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Measuring Intersectoral Knowledge Spillovers: an Application of Sensitivity Analysis to Italy

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Abstract

R&D spillovers are unanimously considered as one of the main driving forces of technical change, innovation and economic growth. This paper aims at measuring inter-industrial R&D spillovers, as a useful information for policy-makers. We apply an “uncertainty-sensitivity analysis” to the Italian input-output table of intermediate goods split into 31 economic sectors for the year 2000. The value added of using this methodology is the opportunity of distinguishing (separately) between spillover effects induced by productive linkages (the Leontiev forward multipliers) and those activated by R&D investments, capturing also the uncertain and non-linear nature of the relations between spillovers and factors affecting them.

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  • Giovanni Cerulli & Bianca Potì, 2007. "Measuring Intersectoral Knowledge Spillovers: an Application of Sensitivity Analysis to Italy," CERIS Working Paper 200711, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
  • Handle: RePEc:csc:cerisp:200711
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    References listed on IDEAS

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    1. Ina Drejer, 2000. "Comparing Patterns of Industrial Interdependence in National Systems of Innovation - A Study of Germany, the United Kingdom, Japan and the United States," Economic Systems Research, Taylor & Francis Journals, vol. 12(3), pages 377-399.
    2. Zvi Griliches, 1998. "Interindustry Technology Flows and Productivity Growth: A Reexamination," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 241-250, National Bureau of Economic Research, Inc.
    3. Scherer, F M, 1982. "Inter-Industry Technology Flows and Productivity Growth," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 627-634, November.
    4. Adams, James D, 1990. "Fundamental Stocks of Knowledge and Productivity Growth," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 673-702, August.
    5. Goto, Akira & Suzuki, Kazuyuki, 1989. "R&D Capital, Rate of Return on R&D Investment and Spillover of R&D in Japanese Manufacturing Industries," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 555-564, November.
    6. Frederic Scherer, 2003. "Technology Flows Matrix Estimation Revisited," Economic Systems Research, Taylor & Francis Journals, vol. 15(3), pages 327-358.
    7. Bart Los & Bart Verspagen, 2000. "R&D spillovers and productivity: Evidence from U.S. manufacturing microdata," Empirical Economics, Springer, vol. 25(1), pages 127-148.
    8. Roland-Holst, David W & Sancho, Ferran, 1992. "Relative Income Determination in the United States: A Social Accounting Perspective," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 38(3), pages 311-327, September.
    9. Erik Dietzenbacher & Bart Los, 2002. "Externalities of R&D Expenditures," Economic Systems Research, Taylor & Francis Journals, vol. 14(4), pages 407-425, December.
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    Cited by:

    1. Cerina, Fabio & Mureddu, Francesco, 2014. "Is agglomeration really good for growth? Global efficiency, interregional equity and uneven growth," Journal of Urban Economics, Elsevier, vol. 84(C), pages 9-22.
    2. Zuzana Smeets Kristkova & Cornelis Gardebroek & Michiel van Dijk & Hans van Meijl, 2017. "The impact of R&D on factor-augmenting technical change – an empirical assessment at the sector level," Economic Systems Research, Taylor & Francis Journals, vol. 29(3), pages 385-417, July.
    3. Eduardo Gonçalves & Fernando Salgueiro Perobelli & Inácio Fernandes Araújo, 2017. "Estimating intersectoral technology spillovers for Brazil," The Journal of Technology Transfer, Springer, vol. 42(6), pages 1377-1406, December.

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    More about this item

    Keywords

    R&D spillovers; Input-output models; Sensitivity analysis; Monte Carlo simulations;
    All these keywords.

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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