Predicting insurance demand from risk attitudes

Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1730), we elicit measures of utility curvature, probability weighting, loss aversion, and preference for certainty and use them to parameterize seventeen common structural models (e.g., expected utility, cumulative prospect theory). Subjects also make 12 insurance choices over different loss probabilities and prices. The insurance choices show coherence and some correlation with various risk-attitude measures. Yet all the structural models predict insurance poorly, often less accurately than random predictions. This is because established structural models predict opposite reactions to probability changes and more sensitivity to prices than people display. Approaches that temper the price responsiveness of structural models show more promise for predicting insurance choices across different conditions.

Automated summary

Measuring risk attitudes and using associated structural models to predict insurance demand showed poor performance in an experiment, with the models often predicting inaccurately compared to random predictions. This discrepancy is mainly due to the models' higher sensitivity to prices and differing reactions to probability changes compared to actual human behavior. Approaches that adjust the models' price responsiveness may hold more promise for accurately predicting insurance choices across various conditions.