Abstract 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.

Abstract 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.

Abstract 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).

Abstract Mask mandates were controversial policies during the pandemic. Although there is considerable research on the benefits of masks, there has been no research on the distribution of perceived costs of compliance with mask mandates. This article presents the results from a hypothetical set of questions related to mask-wearing behavior and opinions that were asked of a nationally representative sample of over 4,000 participants in early 2022. We use survey valuation methods to assess how much participants would be willing to pay to be exempted from rules of mandatory community masking. The survey asks specifically about a 3-month exemption. We find that the majority of respondents (56%) are not willing to pay to be exempted from mandatory masking. However, the average person was willing to pay $525, and a small segment of the population (0.9%) stated they were willing to pay over $5,000 to be exempted from the mandate. Younger respondents stated higher willingness to pay to avoid the mandate than older respondents. Combining our results with standard measures of the value of a statistical life, we estimate that a 3-month masking order was perceived as cost-effective through willingness to pay questions only if at least 13,333 lives were saved by the policy.

Abstract 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.