College Closures

The End of Easy Student Loans

James Bailey1 Comment

The Senate Health, Education, Labor and Pensions Committee is proposing to cut off student loans for programs whose graduates earn less than the median high school graduate. The House proposed a risk-sharing model where colleges would partly pay back the federal government when their students fail to pay back loans themselves. Both the House and Senate propose to cap how much students can borrow for graduate loans. Both would reduce federal spending on higher ed by about $30-$35 billion per year, cutting the size of the $700 billion higher ed sector by 4-5%. I expected that something like this would happen eventually, especially after the student loan forgiveness proposals of 2022:

While we aren’t getting real reform now, I do think forgiveness makes it more likely that we’ll see reform in the next few years. What could that look like?

The Department of Education should raise its standards and stop offering loans to programs with high default rates or bad student outcomes. This should include not just fly-by-night colleges, but sketchy masters degree programs at prestigious schools.

Colleges should also share responsibility when they consistently saddle students with debt but don’t actually improve students’ prospects enough to be able to pay it back. Economists have put a lot of thought into how to do this in a manner that doesn’t penalize colleges simply for trying to teach less-prepared students.

I’d bet that some reform along these lines happens in the 2020’s, just like the bank bailouts of 2008 led to the Dodd-Frank reform of 2010 to try to prevent future bailouts. The big question is, will this be a pragmatic bipartisan reform to curb the worst offenders, or a Republican effort to substantially reduce the amount of money flowing to a higher ed sector they increasingly dislike?

Of course, there is a lot riding on the details. How exactly do you calculate the income of graduates of a program compared to high school grads? The Senate proposal explains their approach starting on page 58. They want to compare the median income of working students 4 years after leaving their program (whether they graduated or dropped out, but exempting those in grad school) to the median income of those with only a high school diploma who are age 25-34, working, and not in school.

Nationally I calculate that this would make for a floor of $31,000. That is, the median student who is 4 years out from your program and is working should be earning at least $31k. In practice the bill would implement a different number for each state. This seems like a low bar in general, though you could certainly quibble with it. For instance, those 4 years out from a program may be closer to age 25 than age 34, but income typically rises with age during those years. If you compare them to 26 year old high school grads, the national bar would be just $28k.

What sorts of programs have graduates making less than $31k per year?

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Predicting College Closures: Now with Machine Learning

James Bailey4 Comments

Small, rural, private schools stand out to me as the most likely to show up on lists of closed colleges. This summer I discussed a 2020 paper by Robert Kelchen that identified additional predictors using traditional regression:

sharp declines in enrollment and total revenue, that were reasonably strong predictors of closure. Poor performances on federal accountability measures, such as the cohort default rate, financial responsibility metric, and being placed on the most stringent level of Heightened Cash Monitoring

Kelchen just released a Philly Fed working paper (joint with Dubravka Ritter and Doug Webber) that uses machine learning and new data sources to identify more predictors of college closures:

The current monitoring solution to predicting the financial distress and closure of institutions — at least at the federal level — is to provide straightforward and intuitive financial performance metrics that are correlated with closure. These federal performance metrics represent helpful but suboptimal measures for purposes of predicting closures for two reasons: data availability and predictive accuracy. We document a high degree of missing data among colleges that eventually close, show that this is a key impediment to identifying institutions at risk of closure, and also show how modern machine learning algorithms can provide a concrete solution to this problem.

The paper also provides a great overview of the state of higher ed. The sector is currently quite large:

The American postsecondary education system today consists of approximately 6,000 colleges and universities that receive federal financial aid under Title IV of the federal Higher Education Act…. American higher education directly produces approximately $700 billion in expenditures, enrolls nearly 25 million students, and has approximately 3 million employees

Falling demand from the demographic cliff is causing prices to fall, in addition to closures:

Between the early 1970s and mid-2010s, listed real tuition and fee rates more than tripled at public and private nonprofit colleges, as strong demand for higher education allowed colleges to continue increasing their prices. But since 2018, tuition increases have consistently been below the rate of inflation

