Taking a regional approach requires the use of standardized hazard definitions and methods for matching hazard and household data. This is done by considering the main hazard measures and techniques used in comparable country studies, and systematically assessing the choice of hazard, the hazard-household matching approach, and the functional form used in analysis. Specifically, we use gridded historical data on precipitation, vegetation, evapotranspiration, and soil moisture, using measures such as the Normalized Difference Vegetation Index (NDVI), the Standardized Precipitation Evapotranspiration Index (SPEI), and WRSI. These measures reflect the hazard to livelihoods from lack of rain in different ways. Some are direct measures of climate conditions (e.g., precipitation, soil moisture, and SPEI), others aggregate climate data through crop models (WRSI), and others measure vegetation outcomes (NDVI). The gridded hazard measures are then merged to harmonized household surveys from nine countries (Ethiopia, Lesotho, Malawi, Mauritania, Mozambique, Niger, Nigeria, Zambia, and Zimbabwe) with location coordinates for either the household or the community. The surveys used are the ones that are also used for official poverty estimates. A consistent approach to temporal matching was used to take into account the timing of the survey and agricultural seasons. Different approaches to spatial merging were tested.

There are important methodological insights from the analysis that emerge. First, we find that soil moisture and greenness measures provide more consistently reliable results than evapotranspiration or rainfall measures. This is an important finding given most of the extant literature is based on evapotranspiration or rainfall metrics. Second, we find that a linear specification performs adequately for the types of moderate shocks that are well-represented in the survey data used. Third, we find that a hazard measure based on 20 km or 50 km radius around each household performs better than those that use smaller radii, perhaps because a larger radius allows local market effects to also be considered.

The relationship between hazard measures and welfare outcomes is estimated separately for different livelihood zones across SSA. Separate estimation provides needed flexibility, given that drought conditions manifest themselves differently across livelihood zones. Important differences in impacts between agroecological zones and between land and livestock owners are observed. However, the downside of a regional approach is a limited ability to explore the heterogeneity of impacts across households in detail without significant investments in data harmonization, and we leave additional work on the heterogeneity of impacts as something to explore further in future analysis.

There are limitations to the analysis undertaken that should be borne in mind when interpreting results. First, estimates capture the direct impact of local drought conditions and not impacts beyond the local area, such as those that might arise from disruption of markets or price impacts. The degree to which this is a concern depends on the relative importance of direct and indirect impacts. The evidence is mixed on this. Hallegatte et al. (2016) suggest food prices are an important channel of the welfare impacts of climate change. However, Artuc et al. (2023) model trade between locations and find the main impact is the direct production effect, not the impact on prices. To the extent this is the case the local estimates presented in this paper may be quite close to aggregate impacts.

Secondly, the empirical estimates are subject to “survivor bias” in that consumption is only measured for households still in existence after the shock has occurred. If households move or disintegrate (perhaps due to the death of one or more household members) because of drought conditions, the estimated impacts could be over or underestimated. This is much more likely to be the case for severe drought conditions, whereas those that are captured in our surveys tend to be less extreme events that are unlikely to cause movement or disintegration of households.

This brings us to the third potential source of bias in the results. Although the analysis uses survey data across many weather conditions in the analysis, there are very few observations in the survey data that experience drought conditions below two standard deviations from the mean. This limits the degree to which non-linearities can be estimated. It also means there is limited empirical support for predicting the welfare costs of extreme events.

A final source of bias comes from measurement error in the hazard data. Although care was taken to use the best available data and match hazard and household data carefully, any measurement error in the hazard variable will attenuate the estimated coefficients, thereby underestimating the welfare impacts. Additional uncertainty is 3