European regional growth, technology gap and R&D efforts - Lydia Greunz (European Regional Science Association (ERSA), 2001)
Since the early days of European integration, one of the central goals for the European Union has been greater equality in income and productivity among member states and regions. This goal was realised for a long time since form the early 1950s onwards, differences in GDP per capita within European regions declined steadily. However, more recently, THE PROCESS OF EUROPEAN ECONOMIC CONVERGENCE HAS CONSIDERABLY SLOWED DOWN and, according to a widening group of economists, has CEASED AT THE REGIONAL LEVEL after 1980. The paper we wish to present aims to explain the different PATTERNS OF CONVERGENCE AND DIVERGENCE in terms of GDP per capita of the European regions. Is it reasonable to think that catching up will occur for all European regions? What role does the technology gap play for the catching up process? What are the mechanisms and policies to be implemented to upgrade social capability which is a major determinant of catching up? In order to answer these crucial questions we have built a MODEL OF CUMULATIVE GROWTH - a SIMULTANEOUS SYSTEM OF EQUATIONS. In this model, and according to the technology gap literature, one of the major determinants of catching up is the existence of a TECHNOLOGY GAP. The latter offers a potential for faster growth if SOCIAL CAPABILITY is sufficiently developed. In our model, social capability is measured in terms of highly and moderately qualified working population, R&D activity and physical infrastructure endowment. Industrial and service activities as well as innovative output are endogenised in the simultaneous equations framework. Our model is tested onto a sample of European regions that is considerably larger than those used by authors publishing in related fields. Whereas the latter generally use NUTS I samples (about 60 observations), our sample is essentielly based upon NUTS II regions and the NUMBER OF OBSERVATIONS TOTALS 153 REGIONS. If this extended sample constitutes clearly one of the INNOVATIONS of this paper with respect to the existing literature, another originality is the chosen approach to explain the different trajectories of convergence and divergence. Indeed, our approach permits to CLUSTER the European regions with respect to their steady state levels of GDP per capita and, more important, enables to identify for each cluster the appropriate policy measures.
Keywords: 330