Search

Which role do city‐regions play in European innovation network formation? We study the evolution of innovation collaborative networks in European city‐regions outlining two opposite models: in the exclusive network model city‐regions establish a closed network of innovators among themselves; in the inclusive network models city‐regions build a network of innovators which includes the peripheral regions. Employing a temporal exponential random graph model on 248 regions for the period 2000–2016, we find that the two models coexist. We conclude that in the EU the city‐regions act as both engines of generation of innovation and integrators of innovation actors. [ABSTRACT FROM AUTHOR]

The goal of this paper is to analyse the innovation activity convergence process across European regions from 2002 to 2012. A novel methodology that allows either detection of overall convergence or endogenous identification of groups of regions was applied. The results support the club convergence hypothesis for explaining the convergence process of Europe's regions during the period analysed and seven innovation convergence clubs were identified. In addition, the research results indicate that initial regional R&D expenditure is the most relevant factor driving the formation of the convergence clubs after controlling for the effects of regional structural characteristics. [ABSTRACT FROM AUTHOR]

The economic growth literature suggests that knowledge spillovers are subject to distance decay effects. In this paper the main aim is to provide a theoretical framework and empirical evidence on the role played by other kinds of proximities, namely relational, social and technological proximity, in explaining productivity growth. Using a sample of 249 EU 27 NUTS 2 regions in the period 1990-2004, semiparametric spatial autoregressive models are estimated. Results provide evidence of a positive role of social and relational proximities as important channels of knowledge spillovers, and on the fact that, when simultaneously present, different kinds of proximities generate synergic effects on growth. Resumen La literatura sobre el crecimiento económico sugiere que los spillovers de conocimiento están sujetos a efectos de deterioro por distancia. El objetivo principal de este artículo es proporcionar un marco teórico y pruebas empíricas sobre el papel desempeñado por otros tipos de proximidades (relacional, social y tecnológica), a la hora de explicar el crecimiento de la productividad. Utilizando una muestra de 249 regiones NUTS 2 de la UE 27 para el período 1990-2004, se estiman modelos autorregresivos espaciales semiparamétricos. Los resultados aportan pruebas del papel positivo de las proximidades sociales y relacionales como canales importantes de spillovers de conocimientos, y del hecho de que, cuando se presentan simultáneamente, los diferentes tipos de proximidades generan efectos sinérgicos sobre el crecimiento. [ABSTRACT FROM AUTHOR]

This paper uses microeconomic data for more than 100,000 European individuals in order to analyse whether the individual economic returns to education vary between migrants and non-migrants and whether any differences in earnings between these two groups are affected by household and/or geographical (regional and interregional) externalities. The results point out that while education is a fundamental determinant of earnings, European labour markets do not discriminate in the returns to education between migrants and non-migrants. Household, regional and supra-regional externalities influence the economic returns to education in a similar way for local, intranational and supra-national migrants. The results are robust to the introduction of a large number of individual, household and regional controls. [ABSTRACT FROM AUTHOR]

The focus of this study is on cross-region R&D collaborations in Europe. We use data on collaborative R&D projects funded by the 5th EU Framework Programme (FP5). The objective is to identify separation effects – such as geographical or technological effects – on the constitution of cross-region collaborative R&D activities within a Poisson spatial interaction modelling framework. The results provide striking evidence that geographical factors are important determinants of cross-region collaboration intensities, but the effect of technological proximity is stronger. R&D collaborations occur most often between organizations that are located close to each other in technological space. [ABSTRACT FROM AUTHOR]

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. Resumen En este artículo se emplea un modelo semiparamétrico espacial de Durbin para analizar el comportamiento del crecimiento de 155 regiones europeas durante el periodo 1988–2000. Esta especificación combina el enfoque semiparamétrico con técnicas econométricas espaciales paramétricas habituales para reconciliar la dependencia espacial y las no linealidades, tal y como sugieren los modelos de crecimiento neoclásicos recientes con interdependencia tecnológica espacial. Los resultados ofrecen pruebas de no linealidades en el efecto de ingresos per capita iniciales e inversiones en capital humano. Además, la especificación utilizada permite utilizar el efecto de la interacción entre las características (condiciones iniciales y variables estructurales) de cada región y las de sus vecinos. Finalmente, da una idea de spillovers (efectos de derrame) globales entre países y spillovers espaciales locales de regiones vecinas dentro del país. [ABSTRACT FROM AUTHOR]