Christopher A. Powers
U.S. Department of Health and Human Services
Centers for Medicare and Medicaid Services
7500 Security Boulevard
Baltimore, MD 21244
Institutional Affiliation: U.S. Department of Health and Human Services, Centers for Medicare and
NBER Working Papers and Publications
|October 2016||Estimating the Heterogeneous Welfare Effects of Choice Architecture: An Application to the Medicare Prescription Drug Insurance Market|
with Jonathan D. Ketcham, Nicolai V. Kuminoff: w22732
We develop a structural model for bounding welfare effects of policies that alter the design of differentiated product markets when some consumers may be misinformed about product characteristics and inertia in consumer behavior reflects a mixture of latent preferences, information costs, switching costs and psychological biases. We use the model to analyze three proposals to redesign markets for Medicare prescription drug insurance: (1) reducing the number of plans, (2) providing personalized information, and (3) defaulting consumers to cheap plans. First we combine administrative and survey data to determine which consumers make informed enrollment decisions. Then we analyze the welfare effects of each proposal, using revealed preferences of informed consumers to proxy for concealed pref...
|July 2015||Which Models Can We Trust to Evaluate Consumer Decision Making? Comment on “Choice Inconsistencies among the Elderly”|
with Jonathan D. Ketcham, Nicolai V. Kuminoff: w21387
Neoclassical and psychological models of consumer behavior often make divergent predictions for the welfare effects of paternalistic policies, leaving wide scope for researchers’ choice of a model to influence their policy conclusions. We develop a framework to reduce this model uncertainty and apply it to administrative data on consumer decision making in Medicare Part D. Consumers’ choices for prescription drug insurance plans can be explained by Abaluck and Gruber’s (AER 2011) model of utility maximization with psychological biases or by a neoclassical version of their model that precludes such biases. We evaluate these competing hypotheses using nonparametric tests of utility maximization and a trio of model validation tests. We find that 79% of enrollment decisions in Medicare Part D ...