What a year it has been. Between the unfolding of a global pandemic and nationwide protests around the topic of police brutality, 2020 has already been a jam-packed year for public policy, and we haven’t even made it into election season.
Reducing COVID-19 risks requires making extraordinarily difficult decisions that trade-off saving lives and economic damages. Benefit-cost analysis is well-suited for investigating these trade-offs and informing these decisions. However, interpreting and using the results requires understanding the framework and addressing its limitations, including the uncertainties in the value of mortality risk reductions and the distribution of impacts across those who are advantaged and disadvantaged. For related information, view JBCA Editor Tom Kniesner's conversation with W. Kip Viscusi.
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.
More than 450,000 Americans died of an opioid overdose between 1999 and 2018. There were fifteen fatal opioid overdoses for every 100,000 individuals in 2018, a ratio five times greater than in 1999. While public health researchers and policymakers have rightly turned their attention toward remedying the global coronavirus pandemic, the U.S. opioid epidemic continues to take lives. In my dissertation, An Empirical Analysis of Policy Responses to the Opioid Epidemic, I analyzed the effect of various state and federal interventions to reduce opioid abuse and overdoses. This analysis can contribute to the benefit-cost analysis of policies that aim to decrease opioid consumption and overdose deaths.
State responses to the opioid epidemic have taken a variety of forms. Among the most controversial have been naloxone access laws, which make it easier for individuals to acquire the drug that reverses an ongoing opioid overdose. From 2006 to 2016, most states enacted one or more different laws to make naloxone accessible to opioid users and others so that they can administer it to individuals experiencing an overdose. Laws that let individuals purchase naloxone directly from a pharmacist without a patient-specific prescription from another healthcare provider decrease overdose deaths by about ten percent. Three thousand fewer fatal overdoses would occur annually if every state enacted such laws (relative to a world in which no states have such laws). Using a VSL of $10 million, those lives saved amount to approximately $300 billion in monetized benefits between 2006 and 2016. In contrast, laws that immunize prescribers, pharmacists, or naloxone administrators from civil or criminal liability arising out of naloxone provision or administration had no statistically significant effect on overdose deaths. The benefits of naloxone access laws were concentrated in white users, heroin overdoses, and urban areas. Accordingly, the results indicate that making naloxone even more widely available, particularly as an over-the-counter medication, would save a significant number of lives.
Economic management in Australia has effectively been outsourced to health experts. Political and business leaders are deferring exclusively to public health advice about how to contain the COVID-19 virus with no serious public discussion of the economic costs.
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.
The primary audience for these Guidelines is EPA analysts, including economists, who are charged with conducting the analysis and writing the technical materials to accompany regulatory text. However, their impact and use extend far beyond an internal, bureaucratic document that collects dust on a shelf. Indeed, many university professors and other federal agencies turn to these Guidelines for use in teaching benefit-cost analysis and understanding the state of the science for conducting economic analysis.
Being in the SBCA is great because it connects me with so many very smart people who care about public policy and benefit-cost analysis. This value was illustrated by the rapid rate at which the SBCA network began to exchange ideas and estimates about the benefits and costs of interventions to address the COVID-19 situation. As these ideas start to work their way into the more general media, I wanted to share a few observations with readers of On Balance about the challenges facing our community in conducting and interpreting these benefit-cost analyses.
First, given how little we know about COVID-19, the first set of analyses will be preliminary and reflect a higher level of imprecision than analyses of diseases and interventions where we have more history. Nevertheless, these initial responses are essential for informing public policy. Policy makers are going to have to make decisions. They should have the benefit of the best available analyses, even if those analyses will get more accurate as the underlying estimates and assumptions used to produce them. The key here is to inform current decision-making and to then update the analyses as more evidence becomes available in order to inform future decision making.
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.
Both sides agree that smoking combustible cigarettes is dangerous: the Centers for Disease Control estimates that annually 480,000 individuals die from smoking, accounting for about one in every five deaths. Yet, there exists disagreement regarding the risks of e-cigarettes relative to combustible cigarettes. The two sides also disagree as to whether e-cigarettes are gateways to or exit ramps from other tobacco products. Ultimately, this debate centers around whether the benefits of e-cigarettes outweigh the costs; and the FDA’s regulatory impact analysis makes explicit use of such a cost-benefit analysis.
