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Animate the cluster or subsidize collaborative R&D? A multiple overlapping treatments approach to assess the impacts of the French cluster policy

Author

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  • Modou Mar

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Nadine Massard

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

Abstract

This article examines the effectiveness of the French cluster policy on R&D and innovation in terms of the economic performance of participating SMEs. Based on an original dataset, we built different measures of treatment and extended the DiD methodology to a multiple overlapping treatments approach. Joining a cluster's association allows firms to benefit from low-cost animation actions and common services, while participating in R&D projects offers substantial financial support. Our findings indicate a complementarity between the two types of instruments since the policy is most effective when the two treatments are simultaneously used. Interestingly, they also show that belonging to a cluster alone induces much stronger positive impacts on R&D spending and on both the R&D and total employment of SMEs than participating in R&D collaborative projects alone. Therefore, to achieve its impact on SMEs, the cluster policy should not overlook low-cost instruments such as animation actions and common services, both as a standalone instrument and as an effective complement to subsidy policies for collaborative R&D projects.

Suggested Citation

  • Modou Mar & Nadine Massard, 2021. "Animate the cluster or subsidize collaborative R&D? A multiple overlapping treatments approach to assess the impacts of the French cluster policy," Post-Print hal-03222070, HAL.
  • Handle: RePEc:hal:journl:hal-03222070
    DOI: 10.1093/icc/dtab002
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    Cited by:

    1. Stefano Basilico & Uwe Cantner & Holger Graf, 2023. "Policy influence in the knowledge space: a regional application," The Journal of Technology Transfer, Springer, vol. 48(2), pages 591-622, April.
    2. Raphaël CHIAPPINI & Sophie POMMET, 2023. "The impact of public support for innovation on SME performance and efficiency," Bordeaux Economics Working Papers 2023-06, Bordeaux School of Economics (BSE).

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    More about this item

    Keywords

    Cluster policy; R&D; Economic performance; SME; Innovation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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