Testing Adverse Selection with Two-Dimensional Information : Evidence from Singapore Auto Insurance Market

This article examines adverse selection in insurance markets within a two-dimensional information framework, where policyholders differ in both their riskiness and degree of risk aversion. Using this setup, we first build a theoretical model to make equilibrium predictions on competitive insurance screening. We study several variations on the pattern of information asymmetry. The outcomes range from full risk separation, to partial separation, to complete pooling of different risk types. Next, we examine results of this construction with an empirical investigation using a cross sectional observation from a major automobile insurer in Singapore. To test for evidence of adverse selection, we propose a copula regression model to jointly examine the relationship between policyholders' coverage choice and accident occurrence. The association parameter in copula provides evidence of asymmetric information. Furthermore, we invoke the theory to identify subgroups of policyholders for whom one may expect the risk-coverage correlation and adverse selection to arise. The empirical findings are largely consistent with theoretical predictions