with Sheah Deilami, Agricultural and Resource Economics

This project estimates the impacts of historical discrimination by the United States Department of Agriculture using the 1933 Agricultural Adjustment Act. Implementation of the AAA allowed white landowners to retain benefits that should have been passed on the Black tenants. I aim to exploit county differences in AAA spending in agricultural areas to measure the impacts on Black farmer exit, and explore the possibility of USDA discrimination as a force contributing to the Great Migration. Additionally, I plan to track county committee membership using archival data to link members to AAA appeal decisions and benefit distribution. This research relates to a broader interest in the contribution of historically discriminatory institutions to Black farmer land loss in the United States.

with Kendra Marcoux, Agricultural and Resource Economics

Racial minority groups and low-income households tend to be disproportionately exposed to various measures of environmental harm. This gap is often referred to as environmental inequity and is thought to be attributable to four separate mechanisms: residential sorting, firm sorting, coordination between residential and firm sorting, and discriminatory policy and enforcement. Few studies have explored the role that discriminatory politics plays, despite elected officials playing a large role in setting and enforcing local environmental policy. At the same time, racial minorities are historically underrepresented in U.S politics, and minority representation can lead to a higher provision of public goods in majority-minority neighborhoods. I would like to understand the intersection of these trends and ask the question: What is the effect of minority representation on environmental inequity?

with Emily Martell, Economics

Acemoglu et al. (2012, AER) pioneered the environment and directed technical change literature. The authors find that the elasticity between clean and dirty inputs to production is crucial for the design of carbon taxes and research subsidies. However, the empirical evidence for the value of this parameter is scant. I propose to estimate this elasticity on sector-level or ideally micro data, aggregating up to the clean and dirty sector level. I will consider modeling exercises such as focusing on a particular sector such as electricity, auto, or transport, exploring a cross-country model, or relaxing the clean and dirty good assumption in Acemoglu et al. (2012).

with Elena Ojeda, Economics

The frequency and severity of natural disasters are growing. Understanding how marginalized populations respond to different types of disasters is crucial for effective policy response. This project explores the long-run consequences of exposure to varying types of natural disasters using linked Census records from 1900 to 1940, real estate, insurance, and federal disaster assistance data. The project studies how individuals respond to a natural disaster in the short-run through decisions on homeownership and location preferences, and if these behaviors are passed on to their children. I start by asking, what is a homeowner’s short-run response to a local natural disaster? Then, using linked Census data I follow their children to understand their location and housing decisions. Do the children of parents exposed to a natural disaster also become homeowners? I plan to use a spatial regression discontinuity design across the boundary of the impacted areas to identify the effect of a natural disaster on those who lost their home.

with Daniela Paz, Economics

Approximately 15.3% of all food produced globally goes to waste at the farm stage, resulting in significant environmental consequences such as the emission of greenhouse gases and the depletion of natural resources. At the same time, low- and middle-income households face challenges in accessing nutritious food, with projections indicating that obesity rates could exceed 51% of the population by 2030, leading to a range of adverse health impacts such as diabetes, heart disease, and stroke. Food insecurity is particularly acute in developing countries with high poverty rates and limited access to healthy food. Recently, supermarkets in different countries started to sell “misshape” food - fruits and vegetables that do not meet traditional color or shape standards- at a reduced price. In this project, we aim to study if this initiative can impact these coexisting problems, reduce nutritional inequality in consumption in developing countries, and decrease food waste at the farm level.

with Suvy Qin, Agricultural and Resource Economics

This project aims to estimate the local housing market impacts of Harris County’s home buyout program. By enabling government agencies to purchase and demolish flood-prone properties, buyout programs can permanently reduce flood risks, but little is known about their effects on neighboring properties. This project studies the effectiveness and equity implications of home buyout programs by combining parcel-level data on residential properties, buyout locations, historical flood maps, flood insurance claims, and demographic information. I first construct counterfactual buyout areas using Harris County’s buyout eligibility criteria to identify properties that could become buyouts in the future. I then estimate the spillover effects of buyouts on the value and sales likelihood of neighboring (non-buyout) homes. These estimates allow me to then consider the welfare consequences of buyouts, with a focus on equity.

with Alice Elizabeth Schmitz, Economics

This project seeks to construct a spatial equilibrium model to estimate the effects of highway systems on deforestation in the Brazilian Amazon. The model considers not just for the effect of public roads on land use, but also for the informal road network which is constructed in response to state infrastructure. Incorporating the social costs of deforestation into my analysis might suggest the construction of a highway between two cities which is built to circumvent natural areas, or the partial construction of a desired network which will be completed by informal roads. More broadly, I hope to contribute to the economic geography's literature on optimal transportation investment by considering how environmental externalities should be included in welfare analyses.

with Max Snyder, Agricultural and Resource Economics

with Aaron Watt, Agricultural and Resource Economics

This project starts with a simple mathematical model of air pollution regulation and builds computational tools around it to estimate the optimal locations for future air pollution monitors. A key part of the regulation modeling is understanding how pollution measurements from the monitors are used to regulate areas with high pollution. One reason this type of analysis is relatively rare is the high computational complexity, so we will be exploring three tool sets that can help us: (1) high performance computing resources (Savio); (2) programming packages that make some of the computations much quicker; and (3) a type of machine learning that can be trained to output optimal locations given input data. We will be adding mathematical complexity to our model and eventually take it to US data from the EPA and NASA. Julia is the main programming language for this project -- a relatively young, high performance, scientific programming language.