Credit Lines as Insurance: Evidence from Bangladesh

Abstract: Lending institutions often withhold credit from borrowers who have suffered an income shock because they are concerned about default risk. This can be especially debilitating in low-income countries because households have few resources to manage these shocks. I show that a loan product that guarantees credit access to agricultural households following a negative shock increases their welfare through two channels: an ex-ante insurance effect, whereby households increase investments in risky but profitable production; and an ex-post effect, whereby households use the loan to smooth consumption. Repayment is high and the loan is profitable for the lender – demonstrating that guaranteed credit is a valuable risk-mitigation tool for households that need not jeopardize lenders profits.

Monitoring in Target Contracts: Theory and Experiment in Kenyan Public Transit

(with Erin Kelley and David Schönholzer)

Abstract: We develop a relational contracting model to study the role of monitoring in firms and evaluate the model experimentally in the field. Specifically, we introduce monitoring devices into commuter minibuses in Nairobi, Kenya, that track real-time vehicle driving behavior and daily productivity. We randomize which minibus owners have access to these monitoring data using a novel mobile app that we designed for the industry. In line with model predictions, we find that treated vehicle owners modify the terms of the contract by decreasing the transfer they demand in exchange for higher effort and lower risk-taking. Drivers respond accordingly by working more hours and decreasing risky driving behavior associated with higher repair costs. As a result, firm costs fall and profits increase. Structural estimation via simulated method of moments demonstrates a close match of the data to the contract model and suggests overall welfare increases stemming from lower firm costs.

Which jobs are lost during a lockdown? Evidence from vacancy postings in India

(with Gaurav Chiplunkar and Erin Kelley)

Abstract: We rely on real-time data from India’s second largest job portal to study how COVID-19 (and the associated lockdown measures) impacted the Indian labor market. Detailed firm-level vacancy postings on location, industries, occupations and job characteristics allow us to document three facts that suggest a dramatic contraction in hiring, especially for young, less-educated and female job-seekers. First, we observe a substantial decline in the total number of new vacancies posted and the number of firms that post at least one job. Second, we see an increase in jobs that can be completed from home and fewer jobs in occupations that can be easily automated. Finally, we find evidence that certain job-seekers are more affected than others, as employers post fewer entry-level jobs, require higher levels of experience and education, and advertise fewer jobs in female-dominated occupations.

Works in Progress