City of Dallas Burden Model
Analysis of Financial Burden in the City of Dallas, as part of the Cities Addressing Fines and Fees Equitably (CAFFE) Initiative
Background
The City of Dallas, as part of their participation in the Cities Addressing Fines and Fees Equitably (CAFFE) Initiative cohort, addressed disparities of Dallas Animal Services (DAS) Pet Animal at Large (loose) Fines and Fees associated with pet care civil citations without compromising public safety. Through a dedicated city team consisting of the Office of Equity and Inclusion, the Office of Community Care & Empowerment, and Dallas Animal Services, $12,513.10 of fines and fees debt were reduced for residents, and a consultant was additionally procured to support policy analysis with regards to alternatives to paying and to address disparities in assessing fines and fess through policy recommendations.
Supporting this effort, the National League of Cities utilized public Census data and a financial burden model to identify key characteristics of those who would be disproportionately impacted by fines/fees and therefore benefit the most from reform. The data set used in this analysis includes multiple variables on social, economic, housing, and demographic characteristics of the U.S. population. By conducting this analysis, the National League of Cities uses a data driven approach to paint the picture of financially burdened households and both validate and challenge existing assumptions.
Methodology
Utilizing the 2018-2022 5-Year American Community Survey (ACS) Public Use Micro Data Sample (PUMs), we are able to calculate the disposable income of households by subtracting housing and utilities costs from the household income. Based on the ACS, we utilized the following variables for calculating housing and utilities costs: Condo Fees, Electricity, Fuel, Gas, Home Insurance, Mobile Home, Mortgage, Property, Rent, Taxes, Water.
The City of Dallas team identified that the average debt from fines/fees that they helped relieve in their pilot program was $782.07. By dividing this amount by the household disposable income for each unit in the dataset, we were able to calculate the financial burden as a % of the monthly disposable income.
We can then sort houses into 5 cohorts based on the percentage. Households in the highest range (80%-100%) are defined as "Extremely Burdened".
After identifying the cohorts, we then analyze the differences in various demographic and economic indicators available to us in the PUMs dataset between the "Extreme Burden" group and the rest of the population.
Furthermore, the PUMS dataset is broken down into areas (PUMAs) that roughly represent no more than 100,000 individuals. The municipal boundaries of the City of Dallas overlap with 21 PUMAs as shown in the map below.
Findings
Utilizing the burden model analysis, 5% of households in Dallas would be extremely burdened and unable to pay this fee out of pocket.
Who could these residents be? The following charts help shed some light on this population.
Distribution of Household Burden by PUMA
- Over half of the burdened households are concentrated in 5 areas
- The remaining 19 PUMAs each have less than 5% of the burdened population
The Dallas team identified areas south of I-30 as an area of concern - this is highlighted with almost 20% of the burdened households in the Southeast PUMA.
Extreme Burdened Households have incomes that are almost 4.5x below the average for all households.
Housing and utilities costs make up 60% of an extremely burdened household's monthly income compared to 20% for the average household.
Renters are over-represented in the burdened population
High cost of housing and rent increases in recent years have reduced household disposable incomes.
Households with a single income are much more vulnerable to being overly financially burdened by a fine/fee.
While Male and Female is combined for "Single Parent Household", Single Mothers make up 30% of the burdened households - the single largest category.
Both Seniors and Younger individuals are over-represented in the burdened population
Racial Breakdown
The share of Black or African American alone in the burdened population is slightly below double to their share of the total population
Burdened households are more likely to have limited resources at home, but also may have difficulties accessing them.
The ACS defines "Limited English Speaking Household" as: No one in the household 14 or over speaks English only or speaks English 'very well'.
Without access to internet at home (either through a phone plan or internet service provider), families have to rely on getting to public spaces that offer wifi, like libraries.
Language Access
Households with Spanish as the primary language make up 33% of the Burdened population compared to 27% of all individuals.
Vehicle Access
A plurality of households with 48% in the burdened population have only 1 vehicle, and 20% have no vehicle.
Burdened Individuals are less likely to hold a college degree
The "Some College, no degree" population may also be burdened with student debt.
Top Occupations of Burdened Individuals
Other major employment categories are:
- Customer Service Reps
- Oil and Gas Industry - Derrick, Rotary Drill and Service Unit Operators
Conclusions
In Dallas, the burden model helped to illustrate that over half of the burdened households are concentrated in 5 out of 21 statistical areas around the city. The team identified areas south of I-30 in the southwest of the city as an area of concern for their programming and the model highlighted 20% of the burdened households concentrated in the same area.
The model also highlights some key characteristics of the burdened population such as:
- Extreme burdened households have monthly incomes 4.5x below the average and their housing and utilities costs take up 60% of that income
- Renters, Single Family Households, and individuals without college degrees are over-represented in the burdened population
- Burdened households are more likely to have limited resources at home, and access to public resources may be hampered due to language barriers, lack of internet at home and fewer vehicles in the household
This highlights the need for further policy analysis with regards to alternatives to paying and to address disparities in assessing fines and fess. Without these interventions to support households, a fine could potentially trigger a cycle of debt. Adjusting fines, providing financial literacy services, and establishing payment plans to reflect ability to pay represents an equitable solution for residents who would otherwise be financially burdened.
