Several governments set the social discount rate using a Ramsey Rule that treats pure time preference as a separate parameter. Standard arguments for declining discount rates rest on uncertainty about growth or preferences, or on aggregation across people who disagree about the rate. We add a different reason: pure time preference may itself be falling over time. Real interest rates have declined for centuries, even as growth has risen. In a Ramsey framework, that pattern points to a falling pure-time-preference component. One structural explanation is selection towards patience: if patience differs across people and persists across generations, the population-weighted distribution shifts, so that the value of pure time preference used to represent society’s impatience declines not only across horizons but across generations. We illustrate with the UK Green Book, which is the most transparent Ramsey-based framework currently in use and under active review.

This article quantifies the economic benefits of an influential early childhood program in a developing country, tracking participants with outcomes measured through age 31 years. Drawing on published experimental data encompassing life-cycle benefits – that rely primarily on available labor-market outcomes – alongside a rigorous cost analysis, we estimate an internal rate of return (IRR) of 10.4–10.7% and benefit–cost ratio of ~8. Even though estimates are not that precise, a variety of sensitivity analyses confirm the robustness of these findings. Notably, we find meaningful gender heterogeneity in returns: females exhibit a higher IRR of 11.5%, compared to their male counterparts (9.6%), suggesting that – in developing countries – early childhood programs may generate disproportionately larger economic gains for women.

In this review essay, I begin by considering Adler’s commitments, in defending his favored form of welfarism, regarding how we should conceive of well-being across human lives and account for this well-being in the social evaluation of outcomes and policies. Then I turn to Adler’s strategy for comparing his welfarist model(s) with traditional cost–benefit analysis in practical contexts of fatality risk regulation.

Solar photovoltaic (PV) has been increasingly adopted in past years. This study provides empirical analysis using a county-level dataset that supported PV poverty alleviation projects (PPAPs) in rural China over 2008–2019 and finds that the rural per capita income of counties participating in PPAPs on average increased by 5 per cent compared to that of their counterparts. The effect is larger in more economically developed areas, those with abundant sunlight hours and in counties where centralized PV predominates. A cost–benefit analysis suggests that PPAPs can yield a net benefit of 57,690 yuan over their life cycle per rural household, with the ratio of household annual income growth to government fiscal expenditure ranging from 1.2 to 1.6. PPAPs are found to reduce carbon dioxide emissions by 22 million tons annually, accounting for 0.4 per cent of China's carbon emissions reduction in 2020. Our results hold important implications for other countries working to alleviate poverty and reduce carbon emissions.

With the ongoing discussion around the implementation of low-dose computed tomography (LDCT) screening for lung cancer, increasing numbers of model-based economic evaluations have been conducted. We systematically reviewed the published literature for economic evaluations of LDCT screening for lung cancer in people at high risk, updating this review in 2024. We identified a total of 57 economic evaluations; two-thirds of which developed de novo decision-analytic models. In our most recent update, of the 22 economic evaluations identified, only one used a model published before 2021. There may be advantages to developing new models, but the huge possibility of research waste cannot be ignored. Better quality of reporting (both in terms of clarity and completeness), improving the availability of existing models, and comparative analysis of different models can help to advance modeling methods in areas of complexity, such as screening for cancer. Ultimately, this should lead to policy decisions based on the best available evidence.

Epidemiological and economic (Epi-econ) models account for endogenous interactions between the epidemic and the economy. We explore the applicability of an Epi-econ model to isolate the effects of lockdown policies during coronavirus disease 2019 in the Netherlands. To this aim, we recalibrate the seminal Epi-econ model of Eichenbaum and colleagues with updated parameters specific to the Dutch context. We find that the model performs poorly in replicating observed Epi-econ trends under baseline assumptions. Next, we explore possibilities to improve model fit by relaxing policy and transmission parameters, and by incorporating observed “random noise” in infectivity parameters. This approach spectacularly improves model performance in replicating observed trends. Finally, we test the performance of the model in simulating alternative policy scenarios. We use the Containment and Health Index from the Blavatnik School of Government to replace Dutch policy parameters with exemplary countries on opposite sides of the stringency spectrum. We find that a more stringent lockdown policy would reduce peak prevalence, while aggravating peak economic contraction, but with little effect on overall trends. Conversely, a more lenient lockdown policy was estimated to increase the peak and overall prevalence, with little effect on economic outcomes. We conclude that while rigorous adjustments to existing models were required, a combined Epi-econ model could be informative to policymakers in assessing alternative lockdown policy options.

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.