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Analysis on determinants of full-time youth employment in panel count model
Journal of the Korean Data & Information Science Society 2019;30:1309-18
Published online November 30, 2019;  https://doi.org/10.7465/jkdi.2019.30.6.1309
© 2019 Korean Data and Information Science Society.

Jangsik Cho1

1Division of Mathematics and Applied Statistics, Kyungsung University
Correspondence to: Professor, Division of Mathematics and Applied Statistics, Kyungsung University, Busan, 48434, Korea. E-mail : jscho@ks.ac.kr
Received September 20, 2019; Revised October 17, 2019; Accepted October 18, 2019.
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
This study analyzed the determinants of the number of youth recruited by local talents in public institutions in terms of the fact that local transfer policies of public institutions can provide good jobs to local talents. We use panel data from the “Public organization management information disclosure system” (Alio) collected over five years from 2013 to 2017. Since the dependent variable is count data, the Poisson model or negative binomial model, panel Poisson regression model and panel negative binomial regression model is mainly used to analyze the count data. The main results are as follows. First, the panel negative binomial regression model was chosen as the optimal model. Second, the annual results show that the number of full-time youth employment increases significantly over time compared to 2013. Third, it can be seen that the number of full-time youth employment in Jeonla, Daegyeong, Dongnam, and Gangwon and Jeju is significantly higher than that of Chungcheong. Finally, the longer the average number of regular employees working in the previous year, the higher the number of full-time youth employment.
Keywords : Hetero-skedasticity, over-dispersion, panel negative binomial regression model, panel Poisson regression model.