Measuring racial disparities in consumer debt lawsuits

By David McClendon

Data • Public Policy • August 26, 2024

Here at January Advisors, we’ve been expanding our research on civil courts to include not just evictions but also debt collection lawsuits. Consumer debt lawsuits, as they are also known, are among the most common cases in civil courts, often involving unpaid medical bills, credit card defaults, and overdue utility bills. Yet, their impact extends far beyond the legal system. Despite typically involving low-dollar amounts, around $1,500, on average, they can lead to severe financial strain for working families already struggling to make ends meet. As these lawsuits increasingly dominate court dockets, they expose deeper vulnerabilities within our economic system, particularly for communities of color.

Our recent collaboration with The Pew Charitable Trusts’ Courts & Communities Project has focused on using court data to understand consumer debt collection lawsuits across various jurisdictions, including Michigan, Minnesota, Chattanooga, TN, and Oregon. A central question in our research has been how these lawsuits impact BIPOC communities. Decades of research has found racial disparities in the criminal justice system, with Black and Latino Americans facing higher rates of arrests and incarceration compared to Non-Hispanic white Americans. Recent studies have identified a disproportionate impact of civil lawsuits as well, such as evictions and consumer debt, on Black communities.

Measuring racial disparities in civil cases is complex. Unlike criminal courts, civil courts typically don’t record the racial or ethnic backgrounds of defendants. Simply mandating the collection of this data, however, isn’t a straightforward solution. Plaintiffs in civil cases, such as those involving consumer debt collection, are usually unaware of the defendant’s race or ethnicity. Furthermore, in many civil cases, defendants do not appear in court or participate in their cases due to confusing court processes, making it impossible to directly gather such information.

This blog post explores the methodologies we have used to estimate racial and ethnic disparities in debt collection lawsuits. We’ll discuss two different approaches, highlighting their advantages, limitations, and data needs. To illustrate our findings, we’ll compare cases from two states: Michigan and Minnesota, offering insights into how these disparities manifest in different regional contexts.

Approach #1: Neighborhood Demographics

A key method for estimating racial and ethnic disparities in civil cases is to analyze data at the neighborhood level, often defined by census tracts. This involves integrating American Community Survey data on racial and ethnic composition with case filings or outcomes in these areas.

In our Michigan report, we focused on disparities in the volume of consumer debt collection cases filed in Black-majority and white-majority neighborhoods. In order to compare neighborhoods to one another, we calculated the case filing rate for every census tract in Michigan (number of lawsuits filed per 100 adults), and classified neighborhoods based on the racial breakdown of the census tract. We also incorporated neighborhood median household income, which is also associated with case filing rates, to look at the interplay of race and income.

We found that neighborhoods with a Black majority faced double the number of cases compared to Non-Hispanic white majority neighborhoods. Intriguingly, race emerged as a more significant factor than neighborhood income in these cases. Regardless of income level, Black-majority neighborhoods consistently showed higher filing rates than Non-Hispanic white-majority neighborhoods with similar income levels.

For this analysis, the crucial data point is the defendant’s address. Many states and localities we’ve worked with provide this information through case lookup systems or make it available to researchers upon request. However, it’s important to note that not all civil court systems collect or publish this information. Furthermore, linking address information with census tract demographics requires geocoding addresses and performing a spatial join of the two datasets.

Analyzing neighborhood-level data offers several benefits. Primarily, it highlights the community-wide impact of debt collection cases, rather than focusing on the individual. This approach circumvents the risk of victim-blaming, a common issue in data-driven studies of racial disparities. By focusing on neighborhoods, we avoid unfairly attributing responsibility to minority groups (e.g., for taking on debt or not paying bills) and instead promote a broader understanding of the structural factors driving these disparities and the community-wide impact.

However, this method isn’t without limitations. In areas where minority groups are less concentrated or less numerous, such as certain regions in Michigan, the approach may be less effective. For example, there were few Hispanic-majority, Asian-majority, or Native American-majority neighborhoods in Michigan, and some areas had no clear majority group. Similarly, in Minnesota, the lack of diversity in many areas, with only a few majority Black or African American census tracts mostly in the Twin Cities, limits the applicability of this neighborhood-level analysis.

