The Supreme Court Just Blocked Student Loan Forgiveness. Now What?

In a conclusion to one of the most consequential Supreme Court sessions in many years, the Court released an opinion today on the Biden administration’s proposed plan to forgive up to $20,000 in federal student loan debt per borrower. After dismissing one case due to lack of standing from the plaintiffs, the Court voted 6-3 to block forgiveness in the second case (giving standing based on the servicer MOHELA).

This decision will have major implications for higher education policy. Here are the things that I will be looking for in the coming months and years:

Restarting student loan repayment was already going to be a nightmare, and this creates additional challenges. The first challenge is the sheer number of borrowers re-entering repayment. Roughly 43 million Americans have federal student debt, and the Biden administration estimated that about 20 million would have their loans completely forgiven by their proposal. I have little confidence that the Department of Education, student loan servicers, and colleges can smoothly handle 23 million borrowers that would have remained, let alone 43 million. Federal Student Aid badly needed additional resources to manage a return to repayment, but Republicans were only willing to provide the funds if it came with a rider blocking its use on debt relief. Since both parties agreed on no riders in last year’s omnibus spending bill, no additional funding was provided.

In an overlooked item due to yesterday’s important decision on college admissions, the Department of Education released information about how they plan to manage the return to repayment. ED plans to give a 90-day grace period for missed payments and is considering future grace periods. Needless to say, Republicans are not happy and may go to court to stop grace periods based on the agreement in this summer’s debt ceiling legislation.

How many borrowers are willing to start making payments? There is going to be a group of people who are livid about having to resume payments after not getting the loan forgiveness they were expecting. I am expecting a substantial group of borrowers to not make any payments until they get to the brink of default—which could take a while. These borrowers may still hold out hope for another forgiveness effort (more on that in the next section) and they may not proactively reach out to servicers to update their information if they have moved since March 2020. A particularly interesting group is the 20 million students who would have received complete forgiveness, as the frustration factor is likely higher among this group than among students who knew they would still have a balance remaining under this plan.

As a note, with income-driven repayment, students at least in theory should be able to start making some payments. But adding an expense back to the monthly budget is painful and income-driven repayment is still complicated to navigate. So there will be challenges even among people who are not as upset about this decision.

How will Democrats respond? The progressive wing of the Democratic Party has been pressuring the Biden administration to forgive all student debt and immediately pivot to using the Higher Education Act instead of the HEROES Act. That is likely not happening given today’s court decision. But a few moderate Democrats voted in favor of a Republican-led resolution disapproving of debt forgiveness and ending the repayment pause. The Biden administration will point to its expanded income-driven repayment plan, which could also face legal challenges in light of this decision. Free college and debt forgiveness were key issues in the 2020 Democratic presidential primary, and they will continue to be key issues in contested Democratic primaries for the next several years.

How will Republicans respond? By the time you read this, there will be plenty of press releases from Republican politicians celebrating the discussion. But there are still concerns about a future administration trying another avenue to forgiveness, particularly through income-driven repayment. There are some thoughtful efforts among Republicans to maintain income-driven repayment while reversing most of the Biden administration’s proposed changes. But Republicans are also seeking to limit borrowing for graduate students, which is something that I have been expecting for years.  

This week’s Supreme Court decisions are likely to influence the direction of American higher education for years to come, and some of the influences are not going to be immediately obvious. But the items discussed above are going to play an outsized role in policy discussions for a good while.

My 2023 Higher Education Finance Reading List

I have the pleasure of teaching my PhD class in higher education finance again at Tennessee this summer. Our students take classes year-round, and I am offering the class in a condensed five-week format this summer to best meet the needs of our students. That means a lot of reading for all of us in a short period of time, but I’m excited as always for this class.

The last three times that I taught the course (spring 2022, spring 2020, and fall 2017), I shared my reading list for the class on this blog. I do not use a textbook for the course because the field is moving so quickly and there are more topics to cover than a textbook could ever include. Instead, I use articles, working papers, and other online resources to provide a current look at the state of higher education finance. As a result, the reading list for my class changes considerably each time.

