Do Lenders Still Discriminate? A Robust Approach for Assessing Differences in Menus

Zhang, David Hao; Willen, Paul S.

doi:10.2139/ssrn.3688604

Cited by 1 publication

(1 citation statement)

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“…Related literature Our paper contributes to a large literature on the causes and consequences of credit market disparities. Much existing research focuses on human frictions such as discrimination, bias, or agency conflict after controlling for observables such as credit score (Dobbie et al, 2019;Bartlett et al, 2019;Bhutta and Hizmo, 2020;Heimer and Yu, 2021;Bayer et al, 2018;Zhang and Willen, 2020;Hanson et al, 2016;Ross et al, 2008;Butler et al, 2020). We instead study how these observables themselves can drive credit misallocation and disparities.…”

Section: Introductionmentioning

confidence: 99%

How Costly is Noise? Data and Disparities in Consumer Credit

Blattner,

Nelson

2021

Preprint

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We show that lenders face more uncertainty when assessing default risk of historically under-served groups in US credit markets and that this information disparity is a quantitatively important driver of inefficient and unequal credit market outcomes. We first document that widely used credit scores are statistically noisier indicators of default risk for historically under-served groups. This noise emerges primarily through the explanatory power of the underlying credit report data (e.g., thin credit files), not through issues with model fit (e.g., the inability to include protected class in the scoring model). Estimating a structural model of lending with heterogeneity in information, we quantify the gains from addressing these information disparities for the US mortgage market. We find that equalizing the precision of credit scores can reduce disparities in approval rates and in credit misallocation for disadvantaged groups by approximately half.

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