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Reading China: Predicting policy change with machine learning

Author

Listed:
  • Weifeng Zhong
  • Julian TszKin Chan

Abstract

The authors develop a quantitative indicator of the Chinese government’s policy priorities over a long period of time, which they call the Policy Change Index (PCI) of China.

Suggested Citation

  • Weifeng Zhong & Julian TszKin Chan, 2018. "Reading China: Predicting policy change with machine learning," AEI Economics Working Papers 998561, American Enterprise Institute.
    • Handle: RePEc:aei:rpaper:998561
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    File URL: http://www.aei.org/publication/reading-china-predicting-policy-change-with-machine-learning
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    Citations

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    Cited by:

    1. Huang, Yun & Luk, Paul, 2020. "Measuring economic policy uncertainty in China," China Economic Review, Elsevier, vol. 59(C).
    2. Zhong, Weifeng & Chan, Julian & Ho, Kwan-Yuet & Lee, Kit, 2020. "Words Speak Louder Than Numbers: Estimating China’s COVID Severity with Deep Learning," Working Papers 10955, George Mason University, Mercatus Center.
    3. Zhong, Weifeng & Chan, Julian, 2020. "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers 10464, George Mason University, Mercatus Center.

    More about this item

    Keywords

    China; Communist Party; Media and Technology; artificial intelligence (ai); Policy Change Index (PCI);
    All these keywords.

    JEL classification:

    • A - General Economics and Teaching

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