ALL DEBT YOUNG ADULTS c MEDICAL STUDENT AUTO/RETAIL
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Click on the variables below to see how debt affects communities across the US a

a White communities and communities of color are based on zip codes where most residents are white (at least 50 percent of the population is white) or most residents are people of color (at least 50 percent of the population is of color).

b Not available because sample size is too small.

ALL DEBT MEDICAL STUDENT AUTO/RETAIL
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    a White communities and communities of color are based on zip codes where most residents are white (at least 50 percent of the population is white) or most residents are people of color (at least 50 percent of the population is of color).

    b Not available because sample size is too small.

    About the Data

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    Appendix

    This data tool was first published in 2017 and has been regularly updated since. In September 2024, we updated the data and changed the threshold for white communities and communities of color from at least 60 percent of a given zip code’s population to at least 50 percent. As a result, the data for white communities and communities of color are not directly comparable with prior data releases. Starting in 2022, the three nationwide credit-reporting companies made significant changes to medical debt reporting. These changes reduced the share of people with medical debt in collections reported on their credit records but not necessarily the share of people with medical debt in collections.

    This dashboard contains information derived from a 4 percent nationally representative panel of deidentified, consumer-level records from a major credit bureau. The credit bureau data are from August 2023 and contain more than 10 million records. We also incorporate estimates from summary tables of the US Census Bureau’s American Community Survey (ACS). We use ACS one-year estimates (2022) where possible, but for areas with smaller populations and for metrics that incorporate zip code–level information, we use the ACS five-year estimates (2018‒22). We report credit bureau but not ACS data at the county level for Connecticut because the Census Bureau implemented significant changes to county boundaries for Connecticut in 2022 and our credit bureau data provider has not yet updated these boundaries. We also incorporate estimates from the 2020 Decennial Census.

    We define people of color as those who are African American, Hispanic, Asian or Pacific Islander, American Indian or Alaska Native, another race other than white, or multiracial. Debt in collections includes past-due credit lines that have been closed and charged-off on the creditor’s books as well as unpaid bills reported to the credit bureau that the creditor is attempting to collect. For example, credit card accounts enter collections once they are 180 days past due. Retail installment loans are retail purchases with installment terms—for example, a loan from a furniture store to buy a couch. The map breaks are determined using the Jenks Natural Breaks method.

    Please contact [email protected] for more information about this dashboard.

    Additional Resources

    Project Credits

    This data dashboard was funded by the Annie E. Casey Foundation, with additional support from the Ford Foundation in 2017. We are grateful to them and to all our funders, who make it possible for Urban to advance its mission. The views expressed are those of the authors and should not be attributed to the Urban Institute, its trustees, or its funders. Funders do not determine research findings or the insights and recommendations of our experts. More information on our funding principles is available here. Read our terms of service here.

    Caroline Ratcliffe and Signe-Mary McKernan had the original vision for Debt in America. We are grateful to Caroline Ratcliffe, Caleb Quakenbush, Cary Lou, Alexander Carther, Jennifer Andre, and Hannah Hassani for their work on previous versions of this dashboard. We thank John van Alst, Henry Chen, and Chris Kukla for helpful counsel in finalizing the auto loan variables and developing the narrative. We also thank Don Baylor and Velvet Bryant (previously) and Irene Lee of the Annie E. Casey Foundation; John Howat, Chi Chi Wu, and Michael Best of the National Consumer Law Center; Heidi Goldberg of the National League of Cities; and Sue Berkowitz of SC Appleseed for their input.