How have preventive and curative medical breakthroughs shaped life expectancy and the dispersion of age at death in the United States over the past century? We address this question by developing a life-cycle model in which both health and lifespan are endogenous. The model distinguishes between preventive innovations, which reduce the incidence of disease, and curative advances, which lower mortality risks associated with existing health conditions. Our quantitative analysis shows that while both types of medical innovation have contributed to increased life expectancy since 1935, curative advances have been the primary driver of the decline in the dispersion of age at death. Medical innovations have also improved welfare – measured in terms of a consumption-equivalent metric – by an average of 0.11% per year, with curative advances representing the most significant contribution. These findings are robust across different scenarios and parametrization strategies.

Many important decisions, for example, applying to college, require an individual to simultaneously submit several applications. These decisions are unique as each application is risky because acceptance is uncertain while also being rival as one can only attend a single college. In an influential theoretical analysis of these problems, Chade and Smith (2006) establish the No Safety Schools Theorem which suggests larger portfolios are riskier than single choices. We offer experimental evidence, using several experiments, that this theorem is routinely violated. In fact, the majority of our subjects violate this theorem. However, performance improves with practice, advice, and feedback.

Although compulsory insurance mitigates the negative externalities caused by uninsured individuals, it raises the issue of insurance crowding out prevention. However, at the theoretical level, compulsory insurance and self-insurance (preventive investments dedicated to loss reduction) are know to be substitutes for risk averters but complements for risk lovers. This paper aims to empirically test these opposite predictions through a laboratory experiment using a model-based design. Our experimental results confirm the theoretical predictions: compulsory insurance and self-insurance are complements for risk lovers and substitutes for risk averters. This study strongly supports public policies advocating mandatory insurance implementation as they enhance risk lovers’ self-insurance investments. Therefore, a risk management scheme combining voluntary top-up and compulsory partial insurance guarantees an optimal risk allocation for risk-averters and increases the investments in self-insurance for risk-lovers.

In this paper we study the idea of consequentialism in dynamic games by considering two versions: A commonly used utility-based version stating that the player’s preferences are governed by a utility function on consequences, and a preference-based version which faithfully translates the original idea of consequentialism to restrictions on the player’s preferences. Utility-based consequentialism always implies preference-based consequentialism, but the other direction is not necessarily true, as is shown by means of a counterexample. In this paper we offer conditions under which the two notions are equivalent.

In meritocratic societies, inequality is considered just if it reflects factors within but not outside individuals’ control. However, individuals often benefit differentially from other people’s efforts. Such passive inequality is simultaneously just and unjust by meritocratic standards, confronting meritocrats with a dilemma. We conducted an experiment with a representative US sample to investigate how people deal with this dilemma. In the experiment, impartial spectators redistribute payments between pairs of individuals. We vary whether initial payments result from luck or effort and whether spectators redistribute between individuals who worked themselves or individuals who benefited from the work of real-life friends. We find that spectators treat inequality based on the efforts of individuals’ friends as if individuals had worked themselves, and very different from inequality resulting from differential luck. This indicates that most people accept inequality if it is merited at some stage, which may explain opposition to redistributive policies.

In this paper, we study a two-period optimal insurance problem for a policyholder with mean-variance preferences who purchases proportional insurance at the beginning of each period. The insurance premium is calculated by a variance premium principle with a risk loading that depends on the policyholder’s claim history. We derive the time-consistent optimal insurance strategy in closed form and the optimal constant precommitment strategy in semiclosed form. For the optimal general precommitment strategy, we obtain the solution for the second period semi-explicitly and, then, the solution for the first period numerically via an efficient algorithm. Furthermore, we compare the three types of optimal strategies, highlighting their differences, and we examine the impact of the key model parameters on the optimal strategies and value functions.

Amnon Rapoport made seminal contributions to research on investment decision-making and individual decision-making under risk. To build on his seminal work, this paper explores the impact of social influence on risk-taking. First, to build predictions for experimental testing, we modify a standard expected utility model by introducing a social norm variable. Using a standard 10-decision paired lottery choice task, we report the results from three experiments with different manipulations to test whether social influence information affects subjects’ own lottery choices. In Experiment 1, we find that participants are more likely to switch to choosing the risky option earlier if they are told that a large majority (>75%) of a large group (N = 100) of others have also chosen the risky option in the past. In Experiment 2, we find there is no effect if the social influence prompt is framed as a small group (N = 10) or the choice of one (N = 1) successful lottery participant, but there is an effect when participants are provided information about the consistently risky choices of one (N = 1) person in the past. In Experiment 3, using an in-person subject pool, we find some mixed effects on risk-taking when the social information is framed as a small group (N = 10) of peers (other students). Altogether, this paper demonstrates that social influence can impact risk-taking in line with a socially normed expected utility model.

