WLD_2020_PFC_v01_M DDI_WLD_2020_PFC_v01_M Development Economics Data Group 2020-10-29 Nesstar Publisher Version 01 (October 2020) Predicting Food Crises 2020 Dataset for reproducing working paper results WLD_2020_PFC_v01_M Bo Pieter Johannes Andree Andres Chamorro Aart Kraay Phoebe Spencer Dieter Wang Nesstar Publisher State and Peace-Building Trust Fund TF0A7049 and TF0A5070 Andres Elizondo Bo Pieter Johannes Andree Famine Food Insecurity Extreme Events Unbalanced Data Cost-sensitive learning C01 - Econometrics C14 - Semiparametric and Nonparametric Methods: General C25 - Discrete Regression and Qualitative Choice Models - Discrete Regressors - Proportions - Probabilities C53 - Forecasting and Prediction Methods - Simulation Methods O10 - Economic Development - General Afghanistan Burkina Faso Chad Congo, Dem. Rep. Ethiopia Guatemala Haiti Kenya Malawi Mali Mauritania Mozambique Niger Nigeria Somalia South Sudan Sudan Uganda Yemen, Rep. Zambia Zimbabwe 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 Data compiled from multiple sources, including surveys and satellite imagery 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. predicting_food_crises_data.NSDstat 183596 28 Nesstar 200801 Name of country 183596 0 Afghanistan 5372 Burki Faso 7110 Chad 10744 Democratic Republic of Congo 7900 Ethiopia 11692 Guatemala 3476 Haiti 6636 Kenya 11218 Malawi 4266 Mali 7900 Mauritania 2054 Mozambique 1738 Niger 10586 Nigeria 15642 Somalia 11692 South Sudan 12324 Sudan 12640 Uganda 8848 Yemen 10902 Zambia 11376 Zimbabwe 9480 Admin 2 code Global Administrative Unit Layers (GAUL) - Food and Agriculture Organization (FAO) 183596 0 Admin 2 code, can be used to match with a GAUL shapefile Frequency: Cross-section / Annual Name of district Global Administrative Unit Layers (GAUL) - Food and Agriculture Organization (FAO) 183596 0 centroid - longitude (x) Global Administrative Unit Layers (GAUL) - Food and Agriculture Organization (FAO) 183596 0 Frequency: Cross-section / Annual centroid - latitude (y) Global Administrative Unit Layers (GAUL) - Food and Agriculture Organization (FAO) 183596 0 Frequency: Cross-section / Annual Year and month 183596 0 2007_01 1162 2007_02 1162 2007_03 1162 2007_04 1162 2007_05 1162 2007_06 1162 2007_07 1162 2007_08 1162 2007_09 1162 2007_10 1162 2007_11 1162 2007_12 1162 2008_01 1162 2008_02 1162 2008_03 1162 2008_04 1162 2008_05 1162 2008_06 1162 2008_07 1162 2008_08 1162 2008_09 1162 2008_10 1162 2008_11 1162 2008_12 1162 2009_01 1162 2009_02 1162 2009_03 1162 2009_04 1162 2009_05 1162 2009_06 1162 2009_07 1162 2009_08 1162 2009_09 1162 2009_10 1162 2009_11 1162 2009_12 1162 2010_01 1162 2010_02 1162 2010_03 1162 2010_04 1162 2010_05 1162 2010_06 1162 2010_07 1162 2010_08 1162 2010_09 1162 2010_10 1162 2010_11 1162 2010_12 1162 2011_01 1162 2011_02 1162 2011_03 1162 2011_04 1162 2011_05 1162 2011_06 1162 2011_07 1162 2011_08 1162 2011_09 1162 2011_10 1162 2011_11 1162 2011_12 1162 2012_01 1162 2012_02 1162 2012_03 1162 2012_04 1162 2012_05 1162 2012_06 1162 2012_07 1162 2012_08 1162 2012_09 1162 2012_10 1162 2012_11 1162 2012_12 1162 2013_01 1162 2013_02 1162 2013_03 1162 2013_04 1162 2013_05 1162 2013_06 1162 2013_07 1162 2013_08 1162 2013_09 1162 2013_10 1162 2013_11 1162 2013_12 1162 2014_01 1162 2014_02 1162 2014_03 1162 2014_04 1162 2014_05 1162 2014_06 1162 2014_07 1162 2014_08 1162 2014_09 1162 2014_10 1162 