Black, White, and Hispanic Americans experience water utility service differently
Over the past couple of years there’s been a growing recognition that drinking water policy is an environmental justice issue in the United States; my research with David Switzer showed racial, ethnic, and socioeconomic disparities in drinking water quality at the community-level—findings that have since been affirmed by other researchers. Identifying racial and ethnic disparities in drinking water service at the individual level is harder. Do people of different races and ethnicities experience water service in markedly different ways?
A few months ago I posted some findings from a Texas A&M Institute for Science, Technology & Public Policy (ISTPP) national public opinion survey. The survey’s carefully-designed sample of nearly 2,000 individuals is representative of the US population, and so offers an extraordinary look at public perceptions about water service. Earlier posts reported on attitudinal differences between water professionals and the general public, how gender predicts opinion on water issues, and the correlation between income and water service experiences.
Today I’m looking at race and ethnicity.
Water service problems
The ISTPP survey asked respondents to say whether they had experienced specific kinds of problems with their drinking water with a simple yes/no answer:
- The water does not taste good (31.5% yes)
- The water is cloudy or dirty (19.5%)
- Water pressure is low (29.2%)
- The water causes sickness (3.8%)
- Water billing or payment problems (10.2%)
56.7% of respondents reported experiencing none of these problems. It’s important to remember that the survey captures perceived water service problems, not actual problems—for example, we don’t know whether a respondent actually experienced low water pressure, we only know whether a respondent thinks (s)he experienced a problem. Happily, a large majority of respondents said that they had not experienced each of these problems.
Less happily, there were notable racial differences in the “yes” responses across all five items, and ethnic differences in two of them. The graph below shows the percent reporting each type of water service problem for Black, Hispanic, and non-Hispanic White respondents (vertical spikes represent 95% confidence intervals).*
It’s not a pretty picture. A generally ordered relationship emerges, with Black respondents reporting the most water problems, followed by Hispanics, with non-Hispanic Whites reporting the fewest in four of the five categories. Non-Hispanic whites were most likely to report no problems at all.
Black respondents reported experiencing water service problems much more frequently than did respondents of other races across all five categories. The differences between Black and non-Hispanic White respondents were large and statistically significant in all categories. For example, 37% of Black respondents reported experiencing low water pressure, compared with 28% of non-Hispanic Whites. 29% of Blacks reported cloudy or dirty water, compared with just 18% of non-Hispanic Whites.
The disparities between Hispanic and non-Hispanic White respondents were less stark, although significantly more Hispanic respondents reported experiencing water bill problems and illness compared with non-Hispanic Whites.
How much do racial/ethnic patterns just reflect income?
Since race and ethnicity correlate with income in the United States, it’s possible that the racial/ethnic disparities are just artifacts of income disparities I discussed in my earlier post. Statistical modeling can help tease out the degree to which race/ethnicity relates to water service experiences after accounting for income. So I fitted logistic regression models to identify correlates of water service experiences by race and ethnicity, while accounting for income, age, urban/rural location and region. These models estimate the likelihood of experiencing each of the five service problems, or no problems. The graph below shows the results, with estimates of racial/ethnic groups at an annual income of $27,500–a relatively low income where we’d expect problems to be most frequent:
When adjusting for income, age, and region, the racial and ethnic disparities persist, but are less pronounced and in most cases not statistically significant by the conventional standard. So while there are clear racial and ethnic differences in water service experiences in the United States, these data suggest that much–but probably not all–of those differences reflect racial/ethnic income disparities.
*The survey captured only two racial categories (Black & White) and one ethnic category (Hispanic), so we can’t analyze other racial or ethnic groups.
Devils (and angels) in the details, Part 5
In early January the California Water Board (SWRCB) published its long-anticipated draft proposal for a statewide low-income water bill assistance program. I’ve blogged about it over the past few weeks*; in this final (I think) post on the proposal, I’ll look at how the SWRCB proposes to pay for the estimated $606 million annual program.
How to raise 600 million dollars
The SWRCB recommends paying for the new water assistance program through “progressive revenue sources… in order not to burden some of the residents that the program seeks to serve.” To that laudable end, the proposal calls for two new taxes:
- A .25% tax on personal incomes over $1 million; and
- A sales tax on bottled water.
The income tax would generate an estimated $466 million annually, while the bottled water tax would generate $154 million. Under California’s Byzantine pubic finance laws both taxes would require supermajorities in the state legislature. The bottled water tax would also have to pass a ballot referendum.
Progressive taxation is crucial to any low-income assistance program, since the whole point is to transfer resources to people who are short on them. A millionaire’s tax makes sense from that perspective; a person whose income tops $1 million annually likely has little difficulty paying his/her water bill, and it’s doubtful that an additional 0.25% tax will much constrain productivity or lifestyle for people with seven-figure incomes.
The bottled water tax is thornier.
It’s common for those of us who work on American drinking water issues to think of bottled water as a luxury good. Bottled water is orders of magnitude more expensive than tap water, after all. It also carries some severe negative externalities: it’s lightly regulated, uses lots of energy to produce and transport, and empty bottles create a huge solid waste problem. For all those reasons, taxing bottled water would be progressive, in theory.
In practice, the progressivity of a bottled water tax isn’t so clear. Counterintuitively, in America bottled water consumption is negatively correlated with income—that is, poor and working-class Americans drink much more bottled water than do middle-class and wealthier Americans. Study after study after study show that low-income people and members of racial/ethnic minorities are much more likely to drink bottled water.
