Conventional benefit–cost analysis plays an important role in informing policy decisions, encouraging systematic investigation of the positive and negative impacts of alternative policies. It is based on strong normative assumptions, however. To measure individual wellbeing, the conventional approach relies on individuals’ willingness to exchange their income for the outcomes they experience. To measure societal welfare, it relies on simple aggregation of these values across individuals. In this “Ethics and Benefit–Cost Analysis” special issue, we explore alternative conceptions of individual and societal welfare, their application, and the implications, from both practical and ethical perspectives.

Unweighted benefit–cost analysis (BCA) based on aggregate willingness to pay might be, at long last, falling into disrepute, as it is widely recognized that it exhibits a bias toward the wealthy, and as alternatives are appearing more and more practicable. However, the choice of alternatives is often framed in terms of choosing an alternative metric to willingness to pay in money, such as willingness to pay in healthy life years, or a measure of subjective well-being. It is argued in this paper that (i) a simple summation of individuals’ willingness to pay in any numeraire (e.g., money, healthy life years) is bound to generate non-transitivity issues in a similar way as money-based BCA, and (ii) a metric such as subjective well-being involves distributional value judgments that are too specific to reflect the relevant spectrum in the public debate. The “orthodox” weighted BCA method, which links BCA to an underlying social welfare function, offers more flexibility and guarantees transitive choices. Fortunately, in some relevant cases, these various methods may provide similar results, and the main options currently proposed all give greater weight to the worse off in the population than does unweighted BCA.

The social welfare function (SWF) framework converts the possible outcomes of governmental policy choice into vectors (lists) of interpersonally comparable well-being numbers, measuring the lifetime well-being of each individual in the population of interest. The SWF proper is a rule for ranking these vectors. The utilitarian SWF adds up well-being numbers. A prioritarian SWF adds up well-being numbers plugged into a strictly increasing and strictly concave transformation function. Governmental policies are conceptualized as probability distributions over well-being vectors. A recent literature applies the SWF framework to health policy. This article first provides a brief overview of the SWF framework and then reviews some of the key concepts and findings that have emerged from this literature. One such concept is the “social value of risk reduction” (SVRR): the marginal social value (as calculated by the SWF) per unit of reduction in fatality risk for a given individual. The SVRR is the analogue, within the SWF framework, to the value-of-statistical-life (VSL) concept within benefit–cost analysis. This article explicates the SVRR concept and reports on recent theoretical findings and simulations that illustrate the properties of utilitarian and prioritarian SVRRs and their differences from VSL.

This article discusses the difference between benefit–cost analysis (BCA) and social welfare analysis in the evaluation of pandemic preparedness policies. Two social welfare approaches are considered: utilitarianism and prioritarianism. BCA sums the individuals’ monetary equivalents of the pandemic impacts. Social welfare analysis aggregates individuals’ well-being impacts. The aggregation rule identifies the normative judgments about what is fair. This article shows that the two methods yield very different estimates of the value of avoiding a future pandemic similar to the COVID-19 one. Compared to BCA, considerations about the distribution of the costs of the hypothetical intervention play a major role in the estimate of both utilitarian and prioritarian pandemic burdens: The more progressive the distribution of the costs is, the larger the net benefits of preventing the pandemic. In contrast, the BCA pandemic burden is indifferent to the distribution of the intervention costs. In addition, BCA tends to underestimate the burden suffered by low-income countries compared to social welfare analysis.

Conventional benefit–cost analysis is well-established and widely used to assess interventions designed to improve public health and welfare. While it has many advantages, it has well-known limitations. Chief among these is its inattention to the distributional equity of the impacts. To measure individual well-being, the conventional approach relies on individuals’ willingness to exchange their own income for the outcomes they experience. To measure societal welfare, it relies on simple aggregation of these values across individuals. This approach reflects a relatively narrow conception of welfare and ignores how impacts are distributed across advantaged and disadvantaged individuals. Social welfare analysis has been proposed as an alternative approach to address these limitations, but real-world applications are rare due largely to the complexity of the calculations. This article provides a pragmatic approach for conducting equity-sensitive benefit–cost analysis globally that addresses data limitations and other challenges, illustrated with example applications. It formally develops and implements equity weights that adjust for the decreasing marginal value of money and for additional moral considerations, prioritizing increases in welfare for those who are worse off.

