Evaluating consumers’ choices of Medicare Part D plans: A study in behavioral welfare economics
Michael Keane (),
Nicolai Kuminoff and
Timothy Neal ()
Journal of Econometrics, 2021, vol. 222, issue 1, 107-140
We propose new methods to model behavior and conduct welfare analysis in complex environments where some choices are unlikely to reveal preferences. We develop a mixture-of-experts model that incorporates heterogeneity in consumers’ preferences and in their choice processes. We also develop a method to decompose logit errors into latent preferences versus optimization errors. Applying these methods to Medicare beneficiaries’ prescription drug insurance choices suggests that: (1) average welfare losses from suboptimal choices are small, (2) beneficiaries with dementia and depression have larger losses, and (3) policies that simplify choice sets offer small average benefits, helping some people but harming others.
Keywords: Random utility model; Mixture of experts; Mixed logit; Market mapping; Hedonic utility; Decision utility; Medicare; Health insurance; Behavioral economics; Welfare (search for similar items in EconPapers)
JEL-codes: C35 C38 C54 D60 D90 I11 I13 M31 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Evaluating Consumers' Choices of Medicare Part D Plans: A Study in Behavioral Welfare Economics (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:107-140
Access Statistics for this article
Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson
More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().