Richard Sellner

Does Globalization Affect Regional Growth? Evidence for Nuts-2 Regions in EU-27

Abstract We analyze the influence of newly constructed globalization measures on regional growth for the EU-27 countries between 2001 and 2006. The spatial Chow-Lin procedure, a method constructed by the authors, was used to construct, on a NUTS-2 level, complete regional data for exports, imports and FDI inward stocks, which serve as indicators for the influence of globalization, integration and technology transfers on European regions. The results suggest that most regions have significantly benefited from globalization measured by increasing trade openness and FDI. In a non-linear growth convergence model, the growth elasticities for globalization and technology transfers decrease with increasing GDP per capita. Furthermore, the estimated elasticity for FDI decreases when the model includes a higher human capital premium for CEE countries and a small significant growth enhancing effect accrues from the structural funds expenditures in the EU.

Macroeconomic Effects of the Services Directive

Making the Single Market more dynamic has been identified as one of the top priorities to improve the EU’s growth performance.1 In contrast to manufacturing industries, where the Single Market appears to be working quite well2, an assessment by the European Commission3 on the state of the internal market for services has identified a large gap between the vision of an integrated European economy and reality in service industries. There are still many impediments to the free movement of services in the EU. Particularly for small and medium-sized enterprises, the bulk of service providers, entry barriers in new EU markets are often prohibitive. With the Directive on Services in the Internal Market (SD) the European Parliament and the Council aim at removing the remaining barriers in this area to enable firms to exploit the full potential of cross-border services. This would be an important step forward in bringing the EU closer to its Lisbon targets.

Sensitivity Analysis of SAR Estimators: A numerical approximation

The estimation of a spatial autoregressive (SAR) model depends on the spatial correlation parameter ρ in a highly nonlinear way, and the least squares (LS) estimators for ρ cannot be computed in a closed form. In this paper, we propose two simple LS estimators and compare them by distance and covariance properties in order to study the local sensitivity behaviour of these estimators. Using matrix derivatives we calculate the Taylor approximation of the LS estimator in the SAR model up to the second order. In a next step, we compare the covariance structure of the two estimators by Kantorovich inequalities and derive efficiency comparisons by upper bounds. Finally, we explore the quality of our new approximations by a Monte Carlo simulation study. The simulation results show significant computation time reductions and a good approximation behaviour of the SAR LS estimator in the neighborhood of ρ=0, when using a non-spatial LS estimator. The results are encouraging and can be used for further developments like quick diagnostic tools to explore the sensitivity of spatial estimators with respect to the size of the spatial correlation.