IDEAS home Printed from https://ideas.repec.org/a/diw/diwvjh/80-3-10.html
   My bibliography  Save this article

Welche Rolle spielt externes Wissen für die sektorale Technologieentwicklung?: Eine empirische Analyse zur Identifikation intersektoraler FuE-Spillovers

Author

Listed:
  • Timo Mitze
  • Björn Alecke

Abstract

In this paper, we analyse the role played by intersectoral R&D-spillovers in determining sectoral productivity growth patterns. Based on panel data for 12 OECD countries throughout the period 1980-2004, our empirical approach builds upon the estimation of so-called "Griliches-Type" production functions, which are prominently driven by knowledge capital stocks. Our results identify positive and statistically significant private as well as social returns to R&D activity. Thereby, both the manufacturing sector as a whole and particularly its hightech subsectors show to benefit considerably from their own sectoral R&D spendings in driving output growth. Moreover, these sectors also show to be a significant source of intersectoral R&D spillovers. Our obtained results thus highlight the macroeconomic importance of the latter sectors and also hint at the likely effectiveness of policy measures aiming to exploit social returns from R&D spillovers. Die vorliegende Arbeit untersucht die Rolle von intersektoralen FuE-Spillovereffekten für das sektorale Produktivitätswachstum anhand eines Datensatzes für zwölf OECD-Volkswirtschaften im Zeitraum 1980 bis 2004. Im Rahmen der empirischen Analyse werden mithilfe von panelökonometrischen Verfahren sogenannte "Griliches-Typ"-Produktionsfunktionen geschätzt, die der Akkumulation von Wissenskapitalstöcken eine besondere Rolle beimessen. Die erzielten Ergebnisse bestätigen die Existenz privater Erträge und sozialer Zusatzerträge von sektoraler FuE-Tätigkeit. So kann gezeigt werden, dass das Verarbeitende Gewerbe und darunter insbesondere "Hightech"-Sektoren einerseits überdurchschnittlich stark von der eigenen FuE-Tätigkeit profitieren und andererseits eine Quelle für positive intersektorale Spillover-Effekte sind. Die Ergebnisse verdeutlichen damit die besondere gesamtwirtschaftliche Rolle dieser Sektoren und geben darüber hinaus Hinweise auf den potenziellen Nutzen einer gezielten politischen Förderung der unternehmerischen FuE-Tätigkeit.

Suggested Citation

  • Timo Mitze & Björn Alecke, 2011. "Welche Rolle spielt externes Wissen für die sektorale Technologieentwicklung?: Eine empirische Analyse zur Identifikation intersektoraler FuE-Spillovers," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 80(3), pages 167-180.
  • Handle: RePEc:diw:diwvjh:80-3-10
    as

    Download full text from publisher

    File URL: http://ejournals.duncker-humblot.de/doi/pdf/10.3790/vjh.80.3.167
    Download Restriction: no

    References listed on IDEAS

    as
    1. Belitz, Heike & Clemens, Marius & Gornig, Martin, 2008. "Wirtschaftsstrukturen und Produktivität im internationalen Vergleich," Studien zum deutschen Innovationssystem 2-2008, Expertenkommission Forschung und Innovation (EFI) - Commission of Experts for Research and Innovation, Berlin.
    2. Jeffrey Bernstein, 1998. "Factor Intensities, Rates of Return, and International R&D Spillovers: The Case of Canadian and U.S. Industries," Annals of Economics and Statistics, GENES, issue 49-50, pages 541-564.
    3. Jeffrey Bernstein, 1997. "Interindustry R&D Spillovers for Electrical and Electronic Products: The Canadian Case," Economic Systems Research, Taylor & Francis Journals, vol. 9(1), pages 111-125.
    4. Bruno Van Pottelsberghe De La Potterie, 1997. "Issues in Assessing the Effect of Interindustry R&D Spillovers," Economic Systems Research, Taylor & Francis Journals, vol. 9(4), pages 331-356.
    5. Kwon, Hyeog Ug, 2003. "Measuring the Rate of Return to R&D, Interindustry R&D Spillovers in Korean Manufacturing Industries," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 44(1), pages 49-57, June.
    6. Jaffe, Adam B, 1986. "Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits, and Market Value," American Economic Review, American Economic Association, vol. 76(5), pages 984-1001, December.
    7. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    8. Dirk Czarnitzki & Kornelius Kraft, 2010. "On the profitability of innovative assets," Applied Economics, Taylor & Francis Journals, vol. 42(15), pages 1941-1953.
    9. Jaffe, Adam B, 1989. "Real Effects of Academic Research," American Economic Review, American Economic Association, vol. 79(5), pages 957-970, December.
    10. Bernstein, Jeffrey I. & Nadiri, M. Ishaq, 1988. "Interindustry R&D, Rates of Return and Production in High-Tech Industries," Working Papers 88-04, C.V. Starr Center for Applied Economics, New York University.
    11. Bart Verspagen, 1997. "Estimating international technology spillovers using technology flow matrices," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 133(2), pages 226-248, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Production functions; R&D spillovers; panel econometrics;

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:diw:diwvjh:80-3-10. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bibliothek). General contact details of provider: http://edirc.repec.org/data/diwbede.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.