2014_11 1162 2014_12 1162 2015_01 1162 2015_02 1162 2015_03 1162 2015_04 1162 2015_05 1162 2015_06 1162 2015_07 1162 2015_08 1162 2015_09 1162 2015_10 1162 2015_11 1162 2015_12 1162 2016_01 1162 2016_02 1162 2016_03 1162 2016_04 1162 2016_05 1162 2016_06 1162 2016_07 1162 2016_08 1162 2016_09 1162 2016_10 1162 2016_11 1162 2016_12 1162 2017_01 1162 2017_02 1162 2017_03 1162 2017_04 1162 2017_05 1162 2017_06 1162 2017_07 1162 2017_08 1162 2017_09 1162 2017_10 1162 2017_11 1162 2017_12 1162 2018_01 1162 2018_02 1162 2018_03 1162 2018_04 1162 2018_05 1162 2018_06 1162 2018_07 1162 2018_08 1162 2018_09 1162 2018_10 1162 2018_11 1162 2018_12 1162 2019_01 1162 2019_02 1162 2019_03 1162 2019_04 1162 2019_05 1162 2019_06 1162 2019_07 1162 2019_08 1162 2019_09 1162 2019_10 1162 2019_11 1162 2019_12 1162 2020_01 1162 2020_02 1162 Year 183596 0 2007 13944 2008 13944 2009 13944 2010 13944 2011 13944 2012 13944 2013 13944 2014 13944 2015 13944 2016 13944 2017 13944 2018 13944 2019 13944 2020 2324 Month 183596 0 1 16268 2 16268 3 15106 4 15106 5 15106 6 15106 7 15106 8 15106 9 15106 10 15106 11 15106 12 15106 IPC Phase as classified by FEWS NET FEWS NET 40952 142644 IPC Phase as classified by FEWS NET. Source at the livelihood zone level has been spatially joined to districts using largest spatial overlap rule. 1 Minimal 24162 2 Stressed 10736 3 Crisis 5131 4 Emergency 895 5 Famine 28 Sysmiss 142644 Frequency: 3-4 monthly Humanitarian Assistance Tag FEWS NET 29722 153874 Tag denoting presence of humanitarian assistance as marked by(!) on FEWS NET maps. 0 0 27548 1 1 2174 Sysmiss 153874 Frequency: 3-4 monthly FEWS Near-term projection (3 or 4 months ahead) FEWS NET 33669 149927 IPC Phase (1 to 5) as classified by FEWS NET in near-term outlooks. Source at the livelihood zone level has been spatially joined to districts using largest spatial overlap rule. 1 Minimal 19872 2 Stressed 8276 3 Crisis 4536 4 Emergency 931 5 Famine 54 Sysmiss 149927 Frequency: 3-4 monthly FEWS Near-term projection Humanitarian Assistance Tag FEWS NET 31583 152013 Tag denoting presence of humanitarian assistance as marked by(!) on FEWS NET near-term projection maps. 0 0 29626 1 1 1957 Sysmiss 152013 Frequency: 3-4 monthly FEWS Medium-term projection (6 or 8 months ahead) FEWS NET 33426 150170 1 Minimal 20204 2 Stressed 7792 3 Crisis 4226 4 Emergency 1163 5 Famine 41 Sysmiss 150170 Frequency: 3-4 monthly FEWS Medium-term projection Humanitarian Assistance Tag FEWS NET 31340 152256 Tag denoting presence of humanitarian assistance as marked by(!) on FEWS NET medium-term projection maps. 0 0 30010 1 1 1330 Sysmiss 152256 Frequency: 3-4 monthly Normalized Difference Vegetation Index (NDVI) MODIS (Terra 16-day 250m), processed in Google Earth Engine). Cropland mask (GFSAD), pasture mask (FAO - FGGD) 183596 0 -0.0426 0.862 Normalized Difference Vegetation Index (NDVI) is a measure of the “greenness,” the relative density and health of vegetation, of the earth’s surface. The values range from -1 and +1. Values greater than .1 generally denote increasing degrees in the greenness and intensity of vegetation. Values between 0 and .1 are commonly characteristic of rocks and bare soil, and values less than 0 sometimes indicate clouds, rain, and snow. Frequency: Raw images (16-day) are aggregated to the monthly level using the average, min and max pixel value. Monthly images are summarized