Essays on the Impact of Technology, Health, and Financial Shocks on Economic Development Outcomes

Abstract

This dissertation consists of three chapters in development economics and finance. A common theme throughout these chapters is using empirical techniques to study how technological, health, and financial innovations impact development outcomes. Chapter 1 studies how loan officers and machine learning algorithms differentially respond to revealed demographics of loan applicants. I conducted an experiment in Pakistan involving 30 loan officers and 5,500 digitally submitted loans. The intervention assigned loans to either the officers or an algorithm and provided applicant identities for a subset of loans to each decision-maker. I find that the algorithm achieves substantially lower loan default versus a loan officer when both agents view an anonymized set of applicant characteristics. I also provide novel experimental estimates of discrimination and find that displaying applicant identities lowers the officers’ discrimination against women; an officer preference for gender equity drives this result. Conversely, algorithmic discrimination against women worsens with the disclosure of identities. The results show that blinding algorithms to applicant demographics may boost efficiency and ensure equity in developing economy credit markets. Chapter 2, co-authored with Emily Battaglia, studies the impact of malaria eradication programs on Black-white economic disparities in the early 1900s U.S. South. Using linked census records, we find that while the health benefits of disease eradication accrued to both races, only white men experienced the associated economic benefits. Blacks exposed to malaria eradication were more likely to be farm laborers, and both Blacks and whites were more likely to migrate out of state. Our findings suggest that malaria eradication, a broadly applied intervention, widened racial gaps. Finally, Chapter 3, co-authored with Minh Phan, investigates whether a consumer demand shock propagates through industry input-output networks. In November 2016, India demonetized 86% of its currency, creating a nationwide demand shock. We construct upstreamness measures to evaluate the impact of demonetization on firms based on their network position. In contrast to current literature, the shock does not propagate through the network. We identify pricing power, inventory frictions, and export intensity as viable explanatory mechanisms. These frictions suggest that downstream firms are particularly vulnerable to demand shocks.