We investigate the effect major catastrophes are expected to have on equilibrium price and quantity in the insurance market. In particular, we examine whether investors expect total industry revenue to increase following a disaster's shock to insurers' financial capital. Rather than examine insurers directly, we study insurance brokers, who earn commissions on premium revenue but do not pay losses following a disaster. We conduct an event study on insurance broker stock returns surrounding the 43 largest insuredloss catastrophes since 1970. We find that brokers earn positive abnormal returns on the day of the event, and that these returns are sustained following the top 20 largest events. We then investigate factors influencing these returns and find that returns are positively related to the size of the loss and negatively related to existing insurer capital. From this, we conclude that catastrophe shocks are expected to increase net industry revenue, benefiting brokers most immediately. This investor response is consistent with economic theories of a negative relationship between capital and insurance prices and price-inelastic demand for commercial insurance. . We would like to thank the two anonymous referees for their insights and suggestions. Thanks also are due to

We examine the ability of insurers to influence the coverage limit decisions of 180,000 households in the National Flood Insurance Program. In this program, private insurers sell identical flood contracts at identical rates and bear no risk of paying claims. About 12 percent of new policyholders overinsure, selecting a coverage limit that exceeds their home's estimated replacement cost. Overinsuring is expensive relative to expected loss, making it difficult to explain with standard decision-making models. The rate of overinsuring differs substantially across insurers, ranging from zero to one-third of new policies. Insurer effects on the likelihood of overinsuring are statistically significant after controlling for the policyholder's characteristics. Additionally, some insurers seem to encourage households to overinsure in percentage terms (e.g., buy 110 percent of replacement cost) while others encourage rounding up in dollars (e.g., to the next $10,000). We find that insurers' distribution systems and commission rates influence whether their policyholders overinsure.

Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1,730), 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 twelve 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. Simpler prediction heuristics show more promise for predicting insurance choices across different conditions.

Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1,730), 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 twelve 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. Simpler prediction heuristics show more promise for predicting insurance choices across different conditions.

Can measured risk attitudes and associated structural models predict insurance demand? In an experiment (n = 1,730), 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 twelve 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. Simpler prediction heuristics show more promise for predicting insurance choices across different conditions.