Measuring Bias in Consumer Lending

Will Dobbie, Andres Liberman, Daniel Paravisini and Vikram Pathania

No 24953, NBER Working Papers from National Bureau of Economic Research, Inc

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 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.

JEL-codes: G41 J15 J16 (search for similar items in EconPapers)
Date: 2018-08
New Economics Papers: this item is included in nep-big and nep-ure
Note: LE LS
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Citations: View citations in EconPapers (13)

Published as Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania & Nicola Gennaioli, 2021. "Measuring Bias in Consumer Lending," The Review of Economic Studies, vol 88(6), pages 2799-2832.

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Related works:
Journal Article: Measuring Bias in Consumer Lending (2021)
Working Paper: Measuring bias in consumer lending (2021)
Working Paper: Measuring Bias in Consumer Lending (2019)
Working Paper: Measuring Bias in Consumer Lending (2018)
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