Article 2025

Minimum wage and labor self-funded training: evidence from China

Journal of Economic Behavior and Organization
Journal · Vol. 235 · Art. 107050
Abstract

We examine the impact of minimum wage increases on labor self-funded training by first constructing a theoretical model that explores the effects under both perfectly and imperfectly competitive market conditions. We then empirically analyze the impact using data on training enterprise registrations and household spending on training. Theoretically, we find an increase in the minimum wage is expected to suppress demand for low-skilled labor, leading affected workers to engage in self-funded training to compete for a limited number of job positions. Empirically, a minimum wage increase significantly boosts the number of newly registered training enterprises and household expenditures on skill training. Mechanism analysis reveals that a higher minimum wage increases labor costs for enterprises, leading them to raise skill requirements during recruitment, thereby encouraging job market participants to pursue self-funded skill training. © 2025 Elsevier B.V.

Keywords

Author Keywords

China Labor self-funded training Minimum wage On-the-job training

Index Keywords

Author Affiliations
School of Economics and Statistics, Guangzhou University, Guangzhou, Guangdong, China
School of Economics, Nankai University, Tianjin, China
Funding & Acknowledgements
National Social Science Fund of China, NSSFC
Grant: 23AJY011
This work was supported by the National Social Science Fund of China (Grant No. 23AJY011 ) and the National Natural Science Foundation of China (Grant No. 72403136 ).
National Social Science Fund of China, NSSFC
This work was supported by the National Social Science Fund of China (Grant No. 23AJY011 ) and the National Natural Science Foundation of China (Grant No. 72403136 ).
National Natural Science Foundation of China, NNSF
Grant: 72403136
This work was supported by the National Social Science Fund of China (Grant No. 23AJY011 ) and the National Natural Science Foundation of China (Grant No. 72403136 ).
National Natural Science Foundation of China, NNSF
This work was supported by the National Social Science Fund of China (Grant No. 23AJY011 ) and the National Natural Science Foundation of China (Grant No. 72403136 ).
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