Abstract

I present a two-period asymmetric learning model to study accident forgiveness as an optional insurance policy in the automobile insurance contract. Accident forgiveness, often considered as “premium insurance,” protects the insured against a premium increase if an at-fault accident occurs. By offering this feature to all policyholders at the beginning, I design an accident forgiveness policy that charges policyholders higher-than-market premiums based on their risk types in the first period and then experience rates both types in the second period contingent on their previous at-fault accidents. In this model, experience rating still serves as the sorting device although its effectiveness is tempered by the presence of an accident forgiveness policy. Moreover, I find that both individual time and risk preferences contribute to the accident forgiveness purchases.

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