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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.

Suggested Citation

  • Vincent Wenxiong Yao, 2006. "Intra-industry spillovers and innovation: An econometric analysis at the firm level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(2), pages 119-135.
  • Handle: RePEc:taf:ecinnt:v:15:y:2006:i:2:p:119-135 DOI: 10.1080/10438590500131108

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    References listed on IDEAS

    1. Jeffrey I. Bernstein & M. Ishaq Nadiri, 1989. "Research and Development and Intra-industry Spillovers: An Empirical Application of Dynamic Duality," Review of Economic Studies, Oxford University Press, vol. 56(2), pages 249-267.
    2. Crepon, Bruno & Duguet, Emmanuel, 1997. "Research and development, competition and innovation pseudo-maximum likelihood and simulated maximum likelihood methods applied to count data models with heterogeneity," Journal of Econometrics, Elsevier, vol. 79(2), pages 355-378, August.
    3. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 577-598.
    4. Richard Blundell & Stephen 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.
    5. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
    6. Jinyoung Kim & Gerald Marschke, 2004. "Accounting for the recent surge in U.S. patenting: changes in R&D expenditures, patent yields, and the high tech sector," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 13(6), pages 543-558.
    7. Bruno Crepon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation And Productivity: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
    8. 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 909-938, July.
    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. 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 243-263, May-June.
    12. Zvi Griliches, 1998. "The Search for R&D Spillovers," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 251-268 National Bureau of Economic Research, Inc.
    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. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Oxford University Press, vol. 58(2), pages 277-297.
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