A Look at Federal Student Loan Borrowing by Field of Study

The U.S. Department of Education’s Office of Federal Student Aid has slowly been releasing interesting new data on federal student loans over the last few years. In previous posts, I have highlighted data on the types of borrowers who use income-driven repayment plans and average federal student loan balances by state. But one section of Federal Student Aid’s website that gets less attention than the student loan portfolio page (where I pulled data from for the previous posts) is the Title IV program volume reports page. For years, this page—which is updated quarterly with current data—has been a useful source of how many students at each college receive federal grants and loans.

While pulling the latest data on Pell Grant and student loan volumes by college last week, I noticed three new spreadsheets on the page that contained interesting statistics from the 2015-16 academic year. One spreadsheet shows grant and loan disbursements by age group, while a second spreadsheet shows the same information by state. But in this blog post, I look at a third spreadsheet of student loan disbursements by students’ fields of study. The original spreadsheet contained data on the number of recipients and the amount of loans disbursed, and I added a third column of per-student annual average loans by dividing the two columns. This revised spreadsheet can be downloaded here.

Of the 1,310 distinct fields of study included in the spreadsheet, 14 of them included more than $1 billion of student loans in 2015-16 and made up over $36 billion of the $94 billion in disbursed loans. Business majors made up 600,000 of the 9.1 million borrowers, taking out $6.1 billion in loans, with nursing majors having the second most borrowers and loans. The majors with the third and fourth largest loan disbursements were law and medicine, fields that tend to be almost exclusively graduate students and can thus borrow up to the full cost of attendance without the need for Parent PLUS loans. As a result, both of these fields took out more loans than general studies majors in spite of being far fewer in numbers. On the other end (not shown here), the ten students majoring in hematology technology/technician drew out a combined $28,477 in loans, just ahead of the 14 students in explosive ordinance/bomb disposal programs who hopefully are not blowing up over incurring a total of $61,069 in debt.

Turning next to programs where per-student annual borrowing is the highest, the top ten list is completely dominated by health sciences programs (the first two-digit CIP not from health sciences is international business, trade, and tax law at #16). It is pretty remarkable that dentists take on $71,451 of student loans each year while advanced general dentists (all 51 of them!) borrow even more than that. Given that dental school is four years long and that interest accumulates during school, an average debt burden of private dental school graduates of $341,190 seems quite reasonable. Toss in income-driven repayment during additional training and it makes sense that at least one of the 101 people with $1 million in federal student loan debt is an orthodontist. On the low end of average debt, the 164 bartending majors ran up an average tab of $2,963 in student loans in 2015-16 while the 144 personal awareness and self-improvement majors are well into their 12-step plan to repay their average of $4,361 in loans.

Trends in Net Prices by Family Income

I continue my look through newly-released data from the National Postsecondary Student Aid Study by turning to trends in the net price of attendance by family income. The net price, which is the full cost of attendance (tuition and fees, books and supplies, room and board, and miscellaneous living expenses) less all grant aid received, is a key college affordability measure as it represents how much money students and their families have to come up with each year to attend college. This net price can be covered by a combination of savings, work income, and student loans, but it is worth noting that student loan limits for many undergraduate students are far below the net price. This means that many families face challenges in paying for college if the net price is a large share of their income.

The first figure here shows trends (since 2004) in the percentage of family income needed to cover the net price. In 2015-16, 48% of students faced net prices of less than 25% of their family income, 20% were between 26% and 50%, 9% were between 51% and 99%, and 23% of students had net prices greater than their family incomes. The good news is that the distribution of net prices held almost constant since 2011-12 after having taken a jump during the Great Recession.

In the second figure, I break down the percentage of students with net prices higher than their family income by type of college attended. Nearly half of students attending for-profit college were in this category, which is not surprising given the high prices charged by many for-profit colleges and students’ low household incomes. About one in five students attending public and private nonprofit four-year colleges are also in this category. Meanwhile, even 18% of community college students had net prices higher than their family’s income, which is a particular concern as quite a few colleges do not allow their students to take out federal loans.

A Look at College Students’ Living Arrangements

Those of us in the research and policy worlds generally had a different college experience than most American college students have today. One example of this is where students live during college. I had a very traditional college experience, which began with me as a recent high school graduate moving into my (non-air conditioned) dorm room in Truman State University’s Ryle Hall in the sweltering August heat.[1] Yet that residential experience is not what most students experience, as I show in my fourth blog post using newly-released data from the National Postsecondary Student Aid Study (NPSAS).

