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).

Redistribution generates equity benefits and deadweight loss. A canonical result in economic theory holds that policymakers generally cannot escape the problem of deadweight loss by redistributing through non-tax rules. Nonetheless, the Office of Management and Budget (OMB) released a revised framework for regulatory analysis in November 2023 that encouraged federal agencies to consider distributional benefits – but not the deadweight loss of redistribution – when choosing which non-tax rules to promulgate. By omitting deadweight loss from distributional analysis, OMB’s framework will lead to inaccurate estimates of the welfare effects of regulatory changes and will leave agencies vulnerable to legal attack on the ground that they have ignored an important aspect of regulatory redistribution in their decision-making. This article illustrates how agencies can incorporate deadweight loss into distributional analysis and thereby place their redistributive rules on firmer economic and legal footing. It shows how the “elasticity of taxable income” approach – widely used by tax economists – can be modified to the regulatory context so that agencies can estimate the deadweight loss of redistributive regulations while incurring relatively modest additional analytical burdens. Explicitly incorporating deadweight loss into distributional analysis will provide the public with a more accurate view of the welfare consequences of regulatory actions and will make regulations more robust to legal challenges.

The social cost of greenhouse gases is important in many regulatory impact analyses. However, calculations of the social cost of greenhouse gases are highly complex and periodically revisited. We offer seven recommendations to improve current estimates. These include recommendations to use both country-level and global measures of the social cost of greenhouse gases, to use country-specific values for monetizing climate damages, to represent uncertainties by reporting distributions instead of using only central values, and to conduct a temporal distributional analysis that shows the magnitudes of climate damages across generations. We also provide recommendations for the discount rates that should be used when estimating the social cost of greenhouse gases, and the appropriate discount rates for regulatory impact analyses that include the social cost of greenhouse gases.

The Sustainable Development Goals (SDGs) are ambitious but in deep trouble. Benefit–cost analysis can help. This Special Issue highlights 12 of the most efficient interventions to speed up progress on the SDGs with Benefit–Cost Ratios (BCRs) above 15. The approaches cover tuberculosis, education, maternal and newborn health, agricultural R&D, malaria, e-procurement, nutrition, land tenure security, chronic diseases, trade, child immunization, and skilled migration. Spanning 2023–2030, these policy approaches are estimated to cost an annual average of $41 billion (of which $6 billion is non-financial). They will realistically deliver $2.1 trillion in annual benefits, consisting of $1.1 trillion in economic benefits and 4.2 million lives saved. The pooled benefit–cost ratio of all 12 investments is 52. By prioritizing these high-impact “best buy” interventions, decision-makers can enhance resource allocation and contribute most efficiently to the SDGs.

Title IX greatly expanded adolescent females’ participation in athletic activities, which may have led to health benefits that extend into later life. Previous research has not explored whether health benefits arising from Title IX differ by race or ethnicity and has not examined women at older ages when health problems become more evident. This article examines the effect of Title IX on racial and ethnicity disparities in health outcomes by considering women aged 42–52 years. White women in these age groups exhibit declines in their self-assessed health status and increases in many health-related ailments, consistent with other evidence on temporal trends in health for women in this age range. Compared to white women, both Black and Hispanic women report the opposite pattern, as there is greater improvement in the post-Title IX period in overall health status. Black and Hispanic women also exhibit greater declines relative to white women in smoking rates post-Title IX, which should confer a broad range of risk reductions. The more favorable impact of Title IX on Black and Hispanic women indicates that investments in women’s sports may enhance both equity and efficiency.

As its practitioners know well, benefit-cost analysis (BCA) walks a fine line between the positive and normative, between the science of economics and the art of political economy. Missteps threaten to undermine its credibility as a value-free science, while overcaution risks irrelevance to the pressing questions of the day. As BCA adapts to give more weight to distributional concerns, while operating in a more highly charged political environment than ever before, these tensions will only grow. For perspective, I reexamine three prominent episodes in the history of economics where these issues were vigorously debated: (i) The founding of the NBER by Wesley Clair Mitchell, who insisted that the organization eschew all policy recommendations; (ii) the introduction of the modern definition of economics as the study of tradeoffs by Lionel Robbins, who insisted welfare effects could never be aggregated; and (iii) the origins of BCA as a measure of income, which to first-generation practitioners seemed to foreclose the possibility of measuring “intangible” benefits like recreation opportunities, mortality risks, and equity. These episodes, together with critiques of economics from philosophers of science, suggest we are best served by being as transparent as possible about the ways values influence BCA reasoning, without arrogating political decisions into it.

