IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_839_24.html
   My bibliography  Save this paper

Improving survey information on household debt using granular credit databases

Author

Listed:
  • Antonietta di Salvatore

    (Bank of Italy)

  • Mirko Moscatelli

    (Bank of Italy)

Abstract

Distributional information on the debt held by households and on the characteristics of debtors is fundamental for creating and updating policy-relevant indicators and models. The primary source for this information in Italy is the Survey on Household Income and Wealth (SHIW), carried out periodically by the Bank of Italy. Its estimates, however, are affected by several types of non-sampling errors inevitably present in surveys. In this work, we use granular credit registers to improve debt estimates and determine which households are more likely to have measurement errors for their debts. The results show that integrating the SHIW with information derived from the credit registers increases household debt participation and the amount of debt households hold. Moreover, we find that households belonging to the wealthiest quintiles of the population, residing in the South and Islands, and for which the reference person has a low level of financial education are more likely not to report their loans for property purchases to the SHIW.

Suggested Citation

  • Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_839_24
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2024-0839/QEF_839_24.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahnert, Henning & Kavonius, Ilja Kristian & Honkkila, Juha & Sola, Pierre, 2020. "Understanding household wealth: linking macro and micro data to produce distributional financial accounts," Statistics Paper Series 37, European Central Bank.
    2. Carmela Aurora Attin� & Francesco Franceschi & Valentina Michelangeli, 2019. "Modelling households� financial vulnerability with consumer credit and mortgage renegotiations," Questioni di Economia e Finanza (Occasional Papers) 531, Bank of Italy, Economic Research and International Relations Area.
    3. Valentina Michelangeli & Mario Pietrunti, 2014. "A Microsimulation Model to evaluate Italian Households Financial Vulnerability," International Journal of Microsimulation, International Microsimulation Association, vol. 7(3), pages 53-79.
    4. Romina Gambacorta & Eleonora Porreca, 2022. "Bridging techniques in the redesign of the Italian Survey on Household Income and Wealth," Questioni di Economia e Finanza (Occasional Papers) 719, Bank of Italy, Economic Research and International Relations Area.
    5. Muchlinski, David & Siroky, David & He, Jingrui & Kocher, Matthew, 2016. "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data," Political Analysis, Cambridge University Press, vol. 24(1), pages 87-103, January.
    6. Engel, Janina & Riera, Pau Gayà & Grilli, Joseph & Sola, Pierre, 2022. "Developing reconciled quarterly distributional national wealth – insight into inequality and wealth structures," Working Paper Series 2687, European Central Bank.
    7. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.
    8. Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.
    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. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    2. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    3. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
    4. Yannic Rehm & Lucas Chancel, 2022. "Measuring the Carbon Content of Wealth Evidence from France and Germany," PSE Working Papers halshs-03828939, HAL.
    5. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    6. Carlo Campajola & Marco D'Errico & Claudio J. Tessone, 2022. "MicroVelocity: rethinking the Velocity of Money for digital currencies," Papers 2201.13416, arXiv.org, revised May 2023.
    7. Erik Gogola, 0000. "Financial Vulnerability of Slovak Households: Evidence from household balance sheet stress tests using HFCS microdata," Proceedings of Economics and Finance Conferences 14316305, International Institute of Social and Economic Sciences.
    8. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    9. Giordana, Gastón & Ziegelmeyer, Michael, 2020. "Stress testing household balance sheets in Luxembourg," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 115-138.
    10. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Revista de Estabilidad Financiera, Banco de España, issue Otoño.
    11. Zixue Zhao & Tianxiang Cui & Shusheng Ding & Jiawei Li & Anthony Graham Bellotti, 2024. "Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction," Mathematics, MDPI, vol. 12(5), pages 1-27, February.
    12. Jaanika Meriküll & Tairi Rõõm, 2020. "Stress Tests of the Household Sector Using Microdata from Survey and Administrative Sources," International Journal of Central Banking, International Journal of Central Banking, vol. 16(2), pages 203-248, March.
    13. Liaqat Ali & Muhammad Kamran Naqi Khan & Habib Ahmad, 2020. "Financial Fragility of Pakistani Household," Journal of Family and Economic Issues, Springer, vol. 41(3), pages 572-590, September.
    14. Faiella, Ivan & Lavecchia, Luciano & Michelangeli, Valentina & Mistretta, Alessandro, 2022. "A climate stress test on the financial vulnerability of Italian households and firms," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 396-417.
    15. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
    16. Raffaele Marchi & Alessandro Moro, 2024. "Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models," Open Economies Review, Springer, vol. 35(1), pages 189-213, February.
    17. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.
    18. David Siroky & Carolyn M. Warner & Gabrielle Filip-Crawford & Anna Berlin & Steven L. Neuberg, 2020. "Grievances and rebellion: Comparing relative deprivation and horizontal inequality," Conflict Management and Peace Science, Peace Science Society (International), vol. 37(6), pages 694-715, November.
    19. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    20. Engel, Janina & Ohlwerter, Dennis & Scherer, Matthias, 2023. "On the estimation of distributional household wealth: addressing under-reporting via optimization problems with invariant Gini coefficient," Working Paper Series 2865, European Central Bank.

    More about this item

    Keywords

    survey; administrative data; residential mortgages; consumer credit;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth

    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:bdi:opques:qef_839_24. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.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.