The data set is constructed using panel data from the Demographic and Health Surveys for 108 regions from 19 Sub-Saharan African countries between 1992 and 2001 and climate data from the Africa Rainfall and Temperature Evaluation System (ARTES) from 1980 and 2001.

The sub-national regions are overlaid with the Köppen-Geiger (Kottek et al., 2006) climate map for Sub-Saharan Africa which we use to identify climate zones. The interpretation of the effects of extreme weather can then be made net of climatic zone effect and in conjunction with the particular climatic zone under study at a regional level.

The panel element of the data makes it possible to estimate better the independent impact of the explanatory variables controlling for unobservable confounding factors and regional-level fixed effects. The sub-national level information can substantially overcome the limitation of national level analyses that can obscure the strong patterns in biophysical or geographical correlates of mortality, malnutrition or disease across regions within a country. For example, sub-national data are particularly relevant in studying infant mortality rates, which vary considerably within countries, with some areas representing infant mortality hot spots (Storeygard et al., 2008). The evident clustering of diarrheal outbreaks also indicates the importance of sub-national level analysis (Luby et al., 2005).

The included socio-economic variables capture the local population’s capacity to adapt and cope with weather shocks. There is well established literature on the socioeconomic determinants of child health which relates to access to health care, maternal education, access to safe water and sanitation. We are particularly interested in the potential use of our analysis to assess the extent to which public health interventions in these areas may mitigate the adverse effects of extreme weather events, that is, through the interaction between weather shocks and access to basic services.

Our findings assess how extreme weather events have already impacted on child health using historical data, but can also be used to simulate the future health burden from increased frequency of weather shocks as a result of climate change. We estimate the potential future health cost from an increased incidence of diarrhea caused by more frequent extreme weather events in Sub-Saharan Africa. However, the estimated relationship between health outcome indicators and extreme weather variables is particularly useful as it provides the statistical basis for more precise health projections in conjunction with localized or down-scaling climate models in Africa.

This paper is organized as follows. In the next section we present some of the existing evidence of how climate and its variations impact our chosen indicators of health. In Section 3 we describe the data. In Section 4 we describe our analytical strategy. In Section 5 we present the results of our analyses of the effects of extreme climate events on child health outcomes. In Section 6 we simulate the economic cost of a projected increase in weather variability in terms of cases of diarrhea and conclude with some policy recommendations for our results in Section 7.

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