• Korean Journal of Labor Studies
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• v.23 no.2
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• pp.47-77
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• 2017

This paper analyzes the changes in wage inequality and its contributing factors since the mid-2000s. Although trends vary by data and wage indices, the Gini coefficient of the total wage of all workers shows an increasing trend due to the part-time increase of less than 35 hours per week, while the wage Gini coefficient of hourly wages and the total wage Gini coefficient of full-time workers showed a declining trend. Part-time increases have increased inequality based on total wages, but part-time hourly wage increases can be considered to have reduced hourly wage inequality. Therefore, as a result of decomposing the factor of Gini coefficient reduction only for full-time workers, factors that contributed absolutely to inequality reduction were variables such as job tenure, career, and occupation, and employment type variable has little effects, and the establishment size variable deepens inequality. The variables such as industry, age, and education did not contribute significantly to the inequality change. This is attributed to the decline in wage premiums for job tenure and management and professional jobs and the increase in wage premiums for large-scale businesses.

• Korean Journal of Social Welfare Studies
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• v.41 no.1
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• pp.65-84
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• 2010

The purpose of Inequality Index Decomposition is to know the cause of overall inequality through decomposing aggregate inequality index into relevant components. Previous studies have mostly focused on the absolute contribution meaning that how much each component possesses out of overall inequality. However it could be more important to know the marginal contribution and inequality effect of each component in order to implement policies reducing overall inequality effectively. For this, we decomposed Gini Coefficient as representative inequality index into income sources or social welfare programs and calculated each Gini Income Elasticity(GIE). Analysis result says that regular employee income and employer(or the self-employed) income, of which GIE are 1.205 and 1.867, are classified as inequality-increasing income source. GIE can be one of good methods to analyse the inequality effect of various income sources and social welfare programs.

• 한국노년학
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• v.29 no.4
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• pp.1445-1461
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• 2009

The primary purpose of this study is to find policy implications by examining the trends in income inequality of the Korean aged and factors contributing to these. For analysis, this study used the 2nd, 5th, 7th and 9th wave of 'Korean Labour and Income Panel Study'. The findings are as follows. First, total income inequality of the elderly rose greatly after 1998 and is decreasing after 2001. Secondly, the Gini coefficient decomposition by income sources shows that earned income was the factor most responsible for the income inequality of the elderly. But its influences of the elderly income inequality is gradually decreasing during analysing periods. Third, assets income and public pensions have a great effects on the elderly income inequality. They increases the income inequality of the elderly households. Forth, interfamily transfer income and public assistance income reduces income inequality of the elderly.

• Korean Journal of Social Welfare Studies
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• v.40 no.1
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• pp.235-260
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• 2009

This study aims to analyze consumption inequality of Korean elderly households. The justification for analyzing consumption inequality during old age could be summarized as follows. First, due to the rapid growth of elderly population, the intra generational inequality of older people will bring greater consequences to the society in the coming years. Second, inequality is more actualized during old age when income stops playing a major role and the everyday lives are based mostly on consumption activities. For analysis, this study used the 2nd, 5th, 7th and 9th wave of 『Korea Labor and Income Panel Study』. The findings are as follows. First, total consumption inequality of elderly households is gradually decreasing after the economic crisis. Also, the gini coefficient of consumption items representing modern consumption culture, such as expenditures on eating out and car maintenance is decreasing. However, the inequality contribution rate of such items is continually rising, indicating that whereas the elderly households in general are being assimilated to the mainstream consumption culture, the disparity between classes is continually expanding. Second, gini coefficient and inequality contribution rate of the essentials such as food and housing has decreased indicating that basic livelihoods in general has risen. Third, the inequality of education expenditure is increasing after the year 2000 which implies that the problem of education inequality in general might have an effect on elderly households.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.99-108
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• 2020

Although the relationship between income and income inequality has previously been discussed, the present study applies a dynamic approach to analyze the specific relationship between forest household income and income inequality. For this analysis, a unit root test and a cointegration test were conducted to characterize the nature of income time-series data. After converting unstable time-series data into stable time-series data, a VAR model was estimated. Based on this model, an impulse-response was generated and variance-decomposition analysis was performed. These analyses showed that the effect of forest household income was relatively larger than that of the Gini coefficient, and that the impact of forest household income not only caused income to increase but also caused the Gini coefficient to decrease. In addition, the impact of the Gini coefficient had an impact on reducing forest household income and further increasing income inequality. We conclude that, with the aim of alleviating the inequality of forest household income, an income growth policy would be more effective than an income distribution policy.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.3
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• pp.502-516
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• 2014

