IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02274133.html
   My bibliography  Save this paper

Dancing with the Stars: Innovation Through Interactions

Author

Listed:
  • Ernest Miguelez

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

  • Ufuk Akcigit

    (University of Chicago)

  • Stefanie Stantcheva

    (MIT - Massachusetts Institute of Technology)

  • Valerio Sterzi

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

  • Santiago Caicedo

Abstract

An inventor's own knowledge is a key input in the innovation process. This knowledge can be built by interacting with and learning from others. This paper uses a new large-scale panel dataset on European inventors matched to their employers and patents. We document key empirical facts on inventors' productivity over the life cycle, inventors' research teams, and interactions with other inventors. Among others, most patents are the result of collaborative work. Interactions with better inventors are very strongly correlated with higher subsequent productivity. These facts motivate the main ingredients of our new innovation-led endogenous growth model, in which innovations are produced by heterogeneous research teams of inventors using inventor knowledge. The evolution of an inventor's knowledge is explained through the lens of a diffusion model in which inventors can learn in two ways: By interacting with others at an endogenously chosen rate; and from an external, age-dependent source that captures alternative learning channels, such as learning-by-doing. Thus, our knowledge diffusion model nests inside the innovation-based endogenous growth model. We estimate the model, which fits the data very closely, and use it to perform several policy exercises, such as quantifying the large importance of interactions for growth, studying the effects of reducing interaction costs (e.g., through IT or infrastructure), and comparing the learning and innovation processes of different countries.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ernest Miguelez & Ufuk Akcigit & Stefanie Stantcheva & Valerio Sterzi & Santiago Caicedo, 2018. "Dancing with the Stars: Innovation Through Interactions," Post-Print hal-02274133, HAL.
  • Handle: RePEc:hal:journl:hal-02274133
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-02274133
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Raffo, Julio & Lhuillery, Stéphane, 2009. "How to play the "Names Game": Patent retrieval comparing different heuristics," Research Policy, Elsevier, vol. 38(10), pages 1617-1627, December.
    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. Ufuk Akcigit & Stefanie Stantcheva, 2020. "Taxation and Innovation: What Do We Know?," NBER Chapters, in: Innovation and Public Policy, National Bureau of Economic Research, Inc.
    2. MORI Tomoya & SAKAGUCHI Shosei, 2018. "Collaborative Knowledge Creation: Evidence from Japanese patent data," Discussion papers 18068, Research Institute of Economy, Trade and Industry (RIETI).
    3. König, Michael & Song, Zheng & Storesletten, Kjetil & Zilibotti, Fabrizio, 2020. "From Imitation to Innovation: Where Is all that Chinese R&D Going?," CEPR Discussion Papers 14966, C.E.P.R. Discussion Papers.
    4. Hiroyasu Inoue & Kentaro Nakajima & Yukiko Umeno Saito, 2019. "Localization of collaborations in knowledge creation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(1), pages 119-140, February.
    5. Kjetil Storesletten & Bo Zhao & Fabrizio Zilibotti, 2019. "Business Cycle during Structural Change: Arthur Lewis' Theory from a Neoclassical Perspective," NBER Working Papers 26181, National Bureau of Economic Research, Inc.
    6. Matteo Tubiana & Ernest Miguelez & Rosina Morneo, 2020. "“In knowledge we trust: learning-by-interacting and the productivity of inventors”," AQR Working Papers 2012005, University of Barcelona, Regional Quantitative Analysis Group, revised Sep 2020.
    7. Matthew O. Jackson, 2020. "A typology of social capital and associated network measures," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 54(2), pages 311-336, March.
    8. Asier Minondo, 2020. "Who presents and where? An analysis of research seminars in US economics departments," Papers 2001.10561, arXiv.org, revised May 2020.
    9. Steven Bond-Smith, 2019. "The unintended consequences of increasing returns to scale in geographical economics," Bankwest Curtin Economics Centre Working Paper series WP1904, Bankwest Curtin Economics Centre (BCEC), Curtin Business School.
    10. Matteo Tubiana & Ernest Miguelez & Rosina Morneo, 2020. "“In knowledge we trust: learning-by-interacting and the productivity of inventors”," AQR Working Papers 2012005, University of Barcelona, Regional Quantitative Analysis Group, revised Sep 2020.
    11. Hornuf, Lars & Jeworrek, Sabrina, 2018. "Crowdsourced innovation: How community managers affect crowd activities," IWH Discussion Papers 13/2018, Halle Institute for Economic Research (IWH).
    12. Gregor Jarosch & Esteban Rossi-Hansberg & Ezra Oberfield, 2018. "Learning from Coworkers," 2018 Meeting Papers 838, Society for Economic Dynamics.
    13. Favaro, Donata & Ninka, Eniel, 2019. "Inventors’ working relationships and knowledge creation: a study on patented innovation," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 45, pages 55-76.
    14. Baruffaldi, Stefano & Pöge, Felix, 2020. "A Firm Scientific Community: Industry Participation and Knowledge Diffusion," IZA Discussion Papers 13419, Institute of Labor Economics (IZA).
    15. Ufuk Akcigit & John Grigsby & Tom Nicholas & Stefanie Stantcheva, 2018. "Taxation and Innovation in the 20th Century," NBER Working Papers 24982, National Bureau of Economic Research, Inc.
    16. Moretti, Enrico, 2019. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," CEPR Discussion Papers 13992, C.E.P.R. Discussion Papers.
    17. Tomoya Mori & Shosei Sakaguchi, 2019. "Creation of knowledge through exchanges of knowledge: Evidence from Japanese patent data," Papers 1908.01256, arXiv.org, revised Aug 2020.
    18. Moretti, Enrico, 2019. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," IZA Discussion Papers 12610, Institute of Labor Economics (IZA).
    19. Enrico Moretti, 2019. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," NBER Working Papers 26270, National Bureau of Economic Research, Inc.
    20. Lars Hornuf & Sabrina Jeworrek, 2018. "How Community Managers Affect Online Idea Crowdsourcing Activities," CESifo Working Paper Series 7153, CESifo.
    21. Krishna Dasaratha, 2019. "Innovation and Strategic Network Formation," Papers 1911.06872, arXiv.org, revised Feb 2020.
    22. Santiago Caicedo, 2019. "Note on Idea Diffusion Models with Cohort Structures," Economica, London School of Economics and Political Science, vol. 86(342), pages 396-408, April.

    More about this item

    JEL classification:

    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

    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:hal:journl:hal-02274133. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.