In this article a semiparametric spatial Durbin model is employed to analyse the growth behaviour of 155 European regions in the period 1988-2000. This specification combines the semiparametric approach with the usual parametric spatial econometric technique to accommodate both spatial dependence and nonlinearities as suggested by recent neoclassical growth models with spatial technological interdependence. The results provide evidence of nonlinearities in the effect of initial per capita incomes and human capital investments. Moreover, the specification used allows identifying the effect of the interaction between the characteristics (initial conditions and structural variables) of each region and those of its neighbours. Finally, it shows some indication of global spillovers across-country and local spatial spillovers from domestic neighbours. Copyright (c) 2008 the author(s). Journal compilation (c) 2008 RSAI.

In this paper we use spatial dependence panel data models to analyse regional growth behaviour in Italy. Controlling for fixed-effects allows us to disentangle the effect of spatial dependence (or spatial interaction) from that of spatial heterogeneity and of omitted variables and, thus, to properly investigate the regional convergence process within the country.

In this paper, we analyze the role of economic complexity as a driver of regional labor productivity growth in Italy. The Economic Complexity Index ( ECI ) measures the multiplicity of knowledge embedded in an economy’s productive structure and can be interpreted as an indicator of “qualified diversity”. We assess the impact of ECI on the distribution dynamics of labor productivity by combining growth regression analysis with conditional density estimates. Counterfactual analysis results suggest that ECI plays a key role in the observed tendency to polarization of regional labor productivity. We also provide evidence of a long-run relationship between labor productivity and ECI by using a panel cointegration analysis.