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Predicting Food Crises 2020, Dataset for reproducing working paper results

Afghanistan, Burkina Faso, Chad, Congo, Dem. Rep., Ethiopia, Guatemala, Haiti, Kenya, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Somal..., 2007 - 2020
Central
Bo Pieter Johannes Andree, Andres Chamorro, Aart Kraay, Phoebe Spencer, Dieter Wang
Created on November 02, 2020 Last modified November 02, 2020 Page views 3527 Download 346 Documentation in PDF Metadata DDI/XML JSON

Identification

Survey ID Number
WLD_2020_PFC_v01_M
Title
Predicting Food Crises 2020, Dataset for reproducing working paper results
Subtitle
Dataset for reproducing working paper results
Country
Name Country code
Afghanistan AFG
Burkina Faso BFA
Chad TCD
Congo, Dem. Rep. COD
Ethiopia ETH
Guatemala GTM
Haiti HTI
Kenya KEN
Malawi MWI
Mali MLI
Mauritania MRT
Mozambique MOZ
Niger NER
Nigeria NGA
Somalia SOM
South Sudan SSD
Sudan SDN
Uganda UGA
Yemen, Rep. YEM
Zambia ZMB
Zimbabwe ZWE
Abstract
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 foresting 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.

Version

Version Date
2020-09

Scope

Topics
Topic Vocabulary URI
C01 - Econometrics Journal of Economic Literature (JEL) https://www.aeaweb.org/econlit/jelCodes.php
C14 - Semiparametric and Nonparametric Methods: General Journal of Economic Literature (JEL) https://www.aeaweb.org/econlit/jelCodes.php
C25 - Discrete Regression and Qualitative Choice Models - Discrete Regressors - Proportions - Probabilities Journal of Economic Literature (JEL) https://www.aeaweb.org/econlit/jelCodes.php
C53 - Forecasting and Prediction Methods - Simulation Methods Journal of Economic Literature (JEL) https://www.aeaweb.org/econlit/jelCodes.php
O10 - Economic Development - General Journal of Economic Literature (JEL) https://www.aeaweb.org/econlit/jelCodes.php
Keywords
Keyword
Famine
Food Insecurity
Extreme Events
Unbalanced Data
Cost-sensitive learning

Coverage

Geographic Coverage
Afghanistan, Burkina Faso, Chad, Democratic Republic of Congo, Ethiopia, Guatemala, Haiti, Kenya, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Somalia, South Sudan, Sudan, Uganda, Yemen, Zambia, Zimbabwe

Producers and sponsors

Primary investigators
Name Affiliation
Bo Pieter Johannes Andree World Bank
Andres Chamorro World Bank
Aart Kraay World Bank
Phoebe Spencer World Bank
Dieter Wang World Bank
Funding Agency/Sponsor
Name Abbreviation
State and Peace-Building Trust Fund SPF

Data Collection

Dates of Data Collection
Start End
2007 2020
Time periods
Start date End date
2007 2020
Data Collection Notes
Data compiled from multiple sources, including surveys and satellite imagery

Access policy

Contacts
Name Affiliation Email
Andres Elizondo World Bank achamorroelizond@worldbank.org
Bo Pieter Johannes Andree World Bank bandree@worldbank.org
Citation requirements
Andree, Bo Pieter Johannes; Chamorro, Andres; Kraay, Aart; Spencer, Phoebe; Wang, Dieter. 2020. Predicting Food Crises. Policy Research Working Paper; No. 9412. World Bank, Washington, DC.

Metadata production

DDI Document ID
DDI_WLD_2020_PFC_v01_M
Producers
Name Abbreviation Affiliation Role
Development Economics Data Group DECDG The World Bank Documentation of the DDI
Date of Metadata Production
2020-10-29
DDI Document version
Version 01 (October 2020)

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