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Intra-industry spillovers and innovation: An econometric analysis at the firm level

  • Vincent Wenxiong Yao

This paper studies spillover effects of innovation at the firm level and the comparability of generalized method of moments (GMM) estimators with maximum likelihood estimators of the earlier studies. Two sources of spillovers are identified, i.e. intra-industry R&D expenditure and intra-industry innovation output. This paper estimates a negative R&D spillover effect and a positive output spillover effect. Because of the substitution effect of intra-industry R&D spillovers, the elasticity of patent with respect to firm's own R&D expenditure is greater than those estimated in the earlier studies. With GMM, individual effects are incorporated into the models either by developing proxies for them or attempting to eliminate them.

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File URL: http://www.tandfonline.com/doi/abs/10.1080/10438590500131108
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Article provided by Taylor & Francis Journals in its journal Economics of Innovation and New Technology.

Volume (Year): 15 (2006)
Issue (Month): 2 ()
Pages: 119-135

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Handle: RePEc:taf:ecinnt:v:15:y:2006:i:2:p:119-135
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  1. Zvi Griliches, 1990. "Patent Statistics as Economic Indicators: A Survey," NBER Working Papers 3301, National Bureau of Economic Research, Inc.
  2. Jeffrey I. Bernstein & M. Ishaq Nadiri, 1986. "Research and Development and Intraindustry Spillovers: An Empirical Application of Dynamic Duality," NBER Working Papers 2002, National Bureau of Economic Research, Inc.
  3. Crepon, B. & Duguet, E., 1995. "Research and Development, Competition and Innovation; Pseudo Maximum Likelihood and Simulated Maximum Likelihood Methods Applied to Count Data Models with Heterogeneity," Papiers d'Economie Mathématique et Applications 95.08, Université Panthéon-Sorbonne (Paris 1).
  4. Zvi Griliches, 1991. "The Search for R&D Spillovers," NBER Working Papers 3768, National Bureau of Economic Research, Inc.
  5. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-38, July.
  6. Jaffe, A.B. & Trajtenberg, M., 1992. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," Papers 14-92, Tel Aviv.
  7. Gerald Marschke & Jinyoung Kim, 2002. "Accounting for the recent surge in U.S. patenting: Changes in R&D expenditures, patent yields, and the high tech sector," Discussion Papers 02-10, University at Albany, SUNY, Department of Economics.
  8. Crepon, Bruno & Duguet, Emmanuel, 1997. "Estimating the Innovation Function from Patent Numbers: GMM on Count Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 243-63, May-June.
  9. Bronwyn H. Hall & Zvi Griliches & Jerry A. Hausman, 1984. "Patents and R&D: Is There A Lag?," NBER Working Papers 1454, National Bureau of Economic Research, Inc.
  10. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  11. 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.
  12. Richard Blundell & Steve Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
  13. Link, Albert N. & Paton, David & Siegel, Donald S., 2002. "An analysis of policy initiatives to promote strategic research partnerships," Research Policy, Elsevier, vol. 31(8-9), pages 1459-1466, December.
  14. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
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