This special issue of the Journal of Benefit–Cost Analysis is dedicated to the memory of Jerry Ellig, a brilliant economist whose untimely death in 2021 cut short a productive and influential career in government and academia. Jerry was adept at applying economic concepts and empirical analysis to improve public policy, and he enjoyed not only studying real policy problems, but finding practical solutions to them. He was also a generous mentor, supporting and collaborating with graduate students and colleagues to publish prolifically in peer-reviewed economics, public administration, and political science journals, as well as law reviews and more popular outlets. He was a great communicator, able to take his academic work and translate it for different audiences, including through testimony, seminars, op-eds, short presentations, and classroom teaching. In addition to these immense talents, Jerry was genuinely kind and unpretentious; he wore Wal-Mart suits and garish ties. And he was laugh-out-loud funny, a master at diffusing difficult situations with a witty, but never mean, quip.

Heat-related mortality risks are a substantial component of the looming costs of climate change in the United States and globally. This article presents the results from a risk-risk survey to test whether U.S. respondents place a valuation premium on mortality risks from heat relative to cancer and transportation risks. The questionnaire exploits exogenous shocks to temperatures during a heat wave and randomized elements to further test whether preferences vary with heat exposure or the age of individuals exposed to heat risks. The results provide strong evidence that there is no valuation premium in the U.S. for heat-related risks. Subjects valued cancer risks twice as highly as heat and transportation risks, the latter of which are a common benchmark for general traumatic fatalities. While there is some evidence that subjects value heat risks more when exposed to a heat shock of approximately 3–4 °C, the size of the differential is too small to establish a statistically significant heat risk premium. Finally, subjects’ responses demonstrate no differential valuation of mortality risks to seniors versus the general population based on the preferences of the general population or the senior subsample.

This article provides a brief Introduction to this special Journal of Benefit–Cost Analysis issue dedicated to comments filed by former presidents of the Society for Benefit Cost Analysis and editors of this journal on the Office of Management and Budget’s draft Circular A–4 on regulatory impact analysis guidelines.

The current method used by the US Government to calculate benefits and costs does not accurately measure the monetary value of some regulations. The problem is that the method fails to recognize the possibility that individual valuations, reflecting judgments in a relatively isolated, uncoordinated situation, might be significantly different from individual valuations in a situation of coordination. For example, people might be willing to pay $X for a good, supposing that other people have that good, but might be willing to pay $Y to abolish that good, supposing that no one will have that good. Or people might be willing to pay $X to protect members of an endangered species in their individual capacity, but far more than $X for the same purpose, assuming that many others are paying as well; one reason may be that an individual expenditure seems futile. We sketch, identify, and explain this unmeasured value, which we define as coordination value, meant as an umbrella concept to cover several categories of cases in which individual valuation measured in the uncoordinated state might be inadequate. Changing the methodology of benefit–cost analysis to consider coordination value would present serious empirical challenges, but would eliminate the estimation error.

In the first analysis purporting to causally link environmental pollution to personality, Schwaba and colleagues leveraged a natural experiment driven by the United States. They used the Clean Air Act to assess the impact of decreased atmospheric lead on the “big five” personality traits. Using data from an online personality test taken by more than 1.2 million U.S. residents, Schwaba et al. reported that people born after lead levels had peaked in their county of birth had more mature, psychologically healthy personalities in adulthood (higher agreeableness and conscientiousness, and lower neuroticism) than cohorts born earlier and exposed to higher levels of atmospheric lead. One concern with their findings is that personality differences among people born in different periods could come from factors unrelated to lead, for example, access to abortion and birth control, or demographic, cultural, or technological changes. Schwaba et al. recognized this possibility but did not fully explore it. When we account for cohort-wide changes by introducing birth year fixed effects into Schwaba et al.’s models, the estimated effects of the lead phaseout on personality largely disappear, becoming indistinguishable from zero while remaining precise. Meanwhile, the estimated birth year fixed effects are jointly significant, suggesting differences in personality traits across cohorts. These results indicate that any effects of the lead phaseout on more mature, psychologically healthy adult personalities are not consistently observable in the data used by Schwaba et al. More broadly, they caution against making causal inferences without controlling for time period effects.