How much impact is your economic appraisal leaving out?” This is the perennial question that CBA practitioners face when putting forward their results of, normally, a poorly performing project. In investment appraisal, my area of work, the question is often decisive. I presume the same applies to CBA practice in policy appraisal.
AS A YOUNG CLINICIAN, I was interested in making a difference; it did not matter how much of a difference, as long as I could claim some patient benefit. And I really didn’t care what benefit: better survival, less local recurrence, shorter hospital stays, fewer narcotics—the specifics did not matter. With retrospective observational studies, it was easy to claim a benefit when I was demonstrating a potential or probable association. I intervened, and the patient improved! If I could prove that claim and make it look scientific with a randomized controlled trial, was I then done?
Outer space probes, radio telescopes, large particle accelerators, genomic platforms, and similar entities are fascinating focal points of intellectual curiosity and discovery. While we marvel at what we learn from them, we can be taken aback by their costs. The $150 billion cost for the International Space Station has been shared by taxpayers in the United States, Europe, Russia, Japan, and Canada. The Large Hadron Collider near Geneva, Switzerland that enabled the 2012 discovery of the elusive Higgs Boson is estimated to cost about 13.5 billion Euros over the 1993-2025 period for taxpayers in the participating countries. According to a recent article in The Economist, proposals are being made to build new infrastructures: A Future Circular Collider in Switzerland, an International Linear Collider in Japan, and a Circular Electron-Position Collider in China. Such are the topic of Massimo Florio’s book, Investing in Science: Social Cost-Benefit Analysis of Research Infrastructures. In it, he demonstrates that benefit-cost analysis (BCA) can be useful in answering the question: Are these costly research infrastructures worth it? He draws upon his substantial experience to adapt the traditional framework to the specific characteristics of research infrastructure (RI). He identifies elements common to RIs, describes how they can be measured and valued, and gives examples from work that he and others have done. This pioneering book fills a gap in that such large-scale investments in science only infrequently have been evaluated using BCA.
Sometimes conferences actually do stimulate research that otherwise might not be done. Six years ago, I was enjoying a breakfast conversation at the SBCA annual research conference with our immediate past president, Clark Nardinelli. At that time, he was overseeing the Regulatory Impact Analysis (RIA) of Federal Drug Administration (FDA) rules implementing the Food Safety Modernization Act, which included provisions to reduce the risks posed to people and pets from adulterated pet food. He complained that, unlike the case of human mortality risks, he had no sound basis for monetizing changes in mortality risks for dogs and other pets. As dogs do not freely make tradeoffs between risks and wages in labor markets, I suggested that finding a value of statistical dog life would likely require a contingent valuation study. Would his unit provide the approximately $50 thousand dollars needed to do the contingent valuation survey? Unfortunately, he had nothing but praise for the suggestion.
So, you've done this great research, had this novel idea, attended a phenomenal event, or read a paradigm-shifting book, and you want to enlighten everyone in the benefit-cost analysis community. Yes, we absolutely want your blog at the Society for Benefit-Cost Analysis.
This post describes a new article forthcoming in the Journal of Benefic Cost Analysis, “When Benefit-Cost Analysis Becomes Optional: Regulatory Analysis at the Consumer Product Safety Commission in the CPSIA Era.” The article describes a familiar scenario for practitioners of regulatory benefit cost analysis: a high impact event followed by swift and forceful congressional action. In the Consumer Product Safety Commission (CPSC) case, the high impact event was a series of product recalls involving toys contaminated with lead. Congress’s response was to pass the Consumer Product Safety Improvement Act (CPSIA) and require several rulemakings.
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.
While DHS sought a monetized benefit-cost analysis based on case studies, early scoping of the project left the way open for multi-criteria decision analysis, cost-effectiveness, and qualitative analysis. Our team developed new ways to modify and implement standard models and, somewhat to our surprise, found that benefit-cost analyses were possible in almost all cases. Along the way, we learned a lot about the power of basic benefit-cost analysis, but also learned lessons about baselines, context, and using sparse data with sensitivity and uncertainty analysis.
