Predicting Food Crises

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9413Recent 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.This paper is a product of the Fragility, Conflict and Violence Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world.

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“…The treatment of the raw data is discussed by (Andrée et al, 2020). We obtain food price data from the FAO and the WFP.…”

Section: Figure 2: Hypothetical Scenarios Leading To Ipc4mentioning

confidence: 99%

Stochastic Modeling of Food Insecurity

Wang

Andrée²,

Chamorro

et al. 2020

Self Cite

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“…The limitation of this approach is that up-to-date household level data is required not only during model training but also when using the trained models to perform out-ofsample predictions. More recently, the World Bank developed a suite of statistical models to forecast transitions into critical states of food insecurity and famine risk from secondary data [19,20]. In this study, we focus on food security nowcasting, proposing a methodology that allows, for the first time, to estimate the current prevalence of people with insufficient food consumption and of people using crisis or above crisis food-based coping at sub-national level at any given time from secondary data, when primary data is not available.…”

mentioning

confidence: 99%

Nowcasting food insecurity on a global scale

Martini

Bracci

Jaiswal

et al. 2021

Preprint

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Lack of regular physical or economic access to safe, nutritious and sufficient food is a critical issue affecting millions of people world-wide. Estimating how many and where these people are is of fundamental importance for governments and humanitarian organizations to take informed and timely decisions on relevant policies and programmes. In this study, we propose a machine learning approach to predict the prevalence of people with insufficient food consumption and of people using crisis or above crisis food-based coping when primary data is not available. Making use of a unique global data set, we show that the proposed models can explain up to 78% of the variation in insufficient food consumption and crisis or above food-based coping levels. We also show that the proposed models can be used to nowcast the food security situation in near-real time and propose a method to identify which variables are driving the changes observed in predicted trends, which is key to make predictions serviceable to decision makers.

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“…[ 20 , 40 , 41 ] show how observation-driven models may be used to not only investigate how observations impact future observations, but also future parameter values, which may empirically be interesting if those parameters carry an economic interpretation. Finally, many popular machine learning algorithms, such as neural networks, can be reduced to equations that show how parameter values change according to levels in the data [ 42 ].…”

Section: Causality In Terms Of True Probability Measuresmentioning

confidence: 99%

Conducting Causal Analysis by Means of Approximating Probabilistic Truths

Andrée

2022

Entropy

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The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context.

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Predicting Food Crises

Cited by 36 publications

References 0 publications

Stochastic Modeling of Food Insecurity

Stochastic Modeling of Food Insecurity

Nowcasting food insecurity on a global scale

Conducting Causal Analysis by Means of Approximating Probabilistic Truths

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