Another advantage offered by the standardization of the deviation from the local mean is that it offers the opportunity to gauge the magnitude of the shock experienced across a variety of geographic contexts. We use z- scores for the hazard anomalies, which means that in addition to being differenced from the mean, the hazard variable is scaled by the standard deviation. This approach assumes that changes in the hazard matter not in an absolute sense, but in proportion to an area’s usual variation. This assumption is useful in taking some production and livelihood differences into account across space. A 3 mm decrease in rain does not have the same impact in wet and dry regions. Considering variations relative to an area’s usual variation is more important when there is no control for this in the hazard measure being used, e.g., total rainfall, soil moisture. It is arguably less important when using an outcome measure that already takes crop choice into account, such as NDVI or WRSI.

Table 3.6 presents the means and standard deviations for the hazard variables used in the analysis. The means indicate lower than average years for many of the hazards across the livelihood zones. The standard deviations highlight substantial variation around these means. It is this variation that the analysis exploits.

**Table 3.6. Mean and standard deviation of hazards used in the analysis**

|**Hazard variable**|**Maize**|**Highland**|**Roots**|**Pastoral**|**Agropastoral**|
|---|---|---|---|---|---|
|||**Growin**|**g season**|||
|Soil moisture|-0.312|-0.322|-0.293|0.098|-0.359|
||(0.929)|(1.126)|(0.769)|(1.086)|(1.000)|
|NDVI|-0.284|-0.349|-0.472|0.334|0.058|
||(0.949)|(1.038)|(0.923)|(1.046)|(0.955)|
|SPEI|0.111|-0.345|-0.600|0.367|0.067|
||(0.721)|(0.820)|(0.850)|(0.834)|(1.183)|
|Rainfall|0.035|-0.378|-0.291|0.417|-0.002|
||(1.135)|(1.170)|(0.909)|(0.980)|(1.183)|
|WRSI|99.050|98.860|99.339|101.488|100.866|
||(9.785)|(9.789)|(2.118)|(11.026)|(10.109)|
|**Rai**|**ny season(us**|**ed inpastoral**|**and agropast**|**oral specificatio**|**ns)**|
|Soil moisture||||0.693|0.610|
|||||(0.497)|(0.173)|
|NDVI||||0.396|0.161|
|||||(1.038)|(0.852)|
|Rainfall||||0.205|0.334|
|||||(0.942)|(0.826)|

# _Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index. Countries and surveys included are: Ethiopia (2011, 2016), Lesotho (2017), Malawi (2010, 2016, 2019), Mauritania (2014, 2109/20), Mozambique (2014), Niger (2018), Nigeria (2011, 2013, 2016), Zambia (2015), and Zimbabwe (2017).

hazard as a level along with the fixed effects method or it uses the specification of the hazard as a standardized anomaly from the local normal (over the 20-year period). In fact, in the extreme case in which only one crosssectional round of survey data is available, then the specification of the hazard as a standardized anomaly is the only sensible option, since in this instance, use of the level of hazard with the fixed effects for districts (for example) would simply express the hazard as a deviation from the district mean of the hazard values experienced in the same survey round by other households in the survey.

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