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Large-scale online job search behaviors reveal labor market shifts amid COVID-19

Nature Cities volume 1, pages 150–163 (2024)Cite this article

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Abstract

The COVID-19 pandemic has had an unprecedented impact on labor markets, significantly altering the structure of labor supply and demand in various regions. We use large-scale online job search queries and job postings in China as indicators to assess and understand the evolving dynamics in regional labor markets. Our analysis reflects the changing landscape of regional agglomeration and potential misalignment of the supply and demand of jobs in labor markets. Specifically, we observe that the intention of labor flow recovered quickly from pandemic conditions, with a trend of the central role shifting from large to small cities and from northern to southern regions, respectively. Following the pandemic, the demand for blue-collar workers was substantially reduced compared with demand for white-collar workers. In particular, our analysis reveals a decreased central role of the metropolises and a decreased regional supply–demand mismatch of labor markets. This implies that, under the unprecedented levels of uncertainty and stress amid the pandemic, workers show relatively rational career choices that align with regional demand. Overall, our approach provides timely information for confronting the dynamic change in labor markets during extreme events. In addition, our findings can assist policymakers in providing appropriate policies to support the sustainable development of regional economies.

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Fig. 1: Labor flow intentions and key COVID-19 events in China (2019Q4–2022Q4).
Fig. 2: Overview of regional labor attractiveness from 2019Q4 to 2022Q4.
Fig. 3: Analysis of quarterly labor flow intention among different city tiers.
Fig. 4: Spatial distribution of the shift in authority and hub centrality scores.
Fig. 5: The spatial and temporal visualization of labor demand.
Fig. 6: Supply–demand misalignment in major urban agglomerations during the fourth quarter of each year from 2019 to 2021.

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Data availability

Source data are provided with this paper. The statistical source data that support the findings of this study are available on figshare57 at https://doi.org/10.6084/m9.figshare.24763752.

Code availability

The code for this paper is available on github at https://github.com/sunyinggilly/flowIntention.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under grant no. 92370204 (H.X.) and no. 62306255 (Y.S.), the Guangzhou-HKUST(GZ) Joint Funding Program under grant no. 2023A03J0008 (H.X.) and the Education Bureau of Guangzhou Municipality (H.X.). We are grateful to P. Zhao for his advanced insights and sustained investment in career science research, and to L. Chen, Y. Cheng, W. Zhao and the team of the BOSS Zhipin Career Science Lab for their support and encouragement.

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Authors and Affiliations

  1. The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Ying Sun & Hui Xiong

  2. Career Science Lab, BOSS Zhipin, Beijing, China

    Hengshu Zhu & Lu Wang

  3. Business Intelligence Lab, Baidu Research, Beijing, China

    Le Zhang

  4. The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China

    Hui Xiong

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Contributions

H.Z. proposed the idea of analysing the labor market shifts using online job search data. Y.S. and L.Z. processed the data. Y.S., H.Z. and L.W. designed the experiments and analysed the results. L.W. and L.Z. conducted the literature review and discussion. H.Z. and H.X. supervised the literature review, data processing, methodology, analysis and discussion. Y.S., H.Z. and L.W. wrote the paper. H.Z. and H.X. managed this project.

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Correspondence to Hengshu Zhu or Hui Xiong.

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Nature Cities thanks Ylenia Casali, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Sun, Y., Zhu, H., Wang, L. et al. Large-scale online job search behaviors reveal labor market shifts amid COVID-19. Nat Cities 1, 150–163 (2024). https://doi.org/10.1038/s44284-023-00022-4

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