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Uncertainty and Learning in Pharmaceutical Demand

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Author Info
Gregory S. Crawford
Matthew Shum

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Abstract

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. Copyright The Econometric Society 2005.

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File URL: http://hdl.handle.net/10.1111/j.1468-0262.2005.00612.x
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Publisher Info
Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 73 (2005)
Issue (Month): 4 (07)
Pages: 1137-1173
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Handle: RePEc:ecm:emetrp:v:73:y:2005:i:4:p:1137-1173

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  5. Michael Chernew & Gautam Gowrisankaran & Dennis P. Scanlon, 2002. "Learning and the value of information: the case of health plan report cards," Working Papers in Applied Economic Theory 2002-17, Federal Reserve Bank of San Francisco. [Downloadable!]
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  14. Fernandez, Jose, 2008. "An Empirical Model of Learning under Ambiguity: The Case of Clinical Trials," MPRA Paper 8621, University Library of Munich, Germany. [Downloadable!]
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  17. Michael Chernew & Gautam Gowrisankaran & Dennis P. Scanlon, 2001. "Learning and the Value of Information: Evidence From Health Plan Report Cards," NBER Working Papers 8589, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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