This paper exploits a large-scale natural experiment to study the equilibrium effects of information restrictions in credit markets. In 2012, Chilean credit bureaus were forced to stop reporting defaults for 2.8 million individuals (21% of the adult population). We show that the effects of information deletion on aggregate borrowing and total surplus are theoretically ambiguous and depend on the pre-deletion demand and cost curves for defaulters and non-defaulters. Using panel data on the universe of bank borrowers in Chile combined with the deleted registry information, we implement machine learning techniques to measure changes in lenders' cost predictions following deletion. Deletion reduces (raises) predicted costs the most for poorer defaulters (non-defaulters) with limited borrowing histories. Using a difference-in-differences design, we find that individuals exposed to increases in predicted costs reduce borrowing by 6.4%, while those exposed to decreases raise borrowing by 11.8% following the deletion, for a 3.5% aggregate drop in borrowing. Using the difference-in-difference estimates as inputs into the theoretical framework, we find evidence that deletion reduced aggregate welfare under a variety of assumptions about lenders' pricing strategies.

Credit information affects the allocation of consumer credit, but its effects on other markets that are relevant for academic and policy analysis are unknown. This paper measures the effect of negative credit information on the employment and earnings of Swedish individuals at the margins of the formal credit and labor markets. We exploit a policy change that generates quasi-exogenous variation in the retention time of past delinquencies on credit reports and estimate that one additional year of negative credit information causes a reduction in wage earnings of $1,000. In comparison, the decrease in credit is only one-fourth as large. Negative credit information also causes an increase in self-employment and a decrease in mobility. We exploit differences in the information available to employers and banks to show suggestive evidence that this cost of default is borne inefficiently by the relatively more creditworthy individuals among previous defaulters.

This paper investigates the effects of college tuition on student debt and human capital accumulation. We exploit data from a random sample of undergraduate students in the United States and implement a research design that instruments for tuition with relatively large changes to the tuition of students who enrolled at the same school in different cohorts. We find that $10,000 in higher tuition causally reduces the probability of graduating with a graduate degree by 6.2 percentage points and increases student debt by $2,961. Higher tuition also reduces the probability of obtaining an undergraduate degree among poorer, credit-constrained students. Thus, the relatively large increases in the price of education in the United States in the past decade can affect the accumulation of human capital.

We exploit a regulation change that reduced from 90 to 30 the number of days a large supermarket chain in Chile could wait to pay its suppliers. The regulation serves as a natural experiment as it was only binding for small suppliers, defined as those firms whose yearly revenues were less than an arbitrary cutoff. Focusing on a narrow window of firms with yearly revenues around the cutoff, we find that small suppliers sell their products at prices 5%-10% lower than large suppliers after the regulation change. We also provide suggestive evidence to isolate the mechanisms through which trade credit (or a lack thereof) affect market outcomes. These results help evaluate the tradeoffs small firms face when negotiating trade credit terms with large clients.

I exploit a natural experiment to estimate borrowers' willingness to pay for a good credit reputation. A lender in Chile offered lower installments to borrowers who were in default. Those who owed more than a fixed arbitrary cutoff were additionally offered a clean public repayment record. Using the cutoff in a fuzzy regression discontinuity design, I show that borrowers are willing to pay the equivalent of 11% of their monthly income for a good reputation. Borrowers use their reputation to take on more debt with other banks, but default more. Thus, renegotiations may impose informational externalities on other lenders.

This paper tests for bias in consumer lending decisions using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a simple model of bias in lending, which predicts that profits should be identical for loan applicants from different groups at the margin if loan examiners are unbiased. We identify the profitability of marginal loan applicants by exploiting variation from the quasi-random assignment of loan examiners. We find significant bias against both immigrant and older loan applicants when using the firm's preferred measure of long-run profits. In contrast, there is no evidence of bias when using a short-run measure used to evaluate examiner performance, suggesting that the bias in our setting is due to the misalignment of firm and examiner incentives. We conclude by showing that a decision rule based on machine learning predictions of long-run profitability can simultaneously increase profits and eliminate bias.

We exploit a natural experiment in the largest online consumer lending platform to provide the first evidence that loan terms, in particular maturity choice, can be used to screen borrowers based on their private information. We compare two groups of observationally equivalent borrowers who took identical unsecured 36-month loans; for only one of the groups, a 60-month loan was also available. When a long-maturity option is available, fewer borrowers take the short-term loan, and those who do default less. Additional findings suggest borrowers self-select on private information about their future ability to repay.