This paper studies the question of how to target aid after a natural disaster. Disaster aid programs often use property damage as a criterion for eligibility. A household's ability to insure against shocks may be harder to observe but more important in determining how the disaster affects welfare. We develop a model of household demand for reconstruction aid and estimate the model parameters using a household survey following the 2015 Nepal earthquake. Key model predictions are validated using a spatial regression discontinuity design. We use the model to estimate welfare from counterfactual targeting strategies. Conditioning aid on property damages does not significantly improve welfare relative to allocating aid randomly. An untargeted approach that divides the aid budget equally among all households in the affected areas yields larger welfare gains. Using resources to assess property damages for targeting purposes may not be cost effective. 

[VoxDev Summary] [SSRN]

Advances in machine learning and the increasing availability of satellite imagery have led to the proliferation of social science research that uses remotely sensed measures of human activity or environmental outcomes to infer the impact of policy. However, when machine learning models minimize a standard loss function the predictions they generate can produce biased estimates when used for causal inference. In this paper, we show how this bias can arise, and we propose the use of an adversarial debiasing algorithm in order to correct this issue when generating machine learning predictions for use in causal inference. 

Negishi weights are commonly used in heterogeneous agent integrated assessment models in order to separate distributional questions from questions of efficiency. We show that, in an economy with public goods, two intuitive notions of distributive fairness are in direct conflict. A Pareto-efficient allocation may respect each agent's budget constraint, or it may make all agents better off relative to the Nash equilibrium, but it cannot in general do both. Negishi weights respect individual budget constraints, but may result in an allocation that makes some actors worse off relative to a non-cooperative baseline. We also identify a set of weights consistent with the Lindahl equilibrium concept. These weights require transfers, but all agents would agree to make them. Furthermore, the transfers can be thought of as payments for public good provision. Thus the Lindahl weights with transfers implement a 'Coasian' solution to the public goods problem. 

What are the impacts of the global trade in plastic waste? Since a 1991 World Bank memo argued, “the economic logic behind dumping a load of toxic waste in the lowest wage country is impeccable” cross country flows of plastic waste have increased seven-fold. Yet the consequences of this trade are poorly understood, partially due to the lack of basic descriptive data about what happens to exported waste at its many destinations. Using satellite data and a machine-learning algorithm trained on crowd-sourced data, we study the spread of informal dumps before and after a major shock to the global trade in waste. Our preliminary findings show that the dumps create severe externalities, while the benefits to jobs and wages appear modest.

[Coverage in The Guardian]

Several observational studies from locations around the globe have documented a positive correlation between air pollution and the severity of COVID-19 disease. Observational studies cannot identify the causal link between air quality and the severity of COVID-19 outcomes, and these studies face three key identification challenges: 1) air pollution is not randomly distributed across geographies; 2) air-quality monitoring networks are sparse spatially; and 3) defensive behaviors to mediate exposure to air pollution and COVID-19 are not equally available to all, leading to large measurement error bias when using rate-based COVID-19 outcome measures (e.g., incidence rate or mortality rate). Using a quasi-experimental design, we explore whether traffic-related air pollutants cause people with COVID-19 to suffer more extreme health outcomes in New York City (NYC). When we address the previously overlooked challenges to identification, we do not detect causal impacts of increased chronic concentrations of traffic-related air pollutants on COVID-19 death or hospitalization counts in NYC census tracts.

If the material intensive enterprises in an urban area of several million people shared physical resources that might otherwise be wasted, what environmental and public benefits would result? This study develops an algorithm based on lifecycle assessment tools for determining a city's industrial symbiosis potential—that is, the sum of the wastes and byproducts from a city's industrial enterprises that could reasonably serve as resource inputs to other local industrial processes. Rather than report, as do many previous papers, on private benefits to firms, this investigation focuses on public benefits to cities by converting the maximum quantity of resources recoverable by local enterprises into an estimate of the capacity of municipal infrastructure conserved in terms of landfill space and water demand. The results here test this novel approach for the district of Mysuru (Mysore), India. We find that the industrial symbiosis potential calculated based on analysis of the inputs and outputs of ∼1000 urban enterprises, translates into 84 000 tons of industrial waste, greater than 74 000 tons of CO2e, and 22 million liters per day of wastewater. The method introduced here demonstrates how industrial symbiosis links private production and public infrastructure to improve the resource efficiency of a city by creating an opportunity to extend the capacity of public infrastructure and generate public health co-benefits.

For decades Americans have grown up learning fundamental waste management principles: reduce, reuse, recycle. Yet, over half of what we generate from homes and businesses—known as municipal solid waste—ends up being discarded in landfills. While recycling has increased, so has our understanding of its limits. New policies and strategies can refocus waste management so that it is less costly for residents and more beneficial for the environment. In particular, twenty-first century waste and materials policy should seek to preserve, rather than throw away, the materials and energy that go into the products we use. This priority requires a new focus on material diversion, rather than waste treatment and disposal.