Skill sets and wage premium: A network analysis based on Chinese agriculture online job offers

Abstract

Technology and globalization have significantly impacted the labor market, leading to employment polarization and economic inequality. Yet, our comprehension of how workplace skills shape these processes remains limited, and empirical evidence that considers regional and industry disparities is scarce. Analyzing Chinese agricultural job vacancy data enables us to discern precise labor market characteristics, facilitating inferences about employment trends and future skill training needs. Employing text mining and network science, we create a skill network, classifying skills into categories based on their interconnections. Our analysis reveals a certain degree of skill polarization in China's agricultural labor force. Skill combinations elucidate diverse labor market dynamics, with digital and soft skills being highly sought after by agricultural employers due to their complementarity with cognitive skills, enhancing workers' competitiveness. Furthermore, we link two skill diversity metrics from the skill network to labor market outcomes, specifically wages, discovering that wage disparities are prominent in single-skill positions, while roles requiring a range of skills command higher wages compared to specialized positions. In summary, our research provides fresh insights into labor market trends, human capital complexity, and the economic inequalities stemming from agricultural digitization and automation, especially in rural areas.