Epidemiological and economic (Epi-econ) models account for endogenous interactions between the epidemic and the economy. We explore the applicability of an Epi-econ model to isolate the effects of lockdown policies during coronavirus disease 2019 in the Netherlands. To this aim, we recalibrate the seminal Epi-econ model of Eichenbaum and colleagues with updated parameters specific to the Dutch context. We find that the model performs poorly in replicating observed Epi-econ trends under baseline assumptions. Next, we explore possibilities to improve model fit by relaxing policy and transmission parameters, and by incorporating observed “random noise” in infectivity parameters. This approach spectacularly improves model performance in replicating observed trends. Finally, we test the performance of the model in simulating alternative policy scenarios. We use the Containment and Health Index from the Blavatnik School of Government to replace Dutch policy parameters with exemplary countries on opposite sides of the stringency spectrum. We find that a more stringent lockdown policy would reduce peak prevalence, while aggravating peak economic contraction, but with little effect on overall trends. Conversely, a more lenient lockdown policy was estimated to increase the peak and overall prevalence, with little effect on economic outcomes. We conclude that while rigorous adjustments to existing models were required, a combined Epi-econ model could be informative to policymakers in assessing alternative lockdown policy options.

This paper explores the role of microeconomic analysis in policy formulation by assessing how the regulatory impact analyses (RIAs) that federal regulatory agencies prepare for important proposed rules may affect outcomes when regulations are challenged in court. Conventional wisdom among economists and senior regulatory officials in federal agencies suggests that high-quality economic analysis can help a regulation survive such challenges, particularly when the agency explains how the analysis affected decisions. However, highlighting the economic analysis may also increase the risk a regulation could be overturned by inviting court scrutiny of the RIA. Using a dataset of economically significant, prescriptive regulations proposed between 2008 and 2013, we put these conjectures to the test, studying the relationships between the quality of the RIA accompanying each rule, the agency’s explanation of how the analysis influenced its rulemaking decisions, and whether the rule was overturned when challenged in court. The regression results suggest that higher-quality RIAs are associated with a lower likelihood that the associated rules are later invalidated by courts, provided that the agency explained how it used the RIA in its decisions. Similarly, when the agency described how the RIA was used, a poor-quality analysis appears to increase the likelihood that the regulation is overturned, perhaps because it invites a greater level of court scrutiny. In contrast, when the agency does not describe how the RIA was utilized, there is no correlation between the quality of analysis and the likelihood that the regulation will be invalidated.

The Federal Communications Commission (FCC) established a separate Office of Economics and Analytics (OEA) in December 2018 to promote more consistent quality and use of economic analysis in its decisions. The agency’s reorganization concentrated economists who previously were dispersed across different offices and bureaus. This paper describes key organizational choices that were made in the period preceding and soon after the establishment of OEA. We show how these decisions – which relate to decision rights, formal control systems, and informal practices and procedures – are consistent with organizational theory and practice. We also draw lessons from the FCC’s experience that may apply to those tasked with managing economists and other specialized or technical staff in large and/or complex organizations.

We develop a model to perform a cost–benefit analysis of bus fare subsidies under financial constraints that preclude the purchase of additional buses. Our model considers users’ costs in the provision of bus services and the lack of road pricing to internalize urban transport externalities in a context where financial constraints severely limit the institutional ability to plan and design the bus system. Because of financial constraints, the bus system can hardly accommodate demand during peak times: buses travel overcrowded, passengers cannot board the first bus to arrive at the bus stop, and they cannot arrive at their destination at their desired time. Another salient aspect of our model is the inclusion of motorcycles as a second private transport mode. Motorcycles are typical in many urban agglomerations in the emerging world and engender many negative transport externalities. According to our results, fare subsidies provide social benefits in Metropolitan Asunción. During peak hours, a higher subsidy is justified as the reduction of the unpriced external costs of substitute modes compensates for the increased cost created by an additional bus passenger. In the off-peak, a higher subsidy is justified (i) as the higher frequencies induced by the new bus ridership reduce waiting times and (ii) because of the reduction of the unpriced external costs of substitute modes. Although our model does not explicitly include inequality aversion, we discuss the distributional aspects of subsidies in the context of middle-income countries.

