The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidized collaborative R&D projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers.

Interactive regional learning involving various actors is considered a precondition for successful innovations and, hence, for regional development. Diasporas as non-native ethnic groups are regarded as beneficial since they enrich the creative class by broadening the cultural base and introducing new routines. Using data on research and development (R&D) collaboration projects, the analysis provides tentative evidence that the size of diasporas positively affects the region’s share of outward R&D linkages enabling the exchange of knowledge. The empirical analysis further confirms that these interactions mainly occur between regions hosting the same diasporas, pointing to a positive effect of ethnic proximity rather than ethnic diversity.

R&D collaborations and the role of proximity. Regional Studies. This paper explores the impact of proximity measures on knowledge exchange measured by granted research and development (R&D) collaboration projects in German NUTS-3 regions. The results are obtained from a spatial interaction model including eigenvector spatial filters. Not only geographical but also other forms of proximity (technological, organizational and institutional) have a significant influence on the emergence of collaborations. Furthermore, the results suggest interdependences between proximity measures. Nevertheless, the analysis does not show that other forms of proximity may compensate for missing geographical proximity. The results indicate that (subsidized) collaborative innovation activities tend to cluster.

Subsidies for research and development (R&D) are an important tool of public R&D policy, which motivates extensive scientific analyses and evaluations. This article adds to this literature by arguing that the effects of R&D subsidies go beyond the extension of organizations’ monetary resources invested into R&D. It is argued that collaboration induced by subsidized joint R&D projects yield significant effects that are missed in traditional analyses. An empirical study on the level of German labor market regions substantiates this claim, showing that collaborative R&D subsidies impact regions’ innovation growth when providing access to related variety and embedding regions into central positions in cross-regional knowledge networks.

The contribution of this thesis is to widen the focus of the literature on related variety. So far, this line of research has a strong focus on technological relatedness and cognitive proximity (Boschma et al. 2015). This thesis puts emphasis on three dimensions of relatedness that not yet been at the heart of research: input-output linkages and vertically related variety, the occupational dimensions of relatedness as well as insights from a project level R&D efforts and their relevance for the analysis of technological relatedness. We contribute to the understanding of how the specific composition and the degree of relatedness of economic agents in space shape their ability to generate variety in terms of entry of new occupational specialisations in regions. We enhance the understanding of how related variety affects different regional outcomes such employment growth and innovation and offer insights into the structure of inter-industry relations from a technological perspective and explains how the rise of certain technologies shapes inter-industry relatedness patterns.