The COVID-19 pandemic spotlighted the ever-increasing amount and crushing effects of student debt, including debts owed directly to postsecondary institutions. In an earlier report, Solving Stranded Credits, we estimated that roughly 6.6 million students owe over $15 billion in unpaid balances to colleges and universities in the United States. The weight of institutional debt can leave students feeling defeated, forcing many to avoid pursuing postsecondary education altogether. On a national scale, these debts prohibit millions of students from gaining meaningful employment and economic success, a burden that disproportionately falls on students of color and lower-income students.        

Thankfully, recent federal stimulus efforts aim to alleviate some of this burden for students, primarily through the Higher Education Emergency Relief Fund (HEERF).* These funds provided colleges and universities with three avenues of financial support: funds to keep institutional operations afloat, funds to provide students with extra support, and an explicit allowance to apply a portion of the institution’s funds to clear outstanding student balances. News reports from last summer (about South Texas College and the Connecticut state college system, for instance) indicate that institutions have taken advantage of this allowance to forgive debts for as many as 700,000 students, allowing them to continue their education, re-enroll, or graduate debt-free. 

The provision of these relief funds creates an interesting opportunity for researchers to observe relationships between the application of debt forgiveness and students’ subsequent academic outcomes: Does debt forgiveness improve student persistence? Does debt forgiveness improve student transfer rates and transfer experiences? The first step in answering these, and other, research questions is to understand the scope of these HEERF-funded programs, both the number of institutions deploying them and the characteristics of the programs themselves. 

The US Department of Education currently collects this information through their Education Stabilization Fund reporting tool. Practitioners and policymakers can view institution-specific information on the amount of HEERF dollars used relative to how much each school was awarded, the institution’s enrollment statistics, and characteristics of students receiving emergency financial aid grants. However, to date, no comprehensive database exists related to the specific usage of HEERF dollars for debt forgiveness, though the US Department of Education intends to administer a survey in Spring 2022 to collect this information.

Until more comprehensive data exists, our team set out to collect and organize publicly-available information on a sample of debt relief programs to gain preliminary insight into how these programs are structured, which types of institutions offer them, and how many and which types of students may benefit. To broaden our understanding of these programs, we did not limit our search to just HEERF-funded programs, since many programs that predated HEERF are similarly structured. However, our analysis and the insights we draw are restricted to HEERF-funded programs—looking at these specific programs can inform future research efforts and give additional context to emerging policies and programs addressing debt forgiveness. In this post, we briefly describe our methods for collecting these data and trends we see in the characteristics of the institutions and their programs. 

Our Approach

To capture information on HEERF-funded debt forgiveness programs, we primarily relied on Google searches, news reports, and Ithaka S+R’s past research on debt forgiveness programs.** Between September 1, 2021 and October 26, 2021, we identified 33 debt forgiveness programs across 64 institutions (some of the programs spanned multiple institutions such as CUNY’s 21 campuses and the Connecticut State University System, which includes its 12 community colleges). We know this is but a small subset of the existing debt forgiveness programs, both those that pre-dated HEERF and those using HEERF funds. Our search only included formalized programs, and thus excludes any institutions using HEERF funding to forgive outstanding debt in a less systematic or public way.

For each of the 33 programs, we collected information on the characteristics of the programs, such as the total amount of funding allocated to debt forgiveness, the amount of debt forgiven (in some cases where institutions reported clearing all outstanding debt, we were also able to collect the average amount of debt cleared per student), and any student eligibility criteria. We define “debt forgiveness” as relief for any outstanding balances owed by a student to the institution (tuition, student fees, housing, books, etc.), exclusive of government or private student loans. Wherever possible, we also collected information on the potential or actual impact of the program—such as on the number of students eligible to receive aid or the number of students who received aid—though these numbers are likely to change as the programs evolve. In most cases, there is limited information on the application process for eligible students and how the institution decides which students receive forgiveness.

After identifying each program and pulling out key program characteristics, we downloaded data for each institution from the Integrated Postsecondary Education Data System to identify broader trends in institutional characteristics and student enrollments at colleges and universities providing debt forgiveness. We included measures such as: total enrollment, percentage of Pell students enrolled, graduation rates, institution classifications (e.g., Historically Black College and Universities (HBCUs) or Minority Serving Institutions (MSI)), and student demographics. 