Here is the reading list I am assigning my students for the course. I link to the final versions of the articles whenever possible, but those without access to an academic library should note that earlier versions of many of these articles are available online via a quick Google search.

The higher education finance landscape and data sources

Chetty, R., Friedman, J. N., Saez, E., Turner, N., & Yagan, D. (2017). Mobility report cards: The role of colleges in intergenerational mobility. Working paper. (link)

Schanzenbach, D. W., Bauer, L., & Breitwieser, A. (2017). Eight economic facts on higher education. The Hamilton Project. (link)

Webber, D. A. (2021). A growing divide: The promise and pitfalls of higher education for the working class. The ANNALS of the American Academy of Political and Social Science, 695, 94-106. (link)

Recommended data sources:

College Scorecard: https://collegescorecard.ed.gov/ (underlying data at https://collegescorecard.ed.gov/data/)

Equality of Opportunity Project: http://www.equality-of-opportunity.org/college

IPEDS: https://nces.ed.gov/ipeds/use-the-data

NCES Data Lab: https://nces.ed.gov/datalab/index.aspx

Postsecondary Value Commission’s Equitable Value Explorer: https://www.postsecondaryvalue.org/equitable-value-explorer/

ProPublica’s Nonprofit Explorer: https://projects.propublica.org/nonprofits/

Urban Institute’s Data Explorer: https://educationdata.urban.org/data-explorer/colleges/

Institutional budgeting

Barr, M.J., & McClellan, G.S. (2010). Understanding budgets. In Budgets and financial management in higher education (pp. 55-85). Jossey-Bass. (link)

Jaquette, O., Kramer II, D. A., & Curs, B. R. (2018). Growing the pie? The effect of responsibility center management on tuition revenue. The Journal of Higher Education, 89(5), 637-676. (link)

Rutherford, A., & Rabovsky, T. (2018). Does the motivation for market-based reform matter? The case of responsibility-centered management. Public Administration Review, 78(4), 626-639. (link)

University of Tennessee System’s FY2023 budget: https://finance.tennessee.edu/budget/documents/

University of Tennessee System’s FY2022 annual financial report: https://treasurer.tennessee.edu/reports/

UTK’s Budget Allocation Model website: https://budget.utk.edu/budget-allocation-model/

Higher education expenditures

Archibald, R. B., & Feldman, D. H. (2018). Drivers of the rising price of a college education. Midwestern Higher Education Compact. (link)

Cheslock, J. J., & Knight, D. B. (2015). Diverging revenues, cascading expenditures, and ensuing subsidies: The unbalanced and growing financial strain of intercollegiate athletics on universities and their students. The Journal of Higher Education, 86(3), 417-447. (link)

Commonfund Institute (2021). 2021 higher education price index. (link)

Griffith, A. L., & Rask, K. N. (2016). The effect of institutional expenditures on employment outcomes and earnings. Economic Inquiry, 54(4), 1931-1945. (link)

Hemelt, S. W., Stange, K. M., Furquim, F., Simon, A., & Sawyer, J. E. (2021). Why is math cheaper than English? Understanding cost differences in higher education. Journal of Labor Economics, 39(2), 397-435. (link)

State sources of revenue

Chakrabarti, R., Gorton, N., & Lovenheim, M. F. (2020). State investment in higher education: Effects on human capital formation, student debt, and long-term financial outcomes of students. National Bureau of Economic Research Working Paper 27885. (link)

Gándara, D. (2020). How the sausage is made: An examination of a state funding model design process. The Journal of Higher Education, 91(2), 192-221. (link)

Kelchen, R., Lingo, M., Baker, D., Rosinger, K. O., Ortagus, J. C., & Wu, J. (2023). A typology and landscape of state funding formulas for public colleges and universities from 2004 to 2020. InformEd States. (link)

Kunkle, K., & Laderman, S. (2023). State higher education finance: FY 2022. State Higher Education Executive Officers Association. (link)

Ortagus, J. C., Kelchen, R., Rosinger, K. O., & Voorhees, N. (2020). Performance-based funding in American higher education: A systematic synthesis of the intended and unintended consequences. Educational Evaluation and Policy Analysis, 42(4), 520-550. (link)

