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Modeling the Revolving Revolution: The Debt Collection Channel

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  • Ricardo Serrano-Padial

    (Drexel University)

  • Lukasz Drozd

    (Federal Reserve Bank of Philadelphia)

Abstract

We investigate the role of information technology (IT) in the collection of delinquent consumer debt. We argue that the widespread adoption of IT by the debt collection industry in the 1990s contributed to the observed expansion of unsecured risky lending such as credit cards. Our model stresses the importance of delinquency and private information about borrower solvency. The prevalence of delinquency implies that the costs of debt collection must be borne by lenders to sustain incentives to repay debt. IT mitigates informational asymmetries, allowing lenders to concentrate collection efforts on delinquent borrowers who are more likely to repay.
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Suggested Citation

  • Ricardo Serrano-Padial & Lukasz Drozd, 2016. "Modeling the Revolving Revolution: The Debt Collection Channel," 2016 Meeting Papers 1666, Society for Economic Dynamics.
  • Handle: RePEc:red:sed016:1666
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gajendran Raveendranathan & Georgios Stefanidis, 2020. "The Unprecedented Fall in U.S. Revolving Credit," Department of Economics Working Papers 2020-05, McMaster University.
    2. Kyle F. Herkenhoff & Gajendran Raveendranathan, 2019. "Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry," Working Papers 2019-071, Human Capital and Economic Opportunity Working Group.
    3. Satyajit c & Dean Corbae & Kyle Dempsey & Jose-Victor Rios-Rull, 2020. "A Quantitative Theory of the Credit Score," PIER Working Paper Archive 20-030, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    4. 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.
    5. Kim, Hyeongjun & Cho, Hoon & Ryu, Doojin, 2018. "An empirical study on credit card loan delinquency," Economic Systems, Elsevier, vol. 42(3), pages 437-449.
    6. Jessica LaVoice & Domonkos F. Vamossy, 2019. "Racial Disparities in Debt Collection," Papers 1910.02570, arXiv.org, revised Jun 2023.
    7. Exler, Florian & Tertilt, Michèle, 2020. "Consumer Debt and Default: A Macro Perspective," IZA Discussion Papers 12966, Institute of Labor Economics (IZA).
    8. Pattison, Nathaniel, 2020. "Consumption smoothing and debtor protections," Journal of Public Economics, Elsevier, vol. 192(C).
    9. Lukasz A. Drozd & Ricardo Serrano-Padial, 2017. "Modeling the Revolving Revolution: The Debt Collection Channel," American Economic Review, American Economic Association, vol. 107(3), pages 897-930, March.
    10. Raveendranathan, Gajendran, 2020. "Revolving credit lines and targeted search," Journal of Economic Dynamics and Control, Elsevier, vol. 118(C).
    11. Lukasz Drozd & Michal Kowalik, 2019. "Credit Cards and the Great Recession: The Collapse of Teasers," 2019 Meeting Papers 1047, Society for Economic Dynamics.
    12. Gordon, Grey, 2017. "Optimal bankruptcy code: A fresh start for some," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 123-149.
    13. Tertilt, Michèle & Exler, Florian, 2020. "Consumer Debt and Default: A Macroeconomic Perspective," CEPR Discussion Papers 14425, C.E.P.R. Discussion Papers.
    14. Danisewicz, Piotr & Elard, Ilaf, 2023. "The real effects of financial technology: Marketplace lending and personal bankruptcy," Journal of Banking & Finance, Elsevier, vol. 155(C).
    15. Xiyang Hu & Yan Huang & Beibei Li & Tian Lu, 2022. "Uncovering the Source of Machine Bias," Papers 2201.03092, arXiv.org.
    16. Johannes Kriebel & Kevin Yam, 2020. "Forecasting recoveries in debt collection: Debt collectors and information production," European Financial Management, European Financial Management Association, vol. 26(3), pages 537-559, June.
    17. Xianghua Lu & Tian Lu & Chong (Alex) Wang & Ruofan Wu, 2021. "Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2564-2585, August.

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    18. Gordon, Grey, 2017. "Optimal bankruptcy code: A fresh start for some," Journal of Economic Dynamics and Control, Elsevier, vol. 85(C), pages 123-149.
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    20. Borghan Nezami Narajabad, 2012. "Information Technology and the Rise of Household Bankruptcy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(4), pages 526-550, October.

    More about this item

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • L84 - Industrial Organization - - Industry Studies: Services - - - Personal, Professional, and Business Services
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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