Measuring Bias in Consumer Lending

89 Pages Posted: 24 Oct 2018 Last revised: 23 Nov 2019

See all articles by Will Dobbie

Will Dobbie

Princeton University

Andres Liberman

Betterfly

Daniel Paravisini

London School of Economics & Political Science (LSE)

Vikram Pathania

University of Sussex - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: August 1, 2018

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 discrimination in 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 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 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.

Keywords: Discrimination, Consumer Credit

JEL Classification: G41, J15, J16

Suggested Citation

Dobbie, Will and Liberman, Andres and Paravisini, Daniel and Pathania, Vikram, Measuring Bias in Consumer Lending (August 1, 2018). Available at SSRN: https://ssrn.com/abstract=3258699 or http://dx.doi.org/10.2139/ssrn.3258699

Will Dobbie

Princeton University ( email )

Andres Liberman (Contact Author)

Betterfly ( email )

Santiago
Chile

Daniel Paravisini

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

Vikram Pathania

University of Sussex - Department of Economics ( email )

Falmer, Brighton BN1 9SL
United Kingdom

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