This paper tests for bias in consumer lending using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a new principal-agent model of bias where loan examiners maximize a short-term outcome, not long-term profits, leading to bias against illiquid applicants at the margin of loan decisions. We identify the profitability of marginal applicants using the quasi-random assignment of loan examiners. Consistent with our model, we find significant bias against immigrant and older applicants when using the firm’s preferred measure of long-run profits, but not when using the short-run measure used to evaluate examiner performance.

We show that high-cost debt exacerbates financial constraints by affecting lenders’ perception of credit risk. Using data from a high-cost lender in the UK, we show that high-cost credit reduces applicants' credit score and future bank credit even though it does not affect future debt repayment. These effects are not present among borrowers who are already tagged as high risk at application, consistent with high-cost credit affecting lenders' beliefs about borrowers' creditworthiness. The results highlight a novel channel through which high-cost credit can harm consumers’ financial health: a self-reinforcing stigma of high risk.

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, only one of which had also a higher rate 60-month maturity choice 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.

We exploit a natural experiment to provide one of the first measurements of the causal effect of negative credit information on employment and earnings. We estimate that one additional year of negative credit information reduces employment by 3\% and wage earnings by \$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. Further evidence suggests this cost of default is borne inefficiently by the relatively more creditworthy individuals among previous defaulters.

We present evidence that restrictions to the set of feasible financial contracts affect buyer - supplier relationships and the organizational form of the firm. We exploit a regulation that restricted the maturity of the trade credit contracts that a large retailer could sign with some of its small suppliers. Using a within-product difference-in-differences identification strategy, we find that the restriction reduces the likelihood of trade by 11 percentage points. The large retailer also responds by internalizing procurement to its own subsidiaries and reducing overall purchases. Finally, we find evidence that relational contracts can help mitigate the inability to extend long trade credit terms.

This article exploits a natural experiment to estimate borrowers' willingness to pay for a good credit reputation. A department store in Chile offered lower monthly installments to borrowers who were in default. Some of these borrowers---those whose balance was higher than a fixed arbitrary cutoff---were additionally offered a clean public repayment record (a “good credit reputation”). Using this cutoff in a fuzzy regression discontinuity design, I estimate that borrowers are willing to pay an amount equal to 11% of their monthly income to have a good credit reputation. Furthermore, I find that borrowers use their improved reputation to borrow more from banks. However, after they've cashed-in on their reputation, borrowers default more on their bank debt, suggesting that renegotiations may impose informational externalities on other credit market participants.

We show empirically that public credit information increases competition in credit markets. We access data that cover all credit card borrowers in Chile and include details about relationship borrowers have with each lender. We exploit a natural experiment whereby a non-bank lender's portfolio was sold to a bank. Because of this transaction, the lender's borrowers, who were previously not identifiable unless in default, become observable by banks through the credit bureau but remain unobservable to other non-bank lenders. Using a difference-in-differences strategy, we find that after the transaction the lender's borrowers receive higher credit limits from other banks relative to other non-bank borrowers. This result is mediated by individuals whose predicted probability of bank default drops as a result of the change to banks' information set. After the transaction, the lender shifts originations to safer borrowers with higher initial limits, a result that is consistent with cross sectional evidence that banks tend to lend to safer borrowers. Our results imply that by increasing competition, public credit information can reduce lenders' incentive to “learn by lending”, potentially excluding riskier populations from access to credit.

This paper uses a large-scale natural experiment to study the equilibrium effects of restricting information provision in credit markets. In 2012, Chilean credit bureaus were forced to stop reporting defaults for 21% of the adult population. Using panel data on the universe of bank transactions in Chile combined with the deleted registry information, we implement machine learning techniques to measure changes in the predictions lenders can make about default rates following deletion. Using a difference-in-differences design, we show that individuals exposed to increases in predicted default reduce borrowing by 6.4% following deletion, while those exposed to decreases raise borrowing by 11.8%. In aggregate, deletion reduces borrowing by 3.5%. Taking the difference-in-difference estimates as inputs into a model of borrowing under adverse selection, we find that deletion reduces surplus under a variety of assumptions about lenders’ pricing strategies.

This paper investigates the effect of tuition on student debt and on measures of human capital accumulation. We exploit data from a random sample of undergraduate students in the US and implement a research design that instruments for the level of tuition with relatively large tuition changes across students 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% and increases student debt by $2,961. Higher tuition also reduces undergraduate completion rates among poorer, credit constrained students. Thus, the large changes in the price of education in the US in the last decade can affect the accumulation of human capital.