Measuring Bias in Consumer Credit (joint with Will Dobbie, Daniel Paravisini, and Vikram Pathania)

Abstract:

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 lending, which predicts that profits should be identical for different groups at the margin if loan examiners are unbiased. We identify the profitability of marginal applicants exploiting variation from the quasi-random assignment of loan examiners. We find significant bias against non-native 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 our results are 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.