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Who's Who in Patents. A Bayesian approach

Author

Listed:
  • Lorenzo Cassi

    () (CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne)

  • Nicolas Carayol

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper proposes a bayesian methodology to treat the who's who problem arising in individual level data sets such as patent data. We assess the usefullness of this methodology on the set of all French inventors appearing on EPO applications from 1978 to 2003.

Suggested Citation

  • Lorenzo Cassi & Nicolas Carayol, 2009. "Who's Who in Patents. A Bayesian approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00631750, HAL.
  • Handle: RePEc:hal:cesptp:hal-00631750
    Note: View the original document on HAL open archive server: https://hal-paris1.archives-ouvertes.fr/hal-00631750
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    References listed on IDEAS

    as
    1. Manuel Trajtenberg & Gil Shiff & Ran Melamed, 2009. "The "Names Game": Harnessing Inventors, Patent Data for Economic Research," Annals of Economics and Statistics, GENES, issue 93-94, pages 67-77.
    2. Francesco Lissoni & Bulat Sanditov & Gianluca Tarasconi, 2006. "The Keins Database on Academic Inventors: Methodology and Contents," KITeS Working Papers 181, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Sep 2006.
    3. repec:adr:anecst:y:2007:i:87-88:p:13 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Laurent Bergé & Nicolas Carayol, GREThA, UMR CNRS 5113, Université de Bordeaux & Pascale Roux, 2017. "How do inventor networks affect urban invention?," CREA Discussion Paper Series 17-03, Center for Research in Economic Analysis, University of Luxembourg.
    2. Lorenzo Cassi & Anne Plunket, 2014. "Proximity, network formation and inventive performance: in search of the proximity paradox," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 395-422, September.
    3. repec:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1375-7 is not listed on IDEAS
    4. Ernest Miguélez & Ismael Gómez-Miguélez, 2011. "“Singling out individual inventors from patent data”," IREA Working Papers 201105, University of Barcelona, Research Institute of Applied Economics, revised May 2011.
    5. Michele Pezzoni & Francesco Lissoni & Gianluca Tarasconi, 2014. "How to kill inventors: testing the Massacrator© algorithm for inventor disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 477-504, October.
    6. Ventura, Samuel L. & Nugent, Rebecca & Fuchs, Erica R.H., 2015. "Seeing the non-stars: (Some) sources of bias in past disambiguation approaches and a new public tool leveraging labeled records," Research Policy, Elsevier, vol. 44(9), pages 1672-1701.
    7. Nicolas CARAYOL & Lorenzo CASSI & Pascale ROUX, 2014. "Unintended triadic closure in social networks: The strategic formation of research collaborations between French inventors," Cahiers du GREThA 2014-13, Groupe de Recherche en Economie Théorique et Appliquée.
    8. repec:spr:scient:v:97:y:2013:i:1:d:10.1007_s11192-013-1025-5 is not listed on IDEAS
    9. Julie Le Gallo & Anne Plunket, 2016. "Technological gatekeepers, regional inventor networks and inventive performance," Working Papers hal-01422916, HAL.
    10. Li, Guan-Cheng & Lai, Ronald & D’Amour, Alexander & Doolin, David M. & Sun, Ye & Torvik, Vetle I. & Yu, Amy Z. & Fleming, Lee, 2014. "Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010)," Research Policy, Elsevier, vol. 43(6), pages 941-955.

    More about this item

    Keywords

    Patents; homonymy; Bayes rule;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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