cohort and use these as weights – for example, all observations from children at an age of one month are assigned a weight equal to (1/# of observations with age = one month). This weighting makes possible the translation of our point estimates, which come from monthly observations, into aggregate changes in mortality risk across the whole full first year of an infant’s life.<sup>12</sup> The remainder of the columns in Table 2 separate out these one-year mortality results into impacts in the first month of life vs. in months two through twelve. Given a baseline neonatal mortality risk of 12.1 percent in the treatment group prior to the verdicts, columns 2 and 5 indicate large impacts, with statistically significant point estimates of -0.024 (or 19.5 percent) and -0.020 (or 16.2 percent), respectively. Meanwhile, the estimated mortality impact in months two through twelve is a much smaller and statistically insignificant -0.001 in both column 3 and column 6. Together, Tables 1 and 2 reveal that most infant mortality events are neonatal and that the environmental policy in question here is associated with large-magnitude reductions in neonatal mortality – on the order of 16-20 percentage points. The adverse effect of pollution beyond the first month is more likely to be captured by morbidity, which we do not observe over time. We thus focus exclusively on neonatal mortality for the remainder of this paper, touching on possible mechanisms of exposure in Section 5.

We explore the timing and geography of the verdicts’ impacts on neonatal health in Table 3, using the same child-month mortality panel as in Table 2. Columns 1-3 show results from neonatal mortality regressions using post-periods of different lengths, while column 4 shows results from simultaneous estimation of short-, medium-, and long-run policy impacts. Column 5 tests the robustness of the main effect to the use of all of India as a control group. All estimates are statistically significant at the five-percent level or lower. Magnitudes range from 2 to 3.5 percentage points in these columns, which imply a 16-29 percent drop in neonatal mortality rates as a result of the rulings. Pairwise F-tests reject the null hypothesis that the 1995-1999 (medium-run) coefficient is equal to either of the other two period-specific coefficients (p-value _<_ 0.01).

In column 6, we investigate the extent to which the mortality reduction is driven by reduced _in utero_ exposure to pollution. To do so, we categorize treatment-group births by their degree of _in utero_ exposure. _All_ children born after the Supreme Court verdicts experience lower post-natal exposure. However, children born in the first nine months after the Supreme Court verdicts (November 1987 to July 1988) experience only partial _in utero_ reduction in exposure to contaminated water, while children born after July 1988 fully benefit from the effect of the verdicts. We define dummies for these two groups of children in the treatment region and estimate impacts for each group simultaneously. The predictive effect of fully benefiting from the verdicts while _in utero_ is a statistically-significant 2.9

> 12For example, column 1 indicates that, after the verdicts, the probability of infant death in a given month drops by 0.005 in the Kanpur region. This translates to a 28.9 percent reduction in the aggregate risk of infant mortality. The significance of point estimates in Table 2 is unaffected by choice of weights.

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