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EU Cohesion Policy and new convergence clubs
Language: English This thesis is written in English
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Author
Chiara Pontillo, Università degli Studi di Bologna, 2016-17
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Academic area
Economics
Abstract
This study provides empirical evidence on club convergence among European regions and on the impact of regional peculiar structural features on club formation.
Building on previous EU convergence analysis, the main novelties are: the inclusion of new EU Member States from CEE in our sample; the use of GDP instead of GVA as our dependent variable; the inclusion of additional control variables to account for the socio-economic and demographic features of new Members; and a checkup on the resources allocations of EU Structural Funds to all regions.
To begin with, chapter 1 is devoted to the definition and classification of European regions and to a brief historical overview of Cohesion Policy and Structural Funds. Then, chapter 2 reviews the literature on convergence, from the neoclassical growth model to the more recent steady state economy and economic degrowth, also focusing on time series and panel convergence analysis.
Eventually, chapter 3 investigates the presence of club convergence in GDP per capita within the 276 NUTS 2 regions for the 2000-2015 time period. To do so, a two-step procedure is applied: first, the non-linear log t test developed by Phillips and Sul is implemented and six convergence clubs are identified; second, an ordered logit model is run to detect how structural characteristics drive the formation of such clubs. The final section 3.4 discusses the results obtained under the models’ conditions: each club shows specific features, starting from club 1, where North-Western and also Central-Eastern capital regions converge to the same steady state, and ending with club 6, where the dramatic effects of the 2009 crisis inevitably made Southern and some CEE regions cluster together in the lowest GDP club. Such cluster structure is well reflected by the results from the ordered logit model, which highlighted the strong impact mostly of population density, unemployment rates, GVA in industry and in financial activities on the clubs formation.