인과 포레스트를 활용한 대졸 청년층의 직무와 전공 일치가 첫 직장 만족도에 미치는 효과 분석

Abstract

This study was conducted to explore the impact of major-job matching on first job satisfaction
 among college graduates. To do so, the casual forest technique, a machine learning-based causal
 inference method, was applied to the Graduates Occupational Mobility Survey (GOMS) 2019 data to
 analyze the average treatment effects of major-job matching on first job satisfaction and the
 heterogeneous treatments effects by covariates. The main findings are as follows. First, major-job
 matching had a positive and significant effect on first job satisfaction even after controlling for
 personal, university, and workplace characteristics as covariates. Second, heterogeneous treatment effects
 were observed by gender, participation in job camps, and business type. Based on the key findings,
 the implications for high job satisfaction among young college graduates through employment related
 to their majors were discussed.