Thomas Scherngell

Spatial interaction modelling of cross‐region R&D collaborations: empirical evidence from the 5th EU framework programme*

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. Copyright (c) 2009 the author(s). Journal compilation (c) 2009 RSAI.

Distinct spatial characteristics of industrial and public research collaborations: evidence from the fifth EU Framework Programme

This study compares the spatial characteristics of industrial R&D networks to those of public research R&D networks (i.e. universities and research organisations). The objective is to measure the impact of geographical separation effects on the constitution of cross-region R&D collaborations for both types of collaboration. We use data on joint research projects funded by the fifth European Framework Programme (FP) to proxy cross-region collaborative activities. The study area is composed of 255 NUTS-2 regions that cover the EU-25 member states (excluding Malta and Cyprus) as well as Norway and Switzerland. We adopt spatial interaction models to analyse how the variation of cross-region industry and public research networks is affected by geography. The results of the spatial analysis provide evidence that geographical factors significantly affect patterns of industrial R&D collaboration, while in the public research sector effects of geography are much smaller. However, the results show that technological distance is the most important factor for both industry and public research cooperative activities.

Determinants of Collaboration in European R&D Networks: Empirical Evidence from a Discrete Choice Model

This paper focuses on inter-organizational R&D collaborations as captured by joint research projects funded within the European Framework Programmes for Research and Technological Development (EU-FPs). We identify determinants of collaboration, including actor characteristics, relational and network effects as well as geographical effects by means of a discrete choice model. Using data on EU-FP projects from the EUPRO database and from a representative survey of participants, we produce statistically significant evidence that collaboration choices in EU-FPs are primarily facilitated by prior acquaintance, thematic proximity and geographical proximity. Also, network effects are significantly related to collaboration choice. Moreover, the study shows that the impact of geographical effects increases for more intensive collaboration. The results are promising since a deeper understanding of these collaboration processes is needed for future governance of research policies in the EU.

Knowledge Spillovers and Total Factor Productivity: Evidence Using a Spatial Panel Data Model

This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity

Patents, Patent Citations and the Geography of Knowledge Spillovers in Europe

As interest in questions of the knowledge economy has grown, knowledge spillovers have received increased attention in recent years. For the purpose of this paper we use externalities and knowledge spillovers interchangeably to denote the non-pecuniary benefit of knowledge to firms, not responsible for the original investment in the creation of this knowledge. Such spillovers arise when some of the R&D activities have the characteristics of a non-rivalrous good and cannot be appropriated entirely. A fundamental question addressed by empirical research on knowledge spillovers is whether these spillovers are geographically bounded or not (see Karlsson and Manduchi 2001). Most of the studies on this issue thus far have concentrated on the spatial extent of local geographic effects that university research may have on the innovative capacity in a region, both directly and indirectly through its interaction with private sector R&D efforts. The studies vary somewhat in terms of research design, but they all find a strong and positive relationship between innovative activity and both industry R&D and university research at the state level in the USA. But the situation is different in terms of the significance of a local geographic spillover effect. Overall, the evidence is non-existent, weak or mixed, and only pertaining to a few individual sectors (Anselin et al. 1997). This may be due to the fact that knowledge spillovers are measured indirectly rather than directly.

Embeddedness of European Regions in European Union-Funded Research and Development (R&D) Networks: A Spatial Econometric Perspective

Wanzenböck I., Scherngell T. and Lata R. Embeddedness of European regions in European Union-funded research and development (R&D) networks: a spatial econometric perspective, Regional Studies. This study focuses on the embeddedness of regions in research and development (R&D) networks within European Union Framework Programmes by estimating how distinct regional factors affect a regions network positioning. Graph theoretic centrality measures in terms of betweenness and eigenvector centrality are calculated at the organizational level to reflect the relevant network structure before aggregation to the region level. Panel spatial Durbin error models (SDEM) reveal that region-internal knowledge production capacities, a regions level of economic development as well as spatial spillovers are important determinants for a regions positioning in the European Union-funded R&D network, but their significance differs depending on the centrality concept.

Is the European R&D Network Homogeneous? Distinguishing Relevant Network Communities Using Graph Theoretic and Spatial Interaction Modelling Approaches

Barber M. J. and Scherngell T. Is the European R&D network homogeneous? Distinguishing relevant network communities using graph theoretic and spatial interaction modelling approaches, Regional Studies. This paper characterizes the geography of communities in the European research and development (R&D) network using data on R&D projects funded by the European Unions Fifth Framework Programme. Communities are sub-networks whose members are more tightly linked to one another than to other members of the network. The paper characterizes the communities by means of spatial interaction models, and estimates the impact of separation factors on the variation of cross-region collaboration activities in a given community at the level of 255 NUTS-2 regions. The results demonstrate that European R&D networks are not homogeneous, showing distinct, relevant substructures characterized by spatially heterogeneous community groups.

The geography of networks and R&D collaborations

Editorial Introduction.- Part A: Analytic Advances and Methodology.- Part B: Structure and Spatial Characteristics of R&D Networks.- Part C: Impact of R& D Networks and Policy Implications.- Epilogue.

Is the United States Still Dominant in the Global Pharmaceutical Innovation Network

The dramatic growth of research and development activities in the pharmaceutical sector in emerging economies raises the question of whether the United States still keeps its dominant role in the global pharmaceutical innovation landscape. This paper focuses on investigating the role of the United States in global pharmaceutical innovation, and differs from previous studies by shifting attention to a network analytic perspective to track the global distribution of pharmaceutical inventions. Our sample is composed of key patents covering all new drugs approved by the Food and Drug Administration between 1996 and 2010. The results show that the United States still dominates in the global pharmaceutical innovation network, especially when it comes to essential core inventions. However, the United States shows a slightly decreasing prominence in the networks of either total new drugs or New Molecular Entity (NME) drugs in the time period 2006–2010 as compared to previous time periods, revealed by subtle traces of network centralities.

Determinants of Collaboration in European R&D Networks: Empirical Evidence from a Binary Choice Model Perspective

R&D collaboration networks stimulated by the European Union in its Framework Programs (FP) on Research and Technological Development exhibit - like many other large social networks - characteristic features of complex networks. Existing analyzes of these networks, however, are mostly built on strongly simplifying assumptions for network construction, like fully connected project graphs or star-graphs. Still, apart from qualitative evidence, little is known about the determinants that affect partner choice and network dynamics. It is the aim of this paper to provide empirical evidence for a more differentiated picture of partner selection - and thus network formation - in the European FPs. We adopt an econometric perspective to identify determinants of link formation, including various actor characteristics, relational and network effects as well as geographical effects. We employ a binary choice model estimated by means of logistic regressions, with a dependent variable that represents the establishment of a formal cooperation between two organizations in FP5. We use data on EU FP projects from the sysres EUPRO database and from a representative survey of FP 5 participants. The study produces statistically significant evidence that R&D collaboration choices of organizations participating in European FPs are affected by geography, FP experience and relational factors including network characteristics. Thematic proximity matters more than geographic proximity, while most influential for collaboration appears to be prior acquaintance of the actors. Also, network effects significantly determine the collaboration choice, but to a slightly smaller extent than geographical effects.