Individuals' monetary values of decreases in mortality risk depend on the magnitude and timing of the risk reduction. We elicited stated preferences among three time paths of risk reduction yielding the same increase in life expectancy (decreasing risk for the next decade, subtracting a constant from or multiplying risk by a constant in all future years) and willingness to pay (WTP) for risk reductions differing in timing and life-expectancy gain. Respondents exhibited heterogeneous preferences over the alternative time paths, with almost 90 percent reporting transitive orderings. WTP is statistically significantly associated with life-expectancy gain (between about 7 and 28 days) and with respondents' stated preferences over the alternative time paths. Estimated value per statistical life year (VSLY) can differ by time path and averages about $500,000, roughly consistent with conventional estimates obtained by dividing estimated value per statistical life by discounted life expectancy.

As health care becomes increasingly personalized to the needs and values of individual patients, informational interventions that aim to inform and debias consumer decision-making are likely to become important tools. In a randomized controlled experiment, we explore the effects of providing participants with published fact boxes on the benefits and harms of common cancer screening procedures. Female participants were surveyed about breast cancer screening by mammography, while male participants were surveyed about prostate cancer screening by prostate-specific antigen (PSA) testing. For these screening procedures, we expect consumers to have overly optimistic prior beliefs about the benefits and harms. We find that participants update their beliefs about the net benefits of screening modestly, but we observe little change in their stated preferences to seek screening. Participants who scored higher on a numeracy test updated their beliefs about screening benefits more in response to the fact boxes than did participants who scored lower on the numeracy test.

We study the impact of new information on people's perceptions of the risks of e-cigarettes. In September 2019 the U.S. experienced an outbreak of e-cigarette, or vaping, associated lung injuries (EVALI). The EVALI outbreak created an information shock, which was followed by additional new information in a later CDC recommendation. We use data on consumer risk perceptions from two sets of surveys conducted before (HINTS survey data) and during the EVALI outbreak (Google Survey data). The empirical model examines changes in risk perceptions during the early crisis period when the CDC was warning consumers that they should avoid all vaping products and during a later period when the message was refined and focused on a narrower set of illegal vaping products that contain THC (the main psychoactive compound in marijuana). Econometric results suggest that the immediate impact of the first information shock was to significantly increase the fraction of respondents who perceived e-cigarettes as more harmful than smoking. As the outbreak subsided and the CDC recommendation changed to emphasize the role of THC e-cigarette products, e-cigarette risk perceptions were only partially revised downwards. Individuals who had higher risk perceptions showed a weaker response to the first information shock but were more likely to later revise their risk perceptions downwards. We conclude the paper by discussing the public policy issues that stem from having risk perceptions of e-cigarette relative to combustible cigarettes remain at these elevated levels where a substantial portion of consumers believe that e-cigarettes are more harmful than cigarettes.

We study the effects of traditional cigarette and e-cigarette taxes on use of these products among adults in the United States. Data are drawn from the Behavioral Risk Factor Surveillance System and National Health Interview Survey over the period 2011 to 2018. Using two-way fixed effects models, we find evidence that higher traditional cigarette tax rates reduce adult traditional cigarette use and increase adult e-cigarette use. Similarly, we find that higher e-cigarette tax rates increase traditional cigarette use and reduce e-cigarette use. Cross-tax effects imply that the products are economic substitutes. Our results suggest that a proposed national e-cigarette tax of $1.65 per milliliter of vaping liquid would raise the proportion of adults who smoke cigarettes daily by approximately one percentage point, translating to 2.5 million extra adult daily smokers compared to the counterfactual of not having the tax.

