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Policy influence in the knowledge space: a regional application

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Abstract

Cluster policies aim at improving collaboration between co-located actors to address systemic failures. As yet, cluster policy evaluations are mainly concerned with effects on firm performance. Some recent studies move to the system level by assessing how the structure of actor-based knowledge networks is affected by such policies. We continue in that direction and analyze how technology-based regional knowledge spaces are shaped by the introduction of a cluster policy. Taking the example of the German BioRegio contest, we examine how such knowledge spaces in winning and non-winning regions evolved before, during and after the policy. Using a difference-in-differences approach, we identify treatment effects of increased knowledge space embeddedness of biotechnology only in the post-treatment period. Our findings imply that cluster policies can have long-term structural effects typically not accounted for in policy evaluations.

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Notes

  1. At the IPC 4 digit level, these are A01H, A61K, C02F, C07G, C07K, C12M, C12N, C12P, C12Q, C12S and G01N.

  2. Structural embeddedness, as measured by betweenness centrality, is addressed in Sect. 5.

  3. We calculate the node betweenness centrality with the igraph package for R (R Core Team 2018; Csardi and Nepusz 2006).

  4. As explained by Basilico and Graf (2020) the usage of a different methodology to map the knowledge space can change the results when calculating centrality measures. Using a simple co-occurrence matrix instead of a relatedness matrix, the results on the calculated betweenness centrality do not vary.

  5. These results are robust to the selection of regions. We performed the same analyses with a more homogeneous subsample of regions. For each winning region, we manually select the most similar non-winning region in terms of the number of biotechnology patents during the pre-funding period and ran models 2a and b. Since the results do not change much (slightly higher model fit), we refrain from presenting them here. Tables are available upon request.

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Acknowledgements

The authors would like to thank the participants of the 3rd Rethinking Clusters workshop in Valencia (Spain) and the 2020 GeoInno conference in Stavanger (Norway) for useful comments. Furthermore, the authors are glad for helpful comments and discussions with the TechSpace project members on earlier versions of this paper. The authors gratefully acknowledge financial support from the German Federal Ministry of Education and Research (BMBF), grant number: 16IFI017. All remaining errors are our own.

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Correspondence to Stefano Basilico.

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A correlation tables

A correlation tables

Table 8 Correlation table for models 1a and b (table 5)
Table 9 Correlation table for models 2a and b (table 7)

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Basilico, S., Cantner, U. & Graf, H. Policy influence in the knowledge space: a regional application. J Technol Transf 48, 591–622 (2023). https://doi.org/10.1007/s10961-022-09925-1

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  • DOI: https://doi.org/10.1007/s10961-022-09925-1

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