As the chart below shows, only 15.6% of all undergraduate students lived on campus in the 2015-16 academic year, a percentage that has largely been consistent since 2000. 56.9% of students lived off campus away from their parent(s), while 27.5% lived off campus with their parents. Aside from a strange blip in 2011-12, these percentage have also been fairly consistent over time.[2]

This low percentage could be explained in part by students living on campus during their first year of college and then moving off campus later on in an effort to either save money or gain more independence. I then focused the next chart on the roughly 38%-40% of students who were first-year students (about 25% at four-year public and private nonprofit colleges and 50% at community colleges and for-profits) to get an idea of whether patterns changed among new students only.[3] Interestingly, the percentages of first-year students living on campus (12.9%) and off campus away from their parent(s) (53.8%) were lower than for all students, which I figured was due to the smaller percentage of four-year students among the first-year student cohort.

I then broke down student living arrangements by institutional type for the 2015-16 academic year, showing numbers both for all students and only for first-year students. The finding that will surprise many is that less than 50% of first-year students at four-year colleges live on campus, in spite of this being viewed as the traditional college experience. 49% of first-year students at private nonprofit colleges and 36% of first-year students at public four-year colleges lived on campus, while very few community colleges or for-profit colleges even have campus housing. The most common living arrangement for both the community college and for-profit sectors was to live off campus away from parent(s) , with about 60% of community college and 75% of for-profit students doing this regardless of year in college. About 40% of community college students lived with their parent(s), with private nonprofit students being least likely to do this (13%).

These data show that the “typical” residential college experience that many of us had was not the typical experience even when we went to college.[4] A more typical college student is the young woman who rang me up as a outlet mall cashier last weekend. She was an education major at the local community college and said that she lived at home to save money. After I introduced myself as a professor, she mentioned that she was hoping to continue living at home and commuting to a nearby four-year college. Although I was unable to get an extra teacher discount from her at the cash register, it was a good reminder that most students never live in a residence hall.

[1] Air conditioning matters a lot in education, folks. For empirical evidence in a K-12 setting, see this great new NBER working paper by Josh Goodman and colleagues.

[2] Fellow data nerds, any idea what happened in 2011-12? I looked at each sector and the pattern is still there (with it being strongest among four-year colleges). For that reason, I am hesitant to place much value on the 2011-12 off campus percentages.

[3] I used the NPSAS variable of year in school for financial aid purposes, as the year in school for credit accumulation purposes could be skewed based on attendance status. However, the general pattern of results held across both definitions.

[4] I’m represented by the 2003-04 NPSAS cohort, where about 46% of first-year students on public university campuses lived in residence halls.

Trends in Zero EFC Receipt

In my third blog post using newly-released data from the 2015-16 National Postsecondary Student Aid Study (NPSAS), I turn my attention away from graduate and professional students and toward undergraduate students. Here, I update a 2015 article that I wrote for the Journal of Student Financial Aid examining trends in the share and types of students who have an expected family contribution of zero—the students who have the least financial ability to pay for college and thus qualify for the maximum Pell Grant.

Using the handy TrendStats tool on the National Center for Education Statistics’s DataLab website, I looked at six NPSAS waves from the 1995-96 to 2015-16 and pulled data for all students and then by student and institutional characteristics. The full spreadsheet can be downloaded here (including data by gender and age that I do not cover in this post), and I go through some of the highlights below.

Overall, the percentage of students with a zero EFC has steadily increased every four years since the 1999-2000 academic year in spite of ebbs and flows in the economy. Part of this is likely due to changes in the rules of who automatically qualifies for a zero EFC based on family income and means-tested benefit receipt (currently, the income limit is $25,000 per year), but increased student diversity in American higher education also plays a role. The percentages in each year are as follows:

1995-96: 18.6%

1999-2000: 17.7%

2003-04: 20.7%

2007-08: 25.4%

2011-12: 37.9%

2015-16: 39.1%

There are stark differences in the percentage of students with a zero EFC by dependency status that have grown larger over time. Independent students with dependents of their own have always been the most likely to have a zero EFC, especially because childcare obligations often limit work hours (resulting in a lower household income). The percentage of students in this category with a zero EFC remained between 35 and 40 percent through 2007-08 before spiking to 61% in 2011-12 and 67.3% in 2015-16. Dependents and independent students with no dependents had generally similar zero EFC rates in the teens through 2003-04, but then independent students started to qualify for zero EFCs at much higher rates. By 2015-16, the gap grew to 18 percentage points (42.2% versus 24.2%).

