Environmental degradation is the largest public health challenge of the century and is likely to be exacerbated by climate change. This study undertakes a comprehensive examination of the health implications of environmental hazards in Cambodia, simultaneously addressing extreme temperatures, precipitation patterns, and air pollution. It leverages data from the Demographic and Health Surveys and satellite-derived metrics on temperature, precipitation, and fine particulate matter. The analysis identifies a positive association between temperature and the occurrence of diarrhea and cough among children and a nonlinear relationship between precipitation and these health outcomes. Furthermore, the study demonstrates that pollution significantly impacts cough incidence. To anticipate future trends, climate simulations are employed to forecast the incidence of child diarrhea in Cambodia under different climate and development scenarios. The projections indicate that diarrhea incidence could increase to 19 percent by 2040 without significant adaptation measures that would lessen the adverse impact of weather. For instance, the acceleration in toilet ownership over the last decade reduced diarrhea incidence by at least 1.2 to 1.4 percentage points. Nevertheless, the path ahead requires proactive efforts to improve sanitation and hygiene. The forecasts suggest that, without additional strategies to counter climate change’s adverse effects, only universal toilet ownership would contain the climate-driven increase in diarrhea incidence expected by 2040.

This paper quantifies the impact of drought on household consumption for five main agroecological zones in Africa, developing vulnerability (or damage) functions of the relationship between rainfall deficits and poverty. Damage functions are a key element in models that quantify the risk of extreme weather and the impacts of climate change. Although these functions are commonly estimated for storm or flood damages to buildings, they are less often available for income losses from droughts. The paper takes a regional approach to the analysis, developing standardized hazard definitions and methods for matching hazard and household data, allowing survey data from close to 100,000 households to be used in the analysis. The damage functions are used to quantify the impact of historical weather conditions on poverty for eight countries, highlighting the risk to poverty outcomes that weather variability causes. National poverty rates are 1–12 percent higher, depending on the country, under the worst weather conditions relative to the best conditions observed in the past 13 years. This amounts to an increase in the total poverty gap that ranges from US$4 million to US$2.4 billion (2011 purchasing power parity).

Air pollution profoundly impacts welfare, causing more deaths globally than malnutrition, AIDS, tuberculosis, and malaria combined. In the Georgian capital, Tbilisi, air pollution levels exceed international standards and surpass levels in other cities in the region. The average monthly PM2.5 concentration in Tbilisi is 20 µg/m3, four times higher than the World Health Organization’s annual recommended limit. This paper uses multiple data sources —administrative data, satellite imagery, private real estate transactions, and traffic data—to estimate the impact of air pollution on the health and productivity of people in Tbilisi. It estimates that a 1% increase in PM2.5 levels corresponds to a 0.24% increase in respiratory hospitalization rates. A 1% increase in PM2.5 is also associated with a 0.2% decrease in rental prices. The study shows that traffic and industrial activity are significant drivers of air pollution in Tbilisi. We paper also estimates the positive co-benefits of potential carbon pricing policies from air pollution reduction. Adopting a carbon tax of $25 per ton would reduce hospitalizations by 0.44% per district by 2036, while increasing rental prices by 0.38%.

Recent surveys and censuses show that Indian internal migration amounts to one of the highest in the world with 30% of its population migrating. Given migrants' demographics, such a significant population movement could have a vast potential to alleviate poverty as well as decrease forest pressures. Despite migration's magnitude, estimates of this double dividend are scarce. This paper addresses this gap by assessing returns to migration for households and forest in Central India. We estimate the potential of internal migration to alleviate poverty by understanding migrant characteristics and their investments. We then evaluate whether there are short term benefits for Central Indian forests by assessing the relationship between migration and changes in forest uses over a five-year timespan. Finally, we study the village-level association between a measure of forest degradation and the proportion of migrating households in the village. We show that internal migration has the potential to alleviate poverty, although not drastically, in a five-year timespan. On the other hand, migration does not change forest pressures in such a short term.

Severe weather shocks recurrently hit Malawi, where they adversely affect the incomes of a larger number of farm households as well as small businesses. A clear understanding of households’ vulnerability to shock-induced poverty is critical for disaster risk management and the design of scalable social safety net programs. This study uses a nationally representative household survey and exogenously measured weather shocks to assess households vulnerability to poverty in Malawi. We find that drought during the growing season decreases non-assistance consumption per capita by 5-12 percent, depending on its intensity. Excess rainfall at the onset of the growing season reduces food consumption by 1.8 percent. Moreover, vulnerability to poverty is generally higher than static poverty because chances of falling below the poverty line as a result of an extreme weather event are high. Our results present an estimate of the geographic distribution of vulnerability that can be used to design adaptive social safety net programs that quickly scale up to cover those at risk of falling into poverty in the aftermaths of weather shocks.

Each year in the U.S., antibiotic-resistant infections affect nearly 3 million people and cause over 30,000 deaths. One of the main drivers of antibiotic resistance is antibiotic prescriptions. Antibiotic use in humans cures bacterial infections and limits their spread while also spurring antibiotic resistance. This paper studies the trade-off between these positive and negative effects of antibiotics on human health and makes two main contributions. First, I provide one of the first causal estimates of the impact of antibiotic prescriptions on antibiotic resistance at the population level using data from Medicare Part D and a collection of hospitals’ annual antibiotic resistance measures. I address the endogeneity of antibiotic prescriptions with an instrumental variable strategy. I find that a 1 pp increase in the annual share of Medicare beneficiaries receiving antibiotic treatment increases annual bacterial resistance in their community by 6 pp on average. Second, I develop a model that assesses the trade-off between curing infections and preserving antibiotic effectiveness, in which bacterial infection evolves according to an epidemiological model. This model quantifies the welfare loss due to overprescribing compared to the socially optimal allocation under a constant tax. When considering amoxicillin—one of the most commonly prescribed antibiotics—and a bacterial infection eventually spreading to 15% of the Medicare population in the steady state, the annual deadweight loss amounts to $183 million. The constrained optimal allocation is reached with an optimal tax of $3.5 per claim, equivalent to decreasing Medicare reimbursement on amoxicillin by 57%.