This study has decomposed the Gini coefficient using Korean Labor & Income Panel Study data and empirically analyzed the impact of demographic characteristics and source-specific income of householder on the household income gap using panel analysis. The scope of areas were divided into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas,' and the period before and after the global financial crisis was examined. The analysis findings are as follows. First, when the entire period was examined by income source using Gini decomposition with division of areas into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas', the following results were revealed. The absolute and relative contribution level of property income to the gross income was the largest in the category of 'nationwide' and 'metropolitan areas,' while the contribution level of earned income was the largest in the category of 'non-metropolitan areas'. In addition, property income worsened the household income gap the most in the category of 'nationwide' and 'metropolitan areas.' Second, property income worsened the household income gap less after the financial crisis than before the crisis. It is probably because the price of real estate skyrocketed before the global financial crisis, worsening the household income gap, whereas the price drop after the crisis temporarily alleviated the gap. Third, a correlation analysis revealed that households with older householders whose education is high school graduation or below had relatively low gross income, and households with higher source-specific income, especially earned income, had relatively high gross income. Fourth, when the household income determinants were compared through panel analysis with division of areas into 'nationwide,' 'metropolitan areas,' and 'non-metropolitan areas,' the following results were obtained. While the impact of earned income, financial income, and other incomes was greater in non-metropolitan areas than in metropolitan areas, the impact of property income was greater in metropolitan areas than in non-metropolitan areas. To reduce the income gap, the government should impose higher taxes on the high-income class and provide tax benefits to the low-income class, with efforts to create a wide variety of jobs. In addition, since income inequality gets worse as the proportion of incomes generated through asset holdings becomes higher, the government should focus on stabilizing property prices while paying attention to the regional differentiation when carrying out related policies.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.2
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• pp.196-213
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• 2019

This paper attempts to analyze the contribution of different socioeconomic factors such as income, age, gender, household composition, education and employment status etc. to the difference between the Capital and Non-Capital Regions in the net wealth inequality of household in Korea. To this end, a two-stage Oaxaca-Blinder type decomposition is employed regarding the regional gap in the inequality of net wealth based upon the Recentered Influence Function of the Gini index for 'the 2018 Household Finance and Living Conditions Survey.' Despite the shortcomings of the survey data on wealth, the findings reveal that regional differences in income, marriage status (divorce), job type (agriculture, forestry and fishery related, and technical and assembly), family type (multi-cultural) variables deepen the regional gap in the net-wealth inequality, but employment status (full-time), job type (administrative and specialized, and service sales), household size variables mitigate the gap, and that regional differences in life cycles play an offsetting role.

• The Journal of Fisheries Business Administration
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• v.45 no.1
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• pp.17-31
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• 2014

This study examines trends in the overall income inequality of fishery household from 2003 to 2012 with the panel data of the Fishery Household Economy Survey. To investigate the potential determinants of income inequality, we decomposes the Gini coefficients into five income sources, fishery income, non-fishery income(non-fishery business income, non-business income), transfer income, irregular income and calculate the impact of each income sources on total income inequality. An evident trend toward increasing inequality of household income was found. Also, we find rising fishery income and non-fishery income play important role in the rapid increase of income inequality. Only transfer income appear to reduce total income inequality.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.392-404
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• 2019

The purpose of this study was to identify the impact of various income sources on income inequality of forestry households. Data from the Forestry Household Economy Survey from 2013-2016 were analyzed using the Gini coefficient decomposition method via income source. In particular, the income inequality analysis of forestry households was broken down into separate analyses based on group, i.e., the whole of forestry households and the five income quintile classes. The results of the analyses showed that income inequality of forestry households is primarily affected by forestry and nonforestry incomes and income quintile class. Moreover, income inequality of the highest income quintile class was largely affected by forestry income compared with other sources of income, whereas that of other income quintile classes was largely affected by nonforestry income. Therefore, in order to reduce income inequality in forestry households, it is necessary to increase the proportion of forestry income in the lower four quintile classes. Given that the income of the lowest quintile class is negative, it is necessary to devise ways to improve the proportion and quantity of forestry income. At the same time, as forestry income increases, a policy alternative is also required to improve inequality in forestry income.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.55-62
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• 2019

This study analyzed the relationship between housing price, purchase price, Gini coefficient, interest rate, and the employment, considering that the change in housing price was an important factor influencing macroeconomic variables and income inequality. The panel VAR model was constructed considering the panel data, and the Granger causality, Impulse response and Variance dispersion analysis were performed. As a result, when compared to before and after the global financial crisis, it was shown that the rent price had an effect on income inequality, but in the following period, both the rent price and the selling price affected the income inequality. And that it has a large impact on inequality. In addition, the causality between income inequality and employment rate, interest rate, and tax rate was confirmed. Therefore, it is expected that it will be a desirable policy to mitigate income inequality considering the influence of policy variables for economic activation including government real estate policy.