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

Bernhard Ganglmair et al.

American Economic Journal: Microeconomics2025https://doi.org/10.1257/mic.20190321article
AJG 3ABDC A*
Weight
0.47

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 D83, L86, O31, O38)

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https://doi.org/https://doi.org/10.1257/mic.20190321

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@article{bernhard2025,
  title        = {{Learning When to Quit: An Empirical Model of Experimentation in Standards Development}},
  author       = {Bernhard Ganglmair et al.},
  journal      = {American Economic Journal: Microeconomics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1257/mic.20190321},
}

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

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Evidence weight

0.47

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.