Complexity in insurance selection: Cross-classified multilevel analysis of experimental data

Abstract

This paper uses laboratory experiments to test the predictions of expected utility theory applied to the context of coverage selection and the effects of complexity on the quality of decision making. Participants choose from menus of stylized insurance plans that vary across three measures of complexity. In each menu one insurance plan stochastically dominates the other options. Out of the three complexity measures, Coverage Similarity and State Complexity, have a statistically significant negative effect on participants’ ability to choose efficiently. Individuals’ decisions can be predicted by the expected utility paradigm only to a certain extent. When the number of payoff relevant states associated with an insurance plan increases, participants violate rationality axioms by selecting first order dominated plans. Similarly, the rate of dominated choices increases when the difference in the level of coverage between plans in the same choice menu is low.