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Korean women wage analysis using selection models
Journal of the Korean Data & Information Science Society 2017;28:1077-85
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Mi Ryang Jeong1 · Mijeong Kim2

12Department of Statistics, Ewha Womans University
Correspondence to: Mijeong Kim
Associate professor, Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul 03760 Korea. E-mail:
Received August 17, 2017; Revised September 13, 2017; Accepted September 14, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this study, we have found the major factors which affect Korean women’s wage analysing the data provided by 2015 Korea Labor Panel Survey (KLIPS). In general, wage data is difficult to analyze because random sampling is infeasible. Heckman sample selection model is the most widely used method for analysing the data with sample selection. Heckman proposed two kinds of selection models: the one is the model with maximum likelihood method and the other is the Heckman two stage model. Heckman two stage model is known to be robust to the normal assumption of bivariate error terms. Recently, Marchenko and Genton (2012) proposed the Heckman selection-t model which generalizes the Heckman two stage model and concluded that Heckman selection-t model is more robust to the error assumptions. Employing the two models, we carried out the analysis of the data and we compared those results.
Keywords : Heckman selection model, Heckman selection-t model, missing not at random, sampling bias