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This paper proposes a new technique based on an iterated spatially weighted regression procedure to verify the presence of economic growth heterogeneities in EU regions in the period 1981-2009. The approach extends a procedure originally proposed in the field of image analysis based on the assumption of local homogeneity of the signal. The presence of the heterogeneity is a criterion to divide the sample of observations (i.e. regions) into smaller homogeneous groups. Our results highlight the presence in the EU regions of five different clubs with different growth paths within each subgroup. Spatial dependence is also considered in the definition of the economic convergence model. [ABSTRACT FROM AUTHOR]

In the last year, a central issue in regional economic growth debate has been represented by the empirical analysis of Verdoorn's law related to the long-term dynamic relationship between the rate of growth in output and the productivity growth due to increasing returns. Several papers have tested Verdoorn's law on European countries as well as many other world economies. Recently, attempts have been made to provide foundations for a spatial version of the original law specification. The main contributions were dedicated to the inclusion of spatial dependence in the economic model. Surprisingly, in the literature on Verdoom's law the analysis of the spatial heterogeneity is not often considered. The aim of this paper is the regional analysis of the spatial dependence and heterogeneity in Verdoorn's law, identifying spatial regimes that can be interpreted as clusters of productivity growth in European regions at NUTS 2 level. To pursue this objective, an optimization algorithm for the identification of groups is used. This constitutes a modified version of Simulated Annealing. [ABSTRACT FROM AUTHOR]

In the last years a central issue in regional economic growth debate is represented by the convergence problem. Many empirical economists have noticed that per-worker GDP of poor regions tend to converge to those of richer regions. However more recently it has been observed that the economic convergence might not be achieved if, in the empirical analysis, we consider the entire data set as one sample. The phenomenon should be analyzed considering regions as belonging to different sub-samples with quite similar economy. Many authors refer to this hypothesis as economic convergence clubs. The definition of these homogeneous groups represents a crucial issue in many regional economic growth studies. The aim of this paper is to propose a method for the identification of convergence clubs for the European regions at NUTS 2 level. The econometric specification used is based on the classical, and spatial augmented version of the conditional β-convergence model. Two different optimization algorithms for the identification of convergence clubs are proposed and compared: Simulated Annealing and Iterated Conditional Modes. [ABSTRACT FROM AUTHOR]