Abstract
China, the world’s second largest economy, is transitioning into an advanced, knowledge-based economy after four decades of rapid economic development. However, China still lacks a detailed understanding of the skills that underly the Chinese labor force, and the development and spatial distribution of these skills. Similar data has proven essential in other contexts; for example, the US standardized skill taxonomy, Occupational Information Network (O*NET), played an important role in understanding the dynamics of manufacturing and knowledge-based work, and the potential risks from automation and outsourcing. Here, we use Machine Learning techniques to bridge this gap, creating China’s first workforce skill taxonomy, and map it to O*NET. This enables us to reveal workforce skill polarization into social-cognitive skills and sensory-physical skills, and to explore China’s regional inequality in light of workforce skills, and compare it to traditional metrics such as education. We build an online tool for the public and policy makers to explore the skill taxonomy: skills.sysu.edu.cn. We also make the taxonomy dataset publicly available for other researchers.
Highlights
Workers rely on their skills to earn a living
Labor market polarizations have been observed in the U.S and many European countries since the 1980s (Autor et al, 2003; Alabdulkareem et al, 2018)
Lacking the occupation-specific data, scholars can only draw conclusions upon the macro-data, like employment data and manufacturing sector data, or the microdata, like workforce survey. This data gap has produced conflicting results: one study (Lv and Zhang, 2015) observed labor polarization while the other (Du et al, 2017) was not conclusive. We revisit this question of labor polarization in China, which is of significance for policymakers
Summary
Workers rely on their skills to earn a living. Fluctuations in the demand for particular skills translate to changes in wages and employment opportunities for individual workers. Technology has long driven such changes, which can result in significant economic and social upheavals (Mokyr et al, 2015). Routine work has proven most susceptible to substitution, while nonroutine tasks have been complemented by technology (Autor et al, 2003). For those with complementary skills, technology increases their productivity, and the wages their skills are likely to command (Brynjolfsson and McAfee, 2014); for those without such skills the outlook is less promising
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