Stochastic Modeling of Food Insecurity

Abstract

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.

On the forecastability of food insecurity

“…In this context, our study represents an initial step towards the application of forecasting approaches to food insecurity at a high spatial and temporal granularity. Our results confirm that nowcasting or one-step-ahead forecasting are feasible, as reported in recent studies [36, 38], but long-term forecasts are challenging and strongly conditioned by data availability. The methods presented in this study come with limitations, and they could be further improved through several approaches.…”

“…Both studies propose methods to predict the current situation when primary data is not available, but they do not address the challenge of making projections for the future. The World Bank recently proposed a machine learning approach to forecast transitions into critical states of food insecurity [18] and a stochastic model to forecast famine risk [19]. These studies focus on forecasting month-to-month transitions to different phases of food insecurity, based on the Integrated Food Security Phase Classification (IPC) framework [20].…”

Nowcasting food insecurity on a global scale

“…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.…”

Fine-grained prediction of food insecurity using news streams

“…To uncover the predictive value of our news factors, we compare them with traditional measures of food insecurity risk factors obtained from recent studies on food insecurity (8,12,27). Our risk indicators include:…”