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Estimating the local average treatment effect of R&D subsidies in a pan-European program

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  • Paul Hünermund
  • Dirk Czarnitzki

Abstract

We investigate the eect of Europe's largest multilateral subsidy program for R&D-performing, small and medium-sized enterprises on rm growth. The program was organized under a specic budget allocation rule, referred to as Virtual Common Pot (VCP), which is designed to avoid cross-subsidization between participating countries. This rule creates exogenous variation in treatment status and allows us to identify the local average treatment effect of public R&D grants. In addition, we compare the program's effect under the VCP rule with the standard situation of a Real Common Pot (RCP), where program authorities allocate a single budget according to uniform project evaluation criteria. Our estimates suggest no average eect of grants on rm growth but treatment eects are heterogeneous and increase with project quality. A Real Common Pot would have reduced the cost of policyinduced job creation by 27%. We discuss the implications of our ndings for the coordination of national policy programs within the European Research Area.

Suggested Citation

  • Paul Hünermund & Dirk Czarnitzki, 2016. "Estimating the local average treatment effect of R&D subsidies in a pan-European program," Working Papers of Department of Management, Strategy and Innovation, Leuven 541177, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven.
  • Handle: RePEc:ete:msiper:541177
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    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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