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

Bernhard Ganglmair (), Timothy Simcoe and Emanuele Tarantino

American Economic Journal: Microeconomics, 2025, vol. 17, issue 3, 164-90

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.

JEL-codes: D83 L86 O31 O38 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Learning When to Quit: An Empirical Model of Experimentation in Standards Development (2018)
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DOI: 10.1257/mic.20190321

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