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Evaluating the Effect of Public Subsidies on firm R&D activity: an Application to Italy Using the Community Innovation Survey

The aim of the paper is twofold: to verify a full policy failure of public support on private R&D effort, when in presence of a potential plurality of public incentives; to compare the most recent econometric methods used for the analysis of the input additionality. Compared to previous studies our work wants to trace out an advance in two directions: adding more robustness by comparing results from various econometric techniques and providing an analysis of the R&D policy effect behind the average results. A by-product of the paper is a taxonomy of the econometric methods used in the literature, according to the structure of the models, the type of dataset and the available policy information. We exploit the third wave of the Community Innovation Survey for Italy (1998-2000) with a sample size of 1,221 supported and 1,319 non-supported firms. Given the used type of data, the article presents two main limits: first, we do not know the level of the subsidy, so that we can control only for the presence of a total crowding-out; second, we can check only the short-run effect of the supporting policy, while an increase in the private R&D effort could be more likely in the medium term. Our results suggest that: 1. the main factors influencing the probability to participate to the incentive policy are R&D experience, human skills, liquidity constraints, but also foreign capital ownership; 2. on average, the total substitution of private funding by the public one is excluded for Italy as a whole, although some cases of total crowding-out are found: low knowledge intensive services, very small firms (10-19 employees) and the auto-vehicle industry. We get, on average, 885 additional thousand Euros of R&D expenditure per firm with a ratio equal to 4.62: it means that if a generic control unit does 1 thousand Euros of R&D expenditure a matched treated does 4.62 thousand Euros. The additionality for the R&D intensity is about 0.014 with a ratio of about 2.67.

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Paper provided by Institute for Economic Research on Firms and Growth - Moncalieri (TO) in its series CERIS Working Paper with number 200809.

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Length: 46 pages
Date of creation: Dec 2008
Date of revision:
Handle: RePEc:csc:cerisp:200809
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  1. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
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  3. Charles Bérubé & Pierre Mohnen, 2009. "Are firms that receive R&D subsidies more innovative?," Canadian Journal of Economics, Canadian Economics Association, vol. 42(1), pages 206-225, February.
  4. Bronwyn H. Hall., 1992. "Investment and Research and Development at the Firm Level: Does the Source of Financing Matter?," Economics Working Papers 92-194, University of California at Berkeley.
  5. Arthur M. Diamond, 1999. "Does Federal Funding "Crowd In" Private Funding Of Science?," Contemporary Economic Policy, Western Economic Association International, vol. 17(4), pages 423-431, October.
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  7. Hall, Bronwyn H., 2002. "The Financing of Research and Development," Department of Economics, Working Paper Series qt34c1c643, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  8. DUGUET Emmanuel, 2004. "Are R&D subsidies a substitute or a complement to privately funded R&D? Evidence from France using propensity score methods for non- experimental data," Public Economics 0411007, EconWPA.
  9. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
  10. Almus, Matthias & Czarnitzki, Dirk, 2003. "The Effects of Public R&D Subsidies on Firms' Innovation Activities: The Case of Eastern Germany," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 226-36, April.
  11. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
  12. James J. Heckman, 1977. "Dummy Endogenous Variables in a Simultaneous Equation System," NBER Working Papers 0177, National Bureau of Economic Research, Inc.
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  15. Klette, T.J. & Moen, J. & Griliches, Z., 1999. "Do Subsidies to Commercial R&D Reduce Market Failures? Microeconometric Evaluation Studies," Papers 16/99, Norwegian School of Economics and Business Administration-.
  16. Stead, H., 1976. "The costs of technological innovation," Research Policy, Elsevier, vol. 5(1), pages 2-9, January.
  17. Lichtenberg, Frank R, 1988. "The Private R&D Investment Response to Federal Design and Technical Competitions," American Economic Review, American Economic Association, vol. 78(3), pages 550-59, June.
  18. James J. Heckman, 2001. "Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 673-748, August.
  19. Heshmati, Almas & Loof, Hans, 2005. "The Impact of Public Funds on Private R&D Investment: New Evidence from a Firm Level Innovation Study," Discussion Papers 11862, MTT Agrifood Research Finland.
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