Spatial Divergence of Urban Human Capital in China from the Perspective of Industrial Development:Based on Panel Data of 288 Chinese Cities from 2008 to 2019
With the rise of the knowledge economy and advancements in information technology,innovation-driven development and skill complementarity have increasingly become pivotal in driving high-quality industrial development.Based on panel data of 288 prefecture-level cities from 2008 to 2019 in China,this paper delves into the changing trends of skill complementarity level of urban labor and spatial evolution of urban human capital,then analyzes the intrinsic mechanism of spatial divergence of human capital from the perspective of the supply and demand of industrial labor.The results show that spatial agglomeration of high-skilled labor and the transition from spatial agglomeration to dispersion among low-skilled labor have prompted an initial increase followed by a decrease in the skill complementarity level of urban labor since 2008.This trend thereby accentuates the pattern of spatial divergence in urban human capital,especially in mega cities and cities with advanced industries,where declining skill complementarity level relates to sluggish growth of life service sector and reduced demand for low-skilled labor by mechamism analysis.Correspondingly,reduced demand for low-skilled labor result in sluggish growth of life service sector,so that wage growth is weak,which exacerbates the wage gap and reduce low-skilled workers'migration intent.However,low-skilled workers still earn significant urban wage premiums in mega cities,and the decreasing proportion of low-skilled labor may impede urban industrial potential.To coordinate industrial development with labor market,the paper suggests prioritizing supply-side reforms in the life service sector and optimizing the allocation of a vast and skill-diverse human capital,then,fostering the integration of life services and emerging industries by skill complementavity to boost the quality and efficiency of industrial development.
国家科技期刊平台
NSTL
万方数据
