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Estimating the Local Average Treatment Effect of R&D Subsidies in a Virtual Common Pot

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  • Hünermund, Paul
  • Czarnitzki, Dirk

Abstract

We investigate the additionality effects of Eurostars, Europe's largest multilateral subsidy program for R&D-performing small and medium sized enterprises. A specific budget allocation rule serves as an instrument and allows us to identify the local average treatment effect of public R&D grants. This rule, referred to as Virtual Common Pot (VCP), is designed to avoid cross-subsidization between participating countries. We compare the program's effect under a VCP with the counterfactual situation under a Real Common Pot (RCP), where project authorities allocate a single budget according to uniform project evaluation criteria. Our estimates suggest a large positive impact on job creation whereas there is no treatment effect on patenting. In addition, we find a relative inefficiency of 19.4% more jobs which could be created by the program under a RCP.

Suggested Citation

  • Hünermund, Paul & Czarnitzki, Dirk, 2015. "Estimating the Local Average Treatment Effect of R&D Subsidies in a Virtual Common Pot," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112869, Verein für Socialpolitik / German Economic Association.
    • Handle: RePEc:zbw:vfsc15:112869
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    References listed on IDEAS

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    Cited by:

    1. Thomas H. W. Ziesemer, 2021. "The Effects of R&D Subsidies and Publicly Performed R&D on Business R&D: A Survey," Hacienda Pública Española / Review of Public Economics, IEF, vol. 236(1), pages 171-205, March.

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    More about this item

    JEL classification:

    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • 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

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