# 1. Introduction

Climate disasters reverse gains and limit progress in efforts to reduce poverty. Global extreme poverty is increasingly concentrated in Sub-Saharan Africa (SSA) where drought is a major risk, a risk that is increasing in many parts of the continent because of climate change. Investments to reduce the impact of drought on welfare are thus essential to poverty reduction efforts in the region. Quantifying the welfare needs that arise when climate shocks occur is an important part of targeting investments to reduce their impact (Clarke and Dercon 2016). This requires vulnerability or damage functions that relate losses in welfare to climate conditions (Auffhammer 2018). While these are widely available for the impact of floods and cyclones on buildings, this is not the case when it comes to quantifying the impact of drought on household consumption in Sub-Saharan Africa.

This paper pilots a regional approach to estimating vulnerability functions for drought and consumption using methods widely employed in the microeconomic literature (Dell, Jones, and Olken 2014). The analysis uses survey data from close to 100,000 households across nine countries and five livelihood zones, allowing considerable variation in climatic conditions to be utilized in estimation. The result is stable estimates that are not driven by any one climate event. We find that consumption is reduced by 10-20 percent in the worst weather observations in our surveys (corresponding to about a 1 in 10-year drought event). We use the estimated vulnerability functions and the full hazard distribution in each household location to calculate the risk drought poses to welfare outcomes. The results indicate that risk is large. Poverty is 1–12 percent higher under the worst weather conditions relative to the best conditions observed in the past 13 years. This amounts to an increase in the total national poverty gap that ranges from US$4 million to US$2.4 billion (2011 PPP) across countries. To our knowledge, these are the first crosscountry estimates of the poverty impact of drought in Africa.

There are several ways in which damage functions for rainfall deficits have been estimated in the climate economics literature. Auffhammer (2018) provides a review and examples of the approaches applied to US farm incomes. The one used for estimating the impact of extreme weather events uses observed data to identify the causal impact of weather changes on outcomes of interest (Dell, Jones, and Olken 2014). Data on hazards are matched to household observations in survey data using information on the geographic location of a household, and a fixed effects regression is run to identify the impact of weather in determining economic outcomes.

Although not undertaken with the purpose of estimating vulnerability functions, several studies have used this method to estimate the impact of drought on welfare. For example, in Africa, Hill and Porter (2017) look at the impact of a water requirement satisfaction index (WRSI) on consumption and poverty, and Hill and Mejia-Mantilla (2017) look at the impact of WRSI on household income and consumption in Uganda. In Malawi, Baquie and Fuje (2020) look at the impact of rainfall on consumption and poverty. Wineman, Mason, Ochieng and Kirimi (2019) examine the relationship between rainfall and income, calorie consumption and poverty in Kenya. Baez, Kshirsagar and Skoufias (2019) look at the impact of rainfall defined dry spells on nutrition outcomes. Outside Africa, Kochhar and Knippenberg (2023) look at the impact of NDVI, rainfall and temperature on consumption and poverty in Afghanistan. Studies that use estimated impacts of drought to predict the welfare cost of other weather events include Hill and Porter (2017), Porter and White (2016), Kochhar and Knippenberg (2023).<sup>2</sup> This study uses the same methods as these papers but takes a regional approach (as in Baez, Kshirsagar and Skoufias 2019) with the objective of developing robust estimates of the relationship between drought conditions and consumption that is not specific to one context or event. This relationship can then be used to predict the impact of drought for other events or locations. The estimates presented in this paper are within the range of results found in country-specific papers. For example, a moderate (about a 1 in 10 year) drought is predicted to reduce consumption by 15 percent and 9 percent in Uganda and Ethiopia, akin to the 10 percent loss we estimate for crop cultivating households in maize and highland zones in this paper.

> 2 Anttila-Hughes and Sharma (2015) take the same approach for storms.

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