E-cigarettes provide nicotine in a vapor form, which is considered less harmful than the smoke from combustible cigarettes because it does not contain the toxins that are found in tobacco smoke. E-cigarettes may be effective in helping smokers to quit or they might simply provide smokers a method of bypassing smoking restrictions. There is very little causal evidence to date on how e-cigarette use impacts smoking cessation among adults. Minnesota was the first to impose a tax on e-cigarettes. This tax provides a plausibly exogenous deterrent to e-cigarette use. We utilize data from the Current Population Survey Tobacco Use Supplements from 1992 to 2015 to assess how the Minnesota tax increase impacted smoking cessation among adult smokers. Estimates suggest that the e-cigarette tax increased adult smoking and reduced smoking cessation in Minnesota, relative to the control group, and imply a cross elasticity of current smoking participation with respect to e-cigarette prices of 0.13. Our results suggest that in the sample period about 32,400 additional adult smokers would have quit smoking in Minnesota in the absence of the tax. If this tax were imposed on a national level about 1.8 million smokers would be deterred from quitting in a ten year period. The taxation of e-cigarettes at the same rate as cigarettes could deter more than 2.75 million smokers nationally from quitting in the same period. The public health benefits of not taxing e-cigarettes, however, must be weighed against effects of this decision on efforts to reduce vaping by youth.

Policies to address the coronavirus disease 2019 (COVID-19) require a balancing of the health risk reductions and the costs of economic dislocations. Application of the value of a statistical life (VSL) to monetize COVID-19 deaths produces a U.S. mortality cost estimate of $1.4 trillion for deaths in the first half of 2020. This article presents worldwide COVID-19 costs for over 100 countries. The total global mortality cost through July 2, 2020 is $3.5 trillion. The United States accounts for 25% of the deaths, but 41% of the mortality cost. Adjustments for the shorter life expectancy and lower income of the victims substantially reduces the estimated monetized losses, but may raise fundamental equity concerns. Morbidity effects of COVID-19 affect many more patients than do the disease's mortality risks. Consideration of the morbidity effects increase the expected health losses associated with COVID-19 illnesses by 10% to 40%.

Our research estimates COVID-19 non-fatal economic losses in the U.S. using detailed data on cumulative cases and hospitalizations from January 22, 2020 to July 27, 2020, from the Centers for Disease Control and Prevention (CDC). As of July 27, 2020, the cumulative confirmed number of cases was about 4.2 million with almost 300,000 of them entailing hospitalizations. Due to data collection limitations the confirmed totals reported by the CDC undercount the actual number of cases and hospitalizations in the U.S. Using standard assumptions provided by the CDC, we estimate that as of July 27, 2020, the actual number of cumulative COVID-19 cases in the U.S. is about 47 million with almost 1 million involving hospitalizations. Applying value per statistical life (VSL) and relative severity/injury estimates from the Department of Transportation (DOT), we estimate an overall non-fatal unadjusted valuation of $2.2 trillion for the U.S. with a weighted average value of about $46,000 per case. This is almost 40% higher than the total valuation of $1.6 trillion (using about $11 million VSL from the DOT) for all approximately 147,000 COVID-19 fatalities. We also show a variety of estimates that adjust the non-fatal valuations by the dreaded and uncertainty aspect of COVID-19, age, income, and a factor related to fatality categorization. The adjustments show current overall non-fatal valuations ranging from about $1.5 trillion to about $9.6 trillion. Finally, we use CDC forecast data to estimate non-fatal valuations through November 2020, and find that the overall cumulative valuation increases from about $2.2 trillion to about $5.7 trillion or to about 30% of GDP. Because of the larger numbers of cases involved our calculations imply that non-fatal infections are as economically serious in the aggregate as ultimately fatal infections.

In evaluating the appropriate response to the COVID-19 pandemic, a key parameter is the rate of substitution between wealth and mortality risk, conventionally summarized as the value per statistical life (VSL). For the United States, VSL is estimated as approximately $10 million, which implies the value of preventing 100,000 COVID-19 deaths is $1 trillion. Is this value too large? There are reasons to think so. First, VSL is a marginal rate of substitution and the potential risk reductions are non-marginal. The standard VSL model implies the rate of substitution of wealth for risk reduction is smaller when the risk reduction is larger, but a closed-form solution calibrated to estimates of the income elasticity of VSL shows the rate of decline is modest until the value of a non-marginal risk reduction accounts for a substantial share of income; average individuals are predicted to be willing to spend more than half their income to reduce one-year mortality risk by 1 in 100. Second, mortality risk is concentrated among the elderly, for whom VSL may be smaller and who would benefit from a persistent risk reduction for a shorter period because of their shorter life expectancy. Third, the pandemic and responses to it have caused substantial losses in income that should decrease VSL. In contrast, VSL is plausibly larger for risks (like COVID-19) that are dreaded, uncertain, catastrophic, and ambiguous. These arguments are evaluated and key issues for improving estimates are highlighted.

