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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
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2024-0839/QEF_839_24.pdf
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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.
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    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.
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    8. 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.
    Full references (including those not matched with items on IDEAS)

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    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

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