Forecasting Social Unrest: A Machine Learning Approach

November 5, 2021

Preview Citation

Format: Chicago

Chris Redl, and Sandile Hlatshwayo. Forecasting Social Unrest: A Machine Learning Approach, (USA: International Monetary Fund, 2021) accessed December 11, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial, socioeconomic, development and political variables. The prediction model correctly forecasts unrest in the following year approximately two-thirds of the time. Shapley values indicate that the key drivers of the predictions include high levels of unrest, food price inflation and mobile phone penetration, which accord with previous findings in the literature.

Subject: Food prices, Inflation, Machine learning, Population and demographics, Prices, Technology

Keywords: Food prices, Global, IMF working, Inflation, Machine learning, Machine learning approach, Machine learning model, Machine learning., Prediction model, Risk index, Social unrest, Unrest event

Publication Details

Pages:

29
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2021/263
Stock No:

WPIEA2021263
ISBN:

9781557758873
ISSN:

1018-5941

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