Structural Estimation of Decision Making under Natural Hazard Risk

Abstract

The use of structural models for decision-making under risk and uncertainty in applied economics is scarce compared to reduced form approaches. This is unfortunate, as structural models have clear connections to theory and permit direct tests of hypotheses and exploration of potential causal mechanisms. In this paper, we derive and estimate a structural model of decision making in the presence of natural hazard risk. Utilizing a unique data set that includes information on risk preferences, subjective likelihood of hurricane strike, and expectations of damage, we estimate several variants of a subjective expected utility model for coastal households’ decisions to purchase flood insurance. We benchmark our model’s out of sample accuracy by comparing it against both reduced-form and machine learning estimates. Overall, we find our structural models perform about as well as a reduced-form probit model in predicting out of sample behavior, but offer increased external validity and insight into theory and mechanisms.