Asymmetric Information and Learning: Evidence from the Automobile Insurance Market

30 Pages Posted: 2 Apr 2003  

Alma Cohen

Tel Aviv University - Eitan Berglas School of Economics; Harvard Law School; National Bureau of Economic Research (NBER)

Abstract

This paper tests the predictions of adverse selection models using data from the automobile insurance market. I find that, in contrast to what recent research has suggested, the evidence is consistent with the presence of informational asymmetries in this market: new customers choosing higher insurance coverage are associated with more accidents. Consistent with the presence of learning by policyholders about their risk type, such a coverage-accident correlation exists only for policyholders with three or more years of driving experience prior to joining their insurer. The informational advantage that new customers with driving experience have over the insurer appears to arise in part from under-reporting of past claim history. I find evidence that policyholders switching to new insurers are disproportionately ones with a poor claims history and that new customers under-report their past claims history when joining a new insurer.

Keywords: Asymmetric information, adverse selection, screening, sorting, moral hazard, insurance, deductible, learning, information transmission, repeat customers

JEL Classification: D40, D80, D82, D83, L10, G22

Suggested Citation

Cohen, Alma, Asymmetric Information and Learning: Evidence from the Automobile Insurance Market. The Review of Economics and Statistics, Vol. 87, pp. 197-207, 2005. Available at SSRN: https://ssrn.com/abstract=289966 or http://dx.doi.org/10.2139/ssrn.289966

Alma Cohen (Contact Author)

Tel Aviv University - Eitan Berglas School of Economics ( email )

Ramat Aviv, Tel Aviv, 69978
Israel

Harvard Law School ( email )

Cambridge, MA 02138
United States
(617) 496-4099 (Phone)
(617) 812-0554 (Fax)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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