A Data-Based Approach to Mitigate the Unintended Racial Inequities Associated with Municipal Fines and Fees

Project Summary

The project team is examining whether a city that uses water shut-offs and late fees to deter missed payments can achieve improved welfare and fairness for the Black and Latino communities in Portland, Oregon through a new payment model. The team has established a partnership with the Portland water bureau to implement a more equitable “ability to pay” model that will aim to reduce water shut offs across the city. SERVUS will help Portland implement a tailored debt forgiveness program correlating with each individual’s ability to pay, that can simultaneously address the municipal financial burden of unpaid debt and alleviate the individual struggle with payment compliance. To determine the ideal amount of debt forgiveness to offer, or the greatest amount that a payer would be likely to actually pay, the team will utilize machine learning and create a prediction model that could, in theory, be used by water departments across the country.

Research Question(s)

  1. Can the personalization of water bills alleviate the burden on Black and Latino/a/x ratepayers, while simultaneously having a non-negative impact on  municipal revenues? 
  2. Can the implementation of data, automation, and machine learning be used to design an objective pricing mechanism?

Actionability

  1. Redesign of policy to help low-income households pay their water bills
  2. Provide a pathway for many Black and Latino/a/x individuals to become free of government debt

Racial Equity Implications

In the wake of the COVID-19 pandemic, large U.S. cities and states experienced a surge in billions of dollars of unpaid water bills, predominantly among populations with low incomes. People of color are more likely to experience economic disadvantage due to historical and systemic discrimination, which has limited their economic opportunities. In the context of utility bills, the interplay between racial and economic disadvantage implies that people of color are disproportionately affected by high utility bills rendering them unable to pay their total balance. The city of Portland provided the research team with data on the entire population of residential water accounts between 2019-2022, and households in predominantly Black and Latino neighborhoods are at considerably higher risk of water shut-off. Debt accumulation is intricately linked with a practice called uniform pricing in economics, which means water bills are the same price for everyone. While seemingly fair, uniform pricing is regressive as it inadvertently places a heavier burden on Black and Latino/a/x people who will pay proportionately more of their income than individuals with higher incomes.

Outcomes

Health outcomes: less stress and anxiety, and better physical health 

Other: payment delinquencies, water shut offs, average water bill amount among Black and Latino/a/x households

Methodology

The team is running a randomized field experiment and combining the data with secondary sources to test and understand the impact of fee forgiveness on the propensity to pay water bills. This will allow the team to design a personalized payment program by combining the field experiment data with internal and external sources that will simultaneously expand the number of Black and Latino/a/x individuals who receive a discount and reduce their shut-offs and missed payments. The team will then use the program to optimize the rates and discounts, with plans to have it implemented by any local government looking to reduce water shut-offs. The study population will consist of all single-family accounts across the city due to ease of water billing data because multi-family properties often pay directly to their landlord and only have one water meter per building.


Faucet with running water into a sink
Grantee and Partner organizations

SERVUS
University of Chicago
The Government Finance Officers Association, (GFOA)
City of Portland Oregon, Portland Water Bureau

Grant status
In Progress
Principal investigators
Bryan Glenn, President of SERVUS
Jean-Pierre H. Dubé, PhD
Start date
Award amount
$568,532
Duration
27 months

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