How the United States’s legacy of slavery influences unequal credit scores and what we can do about it

The credit scoring system in the United States, particularly the FICO score, significantly impacts individuals’ access to affordable financing, housing, and employment opportunities. In this blog article, recent ISS MA graduate Conor Farrell shows that though deemed “colour-blind,” the model inadvertently perpetuates racial disparities rooted in historical injustices, particularly slavery. Reforms, including AI-enabled credit scoring and policy changes like excluding medical debt from credit considerations, are essential to address these inequities and break the cycle of intergenerational poverty and racial inequality, he writes.

Acting as the economic gatekeeper in the United States, the credit score, most commonly known in its FICO form and that which is referenced throughout this blog article, scores individuals on a range from 300 to 850, with lower scores representing greater risk of default or missing payments, and higher scores the opposite. Within the FICO form, an individual’s score is primarily calculated across five components, each with an approximate weight (1):

• Payment history (35%):an analysis of timely payments on outstanding debts and the severity of late payments (i.e., 30 or 60+ days late).
• Amounts owed (30%):calculated as a percentage of total credit available and amount currently employed with a rule of thumb to keep this percentage lower than 10%.
• Length of credit (15%):calculated as the amount of time an individual has accessed credit.
• New credit (10%):how recently the individual opened a new account.
• Type of credit(10%): considers the different revolving and installment loans you have active.

Poor credit scores almost always mean far fewer lending options and far more expensive options when available. Putting this into hard numbers, according to data from the Consumer Financial Protection Bureau, a change from a “subprime” 640 credit score to a 740 credit score in one example might allow a potential home buyer to access a mortgage interest rate as low as 5.75% instead of 7.625%, resulting in almost $90,000 in lower interest costs over the life of a thirty-year loan for the same house (2).

The five factors determining the level of risk are claimed not to consider demographic characteristics. However, the map of average percentages of county populations with subprime credit scores in the United States (Figure 1) shows the stark differences between the American south and north.

The percentage of people per county with subprime credit scores in the United States. Orange indicates counties with fewer subprime credit scores and blue counties with more subprime credit scores. The red line on the map is the Mason-Dixon line and Ohio River extension, the traditional division between northern and southern states. Source: (Equifax and Federal Reserve Bank of New York, 2024).

In particular, given that over 60% of the total African American population resides in the American south, as we further break out the average credit score by race, it is therefore unsurprising to find a mirrored divergence in subprime credit scores, particularly between Black and White Americans (Figure 2).

Seen across race, there is a significant divergence between the average credit scores of White Americans (734) and Black Americans (677) with the average score for Black Americans averaging near the subprime threshold. Source: (Dual Payments, 2020)

This brings into question whether these differences can be explained by other factors not accounted for by the model. Given that credit scores play such a significant factor in one’s ability to obtain affordable finance and in certain cases may impact where one can live or rent or inhibit one’s ability to obtain employment opportunities, how is it then that such a “colour-blind” model appears to be disproportionately impacting Black Americans? In this blog article, I show that a historical institution fundamental to economic development of the United States and the racial and geographic divisions still present in the present — the institution of slavery — can provide an alternative explanation for injustices in the credit score system.

A quick history lesson

While present for the initial two centuries of colonial expansion in North America, slavery rapidly grew in the early 1800s as the United States solidified into a nation-state (Figure 3). Ultimately abolished after a bloody civil war, the history of the United States since slavery’s abolition in 1865 has been characterized by a various forms of institutionalized and explicit forms of race-based discrimination and exclusion, including the sharecropping system, housing discrimination in the form of redlining, and segregation in the education system. While each of these systems and institutions can each be understood to be extensions of the historically unequal forms of development in the United States with their own unique impact of the historical inequalities in their respective period of development, my research as part of my MA thesis set out to determine how is it that the Figure 3 and that of Figure 1 bear strikingly similarities to one another.

Figure 3 presents the relative slave population as a share of the total population in 1860. Counties with dark yellow shades have the largest slave population relative to the total county population, while light blue are the counties with lower slave populations. Counties in dark blue are either unreported or have zero slave population according to the census. It is important to note that this data may not be fully reflective of the actual slave population, but it is the best official data that is available. Source: (United States Census Bureau, 1864)

Current models are far too simplistic

Hypothesizing that the current model claims to be colour-blind in its analysis and that its simplistic model focuses solely on the present-day actions of an individual without acknowledging the persistent inequalities already present within our society, my research analysed 1860 census data alongside contemporary panel data from 2014–2021 through an instrumental variable specification. Through the most stringent specification applied,  I found a 10-percentage-point increase in the relative slave population of a county in 1860 results estimated average effect of 0.791 percentage point increase in the percentage of the current population with a subprime credit score in 2021, holding all else constant; a result that remains highly significant even in the most stringent model employed (3). Put simply, counties that had higher proportions of enslaved people in 1860 tend to have a higher percentage of residents with poor credit scores today, even after considering other factors that might influence this outcome.

