Publication: Stochastic Modeling of Food Insecurity
Date
2020-09
ISSN
Published
2020-09
Author(s)
Wang, Dieter
Andree, Bo Pieter Johannes
Chamorro, Andres Fernando
Girouard Spencer, Phoebe
Abstract
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.
Citation
“Wang, Dieter; Andree, Bo Pieter Johannes; Chamorro, Andres Fernando; Girouard Spencer, Phoebe. 2020. Stochastic Modeling of Food Insecurity; Stochastic Modeling of Food Insecurity. Policy Research Working Paper;No. 9413. © World Bank, Washington, DC. http://openknowledge.worldbank.org/handle/10986/34511 License: CC BY 3.0 IGO.”
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