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Forecasting Social Unrest: A Machine Learning Approach

Author

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  • Chris Redl
  • Sandile Hlatshwayo

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.

Suggested Citation

  • Chris Redl & Sandile Hlatshwayo, 2021. "Forecasting Social Unrest: A Machine Learning Approach," IMF Working Papers 2021/263, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2021/263
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    Cited by:

    1. Barrett, Philip & Appendino, Maximiliano & Nguyen, Kate & de Leon Miranda, Jorge, 2022. "Measuring social unrest using media reports," Journal of Development Economics, Elsevier, vol. 158(C).

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