In my paper for this journal earlier this year (Abelson, 2020), I discussed how seven official guides to benefit-cost analysis (BCA) and the leading international text on BCA (Boardman et al., 2018) deal with eight contentious issues: the issue of standing, core valuation principles, the scope of CBA, changes in real values over time, the marginal excess tax burden, the social discount rate, the use of benefit-cost ratios, and the treatment of risk. I did not discuss, however, arguably the most potent cause of poor BCA studies: appraisal optimism, which is sometimes referred to less courteously as appraisal bias. Indeed, appraisal optimism receives little attention in most BCA textbooks and official guides. I will attempt here a partial rectification of these omissions.

Benefit-cost analysis (BCA) is loosely interpreted as a method for determining whether a policy is ‘in the public interest’. More formally, BCA measures the effect of a policy change on each individual’s wellbeing as a monetary value and sums these values over the population. If the sum is positive, the policy change is declared a potential Pareto improvement, meaning the change plus some set of cost-free money transfers would be Pareto superior to the status quo (or other comparator).

BCA is described as evaluating efficiency or the size of the social pie. It does not depend on how the pie is distributed. Recognizing that the public interest depends on distribution as well as efficiency, policy-evaluation guidance routinely calls for analysis of the distributional effects of a policy. Moreover, out of concern for distributional effects, BCA in practice sometimes departs from its textbook foundation by substituting population-average values for individual-specific values. For example, although an individual’s value per statistical life (VSL) depends on her income, age, and other characteristics, BCA in  practice almost always applies the same value across the population.

On the 26th of March, the Associated Press reported that, "Gov. Andrew Cuomo of New York has said that if all of his sweeping, expensive measures to stem the coronavirus saved one life, it would be worth it." This surely must be hyperbole, but he has also said, "We’re not going to accept a premise that human life is disposable… And we’re not going to put a dollar figure on human life." At the same time, it seems that there is an increasing call in public discourse for a sensible weighing of the pros and cons of "lockdown" measures, which I suspect anyone reading this blog would agree is a necessary component of good decision making. If Cuomo's sentiment about the value of life is shared among decision makers and the public, how do we get our benefit-cost message across in an effective way, that can actually improve the quality of public decision making, at this particular moment, in this particular context? 

In my view, the crucial takeaway from Cuomo's public statements isn't that there are decision makers out there who actually think that every single life should be preserved at any cost, but that he finds the explicit assignment of a dollar value to a human life is abhorrent. And, in the sense that Cuomo is thinking of. it is abhorrent. We know that the value of a statistical life (VSL) isn't the dollar value to society of saving a human life, but rather the dollar value of an amount of risk reduction sufficient to save one life, which we know is not the same thing, and captures only a portion of the value society places on human life.  The usefulness of the VSL for the purposes of measuring the welfare impacts of regulation is undeniable.The trouble is, explaining what it really is, to non-economists, is challenging at the best of times, and probably more or less impossible right now. People don't want to hear it, and there seems to be a real risk that at this very particular moment in time, some may simply disregard any analysis that involves monetization of human life.

The U.S. Environmental Protection Agency (EPA) has a long history of providing comprehensive guidance for conducting economic analyses, including benefit-cost analyses for environmental regulations. Much of that guidance is distilled in the EPA’s Guidelines for Preparing Economic Analyses, which was first released in 1983. The Guidelines are a mix of theory, empirical evidence, and practical recommendations and directives for ensuring the agency is providing the best available economic science for policy makers to consider when making decisions about regulatory actions.

Currently, e-cigarette supporters and opponents are passionately debating what regulations to impose on the products, if any. These debates have been playing out in legislative chambers across the United States, ranging from city halls to Congress, and in federal agencies including the Food and Drug Administration (FDA). Proponents who argue for no or little regulation maintain that e-cigarettes save lives by helping people quit smoking. Opponents, meanwhile, argue that e-cigarettes themselves are addicting teenagers to nicotine.

Applying benefit-cost analysis to homeland security regulations and related applications is difficult, in part due to issues in measuring security or risk avoided (Roberts, 2019; Farrow and Shapiro, 2009). The Office of University Programs within the Department of Homeland Security (DHS) asked our team of benefit-cost, decision, and risk analysts to evaluate changes in their security practices based on DHS funded research and development (R&D) projects over the last 15 years. Initial results have been published in von Winterfeldt, et al. (2019) with additional submissions planned for peer reviewed journals.