AN ECONOMIC ANALYSIS OF STRUCTURAL AND COHESION FUNDS

With over 500 millions of citizens, the European Union (EU) as a whole generates, in 2009, an estimated 28% share of the nominal gross world product and about 21.3% of the gross world product. Compared to the EU average, the United States per capita gross domestic product (GDP) is 35% higher and the Japanese per capita GDP is approximately 15% higher. However, there are substantial economical disparities within the EU. On the high end, Inner London has a per capita GDP equal to EUR 83,200 (334% of the EU27 average), while the poorest region Severozapaden, in Bulgaria, has a per capita GDP of only EUR 6,400 (26% of the EU27 average). Regional policy has been at the heart of EU policies since the Treaty of Rome in 1957. The Treaty’s preamble refers to the need “to strengthen the unity of their economies and to ensure their harmonious development by reducing the differences existing among the various regions and the backwardness of the less-favoured regions”. To this goal there are a number of Structural Funds and Cohesion Funds supporting development of poor regions. Nowadays with the current financial crisis, EU is also facing a new range of challenges owing to the enlargement of the Union which is actually strengthening European economic stability and welfare. In this thesis we will try to answer to the following questions: Which are the determinants of productivity dynamics of EU regions? Which policy was adopted by the EU in the last thirty years to achieve its goals of convergence and competitiveness of European regions? Has this policy been effective? In particular, the thesis is organized in three chapters. Chapter 1 traces the history of the EU regional policy explaining how it has evolved from 1957 to 2006. We discuss how the use as unit of observation of the so called NUTS 2 regions (see http://ec.europa.eu) can be not appropriate for the type of issues under scrutiny. Then we show how we built our database on Structural and Cohesion Funds for the first three programming periods (i.e. 1975-1988, 1989-1993 and 1994-1999), for which no databases are directly available by the European Commission. In this we paid particular attention to the reallocation across regions when information was available only at multiregional or country level, in the light of the constant changes that the EU regional policy underwent from 1975 to 1999. We find that Structural and Cohesion Funds mainly flowed to regions with lower per capita income and, within the Objective 2 regions, with higher unemployment rate and employment share in the industrial sector. However, recipients were not always regions with the least favourable economic conditions and there exists a significantly share of funds allocated independently of the eligibility criteria. Moreover, we find that over time there is an increasing discrepancy between the funds committed and those actually spent by the regions. Finally, taking the ratio of payments on commitments as a measure of the administrative capacity for using EU funds, we find that countries greatly differ in their efficiency in managing the funds. Chapter 2 analyzes the impact of the European Union regional policy on the productivity growth of European regions over the period 1980-2002. In particular, we separately consider three programming periods (1975-1988, 1989-1993 and 1994-1999), and study the effects of various types of funds on labour productivity (i. e. Structural, Cohesion, Objective 1, etc.). In order to capture the main features of the funds, i.e. their size and composition, we also propose a simple growth model, which is subsequently utilized as a guide to the empirical analysis. In all the three programming periods we find that, on average, funding had a positive and concave effect on productivity growth. In particular, a share of funds on GVA of 10% is estimated to raise the regional growth rate of about 0.9% per year. However, by separately considering the three programming periods and the composition of the funds according to different “objectives”, we find that: i) only the funds allocated in the second and third programming periods, when their amount remarkably increased, had a significant impact; and ii) only Objective 1 and Cohesion funds had a positive and significant impact, while the impact of funds devoted to Objectives 2, 3, 4 and 5 appears negative or non significant. The results are robust to funds’ endogeneity and spatial dependence. Chapter 3 analyzes the determinants of the distribution dynamics of labour productivity of European regions over a shorter period, i.e. 1992-2002. We propose a novel methodology which combines the growth regression approach `a la Barro and Sala-i-Martin, in a semiparametric framework, with the stochastic kernel approach `a la Quah. In particular, the distributional impact of a given variable is evaluated by the comparison of actual and counterfactual distributions and the related actual and counterfactual stochastic kernels and ergodic distributions. Counterfactual distribution is calculated by the estimated growth regression, taking the variable to sample average for all regions. The methodology also allows for measuring the marginal effect of the variable on distribution and for testing for possible presence of distributional effects in the residuals of growth regression. We find that initial productivity accounts for a large decrease in the dispersion of productivity. Instead, country unexplained component (country dummies) has an ambiguous effect (benefiting regions around but below the average but hurting regions far below), while employment growth has not any distributional effect. Objective 1 and Cohesion Funds have a reducing-dispersion effect, but their very limited size produces a negligible effect on the overall distribution. This also holds for structural change, as measured by the change in the share of Agriculture sector on total GVA, and Wholesales and Retail; on the opposite Hotel and Other Market Services result enhancing-dispersion sectors. Finally, financial sector has an ambiguous effect, mostly benefiting regions with productivity around but below the average. No variable considered in the analysis appears to affect the polarization of productivity, but initial productivity