Asymmetric Information and Learning in the Automobile Insurance

This paper tests the predictions of adverse selection models, using data from the automobile insurance market. Consistent with the presence of adverse selection, I find that low-deductible choices are correlated with more accidents, higher losses from accidents, and different distributions of the number of accidents and the damage in the event of an accident. Consistent with the presence of learning by policyholders, I find that this correlation between low deductibles and more accidents exists only for drivers who have three or more years of driving experience. Consistent with the presence of learning by insurers, I find that the magnitude of the identified correlation is smaller for repeat customers; that customers who stay with their insurer are disproportionately ones with a good claim history with the insurer; and that insurers make greater profits on repeat customers than on new customers