Zinman, and numerous seminar and conference participants for helpful comments. Jesper Bojeryd provided excellent research assistance. Funding from VINNOVA is gratefully acknowledged. All errors are our own. The views expressed here are those of the authors and do not necessarily represent those of the Federal Reserve Bank of Philadelphia, the Federal Reserve System, or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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%. The retailer also responds by internalizing procurement to its own subsidiaries and reducing overall purchases. Finally, we find that relational contracts can mitigate the inability to extend long trade credit terms.

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.

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