Over-indebted Households in Poland: Classification Tree Analysis

Grzegorz Wałęga () and Agnieszka Wałęga
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Grzegorz Wałęga: Cracow University of Economics
Agnieszka Wałęga: Cracow University of Economics

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2021, vol. 153, issue 2, No 7, 584 pages

Abstract: Abstract Increasing a personal debt burden implies greater financial vulnerability and threats for macroeconomic stability. It also generates a risk of the households over-indebtedness. The assessment of over-indebtedness is conducted with the use of various objective and subjective measures based on the micro-level data. The aim of the study is to investigate over-indebted households in Poland using a unique dataset obtained from the CATI survey. We discuss and compare the usefulness of various over-indebtedness measures across different socio-economic characteristics. Due to the differences in over-indebtedness across single measures, we perform a more complex assessment using a mix of indicators. As an alternative to other commonly criticised over-indebtedness measures, we apply the “below the poverty line” (BPL) measure. In order to obtain the profile of over-indebted households, we use classification and regression tree analysis as an alternative to logit or probit models. We find that DSTI (“debt service to income”) ratio underestimates the extent of over-indebtedness in vulnerable groups of households in comparison with the BPL. We highlight the necessity to use different measures depending on the adopted definition of over-indebtedness. A psychological burden of debts is particularly strong among older and poorly educated respondents. We also find that the age structure of over-indebted households in Poland differs from this structure in countries with a broader access to consumer credits. Our results can be used to enrich the methods of assessing the household over-indebtedness.

Keywords: Over-indebtedness; Classification tree analysis; Poverty line; Survey data (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11205-020-02505-6

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