IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v103y2021i2p236-250.html

Does Knowing Your FICO Score Change Financial Behavior? Evidence from a Field Experiment with Student Loan Borrowers

Author

Listed:
  • Tatiana Homonoff

    (Robert F. Wagner School of Public Service, New York University and NBER)

  • Rourke O'Brien

    (Yale University)

  • Abigail B. Sussman

    (University of Chicago Booth School of Business)

Abstract

One in five consumer credit accounts incurs late fees each quarter. Evidence on the efficacy of regulations to improve behavior through enhanced disclosure of financial product attributes is mixed. We test a novel form of disclosure that provides borrowers with a personalized measure of their creditworthiness. In a field experiment with over 400,000 student loan borrowers, treatment group members received communications about the availability of their FICO Score. The intervention significantly reduced late payments and increased borrowers' FICO Scores. Survey data show treatment group members were less likely to overestimate their FICO Scores, suggesting the intervention may correct for overoptimism.

Suggested Citation

  • Tatiana Homonoff & Rourke O'Brien & Abigail B. Sussman, 2021. "Does Knowing Your FICO Score Change Financial Behavior? Evidence from a Field Experiment with Student Loan Borrowers," The Review of Economics and Statistics, MIT Press, vol. 103(2), pages 236-250, May.
  • Handle: RePEc:tpr:restat:v:103:y:2021:i:2:p:236-250
    DOI: 10.1162/rest_a_00888
    as

    Download full text from publisher

    File URL: https://doi.org/10.1162/rest_a_00888
    Download Restriction: Access to PDF is restricted to subscribers.

    File URL: https://libkey.io/10.1162/rest_a_00888?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kondratjeva, Olga & Roll, Stephen P. & Bufe, Sam & Grinstein-Weiss, Michal, 2021. "Using financial tips to guide debt repayment: Experimental evidence from low- and moderate-income tax filers," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    2. L’Esperance, Madelaine, 2023. "Nudging credit union members to check their credit: Evidence from a field experiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    3. Christa Gibbs & Benedict Guttman-Kenney & Donghoon Lee & Scott Nelson & Wilbert van der Klaauw & Jialan Wang, 2025. "Consumer Credit Reporting Data," Journal of Economic Literature, American Economic Association, vol. 63(2), pages 598-636, June.
    4. Neil Bhutta & Aurel Hizmo & Daniel R. Ringo, 2024. "How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions," Working Papers 24-09, Federal Reserve Bank of Philadelphia.
    5. Claire Greene & Julian Perry & Joanna Stavins, 2024. "Consumer Payment Behavior by Income and Demographics," Working Papers 24-8, Federal Reserve Bank of Boston.
    6. Matteo Crosignani & Jonathan Kivell & Daniel Mangrum & Donald P. Morgan & Ambika Nair & Joelle Scally & Wilbert Van der Klaauw, 2025. "Financial Inclusion in the United States: Measurement, Determinants, and Recent Developments," Economic Policy Review, Federal Reserve Bank of New York, vol. 31(3), pages 1-49, September.
    7. Keegan Harris & Anish Agarwal & Chara Podimata & Zhiwei Steven Wu, 2022. "Strategyproof Decision-Making in Panel Data Settings and Beyond," Papers 2211.14236, arXiv.org, revised Dec 2023.
    8. Choi, Yiseon & Dormady, Noah, 2025. "Can FICO scores be used to explain managerial decision making?: Evidence from a supply-chain resilience experiment," International Journal of Production Economics, Elsevier, vol. 288(C).
    9. Neil Bhutta & Aurel Hizmo & Daniel R. Ringo, 2022. "How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions," Finance and Economics Discussion Series 2022-067, Board of Governors of the Federal Reserve System (U.S.).
    10. J. Michael Collins & Sarah Halpern‐Meekin & Melody Harvey & Jill Hoiting, 2023. "“If I don't have credit, I don't have anything”: Perspectives on the credit scoring system among mothers with low incomes," Journal of Consumer Affairs, Wiley Blackwell, vol. 57(4), pages 1605-1622, October.
    11. Saulı̄tis, Andris, 2023. "Nudging debtors with non-performing loans: Evidence from three field experiments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).

    More about this item

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:103:y:2021:i:2:p:236-250. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: The MIT Press (email available below). General contact details of provider: https://direct.mit.edu/journals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.