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Knowledge Spillovers through Networks of Scientists

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  • Paolo Zacchia

Abstract

In this article, I directly test the hypothesis that interactions between inventors of different firms drive knowledge spillovers. I construct a network of publicly traded companies in which each link is a function of the relative proportion of two firms’ inventors who have former patent collaborators in both organizations. I use this measure to weigh the impact of R&D performed by each firm on the productivity and innovation outcomes of its network linkages. An empirical concern is that the resulting estimates may reflect unobserved, simultaneous determinants of firm performance, network connections, and external R&D. I address this problem with an innovative IV strategy, motivated by a game-theoretic model of firm interaction. I instrument the R&D of one firm’s connections with that of other firms that are sufficiently distant in network space. With the resulting spillover estimates, I calculate that among firms connected to the network the marginal social return of R&D amounts to approximately 112% of the marginal private return.

Suggested Citation

  • Paolo Zacchia, 2020. "Knowledge Spillovers through Networks of Scientists," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(4), pages 1989-2018.
    • Handle: RePEc:oup:restud:v:87:y:2020:i:4:p:1989-2018.
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    File URL: http://hdl.handle.net/10.1093/restud/rdz033
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    Cited by:

    1. Benjamin Niswonger, 2022. "What You See is What You Get: Local Labor Markets and Skill Acquisition," Papers 2209.03892, arXiv.org.
    2. Ma, Xiao, 2020. "College Expansion, Trade and Innovation: Evidence from China," MPRA Paper 109469, University Library of Munich, Germany.
    3. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2021. "MCMC Conditional Maximum Likelihood for the two-way fixed-effects logit," MPRA Paper 110034, University Library of Munich, Germany.
    4. Zhou, William Chongyang & Yan, Daying & Sun, Sunny Li, 2022. "Climbing the Ladder: Inward Sourcing as an Upgrading Capability in Global Value Chains," Research Policy, Elsevier, vol. 51(3).
    5. Cui Zhang & Xiongjin Feng & Yanzhen Wang, 2022. "Technology Spillovers among Innovation Agents from the Perspective of Network Connectedness," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
    6. Conlin, Andrew & Nerg, Iiro & Ala-Mursula, Leena & Räihä, Tapio & Korhonen, Marko, 2023. "The association between chronotype and wages at mid-age," Economics & Human Biology, Elsevier, vol. 50(C).
    7. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
    8. He, Siyi & Liu, Jinsong & Ying, Qianwei, 2023. "Externalities of government-oriented support for innovation: Evidence from the national innovative city pilot policy in China," Economic Modelling, Elsevier, vol. 128(C).
    9. Zhang, Zhaowen & Jiang, Yaohui, 2022. "Can green public procurement change energy efficiency? Evidence from a quasi-natural experiment in China," Energy Economics, Elsevier, vol. 113(C).
    10. René Belderbos & Jinhyuck (Joseph) Park & Martin Carree, 2021. "Do R&D investments in weak IPR countries destroy market value? The role of internal linkages," Strategic Management Journal, Wiley Blackwell, vol. 42(8), pages 1401-1431, August.
    11. Kairui Zheng & Yijie Li & Xiaohui Xin, 2022. "The Influencing Mechanism of High-Speed Rail on Innovation: Firm-Level Evidence from China," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    12. Wei Cheng & Bruce A. Weinberg, 2021. "Marginalized and Overlooked? Minoritized Groups and the Adoption of New Scientific Ideas," NBER Working Papers 29179, National Bureau of Economic Research, Inc.
    13. Weihua Li & Sam Zhang & Zhiming Zheng & Skyler J. Cranmer & Aaron Clauset, 2022. "Untangling the network effects of productivity and prominence among scientists," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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