significant average drop in exceedance of the water quality standard, and the point estimate monotonically drops in the next two groups downstream. The evidence thus implies that the rulings’ impacts on both water quality and neonatal mortality persist some distance downstream of the cases’ focus.

In columns 7 and 8, we use the logarithm of the BOD level as our outcome variable instead of the bathing class dummy. With this specification of BOD, and over the full time period (column 7), the rulings are associated with a marginally significant drop in log-BOD, but the association is smaller and insignificant in the short run. To investigate the divergent results between a dummy variable and the continuous logarithm form, we graphically examine the distribution of the logarithm of BOD by time and treatment status. Figure 5 provides kernel densities of the residuals from regressing log- BOD on weather controls and district and year-month fixed effects. Panel A suggests that the verdicts are associated with significant, yet non-uniform, drops in BOD levels; the central peak shifts downwards, but the left tail is relatively unchanged. In contrast, Panel B illustrates a symmetric tightening of the log-BOD distribution around the mean. Put together, the two kernel density plots paint a picture of treatment-group reductions in BOD that are relatively concentrated in the middle to upper half of the distribution.

Panel B of Table 4 provides estimates of the impact of other pollutants that are relevant to the tanning industry and the Supreme Court verdicts. Calcium, sulfate, and chloride ions are all byproducts of the tanning process; all drop significantly in the aftermath of the rulings (columns 1, 2, and 3). FCOLI, meanwhile, is a strong indicator of domestic pollution and thus should be observed to drop if the second of the two Supreme Court verdicts was successful. We find no evidence of such a drop (column 4).

# **4.4 Mechanisms of policy transmission**

Having identified a strong link between the Ganga Pollution Cases and the local health of both infants (Tables 2 and 3) and rivers (Table 4), we now turn to the questions of how water pollution affects infant health and what that means for Indian environmental policy. Our strategy for answering these questions relies on an estimation of a dose-response function for water pollution and neonatal mortality. We use the merged sample of water pollution and neonatal mortality in this phase of analysis, which limits us to the 1986-2004 time period. To utilize our IV, we must further restrict our geographic focus to only those districts with non-missing upstream pollution measurements. Appendix Table A2 shows how the pool of available pollution monitors and their distance from upstream counterparts varies with our definition of upstream (which is detailed in Section 2). Appendix Table A3 summarizes the merged sample using our preferred upstream definition of [75 km, 200 km].<sup>13</sup> Finally, Appendix Table

> 13The 75-km lower bound minimizes the risk of off-river spatial correlation, while the 200-km upper bound excludes locations that are so far upstream as to not physically affect downstream pollution levels. We test the robustness of the

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