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Multidimensional Poverty Analysis of Elderly Households by Cohort

노인가구의 코호트별 다차원빈곤 분석

  • Kim, Soon-Mi (Department of Consumer Sciences, Chungnam National University) ;
  • Cho, Kyung-Jin (Department of Consumer Sciences, Chungnam National University)
  • 김순미 (충남대학교 소비자학과) ;
  • 조경진 (충남대학교 소비자학과)
  • Received : 2018.12.21
  • Accepted : 2019.01.19
  • Published : 2019.02.28

Abstract

This study analyzed the poverty rate by poverty dimension, correlation between multidimensional poverty, variables that affected the number of poverty dimension and the probability of the poor or not. The sample consisted of 6,361 elderly households (1,561 baby boom birth cohort, 1,793 post-liberation birth cohort, 3,007 Japanese colonial period birth cohort) taken from the $12^{th}$ Korean Welfare Panel Study. First, the highest poverty rate among the baby boom birth cohort was 62.8% of employment poverty. The highest rate among the post-liberation birth cohort and Japanese colonial period birth cohort, was 82.5%, 92.3% of health poverty, respectively. Second, the highest coefficient in the baby boom birth cohort was .354 for asset poverty and relation poverty. In the remaining two cohorts, the coefficient for asset poverty and relation poverty was the highest at .268, .284, respectively. Third, the average number of poverty dimensions was 2.318 of the baby boom birth cohort, 2.921 of the post-liberation birth cohort, 3.564 of the poverty in the Japanese colonial period birth cohort. Also, the poverty rate for each cohort was 20.179%, 28.779%, and 50.083%, respectively. Fourth, the significant variables in all cohorts were gender, education, marital status, residence, and equalized ordinary income for the multiple regression analysis on the number of poverty dimensions. Additionally, age of the post-liberation birth cohort was significant, age and family numbers of the Japanese colonial period birth cohort were significant. Significant variables in logistic analysis on the probability of poverty or not were the same as those of regression analysis.

Acknowledgement

Supported by : Chungnam National University

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