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Bay Area Borrowers

Quantifying and Addressing the Student Debt Crisis in the Bay Area

The Federal Reserve Bank of San Francisco estimated in 2019 that the Bay Area is home to approximately 735,000 student loan borrowers who owe a collective $26.6 billion in student debt. For these borrowers, the communities they live in, and the Bay Area as a whole, the prevalence and impacts of this unprecedented burden amount to a crisis.

But the fallout of this crisis is not borne evenly. Instead, low-income communities and communities of color have been forced to take on a disproportionate share of this debt, are more likely to fall behind in repayment, and are much more frequently the targets of predatory practices from student loan companies.

Moreover, there is evidence that the affordable repayment options that borrowers are entitled to under the law are not reaching those most in need. We know that Income-Driven Repayment (IDR) works—the government’s own data indicates that borrowers repaying through an income-based plan are 28 times less likely to default. And yet, every single day, borrowers in communities of color across the Bay Area continue to share an unequal burden of the worst outcomes of the student debt crisis.

The following map offers insight at the county level on the situation of unmet need for IDR in the Bay Area. The map includes detail on new IDR demand attributable to the economic fallout of Covid-19, a crisis that—like student debt—has had a disproportionate impact on communities of color. Further methodological detail for this map is available below. [1]

To address the student debt crisis head-on while acknowledging its role as a civil rights crisis, it is critical for interventions to be specifically targeted to focus first on the hardest-hit minority communities. Accordingly, the following visualization identifies six Bay Area zip codes as student debt “distress hotspots” for people of color and offers tools to compare those hotspots to other Bay Area zip codes across various demographic and student debt-related measures. These hotspots were selected because they were among the top ten Bay Area zip codes for all three of the key borrower distress metrics that the Federal Reserve Bank of San Francisco identified in its 2019 report on Bay Area student debt: delinquency, default, and cumulative default since 2003. Further methodological detail for this visualization is available below. [2]

Note that certain zip codes are excluded due to data limitations.

The six student debt distress hotspots identified here are:

  • 94589: American Canyon
  • 94509: Antioch
  • 94621: East Oakland – Coliseum
  • 94124: Bayview-Hunters Point
  • 94130: Treasure Island
  • 94590: Vallejo

These two charts combine to articulate a clear message: unmet IDR need is surging in the Bay Area, and there are key hotspots where efforts to meet that need should be focused.


[1] This map estimates the county-wide and Bay Area-wide instance on unmet IDR need by estimating the population of IDR-eligible student loan borrowers not making payments in an IDR plan before the onset of the coronavirus crisis, and then by estimating the population of IDR-eligible student loan borrowers made newly unemployed by the coronavirus pandemic.

First, the map calculates the number of borrowers eligible for IDR in each county that were not making payments in an IDR plan before the coronavirus crisis began. To begin, estimates of each Bay Area county’s adult population are calculated based on estimates of each county’s overall population and the reported proportion of each county’s population aged 18+ from the Census Bureau. The number of student loan borrowers in each county is then calculated by multiplying the number of adults in each county by the proportion of adults in each county with student debt. The proportion of adults with student debt is assumed as a single ratio for the entire Bay Area. This field can be toggled by the viewer, but the default value (reflecting a 2019 estimate from the Federal Reserve Bank of San Francisco) is 12.2%. The number of student loan borrowers eligible for IDR is then calculated by multiplying the number of borrowers in each county by the proportion of borrowers generally eligible for IDR. The proportion of borrowers eligible for IDR is assumed as a single ratio for the entire Bay Area. This field can be toggled by the viewer, but the default value (reflecting a 2015 estimate from the GAO) is 51.0%. Note that this proportion is applied to all student loan borrowers, including borrowers with only private student loans. Because private student loans are generally taken on as a supplement to federal student loans, the number of borrowers with only private student loans is likely extremely small. The number of student loan borrowers in each county successfully utilizing IDR pre-Covid is then calculated by multiplying the number of borrowers in each county by the proportion of borrowers likely to have successfully accessed IDR. This is assumed as a single ratio for the entire Bay Area. This field can be toggled by the viewer, but the default value (reflecting SBPC analysis of data from the Department of Education) is set at 51.8%. Finally, the number of borrowers with unmet IDR need is calculated by subtracting the number of borrowers in each county eligible for IDR but not making payments in an IDR plan from the overall population of borrowers in each county eligible for IDR.

