IDEAS home Printed from https://ideas.repec.org/a/taf/ecsysr/v21y2009i4p409-436.html

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

Author

Listed:
  • Giovanni Cerulli
  • Bianca Poti`

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 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
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/09535310903569216
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/09535310903569216?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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. 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.
    4. 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.
    5. 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.
    6. 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.

    More about this item

    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

    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:taf:ecsysr:v:21:y:2009:i:4:p:409-436. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CESR20 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.