Predicting Food Crises 2020, Dataset for reproducing working paper results
Afghanistan, Burkina Faso, Ethiopia, Guatemala, Haiti, Kenya, Mali, Mozambique, Mauritania, Malawi, Niger, Nigeria, Sudan, Somalia, South Sudan, ..., 2007 - 2020
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Reference ID
WLD_2020_PFC_v01_M
Producer(s)
Bo Pieter Johannes Andree, Andres Chamorro, Aart Kraay, Phoebe Spencer, Dieter Wang
Collection(s)
Metadata
Created on
Nov 02, 2020
Last modified
Apr 26, 2021
Page views
10337
Downloads
978
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/Economy
| Name | Country code |
|---|---|
| Afghanistan | AFG |
| Burkina Faso | BFA |
| Ethiopia | ETH |
| Guatemala | GTM |
| Haiti | HTI |
| Kenya | KEN |
| Mali | MLI |
| Mozambique | MOZ |
| Mauritania | MRT |
| Malawi | MWI |
| Niger | NER |
| Nigeria | NGA |
| Sudan | SDN |
| Somalia | SOM |
| South Sudan | SSD |
| Chad | TCD |
| 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.
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) | Link |
| C14 - Semiparametric and Nonparametric Methods: General | Journal of Economic Literature (JEL) | Link |
| C25 - Discrete Regression and Qualitative Choice Models - Discrete Regressors - Proportions - Probabilities | Journal of Economic Literature (JEL) | Link |
| C53 - Forecasting and Prediction Methods - Simulation Methods | Journal of Economic Literature (JEL) | Link |
| O10 - Economic Development - General | Journal of Economic Literature (JEL) | Link |
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 | |
|---|---|---|
| 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_v02_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
DDI Document - Version 02 - (04/21/21)
This version is identical to DDI_WLD_2020_PFC_v01_M but country field has been updated to capture all the countries covered by survey.
Version 01 (October 2020)
This version is identical to DDI_WLD_2020_PFC_v01_M but country field has been updated to capture all the countries covered by survey.
Version 01 (October 2020)