Asymmetric learning in repeated contracting an empirical study / Alma Cohen.

Author/creator Cohen, Alma
Other author National Bureau of Economic Research.
Format Electronic
Publication InfoCambridge, MA : National Bureau of Economic Research,
Supplemental ContentFull text available from NBER Working Papers

SeriesNBER working paper series ; working paper 13752
Working paper series (National Bureau of Economic Research : Online) ; working paper no. 13752. UNAUTHORIZED
Summary "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"--National Bureau of Economic Research web site.
General noteTitle from PDF file as viewed on 6/12/2008.
Bibliography noteIncludes bibliographical references.
Access restrictionAvailable only to authorized users.
Other formsAlso available in print.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2008610512

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