Why do lower-income people pay orders of magnitude more for bottled water, when affordable tap water is available? From a health and efficiency perspective, that isn’t rational behavior. It might be cultural, it might be taste/odor preferences, it might be about distrust in government, or it might be something else entirely. In some cases, people served by the smallest, poorest communities that suffer from poor water quality might need bottled drinking water. Raising the cost of bottled water might have the perverse effect of pushing low-income households to drink more soda and sugary beverages.
Whatever the reason for the income-bottled water relationship, the distributional effect of the SWRCB’s proposed tax is clear: the poor will bear a disproportionate burden of any bottled water tax. When you consider that a significant proportion of eligible households will never actually participate in the assistance program, a bottled water tax becomes doubly regressive.
So where do we get the other $150 million?
There’s an intuitive political appeal to using a water-related tax to raise money for water bill assistance.† What kinds of water taxes could be progressive? Here are a couple of half-baked ideas (I don’t have the time or data to bake ’em).
- A tax on “luxury” bottled water. Poor folks aren’t buying $5.00 bottles of FijiWater; they’re buying $9.99 cases of Ozarka at Walmart. A sales tax on water that retails for more than $1.00 per liter would spare the lowest-income households, and probably wouldn’t push them into drinking sugary beverages.
- A tax on residential tap water consumption over 12,000 gallons per month. At 50 gallons per capita per day (reasonably efficient indoor use), a family of four uses about 6,000 gallons per month. In the vast majority of situations, residential water consumption beyond 12,000 gallons a month is for discretionary outdoor use. A main drawback to this kind of tax is that it would irritate water utility managers, who don’t want to act as the state’s tax collectors.
A combination of these two taxes could generate significant revenue without putting the revenue burden of low-income assistance onto the people that it’s intended to help.
*In the past few posts, I’ve summarized the proposal, discussed its potentially perverse incentives for ratemaking, pondered its implications for struggling small systems, and options for administering assistance.
†The SWRCB’s carefully avoids alluding to political appeal, but does note that “fees on bottled water or alcohol would have a nexus to water use.”
Devils (and angels) in the details, Part 3
In early January the California Water Board released its draft proposal for a statewide low-income water bill assistance program. My last couple posts summarized the proposal and discussed the perverse incentives for rate design that the subsidy might create. Continuing the theme of unintended consequences, this one looks at what the statewide assistance program might mean for poor-performing small water systems.
All else equal, when it comes to water systems, bigger is better. Water and sewer systems require big infrastructure investments and lots of operating costs to work. When those costs can be spread across large numbers of people and businesses, the average price of water is lower. That makes utilities textbook examples of economies of scale.
Less obviously, the organizations that operate utilities also are subject to economies of scale. Larger organizations can hire and retain higher-quality employees and maintain specialized personnel. As my past research work with David Switzer has shown, these advantages help larger water systems take advantage of human capital in ways that smaller systems can’t.
Larger systems also enjoy significant financial advantages. Larger customer bases tend to be more economically diverse, providing a degree of financial stability to a utility. Financial markets favor large utilities, too, allowing them access to finance capital at lower interest rates than their smaller cousins.
Thing is, just as in the rest of the United States, most of California’s water utilities are really, really small. More than three quarters of California’s 2,800 water systems serve fewer than 3,300 people, but collectively serve just 2% of the state’s population. Meanwhile, the 183 largest systems serve 80% of the state’s population.
Small system struggles
Unfortunately, small systems tend to suffer disproportionately from poor water quality. Here’s the correlation* between annual Safe Drinking Water Act health violations and utility size in California, based on EPA data from 2010-2018:
Affordability also correlates with system size. So do poverty, income, and unemployment. My recent analysis of water and sewer rates nationwide shows that, all else equal, both AR20 and HM decline as utilities increase in size—that is, on average, water and sewer affordability gets better as utilities get bigger.
That small systems struggle is no secret, and California has in recent years sought to encourage system consolidation through a variety of carrots and sticks. The process is slow, however, and fraught with political conflict. Consolidation is not a panacea, and there are plenty of struggling large and medium-sized systems. But the data are clear on this point: on average, with water utilities, bigger is better.
Props and levers
What does all this have to do with low-income assistance? The proposed program would transfer significant resources from relatively affluent to relatively poor communities. In many cases, those communities are served by the small, perennially underperforming utilities that too often provide lousy water quality at very high prices. Financial pressures from ratepayers are among the strongest incentives for small utilities to consolidate. A low-income assistance program will disproportionately benefit customers of those systems (which is good), but could inadvertently prop up systems that would otherwise move toward consolidation due to financial pressures (which is bad). To some failing small systems, low-income assistance could be financial life support system.
But if structured correctly and implemented carefully, low-income water assistance could help drive small system consolidation. Financial assistance for low-income customers could make consolidation more attractive for the larger utilities and private firms that might otherwise be reluctant to take on the responsibility for troubled small systems with less affluent customers. More directly, the state could make assistance funds contingent on consolidation or compliance with SDWA management requirements. Statewide assistance might be a potent lever to push recalcitrant small systems toward consolidation and its blessings.
That’s why statewide low-income water bill assistance should not be considered in isolation, but rather as part of a comprehensive strategy to improve water systems. The same applies to any plan for a national water affordability assistance program.
Whether a statewide assistance program would prop up failing small systems or lever them into consolidation and sustainability will depend in large part on the administrative structures and processes used to implement the program. I’ll take up those administrative angels and devils in my next post.
*Fitted with a negative binomial regression.
†There are probably another dozen or so studies that I could link here.