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KMID : 1001320210480040079
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Social Welfare Policy
2021 Volume.48 No. 4 p.79 ~ p.106
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A Study on the Classification Types and Prediction Factors of Housing Poverty Among the Elderly - Using Latent Class Analysis -
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Park Eun-Joo
Gweon Hyun-Soo
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Abstract
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This study aims to suggest the need to better understand the diverse and complex characteristics of housing poverty among the elderly through their classification types. Additionally, the study establishes principal data for the preparation of housing poverty policy for the elderly by analyzing its predictive factors by type.
Data from the 2019 Korea Welfare Panel Study Year 14 were used for analysis, and the subjects were 3,141 household owners aged 65 or older. Representative housing poverty indicators and influencing factors were selected through a review of previous studies, and latent class analysis and multinomial logistic regression analysis were performed as analysis methods. The results showed that housing poverty among the elderly was classified into three groups: “housing occupancy anxiety type (20.7%),” “facility and structure unfilled type (3.5%),” and “housing stability type (75.7%).” There were differences according to the demographic, socioeconomic, and residential characteristics of each type of housing poverty. The non-receipt of public pensions is a common factor that increases the possibility of housing poverty. Non-ground-floor residences have a strong effect that increases the risk of falling into the “housing occupancy anxiety type” and detached house dwellings into the “facility and structure unfilled type,” indicating that they can be used as important indicators to predict housing poverty by type. The results of this study suggest the need for a housing welfare policy considering the housing poverty characteristics of the elderly, such as setting appropriate policy priorities, providing institutional and legal mechanisms for stabilizing the housing of the elderly living on the ground, and improving the residential environment of the elderly living in single-family homes, improving the old age income security policy, etc.
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KEYWORD
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Housing Poverty, Latent Class Analysis, Elderly, Multinominal logistic regression analysis
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