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

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  • Bruno Crépon

    (Crest)

  • Emmanuel Duguet

    (Crest)

  • Jacques Mairesse

    (Crest)

Abstract

This paper studies the links between productivity, innovation and research at the firm level. We introduce three new features: (i) A structural model that explains productivity by innovation output, and innovation output by research investment: (ii) New data on French manufacturing firms, including the number of European patents and the percentage share of innovative sales, as well as firm-level demand pull and technology push indicators; (iii) Econometric methods which correct for selectivity and simultaneity biases and take into account the statistical features of the available data: only a small proportion of firms engage in research activities and/or apply for patents; productivity, innovation and research are endogenously determined; research investment and capital are truncated variables, patents are count data and innovative sales are interval data. We find that using the more widespread methods, and the more usual data and model specification, may lead to sensibly different estimates. We find in particular that simultaneity tends to interact with selectivity, and that both sources of biases must be taken into account together. However our main results are consistent with many of the stylized facts of the empirical literature. The probability of engaging in research (R&D) for a firm increases with its size (number of employees), its market share and diversification, and with the demand pull and technology push indicators. The research effort (R&D capital intensity) of a firm engaged in research increases with the same variables, except for size (its research capital being strictly proportional to size). The firm innovation output, as measured by patent numbers or innovative sales, rises with its research effort and with the demand pull and technology indicators, either directly or indirectly through their effects on research. Finally, firm productivity correlates positively with a higher innovation output, even when controlling for the skill composition of l
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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

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    1. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    2. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    3. Zvi Griliches, 1998. "Productivity and R&D at the Firm Level," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 100-133, National Bureau of Economic Research, Inc.
    4. Zvi Griliches, 1984. "R&D, Patents, and Productivity," NBER Books, National Bureau of Economic Research, Inc, number gril84-1.
    5. 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).
    6. Ariel Pakes & Zvi Griliches, 1984. "Patents and R&D at the Firm Level: A First Look," NBER Chapters, in: R&D, Patents, and Productivity, pages 55-72, National Bureau of Economic Research, Inc.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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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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