Turning next to institutional type, for-profit colleges (which tend to enroll more independent students with families of their own) have traditionally had higher zero EFC rates than other sectors. 62.2% of students at for-profits had a zero EFC in 2015-16, up from 56.8% in the last NPSAS wave and around 40% before the Great Recession. In the 1990s, community colleges, public 4-year colleges, and private nonprofit 4-year colleges all had zero EFC rates of around 15%. Community colleges’ rates passed 40% in 2011-12, while four-year public and nonprofit colleges’ rates exceeded 30% in 2015-16. Notably, the percentage of zero EFC students at four-year private nonprofit colleges jumped from 25.7% to 30.5% in this NPSAS wave, a much larger increase than among public 4-year colleges.

Readers of my last two blog posts should not be terribly surprised to see that African-American students have been the most likely to have a zero EFC across the last six NPSAS administrations, although there was a slight decrease between 2011-12 and 2015-16 (60.0% to 58.2%). American Indian/Alaska Native students had the next highest zero EFC percentage (51.2%), followed by Hispanic/Latino students (47.6%), Asian students (39.2%), and white students (29.8%). Multiracial students saw an increase in zero EFC rates from 39.1% to 41.8%, but this group is not shown in the chart due to changes in how the Department of Education has classified race and ethnicity over time.

Finally, I examine zero EFC receipt trends by parental education—beginning in the 1999-2000 academic year due to changes in the survey question following the 1995-96 NPSAS. There is a clear relationship between parental education and zero EFC rates, with more than half of all students whose parents never attended college having a zero EFC in 2015-16 and progressively lower rates for students with highly-educated parents. However, two trends stand out among non-first-generation students. The largest increase in zero EFC rates by parental education in the last two NPSAS waves was among families with some college experience or an associate degree (rising from 37.9% to 42.6%). Meanwhile, even among students who had at least one parent with a graduate degree, 27.5% still qualified for a zero EFC.

Readers, if there are any pieces of the new NPSAS data that you would like me to examine in a future blog post, leave me a note in the comments section or send me a tweet. I’m happy to dig into other pieces of the dataset!

What Explains Racial Gaps in Large Graduate Student Debt Burdens?

In my previous blog post, I used brand-new data from the 2015-16 National Postsecondary Student Aid Study (NPSAS) to look at trends in debt burdens among graduate students. The data point that quickly got the most attention was the growth in the percentage of African-American graduate students with at least $100,000 in debt between their undergraduate and graduate programs, with 30% of black students having six-figure debt burdens in 2015-16 compared to just 12% of white borrowers. This means that roughly 150,000 black borrowers had $100,000 in debt, more than half of the number of white borrowers with the same debt level (250,000) despite white graduate student enrollment being four times as white as black grad student enrollment.

My next step is to examine whether the black-white borrowing gap could be explained by other demographic and educational factors. I ran two logistic regressions with the outcome of interest being $100,000 or more in total educational debt using PowerStats, with the results presented in odds ratios. (To interpret odds ratios, note that they are percent changes from 1. So a coefficient of 0.5 means that something is 50% less likely to happen and 1.5 means that something is 50% more likely to happen.) The first regression below only controls for race/ethnicity.

Table 1: Partial regression predicting likelihood of $100,000 or more in debt among graduate students.
  Coefficient (Odds Ratio)    
Characteristic 95% CI p-value
Race/ethnicity (reference: white)
  Black or African American 2.50 (1.91, 3.26) 0.000
  Hispanic or Latino 1.12 (0.89, 1.41) 0.347
  Asian 0.62 (0.46, 0.83) 0.002
  American Indian or Alaska Native 1.31 (0.49, 3.50) 0.595
  Native Hawaiian/other Pacific Islander 1.35 (0.38, 4.74) 0.640
  More than one race 1.73 (1.08, 2.77) 0.023
Source: National Postsecondary Student Aid Study 2015-16.    

 

This shows that black students were 150% more likely to have six-figure debt than white students (p<.001), while Asian students were 38% less likely (p<0.01). Hispanic students had a slightly higher point estimate, but it was not statistically significant.

I then controlled for a number of factors that could be associated with high graduate student debt amounts, including other demographic characteristics (gender, age, and marital status), level of study (master’s or doctoral), institution type, and field of study. The regression results are shown below.

