Predicting Food Crises

Bo Pieter Johannes Andree, Andres Chamorro, Aart Kraay, Phoebe Spencer, Dieter Wang

Research output: Working paperProfessional

Abstract

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.
Original languageEnglish
PublisherWorld Bank
Pages1
Number of pages35
Publication statusPublished - 22 Sep 2020

Publication series

NamePolicy Research Working Papers
PublisherWorld Bank
Volume9413

Keywords

  • Food insecurity
  • Humanitarian intervention
  • Humanitarian finance
  • Famine risk
  • Poverty
  • Machine Learning
  • Prediction models

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  • Stochastic Modeling of Food Insecurity

    Wang, D., Andree, B. P. J., Chamorro, A. & Spencer, P., 22 Sep 2020, World Bank, p. 1, 30 p. (Policy Research Working Papers; vol. 9413).

    Research output: Working paperProfessional

    Open Access

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