The impact and economic merits of President Biden’s Executive Order 13985 on equity depend on how the executive order is implemented. While policy discussion to date has focused on equitable outcomes, we propose framing risk equity policies in terms of equitable risk tradeoff rates based on six policy guidelines. The starting point for ex ante evaluation of equity for mortality risk policies should be the symmetric application of the value of a statistical life (VSL) to all groups. Because of the substantial heterogeneity in VSLs by income and demographic characteristics, symmetric tradeoff rates generate subsidies and deficits relative to private values of risk. Efforts to provide for distributional preferences should be grounded in an understanding of the differentials already provided through application of a uniform VSL. Targeting government programs to specific groups ex ante should be coupled with estimates of the efficiency loss based on symmetric tradeoff rates and the implicit tradeoff rate ratio relative to the average VSL needed to support the redistributive policy. Here, we propose equity guidance that could be incorporated in a revised version of Office of Management and Budget Circular A-4. We contrast the ex ante equity guidance approach with the ex post risk equity evaluation procedure that is incorporated in the Biden Administration’s recently proposed Justice40 plan, where 40% of the benefits of existing programs must be targeted to certain minority groups without ex post examination of their cost effectiveness either feasible or currently planned.

This article examines a policy of scaling up LLINs by 10 percentage points from 2020 levels with a 90% cap in the 29 highest-burden countries in Africa along with social and behavioral change (SBC) and information education and communication (IEC) campaigns to increase the use and effectiveness of LLINs. The incremental cost of this scenario compared to a baseline of maintaining malaria interventions at 2020 levels has a present-day (2023) value of 5.7 billion US$ 2021 discounted at 8% over the period 2023–2030 (undiscounted starting at US$ 416 million in 2023 increasing to US$ 1.4 billion in 2030). This investment will prevent 1.07 billion clinical cases and save 1,337,069 lives. With standardized Copenhagen Consensus Center assumptions, the mortality benefit translates to a present value of US$ 225.9 billion. The direct economic gain is also substantial: the incremental scenarios lead to US$ 7.7 billion in reduced health system expenditure from the reduced treatment of cases, a reduction in the cost of delivering malaria control activities, and reduced household out-of-pocket expenses for malaria treatment. The productivity gains from averted employee and caretaker absenteeism and presenteeism add benefits with a present value of US$ 41.7 billion. Each dollar spent on the incremental scenario delivers US$ 48 in social and economic benefits.

In addition to effects from greenhouse gases, climate change is affected by short-lived climate forcers (SLCF). These are often co-emitted with carbon dioxide, and some are regulated air pollutants. In the governance of these pollutants, established estimates of damage costs of pollution inform benefit–cost analyses. However, climate change impact of SLCFs is omitted from these estimates. The purpose of this study is to calculate economic damage costs of air pollutants’ effect on climate change and compare these with established damage costs. Focus is on European emissions governed in the EU National Emission Reduction Commitments Directive during 2020–2050. We use well-known SLCF emission metrics and multiply with literature values on social costs of methane to calculate climate damage costs of SLCFs. The results indicate that average absolute climate damage costs are highest for black carbon ($59,500/ton in 2050) and lowest for nonmethane volatile organic compounds ($661/ton). Our indicative values are likely underestimations. Indicative climate damage costs are usually lower than established damage costs, with notable exceptions. We propose that more detailed studies are necessary, and that inclusion of climate damage costs into economic valuation of SLCFs is important for future air pollution and climate benefit–cost analyses.

Professional sports teams commonly reevaluate their location decisions based on the prospect of building new, more attractive, stadiums. Even though a large economic literature warns about the modest (and possibly negative) effects on the local economy of hosting a professional sports team, the economic effects of professional teams and stadiums remain blurry for the general public, and cities in the United States continue to compete to lure teams with generous public subsidies. This article integrates several contributions of the literature into one cohesive and simple framework based on cost–benefit analysis, and provides estimations of the average local economic effects of teams in the four biggest professional leagues in the United States. If professional sports games do not attract visitors from other cities, or if players and owners do not spend a significant share of their income in the area, hosting a team can negatively affect the local economy.

The current challenge of education systems is learning. Across low-income countries (LICs) and lower-middle-income countries (LMCs), 62 % of 10-year-olds could not read at a minimally sufficient level in 2015. This study provides an overview of recent spending on education and its correlation with learning outcomes. We show that the relationship between education spending and learning is historically weak. From 2000 to 2015, LICs and LMCs increased spending on education in primary schools by ~$137 per student, an 80 % inflation-adjusted increase, with no corresponding change on the average learning outcomes. We then conduct a benefit-cost analysis of candidate interventions that could increase learning at low cost. Two interventions – structured pedagogy and, teaching at the right level, with and without a technology component generate large benefit-cost ratios. If deployed uniformly to reach 90 % of the 467 million students in LICs and LMCs, these interventions would cost on average $18 per student per year or $7.6 billion annually, generating $65 in benefits for every $1 spent. The economic logic behind this finding is that the hard and costly work of getting children into primary schools has mostly been accomplished, leaving open the possibility of learning interventions that improve the efficiency of the existing education system at low cost. Our results show that increasing education expenditure by just 6 % could increase learning by 120 % if directed toward these highly cost-effective interventions.

