The so-called Flyvbjerg database is the largest source of data on the performance of major investment projects. It has generated influential analyses of the magnitude of and reasons for cost overruns and demand shortfalls in major projects. Those analyses have demonstrated, among other things, the systematic presence of large forecast errors in both construction costs and in user demand in the first year of operation. They have also linked those results to the social welfare consequences of the underlying projects, suggesting that the large and systematic forecast errors are indicative of welfare destruction. Given how influential those analyses have been, this paper examines the link between the database, empirical analyses thereof, and social benefit–cost analysis (BCA). To that end, both the measurement of variables in the database and the estimation of forecast errors are contrasted against BCA. The conditions for the estimated forecast errors to approximate those obtained from a BCA are spelled out, and the scope for drawing welfare conclusions based on those estimates is discussed. Furthermore, numerical simulations are presented to explore whether the estimated forecast errors do indeed imply likely welfare destruction. The simulations suggest that as large as the forecast errors are, welfare destruction is no foregone conclusion.

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.

This article uses the well-being valuation (WV) approach to estimate and monetize the well-being impacts of informal care provision on caregivers. Using nationally representative longitudinal data from the UK, the British Household Panel Survey, we address two challenging methodological issues related to the economic valuation of informal care: (i) the anticipatory nature of informal care; and (ii) the sensitivity of income estimates used in valuation. We address the anticipatory issue by focusing on well-being impacts associated with caring for a relative who had recently suffered a serious accident. We use the fixed effects filtered (FEF) estimator to estimate a “time-invariant income” coefficient free from individual fixed effects bias, which helps to partially improve the quality of the income estimate as an alternative to using instrumental variables. This estimate is used in the calculation of shadow prices of informal care. Our estimates suggest that, focusing on the first year of unanticipated care provision, those experiencing the well-being losses from providing unanticipated informal care would be willing to pay approximately £13,167 on average to avoid it.

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).

In this article, we examine the evolving landscape of Regulatory Impact Assessment (RIA) decisions made by the National Health Regulatory Agency (Agência Nacional de Vigilância Sanitária [Anvisa]), a prominent federal agency leading RIA implementation in Brazil. We quantify Anvisa’s RIA usage rates, exploring the influence of emergency and other justifications for RIA exemptions both pre and post the enactment of detailed procedural requirements in a recent Presidential Order No. 10,411/2020. Our quantitative analysis shows a sharp decline in RIA usage after Presidential Order No. 10,411/2020 came into force in April 2021, as well as a diversification of the justifications given in decisions not to use RIA (exception decisions). This effect is present even if we take into consideration a large proportion of exception decisions tied to Anvisa’s regulatory stock review and to urgent measures prompted by the COVID-19 pandemic. Additionally, we conduct a qualitative analysis of exception decisions due to emergency, post-Presidential Order. We find that Anvisa failed to provide compelling justifications for exempting RIA in emergency regulations in several cases and avoids ex post reviews when RIAs are waived due to emergencies. We conclude the article with recommendations to enhance the scrutiny and transparency of Anvisa’s exception decisions to conduct RIA.

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.

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.

Coal is declining in the U.S. as part of the clean energy transition, resulting in remarkable air pollution benefits for the American public and significant costs for the industry. Using the AP3 integrated assessment model, we estimate that fewer emissions of sulfur dioxide, nitrogen oxides, and primary fine particulate matter driven by coal’s decline led to $300 billion in benefits from 2014 to 2019. Conversely, we find that job losses driven by less coal plant and mining activity resulted in $7.84 billion in foregone wages over the same timeframe. While the benefits were greatly distributed (mostly throughout the East), costs were highly concentrated in coal communities. Transferring a small fraction of the benefits to workers could cover these costs while maintaining societal net benefits. Forecasting coal fleet damages from 2020 to 2035, we find that buying out or replacing these plants would result in $589 billion in air quality benefits, which considerably outweigh the costs. The return on investment increases when policy targets the most damaging capacity, and net benefits are maximized when removing just facilities where marginal benefits exceed marginal costs. Evaluating competitive reverse auction policy designs akin to Germany’s Coal Exit Act, we find that adjusting bids based on monetary damages rather than based only on carbon dioxide emissions – the German design – provides a welfare advantage. Our benefit–cost analyses clearly support policies that drive a swift and just transition away from coal, thereby clearing the air while supporting communities needing assistance.

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.

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.

The purpose of this paper is to answer the research question: How may methods of Benefit–Cost Analysis (BCA) influence policy decision-makers? The new research is a continuation of the previous research, and it is updated with new data and new examples. As it is well-known by BCA experts and practitioners, BCA is a relatively simple and widely used technique for deciding whether to make a change. BCA is very important in political and governmental decisions. Governments are policy creators, and Governments are usually formed of politicians. How much do experts inside Governments influence the policymakers? Do politicians allow enough experts to influence policymaking? The research regarding the work of the Croatian Government’s Office for Legislation and its Unit for the system and coordination of the Assessment of the Effects of Regulations (Unit) showed that the analysis of that Unit was not very good in some very important laws. These laws were changed very often because in practice was seen that the application of some laws was not good, and had caused some problems for the citizens, caused legal uncertainty for them, and in some cases had caused a lot of damage.

