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.
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