Approach #2: Estimating race-ethnicity of defendants

In our Minnesota study on debt collection lawsuits, we implemented a different method. This approach combines the defendant’s name and address with a specialized algorithm to estimate the likelihood that a person belongs to one of five broad race-ethnic categories: Non-Hispanic white, Black/African American, Hispanic/Latino, Asian, and “other” race-ethnic groups.

The technique we used is called Bayesian Improved Surname Geocoding (BISG). Originally developed for estimating race-ethnicity in voter and hospital patient records, BISG uses Census Bureau data that links surnames (appearing at least 100 times in the census) with race-ethnicity. It also incorporates voter data from six states where race-ethnicity is self-reported. This algorithm makes an educated guess about a person’s race-ethnic identity, considering both their surname and the racial-ethnic makeup of their neighborhood.

It’s crucial to understand that BISG doesn’t definitively determine a person’s race-ethnicity. Instead, it provides probabilities of belonging to each category. For instance, if the algorithm estimates someone has a 60% probability of being Non-Hispanic white and a 40% probability of being Hispanic/Latino, we don’t just label them as Non-Hispanic white. We split the case into two parts: 60% attributed to Non-Hispanic white and 40% to Hispanic/Latino. This method helps us examine broader patterns without oversimplifying individuals’ identities.

This approach proved effective in Minnesota, allowing us to explore disparities among Black, Hispanic, Latino, and Asian populations. The neighborhood method, focusing on majority-minority areas, wouldn’t have been as useful in Minnesota due to the lack of census tracts dominated by these groups.

In our analysis, we found that Black and Latino residents faced 2-3 times more consumer debt lawsuits than Non-Hispanic white residents across all income levels, while Asian Minnesotans had similar rates to Non-Hispanic white residents.

However, BISG has its limitations. Notably, it struggles to accurately estimate Native American identities and other smaller race-ethnic groups. This is due, in part, to the variability of Native American names across regions and the limited representation of these names in the datasets BISG uses. This limitation was particularly significant in Minnesota, where Native tribes form a substantial minority population.

We also used this approach in our recent analysis of consumer debt collection lawsuits in Oregon. We found a similar pattern of Black Oregonians having disproportionate filing rates relative to their neighbors across all income levels, although the disparities were more muted than in Minnesota.

Why race-ethnic disparities in debt collection lawsuits matter

Our studies in Michigan, Minnesota, and Oregon lead to a similar, concerning conclusion: Black and Latino consumers are more likely to face debt collection lawsuits than their Non-Hispanic white counterparts, and this holds true even in wealthier communities.

While the data clearly show these disparities, they don’t explain why they exist. Other research, however, provides some clues. The legacies of slavery and racial discrimination have led to marked income and wealth disparities between Black Americans and their Non-Hispanic white neighbors. These underlying inequalities contribute to higher rates of consumer debts, defaults, and lawsuits among Black Americans. Predatory lending practices aimed at these communities, especially in low-income neighborhoods, also play an important role.

Of course, the courts and judges who hear these cases typically have little control over these larger historical and social forces. Yet, there are good reasons why courts should care about – and measure – racial disparities in case filings and outcomes:

1. Fairness. Ensuring that all individuals are treated equally under the law is a foundational principle that must be upheld to maintain the integrity of the judicial system. Recognizing racial disparities in debt lawsuits helps highlight systemic biases and points of unequal access to justice for minority communities.
2. Trust in the courts. It is essential that the courts are perceived as fair and impartial to sustain public trust and confidence. Racial disparities in these types of civil cases can erode trust in the courts and breed cynicism over time among affected communities.
3. Social and economic justice. Racial disparities in legal outcomes can perpetuate poverty and exclusion. Whether courts help mitigate these disparities or unintentionally reinforce them has significant implications for the long-term economic and social health of these communities. Addressing these issues is essential for advancing social and economic justice.
4. Catalyst for change. Tracking racial disparities in legal processes can spark systemic change. Recognizing these disparities could lead to broader reforms within courts and related institutions, including reevaluating how cases are brought to court, scrutinizing debt collection practices, and strengthening legal protections for defendants.

Measuring and tracking racial disparities in consumer debt collection lawsuits is hard but vital work for both the courts and the research community. These efforts are crucial for exposing systemic biases and ensuring fairness in our legal system. By shining a light on these disparities, we can take meaningful steps toward greater equity, building a justice system that truly serves all communities.