Next, the map calculates the number of newly unemployed borrowers in each county in the aftermath of the coronavirus crisis. It is assumed that all such borrowers are now eligible for IDR. To begin, data from the Federal Reserve Bank of St. Louis on the number of unemployed people in each Bay Area county in February 2020 are subtracted from data from the same source on the number of unemployed people in each Bay Area county in June 2020 to estimate an overall number of people newly unemployed due to the coronavirus crisis. For each county, this number is then multiplied by the proportion of newly unemployed people likely to have student loan debt (a proportion set using the same toggle for the proportion of adults with student loan debt mentioned above, which defaults at 12.2%). All of these borrowers are considered newly eligible for IDR. Given the long history of borrowers struggling to access affordable repayment options in the face of a crisis, it is assumed that all of these newly eligible borrowers are likely to benefit from assistance accessing IDR.

The total number of eligible borrowers not making payments in an IDR plan pre-Covid is then added to the total number of borrowers made newly eligible for IDR since the pandemic’s outbreak to arrive at an overall number of borrowers who need help.

[2] Student debt distress hotspots were selected for having been identified in the Federal Reserve Bank of San Francisco’s 2019 report on student debt among the Bay Area zip codes with the highest percent of student loan borrowers 90+ days delinquent, the highest percent of student loan in default, and the highest percent of student loan borrowers who have defaulted at least once since 2003 (see figures 9, 12, and 15). They were also selected for their relatively substantial populations of Black and Latinx borrowers. Demographic and borrower outcome data included in the visualization at the neighborhood and regional level are sourced directly from the FRBSF report and from associated interactive maps published by FRBSF. Data on unmet IDR need at the zip code level is calculated based on information from various data sources. First, data on the population of each zip code aged 20 and older (an age barrier used given data limitations) is aggregated from UnitedStatesZipCodes.org. This is taken as the number of adults in each zip code. Data on the proportion of adults in each zip code with student debt is then sourced from the 2019 FRBSF report and associated interactive maps (it is not a field that can be toggled in this visualization), and that proportion is multiplied by the number of adults in each zip code to produce an estimate of the number of borrowers in each zip code. As above, the number of student loan borrowers eligible for IDR is then calculated by multiplying the number of borrowers in each zip code by the proportion of borrowers eligible for IDR, assumed as a single value for the entire Bay Area. This field can be toggled by the viewer, but the default value (reflecting the same 2015 estimate from the GAO as above) is 51.0%. The number of student loan borrowers in each zip code successfully utilizing IDR is then calculated by multiplying the number of borrowers in each zip code by the proportion of borrowers likely to have successfully accessed IDR, assumed as a single value for the entire Bay Area. This field can be toggled by the viewer, but the default value (reflecting SBPC analysis of the same data from the Department of Education) is set at 51.8%. Finally, the number of borrowers with unmet IDR need is calculated by subtracting the number of borrowers in each zip code eligible for IDR but not making payments in an IDR plan from the overall population of borrowers in each zip code eligible for IDR. That number is summed across the six zip codes and displayed at the bottom as a grand total. Note that, unlike the county-level analysis above, the present zip code-level analysis does not additionally consider borrowers made newly unemployed in the wake of the coronavirus crisis due to data limitations.

The average number of small businesses per 1,000 adults is calculated using data from the Census Bureau on the total number of establishments in each county with 250 employees or fewer (per the definition of a small business set forth by the Small Business Administration) and the number of adults in each county, constructed as described above.

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