Template-Type: ReDIF-Paper 1.0
Title: Learning When to Quit: An Empirical Model of Experimentation in Standards Development
Author-Name: Bernhard Ganglmair
Author-Email: ganglmair@uni-mannheim.de
Author-Name: Timothy Simcoe
Author-Email: tsimcoe@bu.edu
Author-Name: Emanuele Tarantino
Author-Email: tarantino@unimannheim.de
Classification-JEL: D83, O31, O32
Keywords: Learning, Experimentation, Standardization, Dynamic Discrete Choice

Abstract: 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.

Note: 
Length: 89
Creation-Date: 2018-09
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp041
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2018_041