This research analyzes household financial structures and ratios to understand factors of household utility. Its main themes are as following: First, what kinds financial structures are found at each level of income? Second, how are they different by the level of income? Third, what factors contribute to appropriate financial ratios? The themes are supported by the texts on financial ratios from both inside and outside of Korea and proved by the Korean Labor and Income Panel Survey, the fifth annual edition. The households are exempted that do not support the household principle record in the principle and household economy record. Accordingly, this survey is from a financial structure analysis of 3,762 households. The analysis utilizes SPSS Window (Version 10.0) program. The following are the results: First, the income level 4 and above, in which the increasing number indicates a higher level of income, are highly ranked on the income-expense level and the asset-debt rate. Also, level 4 has a strong financial structure, whereas level 1 does not. Apparently, the management of the household is complicated by debt redemption and a lower level of assets. Second, Ratio 1, Ratio 2, Ratio 4, and Ratio 5 are different by the level of income. Third, the level of income contributes to the appropriate financial ratio. The financial safety and prospective financial structure at each income level is an important variable. Households with a high income, in particular, have to balance their finances and capital, reducing liabilities and increasing the total assets. In other words, the family must hold assets to enhance efficiency according to the character and income level of the household. This research is a useful resource for such a decision-making as to improve household financial structure stability. Also, it can be adopted to evaluate financial products for specific households and be used for economic and social welfare planning to predict how households influence the nationwide economy.
The purposes of this study were to examine wives' subjective perception about recovery degree of household consumption level after-before IMF and it's influencing variables. The data used were obtained from 250wives in Seoul. Statistics performed for the analysis were Frequency Percentile Means t-test Multiple Regression Analysis. The major findings were as follows; First household consumption level after IMF was lower than before IMF household-wives had little perception of recovery for economics. Second the difference of household consumption level after-before IMF was affected by income income-change The higher income and income-change were the higher difference of consumption level-after-before IMF was. Third the consumption level after IMF as affected by age income assets. The higher age income and assets were the higher consumption level after IMF was.
This study examined the effects of household head’s worker status change type on household income and household head’s income using a total combined sample of 2,578 households from Korea surveyed in 1998 and 2002 KLIPS(Korean Labor and Income Panel Study). Binary logit regression results showed that household incomes were likely to decrease significantly when household head’s changed worker status from a regular employee, a temporary employee or a daily worker, or an employer or a self-employed to no job status, or from a regular employee to a temporary employee or a daily worker, an employer or a self-employed, or from an employer or a self-employed to a regular employee compared to a regular employee status maintenance. In contrast, household head’s incomes were likely to increase significantly when household head’s changed worker status from a temporary to a regular employee compared to a regular employee status maintenance. Women household heads were significantly associated with the likelihood of the decrease of household head’s income compared to men household heads. Household heads beyond their forties were significantly associated with the likelihood of the decrease of household head’s income compared to household heads in their thirties age-group counterparts. Household heads with education level beyond high school graduation were significantly associated with the likelihood of the increase of household head’s income compared to household heads with the education level of high school graduation. This study shows that a more comprehesive labor policy is needed for achieving sustainable household income inflow.
Paying an attention to the issue of energy poverty of low-income households and ensuing regressivity of energy consumption, this study empirically analyzes the effects of both household and housing characteristics on heating energy consumption in an integrated way and identifies their causal structure based on the 2016 Korea Housing Survey data provided by the Korean government. Multiple regression analysis shows that household income and deteriorated level of housing, such as age and degree of cracks have positive effects and floor area of housing has a negative effect on the heating energy consumption per unit area of housing (HECPUH). Path analyses further reveal that the direct effect of household income on HECPUH is offset by the indirect effects that are mediated by deteriorated level and floor area of housing, making the total effect statistically insignificant. As a result, there is no significant difference in HECPUH across all income strata, implying that low-income (high-income) households pay more (less) heating costs relative to their income level, since they reside in the houses with relatively low (high) energy efficiency. To deal with this regressive causal structure of energy consumption, a policy option is recommended to improve energy efficiency of low-income housing through the government assistance in its maintenance and repair.
This study is to investigate the important factor for household private education expenditure. Especially, this study analyzed the influence of financial management characteristics. For this, the income level is classified by comparative poverty and analyzed the influence power The data for this study was "the Korean Labor Panel" conducted by Korea Labor Institute in 2000. The result showed the demographic factors by the income level and financial characteristics have big difference. Also, income level affects private education expenditure. For lower income level, demographic factors affect more than financial factors. This result explained the private education expenditure as luxurious goods. For middle income level, financial factors affect more than demographic factors. This explained the private education expenditure as choice goods. For upper income level, the private education expenditure was explained as investment goods.
