Reading China: Predicting Policy Change with Machine Learning

AEI Economics Working Paper Series (No. 2018-11)

43 Pages Posted: 6 Dec 2018 Last revised: 14 Apr 2019

See all articles by Julian TszKin Chan

Julian TszKin Chan

Bates White Economic Consulting

Weifeng Zhong

Mercatus Center at George Mason University

Date Written: April 12, 2019

Abstract

For the first time in the literature, we develop a quantitative indicator of the Chinese government’s policy priorities over a long period of time, which we call the Policy Change Index (PCI) for China. The PCI is a leading indicator of policy changes that covers the period from 1951 to the first quarter of 2019, and it can be updated in the future. It is designed with two building blocks: the full text of the People’s Daily — the official newspaper of the Communist Party of China — as input data and a set of machine learning techniques to detect changes in how this newspaper prioritizes policy issues. Due to the unique role of the People’s Daily in China’s propaganda system, detecting changes in this newspaper allows us to predict changes in China’s policies. The construction of the PCI does not require the understanding of the Chinese text, which suggests a wide range of applications in other contexts.

Keywords: policy change, machine learning, China, People’s Daily, propaganda

Suggested Citation

Chan, Julian TszKin and Zhong, Weifeng, Reading China: Predicting Policy Change with Machine Learning (April 12, 2019). AEI Economics Working Paper Series (No. 2018-11), Available at SSRN: https://ssrn.com/abstract=3275687 or http://dx.doi.org/10.2139/ssrn.3275687

Julian TszKin Chan

Bates White Economic Consulting ( email )

1300 Eye Street NW
Suite 600
Washington, DC 20005
United States

Weifeng Zhong (Contact Author)

Mercatus Center at George Mason University ( email )

3434 Washington Blvd., 4th Floor
Arlington, VA 22201
United States

HOME PAGE: http://www.weifengzhong.com/

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