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Research, Innovation and Productivity : An Econometric Analysis at the Firm Level

Author

Listed:
  • Bruno Crépon

    (Crest)

  • Emmanuel Duguet

    (Crest)

  • Jacques Mairesse

    (Crest)

Abstract

This paper studies the links between productivity, innovation and research at th level. We introduce three new features: (i) A structural model that explains pro by innovation output, and innovation output by research investment; (ii) New dat manufacturing firms, including the number of European patents and the percentage sales, as well as firm-level demand pull and technology push indicators; (iii) E which correct for selectivity and simultaneity biases and take into account the features of the available data: only a small proportion of firms engage in resea apply for patents; productivity, innovation and research are endogenously determ investment and capital are truncated variables, patents are count data and innov We find that using the more widespread methods, and the more usual data and mode may lead to sensibly different estimates. We find in particular that simultaneit with selectivity, and that both sources of biases must be taken into account tog results are consistent with many of the stylized facts of the empirical literatu of engaging in research (R&D) for a firm increases with its size (number of empl share and diversification, and with the demand pull and technology push indicato capital intensity) of a firm engaged in research increases with the same variabl research capital being strictly proportional to size). The firm innovation outpu patent numbers or innovative sales, rises with its research effort and with the indicators, either directly or indirectly through their effects on research. Fin correlates positively with an higher innovation output, even when controlling fo the skill composition of labor as well as for physical capital intensity.
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Suggested Citation

  • Bruno Crépon & Emmanuel Duguet & Jacques Mairesse, 1998. "Research, Innovation and Productivity : An Econometric Analysis at the Firm Level," Working Papers 98-33, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:98-33
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    References listed on IDEAS

    as
    1. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    2. Crepon, B. & Duguet, E. & Kabla, I., 1995. "A Moderate Support to Schumpeterian Conjectures from Various Innovation Measures," Papiers d'Economie Mathématique et Applications 95.06, Université Panthéon-Sorbonne (Paris 1).
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    4. repec:crs:wpaper:9730 is not listed on IDEAS
    5. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    6. 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.
    7. 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.
    8. Cohen, Wesley M & Klepper, Steven, 1996. "A Reprise of Size and R&D," Economic Journal, Royal Economic Society, vol. 106(437), pages 925-951, July.
    9. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    10. Jacques Mairesse & Philippe Cunéo, 1985. "Recherche-développement et performances des entreprises : une étude économétrique sur données individuelles," Revue Économique, Programme National Persée, vol. 36(5), pages 1001-1042.
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    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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