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Uncertainty and learning in pharmaceutical demand

Crawford, Gregory S. and Shum, Matthew (2005) Uncertainty and learning in pharmaceutical demand. Econometrica, 73 (4). pp. 1137-1173. ISSN 0012-9682.

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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.

Item Type:Article
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Shum, Matthew0000-0002-6262-915X
Additional Information:© 2005 The Econometric Society. Manuscript received March, 2000; final revision received January, 2005. We are grateful to the editor and three anonymous referees for their detailed comments, which substantially improved the paper. We also thank Dan Ackerberg, Steve Berry, Donna Gilleskie, Nadia Soboleva, Scott Stern, and seminar participants at Duke, UNC-Chapel Hill, NBER, NYU, Princeton, Queen’s, UT-Austin, and Virginia. We thank Andrea Coscelli, Giuseppe Traversa, and Roberto Da Cas for introducing us to the data and acquainting us with features of Italian pharmaceutical markets.
Subject Keywords:Uncertainty, learning, pharmaceutical demand, matching, dynamic discrete choice.
Issue or Number:4
Record Number:CaltechAUTHORS:CRAe05
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:12237
Deposited By: Lindsay Cleary
Deposited On:31 Oct 2008 17:27
Last Modified:03 Oct 2019 00:26

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