Uncertainty and learning in pharmaceutical demand
- Creators
- Crawford, Gregory S.
-
Shum, Matthew
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.
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.Attached Files
Published - CRAe05.pdf
Files
| Name | Size | Download all |
|---|---|---|
|
md5:aa61099679a53d77cb7b28cbe34261b3
|
315.3 kB | Preview Download |
Additional details
- Eprint ID
- 12237
- Resolver ID
- CaltechAUTHORS:CRAe05
- Created
-
2008-10-31Created from EPrint's datestamp field
- Updated
-
2023-06-01Created from EPrint's last_modified field