In 2023, the U.S. Office of Management and Budget (OMB) issued guidance documents that specified new procedures for assessing prospective government regulations (Circular A-4) and economic policies more generally (Circular A-94). These revisions to long-standing guidance were not minor updates but shifted policy analyses from an efficiency-oriented perspective to a redistributive approach. OMB broadened the guidelines for reporting distributional consequences of policies and also specified how policy impacts on different income groups should be weighted. The weights assume that the social welfare function is governed by the sum of identical individual utility functions, each of which exhibits a substantial rate of diminishing marginal utility of income. The resulting weights provide a premium for households below the median-income level and a considerable penalty for those at higher-income levels. Application of the weights to property losses creates potentially substantial inefficiencies. If based on current empirical evidence on the income elasticity of the value of a statistical life rather than assuming that there is a complete offset of the weights, application of the weights to mortality risk valuation would generate inequities in protection.

The considerable literature on the value of a statistical life (VSL) documents the wage-mortality risk trade-offs for the working population. Regulatory analyses often must monetize risks to populations at the tails of the age distribution. Because of the longer life expectancy for children, there have been proposals to add a premium to their VSL, which would generate an inconsistency with revealed preference estimates of the VSL trajectory over the life cycle. The shorter life expectancy among older people has led to various arbitrary senior discounts for seniors’ life expectancy. Application of the value of a statistical life year (VSLY) can address valuation of small changes in life expectancy. Examples of inappropriate age adjustments that we discuss include practices by the Consumer Product Safety Commission (CPSC) and the Environmental Protection Agency (EPA).

Some regulations do not only reduce human deaths, injuries, and illnesses; they also protect nonhuman animals. Regulatory Impact Analyses, required by prevailing executive orders, usually do not disclose or explore benefits or costs with respect to nonhuman animals, even when those benefits or costs are significant. This is an inexcusable gap. If a regulation prevents dogs, horses, or cats from being killed or hurt, the benefits should be specified and quantified. This proposition holds even if those benefits are in some sense incidental to the main goal of the regulation. At the same time, turning the relevant benefits into monetary equivalents raises serious challenges, akin to those raised by the valuation of statistical children.

A uniform value of a statistical life (VSL) is part of established practice within the federal government. Some people have applauded a uniform VSL on the ground that it respects the equality of persons; takes harm to poor people as seriously as it does harm to wealthy people; avoids expressive harms; and builds appropriate wealth redistribution into regulatory policy. Other people have strenuously objected to a uniform VSL, emphasizing that to reduce mortality risks, poor people are willing to pay less than rich people are, and urging that poor people should not have to pay more than they are willing to pay. Whether a uniform VSL is in the interest of poor people depends on whether we are dealing with subsidies or regulations. In the case of subsidies, a uniform VSL is highly likely to benefit poor people. If we are dealing with regulations, we cannot know whether a uniform VSL helps or harms poor people without knowing the incidence of costs (and benefits).

The Consumer Product Safety Commission’s regulation of disposable lighters was targeted at preventing injuries due to use of lighters by children not over 4 years of age. Based on a difference-in-differences analysis of national data for 1990–2019, this article estimates that the regulation reduced all injuries to the target population by 71%, burn injuries by 74%, and injuries severe enough to warrant admission to the hospital by 85% overall and by 84% for burn injuries. Unlike the counterproductive performance of safety cap regulations, this safety device enhanced safety levels in the target population group. The safety improvements from lighter safety devices outweigh any lulling effect of viewing products as being “childproof.” The regulation had a broader safety impact beyond the target population group, as it also reduced all types of injuries by at least 50% for children in the 5–17 age groups. Total annual risk reduction benefits were $940–$1465 million. A benefit-cost analysis based on a retrospective assessment of the regulation finds a more favorable impact than was anticipated.

The value of a statistical life (VSL) establishes the money-risk tradeoff that U.S. government agencies have used for four decades to monetize the mortality reduction benefits of proposed regulations. This article advocates the adoption of the VSL more generally both for policy evaluation purposes and for setting the magnitude of regulatory sanctions involving fatalities. Agencies currently employ the inconsistent practice of using the VSL to set the stringency of regulations, while at the same time, reverting to very low monetary values of sanctions for violations that result in fatalities. Reform of penalty levels to reflect the VSL will require increasing the current statutory limits on regulatory penalties. Revamping the penalty structure also will incentivize private companies to incorporate the VSL in their corporate risk analyses. Government agencies, including those concerned with national defense, similarly could profit from greater expansion of the use of the VSL in policy decisions.

Examination of estimates of the income elasticity of the value of a statistical life based on international stated preference studies yields an average between 0.94 and 1.05 overall and 0.65 and 0.80 after controlling for covariates. Quantile regression estimates indicate that the income elasticity is about 0.55 for more affluent countries and 1.0 for lower income nations, i.e., those countries that have estimates of the value of a statistical life below $2 million or per capita income levels below $3212. The estimates distinguish the values of the income elasticity across country either by income level or by the value of a statistical life. These elasticities are similar to those found in revealed preference labor market studies. The estimates are robust, controlling for possible sample selection bias and the influence of covariates, such as the type of risk.