Though our small sample is certainly not representative of the many HEERF-funded forgiveness programs across the nation, we draw out some key insights on program components and institutional characteristics in the next section. Our full dataset is available in this Air Table (with this directory)

Key Insights

Our analysis of program and institutional characteristics yielded four primary insights:

  1. The characteristics of institutions in our sample mirror those of institutions that we estimate have relatively high rates of students in need of debt forgiveness. Institutions in our sample serve relatively high shares of underrepresented students and have fewer resources than the national average. The average Pell share of the 64 institutions in our sample is 66 percent and 25 percent of the institutions are HBCUs. An additional thirty percent of institutions in our sample are designated as non-HBCU MSIs, most of which are Hispanic Serving Institutions (HSIs). Institutions in our sample have an average six-year completion rate of 32 percent, and revenues per FTE are also relatively low, at around $26,000. 
  2. In our sample, over $220 million in HEERF funding has been used to pay students’ debt through these programs. The average allocation to debt forgiveness across programs is $3.2 million, though the amount committed varies substantially across programs, from only $250,000 to $125 million dedicated by the CUNY system across its 21 campuses. If we assume that, on average, 15 percent of students enrolled at the institutions covered by each program receive debt forgiveness, this would amount to an average allocation of around $2,000 per student. 
  3. Many institutions in our sample maximized the number of students eligible for debt forgiveness by setting very few, if any, eligibility requirements to receive it. As put forth by the Department of Education, receipt of aid applies to any student enrolled at an eligible institution from the start of the national emergency, March 13, 2020 and onwards. Twenty-eight of the 33 programs did not set any additional eligibility rules, only requiring enrollment during the period in which students were most affected by the financial impacts of COVID-19.  However, five of the 33 programs had eligibility requirements relating to other student characteristics besides enrollment, such as requiring students to have a debt balance of $500 or more. 
  4. All institutions erased 100 percent of outstanding institutional balances, but several have utilized the aid to provide even more expansive support. All 64 institutions in the sample erased 100 percent of outstanding debt for their students, while five institutions used these federal funds to provide comprehensive support, beyond debt forgiveness for students. This support includes providing grants and partial refunds for housing, textbooks, and even tech support in addition to debt forgiveness. For example, on top of clearing student account balances, Clark Atlanta University used the federal funds to distribute emergency financial aid through a pro-rated refund of housing and meal charges for Spring 2020..

Potential Next Steps

As the Department of Education continues to gather information on the use of HEERF funds, these data will deepen our understanding of the typology of programs so that we can explore the effects of different policy designs, understand the components of an effective debt forgiveness program, and elevate students’ experiences. The cumulative nature of the work and future research opportunities will allow us to understand the issue of stranded credits from multiple angles to better inform institutional and governmental policies, and identify alternative solutions.

In addition to keeping a pulse on how these debt forgiveness programs expand and evolve, we also hope to better understand the impact of federal, state, and institutional policies on students’ experiences with the “stranded credits” that these debt forgiveness programs address. We know that some of these policies are intended to impact students in concrete, measurable ways—encouraging re-enrollment, improving persistence, improving transfer, and removing debts. But, there may also be other, more nuanced, factors to consider and other policies that may influence students accruing these debts in the first place. 

For instance, what are the individual and institutional factors contributing to students’ unpaid balances, including Return to Title IV policies? Dr. Sosanya Jones’ report, Stranded Credits: A Matter of Equity and the Student Borrower Protection Center’s report, Withholding Dreams, provides insight on how Return to Title IV policies add to students’ financial burden. Additionally, the practice of banning transcript withholding has the potential to help students access transcripts, but may result in new institutional policies that could harm students’ educational progress. How do these policies come to be and how do colleges respond to them in ways that promote or impede student persistence? Answering these questions are vital to understanding the crux of the problem: the factors influencing the accrual of institutional debt in the first place.  Through our collection efforts and tangential research, we hope to gain a more comprehensive understanding of the interlocking relationship between “stranded credits” and debt forgiveness programs.

If you’d like to share an example of a debt forgiveness program or report information on your institution’s debt forgiveness program, please contact Pooja Vora (pooja.vora@ithaka.org).

*HEERF funds were authorized through three separate federal bills: The Coronavirus Aid, Relief, and Economic Security Act (CARES Act, 2020), the Coronavirus Response and Relief Supplemental Appropriations Act (CRRSA Act, 2020), and the American Rescue Plan Act (ARP Act, 2021). 

**We used the following keywords for our Google searches: “college debt forgiveness programs,” “HEERF funded debt forgiveness,” and “COVID funds to forgive student debt.” We consulted Ithaka S+R’s earlier work (see “Collecting Data on New Debt Relief Programs”); an op-ed published by Ithaka S+R’s Managing Director, Catherine Bond Hill in Inside Higher Ed); and institutions’ websites (see examples from Wilberforce University and Saint Augustine’s University, with specific links for each program included in the full dataset).