IDEAS home Printed from https://ideas.repec.org/p/ube/dpvwib/dp2206.html
   My bibliography  Save this paper

Life after Default: Credit Hardship and its Effects

Author

Listed:
  • Giacomo De Giorgi
  • Costanza Naguib

Abstract

We analyze the impact of credit default on individual trajectories. Using a proprietary dataset for the years 2004-2020, we find that after default individuals relocate to cheaper areas. Importantly, default has long-lasting negative effects on income, credit score, total credit limit, and home-ownership status.

Suggested Citation

  • Giacomo De Giorgi & Costanza Naguib, 2022. "Life after Default: Credit Hardship and its Effects," Diskussionsschriften dp2206, Universitaet Bern, Departement Volkswirtschaft.
  • Handle: RePEc:ube:dpvwib:dp2206
    as

    Download full text from publisher

    File URL: https://repec.vwiit.ch/dp/dp2206.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
    4. 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.
    5. 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.
    6. Albanesi, Stefania & Nosal, Jaromir, 2015. "Insolvency After the 2005 Bankruptcy Reform," Economics Series 312, Institute for Advanced Studies.
    7. 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.
    8. Mnasri, Ayman, 2018. "Downpayment, mobility and default: A welfare analysis," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 235-252.
    9. Peter Ganong & Pascal J. Noel, 2020. "Why Do Borrowers Default on Mortgages?," NBER Working Papers 27585, National Bureau of Economic Research, Inc.
    10. Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021. "Predicting Mortality from Credit Reports," Papers 2111.03662, arXiv.org.
    11. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    12. Donghoon Lee & Wilbert Van der Klaauw, 2010. "An introduction to the FRBNY Consumer Credit Panel," Staff Reports 479, Federal Reserve Bank of New York.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. De Giorgi, Giacomo & Naguib, Costanza, 2024. "Life after (soft) default," European Economic Review, Elsevier, vol. 167(C).
    2. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    5. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    7. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    8. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    9. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    10. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    11. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    12. Aysegül Kayaoglu & Ghassan Baliki & Tilman Brück & Melodie Al Daccache & Dorothee Weiffen, 2023. "How to conduct impact evaluations in humanitarian and conflict settings," HiCN Working Papers 387, Households in Conflict Network.
    13. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    14. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    15. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," Working Papers 2201, Tulane University, Department of Economics.
    16. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    17. Alaina Barca & Larry Santucci & Leigh-Ann Schultz, 2022. "Foreclosure Kids: Examining the Early Adult Credit Usage of Adolescents Affected by Foreclosure," Working Papers 22-21, Federal Reserve Bank of Philadelphia.
    18. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    19. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
    20. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.

    More about this item

    Keywords

    mobility; bankruptcy; default; credit; income;
    All these keywords.

    JEL classification:

    • J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ube:dpvwib:dp2206. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Franz Koelliker (email available below). General contact details of provider: https://edirc.repec.org/data/vwibech.html .

    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.