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Persistence of Innovation in Dutch Manufacturing: Is It Spurious?

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  • Wladimir Raymond

    (University of Maastricht)

  • Pierre Mohnen

    (University of Maastricht, UNU-MERIT and CIRANO)

  • Franz Palm

    (University of Maastricht and CESifo)

  • Sybrand Schim van der Loeff

    (University of Maastricht)

Abstract

This paper studies the persistence of innovation in Dutch manufacturing using an unbalanced panel of firm data from four waves of the Community Innovation Survey between 1994 and 2002. We estimate by maximum likelihood a dynamic type 2 tobit model accounting for individual effects and handling the initial conditions problem. We find true persistence in the probability of innovating in the high-tech category of industries and spurious persistence in the low-tech category. Furthermore, past innovation output intensity affects, albeit to a small extent, current innovation output intensity in the high-tech category, while no such evidence is found in the low-tech category. © 2010 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 92 (2010)
Issue (Month): 3 (August)
Pages: 495-504

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Handle: RePEc:tpr:restat:v:92:y:2010:i:3:p:495-504

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  1. Bruno Crepon & Emmanuel Duguet & Jacques Mairessec, 1998. "Research, Innovation And Productivi[Ty: An Econometric Analysis At The Firm Level," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 7(2), pages 115-158.
  2. repec:fth:harver:1473 is not listed on IDEAS
  3. Malerba, Franco & Orsenigo, Luigi, 1999. "Technological entry, exit and survival: an empirical analysis of patent data," Research Policy, Elsevier, vol. 28(6), pages 643-660, August.
  4. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
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  6. Kyriazidou, Ekaterini, 2001. "Estimation of Dynamic Panel Data Sample Selection Models," Review of Economic Studies, Wiley Blackwell, vol. 68(3), pages 543-72, July.
  7. 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.
  8. Geroski, Paul A, 1989. "Entry, Innovation and Productivity Growth," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 572-78, November.
  9. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
  10. Luuk Klomp & George Van Leeuwen, 2001. "Linking Innovation and Firm Performance: A New Approach," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 8(3), pages 343-364.
  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 243-63, May-June.
  12. Janz, Norbert & Lööf, Hans & Peters, Bettina, 2004. "Firm Level Innovation and Productivity - Is there a Common Story Across Countries?," Working Paper Series in Economics and Institutions of Innovation 24, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
  13. Amemiya, Takeshi, 1984. "Tobit models: A survey," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 3-61.
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