Asymmetric information in insurance : the impact of big data on low-ses individuals

Abstract

In this thesis, we analyze the effect of big data in insurance markets with heterogeneous insurance takers. Through a theoretical approach, we consider the effects of increased information flows on insurance contracts offered to different types of individuals along dimensions of socioeconomic status and risk. We find that, on a general level, the development of big data, which is likely to alleviate problems of asymmetric information, will have unfavourable effects on individuals of low socioeconomic status. These effects arise due to a social gradient in risk or differences in abilities, or both. Less asymmetric information leads to more actuarially fair pricing of individuals, holding each individual responsible for their own risk to a larger extent than before. We assess this from a normative perspective, and consider redistributory concerns.