Technological diffusion, skill reconfiguration and wage adjustment: evidence from AI-induced mitigation of overeducation penalties

Purpose The rapid growth of artificial intelligence (AI) is transforming skill structures and changing how education relates to labor market outcomes. This study explores the impact of AI diffusion on wage consequences associated with educational mismatch in China's urban labor market. Design/methodology/approach Using microdata from the China Labor-force Dynamics Survey (CLDS) (2014–2018) combined with city-level indicators of AI diffusion, we construct a cohort-based measure of educational mismatch and estimate extensive fixed-effects models to assess the role of AI. Findings Three key findings emerge. First, overeducation leads to a significant wage penalty, while undereducation is associated with a wage premium. Second, although AI diffusion is not significantly associated with individual wages, it reduces the wage penalty for overeducated workers and slightly lowers the wage premium for undereducated workers. Instrumental-variable estimates using lagged AI diffusion produce similar patterns, although the results should be interpreted with caution. Third, these effects vary by occupation: AI mainly benefits overeducated workers in non-manual jobs, where surplus schooling can be effectively absorbed, whereas in manual jobs it compresses the returns to undereducation as tasks become more skill-intensive. Mechanism analysis provides suggestive evidence consistent with the view that AI improves skill utilization and promotion expectations, while general life satisfaction remains unaffected. Originality/value This study shows how AI transforms the value of skills in evolving labor markets and highlights the need for policy efforts to align human-capital development and worker-transition support with technological change.