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An emprically-based taxonomy of Dutch manufacturing: innovation policy implications

Author

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  • Raymond, W.

    (Quantitative Economics)

  • Mohnen, P.

    (Mt Economic Research Inst on Innov/Techn)

  • Palm, F.C.

    (Quantitative Economics)

  • Schim van der Loeff, S.

    (Quantitative Economics)

Abstract

The paper studies the degree of homogeneity of innovative behavior in order to determine empirically an industry classification of Dutch manufacturing that can be used for policy purposes. We use a two-limit tobit model with sample selection, which explains the decisions by business enterprises to innovate and the impact these decisions have on the share of innovative sales. The model is estimated for eleven industries based on the Dutch Standard Industrial Classification (SBI 1993). A likelihood ratio test (LR) is then performed to test for equality of the parameters across industries. We find that Dutch manufacturing consists of three groups of industries in terms of innovative behavior, a high-tech group, a low-tech group and the industry of wood, where firms seem to have a rather different innovative behavior from the remaining industries. The same pattern shows up in the three Dutch Community Innovation Surveys.
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Suggested Citation

  • Raymond, W. & Mohnen, P. & Palm, F.C. & Schim van der Loeff, S., 2004. "An emprically-based taxonomy of Dutch manufacturing: innovation policy implications," Research Memorandum 024, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    • Handle: RePEc:unm:umamet:2004024
    DOI: 10.26481/umamet.2004024
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    References listed on IDEAS

    as
    1. Sabourin, David & Baldwin, John R. & Hanel, Peter, 2000. "Determinants of Innovative Activity in Canadian Manufacturing Firms: The Role of Intellectual Property Rights," Analytical Studies Branch Research Paper Series 2000122e, Statistics Canada, Analytical Studies Branch.
    2. Spyros Arvanitis & Heinz Hollenstein, 1996. "Industrial Innovation in Switzerland: A Model-based Analysis with Survey Data," Palgrave Macmillan Books, in: Alfred Kleinknecht (ed.), Determinants of Innovation, chapter 2, pages 13-62, Palgrave Macmillan.
    3. Amemiya, Takeshi, 1978. "The Estimation of a Simultaneous Equation Generalized Probit Model," Econometrica, Econometric Society, vol. 46(5), pages 1193-1205, September.
    4. John Baldwin & Petr Hanel & David Sabourin, 2002. "Determinants of Innovative Activity in Canadian Manufacturing Firms," Palgrave Macmillan Books, in: Alfred Kleinknecht & Pierre Mohnen (ed.), Innovation and Firm Performance, chapter 5, pages 86-111, Palgrave Macmillan.
    5. Alfred Kleinknecht & Pierre Mohnen (ed.), 2002. "Innovation and Firm Performance," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-59588-0, December.
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    Citations

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    Cited by:

    1. Bert Diederen & Pierre Mohnen & Franz C. Palm & Sybrand Schim van der Loeff, 2006. "Innovation in Enterprise Clusters: Evidence from Dutch Manufacturing," Chapters, in: Louise Earl & Fred Gault (ed.), National Innovation, Indicators and Policy, chapter 4, Edward Elgar Publishing.
    2. Martin Woerter, 2009. "Industry diversity and its impact on the innovation performance of firms," Journal of Evolutionary Economics, Springer, vol. 19(5), pages 675-700, October.
    3. Mairesse, Jacques & Mohnen, Pierre, 2010. "Using Innovation Surveys for Econometric Analysis," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1129-1155, Elsevier.
    4. de Jong, Jeroen P.J. & Marsili, Orietta, 2006. "The fruit flies of innovations: A taxonomy of innovative small firms," Research Policy, Elsevier, vol. 35(2), pages 213-229, March.
    5. Spyros Arvanitis & Juliette von Arx, 2004. "Bestimmungsfaktoren der Innovationstätigkeit und deren Einfluss auf Arbeitsproduktivität, Beschäftigung und Qualifikationsstruktur," KOF Working papers 04-91, KOF Swiss Economic Institute, ETH Zurich.
    6. Bi, Kexin & Huang, Ping & Wang, Xiangxiang, 2016. "Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 275-284.
    7. Woerter, Martin & Roper, Stephen, 2010. "Openness and innovation--Home and export demand effects on manufacturing innovation: Panel data evidence for Ireland and Switzerland," Research Policy, Elsevier, vol. 39(1), pages 155-164, February.
    8. Wladimir Raymond & Pierre Mohnen & Franz Palm & Sybrand Loeff, 2006. "A Classification of Dutch Manufacturing based on a Model of Innovation," De Economist, Springer, vol. 154(1), pages 85-105, March.

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    More about this item

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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