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Job match analysis using the Tobit model
Journal of the Korean Data & Information Science Society 2021;32:1343-52
Published online November 30, 2021;
© 2021 Korean Data and Information Science Society.

Jangsik Cho1 · Jeonghwan Ko2

1Department of Big Data and Applied Statistics, Kyungsung University
2Department of Information Statistics, Andong National University
Correspondence to: 1 Professor, Department of Big Data and Applied Statistics, Kyungsung University, Busan, 48434, Korea.
2 Corresponding author: Professor, Department of Information Statistics, Andong University, Andong, 36729, Korea. E-mail:
Received October 8, 2021; Revised October 28, 2021; Accepted October 28, 2021.
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 paper, the factors affecting job match of college graduates were analyzed. All of the three variables including job match (education level match, skill level match, and major level match) have complex information on whether or not they are employed and the size of their job match. However, many previous studies overlooked the problem of sample selection bias by analyzing using linear regression analysis Tobit model was used for analysis to overcome this problem. The results of summary results focusing on statistically significant results for all of the education level, skill level, and major level, which indicate job agreement, are as follows; First, it was found that the Tobit model was statistically significant compared to the linear regression model. Second, males appeared higher than females, the higher the age and the higher the parent’s income, the higher the job match. Third, science and engineering, arts and other fields showed higher job match than humanities. Finally, the higher the average grade point, the higher the number of vocational trainings while attending school, the higher the job match.
Keywords : Censored data, job match, marginal effect, sample selection bias, tobit model.