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The wage determinants of college graduates using Heckman’s sample selection model
Journal of the Korean Data & Information Science Society 2023;34:1-8
Published online January 31, 2023;
© 2023 Korean Data and Information Science Society.

Jangsik Cho1 · Joenghwan Ko2

1Department of Big Data and Applied Statistics, Kyungsung University
2Department of Information Statistics, Andong National University
Correspondence to: This work was supported by a Research Grant of Andong National University.
1 Professor, Department of Big Data and Applied Statistics, Kyungsung University, Busan 48434, Korea.
2 Professor, Department of Information Statistics, Andong University, Andong 36729, Korea. E-mail:
Received October 20, 2022; Revised November 18, 2022; Accepted November 22, 2022.
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 wages determinants of college graduates were analyzed using data from the 2019 graduates occupational mobility survey conducted by the Korea Employment Information Service. Wages contain two types of complex information about an individual’s employment status and the size of wages. However many prior studies have overlooked the problem of sample selection bias by using only information on the size of wages. Heckman’s sample selection model was used to overcome these problems. The important results are summarized as follows; Heckman’s sample selection model was statistically significant. male were found to have significantly higher employment chances and wages than female. As the average GPA and the number of licenses increase, both the probability of employment and the size of wages increase statistically significantly. On the other hand, wages were statistically significantly higher for regular workers than for non-regular. Also the larger company size, the higher the statistically significant the wage.
Keywords : Censored data, Heckman’s sample selection model, sample selection bias.