A data-driven approach improves food insecurity crisis prediction

Lentz, Erin; Michelson, Hope; Baylis, Katherine R.; Zhou, Yiqiang

doi:10.1016/j.worlddev.2019.06.008

Cited by 51 publications

(30 citation statements)

References 19 publications

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“…The quantitative, empirical framework also allows us to identify the most relevant agronomic, environmental, conflict or economic drivers behind famine dynamics among a vast set of possible candidates, in a systematic way (Pape et al, 2018). As Lentz et al (2019) demonstrate, systematically incorporating such information leads to significant improvements over existing methods.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Food Insecurity

Wang

Andrée²,

Chamorro

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

Stochastic Modeling of Food Insecurity

Wang

Andrée²,

Chamorro

et al. 2020

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“…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. Seminal work by Lentz and collaborators addressed this challenge for the first time, obtaining satisfactory predictions for food consumption, although limited to Malawi only [21]. Here, we make use of a unique data set of sub-national level food consumption and food-based coping data collected during the last 15 years across, respectively, 78 and 41 countries (see Figure 1), allowing for the first time the development and validation of nowcasting predictive models of food security indicators on a global scale.…”

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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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“…The use of a binary outcome is dictated by our preference to tackle the resilience prediction problem as a classification one. Our assumption is that discriminations above or below clear cut-offs are more intuitive for practitioners, policymakers and humanitarian agencies that aim at efficiently targeting their policy interventions, and therefore predicting cut-offs rather than continuous values is more useful for practical purposes (Lentz et al 2019). The choice of a subjective resilience metric is driven by: (i) data availability; (ii) the assumption that households are in the best position to assess the extent of shock impacts on their welfare and their post-shock recovery, as well as existing evidence that self-reported measures of well-being go hand in hand with objective indicators (Knippenberg et al 2019); (iii) the increasing use in recent studies of subjective approaches and self-evaluations as resilience metrics which represent valid alternatives to objective indicators (Jones & Tanner 2017;Jones & d'Errico 2019).…”

Section: Datamentioning

confidence: 99%

“…A separate and nascent body of empirical work has started testing the potential of ML in predicting well-being measures. In development economics, ML has been lately applied to predict and map poverty (Blumenstock et al 2015;Jean et al 2016;Kshirsagar et al 2017;McBride & Nichols 2018;Perez et al 2019;Steele et al 2017) as well as food security (Ganguli et al 2019;Hossain et al 2019;Lentz et al 2019) outcomes, highlighting the great potential of these predictive tools to improve the old problematic issue of the (in)effective targeting of development programmes.…”

Section: Introductionmentioning

confidence: 99%

Predicting household resilience with machine learning: preliminary cross-country tests

Garbero

Letta

2022

Empir Econ

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Using a unique cross-country sample from 10 impact evaluations of development projects, we test the out-of-sample performance of machine learning algorithms in predicting non-resilient households, where resilience is a subjective metrics defined as the perceived ability to recover from shocks. We report preliminary evidence of the potential of these data-driven techniques to identify the main predictors of household resilience and inform the targeting of resilience-oriented policy interventions.

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A data-driven approach improves food insecurity crisis prediction

Cited by 51 publications

References 19 publications

Stochastic Modeling of Food Insecurity

Stochastic Modeling of Food Insecurity

Nowcasting food insecurity on a global scale

Predicting household resilience with machine learning: preliminary cross-country tests

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