Predicting Food Crises 2020, Dataset for reproducing working paper results
Dataset for reproducing working paper results
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.
C01 - Econometrics
Journal of Economic Literature (JEL)
C14 - Semiparametric and Nonparametric Methods: General
Data compiled from multiple sources, including surveys and satellite imagery
Bo Pieter Johannes Andree
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.
DDI Document ID
Development Economics Data Group
The World Bank
Documentation of the DDI
Date of Metadata Production
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)