Asymmetric Learning in Repeated Contracting: An Empirical Study

This paper uses a unique panel data set of an insurer's transactions with repeat customers. Consistent with the asymmetric learning hypothesis that repeated contracting enables sellers to obtain an informational advantage over their rivals, I find that the insurer makes higher profits in transactions with repeat customers who have a good claims history with the insurer, the insurer reduces the price charged to these repeat customers by less than the reduction in expected costs associated with such customers, and repeat customers with bad claim histories are more likely to flee their record by switching to other insurers. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.