|Soil moisture|Soil Water Index from Copernicus Global<br>Land Service|Soil moisture standardized anomaly (based<br>on the average over the first three months<br>of thegrowingseason)|
|---|---|---|
|Availability of water<br>for crops|FEWS NET|End of season WRSI as anomaly (as a ratio<br>of the WRSI to the median historical WRSI)|

_Source:_ World Bank.

_Note:_ CHIRPS = Climate Hazards Group InfraRed Precipitation with Station; FEWS NET = Famine Early Warning Systems Network; MODIS = Terra Moderate Resolution Imaging Spectroradiometer; NASA = National Aeronautics and Space Administration; NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index.

The historical coverage of the data differs. In general, the analysis uses data from the period 2000/01 to 2019/20 at dekadal or monthly intervals to reflect the shocks faced within the context of a changing climate. For example, the NDVI data utilized are a product of the MODIS (Terra Moderate Resolution Imaging Spectroradiometer) satellites and are available for February 2000 and after. However, the precipitation data are from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) global rainfall data set and are available from 1981. Only the soil moisture data cover a shorter period, starting in 2007. Despite the limited history, however, soil moisture data are rapidly proving useful in estimating drought conditions, and they have a memory effect that rainfall data lack.

The hazard data were extracted at a uniform grid resolution of 0.05° by 0.05° (about 5 km by 5 km near the equator) and a crop coverage mask (Copernicus Global Land Cover Layers, 2019) was applied to exclude hazard values in areas that would not affect crop yields. Data on seasonal timing were taken from the UN Food and Agriculture Organization (FAO) for each country. To aggregate the hazard data to an appropriate geographic unit for merging with the welfare data, additional data were incorporated in the data set, including country and administrative area boundaries from the World Bank Group, Global Administration Areas (GADM), and Global Administrative Unit Layers (GAUL) of the FAO.

The standardized anomaly measure is defined as _(level – mean for local area)/standard deviation_ for the local area, where the mean and standard deviation for the local area are calculated based on the 20 years of hazard data, or the maximum data available in the case of soil moisture. The local area is defined by aggregating the data for a 20 km buffer of the household location or for the administrative area if the former is not available.

The unconditional correlation between different hazard measures is not particularly high. Table 3.2 presents results for each livelihood zone used in the analysis. The independent variation across these hazard measures highlights their capture of different atmospheric conditions; hence the difficulty of identifying a measure capturing drought events well. But as the analysis will show, in a regression framework the results are more consistent across most hazard measures (with some exceptions). This suggests that despite capturing different atmospheric conditions, the hazard measures are reasonably effective at capturing situations making households worse off.

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