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

B-Tier
Journal: American Economic Journal: Microeconomics
Year: 2025
Volume: 17
Issue: 3
Pages: 164-90

Authors (3)

Bernhard Ganglmair (Leibniz-Zentrum für Europäisch...) Timothy Simcoe (not in RePEc) Emanuele Tarantino (not in RePEc)

Score contribution per author:

0.670 = (α=2.01 / 3 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:aea:aejmic:v:17:y:2025:i:3:p:164-90
Journal Field
General
Author Count
3
Added to Database
2026-01-25