When the novel coronavirus entered the US, most US states implemented lockdown measures. In April-May 2020, state governments started political discussions about whether it would be worth the risk to reduce protective measures. In a highly politicized environment, risk perceptions and preferences for risk mitigation may vary by political inclinations. In April-May 2020, we surveyed a nationally representative sample of 5517 members of the University of Southern California's Understanding America Study. Of those, 37% identified as Democrats, 32% as Republican, and 31% as Third Party/Independent. Overall, Democrats perceived more risk associated with COVID-19 than Republicans, including for getting infected, being hospitalized and dying if infected, as well as running out of money as a result of the pandemic. Democrats were also more likely than Republicans to express concerns that states would lift economic restrictions too quickly, and to report mask use and social distancing. Generally, participants who identified as Third Party/Independent fell in between. Democrats were more likely to report watching MSNBC or CNN (vs. not), while Republicans were more likely to report watching Fox News (vs. not), and Third Party/Independents tended to watch neither. However, political inclinations predicted reported policy preferences, mask use, and social distancing, in analyses that accounted for differences in use of media sources, risk perceptions, and demographic background. In these analyses, participants' reported media use added to the partisan divide in preferences for the timing of lifting economic restrictions and reported protective behaviors. Implications for risk communication are discussed.

While much literature has focused on preferences regarding risk, preferences over skewness also have significant economic implications. An important and understudied aspect of skewness preferences is how they affect risk taking. In this paper, we design a novel laboratory experiment that elicits certainty equivalents over lotteries where the variance and skewness of the outcomes are orthogonal to each other. This design enables us to cleanly measure both skewness seeking/avoiding and risk taking behavior, and their interaction, without needing to make parametric assumptions. Our experiment includes both left- and right-skewed lotteries. The results reveal that the majority of subjects are skewness avoiding risk takers who correspondingly also take more risk when facing less skewed lotteries. Our second contribution is to link these choices to individual rank-dependent utility preference parameters estimated using a separate lottery choice protocol. Using a latent-class model, we are able to identify two classes of subjects: skewness avoiders with the classic inverse s-shaped probability weighting function and skewness neutral subjects that do not have an inverse s-shaped probability weighting function. Our results thus demonstrate the link between probability distortion and skewness seeking/avoidance choices. They also highlight the importance of accounting for individual heterogeneity.

We experimentally test for the role of choice uncertainty in generating "endowment effects" - the robust empirical finding that endowing participants with an item raises their reported valuation relative to participants being asked to purchase it instead. While there is some compelling evidence concerning trade uncertainty in the literature, there is substantially less evidence regarding the importance of choice uncertainty. This paper provides novel support for the significance of choice uncertainty in the context of both trading and stated valuations. In a primary set of studies, we find that reducing choice uncertainty eliminates under-trading in the exchange setting and decreases (but does not eliminate) the difference in average valuations reported by buyers and sellers, mainly by decreasing the number of extreme valuations by sellers. Interestingly, our treatment does not lead to a significant increase in the number of mutually acceptable trades implied by stated valuations. Comparing the results from our two primary experiments therefore suggests that value uncertainty continues to play a role in generating valuation asymmetries even after relevant product uncertainty has been resolved. A set of follow-up studies with modified designs replicates this finding in the exchange setting but fails to generate a valuation asymmetry in the control condition, possibly due to pandemic-related mitigation measures and less participant time with the endowed item.

Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The Independence Axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA over several months in thousands of stochastic choices using a large variety of binary option sets. Three monkeys showed consistently few outright (8%) but substantial graded (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most in CC (72%) and CR (88%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright may reflect the long experience of our monkeys, their more graded corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms.

Incentivized experiments in which individuals receive monetary rewards according to the outcomes of their decisions are regarded as the gold standard for preference elicitation in experimental economics. These task-related real payments are considered necessary to reveal subjects' "true preferences." Using a systematic, large-sample approach with three subject pools of private investors, professional investors, and students, we test the effect of task-related monetary incentives on risk preferences in four standard experimental tasks. We find no significant differences in behavior between and within subjects in the incentivized and non-incentivized regimes. We discuss implications for academic research and forions in the field.

An important societal problem is that people underinsure against risks that are unlikely or occur in the far future, such as natural disasters and long-term care needs. One explanation is that uncertainty about the risk of non-reimbursement induces ambiguity averse and risk prudent decision makers to take out less insurance. We set up an insurance experiment to test this explanation. Consistent with the theoretical predictions, we find that the demand for insurance is lower when the nonperformance risk is ambiguous than when it is known and when decision makers are risk prudent. We cannot attribute the lower take-up of insurance to our measure of ambiguity aversion, probably because ambiguity attitudes are richer than aversion alone.

In this study, we investigated whether the risk preference systematically changed during the spread of COVID-19 in Japan. Traditionally, risk preference is assumed to be stable over one's life, though it differs among individuals. While recent studies have reported that it changes with a large event like natural disasters and financial crisis, they have not reached a consensus on its direction, risk aversion, or tolerance. We collected panel data of Japanese individuals in five waves from March to June 2020, which covered the period of the first cycle when COVID-19 spread rapidly and then dwindled. We measured risk preference through questions on the willingness to pay for insurance. The main results are as follows: First, people became more risk tolerant throughout the period; and second, people were more averse to mega risk than moderate risk, with the former correlating more strongly with the individual's perception of COVID-19. The first result may be interpreted as "habituation" to repeated stress, as is understood in neuroscience.

When faced with a global threat peoples' perception of risk guides their response. When danger is to the self as well as to others two risk estimates are generated-to the self and to others. Here, we set out to examine how people's perceptions of health risk to the self and others are related to their psychological well-being and behavioral response. To that end, we surveyed a large representative sample of Americans facing the COVID-19 pandemic at two times (N = 1145, N = 683). We found that people perceived their own risk to be relatively low, while estimating the risk to others as relatively high. These risk estimates were differentially associated with psychological well-being and behavior. In particular, perceived personal but not public risk was associated with people's happiness, while both were predictive of anxiety. In contrast, the tendency to engage in protective behaviors were predicted by peoples' estimated risk to the population, but not to themselves. This raises the possibility that people were predominantly engaging in protective behaviors for the benefit of others. The findings can inform public policy aimed at protecting people's psychological well-being and physical health during global threats.

Little is known about how people's beliefs concerning the Coronavirus Disease 2019 (COVID-19) influence their behavior. To shed light on this, we conduct an online experiment ( ) with US and UK residents. Participants are randomly allocated to a control group or to one of two treatment groups. The treatment groups are shown upper- or lower-bound expert estimates of the infectiousness of the virus. We present three main empirical findings. First, individuals dramatically overestimate the dangerousness and infectiousness of COVID-19 relative to expert opinion. Second, providing people with expert information partially corrects their beliefs about the virus. Third, the more infectious people believe that COVID-19 is, the less willing they are to take protective measures, a finding we dub the "fatalism effect". We develop a formal model that can explain the fatalism effect and discuss its implications for optimal policy during the pandemic.