to the district level using the average value. NDVI anomalies MODIS (Terra 16-day 250m), processed in Google Earth Engine). Cropland mask (GFSAD), pasture mask (FAO - FGGD) 183596 0 -5496.615 3790.713 Anomalies are calculated by dividing the current monthly value by the long-term average for that month, multiplied by 100. Values below 100% represent vegetation cover deficits, above 100% vegetation cover above average. Broadly values between 90% and 110% are considered as being within the range of normal variability. Frequency: Raw images (16-day) are aggregated to the monthly level using the average, min and max pixel value. Monthly images are summarized to the district level using the average value. Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) - Mean CHIRPS Pentad, processed in Google Earth Engine). Cropland mask (GFSAD), pasture mask (FAO - FGGD) 183596 0 0 125.927 CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring. CHIRPS data is available at 5 and 10 day accumulations. Frequency: Raw images (5-day) are aggregated to the monthly level using the average value. Monthly images are summarized to the district level using the average value. Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) - Anomalies CHIRPS Pentad, processed in Google Earth Engine). Cropland mask (GFSAD), pasture mask (FAO - FGGD) 183596 0 -42.511 80.804 Anomalies are calculated by substracting the current monthly value from the long-term average for that month. Frequency: Raw images (5-day) are aggregated to the monthly level using the average value. Monthly images are summarized to the district level using the average value. Evapotranspiration mean Modis (Terra 8-day 500m) 183483 113 0 47.904 Frequency: Raw images (5-day) are aggregated to the monthly level using the average value. Monthly images are summarized to the district level using the average value. Evapotranspiration anomalies Modis (Terra 8-day 500m) 183483 113 -17.498 17.113 Frequency: Raw images (5-day) are aggregated to the monthly level using the average value. Monthly images are summarized to the district level using the average value. Count of violent events (ACLED) Armed Conflict Location & Event Data Project (ACLED) 183596 0 0 265 Frequency: Event data is daily, aggregated to monthly using sum Sum of number of fatalities (ACLED data) Armed Conflict Location & Event Data Project (ACLED) 183596 0 0 2394 Frequency: Event data is daily, aggregated to monthly using sum Food Price Index with January 2010 as base month Food Security and Nutrition Analysis Unit (FSNAU), World Food Programme (WFP) 183596 0 Frequency: Market prices are monthly Area of district in (sq. mts.) Hidden Dimension of Poverty Dataset (World Bank) 183596 0 10.298 331292 Frequency: Cross-section / Annual Percentage occurrence of cropland (FAO) Hidden Dimension of Poverty Dataset (World Bank) 183596 0 0 99.24 Frequency: Cross-section / Annual UN-adjusted population count per year 183596 0 UN-adjusted population count per year, interpolation of CIESIN-GPW (GPWv4-adjusted) data - sum of grids Frequency: Cross-section / Annual Hundreds of metres of elevation difference for grid cells - average Hidden Dimension of Poverty Dataset (World Bank) 183596 0 Frequency: Cross-section / Annual Percentage occurrence of pasture (FAO) Hidden Dimension of Poverty Dataset (World Bank) 183596 0 0 99.598 Frequency: Cross-section / Annual