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Automation Impacts on China's Polarized Job Market

Author

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  • Haohui 'Caron' Chen
  • Xun Li
  • Morgan Frank
  • Xiaozhen Qin
  • Weipan Xu
  • Manuel Cebrian
  • Iyad Rahwan

Abstract

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 Simpson's 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.

Suggested Citation

  • Haohui 'Caron' Chen & Xun Li & Morgan Frank & Xiaozhen Qin & Weipan Xu & Manuel Cebrian & Iyad Rahwan, 2019. "Automation Impacts on China's Polarized Job Market," Papers 1908.05518, arXiv.org.
  • Handle: RePEc:arx:papers:1908.05518
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    References listed on IDEAS

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    6. Hoxtell, Annette, 2020. "Automatisierung qualitativer Marktforschung mit Künstlicher Intelligenz," PraxisWISSEN Marketing: German Journal of Marketing, AfM – Arbeitsgemeinschaft für Marketing, vol. 5(01/2020), pages 111-123.

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