Evaluating the Effects of Major-Job Matching on First Job Satisfaction among College Graduates Using a Causal Forest

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.