where).<sup>40</sup> The focus is on living children aged 6 through 14, for whom fostering is most common.<sup>41</sup> First, I empirically examine whether the probability of sending or receiving a child is correlated with household demographic variables, such as the number of biological children (and separately, the number of boys and girls). Second, I construct two variables for the household gender imbalance: ‘ _more sons 6–14_ ’, and ‘ _more daughters 6–14_ ’ (similar to Akresh (2009)).<sup>42</sup> These are dummies equal to one if the number of biological sons aged 6–14 (daughters) exceeds the number of daughters (sons) in the same age range, respectively. The omitted category is a dummy equal to one for households in which the number of sons equals the number of daughters. The latter category also includes households with no children in the relevant age group, which are the ones that typically receive more children in Nigeria.<sup>43</sup> After estimating the regressions on the full sample of households, I also examine households with at least one biological child aged 6–14 (for the fostering-in regressions) and separately households with at least one child, daughter or son (for the corresponding fostering-out regression). This allows a study of fostering decisions for households that have a gender imbalance versus those that are actually balanced because they have the same (strictly positive) number of boys and girls. I focus on the child-labor hypothesis, controlling for a set of additional householdlevel observable characteristics.<sup>44</sup> A foster child is defined as being a resident child whose parents are alive and live elsewhere. Accordingly, dummies for whether the household hosts at least one foster child, girl or boy are created (for the fostering-in regressions). In the NDHS, mothers also report whether each biological child is living in the household or elsewhere. Based on this information, I construct three dummies which are equal to one if at least one biological child, son or daughter of the household head lives elsewhere (for the fostering-out regressions).<sup>45</sup>

# Table A.3 shows some summary statistics. The sample excludes households in which there is no

> 40If the household head has more than one wife, the sum of children over all spouses is considered.

> 41In the child fostering literature, it is common to consider children in this age range. The percentage of households sending out (receiving) at least one child 6–14 is 17% (6%), while it is 3.8% and 1.8% for children 0–5 (2008 NDHS).

> 42The issues of simultaneity of household fostering and fertility decisions as well as selection into fostering are not dealt with in this analysis. However, I control for many observable household characteristics and use the variables for the gender imbalance (in addition to the number of children) which is a more exogenous proxy for demographics.

> 43In the sample of households considered, the percentage of households that has at least one foster child is 6%. This percentage is 8% in the subsample of households with no children in that age group.

> 44The set of controls includes: region fixed effects, urban dummy, wife (or woman head if female headed household) religion and ethnicity, years of education and age of the male head and his wife (or female head), a wealth index, household land ownership, wife and husband working in agriculture, a dummy for polygynous household head, a dummy for female headed household, number of children ever born, number of daughters and sons alive age 0–5, number of household members older than 15 (male and female).

> 45There is no data on the survival status of the father or whether the child is living with the father in a separate compound for non-resident children. Therefore, the variables for fostered-out children are only based on mothers’ survival status (i.e., it overestimates the number of fostered-out children, as it includes paternal orphans and children living with father only).

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