Learning When to Quit: An Empirical Model of Experimentation

90 Pages Posted: 28 Feb 2018

See all articles by Bernhard Ganglmair

Bernhard Ganglmair

ZEW – Leibniz Centre for European Economic Research - Junior Research Group Competition and Innovation; University of Mannheim - Department of Economics; Mannheim Centre for Competition and Innovation (MaCCI)

Timothy Simcoe

Boston University - Questrom School of Business; NBER

Emanuele Tarantino

University of Mannheim - Department of Economics; Tilburg Law and Economics Center (TILEC)

Multiple version iconThere are 2 versions of this paper

Date Written: February 2018

Abstract

Research productivity depends on the ability to discern whether an idea is promising, and a willingness to abandon the ones that are not. Economists know little about this process, however, because empirical studies of innovation typically begin with a sample of issued patents or published papers that were already selected from a pool of promising ideas. This paper unpacks the idea selection process using a unique dataset from the Internet Engineering Task Force (IETF), a voluntary organization that develops protocols for managing Internet infrastructure. For a large sample of IETF proposals, we observe a sequence of decisions to either revise, publish, or abandon the underlying idea, along with changes to the proposal and the demographics of the author team. Using these data, we provide a descriptive analysis of how R&D is conducted within the IETF, and estimate a dynamic discrete choice model whose key parameters measure the speed at which author teams learn whether they have a good (i.e., publishable) idea. The estimates imply that sixty percent of IETF proposals are publishable, but only one-third of the good ideas survive the review process. Author experience and increased attention from the IETF community are associated with faster learning. Finally, 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

Ganglmair, Bernhard and Simcoe, Timothy S. and Tarantino, Emanuele, Learning When to Quit: An Empirical Model of Experimentation (February 2018). NBER Working Paper No. w24358, Available at SSRN: https://ssrn.com/abstract=3131068

Bernhard Ganglmair (Contact Author)

ZEW – Leibniz Centre for European Economic Research - Junior Research Group Competition and Innovation ( email )

L7,1
Mannheim, 68161
Germany

University of Mannheim - Department of Economics ( email )

D-68131 Mannheim
Germany

Mannheim Centre for Competition and Innovation (MaCCI) ( email )

L 7, 1
Mannheim, 68131
Germany

Timothy S. Simcoe

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

NBER ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Emanuele Tarantino

University of Mannheim - Department of Economics ( email )

D-68131 Mannheim
Germany

Tilburg Law and Economics Center (TILEC) ( email )

Warandelaan 2
Tilburg, 5000 LE
Netherlands

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