Abstract:
Since the 1972 U.S. Clean Water Act, government and industry have invested over $1 trillion to abate water pollution, or $100 per person-year. Over half of U.S. stream and river miles, however, still violate pollution standards. We use the most comprehensive set of files ever compiled on water pollution and its determinants, including 50 million pollution readings from 170,000 monitoring sites, to study water pollution's trends, causes, and welfare consequences. We have three main findings. First, water pollution concentrations have fallen substantially since 1972, though were declining at faster rates before then. Second, the Clean Water Act's grants to municipal wastewater treatment plants caused some of these declines. Third, the grants' estimated effects on housing values are generally smaller than the grants' costs....
The share of waters that are not fishable fell on average by about half a percentage point per year, and the share that are not swimmable fell at the same rate. In total over the period 1972-2001, the share of waters that are not fishable and the share not swimmable each fell by 11 percentage points. Each of the four pollutants which are part of these fishable and swimmable definitions declined rapidly during this period. Fecal coliforms had the fastest rate of decrease, at 2.8 percent per year. BOD, dissolved oxygen deficits, and total suspended solids all declined more slowly, at about 1.5 percent per year.
Trends in all these pollutants since the Clean Water Act are large, but trends before the Clean Water Act were larger. For example, BOD was falling by 3 percent per year before the Clean Water Act and 1.5 percent after it. We find pre/post 1972 trend breaks of comparable magnitudes for all the other pollutants. We interpret these pre-1972 trends somewhat cautiously since, as discussed earlier, relatively few monitoring sites recorded data before the 1970s, and fewer long-term monitoring sites operated in the 1960s.
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We find that [Clean Water Act] grants cause large and statistically significant decreases in pollution. Each grant decreases dissolved oxygen deficits by 0.8 percentage points, and decreases the probability that downstream waters are not fishable by 0.7 percentage points. The other pollutants decrease as well | BOD falls by about 3.4 percent, fecal coliforms fall by 8.5 percent, and the probability that downstream waters are not swimmable by about half a percentage point. The point estimate implies that each grant decreases TSS by one percent, though is imprecise. TSS comes primarily from non-point sources like agriculture and urban runoff, so is less closely related to municipal wastewater.
Event study graphs support these results. These graphs are estimated from specifications corresponding to equation. In years before a grant, the coefficients are all statistically indistinguishable from zero, have modest magnitude, and have no clear trend.... This implies that pollution levels in upstream and downstream waters had similar trends before grants were received. In the years after a grant, downstream waters have 1-2 percent lower dissolved oxygen deficits, and become 1-2 percent less likely to violate fishing standards. These effects grow in magnitude over the first ten years, are statistically significant in this period, and remain negative for about 30 years after a grant.
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The cost to increase dissolved oxygen saturation in a river-mile by 10 percentage points.... .The simplest specification ... implies that it cost $0.57 million per year to increase dissolved oxygen saturation in a river-mile by ten percent; the broadest specification ... implies that it cost $0.54 million per year. The annual cost to make a river-mile fishable ranges from $1.8 million in the simplest specification ... to $1.5 million in the richest specification.... The grants program made 16,000 river-miles fishable.
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The estimates ... are generally consistent with near complete pass-through, i.e., little or no crowding out or in beyond the required municipal capital copayment. The Panel A pass-through estimates range from 1.15 to 1.27 in real terms or 1.53 in nominal, which mean that city expenditure increased by around the amount of the typical copay (which was typically a third of the federal grant). Panel B ... includes the local copayment in the main explanatory variable,... and the estimates imply pass-through rates of 0.86 to 0.94 in real terms or 1.09 in nominal terms.
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Table 5 analyzes how Clean Water Act grants affect housing. Column (1) shows estimates for homes within a quarter mile of downstream waters. Column (2) adds controls for dwelling characteristics, and for baseline covariates interacted with year fixed effects. Column (3) include all homes within 1 mile, and column (4) includes homes within 25 miles.
Panel A reports estimates of how grants affect log mean home values. The positive coefficients in the richer specifications of columns (2) through (4) are consistent with increases in home values, though most are statistically insignificant. Column (4) implies that each grant increases mean home values within 25 miles of affected waters by three hundredths of a percentage point. The 0.25 or 1.0 mile estimates are slightly larger than the 25 mile estimate, which is consistent with the idea that residents nearer to the river benefit more from water quality. Panel B analyzes how grants affect log mean rental values. These estimates are generally smaller than the estimates for housing. The estimate in column (4), including homes within a 25 mile radius of downstream rivers, is small but actually negative.
Panels A and B reflect the classic hedonic model, with fixed housing stock. Panels C and D estimate the effect of grants on log housing units (panel C) or the log of the total value of the housing stock (panel D). In the presence of elastic housing, measuring only price effects (as in Panels A and B) could understate willingness-to-pay for local amenities. Moreover, many cities have had substantial waterfront development, which could be related to water quality.
Panels C and D suggest similar conclusions as Panels A and B. Most of these estimates are small and actually negative. One is marginally significant (Panel C, column 1), though the precision and point estimate diminish with the controls of column (2). Column (4) in of Panel D literally implies that each grant decreases the total value of the housing stock within a 25 mile radius of downstream waters by one point five hundredths of a percentage point.
Figure 4 shows event study graphs, which suggest similar conclusions as these regressions. Panel A shows modest evidence that in the years after a plant receives a grant, the values of homes within 0.25 miles of the downstream river increase. The increases are statistically insignificant in most years and small in magnitude. Panel B shows no evidence that homes within 25 miles of the downstream river increase after a treatment plant receives a grant.
We also report a range of sensitivity analyses, which are broadly in line with the main results.
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Considering all owner-occupied homes within 25 miles of the river, the estimated ratio of the grants aggregate effects on home values to the grants’ costs is 0.25. Adding rental units in column (3) does not change this estimate out to two decimal points.
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Under [the] ... three approaches, the ratios of measured benefits to costs are -0.11 (0.16), 0.11 (0.31), and 0.11 (0.10), respectively.
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Row 8 finds that grants to declining urban areas have slightly lower ratios, while the ratio for high amenity areas is greater. Finally, row 9 tests for differences in the housing market response by census region. This specification finds that grants to the Northeast have smaller ratios, while grants to the south have larger ratios around 0.73. None of these ratios in rows 6-9 are significantly different than that of the mean grant.
The map in Appendix Figure 10 shows heterogeneity in the ratio of measured benefits to costs across U.S. counties. This map assumes the same hedonic price function nationally and reflects spatial heterogeneity in the density of housing units. Specifically, these estimates divide treatment plants into ten deciles of the number of people in 2000 living within 25 miles of downstream river segments. They then use the regression estimates from column 4 of Table 5 to calculate the ratio of the change in the value of housing and grant costs, separately for each decile.39 Finally, we average this ratio across all plants in each county.
The map shows that the ratio of measured benefits to costs is much larger in more populated counties. The bottom decile of counties, for example, includes ratios of measured benefits to costs of below 0.01. The top decile of counties includes ratios between 0.31 and 0.45. Grants and population are both highly skewed|37 percent of grant costs and 54 percent of population are in the top decile.
We take three overall conclusions from this analysis of heterogeneity. First, we find suggestive evidence that ratios of measured benefits to costs follow sensible patterns, though not all estimates are precise Second, none of these subsets of grants considered has a ratio of measured benefits to costs above one, though many of the confidence regions cannot reject a ratio of one. The largest ratios of estimated benefits to costs are for areas where outdoor fishing or swimming is common (ratio of 0.57), for high amenity urban areas (ratio of 0.63), and in the South (ratio of 0.74).
