Territorial Patterns of Innovation and Economic Growth in European Regions

Abstract

This paper proposes the notion of territorial patterns of innovation as a new conceptualization to understand the relationship among knowledge, innovation, and economic growth at the regional level. The territorial patterns of innovation approach reject the simplistic view of an invention‐innovation equivalence and advance alternative patterns, alternative ways in which knowledge and innovation can take place and mix in space. Each of them represents a different way of innovating, one not necessarily more efficient than the other. On the empirical ground, the paper demonstrates this statement for all NUTS2 regions of the 27 European Union states by showing that the efficiency in taking advantage of innovation does not only link to the strength of the local knowledge base; rather, territorial patterns of innovation characterized by relatively low knowledge intensity can be relatively more efficient in grasping and exploiting innovation returns for growing. Interesting policy implications can be drawn from the empirical analysis presented. If the results do not deny the importance of research and development (R&D) activities for regional growth, and therefore the right focus put forward by the Europe 2020 on a “smart growth” based on knowledge and innovation, they call for particular attention when the Europe 2020 goal is translated into a regional setting.

The EU Cohesion Policy in context: Does a bottom-up approach work in all regions?

“…Following this line of reasoning the standard regional growth model (Cappelen et al 2003;Capello, 2010 &Capello and Lenzi, 2013;Rodriguez-Pose, 2011 andPaci and Marrocu, 2013;Petrakos et al 2005a andb, 2011) is augmented by: a 'policy matrix' that includes expenditure under the EU Regional Policy, the RDP and the CAP; a 'Territorial conditioning factors matrix' that includes proxies for regional structural conditions; and a 'Policy interactions matrix' that includes the interaction terms between the different policies and between the policies and regional contextual conditions.…”

Determinants of Agri‐food Firms’ Participation in Public Funded Research and Development

“…The collaboration between companies and universities has long been considered (Veugelers & Cassiman, 2005, Un et al, 2010. Capello & Lenzi (2013) highlight the need to consider the spatial context to understand the relationship among knowledge, innovation and economic growth.…”

Productivity clustering and growth in Central and Eastern Europe

“…Other authors have showed that knowledge, innovation, and the ability of countries to successfully adopt foreign technology could also explain the differences in productivity (Capello & Lenzi, 2013;Cuadros & Alguacil, 2014;Dettori, Marrocu, & Paci, 2012;Easterly & Levine, 2001;Paci & Marrocu, 2013). Table 3 displays the productivity convergence in the manufacturing sector.…”