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Dancing with the Stars: Innovation Through Interactions

Author

Listed:
  • Ufuk Akcigit
  • Santiago Caicedo
  • Ernest Miguelez
  • Stefanie Stantcheva
  • Valerio Sterzi

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.

Suggested Citation

  • Ufuk Akcigit & Santiago Caicedo & Ernest Miguelez & Stefanie Stantcheva & Valerio Sterzi, 2018. "Dancing with the Stars: Innovation Through Interactions," NBER Working Papers 24466, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24466
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Gregor Jarosch & Esteban Rossi-Hansberg & Ezra Oberfield, 2018. "Learning from Coworkers," 2018 Meeting Papers 838, Society for Economic Dynamics.
    5. Mori, Tomoya & Sakaguchi, Shosei, 2018. "Collaborative knowledge creation: Evidence from Japanese patent data," MPRA Paper 88716, University Library of Munich, Germany.
    6. 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.
    7. 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.
    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. 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.
    10. Lars Hornuf & Sabrina Jeworrek, 2018. "How Community Managers Affect Online Idea Crowdsourcing Activities," CESifo Working Paper Series 7153, CESifo.
    11. Krishna Dasaratha, 2019. "Innovation and Strategic Network Formation," Papers 1911.06872, arXiv.org, revised Feb 2020.
    12. 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.
    13. 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.
    14. Hornuf, Lars & Jeworrek, Sabrina, 2018. "Crowdsourced innovation: How community managers affect crowd activities," IWH Discussion Papers 13/2018, Halle Institute for Economic Research (IWH).
    15. 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.
    16. 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.
    17. 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.
    18. Baruffaldi, Stefano & Pöge, Felix, 2020. "A Firm Scientific Community: Industry Participation and Knowledge Diffusion," IZA Discussion Papers 13419, Institute of Labor Economics (IZA).
    19. 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.
    20. Moretti, Enrico, 2019. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," IZA Discussion Papers 12610, Institute of Labor Economics (IZA).

    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

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