Shaw, K., Asher, L., & Murphy, S. (2023). Mapping community college finance systems to develop equitable and effective finance policy. HCM Strategists. (link)

Tennessee’s outcomes-based funding formula: https://www.tn.gov/thec/bureaus/ppr/fiscal-policy/outcomes-based-funding-formula-resources/2020-25-obf.html

Federal sources of revenue

Bergman, P., Denning, J. T., & Manoli, D. (2019). Is information enough? The effect of information about education tax benefits on student outcomes. Journal of Policy Analysis and Management, 38(3), 706-731. (link)

Black, S. E., Turner, L. J., & Denning, J. T. (2023). PLUS or minus? The effect of graduate school loans on access, attainment, and prices. National Bureau of Economic Research Working Paper 31291. (link)

Graddy-Reed, A., Feldman, M., Bercovitz, J., & Langford, W. S. (2021). The distribution of indirect cost recovery in academic research. Science and Public Policy, 48(3), 364-386. (link)

Kelchen, R., & Liu, Z. (2022). Did gainful employment regulations result in college and program closures? Education Finance and Policy, 17(3), 454-478. (link)

Ward, J. D. (2019). Intended and unintended consequences of for-profit college regulation: Examining the 90/10 rule. Journal of Student Financial Aid, 48(3), Article 4. (link)

The financial viability of higher education

Ducoff, N. (2019, December 9). Students pay the price if a college fails. So why are we protecting failing institutions? The Hechinger Report. (link)

EY-Parthenon (2018). Transitions in higher education: Safeguarding the interests of students. (link)

Kelchen, R. (2020). Examining the feasibility of empirically predicting college closures. Brookings Institution. (link)

Massachusetts Board of Higher Education (2019). Final report & recommendations. Transitions in higher education: Safeguarding the interest of students (THESIS). (link)

Sullivan, G. W., & Stergios, J. (2019). A risky proposal for private colleges: Ten reasons why the Board of Higher Education must rethink its plan. Pioneer Institute. (link)

Tarrant, M., Bray, N., & Katsinas, S. (2018). The invisible colleges revisited: An empirical review. The Journal of Higher Education, 89(3), 341-367. (link)

College pricing, tuition revenue, and endowments

Baker, D. J. (2020). “Name and shame”: An effective strategy for college tuition accountability? Educational Evaluation and Policy Analysis, 42(3), 1-24. (link)

Baum, S., & Lee, V. (2018). Understanding endowments. Urban Institute. (link)

Cheslock, J. J., & Riggs, S. O. (2023). Ever-increasing listed tuition and institutional aid: The role of net price differentials by year of study. Educational Evaluation and Policy Analysis. (link)

Hatch, B., Myskow, W., & Trivedi, I. (2022, August 15). Stopping the enrollment slide. The Chronicle of Higher Education. https://www.chronicle.com/article/stopping-the-slide.

Kramer II, D. A., Ortagus, J. C., & Lacy, T. A. (2018). Tuition-setting authority and broad-based merit aid: The effect of policy intersection on pricing strategies. Research in Higher Education, 59(4), 489-518. (link)

Ma, J., & Pender, M. (2022). Trends in college pricing and student aid 2021. The College Board. (link)

Webber, D. A. (2017). State divestment and tuition at public institutions. Economics of Education Review, 60, 1-4. (link)

Financial aid policies, practices, and impacts

Anderson, D. M., Broton, K. M., Goldrick-Rab, S., & Kelchen, R. (2020). Experimental evidence on the impacts of need-based financial aid: Longitudinal assessment of the Wisconsin Scholars Grant. Journal of Policy Analysis and Management, 39(3), 720-739. (link)

Bird, K., & Castleman, B. L. (2016). Here today, gone tomorrow? Investigating rates and patterns of financial aid renewal among college freshmen. Research in Higher Education, 57(4), 395-422. (link)

Dynarski, S., Page, L. C., & Scott-Clayton, J. (2022). College costs, financial aid, and student decisions. National Bureau of Economic Research Working Paper 30275. (link)

