Optimal Insurance: Dual Utility, Random Losses, and Adverse Selection
Alex Gershkov,
Benny Moldovanu,
Philipp Strack and
Mengxi Zhang
American Economic Review, 2023, vol. 113, issue 10, 2581-2614
Abstract:
We study a generalization of the classical monopoly insurance problem under adverse selection (see Stiglitz 1977) where we allow for a random distribution of losses, possibly correlated with the agent's risk parameter that is private information. Our model explains patterns of observed customer behavior and predicts insurance contracts most often observed in practice: these consist of menus of several deductible-premium pairs or menus of insurance with coverage limits–premium pairs. A main departure from the classical insurance literature is obtained here by endowing the agents with risk-averse preferences that can be represented by a dual utility functional (Yaari 1987).
JEL-codes: D81 D82 D86 D91 G22 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.aeaweb.org/doi/10.1257/aer.20221247 (application/pdf)
https://www.aeaweb.org/doi/10.1257/aer.20221247.appx (application/pdf)
https://www.aeaweb.org/doi/10.1257/aer.20221247.ds (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:aea:aecrev:v:113:y:2023:i:10:p:2581-2614
Ordering information: This journal article can be ordered from
https://www.aeaweb.org/journals/subscriptions
DOI: 10.1257/aer.20221247
Access Statistics for this article
American Economic Review is currently edited by Esther Duflo
More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().