Forecasting Social Unrest: A Machine Learning Approach

29 Pages Posted: 4 Feb 2022

See all articles by Chris Redl

Chris Redl

International Monetary Fund (IMF)

Sandile Hlatshwayo

International Monetary Fund (IMF)

Date Written: November 1, 2021

Abstract

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.

Keywords: Social unrest, machine learning., machine learning model, risk index, prediction model, machine learning approach, IMF working, Machine learning, Inflation, Food prices, Global, unrest event

JEL Classification: C45, C53, P16, O33, E31, Q50, Q11, J10

Suggested Citation

Redl, Chris and Hlatshwayo, Sandile, Forecasting Social Unrest: A Machine Learning Approach (November 1, 2021). IMF Working Paper No. 2021/263, Available at SSRN: https://ssrn.com/abstract=4026493

Chris Redl (Contact Author)

International Monetary Fund (IMF) ( email )

Kuwait

Sandile Hlatshwayo

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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