Asymmetric Learning in Repeated Contracting: An Empirical Study

Alma Cohen

NBER Working Paper No. 13752
Issued in January 2008
NBER Program(s):Industrial Organization, Law and Economics, Public Economics

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.

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Document Object Identifier (DOI): 10.3386/w13752

Published: Alma Cohen, 2012. "Asymmetric Learning in Repeated Contracting: An Empirical Study," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 419-432, May. citation courtesy of

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