Working Paper: NBER ID: w24953
Authors: Will Dobbie; Andres Liberman; Daniel Paravisini; Vikram Pathania
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.
Keywords: consumer lending; bias; machine learning; profitability; loan examiners
JEL Codes: G41; J15; J16
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| loan examiner assignment (G21) | loan takeup (G51) |
| loan takeup (G51) | long-run profits for marginal applicants (D40) |
| marginal immigrant applicants (K37) | profits (L21) |
| marginal older applicants (J14) | profits (L21) |
| loan takeup (G51) | bias against immigrant applicants (J15) |
| loan takeup (G51) | bias against older applicants (J71) |
| loan takeup (G51) | bias against female applicants (J16) |
| baseline credit history and demographic characteristics (G51) | bias (D91) |