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as of 1980, there were 41 million “missing women” in India; more recent evidence suggests very high rates of sex-selective abortion (Jha et al, 2006). There is strong evidence of discrimination against girls in investments ranging from health care to vaccination to food intake (Das Gupta, 1987; Basu, 1989; Griffiths et al., 2000; Borooah, 2004; Pande, 2003; Mishra et al., 2004; Oster, 2008).

Second, vaccinations fit well into the framework of the model because they are an investment with saturation. A child only needs a certain number of vaccines (the World Health Organization recommends eight) and more than that are not useful. This means that we are more likely to see evidence of both the increase in inequality and the decrease predicted by the model, since the advantaged group is more likely to reach investment saturation. Vaccines in India are also an investment for which there are significant gender differences, and these gender differences actually map into a large share of the difference in child survival (Oster, 2008). This suggests that the analysis here may also have direct policy relevance.

Perhaps the strongest argument for testing the theory in this context is that I am able to take advantage of variations in the availability of “health camps” in India. These camps provide simple maternal and child health services, including vaccinations. The number of health camps varies across villages, generating clear variation in the convenience and cost (in terms of travel) of vaccination. Although placement of camps is not random, I argue that it is unrelated to existing vaccination conditions (either the level or the existing gender difference in vaccination) or to demographics like income, education, or average number of children. Consistent with institutional details, camp concentration does vary by state, as well as with village population and distance to health clinics. These are, however, observable variations that can be controlled for. There is significant variation in the number of camps, meaning it may be possible to observe the relationship between inequality and access over a relatively large range of access levels.

In Section 4, I present evidence on the relationship between health camps and gender differences in vaccination, using data from the National Family and Health Survey in India, which collects detailed data on child health as well as village-level data on the availability of health camps in the last year. Consistent with the theory outlined above and described in more detail in Section 2, I find that initial increases in the number of health camps increase the gender imbalance in vaccination (measured either by total vaccinations or whether the child has any vaccinations), but that further increases decrease the imbalance. As further support for the theory, I find that this non-monotonic effect is stronger for families with a stronger reported gender bias. In Section 5, I discuss two alternative tests of the non-monotonic prediction, relying on information on distance to

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