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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.

Suggested Citation

  • Giovanni Cerulli & Bianca Potì, 2007. "Measuring Intersectoral Knowledge Spillovers: an Application of Sensitivity Analysis to Italy," CERIS Working Paper 200711, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    • Handle: RePEc:csc:cerisp:200711
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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. 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, December.
    7. Soete, Luc & Verspagen, Bart & ter Weel, Bas, 2010. "Systems of Innovation," 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 1159-1180, Elsevier.
    8. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 17-45, National Bureau of Economic Research, Inc.
    9. 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.
    10. 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.
    11. repec:bla:revinw:v:38:y:1992:i:3:p:311-27 is not listed on IDEAS
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    1. Yeo, Yeongjun & Lee, Jeong-Dong, 2020. "Revitalizing the race between technology and education: Investigating the growth strategy for the knowledge-based economy based on a CGE analysis," Technology in Society, Elsevier, vol. 62(C).
    2. 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.
    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.
    4. 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.
    5. Fabrizio Fusillo & Sandro Montresor & Giuseppe Vittucci Marzetti, 2021. "The global network of embodied R&D flows," Discussion Paper series in Regional Science & Economic Geography 2021-05, Gran Sasso Science Institute, Social Sciences, revised Apr 2021.
    6. João Gabriel Pio & Eduardo Gonçalves & Claúdio R. F. Vasconcelos, 2021. "Technology Spillovers Through Exports: Empirical Evidence for the Chinese Case," Journal of Industry, Competition and Trade, Springer, vol. 21(3), pages 423-443, September.

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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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