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Analysis on determinants of turnover using 2-step regression model
Journal of the Korean Data & Information Science Society 2020;31:75-83
Published online January 31, 2020;  https://doi.org/10.7465/jkdi.2020.31.1.75
© 2020 Korean Data and Information Science Society.

Jangsik Cho1 · Changwan Kang2 · Seungbae Choi3

1Division of Mathematics and Applied Statistics, Kyungsung University
23Production Information Technology Engineering Major
Correspondence to: Professor, Division of Mathematics and Applied Statistics, Kyungsung University, Busan 48434, Korea. E-mail: jscho@ks.ac.kr
Received December 20, 2019; Revised January 7, 2020; Accepted January 7, 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
It is generally analyzed only for the employed people when analyzing the determinants of turnover. In this case, the problem of sample selection bias arises because unemployed people are systematically excluded from the population. Therefore, we use a bivariate probit model to solve the sampling selection bias problem. The main results are as follows. First, the bivariate probit model used to analyze the determinants of turnover is statistically significant. The probability of employment for male is higher than for female, but the probability of turnover is reversed. Third, as the average monthly income increases, the turnover probability is significantly lowered. Also, the turnover probability of university graduates with internship experience is significantly lower. Finally, turnover of regular job is a lower probability than one of nonregular job.
Keywords : Bivariate probit model, changing job, sample selection bias, simple probit model.