Yi Xin from the California Institute of Technology will present "Competing under Information Heterogeneity: Evidence from Auto Insurance" joint work with Marco Cosconati, Fan Wu and Yizhou Jin

Abstract: This paper 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 analyze 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 13% boost in consumer surplus, nearly reaching the efficiency benchmark where firms have full knowledge of consumers’ true risk. Aggregating information through the bureau favors low-risk consumers and reduces average costs by 18 euros per contract through more efficient insurer-insuree matching.