Author Name | KAWAKAMI Atsushi (Toyo University) / TSURU Kotaro (Faculty Fellow, RIETI) / KUME Koichi (Toyo University) |
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Creation Date/NO. | October 2024 24-J-026 |
Research Project | Employment and Educational Reform in the AI era |
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Abstract
The similarity between a subject’s main job and second job is measured using two indicators: similarity of industry (whether the two jobs belong to the same or different industries) and similarity of occupation. The analysis utilizes data on individuals with second jobs from original individual data. Information on tasks required for each occupation in both the main job and second job is obtained from the Ministry of Health, Labour and Welfare’s "Japanese O-NET," and task distance, which indicates the similarity between the tasks of the main job and the second job, is created. The results of the analysis show that, for all benefits except for stress relief, it is more effective to choose a second job in the same industry as the main job for three indicators: skills, networks, and ideas, which are measured by whether the experiences from the second job are useful in the main job. An examination of the effects of task distance using interaction terms for cases where the main and second jobs are in the same or different industries confirms that skills acquired through second jobs can benefit the main job when the two jobs are closely related or when the second job is significantly different from the main job. Additionally, human networks are beneficial when the second job is in a different occupation from the main job. Furthermore, there is a tendency to gain ideas that are useful for the main job when choosing second jobs with both close and distant task distances in different industries.
The findings have significant implications for policies, as they empirically demonstrate the effectiveness of second jobs in terms of their capacity to provide experiences in different industries and occupations which allow for the acquisition of business ideas that enhance open innovation and for forming human networks.