Predicting Food Crises

Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are li...

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Main Authors:	Andree, Bo Pieter Johannes, Chamorro, Andres, Kraay, Aart, Spencer, Phoebe, Wang, Dieter
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2020
Subjects:	FAMINE FOOD SECURITY FOOD INSECURITY EXTREME EVENT COST-SENSITIVE LEARNING FOOD CRISIS UNBALANCED DATA HUMANITARIAN CRISIS TARGETING FORECASTING STATISTICAL MODEL
Online Access:	http://documents.worldbank.org/curated/en/304451600783424495/Predicting-Food-Crises http://hdl.handle.net/10986/34510

id	okr-10986-34510
recordtype	oai_dc
spelling	okr-10986-345102022-09-20T00:09:40Z Predicting Food Crises Andree, Bo Pieter Johannes Chamorro, Andres Kraay, Aart Spencer, Phoebe Wang, Dieter FAMINE FOOD SECURITY FOOD INSECURITY EXTREME EVENT COST-SENSITIVE LEARNING FOOD CRISIS UNBALANCED DATA HUMANITARIAN CRISIS TARGETING FORECASTING STATISTICAL MODEL Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical forecasting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action. 2020-09-24T21:02:57Z 2020-09-24T21:02:57Z 2020-09 Working Paper http://documents.worldbank.org/curated/en/304451600783424495/Predicting-Food-Crises http://hdl.handle.net/10986/34510 English Policy Research Working Paper;No. 9412 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	FAMINE FOOD SECURITY FOOD INSECURITY EXTREME EVENT COST-SENSITIVE LEARNING FOOD CRISIS UNBALANCED DATA HUMANITARIAN CRISIS TARGETING FORECASTING STATISTICAL MODEL
spellingShingle	FAMINE FOOD SECURITY FOOD INSECURITY EXTREME EVENT COST-SENSITIVE LEARNING FOOD CRISIS UNBALANCED DATA HUMANITARIAN CRISIS TARGETING FORECASTING STATISTICAL MODEL Andree, Bo Pieter Johannes Chamorro, Andres Kraay, Aart Spencer, Phoebe Wang, Dieter Predicting Food Crises
relation	Policy Research Working Paper;No. 9412
description	Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical forecasting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action.
format	Working Paper
author	Andree, Bo Pieter Johannes Chamorro, Andres Kraay, Aart Spencer, Phoebe Wang, Dieter
author_facet	Andree, Bo Pieter Johannes Chamorro, Andres Kraay, Aart Spencer, Phoebe Wang, Dieter
author_sort	Andree, Bo Pieter Johannes
title	Predicting Food Crises
title_short	Predicting Food Crises
title_full	Predicting Food Crises
title_fullStr	Predicting Food Crises
title_full_unstemmed	Predicting Food Crises
title_sort	predicting food crises
publisher	World Bank, Washington, DC
publishDate	2020
url	http://documents.worldbank.org/curated/en/304451600783424495/Predicting-Food-Crises http://hdl.handle.net/10986/34510
_version_	1764481055682723840

Predicting Food Crises

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