Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Conventional estimation methods on analyzing gender pay gap focus on comparing the earnings premium and gender inequality from the view of mean earnings distribution, highlighting human capital factors (e.g. education attainment, career training). However, mean distribution analysis do not reflect the whole perspective of gender earnings. Therefore, in our study, we adopt quantile regression estimation method to measure the impact of human capital (e.g. returns to education) and other social characteristics factors on wage. In addition, Melly2006 wage decomposition method is employed to reveal the pattern of gender earnings gap through overall distributions. We verified the evidence of ‘glass ceiling effect’ phenomenon in Korean labor market. The finds of our study also imply the female’s returns to education are higher than male, and the magnitude is even higher for upper earnings distribution. Furthermore, the estimation results of conditional and unconditional quantile regression present the differential of human capital variables occupy a big part of the explanatory on gender wage gap.
HongYe Sun. 2015. \u201cHuman Capital and Glass Ceiling: Quantile Regression Decomposition of Gender Pay Gap in Korean Labor Market\u201d. Global Journal of Human-Social Science - E: Economics GJHSS-E Volume 15 (GJHSS Volume 15 Issue E7): .
Crossref Journal DOI 10.17406/GJHSS
Print ISSN 0975-587X
e-ISSN 2249-460X
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Total Score: 132
Country: South Korea
Subject: Global Journal of Human-Social Science - E: Economics
Authors: HongYe Sun, GiSeung Kim (PhD/Dr. count: 0)
View Count (all-time): 153
Total Views (Real + Logic): 4117
Total Downloads (simulated): 1940
Publish Date: 2015 09, Fri
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Conventional estimation methods on analyzing gender pay gap focus on comparing the earnings premium and gender inequality from the view of mean earnings distribution, highlighting human capital factors (e.g. education attainment, career training). However, mean distribution analysis do not reflect the whole perspective of gender earnings. Therefore, in our study, we adopt quantile regression estimation method to measure the impact of human capital (e.g. returns to education) and other social characteristics factors on wage. In addition, Melly2006 wage decomposition method is employed to reveal the pattern of gender earnings gap through overall distributions. We verified the evidence of ‘glass ceiling effect’ phenomenon in Korean labor market. The finds of our study also imply the female’s returns to education are higher than male, and the magnitude is even higher for upper earnings distribution. Furthermore, the estimation results of conditional and unconditional quantile regression present the differential of human capital variables occupy a big part of the explanatory on gender wage gap.
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