A Test of Asymmetric Learning in Competitive Insurance With Partial Information Sharing

Abstract

This article examines whether an insurer could gain advantageous information on repeat customers over its rivals in the Singapore automobile insurance market, which is featured by partial information sharing among insurers. We find that the insurer does update and accumulate more information regarding its policyholders' riskiness through repeated observations and thus make higher profits with repeat customers especially those of lower risk. We also show that the higher profit is driven by the fact that low risks tend to stay longer with the insurer, and in the meanwhile, they are charged a premium higher than their actuarial risk level. IntroductionIn economics and contract theory, information asymmetry refers to the situation where one party possesses information that is not available to another party in a transaction. Asymmetric learning, a particular form of information asymmetry, arises when a "seller" could gain advantageous information on its repeat "buyers" over its rivals through repeated contracting. Asymmetric learning could result in market power, and thus lead to higher profits from repeat customers (see Kunreuther and Pauly, 1985).

Insurer commitment and dynamic pricing pattern

“…We document evidence consistent with the theoretical predictions. Our results expand the empirical evidence on insurer learning (Hendel and Lizzeri, 2003;Cohen 2012;Kofman and Nini, 2013;Shi and Zhang, 2016) and on contractual commitment (Dionne and Doherty, 1994;Hofmann and Browne, 2013). Last but not least, our empirical test using loaner's PA insurance fills the gap in the investigation of pricing pattern with one-sided commitment and asymmetric learning.…”

“…Although we are not able to empirically conclude for a fully competitive market, we cautiously maintain the competition hypothesis following the extant literature (Chan, 2009;Lu, Wang, and Kweh, 2014). 17 We note that many other insurance products feature no-commitment and asymmetric learning, for example, automobile insurance used by D'Arcy and Doherty (1990), Cohen (2012), Kofman and Nini (2013), and Shi and Zhang (2016). This paper first-time presents a non-automobile short-term health insurance portfolio to show the lowballing premium pattern.…”

“…Their evidence challenges Cohen's (2012) argument regarding the important role of asymmetric learning in determining pricing pattern. Shi and Zhang (2016) investigated an intermediate learning scenario in between the no informationsharing market in Cohen (2012) and the complete information-sharing market in Kofman and Nini (2013). The Singapore automobile insurance market has a no-claim-discount (NCD) system and a public information-sharing platform.…”

“…Therefore, such a pricing strategy can coexist in parallel with the actuarial pricing based on risk discrimination (e.g., a bonus-malus system) and with the policyholders' self-selection process (e.g., the design of menu contracts). SeeCohen (2012) andShi and Zhang (2016) for a detailed discussion on how the lowballing strategy works in a bonus-malus system. See alsoCooper and Hayes (1987) for how the highballing strategy works when menu contracts are offered by the insurer.…”

Dynamic Moral Hazard: A Longitudinal Examination of Automobile Insurance in Canada

“…Using a unique longitudinal survey data where both reported and unreported accidents are observed, Dionne, Michaud, and Dahchour (2013) are able to distinguish dynamic moral hazard from asymmetric learning in the French automobile insurance market. Recent evidence of asymmetric learning in automobile insurance markets also includes Cohen (2012) and Shi and Zhang (2014). Note that the learning problem in Dionne, Michaud, and Dahchour (2013) is between the insured and the insurer where the insured learns on his risk by observing accidents, while in Cohen (2012) and Shi and Zhang (2014) the learning problem is between insurance companies about the insured.…”