I use innovative micro-data and empirical methods founded in economic theory to study the drivers of internal migration choices and the matching of healthcare workers and facilities at the country level. My work aims to understand migration and location choices of people under constraints, both positive (driven by central policy choices) and normative (from fairness principles).
Prior to starting my PhD, I was a senior data analyst at the University of Chicago Urban Labs.
I will be on the 2022-2023 Academic Job Market. Here’s my [CV].
Fields: development economics, labor economics, and public economics.
Job Market Paper :
Abstract: Rural development programs often focus on increasing agricultural investment. Yet, many farmers can benefit from investing in a different technology: outmigration. I explore how one common class of policies — input subsidy programs (ISPs) — allows households to sort based on the relative returns of these two technologies. First, I exploit the roll-out of a large-scale Zambian ISP and use a difference-in-differences strategy. I show that the ISP fosters specialization by farmers based on their comparative advantage, resulting in increases in both agricultural yields and outmigration. Second, I estimate a structural model that incorporates a positive learning externality related to fertilizer adoption. With this externality, the ISP offers advantages relative to alternative revenue-neutral policy counterfactuals. Compared to an untargeted cash transfer, I find that an ISP that allows for re-selling of fertilizer would increase migration out of agriculture. A more targeted cash transfer, or an ISP without resale markets, would reduce migration. All three counterfactual policies reduce fertilizer use relative to the ISP and hinder the process of specialization.