Skip navigation
Please use this identifier to cite or link to this item:
Title: Measuring Bias in Consumer Lending
Authors: Dobbie, Will
Liberman, Andres
Paravisini, Daniel
Pathania, Vikram
Keywords: Discrimination
Consumer Credit
JEL Codes: G41, J15, J16
Issue Date: Aug-2018
Series/Report no.: 623
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.
Appears in Collections:IRS Working Papers

Files in This Item:
File Description SizeFormat 
623.pdf2.72 MBAdobe PDFView/Download

Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.