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Income and Water Service Experiences

Another way in which it’s tough to be poor

Better with more money

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.

Golden State Waterers

A California surprise, Part I

Drought porn

Something unexpected happened when California ordered its utilities to save water: the state’s investor-owned private utilities out-conserved local governments.

California’s long-term drought began as early as 2007, but intensified to crisis conditions by 2012. Conditions worsened, and in response 2015 Governor Jerry Brown and the California State Water Resources Control Board imposed restrictions on 408 drinking water utilities designed to reduce urban water usage by 25% statewide. The order required utilities to cut water use, but left individual utilities to choose the means by which to achieve conservation. The mandate assigned each utility its own conservation target, with standards ranging from 4-36% reductions relative to 2013 levels. These standards were formulaic, and varied based on utilities’ historical water consumption.

It was pretty bad

These conservation rules were in place for twelve months—June 2015 through May 2016—and applied to both local government utilities and private, investor-owned utilities. Conservation rules were assigned based on historical demand patterns and supply considerations only, not on ownership or governance.

Happily, the State of California has shared utility-level conservation data lavishly—a boon to water policy researchers! Over the past year, I’ve been sifting through that mountain of data with Youlang Zhang and David Switzer to see how California’s conservation efforts have fared. We’re discovering some fascinating things. The first of our studies is now forthcoming in Policy Studies Journal.

Restricting the flow

Faced with water scarcity, communities frequently restrict residential outdoor water use, such as car washing and especially lawn/garden irrigation. These water restrictions are effective in driving immediate reductions in water consumption. In California those restrictions typically take the form of limiting the number of days when outdoor irrigation is allowed each week. The graph below shows how public and private utilities regulated outdoor irrigation during the drought.

Eyeballing that graph, there doesn’t appear to be much difference between public and private utilities. But after adjusting statistically for a host of factors like utility size, demographic composition, and hydrological conditions, it turns out that private, profit-seeking, investor-owned utilities restricted irrigation about 4% more than public, local government utilities. That may not seem like much, as we’ll see it’s actually huge.

Meeting the mandate

We were also interested in what made utilities more or less likely to comply with the state’s conservation rules. Overall compliance was about 53%–that is, on average 53% of utilities reached their conservation targets each month. We modeled compliance statistically, and found a number of interesting correlates of success and failure. But most notable was a yawning gap between public and private sector: after adjusting for other factors, private utilities were nearly twice as likely as similar public utilities to meet the state’s conservation standards.

Conservation achievements

Finally, we analyzed overall conservation during the mandatory conservation period. And again, we found that, after accounting for other factors, private utilities conserved an average of 3% more water each month than their public counterparts during the mandatory restriction period. Although this difference is small in percentage terms, it reflects an enormous difference in absolute volume of water. This plot presents the distributions of conservation results from June 2015-May 2016 for local government utilities (green), and what it would have been if each utility had saved 3% more:

Monthly conservation relative to same month in 2013

The areas within the white bars on the right side of the distribution represent the conservation that didn’t happen due to differences in ownership. Three percent greater conservation would have boosted public utilities’ restriction compliance rate from 51 to 62 percent.

In substantive terms, three percent greater conservation by California’s local government utilities during the mandate period would have reduced the state’s water consumption by 54.6 billion gallons—enough to supply the City of San Francisco for more than two years.

Well that was unexpected

So what happened?

California is once again in the midst of a hot, dry summer; other parts of the world are, too. So it’s worth trying to figure out what’s behind the public-private disparity in drought response. Although it’s surprising at first blush, it’s actually a logical result of the institutions that govern water in America generally and California specifically.  My next post will explain why.*

 

 

 

*Spoiler: as usual, it’s about money and politics. If you can’t wait for the next post, you can read the forthcoming article.

 

Zombie Metrics

This guy measures water affordability as (Avg bill ÷ MHI)<2.0%

Terrible, horrible, no good, very bad measurement, part 4

My criticism of average bill ÷ Median Household Income (MHI) as a measure of household-level water affordability isn’t especially new. Lots of other people have pointed out the problems with this conventional methodology, and I’ve been presenting and publishing these arguments for more than twelve(!) years. But golden numbers are stubborn, and bad habits are hard to break—even when people know better.

The remarkable persistence of a bad idea

Over the years I’ve presented to hundreds of utility professionals and spoken personally with scores of managers, analysts, and rate consultants about the pathologies of %MHI and the virtues of alternative approaches. The reception is universally warm and agreeable, as most water professionals genuinely care about affordability and immediately recognize the fundamental flaws of the conventional approach.

And yet.

Alas, there’s an and yet.

Even well-informed specialists continue to use and promote the tried-and-false conventional methodology. Researchers who recognize that average-bill-as-%MHI is deeply flawed employ it anyway because it’s easy and widely recognized (for example). Managers who know that %MHI is a misleading statistic continue to put it in front of their elected officials because it’s familiar and they feel that they have to use this metric because everyone else does, and because they believe it’s an EPA standard (it isn’t). Advocates, analysts, and rate consultants who I like and respect persist with the conventional approach in their studies, even when they know these metrics are fundamentally flawed (many have told me as much!).

Examples abound. The Alliance for Water Efficiency has a nice tool that’s designed to help water utilities model the financial impacts of various rate structures. Sensibly enough, their model includes an assessment of affordability. Unfortunately, it uses the familiar flawed metric:

C’mon man

UNC’s Environmental Finance Center continues to feature average-bill-as-%MHI as the sole affordability indicator on its rates dashboards. Folks at EFC know about the problems with this metric (they blogged about it here), but continue to display it prominently nonetheless.

Et tu, EFC?

 

Easy metrics die hard, it seems.

Breaking habits

Water and sewer ratemaking is a niche specialty (to put it mildly). That’s good news, because if the community of specialists who analyze and design rates for a living get affordability metrics right, there’s a good chance that the utilities they serve will get affordability right, too.

I’ve developed better ways to measure affordability; others are working on this issue, too. At this stage there’s no consensus over the best metrics (naturally, I think mine are great). But abandoning the flawed measurement convention is an important first step.