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Can media and text analytics provide insights into labour market conditions in China?

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  • Bailliu, Jeannine
  • Han, Xinfen
  • Kruger, Mark
  • Liu, Yu-Hsien
  • Thanabalasingam, Sri

Abstract

The official Chinese labour market indicators have been seen as problematic given their small cyclical movement and their only partial capture of the labour force. In our paper, we build a monthly Chinese labour market conditions index (LMCI) using text analytics applied to Mainland Chinese-language newspapers over the period from 2003 to 2017. We use a supervised machine learning approach by training a support vector machine classification model. The information content and the forecast ability of our LMCI are tested against official labour market activity measures in wage and credit growth estimations. Surprisingly, one of our findings is that the much-maligned official labour market indicators do contain information. However, their information content is not robust and, in many cases, our LMCI can provide forecasts that are significantly superior. Moreover, regional disaggregation of the LMCI illustrates that labour conditions in the export-oriented coastal region are sensitive to export growth, while those in inland regions are not. This suggests that text analytics can, indeed, be used to extract useful labour market information from Chinese newspaper articles.

Suggested Citation

  • Bailliu, Jeannine & Han, Xinfen & Kruger, Mark & Liu, Yu-Hsien & Thanabalasingam, Sri, 2019. "Can media and text analytics provide insights into labour market conditions in China?," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1118-1130.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:3:p:1118-1130
    DOI: 10.1016/j.ijforecast.2019.03.003
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    Cited by:

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    3. Belabed, Christian Alexander & Theobald, Thomas, 2020. "Why the Chinese recovery will slow: Some lessons from sectoral data," BOFIT Policy Briefs 8/2020, Bank of Finland Institute for Emerging Economies (BOFIT).
    4. Corneli, Flavia & Ferriani, Fabrizio & Gazzani, Andrea, 2023. "Macroeconomic news, the financial cycle and the commodity cycle: The Chinese footprint," Economics Letters, Elsevier, vol. 231(C).

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    More about this item

    Keywords

    China; Labour markets; Inflation; Text analytics; Machine learning;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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