From Affordabiltiy

Water Color

​Water Sector Reform #5: Environmental Justice

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With a major federal investment in water infrastructure possibly on the horizon, the United States has a once-in-a-generation opportunity to leverage that money into a structural transformation of America’s water sector. This is the last in a series of posts outlining broad proposals to reform the management, governance, and regulation of U.S. drinking water, sewer, and stormwater systems. The first proposed reform was consolidation of water utilities; the second was an overhaul of financial regulation; the third was investment in information technology; the fourth was investment in water sector human capital.

​My fifth proposal is to build environmental justice into federal water regulations.

The face of drinking water in the United States.

Drinking water & environmental justice

It’s difficult to overstate just how much the Flint Water Crisis changed the national conversation on drinking water. As I’ve observed before, Flint’s water contamination wasn’t the first, or worst, or largest drinking water crisis in America in recent years, but for it’s the one that put a spotlight on water infrastructure. Last month a similar lead contamination in Newark grabbed headlines across the country again.

The lead contamination crises in Flint, Newark, and elsewhere turned the spotlight on an uncomfortable reality: America’s water systems problems are not just about infrastructure management, they’re also about institutional politics, race, ethnicity, and poverty. There’s a growing recognition that water infrastructure is an environmental justice issue. That’s expanded the political coalition interested in water infrastructure and raised the stakes in the politics of drinking water.

The color of drinking water

Anecdotes and case studies about drinking water and environmental justice abound. Looking for more rigorous evidence, David Switzer and I conducted the first nation-wide analysis of the relationships between race, ethnicity, socioeconomic status, and Safe Drinking Water Act (SDWA) compliance. Here’s what we found:

The red areas of the graph show high likelihood of drinking water violations, the blue areas show low likelihood. ​These results ​ provide strong evidence of a systemic injustice in utilities serving low-income communities of color across the United States. In communities with higher populations of black and Hispanic individuals, SDWA health violations are more common. What’s more, race and ethnicity seem to matter most in determining drinking water quality in the poorest of communities. My analysis of public experiences with drinking water service also reveals important disparities by race and income. The racial disparities are far, far worse in Indian Country. My study with Mellie Haider and David Switzer found that Clean Water Act violations are 23% higher and SDWA violations are 59% higher on tribal facilities compared with non-tribal facilities.*

The reasons for these racial, ethnic, and socioeconomic disparities in water quality and utility service are varied and complex. In some cases it’s a familiar tale of urban de-industrialization and white flight; in others it’s a legacy of racial discrimination in housing or infrastructure development programs. Environmental injustices in drinking water aren’t just urban phenomena, either: majority-black and majority-Hispanic communities suffer from poor water quality across vast swaths of rural America—to say nothing of Indian Country.

Unfortunately, the regulatory response to ongoing water quality problems in poor, minority communities is to loosen regulations or look past violations. Outsiders to the water sector are sometimes surprised to learn that there are no federal laws requiring racial, ethnic, or socioeconomic equity in water quality.  

Environmental justice

If we are going to pour hundreds of billions of federal dollars into water infrastructure, that money must bring us closer to environmental justice. My final water sector reform proposal, then, is to build environmental justice into federal water regulations. That means, at a minimum:

  • Establishing standard metrics to assess racial, ethnic, and socioeconomic equity in environmental conditions and infrastructure investments. I’m working on some new​ environmental equity metrics that I hope to put into practice soon.
  • Utilities must collect and publicly report data on service shutoffs and restorations.
  • Regulators must demonstrate racial, ethnic, and socioeconomic equity in inspections and enforcement actions.

Eligibility for all federal infrastructure funding must be contingent on utilities demonstrating equity in conditions, investment, and administration, or adequate progress toward that goal. Extra funding and technical assistance should be targeted at communities that suffer from significant disparities due to historical or structural disadvantages—most obviously tribal systems.

Together with the other systemic reforms I’ve proposed, this commitment to environmental justice can rebuild trust in America’s water systems and build a broad political coalition in support of investment in the nation’s most essential infrastructure.

*We’ve got more research on tribal water issues in the, er, pipeline.

Environmental injustice in rural America often come as a surprise to big city folks, who often seem to think that “rural” means “white.” Racial/ethnic disparities in water quality don’t surprise anyone who’s spent time in South Texas, Northern Arizona, or Alabama’s Black Belt.

The Bundle

Important developments in California for utility affordability

You probably need all three

California’s Public Utilities Commission (CPUC) is working on establishing methods to measure affordability for utility service. The CPUC governs ratemaking for the state’s investor-owned water, energy, and telecommunications utilities.* The idea behind the CPUC’s process is to craft sensible, valid metrics to gauge low-income households’ ability to pay for essential services.

As part of their efforts, CPUC has been reviewing the latest academic research on affordability measurement. I was involved in this process through a series of conversations with CPUC staff and a workshop in San Francisco earlier this year. It’s been fascinating to watch the CPUC grapple with this important issue, and gratifying to see principles that I’ve advanced take shape in policy.

​The room where it happens...

Comprehensive measurement

I spend a lot of time thinking, researching, and writing about water affordability; other scholars think about energy and telecom—that’s how industries and disciplines are organized. But of course, the same people who use water utilities also use electricity, gas, telephones, and Internet service. The affordability of any one of these services depends in part on the prices of all the others. So a realistic picture of utility affordability has to include all of them.

What’s particularly exciting about the CPUC’s current work is that they’re crafting a single affordability metric to capture the cost of all these utility services. That requires defining essential service levels for each service, measuring the prices for those levels of service, and estimating the ability of low-income households to pay for that bundle of services in combination. It’s an analytically daunting task.

