IDEAS home Printed from https://ideas.repec.org/p/bon/boncrc/crctr224_2018_041.html
   My bibliography  Save this paper

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

Author

Listed:
  • Bernhard Ganglmair

    ()

  • Timothy Simcoe

    ()

  • Emanuele Tarantino

    ()

Abstract

Motivated by a descriptive analysis of standards development within the Internet Engineering Task Force, we develop a dynamic discrete choice model of R&D that highlights the decision to continue or abandon a line of research. Our estimates imply that sixty percent of IETF proposals are publishable, but only one-third of those good ideas survive the review process. Increased attention and author experience are associated with faster learning. We simulate two counterfactual innovation policies: an R&D subsidy and a publication-prize. Subsidies have a larger impact on research output, though prizes perform better when accounting for researchers' opportunity costs.

Suggested Citation

  • Bernhard Ganglmair & Timothy Simcoe & Emanuele Tarantino, 2018. "Learning When to Quit: An Empirical Model of Experimentation in Standards Development," CRC TR 224 Discussion Paper Series crctr224_2018_041, University of Bonn and University of Mannheim, Germany.
  • Handle: RePEc:bon:boncrc:crctr224_2018_041
    as

    Download full text from publisher

    File URL: https://www.crctr224.de/en/research-output/discussion-papers/discussion-papers#DP41
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. 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).

    More about this item

    Keywords

    Learning; Experimentation; Standardization; Dynamic Discrete Choice;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    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:bon:boncrc:crctr224_2018_041. 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: (CRC Office). General contact details of provider: https://www.crctr224.de/en .

    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.

    We have no references for this item. You can help adding them by using 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.