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Estimating dynamic R&D demand: An analysis of costs and long-run benefits

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  • Peters, Bettina
  • Roberts, Mark J.
  • Vuong, Van Anh
  • Fryges, Helmut

Abstract

Using firm-level data from the German manufacturing sector, we estimate a dynamic, structural model of the firm's decision to invest in R&D and quantify the cost and longrun benefit of this investment. The model incorporates and quantifies linkages between the firm's R&D investment, product and process innovations, and future productivity and profits. The dynamic model provides a natural measure of the long-run payoff to R&D as the difference in expected firm value generated by the R&D investment. For the median productivity firm, investment in R&D raises firm value by 3.0 percent in a group of hightech industries but only 0.2 percent in low-tech industries. Simulations of the model show that cost subsidies for R&D can significantly affect R&D investment rates and productivity changes in the high-tech industries. --

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Bibliographic Info

Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 13-089.

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Date of creation: 2013
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Handle: RePEc:zbw:zewdip:13089

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Keywords: R&D demand; Innovation; Productivity; Dynamic structural model;

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  1. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Wiley Blackwell, vol. 70(2), pages 317-341, 04.
  2. Griliches, Zvi, 1992. " The Search for R&D Spillovers," Scandinavian Journal of Economics, Wiley Blackwell, vol. 94(0), pages S29-47, Supplemen.
  3. Bernstein, Jeffrey I & Nadiri, M Ishaq, 1989. "Research and Development and Intra-industry Spillovers: An Empirical Application of Dynamic Duality," Review of Economic Studies, Wiley Blackwell, vol. 56(2), pages 249-67, April.
  4. Hall, Bronwyn H., 2011. "Innovation and productivity," MERIT Working Papers 028, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  5. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation, and Productivity: An Econometric Analysis at the Firm Level," NBER Working Papers 6696, National Bureau of Economic Research, Inc.
  6. Mark J. Roberts & Van Anh Vuong, 2013. "Empirical Modeling of R&D Demand in a Dynamic Framework," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 35(2), pages 185-205.
  7. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 17-45 National Bureau of Economic Research, Inc.
  8. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," Review of Economic Studies, Oxford University Press, vol. 70(2), pages 317-341.
  9. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
  10. Crepon, B. & Duguet, E. & Mairesse, J., 1998. "Research Investment, Innovation and Productivity: An Econometric Analysis at the Firm Level," Papiers d'Economie Mathématique et Applications 98.15, Université Panthéon-Sorbonne (Paris 1).
  11. Jeffrey I. Bernstein & M. Ishaq Nadiri, 1991. "Product Demand, Cost of Production, Spillovers, and the Social Rate of Return to R&D," NBER Working Papers 3625, National Bureau of Economic Research, Inc.
  12. Hall, Bronwyn H., 2011. "Innovation and productivity," MERIT Working Papers 028, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  13. repec:fth:inseep:9833 is not listed on IDEAS
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