Rainfall is the most important dimension of weather variation in Nigeria. Because of its tropical location, temperature shows very little variation in Nigeria, either within years or across them. As noted by Nieuwolt (cited in Odekunle, 2004) temperatures are not a critical factor in tropical agriculture. Therefore, in this study we concentrate on the effects of precipitation shocks.

Perhaps one of the key aspects of this study is to give proper weight to spatial considerations. Although many studies have linked health outcomes with climate, the use of insufficiently matched climate and disease data can lead to biased results. Thus, we carefully interpolate climate and weather data from the network of stations to the precise location of each DHS clusters. Relatively few studies have matched climate variables to outcomes at fine resolution. The usual practice in the economic literature is to match each locality –a cluster or the geographic center of an administrative unit– to the geographically closest weather station (for example Yang and Choi 2007, Maccini and Yang 2009). While simple, this interpolation method may yield poor results if missing records are prevalent in the weather data, stations have been affected by site relocation, or considerable differences exist in terms of topographic characteristics –for example altitude- between the location of the cluster and the location of the closest station. The replacement of missing data with data from the second closest weather station will introduce artificial variation in weather, whereas the differences in topography will introduce a bias for the interpolated values.

Another alternative is to predict climate using statistical methods, similar to those used in climate modeling (for an application in the economic literature see Nordhaus et al, 1994). This approach requires estimating climate (or weather) based on a station’s observable characteristics –latitude, longitude, altitude, distance to the sea, etc.– and then using the regression coefficients to predict the corresponding values to each DHS clusters. We have opted for this approach because it better fits the observed climate at the weather station site.<sup>18</sup> The construction of the relevant weather variables is described in the Appendix.

In order to assess the effect of rainfall shocks on children’s health we examine the deviations with respect to the normal rainfall during the growing season. The growing season is usually defined as the period of the year during which rainfall distribution characteristics are suitable for crop germination, establishment, and full development. Such period would not exhibit significant variation from year to

> 18 Alternatively, one might use climate and weather data from General Circulation Models (GCM). These global climate models use much more complex methods to interpolate observed climate at weather stations into spatial grids, which in turn can be used to interpolate climate at the sampled cluster. While CGM might capture better complex atmospherically process their primarily objective is not to represent weather conditions at a fine spatial resolution. Therefore, the coarse spatial nature of these models would generate much less spatial variability in our main variables of interest.

16