• Journal of the Korean Home Economics Association
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• v.37 no.2
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• pp.127-144
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• 1999

The purposes of this study were to identify the housing maintenance cost, imputed rent fee and total housing cost of households living in rental house, to analyze the factors related to their housing maintenance cost, imputed rent fee and total housing cost and to investigate the factors contributing to total housing cost to total household income ratio. The data used for these purposes, was 97 KHPS of Daewoo Economic Research Institute. Sample size of households living in rental house, was 663. Statistics performed for the analysis were frequencies, percentiles, t-test, Lorenz cutie and Gini coefficient, Tobit analysis, OLS and Logistic analysis. The results of this study were as fellows: First, monthly cost of monthly rent & maintenance and repairs of households living in rental house with a deposit was lower than rental house, while the imputed rent fee of households living rental house with a deposit was higher than monthly rent households'And, total housing cost of households living in rental house with a deposit was higher than monthly rent households'. Second, Gini coefficient of the housing maintenance cost was 0.440, Gini coefficient of imputed rent fee was 0.362, and Gini coefficient of total housing cost was 0.291. Third, the variables related to their housing maintenance cost were family type, total household expenditure of socio-demographic characteristics and residence, type of rent, housing type of housing environmental factor. Also, the variables contributing to imputed rent fee were job type and educational attainment of household hearts, the number of family members, total household expenditure, residence, type of rent, housing type and tole number of rooms. In addition, the variables associated with total housing cost were job type and educational attainment of household head, total household income and residence, type of rent, housing type and the number of room. Finally, age, job type, educational attainment of household head, wife's employment status, the number of family members, family type, total household expenditure, residence, rent type of rent, housing type, the size of living space, and the number of room were significant variables contributing to total household cost to total household income ratio.

• Journal of Korea Port Economic Association
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• v.20 no.1
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• pp.1-20
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• 2004

The purpose of this paper is to analyze the degree of cargo concentration at Korean ports using Theil's Entropy and to compare the results with those of Gini coefficient, Hoyle(1983), and Hirshmann-Herfindahl models. The entropy indices were compared with other models after measuring the cargo concentration for the period of 1981-2000 among the 18 Korean ports. The core results of empirical analysis are as follows: first, the empirical results of entropy indices show the following trends: all the ports(concentration except 1996's slight deconcentration), ports in Western area(deconcentration in 1990s and slight concentration in 2000), ports in Southern area(deconcentration in 1980s and 1990s except concentration in 2000), and ports in Eastern area(continuous trends of concentration). However, competition power will be decreased if concentration is increased, because of the character of entropy index. The empirical results of 4 indices except Hoyle model show the comparatively same directions in terms of trends. This study found out the similar results among the following models: All the ports(entropy index & Gini coefficient & H-H model), ports in Western area(Entropy index &Hoyle model), ports in Southern area(Entropy index & Gini coefficient), and ports in Eastern area(Entropy index & H-H index).The policy planner of Korean ports should find out the determination factors of concentration and deconcentration of each ports and decide the investment priority, size and scope for balancing the development of regional ports.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.373-383
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• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• The Journal of Fisheries Business Administration
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• v.39 no.3
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• pp.25-47
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• 2008

The Fishing Communities (so-called Uchongae) in Korea was legally established in 1962. It has been gradually expanded by quantity, and we have total 1,969 communities in 2006. The major establishment purpose of Uchongae was put 2 functions. The first function is to make up the double industry structure in coastal region, and second function is to make economical condition for Uchongae. Nevertheless the Fishing Communities System in Korea was not successfully developed after first beginning. The Income gap have become heavily between fishing area and non - fishing area, including agricultural area. The income gap has been due to rapid industrialization and urbanization in Korea. And the income gap even have become heavily among Uchongaes. In this paper, It have been researched the degree of Income inequality among Uchongaes in Korea during 1986-2006. The income inequality degree was analyzed by Gini coefficient and Mean Log Deviation (MLD) using Lorenz Curve. According to analysis result, the Gini coefficient of Uchongaes in Korea has been about 2-times high from 0.0847 to 0.1770 during 20 years. And the MLD has been 5.4 times from 0.0125 to 0.0679 during same periods. This means to more wide the general Income Inequality among the Uchongaes in Korea. Especially, It means to more wide the gap of high ranking Uchongaes and low ranking Uchongaes that MLD index multiplier has been more high.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.1
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• pp.237-255
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• 2020

