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Most of the empirical works in regional convergence are based on either cross-sectional or “a-spatial” panel data models. In this paper, we propose the use of panel data econometrics models that incorporate an explicit consideration of spatial dependence effects (Anselin, 1988; Elhorst, 2001; 2003). This allows us to extend the traditional convergence models to include a rigorous treatment of regional spillovers and to obtain more reliable estimates of the parameters. We consider two models respectively based on the introduction of a spatial lag among the explanatory variables (the “spatial lag model”) and imposing a spatial autoregressive structure to the stochastic component (the “spatial error model”). We apply such a modelling framework to the long-run convergence of per-capita GDP of 125 EU-NUTS2 regions observed yearly in the period 1977-2002. A comparison of the results obtained using the two spatial panel data specifications with the main evidence available in the literature is also provided.

Convergence in Per-capita GDP across European Regions using Panel Data Models Extended to Spatial Autocorrelation Effects