IDEAS home Printed from https://ideas.repec.org/p/unm/unumer/2014060.html
   My bibliography  Save this paper

The size of patent categories: USPTO 1976-2006

Author

Listed:
  • Lafond, F.

    (UNU-MERIT)

Abstract

Categorization is an important phenomenon in science and society, and classification systems reflect the mesoscale organization of knowledge. The Yule-Simon-Naranan model, which assumes exponential growth of the number of categories and exponential growth of individual categories predicts a power law Pareto size distribution, and a power law size-rank relation Zipfs law. However, the size distribution of patent subclasses departs from a pure power law, and is shown to be closer to a shifted power law. At a higher aggregation level patent classes, the rank-size relation deviates even more from a pure power law, and is shown to be closer to a generalized beta curve. These patterns can be explained by assuming a shifted exponential growth of individual categories to obtain a shifted power law size distribution for subclasses, and by assuming an asymmetric logistic growth of the number of categories to obtain a generalized beta size-rank relationship for classes. This may suggest a shift towards incremental more than radical innovation.

Suggested Citation

  • Lafond, F., 2014. "The size of patent categories: USPTO 1976-2006," MERIT Working Papers 2014-060, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    • Handle: RePEc:unm:unumer:2014060
    as

    Download full text from publisher

    File URL: https://www.merit.unu.edu/publications/wppdf/2014/wp2014-060.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benjamin F. Jones, 2009. "The Burden of Knowledge and the "Death of the Renaissance Man": Is Innovation Getting Harder?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(1), pages 283-317.
    2. Hall, B. & Jaffe, A. & Trajtenberg, M., 2001. "The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools," Papers 2001-29, Tel Aviv.
    3. Birgitte Andersen, 1999. "The hunt for S-shaped growth paths in technological innovation: a patent study," Journal of Evolutionary Economics, Springer, vol. 9(4), pages 487-526.
    4. Pasinetti,Luigi L., 1983. "Structural Change and Economic Growth," Cambridge Books, Cambridge University Press, number 9780521274104.
    5. Robert J. Gordon, 2012. "Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds," NBER Working Papers 18315, National Bureau of Economic Research, Inc.
    6. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    7. Pier P. Saviotti, 1996. "Technological Evolution, Variety and the Economy," Books, Edward Elgar Publishing, number 727.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.
    2. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    3. Apa, Roberta & De Noni, Ivan & Orsi, Luigi & Sedita, Silvia Rita, 2018. "Knowledge space oddity: How to increase the intensity and relevance of the technological progress of European regions," Research Policy, Elsevier, vol. 47(9), pages 1700-1712.
    4. Anuraag Singh & Giorgio Triulzi & Christopher L. Magee, 2020. "Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description," Papers 2004.13919, arXiv.org.
    5. Ufuk Akcigit & Murat Celik & Daron Acemoglu, 2014. "Young, Restless and Creative: Openness to Disruption and Creative Innovations," 2014 Meeting Papers 377, Society for Economic Dynamics.
    6. Balland, Pierre-Alexandre & Boschma, Ron, 2022. "Do scientific capabilities in specific domains matter for technological diversification in European regions?," Research Policy, Elsevier, vol. 51(10).
    7. Carolina Castaldi & Roberto Fontana & Alessandro Nuvolari, 2009. "‘Chariots of fire’: the evolution of tank technology, 1915–1945," Journal of Evolutionary Economics, Springer, vol. 19(4), pages 545-566, August.
    8. Forman, Chris & van Zeebroeck, Nicolas, 2019. "Digital technology adoption and knowledge flows within firms: Can the Internet overcome geographic and technological distance?," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    9. Yusuke Oh & Koji Takahashi, 2020. "R&D and Innovation: Evidence from Patent Data," Bank of Japan Working Paper Series 20-E-7, Bank of Japan.
    10. Aghion, Philippe & Akcigit, Ufuk & Howitt, Peter, 2014. "What Do We Learn From Schumpeterian Growth Theory?," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 2, chapter 0, pages 515-563, Elsevier.
    11. Jun, Bogang & Kim, Tai-Yoo, 2015. "A neo-Schumpeterian perspective on the analytical macroeconomic framework: The expanded reproduction system," Hohenheim Discussion Papers in Business, Economics and Social Sciences 11-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    12. repec:ers:journl:v:xv:y:2012:i:sie:p:157-194 is not listed on IDEAS
    13. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    14. Kerstin Hotte, 2021. "Demand-pull, technology-push, and the direction of technological change," Papers 2104.04813, arXiv.org, revised Jan 2023.
    15. Ajay Bhaskarbhatla & Luis Cabral & Deepak Hegde & Thomas (T.L.P.R.) Peeters, 2017. "Human Capital, Firm Capabilities, and Innovation," Tinbergen Institute Discussion Papers 17-115/VII, Tinbergen Institute, revised 03 Mar 2020.
    16. Koki Oikawa & Minoru Kitahara, 2017. "Technology Polarization," Working Papers e113, Tokyo Center for Economic Research.
    17. Yang, Chia-Hsuan & Nugent, Rebecca & Fuchs, Erica R.H., 2016. "Gains from others’ losses: Technology trajectories and the global division of firms," Research Policy, Elsevier, vol. 45(3), pages 724-745.
    18. Martinelli, Arianna, 2012. "An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry," Research Policy, Elsevier, vol. 41(2), pages 414-429.
    19. David Rigby, 2012. "The Geography of Knowledge Relatedness and Technological Diversification in U.S. Cities," Papers in Evolutionary Economic Geography (PEEG) 1218, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2012.
    20. Murmann, Johann Peter & Frenken, Koen, 2006. "Toward a systematic framework for research on dominant designs, technological innovations, and industrial change," Research Policy, Elsevier, vol. 35(7), pages 925-952, September.
    21. Alessandro Caiani, 2017. "Innovation Dynamics and Industry Structure Under Different Technological Spaces," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 307-341, November.

    More about this item

    Keywords

    Innovation; R&D; Learning; Knowledge; Classification; Categorization; Pareto distribution; Power law;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:unm:unumer:2014060. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Ad Notten (email available below). General contact details of provider: https://edirc.repec.org/data/meritnl.html .

    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.