Principles in practice

The CPUC staff took up the challenge, and crafted a smart proposal for comprehensive affordability measurement. The proposal sets essential water supply at 50 gallons per person per day (gpcd), Essential energy is set at “baseline quantities,” with end use studies underway. Telecom essential services are defined as 20 Mbps, 1024 GB/month, and 100 minutes per month. The total price of essential service for all three is the real cost of utilities.

The proposal then uses a combination of three metrics to assess affordability: the Affordability Ratio (AR), Hours at Minimum Wage (HM), and the Ability to Pay Index (API). Each of the metrics offers a different but important perspective on affordability. Here’s how the CPUC report summarizes the three metrics:

The report describes each metric in detail and discusses the ways that they can complement each other. I won’t lie—I’m pretty geeked to see the first two of those, since I introduced and have been evangelizing for them in the water sector. CPUC staff have picked up these principles and run with them.

The CPUC affordability rulemaking process is ongoing, but this staff proposal is an exciting development in utility pricing.

*The CPUC’s efforts are running in parallel with similar work by the California State Water Resources Water Board, which regulates the state’s water utilities, public and private.

Making it Work

A Kansas water utility gets affordability measurement right

And lo, there arose from the Kansas City suburbs a mighty measurement

Recently we’ve seen progress in affordability measurement, as more water utilities are using better metrics when evaluating affordability.* Last year I published a new methodology for measuring water and sewer utility affordability (AR20and HM), and followed that up with a national assessment using those metrics. AR20  is the Affordability Ratio of basic water and sewer service price divided by disposable income at the 20th percentile household income. HM  is basic water and sewer service expressed in Hours of labor at Minimum wage.These metrics seek to capture the trade-offs that low-income households must make in paying for water and sewer services. Utilities have begun to use these and other improved metrics, which is encouraging!

Too hard?

The main objection I’ve seen to the real-world use of AR20 is that it can seem too complicated. You need to know the community’s 20th percentile income and essential non-water/sewer costs of living in order to calculate AR20. But there’s no convenient, off-the-shelf source for those numbers. You have to think about economic conditions in your community.

I usually include housing, health care, taxes, food,and home energy as essential non-water/sewer costs, and I use statistical models to estimate those expenses. Statistical models are important in my research because I’m analyzing affordability across hundreds of communities.  Apparently that’s led some to think that regression models of consumer data are the only way to estimate AR20,  which can seem impossibly difficult.

Fortunately, it’s not really that hard. Since publishing my 2018 article, I’ve heard from folks in utilities large and small about efforts to use these metrics exactly as they were intended: adapting AR20  to fit local needs, calculating it with local data, and using it to shape local decisions.

WaterOne’s excellent measurement adventure

An especially encouraging case is WaterOne, a special district that provides drinking water to a population of about 425,000 in the Johnson County suburbs southwest of Kansas City. As in plenty of other utilities, WaterOne’s leaders have long been interested in the affordability of their service, but had also long used the conventional 2%MHI to gauge affordability. Dissatisfied with that nonsensical number, WaterOne’s financial planning team decided to use AR20  to assess affordability and help guide policy for their own system.

Local calibration

From WaterOne’s Affordability Ratio paper

Making AR20 work for WaterOne required adapting it to local preferences and conditions in a few ways. First, the original AR20 was calculated for water and sewer combined; since WaterOne provides only drinking water, its AR20 calculation included only water rates, not sewer rates. Second, WaterOne analyzed its own customer data and decided that 45 gallons per capita per day and a 2.6-person household were the appropriate basic water consumption level for its customers (my published studies assume a 4-person household at 50 gpcd). Third, WaterOne chose to exclude home energy from their calculation of essential non-water/sewer costs. Rather than constructing an econometric model to estimate essential non-water costs, WaterOne’s finance team used available data and guidelines from the Census, IRS, USDA, and Bureau of Labor Statistics to estimate appropriate costs for its service area.

The result was a WaterOne-specific AR20 that showed the remarkable difference between the conventional %MHI method and the more meaningful AR20. After they’d done all that work, WaterOne staff contacted me to ask for feedback. We had a terrific phone call with WaterOne managers where I offered some comments on their execution, but I didn’t have much of a critique to give—they pretty much got it right.

From analysis to decision

The results were reported with the district’s 2019 budget and written up in a white paper for WaterOne’s governing board. Crucially, the paper uses the affordability metrics to frame a discussion of goals and guidelines, not to declare WaterOne’s rates “affordable” or “unaffordable” according to some arbitrary threshold. They also warned against comparing AR20 values to my published works and to other systems’ AR20 values, since WaterOne’s AR20 is based on different assumptions and WaterOne’s values may not align with others.’

Measurement principles in practice

Complexity isn’t an excuse for crummy measurement; it’s a reason to be careful with measurement. A modicum of creativity can get you there. Want to know what low-income families pay for health insurance locally? Go to Need an estimate for local low-income housing costs? Check Ask local charitable organizations what low-income families pay for food or home energy. You don’t need a PhD or advanced econometric skill to do sound affordability analysis.

WaterOne answered the affordability measurement challenge with a thoughtful, nuanced analysis that applied community values to the best available data. Adapting AR20 for  WaterOne—WOAR20?—is a fine example of how utilities can put measurement principles into practice.

*The conventional approach to water affordability measurement (average bill as a percentage of Median Household Income (%MHI) is deeply flawed, as I’ve blogged previously. Despite its well-document problems,use of %MHI remains widespread, mainly because it’s easy and familiar.

My estimates use publicly-available Consumer Expenditure Survey data and Ordinary Least Squares regression. They’re not especially sophisticated.