Achieving ambitious targets to address the global tuberculosis (TB) epidemic requires consideration of the impact of competing interventions for improved identification of patients with TB. Cost-effectiveness analysis (CEA) and benefit-cost analysis (BCA) are two approaches to economic evaluation that assess the costs and effects of competing alternatives. However, the differing theoretical basis and methodological approach to CEA and BCA is likely to result in alternative analytical outputs and potentially different policy interpretations. A BCA was conducted by converting an existing CEA on various combinations of TB control interventions in South Africa using a benefits transfer approach to estimate the value of statistical life (VSL) and value of statistical life year (VSLY). All combinations of interventions reduced untreated active disease compared to current TB control, reducing deaths by between 5,000 and 75,000 and resulting in net benefits of Int$3.2–Int$137 billion (ZAR18.1 billion to ZAR764 billion) over a 20-year period. This analysis contributes to development and application of BCA methods for health interventions and demonstrates that further investment in TB control in South Africa is expected to yield significant benefits. Further work is required to guide the appropriate analytical approach, interpretation and policy recommendations in the South African policy perspective and context.

In this article, we conduct a number of benefit–cost analyses to clarify whether the establishment of ragweed in Denmark should be prevented (pure prevention) or if the damage from this invasive species should be mitigated (pure mitigation). The main impact of the establishment of ragweed in Denmark would be a substantial increase in the number of allergy cases, which we use as a measure of the physical damage from this species. As valuation methods, we use both the cost-of-illness and benefit transfer methods to quantify the total gross benefits of these two policy actions. Based on the idea of an invasion function, we identify the total and average net benefits under both prevention and mitigation and find that all are significantly positive regardless of the valuation method. Therefore, both prevention and mitigation are beneficial policy actions, but the total and average net benefits under mitigation are larger than those under prevention in all the scenarios we consider. This finding implies that the former policy action is more beneficial. Despite this result, we propose that prevention, not mitigation, may be the proper policy because of information externalities, altruistic preferences, possible catastrophic events, and ethical considerations.

Managing large-scale, geographically distributed, and long-term risks arising from diverse underlying causes – ranging from poverty to underinvestment in protecting against natural hazards or failures of sociotechnical, economic, and financial systems – poses formidable challenges for any theory of effective social decision-making. Participants may have different and rapidly evolving local information and goals, perceive different opportunities and urgencies for actions, and be differently aware of how their actions affect each other through side effects and externalities. Six decades ago, political economist Charles Lindblom viewed “rational-comprehensive decision-making” as utterly impracticable for such realistically complex situations. Instead, he advocated incremental learning and improvement, or “muddling through,” as both a positive and a normative theory of bureaucratic decision-making when costs and benefits are highly uncertain. But sparse, delayed, uncertain, and incomplete feedback undermines the effectiveness of collective learning while muddling through, even if all participant incentives are aligned; it is no panacea. We consider how recent insights from machine learning – especially, deep multiagent reinforcement learning – formalize aspects of muddling through and suggest principles for improving human organizational decision-making. Deep learning principles adapted for human use can not only help participants in different levels of government or control hierarchies manage some large-scale distributed risks, but also show how rational-comprehensive decision analysis and incremental learning and improvement can be reconciled and synthesized.

Although cyber security has become widely recognized as a serious threat to our modern world, there are new threats to our security that combine cyber with other modes of “attack.” This article explores the increasingly important theme in homeland and national security that future attacks will be multimodal, in particular including both a cyber and a physical component, where the cyber attack is intended to make it easier to succeed in the physical attack, and is not an end in itself. The article describes sample scenarios of combined cyber and physical attacks in two sectors where even just cyber security efforts have lagged behind: sports stadiums and the maritime transportation system. It presents an approach to comparing the risk of a combined cyber followed by physical attack and that of a “traditional” physical attack on the same target. It then analyzes the different stadium and maritime examples from the point of view of this risk assessment approach.

A commonly held perception is that an elite graduate degree can “scrub” a less prestigious but less costly undergraduate degree. Using data from the National Survey of College Graduates from 2003 to 2017, this article examines the relationship between the status of undergraduate degrees and earnings among those with elite postbaccalaureate degrees. Few graduates of non-selective institutions earn postbaccalaureate degrees from elite institutions, and even when they do, undergraduate institutional prestige continues to be positively related to earnings overall as well as among those with specific postbaccalaureate degrees including business, law, medicine, and doctoral. Among those who earn a graduate degree from an elite institution, the present value of the earnings advantage to having both an undergraduate and a graduate degree from an elite institution generally greatly exceeds any likely cost advantage from attending a less prestigious undergraduate institution.