After Circular A-4 “Regulatory Analysis” had served various Presidential administrations for 20 years, in 2023 revisions were proposed and made to update and modernize regulatory guidance. At the behest of the Office of Information and Regulatory Affairs within the Office of Management and Budget, peer reviewers were nominated, and a peer review of proposed revisions was organized. Joseph Aldy, Cary Coglianese, Joseph Cordes, R. Scott Farrow, Kenneth Gillingham, William Pizer, Christina Romer, W. Kip Viscusi, and I were selected. The consequent peer reviews are the focus of this synopsis. After reading the comments from my fellow peer reviewers, I was impressed with the careful, thoughtful advice given. When asking nine peer reviewers with various backgrounds to comment on proposed revisions to guidance on regulation, we might expect to get at least 10 different views. Yet, I sense basic agreement on several key aspects of the topics of the notable proposed updates. The degree of consensus is reassuring. Less reassuring, and counter to the actual revisions adopted, is that the peer reviewers mostly agree that fundamental aspects of the updates on the discount rate, distributional analysis, and scope are ill-advised.

This study examines coronavirus disease 2019-related law-drafting using Finnish data. The impact assessments of 17 government bills are analyzed using content analysis. These 17 government bills have been selected based on their societal significance and are related to restrictions on fundamental rights under the Finnish constitution. The purpose of this study is to examine how ex ante impact assessments and the planning of forthcoming implementation and ex post impact assessments have been carried out in these regulatory projects. The results indicate that, based on the ex ante assessments presented in the government bills, it is not possible to conclude that the benefits of the proposed restrictions would exceed the costs. Despite the rush, uncertainty and the incompleteness of information caused by the pandemic, the government bills should clearly justify why the presented regulation is the preferred alternative. Furthermore, the subsequent ex post impact assessment measures have been planned to produce information only about the general development of the pandemic situation instead of the functionality of individual restriction regulations. In this respect, special attention should have been paid to the individual restriction measures.

This article presents the first analysis in the literature that measures the economic benefit of drop-off recycling in terms of consumer surplus (CS). CS, a measure of the economic benefit that consumers receive from participating in economic activities, is estimated using a single-site travel cost method application based on survey data. Our findings indicate that the total CS residents of Monongalia County receive from drop-off recycling is $1,317,960 annually.

Cannabis is the most commonly used illicit drug worldwide. In countries with repressive drug policies, the costs of its prohibition plausibly outweigh the benefits. We conduct a cost–benefit analysis of cannabis legalization and regulation in the Czech Republic, taking into consideration alternative scenarios designed using parameters from the known effects of cannabis legalization in selected U.S. states, Canada, and Uruguay. Our analysis focuses on tax revenues, law enforcement costs, the cost of treatment and harm reduction, and the value of Quality Adjusted Life Years (QALYs). Under all the projected scenarios, the identified benefits of legalizing cannabis for personal use exceed the potential costs. The estimated net social benefit of legalization is in the range of 34.4 to 107.6 million EUR per year (or between 3.2 and 10.1 EUR per capita), depending on the size of the cannabis market and the development of cannabis prices after legalization.

Over the last century, the United States has witnessed three approaches to achieving better regulatory outcomes: the removal of “economic” regulations in certain sectors; regulatory impact analysis (RIA) of new “social” regulations; and retrospective analysis of existing regulations. This article reviews the rationale for each approach, the results to date, and the remaining challenges. It finds that both institutional and technical factors influence the success of reform efforts.

