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A dynamic analysis of household debt using a self-organizing map.

ABSTRACT: The Korean consumer credit panel offers a well-organized set of microdata representing various characteristics of individual borrowers. To overcome the difficulty of fragmented microdata details, we construct a cluster of Korean consumers' credit, to develop a self-organizing map that visualizes individuals' characteristics along two dimensions. The result of cluster analysis reveals that most borrowers belong to one large cluster representing diligent borrowers who honor their loan payments. Conversely, several small clusters that represent borrowers with high default probability are identified, and we also found that these borrowers' characteristics vary. No significant change is found in the structure of the cluster, even when the aggregate amount of consumer credit is increased. Moreover, the expansionary monetary policy did not change the quantitative structure of household debt in Korea.

Supplementary information

The online version contains supplementary material available at 10.1007/s00181-021-02120-5.

SUBMITTER: Kim HH

PROVIDER: S-EPMC8391868 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A dynamic analysis of household debt using a self-organizing map.

Kim Hyun Hak HH

Empirical economics 20210827 6

The Korean consumer credit panel offers a well-organized set of microdata representing various characteristics of individual borrowers. To overcome the difficulty of fragmented microdata details, we construct a cluster of Korean consumers' credit, to develop a self-organizing map that visualizes individuals' characteristics along two dimensions. The result of cluster analysis reveals that most borrowers belong to one large cluster representing diligent borrowers who honor their loan payments. Co ...[more]

PMID: 34465938

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Project description:BackgroundA record number of older individuals have household debt, but little is known about possible links between debts and their mental wellbeing. This study examines the extent to which different aspects of household indebtedness predict mental wellbeing among this population.MethodsA sample of 17,091 individuals (72,700 observations) aged 50 and over in England was derived from waves 1-8 of the English Longitudinal Study of Ageing. Mental wellbeing was assessed by two outcome measures: number of depressive symptoms (CES-D 8) and quality of life (CASP-19 score). The predictors of mental wellbeing were examined using quartiles of non-zero overall debt amount, debt-to-income and debt-to-non-housing wealth ratios as alternative measures of debt burden. Linear regression models estimated the associations of mortgage and non-mortgage debt measures with mental wellbeing while adjusting for observable socioeconomic confounding factors. Individual fixed effect models were used to control for all time-constant factors among a longitudinal subsample.ResultsIndividuals in the highest debt-to-wealth quartile were particularly at risk of lower mental wellbeing, that is, a higher number of depressive symptoms and lower quality of life. After covariate adjustment, non-mortgage debt predicted lower mental wellbeing on both measures but mortgage debt was only linked to lower quality of life. Among the subsample who experienced changes in high non-mortgage debt levels, a small association of these changes with mental wellbeing outcomes were observed. Asymmetric within-individual estimation showed that both getting rid of and acquiring new debts during the study period predicted symmetrically (small) increases and decreases, respectively, in mental wellbeing.ConclusionThese findings indicate that among older individuals in England, non-mortgage debt status is linked to poor mental wellbeing. High, non-mortgage, debt-to-wealth ratios may help identify risk of mental wellbeing issues in older people with debts.

Dataset Information

A dynamic analysis of household debt using a self-organizing map.

Supplementary information

Publications

A dynamic analysis of household debt using a self-organizing map.

Similar Datasets

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