Compromised kidney function is associated with an array of environmental contaminants and pathogens that may be considered for regulation. However, there are few valuation estimates for kidney effects for use in benefit–cost analyses, particularly willingness-to-pay estimates. This paper is one of several surveys valuing morbidity developed by the OECD Surveys to elicit Willingness-to-pay to Avoid Chemicals-related negative Health Effects project, which aims to improve the basis for benefit–cost analyses. We report the results of a stated preference survey valuing reduced the risk of symptomatic chronic kidney disease, filling an important gap in the valuation literature and addressing a need for applied benefits analysis of chemical regulation. The survey was administered to representative samples in each of 10 countries: Canada, Chile, China, Denmark, Germany, Italy, Norway, Türkiye, the United Kingdom, and the United States. The mean (median) WTP for an average reduction of 3.5 in 1,000 of the risk of serious kidney disease over 5 years is $2,609 ($764), corresponding to a mean (median) value per statistical case (VSC) of chronic kidney disease of $805,000 ($224,000). The mean VSC varies between $700,000 for Canada and $1,200,000 for Türkiye.

Both Republican and Democratic administrations make regulatory and funding decisions with close reference to benefit–cost analysis (BCA). With respect to regulation, there has been a great deal of academic discussion of BCA and its limits, but almost no attention has been paid to the role of BCA in government funding. That is a serious gap, not least in connection with climate-related risks, such as wildfire, drought, extreme heat, and flooding. Office of Management and Budget (OMB) Circular A-94 sets out guidelines for the BCA required when people are applying to many federal discretionary grant programs. Through Circular A-94, OMB has long required applicants to demonstrate that the benefits of their projects would exceed the costs. But under Circular A-94 as it stood for many years, efficiency-based BCA could produce results that fail to maximize welfare and that are also highly inequitable. The 2023 revision of Circular A-94 focuses more directly on welfare and equity, which are now – not uncontroversially – being brought directly into policy. At the same time, the new Circular A-94 raises fresh questions about how best to promote welfare, and to consider equity, in practice. This article explains the economic foundations for promoting welfare through distributional weighting – and how the old BCA guidance fell short. It then offers recommendations on how to operationalize distributional weighting on the ground specifically for government spending programs – and for BCA more broadly.

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.

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.

This study develops a generalized evaluation framework that can be used to quantify the financial, economic, stakeholder, and environmental impacts of renewable energy support programs. The application of this framework is demonstrated by evaluating the feed-in tariff (FIT) program for solar distributed energy resources (DER) in Ontario, Canada. Our analysis reveals that Ontario’s FIT program has successfully promoted the adoption of solar DER across communities. However, the program has caused inequitable societal outcomes through a cross-subsidization with a present value of 9 CAD billion, paid for by the electricity consumer base for the benefit of only the 0.06 percent of electricity consumers who could install solar systems. The cost imposed on the Canadian economy ranges from 2.86 to 5.37 CAD billion, depending on the discount rate applied. The sensitivity analysis results indicate that the burden of this program on the Canadian economy would have been reduced by 50 percent if the program had been delayed and implemented in 2016 instead of 2010 due to the declining trend in solar system investment costs. The lessons from this analysis provide insights for designing future environmental and emission reduction policies.

Asthma is a non-communicable and non-curable lung disease that affects 10% of children and 4% of adults worldwide and is associated with an array of environmental contaminants and chemicals. This article offers values suitable for use in cost–benefit analyses of the willingness to pay (WTP) for reduced severity of asthma in adults and children and in reduced probability of getting asthma for these two population groups, all in the context of reducing chemical exposures. To this end, an online survey was administered between November 2021 and May 2022 to 12 727 respondents from seven countries of the Organisation for Economic Co-operation and Development (OECD). This article applies two stated preference methods for eliciting WTP: the contingent valuation method for reduced asthma severity and choice experiments for reduced probability of getting asthma of various severities. The context for such elicitations was a set of household products that contain fewer hazardous chemicals than what is currently available in supermarkets but are more expensive. The study finds that the WTP for reducing asthma severity in adults by one step, e.g. from “moderate plus” to “moderate”, is USD2022 529 per year on average. The parental WTP for reducing asthma severity in their children is USD2022 PPP 948 per year and is on average 1.8 times higher than their WTP for themselves. The mean value of a statistical case (VSC) of adult asthma which would be applied to predictions of new cases of asthma avoided by a regulation equals USD2022 280 000, while the mean VSC of childhood asthma equals USD2022 430 000.

President Biden’s first-day memo “Modernizing Regulatory Review” directs the Office of Management and Budget to “propose procedures that take into account the distributional consequences of regulations… to ensure that regulatory initiatives appropriately benefit and do not inappropriately burden disadvantaged, vulnerable, or marginalized communities.” This paper makes two contributions. First, it discusses how economic analysis can transparently provide the information needed to make value-judgments about what distributional effects are appropriate and inappropriate. Second, it discusses the distributional consequences of regulations that are either designed to reduce internalities or might have the additional benefit of reducing internalities. Examples include tobacco product regulations, appliance energy efficiency standards, and automobile fuel efficiency standards. In many cases, the regulations will increase the prices or decrease the availability of goods that disadvantaged consumers prefer. This paper discussed how to determine whether restricting their consumption opportunities creates net benefits or net costs for disadvantaged consumers. Inframarginal consumers who do not change their consumption face higher opportunity costs but do not receive any benefits from reduced internalities. Empirical challenges include the need to quantify the fraction of inframarginal consumers and the size of the internalities.

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.