If an insurer sets a premium based on the average probability of a loss in an entire population, those at higher-than-average risk for a certain hazard will benefit most from coverage, and hence will be the most likely to purchase insurance for that hazard
In an extreme case, the poor risks will be the only purchasers of coverage, and the insurer can expect to lose money on each policy sold. This situation, referred to as adverse selection, occurs when the insurer cannot distinguish between members of good-and poor-risk categories in setting premiums.
An example of adverse selection
The assumption underlying adverse selection is that purchasers of insurance have an informational advantage over providers because they know their own true risk types. Insurers, on the other hand, must collect information to distinguish between risks.
Private information about risk types creates inefficiencies
Suppose some homes have a 10 percent probability of suffering damage (the “good” risks) and others have a 30 percent probability (the “poor” risks). If the loss in the event of damage is $100 for both groups and if there are an equal number of potentially insurable individuals in each risk class, then the expected loss for a random individual in the population is 0.5 × (0.1 × $00) + 0.5 × (0.3 × $100) = $20. If the insurer charges an actuarially fair premium across the entire population, then only the poor-risk class would normally purchase coverage, since their expected loss is $30 (= 0.3 × $100),and they would be pleased to pay only $20 for the insurance. The good risks have an expected loss of $10 (= 0.1 × $100), so they probably would not pay $20 for coverage. If only the poor risks purchase coverage, the insurer will suffer an expected loss of −$10, ($20−$30), on each policy it sells.
Managing adverse selection
There are two main ways for insurers to deal with adverse selection. If the company knows the probabilities associated with good and bad risks, it can raise the premium to at least $30 so that it will not lose money on any individual. This is likely to produce a partial market failure, as many individuals who might want to purchase coverage will not do so at this high rate. Alternatively, the insurer can design and offer a “separating contract”.
More specifically, it can offer two different price-coverage contracts that induce different risk “types” to separate themselves in their insurance-purchasing decisions. For example, contract 1 could offer price = $30 and coverage = $100, while contract 2 might offer price = $10 and coverage = $40. If the poor risks preferred contract 1 over contract 2, and the good risks preferred contract 2 over contract 1, then the insurer could offer coverage to both groups while still breaking even. A third approach is for the insurer to collect information to reduce uncertainty about true risks, but this may be expensive.
Conclusion
In summary, the problem of adverse selection only emerges if the persons considering the purchase of insurance have more accurate private information on the probability of a loss than do the firms selling coverage. If the policyholders have no better data than the insurers, coverage will be offered at a single premium based on the average risk, and both good and poor risks will want to purchase policies.
This article is an edited version of
an entry in the “Encyclopedia of Quantitative Risk Analysis and
Assessment”, Copyright © 2008 John Wiley & Sons Ltd. Used by
permission.