Given the consistent but varying forms of discrimination experienced by Black Americans since the abolition of slavery, I also found that relative slave populations influence different channels’ persistence including through an education system that requires Black Americans to take on higher levels of debt to obtain the same education, only to earn consistently lower wages than their White counterparts. Unable to generate as much wealth as their White counterparts, Black Americans are often far more burdened by greater amounts of relative debt, limiting their ability to obtain larger assets like homes, which are so vital in generating and retaining intergenerational wealth (4).

Such findings demonstrate that the current credit scoring model, one that claims to be unbiased and does not explicitly penalize individuals based on race, fails to account for the multitude of contextual historical factors that continue to privilege certain groups while barring others from accessing the same system. Contemporary economic inequalities may be influenced by the lingering effects of historical factors emphasizing the complex interaction between race, inequality, historical factors, and contemporary economic outcomes.

As such, it also provides clear evidence that policies that do not adequately consider historical inequalities existing and persistent in the system may in fact serve only to continue to perpetuate such inequalities. Particularly in the context of the credit scoring model in the United States and similar systems of economic gatekeeping, not addressing the existing inequalities through the model restricts an individual’s ability to access affordable financing, housing, or decent employment prospects.

Significant reforms are the only way to address persistent injustices

The rapid introduction of artificial intelligence (AI) holds some promise in this context. A greater number of AI-enabled credit scoring algorithms are being tested that could vastly expand the number of variables influencing a credit score. This will hopefully allow a far more comprehensive picture of an individual current financial health. Models with a greater number of variables would increase the diversity of scoring criteria and de-emphasize the potentially discriminatory data points currently prioritized in the FICO model. The recent decision by the Biden administration to remove medical debt as a variable influencing credit scores also helps to address the burden of emergency care costs that can be detrimental to an individual’s ability to meet their financial responsibilities (5).

However, given that poor credit scores have the potential to make financing almost inaccessible for low- and middle-income individuals, additional social safety nets must be considered to ensure that drastic emergency expenses do not create cycles of intergenerational poverty resulting from poor credit scores. Without significant reform, the current credit scoring model will continue to punish low-income families, forcing them to take on more expensive financing to obtain the same assets as their neighbours, inhibit access to home ownership, make higher education less accessible without taking on larger debt, and continue to ensure a cycle of poverty that perpetuates racial inequalities within the United States.

Footnotes

(1) Pritchard, J., (2021) How the FICO Credit Score Is Composed. Available at: https://www.thebalancemoney.com/fico-credit-score-315552 (Accessed 28 July 2024).

(2) Consumer Financial Protection Bureau (CFPB), (2023). Explore interest rates. Available at: https://www.consumerfinance.gov/owning-a-home/explore-rates/ (Accessed 28 July 2024).

(3) Farrell, C. (2024). The lingering legacy of slavery: historical injustices and credit scores in the United States. International Institute of Social Studies (ISS). ISS working papers. General series No. 723

(4) Jones, J., & Neelakantan, U. (2022). How Big Is the Inheritance Gap Between Black and White Families? Richmond: Federal Reserve Bank of Richmond Economic Brief.

(5) The Consumer Financial Protection Bureau (CFPB) (2023). CFPB Kicks Off Rulemaking to Remove Medical Bills from Credit Reports. Washington, D.C.: CFPB. Accessed 4 July 2024.

Consumer Financial Protection Bureau (CFPB) (2023). CFPB Kicks Off Rulemaking to Remove Medical Bills from Credit Reports. Washington, D.C.: CFPB. Accessed 4 July 2024.

Consumer Financial Protection Bureau (2024). Explore Interest Rates. Retrieved from Consumer Financial Protection Bureau: https://www.consumerfinance.gov/owning-a-home/explore-rates/. Accessed 5 July 2024.

Dual Payments. (2020). Credit Score. Retrieved from Dual Payments: https://dualpayments.com/statistics/credit-score/#race. Accessed 28 July 2024.

Equifax and Federal Reserve Bank of New York, Equifax Subprime Credit Population, retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/EQFXSUBPRIME036061. Accessed 28 July 2024.

Farrell, C. (2024). The lingering legacy of slavery: historical injustices and credit scores in the United States. International Institute of Social Studies (ISS). ISS working papers. General series No. 723

Jones, J., & Neelakantan, U. (2022). How Big Is the Inheritance Gap Between Black and White Families? Richmond: Federal Reserve Bank of Richmond Economic Brief.

United States Census Bureau, (1864). 1860 Census: Agriculture of the United States, Washington: United States Census Bureau. Available at: https://www.census.gov/library/publications/1864/dec/1860b.html

About the author: Conor Farrell

Conor Farrell is a graduate of the International Institute of Social Studies where he majored in Economics of Development. He is passionate about the intersection of history and contemporary economic outcomes understanding that history is not a set of fixed beginnings and ends, but continues to live on through the institutions we have created to shape our societies and influence our future.

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