Learning When to Quit: An Empirical Model of Experimentation in Standards Development
Bernhard Ganglmair (),
Timothy Simcoe () and
CRC TR 224 Discussion Paper Series from University of Bonn and University of Mannheim, Germany
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.
Keywords: Learning; Experimentation; Standardization; Dynamic Discrete Choice (search for similar items in EconPapers)
JEL-codes: D83 O31 O32 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:bon:boncrc:crctr224_2018_041
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