affect the obtained results. The same regressions can be repeated for disposable income. The results are similar to the results obtained for GDP per capita in PPP terms. The results are shown in appendix B table B.1 and B.2. There is an indication that regions that lost eligibility are not negatively affected. However, it is possible that the negative effect on the outcome variables came with a certain lag. Therefore, in table 8.1, the time dummy and the difference in difference
  57. 57. 44 Results Table 7.1: Difference in Difference regression: growth rate of GDP per capita in PPP terms Variables grGDPpppcapita grGDPpppcapita grGDPpppcapita grGDPpppcapita treat 0.00208 (0.00432) post2006 -0.0446*** -0.0446*** -0.0434*** -0.0435*** (0.00516) (0.00540) (0.00487) (0.00568) posttreat -0.00272 -0.00272 0.00372 0.00507 (0.00726) (0.00761) (0.00689) (0.00861) lnunemprate -0.0555*** -0.0508*** (0.00765) (0.0143) lncapitalformation 0.0315* (0.0158) lnneet 0.00672 (0.0146) fixed effects no regional regional regional Constant 0.0424*** 0.0359*** 0.210*** -0.162 (0.00276) (0.00245) (0.0222) (0.194) Observations 550 550 517 443 R-squared 0.238 0.292 0.397 0.390 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: time period 2000-2011 at the NUTS2 level is used; The dependent variable grGDPpppcapita: the growth rate of GDP per capita in PPP terms; treat is a dummy equal to 1 if a region received transitional funding between 2007-2013 and equal to 0 if it received full funding under the Convergence objective between 2000 and 2013; Post2006 is a time dummy equal to 0 in the years up to 2006 and 1 after 2006; Posttreat is an interaction term between post2006 and treat; lnunemprate: logarithm of the unemployment rate; lncapitalformation: logarithm of gross fixed capital formation; lnneet: logarithm of the percentage of people between 15-24 not in education or employment.
  58. 58. 45 Table 7.2: Difference in Difference regression: the level of GDP per capita in PPP terms Variables lnGDPpppcapita lnGDPpppcapita lnGDPpppcapita lnGDPpppcapita treat 0.175*** (0.0244) post2006 0.134*** 0.134*** 0.131*** 0.0910*** (0.0131) (0.0136) (0.0142) (0.0156) posttreat 0.00680 0.00680 0.00430 -0.00305 (0.0192) (0.0201) (0.0217) (0.0220) lnunemprate -0.0900*** -0.0438 (0.0175) (0.0319) lncapitalformation 0.247*** (0.0341) lnneet 0.0391 (0.0429) fixed effects no regional regional regional Constant 9.657*** 9.617*** 10.02*** 7.999*** (0.0161) (0.00568) (0.0480) (0.278) Observations 600 600 560 482 R-squared 0.462 0.760 0.803 0.861 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: time period 2000-2011 at the NUTS2 level is used; The dependent variable lnGDPpppcapita: the logarithm of GDP per capita in PPP terms; treat is a dummy equal to 1 if a region received transitional funding between 2007-2013 and equal to 0 if it received full funding under the Convergence objective between 2000 and 2013; Post2006 is a time dummy equal to 0 in the years up to 2006 and 1 after 2006; Posttreat is an interaction term between post2006 and treat; lnunemprate: logarithm of the unemployment rate; lncapitalformation: logarithm of gross fixed capital formation; lnneet: logarithm of the percentage of people between 15-24 not in education or employment.
  59. 59. 46 Results term are decomposed. The coefficient of the variables treat2007, treat2008, treat2009, treat2010 and treat2011 will be our difference in difference estimator. Firstly, it can be noted that GDP per capita was already influenced by the crisis in 2008 while the effect of the crisis only became noticeable in 2009 for disposable income. The decomposition of time does not seem to change the results obtained before. Only in 2009 the interaction term between p2009 and treat became significant at the 10 percent level (treat2009). However, this result is not robust for the inclusion of covariates.

×