Unraveling the Determinants of Overemployment and Underemployment among Older Workers in Japan: A machine learning approach

Overemployment and underemployment being widely existing phenomena, much less is known about their determinants for older workers. We innovatively employ machine learning methods to determine the important factors driving overemployment and underemployment among older workers in Japan. Those with better economic conditions, worse health, less family support, and unfavorable job characteristics are more likely to report overemployment, whereas increasing age, less disposable income, shorter current work hours, holding a job with a temporary nature, and low job and pay satisfaction are predictive to underemployment. Cluster analysis further shows that reasons for having work hour mismatches can be highly heterogeneous within both overemployed and underemployed groups. Subgroup analyses suggest room for pro-work policies among 65+ workers facing financial stress and lacking family support, female workers with unstable jobs and low spousal income, and salaried workers working insufficient hours.