Measuring Bias in Consumer Lending
Daniel Paravisini and
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Will Dobbie: Princeton University and NBER
Daniel Paravisini: London School of Economics and CEPR
Vikram Pathania: University of Sussex
Working Papers from Princeton University, Department of Economics, Industrial Relations Section.
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 firmâ€™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.
Keywords: Discrimination; Consumer Credit (search for similar items in EconPapers)
JEL-codes: G41 J15 J16 (search for similar items in EconPapers)
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Working Paper: Measuring Bias in Consumer Lending (2019)
Working Paper: Measuring Bias in Consumer Lending (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:indrel:623
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