with woman _i_ , resident in state _s_ , born in year _b_ , surveyed in round _r_ . _yi,s,b,r_ is a dummy variable equal to one if the woman is anemic, zero otherwise. _fgi_ indicates whether the first child ever born is a girl, which is also interacted with a dummy equal to one if the woman is aged 30 or older ( _over_ 30 _i_ ). The cutoff at age 30 is chosen based on the nonparametric evidence previously presented (section 4) and the age at which the conditional age distribution for women with firstborn girls shifts to the left of the distribution of those with a first-born boy (table 6). _αs_ , _μb_ , _δr_ are state, cohort of birth, and survey round fixed effects, respectively. In addition to the set of covariates and fixed effects as in regression 1, _Xi,s,b,r_ also includes dummies for whether the woman is currently pregnant or breastfeeding. These women are in fact more likely to be anemic and their status might be correlated with the error term. Another potential concern is whether past health (predetermined to first-birth) affects both the probability of having a first-born girl and current anemia status (further discussed in the next section). For this reason, I also control for height, a proxy for women’s health status during childhood. Regression 3 is estimated using a probit model. Again, identification relies on the assumption that the sex of the first-born is exogenous (uncorrelated with the error term) after conditioning for the observable characteristics.

Table 7 shows the results. Column (1) shows the unconditional estimates. Women with a firstborn girl are 1.1 percentage points more likely to be anemic (significant at 1% level). Columns (2)(3) include most covariates as well as the controls for women’s current pregnancy and breastfeeding status and the coefficient of a first-born girl is slighly reduced (as expected). Height is included in column (4) and does not affect the estimated coefficient for a first-born girl. This allows me to rule out the possibility that past health conditions are correlated with the sex ratio and with current anemia status. Column (5) controls for the number of children ever born and the coefficient of interest is slightly reduced. The patterns shown in figure 7 are consistent with the result in column (6) indicating that a first-born girl significantly increases the incidence of anemia for women below age 30 (the increase is 1.4 percentage points or a 2.5 percent effect), while this effect is significantly reduced for older women. A further decrease in the incidence of anemia for women aged 45 and older is suggested by estimates in column (7), though it is not significant. Columns (8)-(9) split the sample into women by education and shows that the effect is found only among women with no education. This is consistent with increased health risks associated with risky son-preferring fertility behavior that is most common among them. The last two columns check whether the age pattern of anemia is purely reflecting higher fertility. The estimates of the interactions between the number of children ever born and the older age groups show that, as expected, a higher number of live births significanlty increases the prevalence of anemia among younger women. However, as 21