The Ability of Households to Repay Financial Liabilities in the Face of Unscheduled Events: Insights from the COVID-19 Pandemic

Olga Momot, Wioletta Grzenda

Abstract


Theoretical background: Unscheduled events, such as illnesses, disasters, or pandemics, can significantly impact households' ability to meet their financial obligations. The resilience of households to such events depends on their characteristics.

Purpose of the article: This study aims to identify and assess the importance of factors determining the repayment of household financial obligations during the COVID-19 pandemic.

Research methods: The study applied a logistic regression model and a decision tree. Since logistic regression does not provide information on the importance of variables used in the model, and decision trees do not provide information on the direction of the impact of the factors being examined, the Shapley additive explanations method has been applied to evaluate the results obtained using these models. The study used data from the Household Budget Survey for 2020 and 2021.

Main findings: We established that the greatest impact on the inability to meet financial obligations due to the COVID-19 pandemic was the change in total income caused by the pandemic, subjectively assessed by the respondents. Other key factors influencing difficulties in repaying obligations were education level and the household’s available income. However, the importance of these two factors was influenced by the subjective opinions of respondents about the change in total income due to the COVID-19 variable.


Keywords


repayment of financial liabilities; COVID-19; logistic regression; decision trees; Shapley Additive exPlanations method.

Full Text:

PDF

References


Achou, B., Boisclair, D., d’Astous, P., Fonseca, R., Glenzer, F., & Michaud, P.C. (2020). Early impact of the COVID-19 pandemic on household finances in Quebec. Canadian Public Policy, 46(S3), S217–S235. https://www.utpjournals.press/doi/pdf/10.3138/cpp.2020-087

Airgood-Obrycki, W., Herbert, C., Hermann, A., & Wedeen, S. (2022). Making the rent: Household spending strategies during the COVID-19 pandemic. Joint Center for Housing Studies, Harvard University.

Albuquerque, B., & Green, G. (2023). Financial concerns and the marginal propensity to consume in COVID times: Evidence from UK survey data. Journal of Macroeconomics, 78, 103563. https://doi.org/10.1016/j.jmacro.2023.103563

Arya, V., Bellamy, R.K., Chen, P.Y., Dhurandhar, A., Hind, M., Hoffman, S.C., ... & Zhang, Y. (2019). One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques. arXiv preprint arXiv:1909.03012.

Ben-Haim, Y., & Tom-Tov, E. (2010). A streaming parallel decision tree algorithm. Journal of Machine Learning Research, 11(2).

Breiman, L., Friedman, J., Stone, C.J., & Olshen, R.A. (1984).Classification and regression trees. CRC press. https://doi.org/10.1201/9781315139470

Chen, H., Covert, I.C., Lundberg, S.M., & Lee, S.I. (2023). Algorithms to estimate Shapley value feature attributions. Nature Machine Intelligence, 5(6), 590–601. https://doi.org/10.1038/s42256-023-00657-x

Chlebus, M., Kłosok, M., & Biecek, P. (2020). Model interpretability and explainability. In D. Kaszyński, B. Kamiński, & T. Szapiro (Eds.), Credit Scoring in the Context of Interpretable Machine Learning (ch. 7). SGH Publishing House.

Cramer, J.S. (2003). Logit Models from Economics and Other Fields. Cambridge University Press. https://doi.org/10.1017/CBO9780511615412

Drummond, C., & Holte, R.C. (2003). C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II (Vol. 11, No. 1–8).

Fisher, A., Rudin, C., & Dominici, F. (2019). All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously. Journal of Machine Learning Research, 20(177), 1–81. https://doi.org/10.48550/arXiv.1801.01489

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://doi.org/10.4258/hir.2016.22.4.351

Gramegna, A., & Giudici, P. (2021). SHAP and LIME: an evaluation of discriminative power in credit risk. Frontiers in Artificial Intelligence, 4, 752558. https://doi.org/10.3389/frai.2021.752558

Greenberg, E. (2012). Introduction to Bayesian Econometrics. Cambridge University Press. https://doi.org/10.1017/CBO9781139058414

Hewings, G.J., & Mahidhara, R. (2019). Economic impacts: Lost income, ripple effects, and recovery. In S. Changnon, The Great Flood of 1993 (pp. 205–217). Routledge.

Horvath, A., Kay, B., & Wix, C. (2023). The Covid-19 shock and consumer credit: Evidence from credit card data. Journal of Banking & Finance, 152, 106854. https://doi.org/10.1016/j.jbankfin.2023.106854

Hosmer Jr, D.W., Lemeshow, S., & Sturdivant, R.X. (2013). Applied Logistic Regression. John Wiley & Sons.

