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How Costly is Noise? Data and Disparities in Consumer Credit

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  • Laura Blattner
  • Scott Nelson

Abstract

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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  • Laura Blattner & Scott Nelson, 2021. "How Costly is Noise? Data and Disparities in Consumer Credit," Papers 2105.07554, arXiv.org.
  • Handle: RePEc:arx:papers:2105.07554
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    2. Sabrina T. Howell & Theresa Kuchler & David Snitkof & Johannes Stroebel & Jun Wong, 2021. "Lender Automation and Racial Disparities in Credit Access," NBER Working Papers 29364, National Bureau of Economic Research, Inc.
    3. Subhadeep Mukhopadhyay, 2021. "InfoGram and Admissible Machine Learning," Papers 2108.07380, arXiv.org, revised Aug 2021.
    4. Egle Jakucionyte & Swapnil Singh, 2021. "Emergence of Subprime Lending in Minority Neighborhoods," Bank of Lithuania Working Paper Series 94, Bank of Lithuania.
    5. Laura Blattner & Scott Nelson & Jann Spiess, 2021. "Unpacking the Black Box: Regulating Algorithmic Decisions," Papers 2110.03443, arXiv.org.
    6. Vitaly Meursault & Daniel Moulton & Larry Santucci & Nathan Schor, 2022. "One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas," Working Papers 22-39, Federal Reserve Bank of Philadelphia.

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