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Consumer Debt and Poverty: the Default Risk Gap

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  • Sanroman Graciela
  • Bertoletti Lucía
  • Borraz Fernando

Abstract

This paper examines the disparity in default risk between vulnerable and non-vulnerable populations in consumer lending. We merge an exhaustive registry of loans granted in the financial system with microdata on vulnerable individuals applying for social programs. We estimate the sources of this disparity and how loan and individual characteristics influence the probability of default. We find that vulnerable individuals have a higher risk than non-vulnerable individuals. However, this difference is reduced when individual debt characteristics, particularly the interest rate, are considered. Specifically, interest rates explain at least 30 percent of the risk gap. We also find that the default probabilities faced by lending firms are higher than those faced by banks, but we show that this effect is partly due to interest rate divergences. Our study underscores the importance of considering individual characteristics, loan characteristics, and interest rates when assessing default risk. While recognizing their limitations, these results suggest the need for policy interventions to promote financial inclusion, fair interest rate practices, and financial education, especially for vulnerable populations.

Suggested Citation

  • Sanroman Graciela & Bertoletti Lucía & Borraz Fernando, 2024. "Consumer Debt and Poverty: the Default Risk Gap," Asociación Argentina de Economía Política: Working Papers 4765, Asociación Argentina de Economía Política.
  • Handle: RePEc:aep:anales:4765
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    References listed on IDEAS

    as
    1. Satyajit Chatterjee & Dean Corbae & Kyle Dempsey & José‐Víctor Ríos‐Rull, 2023. "A Quantitative Theory of the Credit Score," Econometrica, Econometric Society, vol. 91(5), pages 1803-1840, September.
    2. Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2021. "Measuring Bias in Consumer Lending [Loan Prospecting and the Loss of Soft Information]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 2799-2832.
    3. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    4. Lasse Brune & Eric Chyn & Jason Kerwin, 2021. "Pay Me Later: Savings Constraints and the Demand for Deferred Payments," American Economic Review, American Economic Association, vol. 111(7), pages 2179-2212, July.
    5. Bartlett, Robert & Morse, Adair & Stanton, Richard & Wallace, Nancy, 2022. "Consumer-lending discrimination in the FinTech Era," Journal of Financial Economics, Elsevier, vol. 143(1), pages 30-56.
    6. Hanson, Andrew & Hawley, Zackary & Martin, Hal & Liu, Bo, 2016. "Discrimination in mortgage lending: Evidence from a correspondence experiment," Journal of Urban Economics, Elsevier, vol. 92(C), pages 48-65.
    7. Neil Bhutta & Paige Marta Skiba & Jeremy Tobacman, 2015. "Payday Loan Choices and Consequences," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 223-260, March.
    8. Cecilia Olivieri & Romina Quagliotti & Graciela Sanroman, 2022. "Debit and credit card holdings: effects of the Uruguayan Financial Inclusion Law," Documentos de Trabajo (working papers) 0422, Department of Economics - dECON.
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. ¿Deuda paga deuda?
      by Elisa Failache in Razones y personas: repensando Uruguay on 2024-09-19 19:14:00

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    More about this item

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

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