|Maize(GS)|32%|29%|23%|36%|
|---|---|---|---|---|
|Highland(GS)|23%|27%|33%|29%|
|Roots and lowland(GS)|9%|7%|42%|23%|
|Agropastoral(RS)|35%|28%||28%|
|Pastoral(RS)|40%|36%||27%|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; GS = growing season; RS = rainy season.

To assess whether the extreme values of the hazards are within or outside the range of hazards experienced by households within the districts and years used, we assessed the percentage of households in a given livelihood zone that experienced a hazard value more than two standard deviations lower than the mean or more than three standard deviations lower than the mean. The results for soil moisture for maize, highland and roots and lowland livelihood zones and for NDVI for agropastorals and pastorals are shown in table 5.2. Assessing the common support between the range of possible hazards and those used in estimating coefficients 2 and 3 can help address concerns regarding the plausibility of the welfare impact predictions that use the fixed effects estimates. In sum, while moderate deviations in rainfall are well reflected in the data used to estimate 2 and 3 , more extreme deviations are less well reflected in the ββ̂ ββ̂ data. ββ̂ ββ̂

**Table 5.2. Share of households with an extreme hazard outcome for soil moisture**

|**Livelihood zone**|**Hazard z-score < -3**|**Hazard z-score < -2**|**Negative hazard z-score**|
|---|---|---|---|
|Soil moisture (GS)||||
|Maize|0|0.01|0.63|
|Highland|0|0.05|0.64|
|Roots and lowland|0|0.03|0.63|
|NDVI (RS)||||
|Agropastoral|0|0.00|0.40|
|Pastoral|0|0.01|0.38|

_Source:_ World Bank. _Note:_ GS = growing season; RS = rainy season. We generated the alternate draws of the hazard distribution, k , a in two ways. The first used the historical weather distribution to select k for all households. This provided a first assessment of what an EP curve would look like but is based on a limited set of historical data points—in the case of soil moisture, only 13 years of data. The second way RR used the data set of 10,000 possible weather simulations for Malawi developed by the risk modeling firm AIR, which RR was based on the historical pattern of rainfall in Malawi. This catalog is available only for Malawi but suggests the degree of precision that modeling the weather offers as compared to using historical weather data. The generated k are used with parameter estimates from tables 4.1 to 4.5 to simulate what consumption would be for each household under these weather conditions. Once consumption has been simulated for a given draw of the weather, variables that describe the simulated consumption distribution can be generated. We focus particularly on: (i) RR the poverty headcount rate—the share of households whose simulated consumption falls below the extreme poverty line of $1.90—and (ii) the total poverty gap—the difference between consumption and the $1.90 poverty line added up for all households whose consumption falls below the poverty line.

The increase in the poverty rate and total poverty gap for eight of the nine countries included in this analysis are shown in figures 5.1 and 5.2. Niger was not included given the low percentage of the population in livelihood zones for which there are robust results. Each country comprises more than one livelihood zone, so each country draws on more than one set of parameter estimates. For the simulation results, the linear relationship is used with the soil moisture hazard for countries with growing season. For Mauritania we use the rainy season NDVI. The linear model was chosen 29