of education (of the woman and her husband), a wealth index<sup>21</sup> , a dummy for urban areas, and woman’s ethnicity and religion.<sup>22</sup> Controlling for age at first marriage and husband’s characteristics entails considering the sample of all currently married women. Given that marital status can also be affected by the sex of the first-born (this is analyzed in section 6.1), women with daughters might be more likely to be ever divorced or separated and, as a result, typically have lower fertility than those continuously married. To account for this possible (downward) bias, I first estimate regression 1 using the entire sample of women, and then restrict it to continuously married women (for whom the effect on fertility should be stronger as found in Dahl and Moretti, 2008).<sup>23</sup> _αr_ , _γt_ are region and cohort of birth fixed effects, respectively. I also control for region and ethnic-specific trends, _δrt_ and _χet_ , respectively, to capture region or ethnic-specific trends that may be correlated with the error term. Given that boys are biologically weaker than girls and are more likely to die in infancy, I additionally control for the survival status of the child (a dummy for whether the firstborn child died and its interaction with _firstborngirl_ ).<sup>24</sup> Results with and without controlling for child mortality are shown. As previously noted, since mothers tend to replace a lost child by having more children, omitting to control for differential mortality would lead to a downward bias of the effect of a first-born daughter on fertility. After controlling for the mortality of the first-born child and its interaction with _firstborngirl_ , _β_ 1 is the estimated effect of a first-born _surviving_ daughter on the outcome of interest. Identification relies on the assumption that the sex of the first-born is exogenous (uncorrelated with the error term) after conditioning on observable characteristics. Regression 1 is estimated using OLS and a model for count data (accounting for over-dispersed outcome variables) when the dependent variable is the number of children ever born, and using a probit model for the probability of desiring more children and using contraceptives.

If women practice son-preferring fertility stopping behavior, I expect to find _β_ 1 _>_ 0 for the regressions for realized (and desired) fertility, and _β_ 1 _<_ 0 for the use of contraceptives. Although the effect on fertility should (and is found to) be larger for women who had more subsequent daughters among earlier-born children, the analysis focuses on the sex of the first-born, thus allowing a causal interpretation of the results (Dahl and Moretti, 2008). In fact, while the sex of the first-born can

> 21The DHS wealth index is a measure of a household’s cumulative living standard. It’s generated using principal component analysis based on the households ownership of consumer goods; dwelling characteristics; type of drinking water source; toilet facilities; and other characteristics that are related to a households socio-economic status.

> 22Ethnicity include Hausa-Fulani, Igbo, Yoruba, and other minor ethnicities as the omitted category. Religion include Catholic, other Christians, Muslims, and Traditionalists (omitted).

> 23The rest of the analysis for fertility behavior and birth spacing uses the sample of continuously married women (so that the fertility history at the time of the survey is relevant for the current union).

> 24I also constructed two alternative variables for child death before reaching the age of 1 or 2 years, and results are qualitatively similar to the ones obtained with child death at any age. This is probably because most deaths happen in the first years of life in this context.

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