This study investigated income elasticity of household health expenditures and differences by income level from 1998 through 2003. Data from Korean Labor and Income Panel Study was used for empirical analyses. To estimate the income effects on health expenditure, the two-part model was employed: a logistic regression for any health expenditure-first part-and a Ordinary Least Square regression for health expenditure conditional on any spending-second part. To estimate income elasticity, both health expenditure and income were log transformed in the second part. In addition, the random effects(RE) model was used for a longitudinal panel which was continuously followed from 1998 through 2003 to estimate income effects on health expenditures controlling for within and between unobservable household characteristics. Furthermore, difference in income effects on health expenditure across income level was investigated. Although income slightly increased odds of any health expenditure, there was not no table differences across income level. Income significantly increased health expenditures during study period(overall income elasticity: about 0.2) and the highest 20% income group presented higher income elasticity than the lowest 20% income group.
The purpose of this study was to analyze the life satisfaction of a single household head. This study examined the life satisfaction level of the single household head and analyzed the contributing factors. Data for this study were from the 7th KLIPS (7th Korea Labor and Income Panel Study), and the sample. consisted of 241 single household heads (including 83 male household single heads and 158 female single household heads). The statistics used for the analysis were frequencies, percentile, means, standardization, ANOVA, and multiple regression. The results were as follows. First, 53% of the household heads were due to their spouses' death, 34% of the household heads were single due to divorce, and 13% were single for other reasons. The average age of a single household head was 51 years, and the average education level of a single household head was 9.7 years and 70% of the single household heads were employed. Approximately 46% of the single household heads lived in small-and medium-sized cities. Second, the level of satisfaction with income, leisure, and residence of the single household head was lower than the middle level (3.0 points), while the level of satisfaction with family relationships, relatives, and social relationships was higher than the middle level (3.0 points). Third, there were differences in the life satisfaction level of single household heads in accordance with the single household head's marriage status, residence, recognition of health status, and current financial situation. Finally, significant variables contributing to the life satisfaction level of the single household head were gender, educational level, residence, monthly total income, satisfaction level of leisure, and family relationships. The most influential variable was the residence.
The purpose of this study was to gain a better understanding of households with a negative cash flow. The Household Budget Survey conducted in 2000, 2005, and 2010 by the Bureau of Statistics in South Korea were used for this study. The households used in this study were divided into four groups according to their income levels; they were categorized the lowest, low, middle, and high income groups. This study made several findings regarding households with a negative/positive cash flow. Firstly, the demographic and economic characteristics were different between those who have a negative cash flow and those with a positive cash flow. A female household head, a household head age 65 and older, a household head with a lower educational attainment, an unemployed household head, and the presence of a child/children in schools were related with the household deficit. Secondly, the households with a positive cash flow had a higher income level compared to the households with a negative cash flow, while the households with a negative cash flow had a much higher consumption level compared to the households with a positive cash flow. Thirdly, the household deficit to total income ratio of the lowest income group was higher when compared to any other income group. Lastly, the multivariate statistics showed that households including a child/children in schools are more likely to be a household with a negative cash flow. Especially, the expenditures on education and transportation were related with the likelihood of a household deficit.
This study explored the level and general propensity of the economic distress and the financial management behavior of household financial managers and analyzes the effects of economic distress and the sub dimensions to financial management behaviors. The research results can be summarized as follows. 1. The overall level of economic distress of household financial managers was middle-levels and the financial management behavior were slightly higher than mid-point. 2. Regarding the household variables on the economic distress of household financial managers, education levels and occupations of husbands, monthly income, financial knowledge were the variables that had a significant negative effect on the economic distress. That is, higher education levels of husbands, husbands with management-level/professional-level career, higher monthly income, and higher level of financial knowledge generated lower economic distress. 3. Regarding the household variables and the economic distress on the financial management behavior, monthly income, and financial knowledge were the variables that had a significant positive effect on the financial management behavior. On the contrary, income-asset distress was negative variable. Thus, higher monthly income, higher level of financial knowledge, and lower level of economic distress generated higher financial management behavior. Statistically significant differences were detected in financial management behavior sub dimensions. From this research, it could be concluded that the main variables affecting the level of financial management behaviors are income-asset distresses and financial knowledge.
The purpose of the study was to investigate the characteristics and economic status of deficit households compared to surplus households. Data from The Household Income and Expenditure Survey 2005 by NSO and 50, 207 salary/wage earners' households were used for the analysis. The statistical methods used were GLM, logit, and cluster analyses. The analysis results showed that 25.3 percent of the households were deficit households. Approximately half of the lowest 20% income group were deficit households. Income deficit households earned 1, 273 thousand less than that of surplus households, whereas consumption of deficit households was 1, 006 thousand more than that of surplus households. The average propensity of consumption of deficit households was 142.1. According to the logit analysis, factors contributing to the probability of belonging to a deficit household included income level, household size, age and educational level, occupation, homeownership, car ownership, and wife's employment status. Deficit households were classified into 5 types: 1) health care expenditure-dominated group, 2) housing expenditure-dominated group, 3) education expenditure-dominated group, 4) money transfer-dominated group, and 5) overall-overconsumption group. The overall-overconsumption group was the largest group of all at 58.5%. It was found that for all five groups, the changes in household size, income group, home ownership, and occupation of the individual were variables that influenced the probability of belonging to a certain group.
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