Assessing regional growth and convergence across Europe is a matter of primary importance, either in light of the effectiveness of cohesion policies, or in terms of the expectations of New Entrants. Empirical models that not account for structural heterogeneities and spatial effects could fail to detect club convergence phenomena. In this paper, we adopt a spatially filtered mixture regression approach that endogenously identifies regional clubs of beta-convergence, in order to avoid ad hoc predeterminations, as North-South or centre-periphery divisions. Results indicate that spatial effects matter, and absolute or conditional convergence might be too much restrictive assumptions, not supported by the data. Excluding a small number of regions that behave as outliers, only few regions show fast convergence. The majority of the sample, in fact, exhibits slow convergence, with the remaining part showing no convergence at all. In addition, a dualistic phenomenon seems to be present inside some States, reinforcing the “diverging-convergence” paradox.
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Paper provided by Department of Economic and Business Sciences, LUISS Guido Carli in its series Quaderni DPTEA with number
142.
Find related papers by JEL classification: C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models O40 - Economic Development, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General R11 - Urban, Rural, and Regional Economics - - General Regional Economics - - - Analysis of Growth, Development, and Changes
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