Wednesday, February 14, 2018 at 11:40am to 1:10pm
Ives Hall, 115
B07 Tower Rd, Ithaca, NY 14853, USA
Qing Gong - University of Pennsylvania
Abstract: Physicians often choose among alternative treatment options based on their beliefs over the treatment effectiveness and their skills in delivering the treatment. I examine how two kinds of physician learning jointly shape their treatment choices: Bayesian learning that updates beliefs about treatment-patient match values and learning by doing that improves surgical skills. Using case-level data on the history of brain aneurysm treatments by over 200 physicians, I find that both kinds of learning are present and that physicians are forward-looking. In light of these empirical patterns, I develop and estimate a dynamic structural model of physician learning and treatment choices for heterogeneous patients. I then disentangle the impacts of the two kinds of learning and explore to what extent forward-looking physicians deviate from myopic best choices. Physicians are more than twice as likely to experiment on unhealthy patients than healthier ones, which hurts short-term outcomes but improves overall treatment success rates by 13-17%. I also evaluate the impacts of several alternative payment schedules. Uniform payments across treatments facilitate the adoption of the new treatment while outcome-contingent payments have heterogeneous effects across physicians. The heterogeneity highlights the coexistence of two opposing effects: the incentive to exploit the myopic best option and the incentive to experiment with less familiar options due to the increased return from learning.