FACTORS INFLUENCING THE SPATIAL LABOR EMPLOYMENT STRUCTURE IN THE DIGITAL ECONOMY: BASED ON A SPATIAL PANEL MODEL

Abstract

In the context of the rapid development of the digital economy, global labor markets and employment structures are undergoing profound transformations. This study focuses on the interactive relationship between the digital economy and labor employment structures, exploring the impact of digital economy development on the labor employment structure across different regions in China. By employing global principal component analysis (GPCA) and the global entropy method, this study quantifies the level of digital economy development in various regions. Panel models and spatial panel models are utilized to analyze the changing characteristics of labor employment structures during the digital transformation process across regions. The quantification of digital economy levels across provinces reveals that the eastern regions exhibit the highest levels of digital economy development, followed by the central and western regions. To account for spatial differences, this study innovatively applies spatial panel models and uses the Akaike Information Criterion (AIC) to compare models, ultimately selecting the spatial Durbin model and spatial lag model for regression analysis. The findings indicate that the digital economy exerts differential impacts on labor employment. On the one hand, the digital economy creates opportunities for skill training for workers with low to medium educational attainment, with the level of digital economy development having a significant positive effect on the employment scale of these workers. This suggests that economic expansion and agglomeration effects in high-density regions remain key drivers for absorbing low-skilled labor. On the other hand, higher levels of digital economy development, economic growth, and population agglomeration effects promote the concentration of highly educated workers, though the effect is less pronounced compared to that for workers with lower educational attainment. Based on these findings, this study proposes several policy recommendations. First, it is necessary to strengthen inter-regional coordination of digital infrastructure and facilitate the gradient transfer of industries to leverage population agglomeration effects and expand employment channels for low- to medium-skilled workers. Second, policies should be refined to attract and nurture high-skilled talent by promoting industry-academia-research integration and optimizing urban ecosystems to enhance the absorption capacity for high-end factors. Additionally, attention should be paid to the structural impacts of educational investment, foreign trade policies, and urbanization processes on employment structures, using spatial governance tools to mitigate negative spillovers in regional labor markets. Future research could incorporate micro-level indicators, such as technology penetration rates and enterprise digital maturity, to further explore the heterogeneity of underlying mechanisms.