The Color of Debt: The Black Neighborhoods Where Collection Suits Hit Hardest

The Color of Debt: The Black Neighborhoods Where Collection Suits Hit Hardest

Organisation: ProPublica (United States)

Publication Date: 04/08/2016

Size of team/newsroom:large


When ProPublica attempted to measure, for the first time, the prevalence of judgments stemming from debt collection lawsuits, a clear pattern emerged: they were massed in black neighborhoods. Our analysis of five years of court judgments from three metropolitan areas — St. Louis, Chicago and Newark — showed that even accounting for income, the rate of judgments was twice as high in mostly black neighborhoods as it was in mostly white ones. These findings could suggest racial bias by lenders or collectors. But we found that there is another explanation: That generations of discrimination have left black families with grossly fewer resources to draw on when they come under financial pressure. We created interactive maps to show readers the black neighborhoods suits hit hardest. The app, “The Color of Debt,” was such a powerful, immediate way to understand our findings that Missouri Attorney General Chris Koster used it in his presentation as he proposed a series of changes to state court rules to curb what he called “abusive” collection lawsuits that were saddling his constituents with illegitimate debts.

What makes this project innovative? What was its impact?

For Color of Debt, we sought to go past the basics of our statistical finding and try to eliminate other possible variables that might explain how debt collection lawsuits disproportionately affect African-American communities and to identify possible causes. We published the results of our work in a technical methodology whitepaper, as well as a more basic explanation of our work for non-technical readers. It has become a regular practice at ProPublica to publish both technical methodologies — which experts can use to scrutinize, discuss and repeat our analysis — and non-technical methodologies — which citizens can use to understand our analysis and draw their own conclusions about its validity. Although that practice is rooted in the history of data journalism, we believe we are pioneering this level of openness with our analyses.

Technologies used for this project:

R, Ruby on Rails, HTML/CSS, Javascript, Landline (our open-source SVG mapping library), Google Street View
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