Predictors of customer loyalty in automobile insurance: The role of private information in risky driving behavior and claim history
Contract relevant information asymmetries are known to cause inefficiencies in markets. The information asymmetry is largest in the beginning of the customer insurer relationship but reduces over time; the longer a policyholder stays with the insurer the more the insurer learns about the policyholder’s risk. Two important characteristics of the market studied here imply that the information asymmetry may not be reduced for all policyholders. First, insurers do not have access to traffic violations, which are predictors of risk since policyholders with traffic violations are more likely to report a claim. Second, the insurers do not share information, such as previous claims, which means that the policyholder can flee a poor claim record by switching insurer. Hence, there may be a selection of high risk customers who switch insurer more often, such that the information asymmetry in this group is never reduced. To test this, we compare information asymmetries in two groups of policyholders; new customers who stay with the insurer for a period or less (short term), and long-term customers who stay with the insurer for several periods (loyal). The results indicate that departing policyholders are disproportionately high risks that constitute an adverse selection of risks, while loyal policyholders constitute a propitious (favorable) selection of risks.
|Date of creation:||03 Feb 2011|
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Alma Cohen, 2008. "Asymmetric Learning in Repeated Contracting: An Empirical Study," NBER Working Papers 13752, National Bureau of Economic Research, Inc.
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