If the need to develop a knowledge economy at the regional level is increasingly evident to policy makers in Europe, the right set of policies to accomplish such an aim has not yet been devised, and is an important policy debate also at the European Community level. This special issue is a step forward in this direction. The design and implementation of regional innovation policies should take the specificities of each regional mode of innovation into account, and these peculiarities can be highlighted only through sound scientific conceptual and empirical analyses in which the regional dimension is carefully taken into consideration and the spatial effects of policy tools like R&D investments and innovation incentives clearly assessed in terms of their impact on regional growth in different regional contexts. It is in this spirit that this special issue has been conceived.

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.

This paper examines the impact of knowledge assets—technological and human capital—on the regional productivity levels within a Cobb–Douglas production function model, which includes the traditional tangible inputs, as well as other territorial and industrial features of the regions. Spatial panel econometric techniques are applied to account for geographical association and to examine the role of knowledge spillovers from the neighbouring regions. We apply our empirical model to a large set of regions belonging to the EU27 plus Norway and Switzerland over the period 2000–2008. Our main results, robust to a wide array of sensitivity checks, show that both knowledge assets exert a positive impact on gross domestic product, with human capital being more effective than technological capital in most cases. Moreover, we find evidence of spatial spillovers directly associated with the two knowledge assets, which turn out to have a larger effect in the regions of the 12 new accession countries. These results underline the central role played by highly educated labour forces in allowing a region to absorb the potential knowledge spilling from the external territories, especially in developing countries, and to ensure its effective use in the production process.

Europe's 2020 strategy and the consequent initiative “Innovation Union” call for a particular attention to the territorial dimension of innovation and knowledge creation. To this end, this paper investigates the nature of knowledge production and diffusion among regions in 29 European Union (EU) countries and tries to assess its effectiveness. Data envelopment analysis is thus applied to assess how efficiently European regions use internal and external inputs for the production of new knowledge and ideas. The analysis produces a ranking of the innovative performance of EU regions for two points in time: the beginning of the current century and the second part of this decade. This ranking is then evaluated through the Malmquist productivity index in order to assess the relative importance of its main components. The Data envelopment analysis provides further evidence of a dualistic (centre versus periphery) pattern in the regional innovation activities, with the most efficient territories located in the most central or economically strategic areas of the continent. The application of the Malmquist productivity index shows that both the magnitude and intrinsic features of the productivity dynamics are extremely differentiated across regions. Again, we observe important differences between the core and periphery of Europe and, more specifically, between the rich and industrialized countries that form the so-called “Old Europe” and the relatively poorer ones that have entered the EU quite recently.

This paper looks at the genesis of innovation in the U.S. from a territorial perspective. The analysis aims to disentangle the impact of local research and development (R&D) expenditure from other contextual conditions supportive of the process of innovation. Particular emphasis is devoted to the role of socio-economic factors and systems of innovation (“social filter” conditions) and to their impact on the returns of R&D expenditure in different territorial contexts. The empirical analysis is based on a regional knowledge production function approach, leading to an empirical model estimated by means of panel data analysis for the period between 1994 and 2007 at the U.S. Bureau of Economic Analysis-Economic Area level. The results unveil the complexity of the territorial dynamics of innovation of the U.S. Local R&D investments are important predictors for regional innovative performance and their impact is highly localised. However, social filter conditions are fundamental for the productivity of innovation efforts.

The goal of this paper is twofold. First, we aim to assess the role played by inventors' cross-regional mobility and collaborations in fostering knowledge diffusion across regions and subsequent innovation. Second, we intend to evaluate the feasibility of using mobility and co-patenting information to build cross-regional interaction matrices to be used within the spatial econometrics toolbox. To do so, we depart from a knowledge production function where regional innovation intensity is a function not only of the own regional innovation inputs but also external accessible knowledge stocks gained through interregional interactions. Differently from much of the previous literature, cross-section gravity models of mobility and co-patents are estimated to use the fitted values to build our “spatial” weights matrices, which characterize the intensity of knowledge interactions across a panel of 269 regions covering most European countries over 6 years.

The present debate on regional policy design to fit the Europe 2020 Agenda calls for additional reflections on the way sectoral policies, like innovation policies, can be translated appropriately into a regional setting. The paper enters the debate on smart specialization strategies by stressing the need to overcome the simplistic dichotomy between core and periphery in the Union and between an advanced “research area” (the core) and a “co-application area” of general purpose technologies to local technological specificities (the periphery). The geography of innovation is much more complex than a simple core-periphery model, and the logical pathway toward innovation is much more complex than the linear model of research and development-invention-innovation direct link: the innovation patterns are differentiated among regions according to their regional context conditions. The identification of specific “innovation patterns” is necessary to design “smart innovation” policies. The paper presents a critique to the smart specialization debate, suggests a new taxonomy of European innovative regions based on their innovation patterns, and proposes innovation policies for each regional mode of innovation.