We conduct a benefit-cost analysis of a package of early childhood interventions that can improve nutrition outcomes in Haiti. Using the Lives Saved Tool, we expect that this package can prevent approximately 55,000 cases of child stunting, 7,600 low-weight births and 28,000 cases of maternal anemia annually, if coverage reaches 90% of the target population. In addition, we expect these nutrition improvements will avoid 1,830 under-five deaths, 80 maternal deaths and 900,000 episodes of child illness every year. Those who avoid stunting will experience lifetime productivity benefits equivalent to five times gross national income per capita in present value terms, at a 5% discount rate. While previous benefit-cost analyses of this specific package have only estimated the lifetime productivity benefits of avoided stunting, this paper also accounts for reductions in fatal and non-fatal health risks. In the base case scenario, the annualized net benefits of the intervention equal Haitian gourdes 13.4 billion (USD 211 million) and the benefit-cost ratio (BCR) is 5.2. Despite these substantial benefits, the package may not be the most efficient use of a marginal dollar, with alternative interventions to improve human capital yielding BCRs approximately three to four times higher than the base estimate.

Air pollution is a persistent and well-established public health problem in India: emissions from coal-fired power plants have been associated with over 80,000 premature deaths in 2015. Premature deaths could rise by four to five times this number by 2050 without additional pollution controls. We site a model 500 MW coal-fired electricity generating unit at eight locations in India and examine the benefits and costs of retrofitting the plant with a flue-gas desulfurization unit to reduce sulfur dioxide emissions. We quantify the mortality benefits associated with the reduction in sulfates (fine particles) and value these benefits using estimates of the value per statistical life transferred to India from high income countries. The net benefits of scrubbing vary widely by location, reflecting differences in the size of the exposed population. They are highest at locations in the densely populated north of India, which are also among the poorest states in the country.

Nuclear war is clearly a global catastrophic risk, but it is not an existential risk as is sometimes carelessly claimed. Unfortunately, the consequence and likelihood components of the risk of nuclear war are both highly uncertain. In particular, for nuclear wars that include targeting of multiple cities, nuclear winter may result in more fatalities across the globe than the better-understood effects of blast, prompt radiation, and fallout. Electromagnetic pulse effects, which could range from minor electrical disturbances to the complete collapse of the electric grid, are similarly highly uncertain. Nuclear war likelihood assessments are largely based on intuition, and they span the spectrum from zero to certainty. Notwithstanding these profound uncertainties, we must manage the risk of nuclear war with the knowledge we have. Benefit-cost analysis and other structured analytic methods applied to evaluate risk mitigation measures must acknowledge that we often do not even know whether many proposed approaches (e.g., reducing nuclear arsenals) will have a net positive or negative effect. Multidisciplinary studies are needed to better understand the consequences and likelihood of nuclear war and the complex relationship between these two components of risk, and to predict both the direction and magnitude of risk mitigation approaches.

Benefit-cost analyses of education policies in low- and middle-income countries have historically used the effect of education on future wages to estimate benefits. Strong evidence also points to female education reducing both the under-five mortality rates of their children and adult mortality rates. A more complete analysis would thus add the value of mortality risk reduction to wage increases. This paper estimates how net benefits and benefit-cost ratios respond to the values used to estimate education’s mortality-reducing impact including variation in these estimates. We utilize a ‘standardized sensitivity analysis’ to generate a range of valuations of education’s impact on mortality risks. We include alternative ways of adjusting these values for income and age differences. Our analysis is for one additional year of schooling in lower-middle-income countries, incremental to the current mean. Our analysis shows a range of benefit-cost ratios ranging from 3.2 to 6.7, and net benefits ranging from $2,800 to $7,300 per student. Benefits from mortality risk reductions account for 40% to 70% of the overall benefits depending on the scenario. Thus, accounting for changes in mortality risks in addition to wage increases noticeably enhances the value of already attractive education investments.

Uncertain future risks pose cognitive and analytical challenges to household decision makers. Risks with uncertain probabilities, coupled with potentially severe outcomes pose problems for decision-making and are prone to overreactions. Imprecision in risk estimates generates behavioral distortions such as ambiguity aversion. This article presents new empirical results indicating household overvaluations of uncertain threats posed by several drinking water risks: traces of prescription drugs in drinking water, plastic water bottles with bisphenol-A, and the weed killer atrazine in drinking water. Negative reactions reflect responses to ambiguous risks, but policies driven by these concerns may misallocate regulatory resources due to risk conservatism and “no-regrets” responses.