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How Costly Is Noise? Data and Disparities in Consumer Credit

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  • Blattner, Laura

    (Stanford University)

  • Nelson, Scott

    (Chicago Booth)

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.

Suggested Citation

  • Blattner, Laura & Nelson, Scott, 2021. "How Costly Is Noise? Data and Disparities in Consumer Credit," Research Papers 3978, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:3978
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    File URL: https://www.gsb.stanford.edu/faculty-research/working-papers/how-costly-noise-data-disparities-consumer-credit
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    Cited by:

    1. Hurtado, Agustin & Sakong, Jung, 2022. "The effect of minority bank ownership on minority credit," Working Papers 325, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.
    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. Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," LawFin Working Paper Series 42, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    4. Subhadeep Mukhopadhyay, 2021. "InfoGram and Admissible Machine Learning," Papers 2108.07380, arXiv.org, revised Aug 2021.
    5. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    6. Ströbel, Johannes & Howell, Sabrina & Kuchler, Theresa & Snitkof, David, 2021. "Racial Disparities in Access to Small Business Credit: Evidence from the Paycheck Protection Program," CEPR Discussion Papers 16623, C.E.P.R. Discussion Papers.
    7. Cusato, Antonio & Castillo, José Luis & IDB Invest, 2023. "Access to Credit and the Expansion of Broadband Internet in Peru," IDB Publications (Working Papers) 12922, Inter-American Development Bank.
    8. Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," SAFE Working Paper Series 369, Leibniz Institute for Financial Research SAFE.
    9. Egle Jakucionyte & Swapnil Singh, 2021. "Emergence of Subprime Lending in Minority Neighborhoods," Bank of Lithuania Working Paper Series 94, Bank of Lithuania.
    10. 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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