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The relationship of policy induced R&D networks and inter-regional knowledge diffusion

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  • Marcel Bednarz
  • Tom Broekel

Abstract

Knowledge diffusion is argued to be strongly influenced by knowledge networks and spatial structures. However, empirical studies primarily apply an indirect approach of measuring their impact. Moreover, little is known about how policy can influence the spatial diffusion of knowledge. This paper seeks to fill this gap by empirically testing the effects of policy induced knowledge networks on the propensity of inter-regional patent citations. We use patent citation data for 141 labor market regions in Germany between 2000 to 2009, which is merged with information on subsidized joint R&D projects. Based on the latter, we construct a network of subsidized R&D collaboration. Its impact on inter-regional patent citations is evaluated with binomial and negative binomial regression models. Our findings do not indicate that inter- regional network links created by public R&D subsidies facilitate patent citations and hence, inter-regional knowledge diffusion.

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  • Marcel Bednarz & Tom Broekel, 2019. "The relationship of policy induced R&D networks and inter-regional knowledge diffusion," Papers in Evolutionary Economic Geography (PEEG) 1908, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Mar 2019.
    • Handle: RePEc:egu:wpaper:1908
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    2. Mariane Santos Françoso¹ & Nicholas S. Vonortas, 2023. "Gatekeepers in regional innovation networks: Evidence from an emerging economy," The Journal of Technology Transfer, Springer, vol. 48(3), pages 821-841, June.
    3. Skare, Marinko & Soriano, Domingo Riberio, 2021. "Technological and knowledge diffusion link: An international perspective 1870–2019," Technology in Society, Elsevier, vol. 66(C).
    4. İbrahim Semih & Umut Yılmaz & Erkan Erdil & Müge Özman, 2023. "What drives network evolution? Comparing R&D project and patent networks in the EU," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 32(5), pages 1109-1134.
    5. Fang Han & Sejun Yoon & Nagarajan Raghavan & Hyunseok Park, 2022. "Investigating Company’s Technical Development Directions Based on Internal Knowledge Inheritance and Inventor Capabilities: The Case of Samsung Electronics," Sustainability, MDPI, vol. 14(5), pages 1-19, March.

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    More about this item

    Keywords

    knowledge diffusion; subsidized R&D-networks; gravity model; negative binomial regression; proximity; spillover;
    All these keywords.

    JEL classification:

    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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