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This paper reports a new experimental test of the notion that behavior switches from risk averse to risk seeking when gains are “reflected” into the loss domain. We conduct a sequence of experiments that allows us to directly compare choices under reflected gains and losses where real and hypothetical payoffs range from several dollars to over $100. Lotteries with positive payoffs are transformed into lotteries over losses by multiplying all payoffs by –1, that is, by reflecting payoffs around zero. When we use hypothetical payments, more than half of the subjects who are risk averse for gains turn out to be risk seeking for losses. This reflection effect is diminished considerably with cash payoffs, where the modal choice pattern is to exhibit risk aversion for both gains and losses. However, we do observe a significant difference in risk attitudes between losses (where most subjects are approximately risk neutral) and gains (where most subjects are risk averse). Reflection rates are further reduced when payoffs are scaled up by a factor of 15 (for both real and hypothetical payoffs).

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

Measuring risk aversion is sensitive to assumptions about the wealth in subjects’ utility functions. Data from the same subjects in low- and high-stake lottery decisions allow estimating the wealth in a pre-specified one-parameter utility function simultaneously with risk aversion. This paper first shows how wealth estimates can be identified assuming constant relative risk aversion (CRRA). Using the data from a recent experiment by Holt and Laury (2002a), it is shown that most subjects’ behavior is consistent with CRRA at some wealth level. However, for realistic wealth levels most subjects’ behavior implies a decreasing relative risk aversion. An alternative explanation is that subjects do not fully integrate their wealth with income from the experiment. Within-subject data do not allow discriminating between the two hypotheses. Using between-subject data, maximum-likelihood estimates of a hybrid utility function indicate that aggregate behavior can be described by expected utility from income rather than expected utility from final wealth and partial relative risk aversion is increasing in the scale of payoffs.

This paper investigates whether individuals’ risk-taking behavior is affected by background risk by analyzing individuals’ choices over a series of lotteries in a laboratory setting in the presence and absence of independent, uncorrelated background risks. Overall, our results were mixed. We found some support for the notion that individuals were more risk averse when faced with the introduction of an unfair or mean-preserving background risk than when no background risk was present, but this finding depends on how individuals incorporate endowments and background gains and losses into their utility functions and how error variance is modeled.

We estimate a dynamic programming model of schooling decisions in which the degree of risk aversion can be inferred from schooling decisions. In our model, individuals are heterogeneous with respect to school and market abilities but homogeneous with respect to the degree of risk aversion. We allow endogenous schooling attainments to affect the level of risk experienced in labor market earnings through wage dispersion and employment rate dispersion. We find a low degree of relative risk aversion (0.93) and the estimates indicate that both wage and employment rate dispersions decrease significantly with schooling attainments. We find that a counterfactual increase in risk aversion will increase schooling attainments. Finally, the low degree of risk aversion implies that an increase in earnings dispersion would have little effect on schooling attainments.

We demonstrate in a laboratory experiment that the effectiveness of performance-contingent incentives is inversely related to risk-aversion levels. For about 16.5% of participants, performance fails to improve under performance-pay, and the probability of such failure increases with risk-aversion. This phenomenon works in part through the reduced effort level of more risk-averse individuals when effort level is positively correlated with risk exposure. It is also associated with higher self-reported levels of stress by more risk-averse people working under performance-contingent pay. We find no evidence of such stress causing decrements in the quality of effort affecting performance after controlling for effort level. However, controlling for effort, more risk-averse participants perform better under a fixed salary, leaving less room for improvement under performance-pay.

We review the use of behavior from television game shows to infer risk attitudes. These shows provide evidence when contestants are making decisions over very large stakes, and in a replicated, structured way. Inferences are generally confounded by the subjective assessment of skill in some games, and the dynamic nature of the task in most games. We consider the game shows Card Sharks, Jeopardy!, Lingo, and finally Deal Or No Deal. We provide a detailed case study of the analyses of Deal Or No Deal, since it is suitable for inference about risk attitudes and has attracted considerable attention.

We survey the literature on the Risk Augmented Mincer equation that seeks to estimate the compensation for uncertainty in the future wage to be earned after completing an education. There is wide empirical support for the predicted positive effect of wage variance and the negative effect of wage skew. We discuss robustness of the findings across specifications, potential bias from unobserved heterogeneity and selectivity and consider the core issue of students' information on benefits from education.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.