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Innovative Sales, R&D and Total Innovation Expenditures:Panel Evidence on their Dynamics

Author

Listed:
  • Raymond, Wladimir

    (Maastricht University)

  • Mohnen, Pierre

    (UNU-MERIT, and Maastricht University)

  • Palm, Franz

    (Maastricht University)

  • Schim van der Loeff, Sybrand

    (UNU-MERIT, and Maastricht University)

Abstract

This paper studies the dynamic relationship between input and output of innovation in Dutch manufacturing using an unbalanced panel of enterprise data from five waves of the Community Innovation Survey during 1994-2004. We estimate by maximum likelihood a dynamic panel data bivariate tobit with double-index sample selection accounting for individual effects. We find persistence of innovation input and innovation output, a lag effect of the former on the latter and a feedback effect of the latter on the former. The lag effect remains significant in the high-tech sector even after four years. Firm and industry effects are also important.

Suggested Citation

  • Raymond, Wladimir & Mohnen, Pierre & Palm, Franz & Schim van der Loeff, Sybrand, 2009. "Innovative Sales, R&D and Total Innovation Expenditures:Panel Evidence on their Dynamics," MERIT Working Papers 2009-029, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    • Handle: RePEc:unm:unumer:2009029
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    Cited by:

    1. Eleonora Bartoloni & Maurizio Baussola, 2018. "Driving business performance: innovation complementarities and persistence patterns," Industry and Innovation, Taylor & Francis Journals, vol. 25(5), pages 505-525, May.
    2. Marco Capasso & Elena Cefis & Koen Frenken, 2009. "Do Some Firms Persistently Outperform ?," LEM Papers Series 2009/15, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Yu‐Fu Chen & Michael Funke, 2010. "Booms, Recessions And Financial Turmoil: A Fresh Look At Investment Decisions Under Cyclical Uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 57(3), pages 290-317, July.
    4. Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July.
    5. García-Vega, María & Huergo, Elena, 2019. "The role of international and domestic R&D outsourcing for firm innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 775-792.
    6. Martin Srholec, 2014. "Cooperation and Innovative Performance of Firms: Panel Data Evidence from the Czech Republic, Norway and the UK," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 5(1), pages 133-155, March.
    7. Gabriele Pellegrino & Mariacristina Piva, 2014. "Do innovative inputs lead to different innovative outputs in mature and young firms?," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1497, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    8. Martin Srholec, 2016. "Persistence of Cooperation on Innovation: Econometric Evidence from Panel Micro Data," Prague Economic Papers, Prague University of Economics and Business, vol. 2016(1), pages 53-70.
    9. Maria Plotnikova & Isidoro Romero & Juan A. Martínez-Román, 2016. "Process innovation in small businesses: the self-employed as entrepreneurs," Small Business Economics, Springer, vol. 47(4), pages 939-954, December.
    10. Konstantinos Konstantakis & Panayotis G. Michaelides & Theofanis Papageorgiou, 2014. "Sector size, technical change and stability in the USA (1957-2006): a Schumpeterian approach," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 41(10), pages 956-974, October.
    11. Martin Falk & Eva Hagsten, 2021. "Innovation intensity and skills in firms across five European countries," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 371-394, September.
    12. Martin Srholec, 2011. "Cooperation and innovative performance of firms: Panel data evidence from the Czech Republic, Norway and the United Kingdom," Working Papers on Innovation Studies 20111131, Centre for Technology, Innovation and Culture, University of Oslo.

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    More about this item

    Keywords

    innovation; production function; panel data; CIS data; bivariate dynamic tobit; the Netherlands;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • 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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