The Chinese labor market has reached a Lewis turning point, leading to increased absolute income for migrant workers but declining relative income and satisfaction. This raises concerns about social justice and equality. To address this, the Chinese government has implemented extensive vocational skills training programs for migrant workers. This study aims to determine if government-provided vocational skills training (GPVST) impacts migrant workers' income and income satisfaction, and to explore the underlying mechanisms. Existing research presents conflicting views: some studies show a positive income effect of vocational training, while others find limited or no impact. This discrepancy arises from varying theoretical frameworks (Human Capital Theory, Life-cycle Theory), methodological approaches, and data selection (national, regional, primary, secondary data, focus on different generations). Previous research often overlooks subjective income satisfaction (IS) and the role of livelihood capitals beyond human capital. This study addresses these gaps by employing PSM-DID on CLDS panel data (2016 and 2018) to analyze the impact of GPVST on both objective IL and subjective IS, using a mediation effect model based on livelihood capital theory to analyze the influence mechanism.

Foreign research since the 1980s and 90s shows training returns of around 5%, varying by gender, duration, and content, but mainly focuses on unemployed or general laborers, not migrants. Chinese studies offer conflicting views: some confirm a positive income effect of vocational training, while others point to limitations in training content, quality, and low returns. These differences reflect diverse theoretical perspectives (Human Capital Theory, Life-cycle Theory), varying methodologies, and data choices. Most studies concentrate on the income-enhancing effect of GPVST, focusing on human capital’s role and neglecting IS and other livelihood capitals' contributions. This study builds upon these existing studies by considering a more comprehensive range of factors and utilizing a more robust methodology.

This study uses propensity score matching with difference-in-differences (PSM-DID) and a parallel multiple mediation model. The data comes from the China Labor-force Dynamics Survey (CLDS) for 2016 and 2018, creating a two-period panel data set. The treatment group comprises migrant workers who received GPVST in 2016, while the control group consists of those who did not. PSM addresses self-selection bias, and the DID approach evaluates the treatment effect by comparing changes in income between the two groups before and after the intervention. The model includes control variables like gender, age, marital status, household registration, education, and work experience. Sample weights from CLDS are incorporated. A parallel multiple mediation model, based on the DFID sustainable livelihoods framework, examines how GPVST affects migrant workers' income by influencing their livelihood capitals (human, physical, financial, social, and natural). Fifteen indicators measure livelihood capital, weighted using the entropy method. The study employs the Karlson-Holm-Breen (KHB) method to analyze mediating effects. Robustness checks include changing matching methods (1:1 and 1:3 nearest neighbor matching) and using the number of GPVST participations as an independent variable. Heterogeneity analysis explores differences based on age (older vs. younger generation) and hukou (local vs. foreign).

Descriptive statistics show that migrant workers participating in GPVST had higher IL and IS than the control group in both 2016 and 2018. PSM-DID results confirm that GPVST significantly and positively impacts both IL (14.6% - 34.7% increase) and IS (6.9% - 9.3% increase). Robustness checks support these findings. Heterogeneity analysis reveals that GPVST's positive effect on IL is more pronounced for foreign migrant workers, while its positive effect on IS is stronger for local migrant workers. For the older generation, GPVST significantly increases IS but not IL, possibly because their career expectations diminish with age. The mediation analysis (using KHB) shows that GPVST's impact on IL is primarily indirect, driven by increased livelihood capitals, especially human and financial capital. The indirect effect of livelihood capital on IL is stronger than the direct effect on IL. Conversely, GPVST's effect on IS has a stronger direct effect than indirect, with human, social, and financial capital playing mediating roles. Physical and natural capital showed no significant mediating effect, possibly due to the specific nature of the training programs, imperfect land transfer mechanisms, and the cultural significance of land ownership for migrant workers.

The findings demonstrate that GPVST significantly improves both the objective (IL) and subjective (IS) aspects of migrant workers' income status. This supports the government's investment in vocational training and suggests it is effective in enhancing income and satisfaction. The stronger effect for foreign migrant workers on IL is likely due to the limited transferability of their pre-migration human capital. The stronger effect on IS for local migrant workers might result from reduced discrimination and closer alignment between wages and expectations. The mediating effect of human, social, and financial capital suggests that GPVST's positive impact stems from its role in skill enhancement, improved information access and networking opportunities, and increased financial resources. The lack of significant mediation by physical and natural capital highlights potential areas for policy improvement.

GPVST significantly improves migrant workers' income and satisfaction. The impact is stronger for foreign workers' IL and local workers' IS. The positive effect is channeled through human, social, and financial capital. Future research should use longer panel data, analyze different GPVST types (skills vs. entrepreneurship), and refine livelihood capital indicators.

This study uses CLDS data from only 2016 and 2018. Longer panel data would strengthen the analysis. The study primarily focuses on vocational skills training; future research should explore entrepreneurship training's impact. Refinement of livelihood capital indicators and model selection could further enhance the study's insights.