Minimum payments on credit card debt allow consumers to repay slowly: despite being unsecured, the average $7,000 balance generally amortizes in over 20 years. We study how lenders choose these minimum payments and the impacts of these choices on equilibrium consumer debt outcomes. When short-term illiquidity makes many borrowers unable to make higher payments, lenders set low minimums to limit default costs. Alternatively, if many borrowers make near-minimum payments for reasons besides illiquidity (e.g., due to anchoring), lenders set low minimums to generate interest revenue. To separate these two forces, we use payment-level data from a credit bureau to document a new fact about intra-temporal debt repayment. Consumers often revolve high-interest credit card debt while making excess payments on low-interest installment debt, providing evidence that low payments aren't solely liquidity-driven. We use this fact to estimate an empirical model that predicts realistically low lender minimums. The model suggests that without anchoring, minimums would be over twice as high for most borrowers. Lenders amplify consumer biases, accounting for 20% of the total increase in credit card debt and 85% of defaults from anchoring in our model.

We use data from a major South African payment processor to study how digital payments mitigate asymmetric information challenges in small business "revenue-based financing" contracts, which tie repayment schedules to future revenue. Eight months post-financing, digital payments through the processor are 15% lower for takers than observably similar non-takers. We show this "gap" can be decomposed into three components: moral hazard from revenue hiding, adverse selection, and the causal effect of financing for takers. Two natural experiments suggest that takers shift more revenue off the platform when competition increases (moral hazard), and that financiers can increase repayment by waiting longer before extending offers (adverse selection). With estimates from both experiments, we bound the gap components, finding substantial adverse selection, but also positive short-run causal effects. Our results suggest digital payment platforms with "sticky" features can alleviate classic risk-sharing frictions by imposing hiding costs and limiting hidden information.

This thesis studies the Paycheck Protection Program (PPP)—a 660 billion dollar small business loan program enacted in April 2020 in response to the COVID-19 pandemic. The majority of small businesses could apply for (and receive) a forgivable loan, but I find that PPP uptake differs dramatically across space. To study this geographic variation, I first present a theoretical framework for PPP. One key prediction is that even though supply-side barriers limited PPP uptake in places that had weak banking relationships in the first round, business human capital should have been able to substitute for bank human capital during the second round. Second, I use a combination of county and firm-level data to demonstrate that in line with theory, places that received very little PPP overall had both low bank and low business human capital. Finally, factors such as COVID-19 impact or political attitudes were not key determinants in predicting PPP uptake. Consequently, even though PPP was designed for COVID-19, the distribution of PPP loans was fundamentally about human capital and institutions rather than pandemic impact.