Selection of the best estimates of economic parameters frequently relies on the “best estimates” or a meta-analysis of the “best set” of parameter estimates from the literature. Using an all-set dataset consisting of all reported estimates of the value of a statistical life (VSL) as well as a best-set sample of the best estimates from these studies, this article estimates statistically significant publication selection biases in each case. Biases are much greater for the best-set sample, as one might expect, given the subjective nature of the best-set selection process. For the all-set sample, the mean bias-corrected estimate of the VSL for the preferred specification is $8.1 million for the whole sample and $11.4 million based on the CFOI data, while for the best-set results, the whole sample value is $3.5 million, and the CFOI data estimate is $4.4 million. Previous estimates of huge publication selection biases in the VSL estimates are attributable to these studies’ reliance on best-set samples.

Countries throughout the world use estimates of the value of a statistical life (VSL) to monetize fatality risks in benefit-cost analyses. However, the vast majority of countries lack reliable revealed preference or stated preference estimates of the VSL. This article proposes that the best way to calculate a population-average VSL for countries with insufficient or unreliable data is to transfer a base VSL from the United States calculated using labor market estimates from Census of Fatal Occupational Injuries data, coupled with adjustments for differences in income between the United States and the country of interest. This approach requires estimation of two critical inputs: a base U.S. VSL and the income elasticity of the VSL. Drawing upon previous meta-analyses that include adjustments for publication selection biases, we adopt a base VSL of $9.6 million. We utilize a sample of 953 VSL estimates from 68 labor market studies of the VSL covering fourteen lower-middle income to high income nations. We estimate the income elasticity of the VSL within the United States to be from 0.5 to 0.7 and to be just above 1.0 for non-U.S. countries. Quantile regression reveals that much of the disparity in income elasticities is attributable to income differences between the United States and other countries, as the income elasticity increases for lower income populations. Using income classifications from the World Bank, we calculate average VSLs in lower income, lower-middle income, upper-middle income, and upper income countries to be $107,000, $420,000, $1.2 million, and $6.4 million, respectively. We also present VSL estimates for all 189 countries for which World Bank income data are available, yielding a VSL range from $45,000 to $18.3 million.

The value of a statistical life (VSL) is the most influential single parameter used in calculating the benefits of governmental regulations. While there are some interagency differences, there is a commonality in the conceptual approach, the central role of mortality risk valuation in benefit assessment, and the general range of valuations used. Corporate risk decisions are based on a less rigorous risk analysis procedure. As typified by the General Motors ignition switch recall problems and the company’s lax corporate safety culture, there is often little systematic corporate balancing of cost and risk. This suppression of safety concerns may be attributable to the adverse experiences automobile companies had after conducting risk analyses that valued fatalities based on damages awards for wrongful death, and in response juries levied blockbuster punitive damages awards. Instead, companies should adopt the VSL in its product risk decisions. Companies should also be provided with a safe harbor reference point for responsible risk decisions. Regulatory agencies should use the VSL in setting regulatory sanctions.

The use of wage differential techniques to estimate the value of a statistical life (VSL) leads to the conclusion that willingness to pay for risk reduction increases with income. However, the use of this result in policy-relevant calculations, such as benefit-cost analysis, has led to criticism among ethicists and the lay public, at the same time as it has been defended in the economic literature. In this paper, we argue that differential valuation measures not a differential value that individuals place upon their own lives, but a differential value that they place upon marginal economic resources. Using two sets of VSL estimates from metastudies, we provide an initial estimate of the relative marginal value of income, allowing interpersonal comparison at the societal level. With these results, we propose an empirically determined, ethically justifiable social welfare function that can be easily incorporated into benefit-cost analysis and that has important implications for development economics, although more work is necessary to provide a robust estimate.

The Census of Fatal Occupational Injury (CFOI) provides data for creating objective measures of workplace risk used in estimations of wage-risk premia for value of statistical life (VSL) calculations. This relatively new data set enables a more theoretically defensible measure for use in hedonic wage equations. However, constructing these rates from the CFOI data necessarily involves creating an industry-occupation matrix defining the “jobs,” deciding whether or not to include the self-employed, and selecting a denominator. These choices in the construction of the risk measure alone, as shown here, result in variations of VSL estimates ranging from $8 million to $18 million. Further, risk measures based on the CFOI data, regardless of construction, are sensitive to simple changes in the specification for the hedonic wage equation. In particular, fixed effects describing the industry in which a worker is employed, as well as the worker’s occupation, are primary influences on the magnitude of the VSL estimates.

Many people have wondered why the US government conducts cost-benefit analysis with close reference to the value of a statistical life (VSL). It is helpful to answer that question by reference to the “Easy Cases,” in which those who benefit from regulatory protection must pay for it. In such cases, WTP is usually the right foundation for VSL, because beneficiaries are hardly helped by being forced to pay for regulatory protection that they believe not to be in their interests. In the Easy Cases, arguments from both welfare and autonomy support the use of WTP and VSL (with potentially important qualifications involving imperfect information and behavioral market failures). The analysis is less straightforward in harder cases, in which beneficiaries do not pay for all of the cost of what they receive (and may pay little of that cost). In such cases, arguments from welfare and autonomy might not lead in any clear direction. In the harder cases, regulation might be justified on welfare grounds even if the cost-benefit analysis (based on VSL) suggests that it is not. In principle, a direct inquiry into welfare (the master concept) would be preferable to use of cost-benefit analysis. In the harder cases, distributional considerations might also count in favor of proceeding (as prioritarianism suggests). But at the current time, direct inquiries into welfare consequences and into distributional effects are challenging in practice, and hence regulators should generally rely on cost-benefit analysis, making welfarist adjustments, or adjustments based on distributional considerations, only in compelling cases.