Alternative disposable dinnerware treatments to per- and polyfluoroalkyl substances (PFAS) are under development. A discrete choice experiment of 1,304 U.S. consumers addressed the market’s response to bio-based alternatives. Information nudges were used to assess the impact of health and environmental information on behavior. Data were analyzed using mixed logit models. Bio-based treated plates generated premiums compared to the PFAS-treated plates. Participants exposed to either environmental or health information were willing to pay a price premium of $2.0-$2.12 for bio-based treatments. Both information nudges generated premiums for the USDA Certified Bio-based products relative to the control.

An abundance of statistics has shown gender disparity in hiring decisions. This paper shows that a previously unexplored factor, the decision-making process utilized by a hiring committee, plays a crucial role. Using a laboratory experiment, we find that gender disparity is eliminated when hiring decisions are made unanimously by a group. By comparison, we find that gender disparity is largest when decisions are made by a leader who volunteers. We do not find evidence of heterogeneity by gender as the results persist regardless of the number of women in the group or the leader’s gender. The experimental design allows us to rule out several possible mechanisms including differences in leadership characteristics and communication styles.

The bonus-malus system (BMS) is a widely recognized and commonly employed risk management tool. A well-designed BMS can match expected insurance payments with estimated claims even in a diverse group of risks. Although there has been abundant research on improving bonus-malus (BM) systems, one important aspect has been overlooked: the stationary probability of a BMS satisfies the monotone likelihood ratio property. The monotone likelihood ratio for stationary probabilities allows us to better understand how riskier policyholders are more likely to remain in higher premium categories, while less risky policyholders are more likely to move toward lower premiums. This study establishes this property for BMSs that are described by an ergodic Markov chain with one possible claim and a transition rule +1/-d. We derive this result from the linear recurrences that characterize the stationary distribution; this represents a novel analytical approach in this domain. We also illustrate the practical implications of our findings: in the BM design problem, the premium scale is automatically monotonic.

We report the results of an experiment on selective exposure to information. A decision maker interested in learning about an uncertain state of the world can acquire information from one of two sources that have opposite biases: when informed on the state, they report it truthfully; when uninformed, they report their favorite state. A Bayesian decision-maker is better off seeking confirmatory information unless the source biased against the prior is sufficiently more reliable. In line with the theory, subjects are more likely to seek confirmatory information when sources are symmetrically reliable. On the other hand, when sources are asymmetrically reliable, subjects are more likely to consult the more reliable source even when prior beliefs are strongly unbalanced and this source is less informative. Our experiment suggests that base rate neglect and simple heuristics (e.g., listen to the most reliable source) are important drivers of the endogenous acquisition of information.

We introduce the “Fork Game,” a graphical interface designed to elicit higher-order risk preferences. In this game, participants connect forked pipes to create a final structure. A ball is then dropped into the top opening of this structure and follows a downward path, randomly turning left or right at each forked joint. This construction is effectively isomorphic to the apportionment of binary-outcome lotteries, allowing participants to construct complex gambles. Furthermore, the game is easily comprehensible, highly modular, and provides a flexible means of assessing risk aversion, prudence, temperance, and even higher-order risk preferences.

Our study contributes to the literature on choice shifts in group decision-making by analyzing how the level of risk-taking within a group is influenced by its gender composition. In particular, we investigate experimentally whether group composition affects how preferences ‘shift’ when comparing individual and group choices. Consistent with hypotheses derived from previous literature, we show that male-dominated groups shift toward riskier decisions in a way that is not explained by any simple preference aggregation mechanism. We discuss potential channels for the observed pattern of choice shifts.

In this paper, we present a flexible approach to estimating parametric cumulative Prospect Theory using Hierarchical Bayesian methods. Bayesian methods allow us to include prior knowledge in estimation and heterogeneity in individual responses. The model employs a generalised parametric specification of the value function allowing each individual to be risk-seeking in low-stakes mixed prospects. In addition, it includes parameters accounting for varying levels of model noise across domains (gain, loss, and mixed) and several aspects of lottery design that can influence respondent behaviour. Our results indicate that enhancing value function flexibility leads to improved model performance. Our analysis reveals that choices within the gain domain tend to be more predictable. This implies that respondents find tasks in the gain domain cognitively less challenging in comparison to making choices within the loss and mixed domains.

We revisit the recently introduced concept of return risk measures (RRMs) and extend it by incorporating risk management via multiple so-called eligible assets. The resulting new class of risk measures, termed multi-asset return risk measures (MARRMs), introduces a novel economic model for multiplicative risk sharing. We point out the connection between MARRMs and the well-known concept of multi-asset risk measures (MARMs). Then, we conduct a case study, based on an insurance dataset, in which we use typical continuous-time financial markets and different notions of acceptability of losses to compare RRMs, MARMs, and MARRMs and draw conclusions about the cost of risk mitigation. Moreover, we analyze theoretical properties of MARRMs. In particular, we prove that a positively homogeneous MARRM is quasi-convex if and only if it is convex, and we provide conditions to avoid inconsistent risk evaluations. Finally, the representation of MARRMs via MARMs is used to obtain various dual representations.

