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Unraveling the Role of Public Researcher Mobility for Industrial Innovation

Author

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  • Ejsing, Ann-Kathrine

    (Danish Insurance Association)

  • Kaiser, Ulrich

    (University of Zurich)

  • Kongsted, Hans Christian

    (University of Copenhagen)

Abstract

We estimate the relative contribution of mobile scientists who leave academia for the private sector on the subsequent innovative performance of the firms they join. We use data on the population of Danish firms and their R&D workers for the period 1999-2004 and measure innovation performance by the (value-adjusted) number of patent applications at the European Patent Office. We compare the efficacy of mobile former university scientists to the effects of mobile workers hired from other firms as well as immobile workers on the innovation performance of their employer. Our main result is that mobile university scientists contribute substantially more to innovation than R&D workers hired from other firms who, in turn, contribute slightly less to industrial innovation than recent university graduates. By contrast, immobile workers add little to the innovative activity of their employer. We also find that the contribution of mobile R&D workers to innovation depreciates fairly rapidly. These findings provide us with three main managerial implications: Firstly, hiring scientists from universities is a way of boosting a firm's innovative activity. Secondly, because hires from academia receive lower wages on average than hires from private sector firms, this implies that hiring R&D workers from academia may be a cost-effective way of improving innovation performance. Thirdly, firms need to take measures in order to further public-private researcher interaction to prevent the depreciation of the knowledge stock of their employees.

Suggested Citation

  • Ejsing, Ann-Kathrine & Kaiser, Ulrich & Kongsted, Hans Christian, 2011. "Unraveling the Role of Public Researcher Mobility for Industrial Innovation," IZA Discussion Papers 5691, IZA Network @ LISER.
  • Handle: RePEc:iza:izadps:dp5691
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    References listed on IDEAS

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    1. Julia Porter Liebeskind & Amalya Lumerman Oliver & Lynne Zucker & Marilynn Brewer, 1996. "Social networks, Learning, and Flexibility: Sourcing Scientific Knowledge in New Biotechnology Firms," Organization Science, INFORMS, vol. 7(4), pages 428-443, August.
    2. Galindo-Rueda, Fernando & Haskel, Jonathan, 2005. "Skills, Workforce Characteristics and Firm-Level Productivity: Evidence from the Matched ABI/Employer Skills Survey," IZA Discussion Papers 1542, Institute of Labor Economics (IZA).
    3. Julia Porter Liebeskind & Amalya Lumerman Oliver & Lynne G. Zucker & Marilynn B. Brewer, 1995. "Social Networks, Learning, and Flexibility: Sourcing Scientific Knowledge in New Biotechnology Firms," NBER Working Papers 5320, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Timo Tohmo & Jutta Viinikainen, 2017. "Does intersectoral labour mobility pay for academics?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 83-103, October.
    2. Elizabeth A. Corley & Barry Bozeman & Xuefan Zhang & Chin-Chang Tsai, 2019. "The expanded scientific and technical human capital model: the addition of a cultural dimension," The Journal of Technology Transfer, Springer, vol. 44(3), pages 681-699, June.
    3. Kaiser, Ulrich & Kuhn, Johan M., 2012. "Long-run effects of public–private research joint ventures: The case of the Danish Innovation Consortia support scheme," Research Policy, Elsevier, vol. 41(5), pages 913-927.

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    Keywords

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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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