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Personal decisions about health hazards are the main cause of impaired health and premature death. People smoke and eat too much, and they exercise too little. The lack of preventive efforts is surprising given their proven effectiveness. In the early 1960s, Arrow suggested that moral hazard might be a reason for underprevention, but this explanation was later challenged. In this paper, we show that underprevention might be caused by misperceived probabilities. We derive when and how probability weighting gets in the way of prevention by blurring its benefits. We use a general model of prevention, encompassing several special cases from the literature. We also show how perceived ambiguity makes the problem of underprevention even worse by amplifying the effect of probability weighting.

Surveys typically use hypothetical questions to measure subjective and unverifiable concepts like happiness and quality of life. We test whether this is problematic using a large survey experiment on health and subjective well-being. We use Prelec’s Bayesian truth serum to incentivize the experiment and defaults to introduce biases in responses. Without defaults, the data quality was good and incentives had no impact. With defaults, incentives reduced default biases in the subjective well-being questions by inducing participants to spend more effort. Incentives had no impact on the health questions regardless of whether defaults were used.

Low insurance take-up in low-income populations is not easily explained by the standard single-period expected utility model of insurance that overlooks the relevance of time preference when liquidity is constrained. We design field survey instruments to elicit quasi-hyperbolic time preferences, as well as prospect theory risk preferences, and use them to examine whether time preferences explain health insurance behavior of low-income Filipinos. Consistent with theory, those with stronger parameterized time preference are less likely to insure and the partial association is most pronounced at low wealth where liquidity is most likely to be constrained. Among those with better understanding of insurance, lower take-up is also associated with present bias. We do not find that insurance is significantly associated with risk preferences.

Temporary incentives are offered in anticipation of persistent effects that are seldom estimated. We use a nationwide randomized experiment in the Philippines to estimate effects of two incentives for health insurance three years after their withdrawal. We find that both temporary incentives had persistent effects on enrollment. A premium subsidy had a small but highly persistent effect. Application assistance offered to those initially unresponsive to the subsidy had a much larger but less persistent effect. The subsidy persuaded those with higher initial stated willingness to pay to enroll and keep enrolling. The offer of application assistance to initial non-compliers with the subsidy achieved a larger immediate effect by drawing in those who stated they valued insurance less and were less likely to re-enroll when the incentives were withdrawn.

This paper introduces the Prince incentive system for measuring preferences. Prince combines the tractability of direct matching, allowing for the precise and direct elicitation of indifference values, with the clarity and validity of choice lists. It makes incentive compatibility completely transparent to subjects, avoiding the opaqueness of the Becker-DeGroot-Marschak mechanism. It can be used for adaptive experiments while avoiding any possibility of strategic behavior by subjects. To illustrate Prince’s wide applicability, we investigate preference reversals, the discrepancy between willingness to pay and willingness to accept, and the major components of decision making under uncertainty: utilities, subjective beliefs, and ambiguity attitudes. Prince allows for measuring utility under risk and ambiguity in a tractable and incentive-compatible manner even if expected utility is violated. Our empirical findings support modern behavioral views, e.g., confirming the endowment effect and showing that utility is closer to linear than classically thought. In a comparative study, Prince gives better results than a classical implementation of the random incentive system.

Language use differs between truthful and deceptive statements, but not all differences are consistent across people and contexts, complicating the identification of deceit in individuals. By relying on fact-checked tweets, we showed in three studies (Study 1: 469 tweets; Study 2: 484 tweets; Study 3: 24 models) how well personalized linguistic deception detection performs by developing the first deception model tailored to an individual: the 45th U.S. president. First, we found substantial linguistic differences between factually correct and factually incorrect tweets. We developed a quantitative model and achieved 73% overall accuracy. Second, we tested out-of-sample prediction and achieved 74% overall accuracy. Third, we compared our personalized model with linguistic models previously reported in the literature. Our model outperformed existing models by 5 percentage points, demonstrating the added value of personalized linguistic analysis in real-world settings. Our results indicate that factually incorrect tweets by the U.S. president are not random mistakes of the sender.

We introduce belief hedges, i.e., sets of events whose uncertain subjective beliefs neutralize each other. Belief hedges allow us to measure ambiguity attitudes without knowing those subjective beliefs. They lead to improved ambiguity indexes that are valid under all popular ambiguity theories. Our indexes can be applied to real-world problems and do not require expected utility for risk or commitments to two-stage optimization, thereby increasing their descriptive power. Belief hedges make ambiguity theories widely applicable.

This paper introduces two simple betting mechanisms—top-flop and threshold betting—to elicit unverifiable information from crowds. Agents are offered bets on the rating of an item about which they received a private signal versus that of a random item. We characterize conditions for the chosen bet to reveal the agents' private signal even if the underlying ratings are biased. We further provide microeconomic foundations of the ratings, which are endogenously determined by the actions of other agents in a game setting. Our mechanisms relax standard assumptions of the literature, such as common prior, and homogeneous and risk neutral agents.