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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Uncertainty and learning in pharmaceutical demand
Organization Unit
Authors
  • Gregory S. Crawford
  • Matthew Shum
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Econometrica
Publisher Wiley-Blackwell Publishing, Inc.
Geographical Reach international
ISSN 0012-9682
Volume 73
Number 4
Page Range 1137 - 1173
Date 2005
Abstract Text Exploiting a rich panel data set on anti‐ulcer drug prescriptions, we measure the effects of uncertainty and learning in the demand for pharmaceutical drugs. We estimate a dynamic matching model of demand under uncertainty in which patients learn from prescription experience about the effectiveness of alternative drugs. Unlike previous models, we allow drugs to have distinct symptomatic and curative effects, and endogenize treatment length by allowing drug choices to affect patients' underlying probability of recovery. We find that drugs' rankings along these dimensions differ, with high symptomatic effects for drugs with the highest market shares and high curative effects for drugs with the greatest medical efficacy. Our results also indicate that while there is substantial heterogeneity in drug efficacy across patients, learning enables patients and their doctors to dramatically reduce the costs of uncertainty in pharmaceutical markets.
Digital Object Identifier 10.1111/j.1468-0262.2005.00612.x
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Keywords Economics and Econometrics, uncertainty, learning, pharmaceutical demand, matching, dynamic discrete choice