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Firms’ human capital, R&D and innovation: a study on French firms

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  • Emilie-Pauline Gallié

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  • Diègo Legros

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Abstract

This article investigates the effects of human capital and technological capital on innovation. While the role of technological capital as measured by research and development (R&D) expenditure has been intensively investigated, few studies have been made on the effect of employee training on innovation. This article explores the relationship between innovation and firm employee training. Our methodological approach contributes to the literature in three ways. We propose various indicators of firm employee training. We build a count data panel with a long time-data series to deal with the issue of firms’ heterogeneity. We propose a dynamic analysis. Using dynamic count data models on French industrial firms over the period 1986–1992, we find positive and significant effects of R&D intensity and training on patenting activity. Whatever the indicators of training our results show that the firm employee training has a positive impact on technological innovation. Copyright Springer-Verlag 2012

Suggested Citation

  • Emilie-Pauline Gallié & Diègo Legros, 2012. "Firms’ human capital, R&D and innovation: a study on French firms," Empirical Economics, Springer, vol. 43(2), pages 581-596, October.
  • Handle: RePEc:spr:empeco:v:43:y:2012:i:2:p:581-596
    DOI: 10.1007/s00181-011-0506-8
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    References listed on IDEAS

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    1. Nathalie Greenan & Emmanuel Duguet, 1997. "Le biais technologique : une analyse économétrique sur données individuelles," Revue Économique, Programme National Persée, vol. 48(5), pages 1061-1089.
    2. Shiferaw Gurmu & Fidel Pérez-Sebastián, 2008. "Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms," Empirical Economics, Springer, vol. 35(3), pages 507-526, November.
    3. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    4. Emmanuel Duguet & Stéphanie Monjon, 2002. "Les fondements microéconomiques de la persistance de l'innovation. Une analyse économétrique," Revue économique, Presses de Sciences-Po, vol. 53(3), pages 625-636.
    5. repec:fth:harver:1473 is not listed on IDEAS
    6. Pierre Mohnen & Pierre Therrien, 2001. "How Innovative Are Canadian Firms Compared to Some European Firms? A Comparative Look at Innovation Surveys," CIRANO Working Papers 2001s-49, CIRANO.
    7. Ariél Pakes & Zvi Griliches, 1984. "Estimating Distributed Lags in Short Panels with an Application to the Specification of Depreciation Patterns and Capital Stock Constructs," Review of Economic Studies, Oxford University Press, vol. 51(2), pages 243-262.
    8. 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.
    9. Benhabib, Jess & Spiegel, Mark M., 1994. "The role of human capital in economic development evidence from aggregate cross-country data," Journal of Monetary Economics, Elsevier, vol. 34(2), pages 143-173, October.
    10. repec:adr:anecst:y:1998:i:49-50:p:11 is not listed on IDEAS
    11. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    12. Yannick Carriou & François Jeger, 1997. "La formation continue dans les entreprises et son retour sur investissement," Économie et Statistique, Programme National Persée, vol. 303(1), pages 45-58.
    13. Cockburn, Iain M & Henderson, Rebecca M, 1998. "Absorptive Capacity, Coauthoring Behavior, and the Organization of Research in Drug Discovery," Journal of Industrial Economics, Wiley Blackwell, vol. 46(2), pages 157-182, June.
    14. Cincera, Michele, 1997. "Patents, R&D, and Technological Spillovers at the Firm Level: Some Evidence from Econometric Count Models for Panel Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 265-280, May-June.
    15. 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.
    16. Blundell, Richard & Griffith, Rachel & Van Reenen, John, 1995. "Dynamic Count Data Models of Technological Innovation," Economic Journal, Royal Economic Society, vol. 105(429), pages 333-344, March.
    17. Emmanuel Duguet & Isabelle Kabla, 1998. "Appropriation Strategy and the Motivations to Use the Patent System: An Econometric Analysis at the Firm Level in French Manufacturing," Annals of Economics and Statistics, GENES, issue 49-50, pages 289-327.
    18. 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.
    19. Mark Rogers, 2004. "Networks, Firm Size and Innovation," Small Business Economics, Springer, vol. 22(2), pages 141-153, March.
    20. Bruno Crépon & Emmanuel Duguet & Jacques Mairesse, 2000. "Mesurer le rendement de l'innovation," Économie et Statistique, Programme National Persée, vol. 334(1), pages 65-78.
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    Cited by:

    1. Zakaria Babutsidze & Maurizio Iacopetta, 2016. "Innovation, growth and financial markets," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 1-24, March.
    2. Ipsita Roy & Uwe Cantner & Wolfgang Gerstlberger, 2020. "Works councils, training activities and innovation: a study of German firms," International Journal of Human Resources Development and Management, Inderscience Enterprises Ltd, vol. 20(3/4), pages 269-294.
    3. Stav Rosenzweig, 2017. "The effects of diversified technology and country knowledge on the impact of technological innovation," The Journal of Technology Transfer, Springer, vol. 42(3), pages 564-584, June.
    4. Antonelli, Cristiano & Scellato, Giuseppe, 2019. "Wage inequality and directed technological change: Implications for income distribution," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 59-65.
    5. Mark Vancauteren, 2018. "The effects of human capital, R&D and firm’s innovation on patents: a panel study on Dutch food firms," The Journal of Technology Transfer, Springer, vol. 43(4), pages 901-922, August.
    6. Anna Vuorio & Lasse Torkkeli & Liisa-Maija Sainio, 2020. "Service innovation and internationalization in SMEs: antecedents and profitability outcomes," Journal of International Entrepreneurship, Springer, vol. 18(1), pages 92-123, March.
    7. Yoshitsugu Kitazawa, 2012. "An improved theoretical ground for the linear feedback model and a new indicator," Discussion Papers 58, Kyushu Sangyo University, Faculty of Economics.
    8. Rehman, Naqeeb Ur, 2016. "Does Internal and External Research and Development Affect Innovation of Small and Medium-Sized Enterprises? Evidence from India and Pakistan," ADBI Working Papers 577, Asian Development Bank Institute.

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    More about this item

    Keywords

    Patents; R&D; Employee training; Count panel data; Linear feedback model; C23; C25; J24; L60; O31;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • 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

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