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Life after (Soft) Default

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  • Giacomo De Giorgi
  • Costanza Naguib

Abstract

We analyze the impact of soft credit default (i.e. a delinquency of 90+ days) on individual trajectories. Using a proprietary dataset on about 2 million individuals for the years 2004 to 2020, we find that a soft default has substantial and long-lasting (i.e. up to ten years after the event) negative effects on credit score, total credit limit, home-ownership status, and income.

Suggested Citation

  • Giacomo De Giorgi & Costanza Naguib, 2023. "Life after (Soft) Default," Papers 2306.00574, arXiv.org.
    • Handle: RePEc:arx:papers:2306.00574
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Diamond, Rebecca & Guren, Adam & Tan, Rose, 2020. "The Effect of Foreclosures on Homeowners, Tenants, and Landlords," Research Papers 3877, Stanford University, Graduate School of Business.
    3. Janet Currie & Erdal Tekin, 2015. "Is There a Link between Foreclosure and Health?," American Economic Journal: Economic Policy, American Economic Association, vol. 7(1), pages 63-94, February.
    4. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
    5. Peter Ganong & Pascal J. Noel, 2020. "Why Do Borrowers Default on Mortgages?," NBER Working Papers 27585, National Bureau of Economic Research, Inc.
    6. Albanesi, Stefania & Nosal, Jaromir, 2015. "Insolvency After the 2005 Bankruptcy Reform," CEPR Discussion Papers 10533, C.E.P.R. Discussion Papers.
    7. Luigi Guiso & Paola Sapienza & Luigi Zingales, 2013. "The Determinants of Attitudes toward Strategic Default on Mortgages," Journal of Finance, American Finance Association, vol. 68(4), pages 1473-1515, August.
    8. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    9. Gelman, Michael & Kariv, Shachar & Shapiro, Matthew D. & Silverman, Dan & Tadelis, Steven, 2020. "How individuals respond to a liquidity shock: Evidence from the 2013 government shutdown," Journal of Public Economics, Elsevier, vol. 189(C).
    10. Marieke Bos & Emily Breza & Andres Liberman, 2018. "The Labor Market Effects of Credit Market Information," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2005-2037.
    11. S. Michael Giliberto & Arthur L. Houston, 1989. "Relocation Opportunities and Mortgage Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 17(1), pages 55-69, March.
    12. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    13. Mnasri, Ayman, 2018. "Downpayment, mobility and default: A welfare analysis," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 235-252.
    14. Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021. "Predicting Mortality from Credit Reports," Papers 2111.03662, arXiv.org.
    15. Donghoon Lee & Wilbert Van der Klaauw, 2010. "An introduction to the FRBNY Consumer Credit Panel," Staff Reports 479, Federal Reserve Bank of New York.
    16. Peter Ganong & Pascal J. Noel, 2020. "Why Do Borrowers Default on Mortgages? A New Method For Causal Attribution," Working Papers 2020-100, Becker Friedman Institute for Research In Economics.
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