Another way in which it’s tough to be poor
Drinking water utilities are great, but they aren’t perfect. Sometimes there are problems. Do those problems occur randomly? Or are there observable patterns in the water service problems?
Recently I’ve been posting about 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, and on the ways that gender predicts opinion on water issues. I’m continuing to write up interesting findings from the ISTPP survey as time allows.
Today I’m looking at income.
Water service problems
The ISTPP survey asked respondents to say whether they had experienced each of the following 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%)
Importantly, this survey captures perceived water service problems, not actual problems—we don’t know that any given respondent actually experienced low water pressure, for example. We only know whether a respondent thinks (s)he experienced a problem. Likewise, we don’t know whether water actually caused sickness, only whether the respondent believes that it did. Fortunately, the large majority of respondents said “no” to all of these.
But the “yes” responses didn’t happen by chance. I fitted logistic regression models to identify correlates of water service experiences using the demographic variables in the ISTPP survey, such as race, ethnicity, age, urban/rural location, region, and income. These models estimate the likelihood of experiencing each of the five service problems.
A troubling pattern
The demographic correlates of water service problems vary, but across all five items, household income was the single strongest and most consistent predictor of water service problems. The graph below shows the likelihood of reporting that water billing problems at various income levels, with all else held equal (vertical spikes represent 95% confidence intervals):
At a $20,000 household income, there is a 13% chance of reporting billing problems. At $50,000, the likelihood is to about 9%; at $100,000 the likelihood drops to about 8%. That all makes some sense; we’d generally expect billing problems to correlate with income.
But the same pattern emerges for other kinds of water service problems, too. Here is the likelihood of reporting that water tastes bad at various income levels, again with other variables held constant:
At a $20,000 household income, there is a 37% chance of reporting bad-tasting tap water. At $50,000, the likelihood is to about 30%; at $100,000 the likelihood drops to about 25%.
Here’s the likelihood of experiencing cloudy or dirty water by household income:
Here’s the likelihood of reporting low water pressure by income:
And finally, here’s the likelihood of reporting that water caused illness by income:
Taken together, this is a sobering picture.* There is a clear relationship between income and the way that Americans experience their drinking water utility service. These results resonate with recent research finding a positive relationship between tap water consumption and income, with attendant implications for public health.
*In a future post I’ll look at race and drinking water experience; the picture won’t be much prettier.
Sometimes progress is visible in what you don’t see
Earlier this week I had the pleasure of speaking to the annual conference of the California Water Association, an organization of that state’s investor-owned water utility companies. The theme of the day was affordability. The California Public Utilities Commission and State Water Resources Control Board are working hard to craft rules and guidelines for affordability in the Golden State, with clear implications for the state’s utilities.
During the conference several speakers took to the stage to talk about efforts underway in California to ensure affordability as communities grapple with water infrastructure and supply costs. We heard from utility managers, state agency bureaucrats, and state legislators. These were not dilettantes or casual observers; these were experienced people well-versed in water policy, and I heard lots of exciting things about steps and directions the state and its utilities are taking.
But one of the most exciting things about the conference was something I didn’t hear and didn’t see. In an all-day meeting on the subject of water affordability, nobody mentioned average-bill-as-percent-of-median-household-income.
Indeed, I’m a bit embarrassed to admit that I was the first to mention the %MHI standard when I launched into my familiar attack on that miserable metric. I’ve been excoriating that metric in rooms full of water folks since 2006.
I can do it in my sleep. But the attack wasn’t necessary in that room on that day. The audience was receptive to more careful measurement and analysis—even if the results weren’t pretty or comfortable.
Good policy requires good measurement. In the case of water affordability, good measurement begins with abandoning bad measurement. The California water community has apparently taken that first step; maybe it’s a sign that the rest of the nation is ready to follow. The quiet disappearance of a number from conversation might seem like the smallest of small victories, but policy revolutions begin with such changes in analytical frameworks.