In its 2023 revision of the social cost of carbon, the U.S. Environmental Protection Agency values people’s lives on the basis of their willingness to pay for them, without applying any distributional weights. It justifies this proposal on grounds of the Kaldor–Hicks criterion, which avoids interpersonal comparisons of wellbeing. But this criterion was discredited 70 years ago. Interpersonal comparisons of wellbeing cannot truly be avoided, and they should be used to determine distributional weights. One way of doing so is to identify as a numeraire a good that brings equal wellbeing to each person. A healthy life year is a reasonable, though only approximate, candidate for such a good. This article presents the point of view of a philosopher, regarding the practice of economists from outside the discipline.

Estimating value of statistical life (VSL) is an important input to many benefit-cost analysis (BCA) approaches, but for many low- and middle-income countries, there are limited or no data estimating VSL. Current guidance relies on extrapolation of results from high-income settings, which may be unreliable, leading to low confidence applying VSL. During 2019, we surveyed 1,820 low-income individuals (average consumption per capita USD329) across four diverse regions in Ghana and Kenya, to inform recommendations about effective spending in the development sector. We elicited VSL using a stated-preference approach, capturing the willingness-to-pay to reduce the risk of death for themselves and their children. Additionally, we conducted multiple “policy choice experiments” (PCEs) in which we asked respondents to choose, from the perspective of a decision-maker, between programs that save lives of different ages, and save lives and provide cash transfers. VSL estimates for this population fell in the range of USD66,795–USD90,453 (PPP-adjusted). We found similar results in the PCE but uncovered much stronger preferences for saving younger lives. Overall, our results suggest that VSL in low-income countries may be higher than estimates based on extrapolations from wealthy countries and that within these communities, policymakers should place more weight on saving the lives of young children. We also explore methodological learnings about how to apply and collect data for BCA in particularly low-income, low-education settings. We find that through careful training and gatekeeping, it is feasible to elicit complicated preferences in this population, and both approaches have their benefits and drawbacks.

There is a need to value health technologies in a way that accommodates their broader economic impacts and competing approaches for doing so have emerged. The Pareto principle (PP) requires policymakers to resolve intrapersonal trade-offs by deferring to the preferences of the individuals facing those trade-offs. Many broad value frameworks such as cost-utility analysis and its extensions, health-centric multicriteria decision analysis, and poverty-free life expectancy are not sufficiently deferential to these preferences, violating PP. I propose using the health-augmented lifecycle model (HALM) to value health technologies in a way that flexibly incorporates the interactions among health and economic factors – specifically mortality and morbidity risks, paid and unpaid work, consumption, leisure, and public and private transfer inflows and outflows--over the life course. It relies on individual preferences, satisfying PP. It is compatible with cost-benefit analysis, social welfare functions, and equivalent income approaches. I calibrate the HALM for the US setting and apply it to a pediatric vaccine.

Public sector allocative decisions should reflect, as far as possible, the preferences of those affected by the decisions. Conventional benefit–cost analysis (BCA) will simply aggregate individuals’ private willingness-to-pay (WTP) over all affected individuals to estimate the total benefits of a policy that delivers a public good. Given the nature of a public good, it is not unreasonable to consider that an individual may have altruistic preferences over the consumption of the public good by others. In this paper, we set out the theoretical underpinnings for a new citizen-based WTP, informed by political philosophy. Our model extends the standard social utility model (Bergstrom, 2006) of WTP for a public good when individuals are altruists by incorporating a Veil of Ignorance (VoI; Harsanyi, 1955). Our findings show that our WTP (Citizen) correctly includes altruistic as well as distributional preferences of individuals in society into WTP for use in a BCA. When WTP (Citizen) are aggregated for use in a BCA, equal weight is given to each individual’s preference and the BCA will correctly identify potentially Pareto-improving projects in a consistent manner.

Animal welfare is often ignored in decision-making, despite widespread agreement about its importance. This is partly because of a lack of quantitative methods to assess the impacts of policies on humans and nonhumans alike on a common scale. At the same time, recent work in economics, philosophy, and animal welfare science has made progress on the fundamental theoretical challenge of estimating the well-being potential of different species on a single scale. By combining these estimates of each species’ well-being potential with assessments of how various policies impact the quality of life for these species, along with the number of animals affected, we can arrive at a framework for estimating the impact of policies on animal health and well-being. This framework allows for a quantifiable comparison between policies affecting humans and animals. For instance, it enables us to compare human QALYs to animal QALYs tailored to specific species. Hence, the intrinsic value of animal welfare impacts of policies can be monetized on the same scale as market and non-market impact for humans, facilitating benefit–cost analysis. Many challenges remain though, including issues of population ethics, political feasibility, and new complexities in addressing equity and uncertainty.