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China is the worlds second largest economy. After four decades of economic miracles, Chinas economy is transitioning into an advanced, knowledge-based economy. Yet, we still lack a detailed understanding of the skills that underly the Chinese labor force, and the development and spatial distribution of these skills. For example, the US standardized skill taxonomy O*NET played an important role in understanding the dynamics of manufacturing and knowledge-based work, as well as potential risks from automation and outsourcing. Here, we use Machine Learning techniques to bridge this gap, creating Chinas 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 the Chinas 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 will also make the taxonomy dataset publicly available for other researchers upon publication.
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When facing threats from automation, a worker residing in a large Chinese city might not be as lucky as a worker in a large U.S. city, depending on the type of large city in which one resides. Empirical studies found that large U.S. cities exhibit resilience to automation impacts because of the increased occupational and skill specialization. However, in this study, we observe polarized responses in large Chinese cities to automation impacts. The polarization might be attributed to the elaborate master planning of the central government, through which cities are assigned with different industrial goals to achieve globally optimal economic success and, thus, a fast-growing economy. By dividing Chinese cities into two groups based on their administrative levels and premium resources allocated by the central government, we find that Chinese cities follow two distinct industrial development trajectories, one trajectory owning government support leads to a diversified industrial structure and, thus, a diversified job market, and the other leads to specialty cities and, thus, a specialized job market. By revisiting the automation impacts on a polarized job market, we observe a Simpsons paradox through which a larger city of a diversified job market results in greater resilience, whereas larger cities of specialized job markets are more susceptible. These findings inform policy makers to deploy appropriate policies to mitigate the polarized automation impacts.
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