In 1921, John Maynard Keynes and Frank Knight independently insisted on the importance of making a distinction between uncertainty and risk. Keynes referred to matters about which ‘there is no scientific basis on which to form any calculable probability whatever’. Knight claimed that ‘Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated’. Knightian uncertainty exists when people cannot assign probabilities to imaginable outcomes. People might know that a course of action might produce bad outcomes A, B, C, D and E, without knowing much or anything about the probability of each. Contrary to a standard view in economics, Knightian uncertainty is real, and it poses challenging and unresolved issues for decision theory and regulatory practice. It bears on many problems, potentially including those raised by artificial intelligence. It is tempting to seek to eliminate the worst-case scenario, and thus to adopt the maximin rule, which might seem to be the appropriate approach under Knightian uncertainty. But serious problems arise if eliminating the worst-case scenario would (1) impose high risks and costs, (2) eliminate large benefits or potential ‘miracles’ or (3) create uncertain risks.

This article examines a policy of scaling up LLINs by 10 percentage points from 2020 levels with a 90% cap in the 29 highest-burden countries in Africa along with social and behavioral change (SBC) and information education and communication (IEC) campaigns to increase the use and effectiveness of LLINs. The incremental cost of this scenario compared to a baseline of maintaining malaria interventions at 2020 levels has a present-day (2023) value of 5.7 billion US$ 2021 discounted at 8% over the period 2023–2030 (undiscounted starting at US$ 416 million in 2023 increasing to US$ 1.4 billion in 2030). This investment will prevent 1.07 billion clinical cases and save 1,337,069 lives. With standardized Copenhagen Consensus Center assumptions, the mortality benefit translates to a present value of US$ 225.9 billion. The direct economic gain is also substantial: the incremental scenarios lead to US$ 7.7 billion in reduced health system expenditure from the reduced treatment of cases, a reduction in the cost of delivering malaria control activities, and reduced household out-of-pocket expenses for malaria treatment. The productivity gains from averted employee and caretaker absenteeism and presenteeism add benefits with a present value of US$ 41.7 billion. Each dollar spent on the incremental scenario delivers US$ 48 in social and economic benefits.

Professional sports teams commonly reevaluate their location decisions based on the prospect of building new, more attractive, stadiums. Even though a large economic literature warns about the modest (and possibly negative) effects on the local economy of hosting a professional sports team, the economic effects of professional teams and stadiums remain blurry for the general public, and cities in the United States continue to compete to lure teams with generous public subsidies. This article integrates several contributions of the literature into one cohesive and simple framework based on cost–benefit analysis, and provides estimations of the average local economic effects of teams in the four biggest professional leagues in the United States. If professional sports games do not attract visitors from other cities, or if players and owners do not spend a significant share of their income in the area, hosting a team can negatively affect the local economy.

The world remains off-track for the sustainable development goal (SDG) target 3.4, which calls for a one-third reduction in noncommunicable diseases (NCDs) mortality by 2030. This paper presents benefit–cost analyses of various NCD interventions in low-income (LICs) and lower–middle-income (LMCs) countries. We looked at 30 interventions recommended by the Disease Control Priorities Project, including six intersectoral policies (e.g., taxes) and 24 clinical services. We used a previously published model to estimate intervention costs and benefits through 2030, discounted at 8%. We focused on interventions with benefit–cost ratios (BCRs) > 15 and their contribution toward achieving the SDG target. We found that intersectoral policies often provided great value for money, with BCRs ranging from 40 (trans-fat bans) to 100 (tobacco excise taxes). However, seven clinical interventions (e.g., basic treatment of cardiovascular disease or breast cancer) also had BCRs > 15. The overall population impact of clinical interventions over the 2023–2030 period would be much higher than that of the intersectoral policies, which can take many years to reach their peak effects. Fully implementing the best-investment interventions would accelerate progress toward SDG 3.4 everywhere, but only one in 10 countries would achieve the target. This strategy would require an additional US$ 2.4 billion annually across all LICs and LMCs. We conclude that there are several cost-beneficial opportunities to tackle NCDs in LICs and LMCs. In countries with very limited resources, the best-investment interventions could begin to address the major NCD risk factors and build greater health system capacity, with benefits continuing to accrue beyond 2030.

Motivated by traffic congestion and air pollution, Beijing is one of several major cities to restrict vehicle ownership by requiring residents to win a lottery for the right to obtain an additional car. We examine the welfare cost of preventing people from owning cars because of misallocation: under a lottery, some individuals with low willingness to pay (WTP) for cars can obtain cars, while other individuals with high WTP cannot. We estimate welfare costs using a new contingent valuation method survey of Beijing lottery participants which we designed and conducted explicitly for this purpose. We find that restricting vehicle ownership reduced private welfare by 26 billion yuan. Back-of-the-envelope calculations suggest that the benefits of lower congestion and pollution roughly equal the costs. Our WTP estimates indicate a net welfare gain of approximately 32 billion yuan if Beijing’s lottery were replaced with an auction, which is similar to previous estimates.

