Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Reading China: Predicting policy change with machine learning

Weifeng Zhong (Mercatus Center at George Mason University)
17:2518:05 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Secondary topics:  Text and Language processing and analysis

Who is this presentation for?

Those who are interested in the Chinese economy, novel applications of machine learning, or both.



Prerequisite knowledge

Basic knowledge about neural networks and natural language processing.

What you'll learn

(1) The first index to predict China's "next big things." (2) A new way to uncover hidden patterns from trivial labels. (3) A wide range of potential applications.


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) of China. The PCI is a leading indicator of policy changes that runs from 1951 to the third quarter of 2018, 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 researcher’s understanding of the Chinese context, which suggests a wide range of applications in other settings, such as predicting changes in other (ex-)Communist regimes’ policies, measuring decentralization in central-local government relations, quantifying media bias in democratic countries, and predicting changes in lawmaker’s voting behavior and in judges’ ideological leaning.

Photo of Weifeng Zhong

Weifeng Zhong

Mercatus Center at George Mason University

Weifeng Zhong is a research fellow in economic policy studies at the American Enterprise Institute, where his research focuses on Chinese economic issues and political economy. His recent work has been on the application of text-analytic and machine-learning techniques to political economy issues such as the US presidential election, income inequality, and predicting policy changes in China. He has been published in a variety of scholarly journals, including the Journal of Institutional and Theoretical Economics. In the popular press, his writings have appeared in the Financial Times, Foreign Affairs, The National Interest, and Real Clear Politics, among others. He has a Ph.D. and an M.Sc. in managerial economics and strategy from Northwestern University. He also holds M.Econ. and M.Phil. degrees in economics from the University of Hong Kong and a B.A. in business administration from Shantou University in China.

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