Tuesday, February 28, 2017

Consequences of the Clean Water Act and the Demand for Water Quality

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.
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.
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.
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.  
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.
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.
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.
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).
We ,,, discuss five reasons why these estimates may not reflect the full benefits of these grants. We believe these reasons provide support for interpreting the hedonic estimates as a lower bound on total benefits.

First, people might have incomplete information about changes in water pollution and their welfare implications. Cross-sectional and time series analyses do find statistically significant though imperfect correlation between perceived local water pollution, as reported in surveys, and objectively measured local water pollution.... Residents may observe indirect signals of polluted water, such as fish kills (dead fish floating on the water or sitting on the bank), algae blooms, sulfurous odors like rotten eggs, miscolored waters, and may learn about pollution directly from waterfront warning signs, news coverage, and publicity. People who go fishing value actually catching fish, so if pollution decreases the number of fish someone can catch, then water pollution may discourage fishing. Incomplete information would be especially important if pollution abatement improves health.

Second, these hedonic regressions may include some reasons for which people value water quality, including recreation and aesthetics, though not all. One category not counted in these estimates is "nonuse" or "existence" values. A person may value a clean river even if that person never visits or lives near that river. As mentioned in the introduction, the measurement of nonuse values has been controversial and faces severe challenges. The controversy is not over whether nonuse values exist; it is straightforward to explain their place in economic theory. Instead, the controversy is over the extent to which stated preference or contingent valuation methods accurately reflect nonuse values. 

Third, this analysis abstracts from general equilibrium changes. One possible channel is that wages change to reflect the improvement in amenities. A second general equilibrium channel is that the hedonic price function may have shifted. In the presence of such general equilibrium changes, our estimates could be interpreted as a lower bound on willingness to pay . Other possible general equilibrium channels describe reasons why the effects of cleaning up an entire river system could differ from summing up the effects of site-specific cleanups. One channel involves substitution. Rivers can be thought of as a differentiated product. Cleaning up part of a river in an area with many dirty rivers might have different value than cleaning up a river in an area with many clean rivers. Another possible channel involves ecology. The health of many aquatic species (so indirectly, the benefit people derive from a river) may depend nonlinearly on the area of clean water. Ecologists have well established that the growth rate of a population is an inverted U-shaped function of the population’s size.

Fourth, the 25 mile radius is only designed to capture 95 percent of recreational trips. The magnitude of benefits from the remaining 5 percent of trips is unclear; the last 5 percent of trips might account for more than 5 percent of the total surplus from recreational demand because they represent people willing to travel great distances for recreation.

Finally, while our pass-through estimates suggest that an individual grant did not cause substantial crowd-out or crowd-in of municipal spending, we interpret this estimate cautiously since it reflects only 199 cities, does not use upstream waters as a comparison group, and reflects pass-through of marginal changes in investment, rather than the entire Clean Water Act.

by David A. Keiser and Joseph S. Shapiro
National Bureau of Economic Research (NBER) www.NBER.org
NBER Working Paper No. 23070; Issued in January 2017

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