Abstract
This article focuses on the embeddedness of European regions in networks of R&D collaborations of the EU Framework Programmes. Network embeddedness is defined in terms of network analytic centrality measures calculated for organisations and aggregated to the regional level. The objective is to estimate how region-internal and region-external characteristics affect a region’s positioning in the thematic networks of Information and Communication Technologies, Sustainable Development and Life Sciences. In our modelling approach, we employ panel spatial Durbin error models, linking a region’s centrality in the network to knowledge production and general economic characteristics of regions, and their neighbours, respectively. We found evidence that financial R&D resources, human capital and the level of socio-economic development are important general determinants of a region’s network positioning. By linking European R&D networks with regional innovativeness, the study provides important implications for setting priorities in a regional innovation policy context.
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Notes
- 1.
In our definition of the distinct thematic areas of the FPs we basically follow the study of Hoekman et al. (2012). Our thematic priorities consist of the following programme lines in the distinct FP: FP4 programmes ENV2C, MAST3, JOULE and THERMIE, FP5-EESD, FP6-SUSTDEV for Sustainable Development; FP4-BIOTECH2, FP4-BIOMED2 and FP4-FAIR, FP5-Quality of Life, FP6-Food, FP6-LIFESCIHEALTH for Life Sciences; FP4-ACTS, FP4-ESPRIT4 and FP4-TELEMATICS 2C, FP5-IST, FP6-IST for the thematic priority ICT (Rietschel et al. 2009). The thematic areas we include make up 72.5 % of total funding in FP5, and 63.3 % of total funding in FP6 (Hoekman et al. 2012). Details on the network structure of the thematic FP networks are given in the Appendix.
- 2.
Further centrality measures commonly used in SNA are degree and closeness centrality. Degree centrality focuses on connections directly attached to a vertex; closeness centrality indicates how close a distinct vertex is to all other vertices in the network (Faust 1997).
- 3.
A path is the alternating sequence of vertices and links, beginning and ending with a vertex, so that the shortest path or geodesic distance d uvt between two organisations u and v in time period t. is defined as the number of vertices to be passed in the shortest possible path from one vertex to another (see Wasserman and Faust 1994 for further details).
- 4.
For practical purposes, we draw on the adjacency matrix A t defined by Eq. 15.1 instead of the bipartite graph in our formal description.
- 5.
A common notation used in this context is the eigenvector equation as given by λ x = A x, where x is a vector of centralities x = (x 1, x 2, ....) denoting the eigenvector of the adjacency matrix A with eigenvalue λ (Bonacich 1987).
- 6.
A descriptive analysis of our centrality measures as used in the spatial modelling approach are given in the Appendix.
- 7.
We define w ij = 1 if i and j are spatial neighbours in the form that they are sharing a common border, and zero otherwise, with w ii = 0. We use a row standardized version of W allowing interpretation of the spatial lags of the independent variables being the weighted average impact on region i by their neighbouring regions. For the SDEM, interpretation of both direct and indirect effects is directly associated with the parameter estimates as opposed to specifications that contain spatial lags of the dependent variable, such as the SDM (Le Sage and Pace 2009).
- 8.
The index is defined by \( {c}_{it}^{(4)}=\frac{1}{2}{\displaystyle {\sum}_P\left|{s}_{ip}-\left.{\overline{s}}_p\right|\right.} \) where s ip is the region’s i share of patents in a specific IPC class p and \( {\overline{s}}_p \) is the mean of IPC class p. Patents were taken into account at a three-digit level corresponding to the International Patent Classification (IPC).
- 9.
We include five different main economic sectors, namely agriculture, manufacturing, construction, private services and non-market service sector. The index of specialisation to account for industrial diversity is defined as \( {z}_{it}^{(1)}=\frac{1}{2}{\displaystyle {\sum}_P\left|{o}_{ip}-\left.{\overline{o}}_p\right|\right.} \) where o ip is the region’s i share of gross value added in a specific sector p (indexed p = 1, …., 5) and \( {\overline{o}}_p \) is the mean of sector p for n = 241 regions.
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Acknowledgements
We are grateful to Thomas Scherngell (AIT) for providing valuable research assistance. We thank Michael Barber (AIT) and two anonymous referees for comments on earlier versions of this study.
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Appendix
Appendix
Network characteristics by thematic field
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Wanzenböck, I., Heller-Schuh, B. (2013). The Embeddedness of Regions in R&D Collaboration Networks of the EU Framework Programmes. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_15
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