Has the Structure of Factor Income Distribution Shifted toward Emerging Factors: Contributions of Categorized Factors and Evidence at the Prefecture-Level Cities

With the rapid advancement of AI algorithms and expanding application scenarios,technological and data factors have become increasingly critical.This paper employs an extended C-D production function with panel data from Chinese prefecture-level cities (2003—2021),incorporating data as a production factor to examine the structure and dynamics of income distribution.Findings show that labor and capital remain dominant,together accounting for over 60％ of the income share,while technology and data contribute around 12％,suggesting a potential “Solow Paradox” in the impact of emerging factors.These results remain robust across various checks.Factor income distribution is shaped by city characteristics,location,infrastructure,and resource endowment.First-tier cities lead in technology (14.4％) and data (17.6％) shares,with strong synergy observed in the eastern coastal region.In contrast,resource-based cities in central and western China struggle to harness new factors.Notably,since 2015,the contributions of technology and data have risen significantly,especially data in 2018—underscoring their growing role in economic growth.