Most college revenue comes from tuition or from state support of public schools; gifts and grants are highly concentrated:

Research funding is distributed across a larger group of institutions, although the vast majority of dollars flows to the 146 institutions that are designated as Research I universities in the Carnegie classifications…. Just 136 colleges or university systems in the United States had endowments of more than $1 billion in fiscal year 2023, but they account for more than 80 percent of all endowment assets in American higher education. Going further, five institutions held 25 percent of all endowment assets, and 25 institutions held half of all assets

Now lets get to closures. As I thought, size matters:

most institutions that close are somewhat smaller than average, with the median closed school enrolling a student body of about 1,389 full-time equivalent students several years prior to closure

As does being private, especially private for-profit (states won’t bail you out when you lose money):

As do trends:

variables measuring ratios of financial metrics and those measuring changes in covariates are generally more important than those measuring the level of those covariates

When they throw hundreds of variables into a machine learning model, it can predict most closures with relatively few false positives, though no one variable stands out much (FRC is Financial Responsibility Composite):

My impression is that the easiest red flag to check for regular people who don’t want to dig into financials is “is total enrollment under 2000 and falling at a private school”.

They predict that the coming Demographic Cliff (the falling number of new 18-year-olds each year) will lead to many more closures, though nothing like the “half of all colleges” you sometimes hear:

The full paper is available ungated here. I’ll close by reiterating my advice from the last post: would-be students, staff, and faculty should do some basic research to protect themselves as they consider enrolling or accepting a job at a college. College employees would also do well to save money and keep their resumes ready; some of these closures are so sudden that employees find out they are out of a job effective immediately and no paycheck is coming next month.

Predicting College Closures

James Bailey7 Comments

This week the University of the Arts in Philadelphia announced they were closing effective immediately, leaving students scrambling to transfer and faculty desperate for jobs. U Arts now joins Cabrini University and Birmingham-Southern as some the 20 US colleges closing or being forced to merge so far this year. This trend of closures is likely to accelerate given falling birth rates that mean the number of college-age Americans is set to decline for decades; short-term issues like the FAFSA snafu and rising interest rates aren’t helping either.

All this makes it more important for potential students and employees to consider the financial health of colleges they might join, lest they find themselves in a UArts type situation. But how do you predict which colleges are at significant risk of closing? One thing that jumps out from this year’s list of closures is that essentially every one is a very small (fewer than 2000 undergrad) private school. Rural schools seem especially vulnerable, though this year has also seen plenty of closures in major cities.

Source

There appear to be a number of sources tracking the financial health of colleges, though most are not kept up to date well. Forbes seems to be the best, with 2023 ratings here; UArts, Cabrini, and Birmingham-Southern all had “C” grades. If you have access to them, credit ratings would also be good to check out; Fitch offers a generally negative take on higher ed here.

In a 2020 Brookings paper, Robert Kelchen identified several statistically significant predictors of college closures:

I used publicly available data compiled by the federal government to examine factors associated with college closures within the following two to four years. I found several factors, such as sharp declines in enrollment and total revenue, that were reasonably strong predictors of closure. Poor performances on federal accountability measures, such as the cohort default rate, financial responsibility metric, and being placed on the most stringent level of Heightened Cash Monitoring, were frequently associated with a higher likelihood of closure. My resulting models were generally able to place a majority of colleges that closed into a high-risk category

The Higher Learning Commission reached similar conclusions. Of course, there is a danger in identifying at-risk colleges too publicly:

Since a majority of colleges identified of being at the highest risk of closure remained open even four years later, there are practical and ethical concerns with using these results in the policy process. The greatest concern is that these results become a self-fulfilling prophecy— being identified as at risk of closure could hasten a struggling college’s demise.

Still, would-be students, staff and faculty should do some basic research to protect themselves as they considering enrolling or accepting a job at a college. College employees would also do well to save money and keep their resumes ready; some of these closures are so sudden that employees find out they are out of a job effective immediately and no paycheck is coming next month.