We use data on insurance deductible choices to estimate a structural model of risky choice that incorporates "standard" risk aversion (diminishing marginal utility for wealth) and probability distortions. We find that probability distortionscharacterized by substantial overweighting of small probabilities and only mild insensitivity to probability changes-play an important role in explaining the aversion to risk manifested in deductible choices. This finding is robust to allowing for observed and unobserved heterogeneity in preferences. We demonstrate that neither Kőszegi-Rabin loss aversion alone nor Gul disappointment aversion alone can explain our estimated probability distortions, signifying a key role for probability weighting.(JEL D01, D03, D12, D81, G22) * For helpful comments, we thank seminar and conference participants at Berkeley, Collegio

Using a unique dataset, we test whether households' deductible choices in auto and home insurance reflect stable risk preferences. Our test relies on a structural model that assumes households are objective expected utility maximizers and claims are generated by household-coverage specific Poisson processes. We find that the hypothesis of stable risk preferences is rejected by the data. Our analysis suggests that many households exhibit greater risk aversion in their home deductible choices than their auto deductible choices. Our results are robust to several alternative modeling assumptions. (JEL D11, D83)

We use data on insurance deductible choices to estimate a structural model of risky choice that incorporates "standard" risk aversion (diminishing marginal utility for wealth) and probability distortions. We find that probability distortionscharacterized by substantial overweighting of small probabilities and only mild insensitivity to probability changes-play an important role in explaining the aversion to risk manifested in deductible choices. This finding is robust to allowing for observed and unobserved heterogeneity in preferences. We demonstrate that neither Kőszegi-Rabin loss aversion alone nor Gul disappointment aversion alone can explain our estimated probability distortions, signifying a key role for probability weighting.(JEL D01, D03, D12, D81, G22) * For helpful comments, we thank seminar and conference participants at Berkeley, Collegio

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences—including both expected utility (EU) theory and non-EU models—that have been estimated using field data, and we highlight issues related to identification and estimation of such models using field data. We then survey the literature, giving separate treatment to research that uses individual-level data (e.g., property-insurance data) and research that uses aggregate data (e.g., betting-market data). We conclude by discussing directions for future research. ( JEL C51, D11, D81, D82, D83, G22, I13)

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households' deductible choices across three lines of insurance coverage and a model that nests expected utility theory plus a range of non-expected utility models, we perform a revealed preference analysis that yields household-specific bounds on the model parameters. We then impose stability and other structural assumptions to tighten the bounds, and we explore what we can learn about households' risk preferences from the intervals defined by the bounds. We further utilize the intervals to (i) classify households into preference types and (ii) recover the single parameterization of the model that best fits the data. Our approach does not entail making distributional assumptions about unobserved heterogeneity in preferences. Terms of use: Documents in EconStor may

Standard accident models are based on the expected utility framework and represent agents' beliefs about accident risk with a probability distribution. Consequently, they do not allow for Knightian uncertainty, or ambiguity, with respect to accident risk and cannot accommodate optimism (ambiguity loving) or pessimism (ambiguity aversion). This paper presents a unilateral accident model under ambiguity. To incorporate ambiguity, I adopt the Choquet expected utility framework and represent the injurer's beliefs with a neoadditive capacity. Ishow that neither strict liability nor negligence is generally efficient in the presence of ambiguity. In addition, I generally find that the injurer's level of care decreases (increases) with ambiguity if he is optimistic (pessimistic) and decreases (increases) with his degree of optimism (pessimism). The results suggest that negligence is more robust to ambiguity and, therefore, may be superior to strict liability in unilateral accident cases. Finally, I design an efficient ambiguity-adjusted liability rule.

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.

We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households' deductible choices across three lines of insurance coverage and a model that nests expected utility theory plus a range of non-expected utility models, we perform a revealed preference analysis that yields household-specific bounds on the model parameters. We then impose stability and other structural assumptions to tighten the bounds, and we explore what we can learn about households' risk preferences from the intervals defined by the bounds. We further utilize the intervals to (i) classify households into preference types and (ii) recover the single parameterization of the model that best fits the data. Our approach does not entail making distributional assumptions about unobserved heterogeneity in preferences.