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.

DNA databases are useful tools for improving public safety. While past research examines the effects of national- or state-level databases, little is known about the distinct benefits of a local, District Attorney-run DNA database. Two key advantages of a local database are that (i) more local criminals submit a sample as part of a plea agreement (submission is not restricted to certain crimes and mandatory) and (ii) response times for identifying reoffenders from DNA evidence are shorter. This report performs a retrospective benefit–cost analysis on the Orange County District Attorney’s DNA database. The analysis is run on administrative records that provide costs, entries into the DNA database, and matches that occur between samples taken from a crime scene and individual profiles in the database. We also estimate the deterrence effect of entry into the database with defendant-case-level data. We find that, for every dollar spent on operating the database over the last 10 years, $1.71 is saved due to the estimated reduction in future offenses.

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.

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.

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.

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.

In this paper, we present a standardized approach for using cancer incidence and survival data to account for the timing between a reduction in carcinogen exposure and the subsequent reduction in cancer risk and fatality. While the estimates for this timing between a reduction in carcinogen exposure and reduced cancer risk would ideally come from high-quality studies specifically examining this question, very few such studies are available. Thus, we designed an approach to account for this timing when sufficient data are not available elsewhere. Our approach can be used in estimating monetized values for achieving small reductions in the risks for many common specific types of cancer in benefit–cost analyses of regulatory and non-regulatory policies in the United States that achieve cancer risk reductions by reducing carcinogen exposures. We provide estimated values for 108 different cancer sites and for all cancer sites combined. We accompany this paper with a spreadsheet-based tool that presents our results separately for non-fatal and fatal risks so that results can easily be calculated using different combinations of discount rates, latency between carcinogen exposure and cancer diagnosis, values for the willingness-to-pay to avoid fatal and non-fatal cancer risks, and potentially affected populations.

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.

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.

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.

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.

Healthcare technologies are often appraised under considerable ambiguity over the size of incremental benefits and costs, and thus how decision-makers combine unclear information to make recommendations is of considerable public interest. This paper provides a conceptual foundation for such decision-making under ambiguity, formalizing and differentiating the decision problems of a representative policy-maker reviewing the results from an economic evaluation. A primary result is that presenting information to regulators in an incremental cost-effectiveness ratio or cost-effectiveness analysis (CEA) format instead of a net monetary benefit or cost–benefit analysis (CBA) framework may induce errors in decision-making when there exists ambiguity in incremental benefits and decision-makers use well-known decision rules to combine information. Ambiguity in incremental costs or the value of the cost-effectiveness threshold does not distort decision-making under these rules. In reasonable contexts, I show that the CEA framing may result in the approval of fewer technologies relative to CBA framing. I interpret these results as predictions on how the presentation of information from economic evaluations to regulators may frame and distort recommendations. All the results extend to non-healthcare contexts.

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.

Energy access is often considered a catalyst for development. Yet, the binary classification of household electrification misses important variation in service quality and in how households use electricity. To examine the benefits of household electrification and illustrate the importance of using more nuanced classifications of energy access, this article develops a metric called the Energy Access Dividend (EAD), which quantifies the electrification benefits forgone due to slow and incomplete energy transitions. This framework is flexible, allowing for the estimation of a variety of electrification benefits such as reduced lighting and cell phone charging expenditures, environmental improvements, time use and asset ownership changes, and improvements associated with productive energy use. To demonstrate the applicability of this framework, we calculate the EAD for several proposed electrification trajectory alternatives in Honduras. We find that in Honduras, a country with high rates of basic electricity access, achieving immediate universal, high-quality electricity would generate nearly $697 million in benefits over the period leading up to 2050. We also estimate the EADs associated with more limited immediate electrification as well as geographically based electrification scenarios, demonstrating that these calculations can inform priorities for energy policy design.

We explore the changes in central government administration due to European Union (EU) membership and its consequences for policy outcomes and economic efficiency in Finland and Sweden. Both countries became members of the EU in 1995. Upon joining the union, member states are expected to adopt common legislation and are encouraged to develop similar rule-making procedures. The actual implementation of EU directives varies considerably between member states, however. This is also the case for Finland and Sweden. Despite the two Nordic countries for historical reasons having had similar government systems, upon becoming members of the EU, they started to diverge. Using a model of delegation and comparing the more centralized Finnish system with the decentralized institutional setup in Sweden, we show that the Swedish approach leads to a stricter than optimal environmental policy, which in turn makes EU policy non-optimal from a global point of view, ceteris paribus. We also provide empirical support for our findings in the form of some example cases. We focus on environmental policy since this is an area that has been high on the EU agenda.

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.

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.

Substance use disorder (SUD) is a public health crisis in the United States associated with significant economic costs including healthcare, criminal justice, productivity, and mortality and morbidity costs. In this paper, we present a tool for a customizable economic analysis that can be utilized by different recovery program owners and operators within the SUD continuum of care that considers these program’s operating and capital costs, location, size, and success rate. The goal of this tool is to provide owners and operators with an accessible tool that can estimate their individual program’s economic costs, benefits, and return on investment. In applications of the tool, we find that there are significant benefits associated with SUD recovery-oriented services, even with more conservative modeling of recovery benefits. Specifically, we find that a representative recovery housing program in Florida yields a net benefit of $143 million over 20 years with an associated return on investment of $22.19 per dollar invested. Further, we find that the net benefits of different recovery-oriented modalities including a recovery house, a recovery campus, and a residential inpatient program are positive, with returns on investment varying from nearly $22 per dollar invested to $1 per dollar.