# _6.1. Description of the variables_

The final set of explanatory variables (all community-averages) includes the average time to haul water (in minutes), the average household size, the average number of children below five years of age, the average number of children aged 5 to 15, the average number of women aged 16 to 65, the average number of men aged 16 to 65, the average proportion of male household heads, year and regional dummies, and dummies to control for the month of interview. The education of the household head and the household’s wealth index have not been included in the model because of their strong correlation with the variable measuring hauling time (the coefficient of correlation with hauling time is -0.38 and -0.53, respectively).<sup>13</sup> In 7% of the surveyed clusters, the average time to the water source is zero. In order to take this particular feature of the data into account we include a dummy variable which takes the value 1 if and only if the time to the source is 0, following Battese (1997).

For each cluster, community-averages are calculated using data on households in which at least one girl aged 5 to 15 is present (with no girl aged 5 to 15, the proportion of such girls attending school must be zero). In order to control for possible selection bias, the total number of children aged 5 to 15 is treated as endogenous and instrumented in a first-stage regression. The identifying instruments are the age of the household head (linear and squared versions of this variable) and a dummy variable indicating whether the household head is a widow(er) or is divorced. We also consider the time to haul water as potentially endogenous, and use as identifying instruments the dummy variable indicating whether the household head is a widow(er) or is divorced, whether the household lives in a rural or urban community, and the one-period lagged hauling time. The place of residence (rural or urban community) is assumed exogenous in the sense that water infrastructure was likely not the main factor driving households’ choice in terms of residence location. We also argue that average hauling time in the community in the previous period of observation is a good instrument for current hauling time, since the two should be highly correlated, while past-period hauling time should not explain average school attendance in the current period. These two regressions are estimated using OLS but controlling for unobserved cluster-specific effects (Chamberlain,

> 13 The wealth index is provided in the DHS surveys. It is calculated using principal components analysis based on data concerning the household’s ownership of a number of consumer items such as a television and car; dwelling characteristics such as flooring material; type of drinking water source; toilet facilities; and other characteristics that are related to wealth status. The resulting asset scores are standardized in relation to a standard normal distribution with a mean of zero and a standard deviation of one. These standardized scores are then used to create the break points that define wealth quintiles as: Lowest, Second, Middle, Fourth, and Highest, see http://www.measuredhs.com/.

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