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Predicting Food Crises / Bo Pieter Johannes Andree.
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications") Available online
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications")- Format:
- Book
- Government document
- Author/Creator:
- Andree, Bo Pieter Johannes.
- Series:
- Policy research working papers.
- World Bank e-Library.
- Language:
- English
- Subjects (All):
- Climate and Meteorology.
- Climate Change Economics.
- Cost-Sensitive Learning.
- Development Economics and Aid Effectiveness.
- Economic Forecasting.
- Extreme Event.
- Famine.
- Food Crisis.
- Food Insecurity.
- Food Security.
- Forecasting.
- Humanitarian Crisis.
- Statistical and Mathematical Sciences.
- Statistical Model.
- Targeting.
- Unbalanced Data.
- Local Subjects:
- Climate and Meteorology.
- Climate Change Economics.
- Cost-Sensitive Learning.
- Development Economics and Aid Effectiveness.
- Economic Forecasting.
- Extreme Event.
- Famine.
- Food Crisis.
- Food Insecurity.
- Food Security.
- Forecasting.
- Humanitarian Crisis.
- Statistical and Mathematical Sciences.
- Statistical Model.
- Targeting.
- Unbalanced Data.
- Physical Description:
- 1 online resource (35 pages)
- Place of Publication:
- Washington, D.C. : The World Bank, 2020.
- System Details:
- data file
- Summary:
- Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical foresting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action.
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