The impact of vocational skills training on earnings: Evidence from China

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Authors: Hongye Sun
Cited by: 3
References: 128

Abstract

Vocational skills training (VST) is essential for boosting productivity and economic growth, particularly in the digital era where rapid technological change continuously reshapes skill demands. The association between training and economic outcomes has been extensively debated in previous literature; however, empirical findings on the causal relationship between training participation and earnings remain scarce, particularly for government-supported training programs. This study examines the impact of VST on earnings using instrumental variable estimation with data from Shandong Province, China. The findings demonstrate that VST participation generates substantial earnings returns average 12.1%, with male and female participants experiencing gains of 17.2% and 9.6%, respectively. Furthermore, training intensity exhibits significant earnings effects across program types and training categories. Specifically, each additional program type increases earnings by 13.2%, while each additional training category increases earnings by 11.5%. Moreover, conditional quantile regression incorporating a control function approach reveals a U-shaped pattern of training effects across the earnings distribution. That is, the largest earnings impacts are observed at the lower and upper percentiles. In addition, mediation analysis identifies that skill enhancement and vocational certification play important roles in transmitting training effects to earnings. This study contributes to the limited evidence on vocational training effectiveness in developing economies by providing rigorous empirical findings that inform more efficient and strategically targeted human capital policies.

Fields of Study & Subjects

Labor market dynamics and wage inequality Advanced Causal Inference Techniques Higher Education and Employability

Publication details

Journal / source
International Review of Economics & Finance
Publisher
Elsevier BV
Volume
107
Pages
105079–105079
Publication date
4 March 2026
Language
EN