Table 2: Full regression predicting likelihood of $100,000 or more in debt among graduate students.
  Coefficient (Odds Ratio)    
Characteristic 95% CI p-value
Race/ethnicity (reference: white)
  Black or African American 2.30 (1.79, 2.97) 0.000
  Hispanic or Latino 1.03 (0.80, 1.33) 0.828
  Asian 0.69 (0.48, 0.98) 0.036
  American Indian or Alaska Native 0.97 (0.25, 3.77) 0.964
  Native Hawaiian/other Pacific Islander 1.61 (0.44, 5.84) 0.468
  More than one race 1.82 (1.12, 2.95) 0.015
Female 1.00 (0.84, 1.19) 0.990
Age as of 12/31/2015 1.04 (1.03, 1.04) 0.000
Marital status (reference: single)
  Married 0.68 (0.55, 0.85) 0.001
  Separated 0.94 (0.51, 1.73) 0.840
Graduate institution (reference: public)
  Private nonprofit 1.64 (1.36, 1.98) 0.000
  For-profit 2.15 (1.64, 2.82) 0.000
Graduate degree program (reference: master’s)
  Research doctorate 3.00 (2.38, 3.78) 0.000
  Professional doctorate 7.07 (5.61, 8.90) 0.000
Field of study (reference: education)
  Humanities 0.99 (0.66, 1.48) 0.943
  Social/behavioral sciences 1.85 (1.38, 2.48) 0.000
  Life sciences 1.71 (1.14, 2.56) 0.009
  Math/Engineering/Computer science 0.34 (0.20, 0.57) 0.000
  Business/management 0.91 (0.64, 1.28) 0.577
  Health 1.93 (1.47, 2.53) 0.000
  Law 1.38 (0.90, 2.11) 0.140
  Others 1.26 (0.89, 1.79) 0.186
Source: National Postsecondary Student Aid Study 2015-16.    

 

Notably, the coefficient for being African-American (relative to white) decreased slightly in the regression with additional control variables. Black students were 130% more likely to have six-figure debt burdens than white students, down from 150% in the previous regression. Not surprisingly, doctoral students, students at private nonprofit and for-profit colleges, and students studying health, life sciences, and social/behavioral sciences were more likely to have $100,000 in debt than public university students, master’s students, and those studying education. Meanwhile, STEM students were far less likely to have $100,000 in debt than education students, which is not surprising given the large number of assistantships available in STEM fields.

This regression strongly suggests that the black/white gap in large student debt burdens cannot be explained by other demographic characteristics or individuals’ fields of study. Financial resources (such as the large wealth gap between black and white families) are likely to blame, but this is not well-measured in the NPSAS. The best proxy is a student’s expected family contribution (EFC), which only measures a student’s own resources as an adult student. Including EFC as a variable in the model brings the black/white gap down to 120% (not shown here for the sake of brevity), but a good measure of wealth likely shrinks the gap by a much larger amount.

Examining Trends in Graduate Student Debt by Race and Ethnicity

For many of us in the higher education world, the release of the newest wave of the National Postsecondary Student Aid Study (NPSAS) is something akin to a national holiday. The NPSAS is a nationally-representative dataset of both undergraduate and graduate students that has provided a snapshot every four years of the state of how students pay for higher education. (Going forward, there will be a new dataset produced every two years, which is great news!) The 2015-16 NPSAS dropped on Tuesday morning, which sent nerds everywhere running to their computers to run numbers via PowerStats.

In this post, I look at graduate student borrowing, which is of increasing interest to policymakers given the average size of graduate student loan burdens and the potential implications for taxpayers thanks to income-driven repayment and Public Service Loan Forgiveness. I used the TrendStats tool to look at graduate student loan debt by race/ethnicity every four years from 2000 to 2016, based on concerns raised by Judith Scott-Clayton about the growth in student debt among African-American students.

The first figure looks at overall trends in graduate student borrowing across each of the five cohorts. The percentage with no debt fell from 51% in 2000 to 39% in 2008 before remaining steady throughout the rest of the period. Meanwhile, the percentage with at least $50,000 in debt (for both undergraduate and graduate school) went up from 9% in 2000 to 32% in 2016, with a steady upward trend across every cohort. The increases were even larger among those with more than $100,000 in debt, with that share going from 1.5% to 14.2% during this period. (The introduction of Grad PLUS loans in 2006 probably didn’t hurt that trend, although the jump between 2008 and 2012 was larger than the jump between 2004 and 2008.)

I broke down the borrowing data by race/ethnicity to look at the percentage of graduate students with no debt at all across each cohort. Across each cohort, at least 60% of Asian students had no debt, while the percentage of white students with no debt was 51% in 2000 before meandering around 40% in more recent cohorts. Forty-five percent of Hispanic students had no debt in 2000, which steadily fell to 27% in 2016. Among African-American students, however, the percentage with no debt fell from 37% in 2000 to 17% in both 2012 and 2016. Part of this may be due to the higher likelihood of black students to study in fields with fewer graduate assistantships (such as education), but family resources likely play a crucial role here.