Pareto principle states that, for many events, roughly 80% of the effects come from 20% of the causes. This study clarified if there is Pareto principle in Korean universities' academic information resource sharing network and calculates the Gini efficient about inequality in sharing academic resources. As a result, top 20% libraries led 80% of performance and inequality degree showed 0.8 as very serious condition. Relative Gini efficient which is recalculated considering scale of each libraries stay 0.7 that is adjusted slightly down. It means that such phenomenon is not caused by the difference of each universities scale with high contribution of big university and low contribution of small university. And in comparison of inequality between university's types, inequality between community colleges and private universities is more serious than four-year-course college and national university respectfully. Finally, as a result of visualizing the distribution of participating libraries, there were libraries with overwhelming contributions, and libraries with small but relatively high contribution levels were also distributed.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.591-600
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• 2016

This paper, in the situation of deepening poverty and worsening income inequality, aims to find the impact on income inequality of main income sources such as public income, market income and family income in the elderly and propose polices for weakening the income inequality in the elderly. Main results are as follows. First, Gini coefficients of each income sources in the elderly are total income's 0.4801, public income's 0.4071, market income's 0.6736 and family income's 0.1855. Income inequality in the elderly population is serious in the total income, public income and market income areas. Second, after excepting for public income in total income, Gini coefficient is 0.4864. after excepting for market income in total income, Gini coefficient is 0.3609. And after excepting for family income in total income, Gini coefficient is 0.5784. When market and public income are excepted from total income, Gini coefficient alleviate. Therefore, market income and public income are the major causes of income inequality in the elderly. But, family income alleviate the income inequality in the elderly. In order to alleviating the income inequality of the elderly, we must try to increasing the market income. For example, government must to supply job opportunities for the elderly of low-income.

• Journal of Families and Better Life
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• v.14 no.3
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• pp.223-236
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• 1996

The purpose of this study was to compare the financial status between Aged Households and Non-aged households. The sample obtained from 1993 KHPS, consisted of 3,425 Korean married couples including 2,915 of non-aged households and 510 aged households. Statistics employed for the analysis were frequencies percentile and t-test And Lorenz curve and Gini coefficient were used to compare the financial status of two groups. The results of this study were as follows. First financial status of aged households specially annual total income annual current income earned income and annual total expenditure were lower than those of non-aged households. Second Gini-coefficients of aged households' income and expenditure were greater than expenditure of them. Moreover Gini-coefficients of aged households' total asset and total debt were greater than those of non-aged households' However total asset of aged and non-aged households were smaller than total debt of two groups.

• KDI Journal of Economic Policy
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• v.29 no.3
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• pp.1-28
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• 2007

Upon analyzing several Korean Income data sets, it is confirmed that the relationship between Gini coefficient and bi-polarization measure (EGR) is empirically not different although they each come from different theoretical basis. Furthermore, it is difficult to state that the degree of polarization of income distribution, measured by DER, in Korea has deepened more than that of income inequality, estimated by Gini coefficient, in the periods of before and after the economic crisis.

• Journal of the Korean Home Economics Association
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• v.40 no.9
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• pp.143-159
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• 2002

This study investigates inequality of the private educational expenditure using the Family Expenditure Survey of 1990, 1996, 1998, and 2000. The major results are: first, inequality of the private educational expenditure has been relived between 1990 and 2000; second, despite decrease in household income right after the Korean economic crisis, the private educational expenditure has been increased in the households having middle and high school students; third, the gaps in the private educational expenditure between income groups are mainly due to the differences in the spending levels of the private education rather than differences in the percentages of households who spend any in the private education; fourth, in 2000, the gini coefficient of the private educational expenditure among households having elementary school student is 0.4832, and 0.6468 among households having middle and high school students; fifth, 30% of the households having middle and high school students who show the highest level of the private educational expenditure occupy 80% of the total private educational expenditure made by the whole households.

• Journal of Smart Tourism
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• v.2 no.3
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• pp.17-30
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• 2022

The tourism sector of the Maldives has seen rapid growth since its inception in 1972. One significant development is the transformation of the market composition in recent years. China has surpassed traditional European markets as the single largest source market. In this regard, this study seeks to assess the seasonality in the Maldivian tourism sector using a monthly dataset of visitor arrivals from 2003 to 2019. The seasonality ratio, the seasonality indicator, the Gini coefficient and the seasonal index were used to examine the seasonality patterns. The results of this study show that there are three distinct peaks (January to April, August, and November to December) and two off-peaks (May to July and September) periods. The findings also reveal that the rise of the Chinese market has significantly lessened the seasonality of Maldivian inbound tourism. Finally, some important implications are discussed.