This article is based on comments that I submitted to the Office of Management and Budget (OMB) as one of eight peer reviewers of OMB’s 2023 draft revisions to Circular A-4, “Regulatory Analysis.” Since that time, OMB issued a final version of Circular A-4, which was then rescinded in January 2025. This article summarizes some of my main comments on the 2023 draft version that I submitted as a peer reviewer, along with a “prologue” that summarizes some of the main issues raised in my peer review and an “epilogue” recognizing both the initial revision of Circular A-4 that was initially adopted, as well as the 2025 decision to rescind the revised version and return to the 2003 version of the circular. The 2003 Circular continues to provide useful guidance, but could have been improved by expanding the scope of regulatory impact analysis in ways discussed in my peer review comments.

In 2020 the Federal Communications Commission (FCC) revisited a spectrum allocation decision it made in 1999. The Agency found that frequencies set aside for specific technologies used by vehicles – Intelligent Transportation Services (ITS) – had been left largely unused. It crafted new rules, shifting 45 MHz of the 75 MHz allocation to newly designated wireless services focusing on Wi-Fi applications, while leaving the remaining (40% of bandwidth) reserved for ITS. The FCC decision was premised on a cost–benefit analysis performed by the agency, supported by two similar studies submitted by outside interests. Yet, upon examination, the cost–benefit calculations prove stunningly uncompelling. In their economic logic, their understanding of existing market data and their use of FCC policy, fundamental errors render net benefit estimates irrelevant to decision-making. In particular, the value of marginal products (VMPs) as well as the opportunity costs of rival allocations are ignored. These failings are stunning, both on their own and given that the FCC, in its reallocation, critiqued its 1999 decision as socially unproductive – and yet deployed just the same basic methodological format, relying on FCC administrative determinations to select favored business models for supplying wireless services.

Under President Clinton’s Executive Order 12866, “Regulatory Planning and Review,” U.S. federal agencies have been required to assess the costs, benefits, and other impacts of their major regulations since 1993. The U.S. Office of Management and Budget (OMB), in the Executive Office of the President, is responsible for overseeing this process and issuing related guidance. Under the Biden Administration, in April 2023 OMB issued a draft update of its 2003 Circular A-4 best-practice guidance and requested public comment. That update was finalized in November 2023, then rescinded by the Trump Administration in January 2025. This special issue of the Journal of Benefit-Cost Analysis provides reflections on the revisions of that guidance from past Society for Benefit-Cost Analysis presidents and Journal editors. Although I address several substantive issues in my comments and other work, barriers to implementation of best practices remain a major concern. Most of those who commented on the proposed revisions focused largely on the words on the page rather than on the work needed to implement them. Yet one of the most important sentences in both original and revised Circulars reads: “You will find that you cannot conduct a good regulatory analysis according to a formula. Conducting high-quality analysis requires competent professional judgment…” The challenge is supporting the development of this judgment, and ensuring that analysts have the data and resources necessary to conduct high-quality analyses that are useful for decision-making.

Our research reviews theory and evidence in the economics literature to provide a standard value of a statistical life (VSL) applicable to the Department of Defense (DOD). We follow Viscusi (Best estimate selection bias in the value of a statistical life, Journal of Benefit-Cost Analysis, 9(2), 205–246, 2018a) by conducting a meta-analysis of 1,025 VSL estimates from 68 different labor market studies and find a best-set average VSL estimate of $11.8 million (US$2021) across all studies. For DOD analysts and practitioners, we advocate using our best-set VSL estimate for the vast majority of benefit–cost analyses (BCAs) within the DOD. In addition to providing a VSL benchmark to use in DOD BCAs, we disaggregate casualty types and provide a range of VSL estimates to use in sensitivity analyses. Employing restricted data from the DOD on over 6,700 US military fatalities in Afghanistan and Iraq from 2001 to 2021, we show that (1) fatalities are highly concentrated among young, White and enlisted males, and that (2) the Army and Marines account for the vast majority of the fatality totals (73 and 22%, respectively), in contrast to the low number of fatalities (<5%) in the Air Force and Navy. The monetized cost of US military fatalities in Afghanistan and Iraq would involve individual VSL levels that range from $3.2 to $27.6 million per statistical life (US$2021), after applying standard pay grade and income adjustments.

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.