Measuring Bias in Consumer Lending
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 are extremely grateful to the Lender for providing the data used in this analysis. We also thank Leah Boustan, Hank Farber, Alex Mas, Crystal Yang, and numerous seminar participants for helpful comments and suggestions. Emily Battaglia, Nicole Gandre, Jared Grogan, Bailey Palmer, and James Reeves provided excellent research assistance. All errors and omissions are ours alone. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.