Guzman-Alvarez, A., & Page, L. C. (2021). Disproportionate burden: Estimating the cost of FAFSA verification for public colleges and universities. Educational Evaluation and Policy Analysis, 43(3), 545-551. (link)

Kelchen, R., Goldrick-Rab, S., & Hosch, B. (2017). The costs of college attendance: Examining variation and consistency in institutional living cost allowances. The Journal of Higher Education, 88(6), 947-971. (link)

Student debt and financing college

Baker, D. J. (2019). When average is not enough: A case study examining the variation in the influences on undergraduate debt burden. AERA Open, 5(2), 1-26. (link)

Black, S. E., Denning, J. T., Dettling, L. J., Goodman, S., & Turner, L. (2020). Taking it to the limit: Effects of increased student loan availability on attainment, earnings, and financial well-being. National Bureau of Economic Research Working Paper 27658. (link)

Boatman, A., Evans, B. J., & Soliz, A. (2017). Understanding loan aversion in education: Evidence from high school seniors, community college students, and adults. AERA Open, 3(1), 1-16. (link)

Ritter, D., & Webber, D. (2019). Modern income-share agreements in postsecondary education: Features, theory, applications. Federal Reserve Bank of Philadelphia Discussion Paper 19-06. (link)

Scott-Clayton, J. (2018). What accounts for gaps in student loan default, and what happens after. Brookings Institution Evidence Speaks Report #57. (link)

Returns to education

Darity, Jr., W. A., & Underwood, M. (2021). Reconsidering the relationship between higher education, earnings, and productivity. Postsecondary Value Commission. (link)

Deterding, N. M., & Pedulla, D. S. (2016). Educational authority in the “open door” marketplace: Labor market consequences of for-profit, nonprofit, and fictional educational credentials. Sociology of Education, 89(3), 155-170. (link)

Doyle, W. R., & Skinner, B. T. (2017). Does postsecondary education result in civic benefits? The Journal of Higher Education, 88(6), 863-893. (link)

Ma, J., & Pender, M. (2023). Education pays 2023: The benefits of higher education for individuals and society. The College Board. (link)

Webber, D. A. (2016). Are college costs worth it? How ability, major, and debt affect the returns to schooling. Economics of Education Review, 53, 296-310. (link)

Examining Trends in Debt to Earnings Ratios

I was just starting to wonder when the U.S. Department of Education would release a new year of College Scorecard data, so I wandered over to the website to check for anything new. I was pleasantly surprised to see a date stamp of April 25 (today!), which meant that it was time for me to give my computer a workout.

There are a lot of great new data elements in the updated Scorecard. Some features include a fourth year of post-graduation earnings, information on the share of students who stayed in state after college, earnings by Pell receipt and gender, and an indicator for whether no, some, or all programs in a field of study can be completed via distance education. There are plenty of things to keep me busy for a while, to say the least. (More on some of the ways I will use the data coming soon!)

In this update, I share data on trends in debt to earnings ratios by field of study. I used median student debt accumulated by the first Scorecard cohorts (2014-15 and 2015-16 leavers) and tracked median earnings one, two, three, and four years after graduating college. The downloadable dataset includes 34,466 programs with data for each element.

The below table shows debt-to-earnings ratios for the four most common credential levels. The good news is that the average ratio ticked downward for each credential level, with bachelor’s and master’s degrees showing steep declines in their ratios than undergraduate certificates and associate degrees.

Credential	1 year	2 years	3 years	4 years
Certificate	0.455	0.430	0.421	0.356
Associate	0.528	0.503	0.473	0.407
Bachelor’s	0.703	0.659	0.569	0.485
Master’s	0.833	0.793	0.734	0.650

The scatterplot shows debt versus earnings four years later across all credential levels. There is a positive correlation (correlation coefficient of 0.454), but still quite a bit of noise present.

Enjoy the new data!

Sharing a Dataset of Program-Level Debt and Earnings Outcomes

Within a couple of hours of posting my comments on the Department of Education’s proposal to create a list of programs with low financial value, I received multiple inquiries about whether there was a user-friendly dataset of current debt-to-earnings ratios for programs. Since I work with College Scorecard data on a regular basis and have used the data to write about debt-to-earnings ratios, it only took a few minutes to put something together that I hope will be useful.

