of the distribution of unobservables will have a higher wage than someone near the bottom of the distribution. There are a few alternative strategies that we can use, however, that might allow us to say something about the effect along the current income distribution; one is to attempt to transform the coefficients by quantile on the distribution of _εidt_ into average coefficients by quantile on the distribution of ln ( _yidt_ ). That is, by estimating the OLS regression of ln ( _yidt_ ) on _diseasedt_ and _Xidt_ , we can use the residuals as a proxy for _εidt_ and thus assign each individual a coefficient _βi_ based on their place in the distribution of residuals. Then, we can look at the distribution of _βi_ across the wage or consumption distribution. In panels (D) through (F) of Figure 8, we present the smoothed local means of _βi_ at quantiles of the wage or consumption distributions; given the ad-hoc nature of the calculation, we have not attempted to calculate standard errors, but the means confirm the results so far: the largest effects of the early-life disease environment on wages occur towards the upper end of the distribution. The same appears to be true of the consumption regression as well, though the lack of precision in the quantile estimates make the results less clear than in the wage case.

An alternative strategy is to remove some of the controls, and so in Figure 9, we present the results of quantile regressions without the district and year-of-birth fixed effects. The results are broadly similar to those in Figure 8, though the coefficients are larger, since they are now picking up part of the district and year fixed effects; the consumption estimates are also somewhat altered, in that they now show a larger effect near the bottom of the distribution. However, this approach is not entirely satisfactory, as the regression equation is now almost certainly misspecified, as the omitted fixed effects are surely correlated with both the disease environment and wages.

Finally, Figure 10 presents the results from quantile regressions with the full set of controls: district and year-of-birth fixed effects, but also state _×_ urban fixed effects, state time trends, and social group _×_ urban indicators. The estimates are quite similar to those in Figure 8, though somewhat larger and more significant for consumption and smaller and less significant for sanitation.

# **C Robustness Checks on Fiscal and Welfare Calculations**

In this appendix, we produce alternative estimates of the fiscal and welfare impacts of improved sanitation and early-life disease environment. First, we examine the robustness of our estimates of the increased tax revenues resulting from improved sanitation or IMR, using the results of our quantile regressions. Then we examine how the welfare gains are affected by using either quantile estimates or our regression of consumption on IMR.

## **C.1 Fiscal Impacts Using Quantile Regressions**

In appendix B, we discussed how our quantile regression estimates of the effect of IMR and sanitation on wages could be used to assign each individual in our sample an estimated coefficient. We now make use of these personalized coefficients to produce new estimates of the fiscal effects of the sanitation and disease environment.

Our baseline fiscal and welfare analysis is conducted using 2012-13 dollars, and so we first rescale the incomes in our sample to be representative of the average in 2012. According to the World Bank, GNI per capita in India in 2012 was 83026.8 INR; therefore, we multiply all incomes by a constant to make the mean in our sample 83026.8. Then, to account for the fact that we have 25