The Biden administration has made equity a priority when issuing regulations, encouraging agencies to ensure that their regulations appropriately benefit and do not inappropriately burden disadvantaged groups. But scholarly examinations of agencies’ practices to date on understanding the distributional consequences of their regulations and on promoting equity have revealed significant gaps. In particular, agencies pay very little attention to the incidence of the costs of their regulations. The U.S. Environmental Protection Agency, for example, rarely considers the incidence of regulatory costs among disadvantaged groups, despite being an agency that conducts relatively complete benefit–cost analyses and explicitly analyzes environmental justice implications of its regulations. But this cost-blindness is a mistake; it presents a missed opportunity to use the current equity-focused momentum to make real improvements for disadvantaged groups that could have long-lasting effects. This essay calls for agencies to give more attention to the incidence of regulatory costs in order to identify needs and opportunities for grants and investments to disadvantaged groups. This approach could provide much-needed direction for a program like the Biden administration’s Justice40 initiative.

The generalized risk-adjusted cost-effectiveness (GRACE) analysis method modifies standard cost-effectiveness analysis (CEA), the primary method currently used worldwide to value health improvements arising from healthcare interventions. Generalizing standard CEA, GRACE allows for decreasing or even increasing returns to health. Previous presentations of GRACE have relied extensively on Taylor Series expansion methods to specify key model parameters, including those that properly adjust for illness severity and preexisting disability, consequences of uncertain treatment outcomes, and the marginal rate of substitution between life expectancy and health-related quality of life. Standard CEA cannot account for these sources of value or cost in its valuation of medical treatments. However, calculations of GRACE measures based on Taylor Series are approximations, which may be poorly behaved in some contexts. This paper provides a new approach for implementing GRACE, using exact utility functions instead of Taylor Series approximations. While any proper utility function will suffice, we illustrate with three well-known functions: constant relative risk aversion (CRRA) utility; hyperbolic absolute risk aversion (HARA) utility, of which CRRA is a special case; and expo-power (EP) utility, of which constant absolute risk aversion (CARA) is a special case. The analysis then extends from two-period to multiperiod models. We discuss methods to estimate parameters of HARA and EP functions using two different types of data, one from discrete choice experiments and the other from “happiness economics” methods. We conclude with some reflections on how this analysis might affect benefit-cost analysis studies of healthcare interventions.

In almost every country, government is the largest buyer of works, goods, and services from the private sector. Through the laws and practice of public procurement, governments create competition among firms, thus optimizing public expenditure. However, public procurement is often associated with inefficient allocation of resources and corruption. One method to reduce inefficiencies and abuse in public procurement is the use of e-government procurement (e-GP) platforms. Yet nearly 40% of countries—mostly low- and lower-middle income countries—do not have functioning e-GP platforms. Cost–benefit analysis is used to make the investment case for the development and integration of e-procurement systems in low- and lower middle-income countries. The costs of setting up an e- GP system include an initial investment of $9.03 million, on average, for the planning, design, and build phases spread over a 5-year period. Annual operating and maintenance expenses during pilot and deployment phases are estimated at $1.1 million annually. In total, it is estimated that the net present value of costs to design, build, test, deploy, and operate a robust e-GP system is $16.7 million for a typical low- and middle-income country (at an 8% discount rate). While there are many tangible benefits of e-GP, the benefit assessed here is the reduction in the prices of goods, works, and services paid by government buyers. Using the average percentage reduction in procurement prices of 6.75%, the savings from an e-procurement system are valued at $637.9 million and $5.2 billion for low- and lower middle-income countries, respectively. The benefit–cost ratio of implementing an e-GP system in the average low-income country ranges from 8 to 58 and is 142 to 473 for a lower middle-income country. The size of the procurement market, the reduction in procurement prices, the duration of the implementation process, and the penetration rate of e-GP throughout government are principal determinants in the return on investment.

Motivated by traffic congestion and air pollution, Beijing is one of several major cities to restrict vehicle ownership by requiring residents to win a lottery for the right to obtain an additional car. We examine the welfare cost of preventing people from owning cars because of misallocation: under a lottery, some individuals with low willingness to pay (WTP) for cars can obtain cars, while other individuals with high WTP cannot. We estimate welfare costs using a new contingent valuation method survey of Beijing lottery participants which we designed and conducted explicitly for this purpose. We find that restricting vehicle ownership reduced private welfare by 26 billion yuan. Back-of-the-envelope calculations suggest that the benefits of lower congestion and pollution roughly equal the costs. Our WTP estimates indicate a net welfare gain of approximately 32 billion yuan if Beijing’s lottery were replaced with an auction, which is similar to previous estimates.

