We present evidence consistent with time-varying risk preferences among automobile drivers. Exploiting a unique data set of agents' high-frequency driving behavior collected by a mobile phone application, we show that drivers drive more conservatively following near-miss accidents. In a preferred specification, a near-miss triggers a reduction in driving distance of 12.98 kilometers, in-car cell phone use by more than 100%, and highway use by 43.24%. Structural estimation results indicate that such changes in behavior are consistent with an increase in risk aversion of 10.54% to 43.77% and a reduction in annual insurance cost amounting to 2.04% to 3.31% of the average car insurance premium.

This article studies competition under information heterogeneity in selection markets and examines the impact of public information regulations aimed at reducing information asymmetries between competing firms. We develop a novel model and introduce new empirical strategies to analyse imperfect competition in markets where firms have heterogeneous information about consumers, vary in cost structures, and offer differentiated products. Using data from the Italian auto insurance market, we find substantial differences in the precision of risk ratings across insurers, and those with less accurate risk-rating algorithms tend to have more efficient cost structures. We assess the equilibrium effects of giving firms equal access to aggregated risk information from a centralized bureau. This policy significantly reduces prices by increasing competition, leading to a 15.7% boost in consumer surplus, almost reaching the efficiency benchmark where firms have full knowledge of consumers’ true risk. Aggregating information through the bureau favours low-risk consumers and reduces average costs by 12 euros per contract through more efficient insurer–insuree matching.