Working Paper: NBER ID: w13752
Authors: Alma Cohen
Abstract: 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.
Keywords: No keywords provided
JEL Codes: D40; D80; D82; D83; G22; L10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Length of good record (C41) | Insurer's profits (G22) |
| Insurer's private information (G52) | Higher profits (D33) |
| Poor record (Y30) | Switching insurers (G52) |
| Good claim history (G52) | Insurer's profits (G22) |
| Repeat customers with good records (C52) | Higher profits (D33) |