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Alma Cohen, Asymmetric Learning in Repeated Contracting: An Empirical Study, 94 Rev. Econ. & Stat. 419 (2012).

Abstract: This paper studies a unique panel dataset of transactions with repeat customers of an insurer that operates in a market in which insurers are not required by law or contract to share information about their customers’ records. This dataset is used to test the asymmetric learning hypothesis under which sellers obtain private information about repeat customers and this learning allows them to make higher profits from transactions with repeat customers. Consistent with this learning hypothesis, I find that the insurer in my dataset makes higher profits in transactions with repeat customers who have a good claims history with the insurer – customers about whom the insurer has positive private information not shared by other insurers; that the insurer provides these repeat customers with a reduction in premiums that is lower than the reduction in expected costs associated with such customers; and that policyholders who have bad claim histories with the insurer are more likely to flee their record by switching to other insurers.