Asymmetric Learning in Repeated Contracting

An Empirical Study

Bibliographic Data

ID19316144
AuthorsAlma Cohen (National Bureau of Economic Research, corresponding author)
Year2012
Volume94
Issue2
Pages419-432
Publication date2012-05-01
Open AccessNo
TypeARTICLE
VenueThe Review of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0034-6535 • E-ISSN: 1530-9142
PublisherMIT Press - Journals (PUBLISHER)
DOI10.1162/rest_a_00181
OpenAlexW2891894220
LanguageEN
References cited39

This paper studies a unique panel dataset of transactions with repeat customers of an insurer operating in a market in which insurers are not required by law or contract to share information about their customers' records. I use this dataset to test the asymmetric learning hypothesis that sellers obtain over time private information that some of their repeat customers have low risk, and that this learning enables sellers to make higher profits in transactions with these repeat customers. Consistent with this hypothesis, I find that the insurer in my dataset makes higher profits in transactions with repeat customers and that these profits are driven by transactions with repeat customers with good past claims history with the insurer; that these higher profits result from repeat customers with good claim history receiving a reduction in premiums that is lower than the reduction in expected costs associated with such customers; and that policyholders with bad claim history are more likely to flee their record by switching to other insurers

Actuarial science · Business · Computer security · Information asymmetry · Private information retrieval · Test (biology) · Banking stability, regulation, efficiency · Computer Science · Finance · Insurance and Financial Risk Management · Law, Economics, and Judicial Systems

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