Policymakers are called to act in the present to protect the public against future risks while operating under the constraints of doing so in an economically efficient yet effective manner. Unfortunately, these risks tend to present the most difficulty for traditional analytical tools in support of policymaking. This post describes a recently published article, “Dynamic Benefit-Cost Analysis for Uncertain Futures,” co-authored with Susan E. Dudley, Brian F. Mannix, and Christopher Carrigan as part of the open access Symposium on Analysis for Uncertain Futures in the Journal of Benefit Cost Analysis. The article explores the challenges “uncertain futures” pose for analytical tools to satisfactorily support policymaking and proposes the use of “dynamic benefit-cost analysis” frameworks as a necessary approach to address these challenges.
Professor Martin Weitzman passed away unexpectedly at the end of August 2019. A substantial number of accounts of his many contributions to economics in general and environmental economics in particular are available, and collectively provide a good overview of his contributions and influence on the profession (see links at the end of this post). In this short note, I want to highlight some aspects of his works that are relevant for applied welfare economics in general and cost-benefit analysis in particular. I single out two stellar contributions that opened up new vistas for research and enriched the literature on welfare measurement.
Perhaps the most direct application of his celebrated “dismal theorem” (Weitzman 2009) is that cost-benefit analysis has little to offer when it comes to global climate policy. Indeed, given the assumptions of his model, comparing benefits and costs provides no useful information to decision-makers in this particular context.
The National Center for Children in Poverty reports that 21% of American children are living in poverty, including 46% of African American and 40% of Latino children. Compared to non-poor children, on average impoverished children are 15.6% less likely to graduate high school and 37% more likely to be unemployed as adults.
Research has documented that high-quality early childhood education boosts the skills of disadvantaged children in the short-term. However, a recent series of articles finds that comprehensive early childhood education programs have positive long-term impacts on earnings, criminal activity, and health. Such programs remediate socioeconomic inequality by improving the economic prospects of their participants. While the boost in cognitive skills eventually fades away, the boost in non-cognitive skills persists over the life cycle.
The Carolina Abecedarian Project (ABC) and the Carolina Approach to Responsive Education (CARE)—the continuation of ABC—are early childhood education programs that promote skills (such as language) and cognitive development, providing treatment until age 5 for 50 weeks per year. García et al. (2018a) conducted a benefit cost analysis of these programs by monetizing the benefits of treatment for the participants over their lives, and comparing benefits to programmatic costs.
ABC/CARE improves the language and development skills of children and, subsequently, their years of education completed. Figure 1, which is reproduced from Garcia et al. (2018a), displays the average life-cycle net present values per program participant of the main components of the ABC/CARE programs. Benefits were measured from birth to forecasted death, discounted to birth at a rate of 3%. Programmatic costs are adjusted to reflect the welfare cost of taxation to fund the programs. Both benefit and cost estimates are statistically significant at standard levels, and account for forecasting and estimation error, and are adjusted for. As indicated in the figure, higher future earnings and the avoided cost of crime (to society) are significant components of the benefit of the program.
Over four decades ago I struggled through my first year of graduate school in economics, a struggle made palatable by the extraordinary professor who taught my first economics course. Deirdre McCloskey was (and is) smart, fun, and eccentric, and she enthusiastically led us through our first pass at economics. Part of that passage included learning to write, for McCloskey held – a notion considered eccentric – that economists should write well. Although good writing remains suspect among economists, Deirdre McCloskey taught me long ago that good writing makes for good economics.
In class, McCloskey stressed the value of writing well, passing out class notes on writing. I kept the notes for many years and hopefully studied them as I attempted to become a passable writer. I finally discarded the notes when they and much else appeared in McCloskey’s 1985 article, “Economical Writing.” The article soon became a short book of the same title; a second edition followed thirteen years later. We now are rewarded with a third edition of Economical Writing, with a new subtitle: Thirty-Five Rules for Clear and Persuasive Prose (University of Chicago Press, 2019). McCloskey’s personality pervades her little book, which is fun to read and humbling to those of us who would write better.