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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
DOI
https://doi.org/10.48529/bwmq-rh91
Producer(s)
Bo Pieter Johannes Andree, Andres Chamorro, Aart Kraay, Phoebe Spencer, Dieter Wang
Collection(s)
Fragility, Conflict and Violence
Metadata
Documentation in PDF DDI/XML JSON
Created on
Nov 02, 2020
Last modified
Apr 26, 2021
Page views
25360
Downloads
2108
  • Identification
  • Version
  • Scope
  • Coverage
  • Producers and sponsors
  • Data collection
  • Access policy
  • Contacts
  • Metadata production
  • Citation

    • Identification

    Survey ID number
    WLD_2020_PFC_v01_M
    Title
    Predicting Food Crises 2020
    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.

    Version

    Version Date
    2020-09

    Scope

    Topics
    Topic Vocabulary URI
    C01 - Econometrics Journal of Economic Literature (JEL)
    C14 - Semiparametric and Nonparametric Methods: General Journal of Economic Literature (JEL)
    C25 - Discrete Regression and Qualitative Choice Models - Discrete Regressors - Proportions - Probabilities Journal of Economic Literature (JEL)
    C53 - Forecasting and Prediction Methods - Simulation Methods Journal of Economic Literature (JEL)
    O10 - Economic Development - General Journal of Economic Literature (JEL)
    Keywords
    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
    State and Peace-Building Trust Fund

    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

    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.

    Contacts

    Contacts
    Name Affiliation Email
    Andres Elizondo World Bank achamorroelizond@worldbank.org
    Bo Pieter Johannes Andree World Bank bandree@worldbank.org

    Metadata production

    DDI Document ID
    DDI_WLD_2020_PFC_v02_M
    Producers
    Name Affiliation Role
    Development Economics Data Group The World Bank

    Documentation of the DDI
    Date of Metadata Production
    2020-10-29

    Metadata version

    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)

    Citation

    Citation
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