IDEAS home Printed from https://ideas.repec.org/a/aea/aejmic/v17y2025i3p164-90.html

Learning When to Quit: An Empirical Model of Experimentation in Standards Development

Author

Listed:
  • Bernhard Ganglmair
  • Timothy Simcoe
  • Emanuele Tarantino

Abstract

Using data from the Internet Engineering Task Force (IETF), a voluntary organization that develops protocols for managing internet infrastructure, we estimate a dynamic discrete choice model of the decision to continue or abandon a line of research. The model's key parameters measure the speed at which authors learn whether their project will become a technology standard. We use the model to simulate two innovation policies: an R&D subsidy and a publication prize. While subsidies have a larger impact on research output, the optimal policy depends on the level of R&D spillovers.

Suggested Citation

  • Bernhard Ganglmair & Timothy Simcoe & Emanuele Tarantino, 2025. "Learning When to Quit: An Empirical Model of Experimentation in Standards Development," American Economic Journal: Microeconomics, American Economic Association, vol. 17(3), pages 164-190, August.
  • Handle: RePEc:aea:aejmic:v:17:y:2025:i:3:p:164-90
    DOI: 10.1257/mic.20190321
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/mic.20190321
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.

    File URL: https://doi.org/10.3886/E205961V1
    Download Restriction: no

    File URL: https://www.aeaweb.org/articles/materials/23521
    Download Restriction: no

    File URL: https://www.aeaweb.org/articles/materials/23522
    Download Restriction: no

    File URL: https://libkey.io/10.1257/mic.20190321?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Timothy Simcoe, 2012. "Standard Setting Committees: Consensus Governance for Shared Technology Platforms," American Economic Review, American Economic Association, vol. 102(1), pages 305-336, February.
    2. Michael Roach & Wesley M. Cohen, 2013. "Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research," Management Science, INFORMS, vol. 59(2), pages 504-525, October.
    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. is not listed on IDEAS
    2. Alessandra Allocca, 2023. "“No Man is an Island”: An Empirical Study on Team Formation and Performance," Rationality and Competition Discussion Paper Series 389, CRC TRR 190 Rationality and Competition.
    3. Baron, Justus, 2020. "Counting standard contributions to measure the value of patent portfolios - A tale of apples and oranges," Telecommunications Policy, Elsevier, vol. 44(3).
    4. Baron, Justus & Kanevskaia, Olia, 2023. "Wearing multiple hats—The role of working group chairs’ affiliation in standards development," Research Policy, Elsevier, vol. 52(9).

    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. Wen, Wen & Forman, Chris & Jarvenpaa, Sirkka L, 2022. "The effects of technology standards on complementor innovations: Evidence from the IETF," Research Policy, Elsevier, vol. 51(6).
    2. Tarantino, Emanuele & Simcoe, Timothy S. & Ganglmair, Bernhard, 2018. "Learning When to Quit: An Empirical Model of Experimentation," CEPR Discussion Papers 12733, Centre for Economic Policy Research.
    3. 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.
    4. Xiangfei Yuan & Haijing Hao & Chenghua Guan & Alex Pentland, 2022. "Which factors affect the performance of technology business incubators in China? An entrepreneurial ecosystem perspective," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.
    5. Zaizhou Hu & Zengdong Cao & Anran Du & Qin Tu, 2025. "Where does it matter? Revisiting the role of proximity in knowledge spillovers," American Journal of Economics and Sociology, Wiley Blackwell, vol. 84(2), pages 297-322, March.
    6. Justus Baron & Jorge Contreras & Martin Husovec & Pierre Larouche, 2019. "Making the Rules: The Governance of Standard Development Organizations and their Policies on Intellectual Property Rights," JRC Research Reports JRC115004, Joint Research Centre.
    7. Manuel Acosta & Daniel Coronado & Esther Ferrándiz & Manuel Jiménez, 2022. "Effects of knowledge spillovers between competitors on patent quality: what patent citations reveal about a global duopoly," The Journal of Technology Transfer, Springer, vol. 47(5), pages 1451-1487, October.
    8. Cassiman, Bruno & Veugelers, Reinhilde & Arts, Sam, 2018. "Mind the gap: Capturing value from basic research through combining mobile inventors and partnerships," Research Policy, Elsevier, vol. 47(9), pages 1811-1824.
    9. Leonardo Costa Ribeiro & Glenda Kruss & Gustavo Britto & Américo Tristão Bernardes & Eduardo Motta e Albuquerque, 2014. "A methodology for unveiling global innovation networks: patent citations as clues to cross border knowledge flows," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 61-83, October.
    10. Watzinger, Martin & Schnitzer, Monika, 2019. "Standing on the Shoulders of Science," Rationality and Competition Discussion Paper Series 215, CRC TRR 190 Rationality and Competition.
    11. Nivedita Mukherji & Jonathan Silberman, 2021. "Knowledge flows between universities and industry: the impact of distance, technological compatibility, and the ability to diffuse knowledge," The Journal of Technology Transfer, Springer, vol. 46(1), pages 223-257, February.
    12. Justus Baron & Daniel F. Spulber, 2018. "Technology Standards and Standard Setting Organizations: Introduction to the Searle Center Database," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(3), pages 462-503, September.
    13. Pierre Larouche & Florian Schuett, 2019. "Repeated interaction in standard setting," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 28(3), pages 488-509, June.
    14. Hao Zhou & Jie Lin, 2023. "Impacts of codified knowledge index on the allocation of overseas inventors by emerging countries: evidence from PCT patent activities in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 877-899, February.
    15. Goldberg, Mitchell & Schär, Fabian, 2023. "Metaverse governance: An empirical analysis of voting within Decentralized Autonomous Organizations," Journal of Business Research, Elsevier, vol. 160(C).
    16. Stefano Breschi & Francesco Lissoni & Gianluca Tarasconi, 2014. "Inventor Data for Research on Migration and Innovation: A Survey and a Pilot," WIPO Economic Research Working Papers 17, World Intellectual Property Organization - Economics and Statistics Division.
    17. Matt Marx & Aaron Fuegi, 2020. "Reliance on science: Worldwide front‐page patent citations to scientific articles," Strategic Management Journal, Wiley Blackwell, vol. 41(9), pages 1572-1594, September.
    18. Josh Lerner & Jean Tirole, 2015. "Standard-Essential Patents," Journal of Political Economy, University of Chicago Press, vol. 123(3), pages 547-586.
    19. Ke, Qing, 2020. "Technological impact of biomedical research: The role of basicness and novelty," Research Policy, Elsevier, vol. 49(7).
    20. Basse Mama, Houdou, 2018. "Nonlinear capital market payoffs to science-led innovation," Research Policy, Elsevier, vol. 47(6), pages 1084-1095.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

    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:aea:aejmic:v:17:y:2025:i:3:p:164-90. 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: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.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.