Assessing risk attitudes among physicians, medical students, and non-medical students with experimental data
Recently, laboratory and field experiments have been increasingly used in health economics to predict the behavior of physicians in connection with different payment systems. However, these studies often employ students as decision-makers, assuming that they are a good proxy for the behavior of real physicians, as no qualitative difference between physicians and students’ decisions is often observed. Employing a large sample of experimental data, we investigate whether attitudes toward risk varied significantly between physicians, medical and non-medical students in the monetary domain. The results show significant variation in risk attitude regardless of the estimation technique employed, suggesting constant relative risk aversion as a supported representation of risk preferences. Finally, physicians were less risk-averse than any other participant type in the sample, suggesting that medical risk attitudes differed from other participants, at least in the monetary domain. Given the difficulty in involving real physicians due to their participation barriers, employing medical and nonmedical students in experiments is the second-best option. However, researchers must be careful when designing tasks because choices may differ across various contexts. Additionally, policymakers must be cautious when drawing policy implications from laboratory predictions, not taking it for granted that students’ decisions fully match physicians’ decisions
by:
Massimo Finocchiaro Castro Department of Law, Economics and Humanities, Mediterranean University of Reggio Calabria; Health Econometrics and Data Group, University of York; Institute for Corruption Studies, Illinois State University
Calogero Guccio Department of Economics and Business, University of Catania; Health Econometrics and Data Group, University of York; Institute for Corruption Studies, Illinois State University
Domenica Romeo Department of Economics and Business, University of Catania; Health Econometrics and Data Group, University of York