Economic Effects of Risk Classification Bans

Abstract

Risk classification refers to the use of observable characteristics by insurers to group individuals with similar expected claims, to compute the corresponding premiums, and thereby to reduce asymmetric information. Permitting risk classification may reduce informational asymmetry-induced adverse selection and improve insurance market efficiency. It may also have undesirable equity consequences and undermine the implicit insurance against reclassification risk, which legislated restrictions on risk classification could provide. We use a canonical insurance market screening model to survey and to extend the risk classification literature. We provide a unified framework for analysing the economic consequences of legalised vs banned risk classification, both in static-information environments and in environments in which additional information can be learned, by either side of the market, through potentially costly tests.

Insurance loss coverage and social welfare

“…We have shown that loss coverage can be reconciled with (although it is not the same as) an 'equal-weights' utilitarian social welfare, in the spirit of Hoy (2006) or Dionne & Rothschild (2014). Specifically, if insurance demand is iso-elastic, ranking risk classification schemes by loss coverage always gives the same ordering as ranking by social welfare.…”

“…The loss coverage literature contrasts with economic literature on insurance risk classification, as summarised in surveys such as Hoy (2006), Einav & Finkelstein (2011) and Dionne & Rothschild (2014). Economic literature typically takes a utility-based approach: representative agents from each risk-group make purchasing decisions which maximise their expected utilities, and the outcomes of different risk classification schemes are then evaluated by a social welfare function which is a (possibly weighted) sum of expected utilities over the whole population.…”

Insurance Fraud in a Rothschild–Stiglitz World

“…Proposition 2 demonstrates that asymmetric information about risk type interacts with the fraud externality. Complete unraveling is more commonly observed under so‐called fixed contracts (see Dionne and Rothschild, ) when insurers compete in prices for a prespecified indemnity (see Einav and Finkestein, ; Peter, Richter, and Steinorth, ). Ex post moral hazard induces an implicit premium loading, which allows for complete unraveling in our model.…”

Recent Research Developments Affecting Nonlife Insurance—The CAS Risk Premium Project 2014 Update

“…Risk classification is a traditional and effective tool to reduce adverse selection; however, in various jurisdictions, risk classification based on certain risk factors (e.g., gender) is banned. Schmeiser et al (2014) and Dionne and Rothschild (2014) discuss the potential consequences of such bans, and both suggest that such restrictions result in significant costs.…”