The role of insurance in managing extreme events: implications for terrorism coverage - Statistical Data Included

Business Economics, April, 2002 by Howard Kunreuther

Their study examined changes in pricing strategy as a function of the degree of uncertainty in either the probability and/or loss. A probability is considered to be well specified where there is enough historical and/or scientific information on an event that all experts agree that the probability of a loss is p. When there is wide disagreement between the experts on the estimate of p, this ambiguous probability is referred to as Ap. L represents a known loss--that is, there is a general consensus about what the loss will be if a specific event occurs. When the outcome is uncertain, and the experts' estimates range between [L.sub.min] and [L.sub.max], this uncertain loss is denoted as UL.

Combining the degree of probability and loss uncertainty lead to four cases, which are shown in Table 1, along with a set of illustrative examples of the types of risks that fall in each category.

To see how underwriters reacted to different situations, four scenarios were constructed as shown by the columns in Table 2. When the risk is well specified, the probability of the earthquake is either .01 or .005; the loss, should the event occur, is either $1 million or $10 million. The premium set by the underwriter is standardized at one for the (p, L) case so one can then examine how uncertainty and ambiguity affect pricing decisions. Thus, Table 2 shows the ratio of the other three cases relative to the well-specified risk (p, L) for the four different scenarios, drawn from responses of underwriters in primary insurance companies. For the highly ambiguous case (Ap,UL), the premiums were between 1.43 to 1.77 times the premiums for a well-specified risk. The ratios for the other two cases were always above 1, but less than the (Ap,UL) case.

Adverse Selection. (2) If the insurer sets a premium based on the average probability of a loss, using the entire population as a basis for this estimate, the highest risks will be the ones most likely to purchase coverage and the insurer will face a portfolio of policies that yield a negative expected return. This situation, referred to as adverse selection, occurs when the insurer cannot distinguish between different risk categories in estimating the chances of a claim and/or its magnitude.

Moral Hazard. (3) Moral hazard refers to an increase in the probability and/or size of the loss caused by the behavior of the policyholder. Providing insurance protection to an individual may lead to more carelessness by the insured than before he or she had coverage. If the insurer cannot predict this behavior by relying on scientific estimates of the risk and past data from uninsured individuals to determine rates, the resulting premium is likely to be too low.

Moral hazard represents a challenge for insurers. It is often extremely difficult to control behavior once a person is insured. How do you monitor carelessness? How easy is it to determine if a person will decide to collect more on a policy than he or she deserves by making false claims? For these and other reasons the insurer may charge higher premiums than they would if they based their rates solely on actuarial risk estimates.