Stochastic Modeling of Food Insecurity

Dieter Wang*, Bo Pieter Johannes Andree, Andres Chamorro, Phoebe Spencer

*Corresponding author for this work

Research output: Working paper / PreprintWorking paperProfessional


Recent advances in food insecurity classification have made analytical approaches to predict and inform response to food crises possible. This paper develops a predictive, statistical framework to identify drivers of food insecurity risk with simulation capabilities for scenario analyses, risk assessment and forecasting purposes. It utilizes a panel vector-autoregression to model food insecurity distributions of 15 Sub-Saharan African countries between October 2009 and February 2019. Statistical variable selection methods are employed to identify the most important agronomic, weather, conflict and economic variables. The paper finds that food insecurity dynamics are asymmetric and past-dependent, with low insecurity states more likely to transition to high insecurity states than vice versa. Conflict variables are more relevant for dynamics in highly critical stages, while agronomic and weather variables are more important for less critical states. Food prices are predictive for all cases. A Bayesian extension is introduced to incorporate expert opinions through the use of priors, which lead to significant improvements in model performance.
Original languageEnglish
PublisherWorld Bank
Number of pages30
Publication statusPublished - 22 Sep 2020

Publication series

NamePolicy Research Working Papers
PublisherThe World Bank


  • Food insecurity
  • Humanitarian crisis
  • Time series
  • Famine risk
  • Stochastic simulation
  • Bayesian estimation


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