Drawing upon recent studies that empirically estimate both the benefits and costs of trade, this paper addresses a simple and important question: By how much do the benefits of increased global trade outweigh the costs? To the best of our knowledge, this is the first attempt to answer this question at global and World Bank income-grouping levels using empirically estimated relationships from the trade cost literature. Using a structural gravity model, we simulate changes in three primary trade constraints: a 10% reduction in tariff levels, a 10% reduction in effective distance, and a 10% increase in free trade agreement depth. The projection leads to a roughly 5% increase in global trade by value. Our model suggests that increased trade has an incredibly high benefit–cost ratio (BCR) for the developing world with an order-of-magnitude estimate for low- and lower–middle-income countries of 100 and for upper–middle-income countries of 50. However, the BCR for high-income countries is substantially lower, with a value closer to 5. Overall, the results suggest that free trade leads to substantial net benefits globally, generating US$ 700 billion in benefits (0.83% of global GDP) and US$ 100 billion in costs (0.12% of global GDP) in the first year, a differential that grows over time. Sensitivity analyses suggest that our BCRs are on the lower end of a plausible range. The results point to the incredible value of free trade, particularly for developing countries, and reiterate the importance of considering distributional impacts when implementing trade reforms.

This article compares the U.S. Environmental Protection Agency’s (EPA) ex ante cost analysis of its 1995 Large Municipal Waste Combustor (MWC): New Source Performance Standards and Emissions Guidelines rule to an ex post assessment of its cost. Unlike many retrospective cost analyses, where ex post assessments are limited due to lack of data on compliance costs, this case study is unique because we located and used plant-level survey data from the U.S. Department of Energy and Governmental Advisory Associates in a comparison of ex ante and ex post costs of individual MWCs. We find the ex post capital expenditures for nitrogen oxide control systems are typically lower than the EPA ex ante estimates, while the ex post capital expenditures for mercury control systems tend to be higher than the EPA ex ante estimates. Finally, while we find a few outliers, the average ratio of ex post to ex ante capital expenditures for particulate matter and sulfur dioxide emissions control is near unity.

There are increasing calls for concrete suggestions on how to account for distributional impacts in policy analysis. Within the context of benefit-cost analysis, per se, one possibility is to apply “distributional weights,” to inflate costs and benefits experienced by poor or disadvantaged groups. We distinguish between “utility-weights,” intended to correct for the bias in willingness to pay caused by diminishing marginal utility of income, and “equity-weights,” intended to account for the possibility that decision makers might have disproportional concern about the welfare of the poor or other disadvantaged groups. We argue that utility-weights are appropriate and necessary to maintain the legitimacy of BCA as a measure of aggregate welfare, but that equity-weights are inappropriate because they involve moral judgments that should remain in the domain of democratically accountable decision makers, and because they conflate information about both the welfare and equity impacts of policies, making it impossible for decision-makers to apply their own moral values to the assessment of tradeoffs between welfare and equity. We offer concrete suggestions regarding the application of utility-weights and the calculation of a set of metrics to provide intuitively comprehensible and useful information about, and allow decision makers to quantitatively assess the tradeoffs between, welfare and equity caused by specific policies.

This study is an attempt to determine whether the need to get hydropower project appraisals perfectly right during the pre-construction phase, so as to prevent significant overruns along with benefit shortfalls, should supersede the need to deliver projects at the earliest possible time so as to meet the needs of the people. To achieve the study objective, we test whether the Hiding Hand principle is predominantly benevolent or malevolent. We argue that if the Hiding Hand is benevolent, then project stakeholders are better off focusing on the quick delivery of power projects; however, if it is malevolent, then more attention should be given to perfecting project appraisals. It transpires from the statistical analysis that the Benevolent Hiding Hand dominates the Malevolent Hiding Hand in the selected World Bank-financed hydropower projects (33% v. 21%), and that ultimately, 75% of the projects were even more successful than anticipated—while 25% of the projects failed. Our findings further show that while a total loss of 2.335 billion USD in the sampled dams was caused by the Malevolent Hiding Hand, 11.259 billion USD was gained as a result of the Benevolent Hiding Hand. The predominance of the Benevolent Hiding Hand justifies placing some weight on proceeding with hydropower projects that show significant promise even if all the implantation risks are not fully quantified at the appraisal stage, especially in developing countries.