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.

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从风险态度预测保险需求

衡量的风险态度和相关的结构模型能否预测保险需求？在一个实验中（n = 1730），我们引出了效用曲率、概率加权、损失厌恶和对确定性偏好的度量，并使用它们来参数化 17 种常见的结构模型（例如，预期效用、累积前景理论）。受试者还针对不同的损失概率和价格做出 12 种保险选择。保险选择显示出与各种风险态度测量的连贯性和一定的相关性。然而，所有结构模型对保险的预测都很差，通常不如随机预测准确。这是因为已建立的结构模型预测对概率变化的相反反应以及对价格的敏感度比人们表现出的要高。缓和结构模型的价格响应能力的方法在预测不同条件下的保险选择方面表现出更大的希望。