A4 reproduces the main results of our DiD analysis with the merged sample.

Table 5 establishes a causal relationship between pollution and infant mortality. Column 1 shows the OLS relationship between BOD pollution (i.e., exceedance of the bathing-class standard) and neonatal mortality. It is a statistical zero, which may reflect the upward bias that would result from positive correlations between pollution, urbanization, and economic activity. Columns 2-5 replace the key independent variable with its upstream counterpart – the reduced-form analog of 2SLS with upstream BOD as the instrument. The specifications in these columns vary only by either sample time period or the definition of upstream. In stark contrast to the OLS result, the coefficient on upstream water quality is positive and significant across all four specifications. The interpretation of these coefficients is that exceeding the BOD standard for bathing-class water quality upstream increases the downstream likelihood of neonatal mortality by 2-3 percentage points – a significant spatial externality.

Our empirical strategy hinges on the assumption that upstream river pollution is exogenous once we condition on weather, policy, and district and year-month fixed effects. Implicit in this assumption is that these controls fully capture the endogenous location of households. To bolster this argument, we employ a placebo test that provides additional evidence supporting our exclusion restriction. In columns 6 and 7, each upstream district is replaced with the nearest off-river neighbor with nonmissing pollution data. If the resulting ‘falsified’ upstream pollution variable remains a significant predictor of downstream mortality, then we are likely capturing spurious, spatial correlation between a given district and its neighbors due to the regional nature of many health and environmental shocks. This, however, is not the case: the falsified upstream pollution variable loses both its magnitude and significance, thereby suggesting that our true upstream variable is isolating quasi-random variation in pollution that originates upstream and yet flows downstream to other districts.

The next step is to estimate the dose-response function and use it to test the importance of the pollution mechanism. The results of this step are documented in Table 6, which details the first and second stages of our 2SLS estimation and the associated over-identification tests. In columns 1-4, we employ a single instrument – upstream pollution, as before. Once again, the first three columns pertain to short-, medium-, and long-run periods of observation, while the fourth uses the full time period but parameterizes the policy variable as three period-specific dummies. The first-stage is strong in all four columns.<sup>14</sup> In the second stage, all specifications yield statistically significant point estimates of the pollution coefficient (in Panel B), which is approximately four to six times larger in magnitude than its upstream counterpart in Table 5. The implication is that surpassing the BOD standard of 3 mg/l raises the risk of neonatal mortality by 10-15 percentage points.

> lower bound in columns 4 and 5 of Table 5 as well as in columns 1-3 and 5-7 of Table A5.

> 14The first stage is also very strong in IV models that replace BOD with sulfates, chlorides, or calcium. These results are available upon request.

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