Narrow bracketers who are myopic in specific decisions would fail to consider preexisting risks in investment and neglect hedging opportunities. Growing evidence has demonstrated the relevance of narrow bracketing. We take a step further in empirical investigation and study individual heterogeneity in narrow bracketing. Specifically, we use a lab experiment in investment and hedging that elicits subjects’ preferences on rich occasions to uncover the individual degree of narrow bracketing without imposing distributional assumptions. Combining prospect theory and narrow bracketing can explain our findings: Subjects who invest more also insure more, and subjects insure significantly less in the loss domain than in the gain domain. More importantly, we show that the distribution of the individual degree of narrow bracketing is skewed at two extremes, yet with a substantial share of people in the middle who partially suffer from narrow bracketing. Neglecting this aspect, we would overestimate the severity of narrow bracketing and misinterpret its relation with individual characteristics.

Risk was incorporated into monetary aggregation over thirty-five years ago, using a stochastic version of the workhorse money-in-the-utility-function model. Nevertheless, the mathematical foundations of this stochastic model remain shaky. To firm the foundations, this paper employs richer probability concepts than Borel-measurability, enabling me to prove the existence of a well-behaved solution and to derive stochastic Euler equations. This measurability approach is less common in economics, possibly because the derivation of stochastic Euler equations is new. Importantly, the problem’s economics are not restricted by the approach. The results provide firm footing for the growing monetary aggregation under risk literature, which integrates monetary and finance theory. As crypto-currencies and stable coins garner attention, solidifying the foundations of risky money becomes more critical. The method also supports deriving stochastic Euler equations for any dynamic economics problem that features contemporaneous uncertainty about prices, including asset pricing models like capital asset pricing models and stochastic consumer choice models.

We investigate the role of visual attention in risky choice in a rich experimental dataset that includes eye-tracking data. We first show that attention is not reducible to individual and contextual variables, which explain only 20% of attentional variation. We then decompose attentional variation into individual average attention and trial-wise deviations of attention to capture different cognitive processes. Individual average attention varies by individual, and can proxy for individual preferences or goals (as in models of “rational inattention” or goal-directed attention). Trial-wise deviations of attention vary within subjects and depend on contextual factors (as in models of “salience” or stimulus-driven attention). We find that both types of attention predict behavior: average individual attention patterns are correlated with individual levels of loss aversion and capture part of this individual heterogeneity. Adding trial-wise deviations of attention further improves model fit. Our results show that a decomposition of attention into individual average attention and trial-wise deviations of attention can capture separable cognitive components of decision making and provides a useful tool for economists and researchers from related fields interested in decision-making and attention.

This paper reports a series of experiments designed to evaluate how the advertised participation payment impacts participation rates in laboratory experiments. Our initial goal was to generate variation in the participation rate as a means to control for selection bias when evaluating treatment effects in common laboratory experiments. Initially, we varied the advertised participation payment to 1734 people from $\mathrm{\$}5$ to $\mathrm{\$}15$ using standard email recruitment procedures, but found no statistical evidence this impacted the participation rate. A second study increased the advertised payment up to $\mathrm{\$}100$. Here, we find marginally significant statistical evidence that the advertised participation payment affects the participation rate when payments are large. To combat skepticism of our results, we also conducted a third study in which verbal offers were made. Here, we found no statistically significant increase in participation rates when the participation payment increased from $\mathrm{\$}5$ to $\mathrm{\$}10$. Finally, we conducted an experiment similar to the first one at a separate university. We found no statistically significant increase in participation rates when the participation payment increased from $\mathrm{\$}7$ to $\mathrm{\$}15$. The combined results from our four experiments suggest moderate variation in the advertised participation payment from standard levels has little impact on participation rates in typical laboratory experiments. Rather, generating useful variation in participation rates likely requires much larger participation payments and/or larger potential subject pools than are common in laboratory experiments.

When it comes to experiments with multiple-round decisions under risk, the current payoff mechanisms are incentive compatible with either outcome weighting theories or probability weighting theories, but not both. In this paper, I introduce a new payoff mechanism, the Accumulative Best Choice (“ABC”) mechanism that is incentive compatible for all rational risk preferences. I also identify three necessary and sufficient conditions for a payoff mechanism to be incentive compatible for all models of decision under risk with complete and transitive preferences. I show that ABC is the unique incentive compatible mechanism for rational risk preferences in a multiple-task setting. In addition, I test empirical validity of the ABC mechanism in the lab. The results from both a choice pattern experiment and a preference (structural) estimation experiment show that individual choices under the ABC mechanism are statistically not different from those observed with the one-round task experimental design. The ABC mechanism supports unbiased elicitation of both outcome and probability transformations as well as testing alternative decision models that do or do not include the independence axiom.