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Measuring Intersectoral Knowledge Spillovers: An Application Of Sensitivity Analysis To Italy


  • Giovanni Cerulli
  • Bianca Poti`


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 interindustry R&D spillovers. 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 between spillover effects induced by productive linkages (the Leontief forward multipliers) and those activated by R&D investments, capturing the uncertain and non-linear nature of the relations between spillovers and factors affecting them.

Suggested Citation

  • Giovanni Cerulli & Bianca Poti`, 2009. "Measuring Intersectoral Knowledge Spillovers: An Application Of Sensitivity Analysis To Italy," Economic Systems Research, Taylor & Francis Journals, vol. 21(4), pages 409-436.
  • Handle: RePEc:taf:ecsysr:v:21:y:2009:i:4:p:409-436 DOI: 10.1080/09535310903569216

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    References listed on IDEAS

    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. 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.
    4. Frederic Scherer, 2003. "Technology Flows Matrix Estimation Revisited," Economic Systems Research, Taylor & Francis Journals, vol. 15(3), pages 327-358.
    5. 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.
    6. Erik Dietzenbacher & Bart Los, 2002. "Externalities of R&D Expenditures," Economic Systems Research, Taylor & Francis Journals, vol. 14(4), pages 407-425.
    7. 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.
    8. 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.
    9. 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.
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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. repec:kap:jtecht:v:42:y:2017:i:6:d:10.1007_s10961-016-9528-x is not listed on IDEAS
    3. Kristkova, Z. Smeets & Gardebroek, K. & van Dijk, M. & van Meijl, H., 2015. "The impact of R&D on factor-augmenting technical change- an empirical assessment at the sector level," 2015 Conference, August 9-14, 2015, Milan, Italy 230229, International Association of Agricultural Economists.

    More about this item


    R&D spillovers; Input-output models; Sensitivity analysis; Monte Carlo simulations;

    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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