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 pro- ductivity 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. Sur- prisingly, 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.

Spatial Clusters in EU Productivity Growth

POSTIGLIONE, PAOLO;BENEDETTI, ROBERTO
2017-01-01

Abstract

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 pro- ductivity 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. Sur- prisingly, 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/651461
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