Forecasting Social Unrest: A Machine Learning Approach

Chris Redl and Sandile Hlatshwayo

No 2021/263, IMF Working Papers from International Monetary Fund

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 (search for similar items in EconPapers)
Pages: 29
Date: 2021-11-05
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2021/263

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