To create a debt-to-earnings ratio that covered as many programs as possible, I pulled median student debt accumulated at that institution for the cohorts of students who left college in the 2016-17 or 2017-18 academic years and matched it with earnings for those same cohorts one calendar year later (calendar year 2018 or 2019). The College Scorecard has some earnings data more than one year out at this point, but a much smaller share of programs are covered. I then calculated a debt-to-earnings ratio. And for display purposes, I also pulled median parent debt from that institution.

The resulting dataset covers 45,971 programs at 5,033 institutions with data on both student debt and earnings for those same cohorts. You can download the dataset here in Excel format and use filter/sort functions to your heart’s content.

Comments on a Proposed Federal List of Low-Value Programs

The U.S. Department of Education recently announced that they will be creating a list of low-value postsecondary programs, and they requested input from the public on how to do so. They asked seven key questions, and I put together 3,000-plus words in comments as a response to submit. Here, I list the questions and briefly summarize my key points.

Question 1: What program-level data and metrics would be most helpful to students to understand the financial (and other) consequences of attending a program?

Four data elements would be helpful. The first is program-level completion rates, especially for graduate or certificate programs where students are directly admitted into programs. Second, given differential tuition and different credit requirements across programs, time to completion and sticker/net prices by program would be incredibly valuable. The last two are debt and earnings, which are largely present in the current College Scorecard.

Question 2: What program-level data and metrics would be most helpful to understand whether public investments in the program are worthwhile? What data might be collected uniformly across all students who attend a program that would help assess the nonfinancial value created by the program?

I would love to see information on federal income taxes paid by former students and use of public benefits (if possible). More information on income-driven repayment use would also be helpful. Finally, there is a great need to rethink definitions of “public service,” as it currently depends on the employer instead of the job function. That is a concern in fields like nursing that send graduates to do good things in for-profit and nonprofit settings.

Question 3: In addition to the measures or metrics used to determine whether a program is placed on the low-financial-value program list, what other measures and metrics should be disclosed to improve the information provided by the list?

Nothing too fancy here. Just list any sanctions/warnings from the federal government, state agencies, or accreditors along with general outcomes for all students at the undergraduate level to account for major switching.

Question 4: The Department intends to use the 6-digit Classification of Instructional Program (CIP) code and the type of credential awarded to define programs at an institution. Should the Department publish information using the 4-digit CIP codes or some other type of aggregation in cases where we would not otherwise be able to report program data?

This is my nerdy honey hole, as I have spent a lot of time thinking on these issues. The biggest two issues with student debt/earnings data right now is that some campuses get aggregated together in reporting and that it’s also impossible to separate outcomes for fully online versus hybrid/in-person programs. Those nuts need to be cracked, and then aggregate up if cell sizes are too small.

Question 5: Should the Department produce only a single low-financial-value program list, separate lists by credential level, or use some other breakdown, such as one for graduate and another for undergraduate programs?

Separate out by credential level and ideally have a good search function by program of study. Otherwise, some low-paying programs will clog up the lists and not let students see relatively lousy programs in higher-paying areas.

Question 6: What additional data could the Department collect that would substantially improve our ability to provide accurate data for the public to help understand the value being created by the program? Please comment on the value of the new metrics relative to the burden institutions would face in reporting information to the Department.

I would love to see program-level completion rates (where appropriate) and better pricing information at the program level. Those items aren’t free to implement, so I would gladly explore other cuts to IPEDS (such as the academic libraries survey) to help reduce additional burden.

Question 7: What are the best ways to make sure that institutions and students are aware of this information?

Colleges will be aware of this information without the federal government doing much, and they may respond to information that they didn’t have before. But colleges don’t have a great record of responding to public shaming if they already knew that affordability was a concern, so I’m not expecting massive changes.

The College Scorecard had small changes around the margins for student behaviors, primarily driven by more advantaged students. I’m not an expert in reaching out to prospective students, but I know that outreach to as many groups as possible is key.