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Recently, a significant share of the empirical analysis on the impact of public capital on regional growth has used multivariate time-series frameworks based on vector autoregressive (VAR) models. Nevertheless, not as much attention has been dedicated to the analysis of the long-run determinants of regional growth processes using multi-region panel data and applying panel integration and co-integration techniques. This paper estimates the dynamic domestic effects of public infrastructure using a structural vector autoregressive (S-VAR) methodology for the Spanish regions. From a methodological point of view, the paper contains several features that can be viewed as a contribution to the existing empirical literature. First, the important issues of the stationarity of the data and the existence and estimation of cointegrating relationships in the long-run are addressed in the context of the analysis of panel data. Secondly, the long-run cointegrating production function is embedded within structural vector error correction (S-VEC) shortrun models to produce consistent estimates of impulse responses, contrary to many researchers who have estimated unrestricted VAR models in levels or VAR models in first differences. The estimates reveal new results with respect to the previous empirical evidence.

The debate over the impact of education on economic growth has recentlyled to disagreement when, at the empirical level, the effect of average humancapital on economic growth has been found to be weak. With this paper we revisitthese results by arguing how different educational attainment levels (rather than theaverage human capital stock) impact heterogenously different regions’ economicperformance. We build and test a catch-up model where technology adoption takesplace as a function of each region’s human capital composition. We show for 50NUTS3 Spanish provinces in between 1965 and 1997, how convergence to thefrontier is driven by higher education and, to a lesser extent, by vocational training.Both theoretical and empirical results are alternative to the well known formalizationproposed by Vandenbussche, Aghion and Meghir (2006). Severe endogeneityissues, as well as small sample biases, are tackled by using system GMM estimatorsand the correction proposed by Windmeijer (2005).

The purpose of this paper is to investigate how national interpersonaltransfer policies affect regions’ economic growth. Reviewing the economic literature,we found only incidental evidences and contradictory conclusions. Our objective isto provide some statistical evidence of the relation between interregional interpersonalredistribution towards poor European regions and their relative growth. Usingsome recent developments in the field of spatial data analysis, we found that interpersonaltransfers do not seem to affect negatively the economic growth of poor Europeanregions, while it is not possible to assert their impact on rich regions.

This paper presents a model which explains per capita regional product as a function of spatial and non spatial variables. Spatial variables may act within and beyond its territory and are related to spatial phenomena such as spatial external economies or expenditure urban multipliers. The model is estimated using Spatial Econometrics techniques and empirical evidence from the Spanish provinces, «comarcas» (sub-regions) and municipalities. The results validate the model in the three different levels of spatial aggregation and underline that spatial variables explain from 30 to 50% of regional product generation.