Forecasting Social Unrest : A Machine Learning Approach /
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, socioeconomi...
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| Format: | Journal |
| Language: | English |
| Published: |
Washington, D.C. :
International Monetary Fund,
2021.
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| Series: | IMF Working Papers; Working Paper ;
No. 2021/263 |
| Subjects: | |
| Online Access: | Full text available on IMF |
| 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. |
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| Item Description: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Physical Description: | 1 online resource (29 pages) |
| Format: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Access: | Electronic access restricted to authorized BRAC University faculty, staff and students |