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Who\'s Who in Patents. A Bayesian approach

Author

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  • Nicolas CARAYOL
  • Lorenzo CASSI

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

  • Nicolas CARAYOL & Lorenzo CASSI, 2009. "Who\'s Who in Patents. A Bayesian approach," Cahiers du GREThA (2007-2019) 2009-07, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    • Handle: RePEc:grt:wpegrt:2009-07
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    File URL: http://cahiersdugretha.u-bordeaux.fr/2009/2009-07.pdf
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    References listed on IDEAS

    as
    1. Zvi Griliches, 1984. "R&D, Patents, and Productivity," NBER Books, National Bureau of Economic Research, Inc, number gril84-1, March.
    2. 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.
    3. Nicolas Carayol & Pascale Roux, 2007. "The Strategic Formation of Interindividual Collaboration Networks. Evidence from Co-invention Patterns," Annals of Economics and Statistics, GENES, issue 87-88, pages 275-301.
    4. 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.
    5. 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. Bergé, Laurent & Carayol, Nicolas & Roux, Pascale, 2018. "How do inventor networks affect urban invention?," Regional Science and Urban Economics, Elsevier, vol. 71(C), pages 137-162.
    2. Carayol, Nicolas & Bergé, Laurent & Cassi, Lorenzo & Roux, Pascale, 2019. "Unintended triadic closure in social networks: The strategic formation of research collaborations between French inventors," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 218-238.
    3. 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.
    4. Gallo, Julie Le & Plunket, Anne, 2020. "Regional gatekeepers, inventor networks and inventive performance: Spatial and organizational channels," Research Policy, Elsevier, vol. 49(5).
    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. 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.
    7. 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.
    8. YIN Deyun & MOTOHASHI Kazuyuki, 2018. "Inventor Name Disambiguation with Gradient Boosting Decision Tree and Inventor Mobility in China (1985-2016)," Discussion papers 18018, Research Institute of Economy, Trade and Industry (RIETI).
    9. Deyun Yin & Kazuyuki Motohashi & Jianwei Dang, 2020. "Large-scale name disambiguation of Chinese patent inventors (1985–2016)," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 765-790, February.
    10. Pascal Cuxac & Jean-Charles Lamirel & Valerie Bonvallot, 2013. "Efficient supervised and semi-supervised approaches for affiliations disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(1), pages 47-58, October.
    11. Julie Le Gallo & Anne Plunket, 2016. "Technological gatekeepers, regional inventor networks and inventive performance," Working Papers hal-01422916, HAL.
    12. 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.

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    More about this item

    Keywords

    Patents; homonymy; Bayes rule;
    All these keywords.

    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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