Impact of Consequence Information on Insurance Choice

Insurance choices are often hard to rationalize by standard theory and frequently appear sub-optimal. A key reason may be that people are unable to map the cost-sharing features of plans to their distribution of financial consequences. We develop and experimentally test a decision aid that provides this mapping to simplify comparisons of plan options. In two experiments mirroring typical health insurance decisions, we find that when people choose plans using standard feature-based information, they violate dominance at high rates. Our distribution-based decision aid substantially reduces dominance violations, and also changes choice patterns in situations where there is no dominant option. Choice patterns under feature-based menus can be most easily rationalized by models of heuristic choices, such as minimizing premium or deductible. With the decision aid, though, significantly more people have choice patterns that are better explained by expected utility theory. We compare our distribution-based approach to an alternative of providing estimates of the expected value of costs, which is the most common decision-support available in most insurance markets. Providing expected values affects choices in a similar direction as our consequence-based approach, but in a more muted fashion, and is only about half as effective at reducing dominance violations.