For decision-makers considering new medicines for reimbursement and public use, both value for money and affordability are important considerations. Whereas a cost-effectiveness model provides information about value for money, a budget impact assessment (BIA) is customized to a specific context and estimates the total investment needed; one part of affordability. Both analytic approaches have parameter uncertainty within them, yet comparatively little attention is given to parameter uncertainty in BIA. Currently, within BIA, uncertainty exploration is limited to point estimates for plausible scenarios, prompting the question: can a decision-maker be confident in point estimates? Within this paper, our intent is to revitalize the discussion of uncertainty in BIA. In the context of health technology assessments submitted to support reimbursement decision-making, we propose reliance on probabilistic sensitivity analysis conducted in the cost-effectiveness model. If assumptions made in a cost-effectiveness model are valid, probabilistic cost estimates from the model, with the same perspective adopted as the BIA, should also inform BIA. Mean and variance of population outcomes, given parameter uncertainty in model inputs, are estimable from model outputs. As sufficiently large random samples are drawn from a population, the distribution of sample means will follow an approximately normal distribution. Therefore, when drawing samples from the model to inform estimates of budget impact, the assumption of an approximately normal distribution for costs is reasonable. We propose that the variance in mean costs from the cost-effectiveness model also reflects the variance in budget impact estimates and should be used to estimate budget impact confidence intervals.

The problem of how to evaluate investments in airports has now been studied for over 50 years. This paper analyzes the use of different methods like cost–benefit analysis (CBA), economic impact analysis (EIA), and computable general equilibrium (CGE) models to address the question. It assesses the strength and weaknesses of each method, and it discusses which methods have been used in different countries. The paper argues that the CBA approach and the newer CGE modeling approach address the policy issue well and that both methods are appropriate, although improvements are possible, especially in the newer aspects of evaluation. Furthermore, more data intensive CGE models are able to analyze broader aspects of the evaluation question for which CBA has had difficulty. EIA does not address the problem satisfactorily, and it misleads air transport policy. But this evaluation contrasts sharply with practice. EIA has been extensively used to decide on airport investment. CGE approaches are very promising, though further work is needed for them to reach their full potential. This paper pays particular attention to the relationship between CBA and CGE in airport investment evaluation and also the possible role of wider economic benefits (WEBs) of aviation in evaluation.

Biodiversity points are a quantitative measure for biodiversity. For over a decade, biodiversity points are being applied in the Netherlands for measuring the impact of roads, enclosure dams, and other water management projects on the non-use value of biodiversity. Biodiversity points are quite similar to the quality-adjusted life years used for cost-effectiveness analysis of healthcare treatments. Biodiversity points can be calculated by multiplying the size of the ecotope (e.g., number of hectare), the ecological quality of the ecotope (0–100 %), and the ecological scarcity of each type of ecotope. For many infrastructure projects, the impact on the non-use value of biodiversity can be a principal purpose or a major co-benefit or trade-off, for example, for a park, a fish sluice, a road, an ecoduct, an enclosure dam, or a marine protected area. Biodiversity points are a simple, transparent, and standardized way to aggregate and quantify the qualitative or ordinal assessments by ecological experts. For measuring the non-use value of biodiversity, they are also more informative than valuation by revealed or stated preferences methods. This paper provides the first overview of the application of this method in the Dutch practice of cost–benefit analysis. It also discusses its merits and limitations. The calculation and use of biodiversity points are illustrated by four case studies.

Proponents of the public goods argument (‘PGA’) seek to ground the authority of the state on its putative indispensability as a means of providing public goods. But many of the things we take to be public goods – including many of the goods commonly invoked in support of the PGA – are actually what we might term publicized goods. A publicized good is any whose ‘public’ character results only from a policy decision to make some (otherwise private) good freely and universally available. This fact poses complications for the PGA, insofar as the set of possible publicized goods is quite extensive indeed.

A commonly assumed reason for the delegation of authority from a legislature (politicians) to bureaucracies is that the bureaucrats have an information advantage over the politicians, including knowledge of cost–benefit analysis (CBA). But it is reasonable to assume that the bureaucrats use their information advantage by taking all relevant aspects of policy into account? We model the use of CBA using a delegation model and then test the theoretical predictions with empirical data collected from five Swedish government agencies. The empirical results lend support both for the hypothesis that risk aversion concerning the environmental outcome, the bureaucrats’ environmental attitudes, and the cost of taking CBA information into account have a considerable impact on the probability of using information from a CBA. Hence risk averse and bureaucrats with strong environmental preferences are less likely and bureaucrats with low cost of doing a CBA more likely than other bureaucrats to use CBA information. Finally, a binding governmental budget constraint may positively influence a bureaucrat’s choice of using CBA information. A tentative conclusion is therefore that it may be possible to increase the use of CBA by making the budgetary consequences of policies much clearer and demanding due consideration of costs.

Social discounting conventions vary widely. Some differences reflect institutional constraints, but many reflect differing assumptions about how a social discount rate should be derived and applied. The divide between advocates of social opportunity cost and social time preference (STP) frameworks seems unbridgeable. There is no consensus among STP advocates on whether the social cost of funding $1 of public spending is barely more than $1 of consumption or perhaps more than $2; or on whether the covariance of public service benefits with income merits a discount rate premium that is trivial or a few percentage points. The practicalities of government fund raising are sometimes overlooked. The issues are here reviewed in the light of the literature and of experience with developing and applying social discounting regimes and extended debates within government.