IDEAS home Printed from https://ideas.repec.org/p/zbw/vfsc15/112869.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/112869/1/VfS_2015_pid_44.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Nicholas Bloom & Mark Schankerman & John Van Reenen, 2013. "Identifying Technology Spillovers and Product Market Rivalry," Econometrica, Econometric Society, vol. 81(4), pages 1347-1393, July.
    3. Takalo, Tuomas & Tanayama, Tanja & Toivanen, Otto, 2013. "Market failures and the additionality effects of public support to private R&D: Theory and empirical implications," International Journal of Industrial Organization, Elsevier, vol. 31(5), pages 634-642.
    4. Kenneth Arrow, 1962. "Economic Welfare and the Allocation of Resources for Invention," NBER Chapters, in: The Rate and Direction of Inventive Activity: Economic and Social Factors, pages 609-626, National Bureau of Economic Research, Inc.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    7. Blind, Knut & Edler, Jakob & Frietsch, Rainer & Schmoch, Ulrich, 2006. "Motives to patent: Empirical evidence from Germany," Research Policy, Elsevier, vol. 35(5), pages 655-672, June.
    8. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Hünermund, Paul & Czarnitzki, Dirk, 2019. "Estimating the causal effect of R&D subsidies in a pan-European program," Research Policy, Elsevier, vol. 48(1), pages 115-124.
    3. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    4. Mellace, Giovanni & Ventura, Marco, 2019. "Intended and unintended effects of public incentives for innovation. Quasi-experimental evidence from Italy," Discussion Papers on Economics 9/2019, University of Southern Denmark, Department of Economics.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    7. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    8. Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute of Labor Economics (IZA).
    9. Hottenrott, Hanna & Lopes-Bento, Cindy, 2014. "(International) R&D collaboration and SMEs: The effectiveness of targeted public R&D support schemes," Research Policy, Elsevier, vol. 43(6), pages 1055-1066.
    10. Tarek Azzam & Michael Bates & David Fairris, 2019. "Do Learning Communities Increase First Year College Retention? Testing Sample Selection and External Validity of Randomized Control Trials," Working Papers 202002, University of California at Riverside, Department of Economics.
    11. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    12. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    13. Frölich, Markus & Lechner, Michael, 2010. "Exploiting Regional Treatment Intensity for the Evaluation of Labor Market Policies," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1014-1029.
    14. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    15. Marco Caliendo & Stefan Tübbicke, 2020. "New evidence on long-term effects of start-up subsidies: matching estimates and their robustness," Empirical Economics, Springer, vol. 59(4), pages 1605-1631, October.
    16. Tymon Słoczyński, 2015. "The Oaxaca–Blinder Unexplained Component as a Treatment Effects Estimator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(4), pages 588-604, August.
    17. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    18. Tuomas Takalo, 2012. "Rationales and Instruments for Public Innovation Policies," Journal of Reviews on Global Economics, Lifescience Global, vol. 1, pages 157-167.
    19. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    20. Adda & Dustmann, 2004. "Career Progression and Formal versus on the Job Training," Econometric Society 2004 North American Winter Meetings 492, Econometric Society.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:vfsc15:112869. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/vfsocea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vfsocea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.