IDEAS home Printed from https://ideas.repec.org/h/bis/bisifc/49-44.html
   My bibliography  Save this book chapter

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

In: Are post-crisis statistical initiatives completed?

Author

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

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2019. "Can media and text analytics provide insights into labour market conditions in China?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
  • Handle: RePEc:bis:bisifc:49-44
    as

    Download full text from publisher

    File URL: https://www.bis.org/ifc/publ/ifcb49_44.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. John Knight & Jinjun Xue, 2006. "How High is Urban Unemployment in China?," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 4(2), pages 91-107.
    2. Michelle Alexopoulos & Jon Cohen, 2009. "Uncertain Times, uncertain measures," Working Papers tecipa-352, University of Toronto, Department of Economics.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    4. Burdekin, Richard C.K. & Siklos, Pierre L., 2008. "What has driven Chinese monetary policy since 1990? Investigating the People's bank's policy rule," Journal of International Money and Finance, Elsevier, vol. 27(5), pages 847-859, September.
    5. Giles, John & Park, Albert & Zhang, Juwei, 2005. "What is China's true unemployment rate?," China Economic Review, Elsevier, vol. 16(2), pages 149-170.
    6. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    7. Feng, Shuaizhang & Hu, Yingyao & Moffitt, Robert, 2017. "Long run trends in unemployment and labor force participation in urban China," Journal of Comparative Economics, Elsevier, vol. 45(2), pages 304-324.
    8. Xiaoxia Wang & Wenkai Sun, 2014. "Discrepancy between Registered and Actual Unemployment Rates in China: An Investigation in Provincial Capital Cities," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 22(4), pages 40-59, July.
    9. Mccallum, Bennet T., 1988. "Robustness properties of a rule for monetary policy," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 29(1), pages 173-203, January.
    10. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    11. Mr. Waikei R Lam & Xiaoguang Liu & Mr. Alfred Schipke, 2015. "China’s Labor Market in the “New Normal”," IMF Working Papers 2015/151, International Monetary Fund.
    12. Tobback, Ellen & Naudts, Hans & Daelemans, Walter & Junqué de Fortuny, Enric & Martens, David, 2018. "Belgian economic policy uncertainty index: Improvement through text mining," International Journal of Forecasting, Elsevier, vol. 34(2), pages 355-365.
    13. Klingelhöfer, Jan & Sun, Rongrong, 2018. "China's regime-switching monetary policy," Economic Modelling, Elsevier, vol. 68(C), pages 32-40.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).
    3. Giulio Cornelli & Sebastian Doerr & Leonardo Gambacorta & Bruno Tissot, 2022. "Big Data in Asian Central Banks," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(2), pages 255-269, July.
    4. Jonathan Alexander Muñoz-Martínez & David Orozco & Mario A. Ramos-Veloza, 2023. "Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia," Borradores de Economia 1256, Banco de la Republica de Colombia.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:zbw:bofitp:2018_009 is not listed on IDEAS
    2. Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2019. "Can media and text analytics provide insights into labour market conditions in China?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    3. Yue Yin & Ye Jiang, 2023. "Fertility Effects of Labor Market Conditions at Graduation," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 31(4), pages 120-152, July.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    5. Panagiotidis, Theodore & Printzis, Panagiotis, 2020. "What is the investment loss due to uncertainty?," Global Finance Journal, Elsevier, vol. 45(C).
    6. N. Bloom, 2016. "Fluctuations in uncertainty," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 4.
    7. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    8. Hong, T., 2021. "Revisiting the Trade Policy Uncertainty Index," Cambridge Working Papers in Economics 2174, Faculty of Economics, University of Cambridge.
    9. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    10. Hiroaki Miyamoto, 2016. "Uncertainty shocks and labor market dynamics in Japan," Working Papers SDES-2016-8, Kochi University of Technology, School of Economics and Management, revised Jun 2016.
    11. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2014. "Uncertainty and Monetary Policy in Good and Bad Times," "Marco Fanno" Working Papers 0188, Dipartimento di Scienze Economiche "Marco Fanno".
    12. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    13. Gábor-Tóth, Enikő & Georgarakos, Dimitris, 2018. "Economic policy uncertainty and stock market participation," CFS Working Paper Series 590, Center for Financial Studies (CFS).
    14. Dew-Becker, Ian & Giglio, Stefano & Kelly, Bryan, 2021. "Hedging macroeconomic and financial uncertainty and volatility," Journal of Financial Economics, Elsevier, vol. 142(1), pages 23-45.
    15. Bhanu Pratap & Nalin Priyaranjan, 2023. "Macroeconomic effects of uncertainty: a Google trends-based analysis for India," Empirical Economics, Springer, vol. 65(4), pages 1599-1625, October.
    16. Pang, Ke & Siklos, Pierre L., 2016. "Macroeconomic consequences of the real-financial nexus: Imbalances and spillovers between China and the U.S," Journal of International Money and Finance, Elsevier, vol. 65(C), pages 195-212.
    17. Riikka Nuutilainen, 2015. "Contemporary Monetary Policy in China: An Empirical Assessment," Pacific Economic Review, Wiley Blackwell, vol. 20(3), pages 461-486, August.
    18. Gabriel P. Mathy, 2020. "How much did uncertainty shocks matter in the Great Depression?," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 14(2), pages 283-323, May.
    19. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
    20. Aaron Mehrotra & José R Sánchez-Fung, 2010. "China's Monetary Policy and the Exchange Rate," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 52(4), pages 497-514, December.
    21. Marco Guerrazzi & Ilham Ksebi, 2019. "Measuring Unemployment by Means of Official Data and Administrative Records: Empirical and Theoretical Perspectives," QUADERNI DI ECONOMIA DEL LAVORO, FrancoAngeli Editore, vol. 2019(110), pages 17-49.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bis:bisifc:49-44. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christian Beslmeisl (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.