Space Vs. Networks in the Geography of Innovation: A European Analysis

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Space Vs. Networks in the Geography of Innovation: A European Analysis SummaryIn the last fifteen years, income differences among European Member States have been strongly narrowing while the process has been matched with a widening of the interregional variance within single countries. Traditionally, regional economic disparities in Europe have been ascribed to peripherality and/or to a high level of dependence on declining sectors. Nowadays regional disparities can be no longer defined only in terms of statistical differences in the values of standard macroeconomic indicators, but also according to innovative capacities and knowledge endowment. This paper provides an original framework for the interpretation of the existing relationships between innovation process and research activity in Europe and the structural and geographical features shaping the European scientific and technological map. In order to do so, we focus on two knowledge-based relational phenomena: participation in the same research networks (funded by the EU Fifth Framework Programme) and EPO co-patent applications. Using two complementary econometric techniques we try to assess those factors that determine patenting activity, distinguishing structural features, geographical and relational spillovers. Through these variables we measure the intrinsic relational structure of knowledge flows which directly connects people, institutions and, indirectly, regions, across European countries in order to test whether hierarchical relationships based on a-spatial networks between geographically distant excellence centres prevail over diffusive patterns based on spatial contiguity.

Dimensions of Proximity and Knowledge Bases: Innovation between Spatial and Non-spatial Factors

“…For example, geographical proximity cannot explain connections to partners in distant locations (GERTLER, 2008), and learning often takes place in a combination of both geographically close and distant settings (BATHELT et al, 2004). Relational networks thereby act in a complementary fashion towards purely geographical ones (MAGGIONI et al, 2007). This is why the context of learning should be taken into account in a more structured way to enhance our understanding of the involved spatial and non-spatial dynamics (STORPER, 2009 3 ; cf.…”

The territorial dynamics of innovation: a Europe United States comparative analysis

“…In addition, empirical analyses of the diffusion of spillovers have highlighted the presence of very strong distance decay effects in the US -knowledge spillovers, in general, do not spread beyond a 80 to 110 kms radius from the MSA where they are generated (Varga 2000;Ács 2002) -whereas in Europe the geographical diffusion of spillovers is felt in a radius of 200 to 300 kms from the point of origin (Bottazzi and Peri 2003;Moreno et al 2005a;Rodríguez-Pose and Crescenzi 2006). Greater proximity and lower distance decay suggest a greater potential for European regions to rely on innovative inputs in neighbouring areas as a source of innovation and develop interregional relational networks between innovative actors supported by spatial proximity (Maggioni et al 2006). Greater distance and stronger distance decay effects are, in contrast, likely to lead to the creation of self-contained innovative areas in the US, which necessarily have to rely on their own innovative inputs rather than on spillovers from other MSAs.…”

Network Formation and Geography: Modelling Approaches, Underlying Conceptions, Recent and Promising Extensions

“…The earlier studies using Gravity Models assume that the stochastic process behind tie formation works identically and independently; i.e. any pair of ties, among the same pair of nodes or not, are independent (Ponds 2006, Maggioni 2007, Scherngell and Barber 2009, Hoekman et al 2010). More recent applications (Scherngell and Lata 2011) take the spatial autocorrelation among flow residuals into account and corrects for this by using eigenvector filtering.…”