to reduced import tariffs and relaxed licensing of domestic production was potentially correlated with other trends, for instance, with greater exposure of women to Western media, and with rising incomes for large sections of the population. We therefore interact birth cohort variation in exposure to the new technology with the sex of the firstborn child of the mother, a previously well-tested proxy for the proclivity to commit sex-selection. Since we are interested primarily in gender gaps in parental investments, survival, and sibship size, we use the sex of the child as a third interaction in a triple differences-in-differences (DDD) regression framework.

Previous work has shown that more educated and wealthier women are more likely to engage in sex-selective abortion in India (Jha et al. (2006), Bhalotra and Cochrane (2010)). We directly account for selection into uptake by estimating a specification that includes woman fixed effects. Even in the absence of woman fixed effects, by leveraging variation in firstborn child’s sex, we obtain variation in the risk of sex-selection that is independent of socioeconomic status (SES). It is, therefore, key to our identification strategy to demonstrate that (a) the sex of the firstborn child is quasi-random, (b) pre-ultrasound trends in outcomes did not differ by child gender, and (c) firstborn child sex predicts the proclivity to commit sex-selection (conditional on underlying preferences). We now demonstrate that these three conditions hold.

**Testing exogeneity of firstborn sex.** Our assumption that the sex of the first child is randomly determined is supported by the data. We demonstrate this as follows. First, the top left graph in Figure 3 shows that the proportion of females among first births in India lies within the normal range that one would expect in the absence of manipulation (48.80 percent - 49.26 percent); and it shows no tendency to increase in the post-ultrasound period. This contrasts sharply with the time profile of the sex ratio for higher order births preceded by a girl.<sup>23</sup> Second, Table 2 shows that families with firstborn boys and firstborn girls are similar along a number of observables.<sup>24</sup>

Third, we combine these two stylized descriptive patterns in a regression framework—see Table A.3, where firstborn sex is the dependent variable. Column 1 shows that the share of firstborn girls (as opposed to boys) does not change post-ultrasound, conditional on state fixed effects, linear time trends, and indicators of SES. In column 2, the specification is enriched to allow interactions between every SES indicator and the post-ultrasound indicator. This is relevant because previous work shows that the proclivity to commit sex-selective abortion varies systematically with SES (Bhalotra and Cochrane (2010)). The interaction terms are jointly insignificant, indicating no the costs of ultrasound and abortion were not prohibitive even for relatively poor households.

> 23Figure 3 reproduces Figures 1-4 from Bhalotra and Cochrane (2010).

> 24Table A.4 reports the corresponding summary statistics for the REDS sample. The definition of preultrasound and early diffusion period is the same as in Table 2 but the late diffusion period is shorter (1995-1999) because we use the 1999 survey. The sample sizes in Table 2 are smaller than in other tables because there is only one first birth per woman, but multiple births or multiple years per woman in the other tables. The reason we have more firstborn-girl women in more recent years in the data is that we construct a retrospective woman-year panel from three cross-sections and firstborn-girl mothers have higher mortality as shown in Milazzo (2018), causing fewer of them to appear in the sample when we project backwards from cross-sections of surviving women.

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