In the midst of mass COVID-19 vaccination distribution efforts in the U.S. Texas became the first state to abolish its mask mandate and fully lift capacity constraints for all businesses, effective on March 10, 2021. Proponents claimed that the reopening would generate short-run employment growth and signal a return to normal while opponents argued that it would cause a resurgence of COVID-19 and kill Texans. This study finds that each side was largely incorrect. First, using daily anonymized smartphone data - and synthetic control and difference-in-differences approaches - we find no evidence that the Texas reopening led to substantial changes in mobility, including foot traffic at a wide set of business establishments. Second, we find no evidence that the Texas reopening affected the rate of new COVID-19 cases or deaths during the five weeks following the reopening. Our null results persist across more urbanized and less urbanized counties, as well as across counties that supported Donald Trump and Joe Biden in the 2020 presidential election. Finally, we find no evidence that the Texas reopening impacted short-run employment, including in industries most affected by the reopening. Together, these findings underscore the persistence of late-pandemic era private behavior and stickiness in individuals' risk-related beliefs, and suggest that reopening policies may have impacts that are more muted than policymakers expect.

Economic theory suggests that the deterrence of deviant behavior is driven by a combination of and of punishment. This paper presents the first controlled experiment to study a third important factor that has been mainly overlooked: the of formal sanctions. We consider two dimensions: the timing at which the uncertainty about whether one will be punished is dissolved and the timing at which the punishment is actually imposed, as well as the combination thereof. By varying these dimensions of delay systematically, we find a surprising non-monotonic relation with deterrence: either no delay (immediate resolution and immediate punishment) or maximum delay (both resolution and punishment as much as possible delayed) emerge as most effective at deterring deviant behavior and recidivism. Our results yield implications for the design of institutional policies aimed at mitigating misconduct and reducing recidivism.

The Centers for Disease Control and Prevention (CDC) deem large indoor gatherings without social distancing the "highest risk" activity for COVID-19 contagion. On June 20, 2020, President Donald J. Trump held his first mass campaign rally following the US coronavirus outbreak at the indoor Bank of Oklahoma arena. In the weeks following the event, numerous high-profile national news outlets reported that the Trump rally was "more than likely" the cause of a coronavirus surge in Tulsa County based on time series data. This study is the first to rigorously explore the impacts of this event on social distancing and COVID-19 spread. First, using data from SafeGraph Inc, we show that while non-resident visits to census block groups hosting the Trump event grew by approximately 25 percent, there was no decline in net stay-at-home behavior in Tulsa County, reflecting important offsetting behavioral effects. Then, using data on COVID-19 cases from the CDC and a synthetic control design, we find little evidence that COVID-19 grew more rapidly in Tulsa County, its border counties, or in the state of Oklahoma than each's estimated counterfactual during the five-week post-treatment period we observe. Difference-in-differences estimates further provide no evidence that COVID-19 rates grew faster in counties that drew relatively larger shares of residents to the event. We conclude that offsetting risk-related behavioral responses to the rally-including voluntary closures of restaurants and bars in downtown Tulsa, increases in stay-at-home behavior, displacement of usual activities of weekend inflows, and smaller-than-expected crowd attendance-may be important mechanisms.

How do we judge others' behavior when they are both -when we observe their behavior but not the underlying traits or history that moderate the perceived riskiness of their behavior? We investigate this question in the context of the COVID-19 pandemic: How people make sense of, and judge, -behaviors, such as going to the gym or a bar, which are considered to be more or less risky and appropriate, depending on the target's vaccination status. While decision theoretic models suggest that these judgments should depend on the probability that the target is vaccinated (e.g., the positivity of judgments should increase linearly with the probability of vaccination), in a large-scale pre-registered experiment ( = 936) we find that both riskiness and appropriateness judgments deviate substantially from such normative benchmarks. Specifically, when participants judge a stranger's behavior, without being asked to think about the stranger's vaccination status, they tend to judge these behaviors similarly positively to behaviors of others who are to be fully vaccinated. By contrast, when participants are explicitly prompted to think about the vaccination status of others, they do so, leading them to view others more disparagingly, at times even more negatively than what a normative benchmark would imply. More broadly, these results suggest new directions for research on how people respond to risk and ambiguity. We demonstrate that even subtle cues can fundamentally alter what information is "top of mind," that is, what information is included or excluded when making judgments.