Keese, M. (2012). Who feels constrained by high debt burdens? Subjective vs. objective measures of household debt. Journal of Economic Psychology, 33(1), 125–141. https://doi.org/10.1016/j.joep.2011.08.002

Li, F., Zhang, X., Zhang, X., Du, C., Xu, Y., & Tian, Y. C. (2018). Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets. Information Sciences, 422, 242–256. https://doi.org/10.1016/j.ins.2017.09.013

Li, J., Song, Q., Peng, C., & Wu, Y. (2020). COVID-19 pandemic and household liquidity constraints: Evidence from micro data. Emerging Markets Finance and Trade, 56(15), 3626–3634. https://doi.org/10.1080/1540496X.2020.1854721

Li, X., Wang, Y., Basu, S., Kumbier, K., & Yu, B. (2019). A debiased MDI feature importance measure for random forests. Advances in Neural Information Processing Systems, 32. https://doi.org/10.48550/arXiv.1906.10845

Liu, X.Y., Wu, J., &Zhou, Z.H. (2008). Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(2), 539–550. https://doi.org/10.1109/TSMCB.2008.2007853

Lundberg, S.M., & Lee, S.I. (2017). A Unified Approach to Interpreting Model Predictions. https://ui.adsabs.harvard.edu/link_gateway/2017arXiv170507874L/doi:10.48550/arXiv.1705.07874

Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., ... & Lee, S.I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038%2Fs42256-019-0138-9

Jędrzejczyk, I. (2021). Risk exposure to the COVID-19 pandemic situation and the socio-economic sensitivity of rural areas and agribusiness. Ubezpieczenia w Rolnictwie, 41. https://doi.org/10.48058/urms/73.2020

Korzeniowska, A.M., Rutecka-Góra, J., Swacha-Lech, M., & Waliszewski, K. (2023). Wpływ pandemii COVID-19 na finanse osobiste. Wydawnictwo Uniwersytetu Ekonomicznego. https://doi.org/10.15611/2023.47.3

Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics. Econometric Society Monographs, 3. https://doi.org/10.1017/CBO9780511810176

Mamatzakis, E., Tsionas, M.G., & Ongena, S. (2023). Why do households repay their debt in UK during the COVID-19 crisis?. Journal of Economic Studies, 50(8), 1789–1823. https://doi.org/10.5167/uzh-235836

Mangalathu, S., Hwang, S. H., & Jeon, J. S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927. https://doi.org/10.1016/j.engstruct.2020.110927

Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C.A., ... & Bischl, B. (2020). General pitfalls of model-agnostic interpretation methods for machine learning models. In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (pp. 39–68). Springer. https://doi.org/10.1007/978-3-031-04083-2_4

Okuyama, Y., Sonis, M., & Hewings, G. J. (1999). Economic impacts of an unscheduled, disruptive event: a Miyazawa multiplier analysis. In G. Hewings, M. Sonis, M. Madden, & Y. Kimura (Eds.), Understanding and Interpreting Economic Structure (pp. 113–143). Springer. https://doi.org/10.1007/978-3-662-03947-2_6

Pedregosa, F. et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Puszer, B., & Czech, M. (2021). The impact of the COVID-19 pandemic on the consumer credit market in eurozone countries. Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia, 55(4), 49–71. https://doi.org/10.17951/h.2021.55.4.49-71

Ribeiro, M.T., Singh, S., & Guestrin, C. (2016, August). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). https://doi.org/10.1145/2939672.2939778

Roth, A.E. (Ed.). (1988). The Shapley Value: Essays in Honor of Lloyd S. Shapley. Cambridge University Press. https://doi.org/10.1017/CBO9780511528446

Sekścińska, K., Trzcińska, A., Pankowski, D., Pisula, E., & Wytrychiewicz-Pankowska, K. (2022). Financial factors and psychological distress during the COVID-19 pandemic in Poland. International Journal of Environmental Research and Public Health, 19(3), 1798. https://doi.org/10.3390/ijerph19031798

Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling lime and SHAP: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 180–186). https://doi.org/10.1145/3375627.3375830

Statistics Poland. (2021). Household budget survey in 2020.

Statistics Poland. (2022). Household budget survey in 2021.

Strumbelj, E., & Kononenko, I. (2010). An efficient explanation of individual classifications using game theory. The Journal of Machine Learning Research, 11, 1–18. https://doi.org/10.1145/1756006.1756007

Vuong, Q.H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 57(2), 307–333. https://doi.org/10.2307/1912557

Waliszewski, K., & Warchlewska, A. (2021). Comparative analysis of Poland and selected countries in terms of household financial behaviour during the COVID-19 pandemic. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(3), 577–615. https://doi.org/10.24136/eq.2021.021

Wąsiński, M., & Wnukowski, D. (2020). Skutki pandemii COVID-19 dla gospodarki światowej. Biuletyn PISM. https://www.pism.pl/publikacje/Skutki_pandemii_COVID19_dla_gospodarki_swiatowej

Witoń, A. (2024). Stan finansów osobistych Polaków przed i w trakcie pandemii COVID-19 – na podstawie wybranych wskaźników. Przegląd Prawno-Ekonomiczny, 2, 135–156. https://doi.org/10.31743/ppe.17076

Yannelis, C., & Amato, L. (2023). Household behavior (consumption, credit, and investments) during the COVID-19 pandemic. Annual Review of Financial Economics, 15(1), 91–113. https://doi.org/10.1146/annurev-financial-110821-020744




DOI: http://dx.doi.org/10.17951/h.2025.59.1.131-151
Date of publication: 2025-05-20 12:19:19
Date of submission: 2024-09-17 18:40:31


Statistics


Total abstract view - 0
Downloads (from 2020-06-17) - PDF - 0

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Olga Momot, Wioletta Grzenda

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.