We use data on households' deductible choices in auto and home insurance to estimate a structural model of risky choice that incorporates standard risk aversion (concave utility over final wealth), loss aversion, and nonlinear probability weighting. Our estimates indicate that nonlinear probability weighting plays the most important role in explaining the data. More specifically, we find that standard risk aversion is small, loss aversion is nonexistent, and nonlinear probability weighting is large. When we estimate restricted models, we find that nonlinear probability weighting alone can better explain the data than standard risk aversion alone, loss aversion alone, and standard risk aversion and loss aversion combined. Our main findings are robust to a variety of modeling assumptions.
deductible loss aversion probability weighting risk aversion