Adverse Selection and Moral Hazard in Insurance: Can Dynamic Data Help to Distinguish?

Abstract

A standard problem of applied contracts theory is to empirically distinguish between adverse selection and moral hazard. We show that dynamic insurance data allow to distinguish moral hazard from dynamic selection on unobservables. In the presence of moral hazard, experience rating implies negative occurrence dependence: individual claim intensities decrease with the number of past claims. We discuss econometric tests for the various types of data that are typically available. Finally, we argue that dynamic data also allow to test for adverse selection, even if it is based on asymmetric learning.

The effects of access to health insurance: Evidence from a regression discontinuity design in Peru

“…Chiappori (2000) provides a broad review of the early literature. Chiappori and Salanié (2000), Abbring et al (2003a), and Abbring et al (2003b) investigate moral hazard in the market for car insurance. Finkelstein and Poterba (2004), Bajari et al (2006), Fang et al (2006), Aron-Dine et al (2015) and Einav et al (2013) study adverse selection and moral hazard in the context of health insurance in developed countries.…”

Better Safe than Sorry? Ex Ante and Ex Post Moral Hazard in Dynamic Insurance Data

“…Much of the literature, however, uses static theory and cross-sectional data, which limits both its versatility in dealing with truly dynamic aspects of insurance markets, such as experience rating, and variation in the data that can be turned into robust empirical results. The empirical distinction between moral hazard and selection effects using static methods has turned out to be particularly hard; as argued by Abbring, Chiappori, Heckman, and Pinquet (2003), this is the standard econometric problem of distinguishing causal and selection effects. This paper instead analyzes moral hazard in car insurance using panel data on contracts and claims provided by a Dutch insurance company.…”

“…1 Chiappori (2001) forwarded the idea to exploit the rich variation that can be derived from dynamic theory and found in longitudinal data; Abbring, Chiappori, Heckman, and Pinquet (2003) suggested that we base a test for moral hazard on the dynamic variation in individual risk with the idiosyncratic variation in incentives due to experience rating.…”

Banking Relationships, Managerial Ownership and Operational Performance: A Simultaneous Equations Approach in the Context of SMEs

“…In addition to supporting hypothesis H 1 , this negative effect of the number of banks on operational performance finds support in the literature often evidenced by the following argument: the intensification of the relationship allows greater mutual knowledge, contributing to a reduction in information asymmetries, characterized by the fact that credit applicants possess private and exclusive information, which is difficult to transfer out of the relationship (Castelli et al, 2012). Maintaining and consolidating a banking relationship with a small number of entities allows adverse selection and moral hazard to be reduced (Abbring et al, 2003). Under these circumstances the lender bank can practice differentiated active interest rates and, in short, adjusted credit agreements according to risk level.…”