COVID-19 vaccines have been authorized in multiple countries, and more are under rapid development. Careful design of a vaccine prioritization strategy across sociodemographic groups is a crucial public policy challenge given that 1) vaccine supply will be constrained for the first several months of the vaccination campaign, 2) there are stark differences in transmission and severity of impacts from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across groups, and 3) SARS-CoV-2 differs markedly from previous pandemic viruses. We assess the optimal allocation of a limited vaccine supply in the United States across groups differentiated by age and essential worker status, which constrains opportunities for social distancing. We model transmission dynamics using a compartmental model parameterized to capture current understanding of the epidemiological characteristics of COVID-19, including key sources of group heterogeneity (susceptibility, severity, and contact rates). We investigate three alternative policy objectives (minimizing infections, years of life lost, or deaths) and model a dynamic strategy that evolves with the population epidemiological status. We find that this temporal flexibility contributes substantially to public health goals. Older essential workers are typically targeted first. However, depending on the objective, younger essential workers are prioritized to control spread or seniors to directly control mortality. When the objective is minimizing deaths, relative to an untargeted approach, prioritization averts deaths on a range between 20,000 (when nonpharmaceutical interventions are strong) and 300,000 (when these interventions are weak). We illustrate how optimal prioritization is sensitive to several factors, most notably, vaccine effectiveness and supply, rate of transmission, and the magnitude of initial infections.

As measured by Gini coefficients, fractile inequalities, and tail power laws, wealth is distributed less equally across people than are labor earnings. We study how luck, attitudes that shape saving decisions, and growth rates of labor earnings balance each other in ways that simultaneously shape joint distributions across people of labor earnings, age, and wealth together with an equilibrium rate of return on savings that plays a pivotal role in balancing contending forces. Strong motives for people to save and for firms to demand capital raise an equilibrium interest rate enough to make wealth grow faster than labor earnings. That makes cross-sectional wealth more unevenly distributed and have a fatter tail than labor earnings, as in US data.

Adaptive experimental designs can dramatically improve efficiency in randomized trials. But with adaptively collected data, common estimators based on sample means and inverse propensity-weighted means can be biased or heavy-tailed. This poses statistical challenges, in particular when the experimenter would like to test hypotheses about parameters that were not targeted by the data-collection mechanism. In this paper, we present a class of test statistics that can handle these challenges. Our approach is to adaptively reweight the terms of an augmented inverse propensity-weighting estimator to control the contribution of each term to the estimator’s variance. This scheme reduces overall variance and yields an asymptotically normal test statistic. We validate the accuracy of the resulting estimates and their CIs in numerical experiments and show that our methods compare favorably to existing alternatives in terms of mean squared error, coverage, and CI size.

Estimating the impact of child health investments on adult living standards entails multiple methodological challenges, including the lack of experimental variation in health status, an inability to track individuals over time, and accurately measuring living standards and productivity in low-income settings. This study exploits a randomized school health intervention that provided deworming treatment to Kenyan children, and uses longitudinal data to estimate impacts on economic outcomes up to 20 y later. The effective respondent tracking rate was 84%. Individuals who received two to three additional years of childhood deworming experienced a 14% gain in consumption expenditures and 13% increase in hourly earnings. There are also shifts in sectors of residence and employment: treatment group individuals are 9% more likely to live in urban areas, and experience a 9% increase in nonagricultural work hours. Most effects are concentrated among males and older individuals. The observed consumption and earnings benefits, together with deworming’s low cost when distributed at scale, imply that a conservative estimate of its annualized social internal rate of return is 37%, a high return by any standard.

India has set aggressive targets to install more than 400 GW of wind and solar electricity generation by 2030, with more than two-thirds of that capacity coming from solar. This paper examines the electricity and carbon mitigation costs to reliably operate India’s grid in 2030 for a variety of wind and solar targets (200 GW to 600 GW) and the most promising options for reducing these costs. We find that systems where solar photovoltaic comprises only 25 to 50% of the total renewable target have the lowest carbon mitigation costs in most scenarios. This result invites a reexamination of India’s proposed solar-majority targets. We also find that, compared to other regions and contrary to prevailing assumptions, meeting high renewable targets will avoid building very few new fossil fuel (coal and natural gas) power plants because of India’s specific weather patterns and need to meet peak electricity demand. However, building 600 GW of renewable capacity, with the majority being wind plants, reduces how often fossil fuel power plants run, and this amount of capacity can hold India’s 2030 emissions below 2018 levels for less than the social cost of carbon. With likely wind and solar cost declines and increases in coal energy costs, balanced or wind-majority high renewable energy systems (600 GW or ≈

To reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), most countries closed schools, despite uncertainty if school closures are an effective containment measure. At the onset of the pandemic, Swedish upper-secondary schools moved to online instruction, while lower-secondary schools remained open. This allows for a comparison of parents and teachers differently exposed to open and closed schools, but otherwise facing similar conditions. Leveraging rich Swedish register data, we connect all students and teachers in Sweden to their families and study the impact of moving to online instruction on the incidence of SARS-CoV-2 and COVID-19. We find that, among parents, exposure to open rather than closed schools resulted in a small increase in PCR-confirmed infections (odds ratio [OR] 1.17; 95% CI [CI95] 1.03 to 1.32). Among lower-secondary teachers, the infection rate doubled relative to upper-secondary teachers (OR 2.01; CI95 1.52 to 2.67). This spilled over to the partners of lower-secondary teachers, who had a higher infection rate than their upper-secondary counterparts (OR 1.29; CI95 1.00 to 1.67). When analyzing COVID-19 diagnoses from healthcare visits and the incidence of severe health outcomes, results are similar for teachers, but weaker for parents and teachers’ partners. The results for parents indicate that keeping lower-secondary schools open had minor consequences for the overall transmission of SARS-CoV-2 in society. The results for teachers suggest that measures to protect teachers could be considered.

Do firstborn children have a height advantage? Empirical findings have found mostly that, yes, second or higher-order children often lag behind firstborns in height outcomes, especially in developing countries. However, empirical investigations of birth-order effects on child height overlook the potential impact that birth spacing can have. We provide an explanation for the negative birth-order effect on stunting outcomes for young Indian children and show it is driven by short preceding-birth spacing. We find that firstborn children are taller than children of higher birth order: The height-for-age gap for third (or higher)-order children is twice the gap for children second in birth order. However, this pattern is observed when spacing between later-born children and their immediate elder siblings is fewer than 3 y. Interestingly, the firstborn height advantage disappears when later-born children are born at least 3 y after their elder siblings. Thus, our findings indicate that spacing length between children explains differences in height, over birth order. Although India’s family planning policy has resulted in a substantial reduction in total fertility, its achievement in spacing subsequent births has been less impressive. In showing that spacing can alleviate or aggravate birth-order effects on attained height, our study fills an evidence gap: Reducing fertility alone may not be sufficient in overcoming negative birth-order effects. To reduce the detrimental effects of birth order on child stunting, policy responses—and therefore research priorities—require a stronger focus on increasing the time period between births.