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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. Andrea Neri & Maria Giovanna Ranalli, 2012. "To misreport or not to report? The measurement of household financial wealth," Temi di discussione (Economic working papers) 870, Bank of Italy, Economic Research and International Relations Area.
    7. 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.
    8. Romina Gambacorta & Eleonora Porreca, 2025. "Bridging techniques in the redesign of the Italian Survey on Household Income and Wealth," METRON, Springer;Sapienza Università di Roma, vol. 83(1), pages 43-74, April.
    9. 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.
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    Keywords

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    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

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