Finally, I examined the percentage of students with at least $100,000 in educational debt by race and ethnicity. All groups of students started out at between one and two percent with six-figure debts in 2000, but those rates quickly diverged. By 2012, 7% of Asian students, 11% of white students, 14% of Hispanic students, and 21% of black students had at least $100,000 in educational debt. In the newest NPSAS wave, all racial/ethnic groups except black students stayed within one percentage point of their 2012 level. But in 2016, an astonishing 30% of African-American graduate students had at least $100,000 in debt—nearly three times the rate of white students.

In future posts, I will look at some other interesting tidbits from the new NPSAS data. But for right now, these graphics are so depressing that I need to step away and work on something else. Student loan debt isn’t a crisis for all students, but it’s an increasingly urgent matter for African-American students in particular as well as for taxpayers who will be expected to pay for at least partial loan forgiveness.

[Check out my next post for some regressions that explore the extent to which the black/white gap in the percentage of grad students with $100,000 in debt can be explained by other factors.]

Is Administrative Bloat Really a Big Problem?

I usually begin talks on my book Higher Education Accountability with a discussion of why accountability pressures now are stronger than ever for much of nonprofit higher education. Not surprisingly, one of the key reasons that I discuss is the rising price tag of a college education. I usually get at least one question from audience members in every talk about the extent to which administrative bloat in higher education is driving up college prices. I have written before about how difficult it is to pin the rising cost of providing a college education on any given factor, but I am diving in deeper on the administrative bloat concern in this post.

First, let’s take a look at trends in administrative expenditures and staffing over the last decade or two. Here are charts on inflation-adjusted per-FTE expenditures for instruction, academic support, institutional support, and student services between 2003 and 2013 (courtesy of Delta Cost Project analyses). The charts show that spending on student services and academic support increased faster than both inflation and instructional expenditures, while institutional support expenditures (the IPEDS expenditure category most closely associated with administration) increased about as fast as instructional expenditures.

Turning to staffing trends, I again use Delta Cost Project data to look at the ratios of full-time faculty, part-time faculty, administrators, and staff per 1,000 FTE students. In general, the ratio of full-time faculty and administrators per 1,000 students held fairly constant across time in most sections of higher education. However, the ratio of part-time faculty and professional staff members (lower-level administrators) increased markedly across higher education.

The data suggest that there has not been a massive explosion of high-level administrators, but there has been substantial growth in low- to mid-level academic support and student services staff members. What might be behind that growth in professional staff members? I offer two potential explanations below.

Explanation 1: Students need/want more services than in the past. As most colleges have enrolled increasingly diverse student bodies and institutions respond to pressures to graduate more students, it’s not surprising that colleges have hired additional staff members to assist with academic and social engagement. Students have also demanded additional services, such as more staff members to support campus diversity initiatives. (Lazy rivers and climbing walls could factor in here, but there are limited to such a small segment of higher education that they’re likely to be a rounding error in the grand scheme of things.)

Explanation 2: Staff members are doing tasks that faculty members used to do, which may not necessarily be a bad thing. A good example here is academic advising. Decades ago, it was far more common for faculty members to advise undergraduate students from their first year on. But over the years, professional academic advisers have taken on these responsibilities at many campuses, leaving faculty members to advise juniors and seniors within a major. To me, it seems logical to allow lower-paid professional advisers to work with first-year and second-year students, freeing up the time of higher-paid faculty members to do something else such as teach or do research. (I also have a strong hunch that professional advisers are better at helping students through general education requirements than faculty members, but I’d love to see more research on that point.)

In summary, there are lots of gripes coming from both faculty members and the public about the number of assistant and associate deans on college campuses. But most of the growth in non-faculty employees is among lower-level student and academic affairs staff members, not among highly-paid deans. There is still room for a robust debate about the right number of staff members and administrators, but claims of massive administrative bloat are not well-supported across all of higher education.

It’s hard to believe that a faculty member is writing this, but I do feel that most administrators do serve a useful purpose. As I told The Chronicle of Higher Education in a recent interview (conducted via e-mail while I was waiting for a meeting with an associate dean—I kid you not!), “Faculty do complain about all of the assistant and associate deans out there, but this workload would otherwise fall on faculty. And given the research, teaching, and service expectations that we face, we can’t take on those roles.”

The Potential Role of States in Setting Living Allowance Estimates

For most American college students, the non-tuition portions of the cost of attendance (room and board, books and supplies, and a miscellaneous expenses category) are larger than tuition and fees. Colleges can set these estimates as they deem fit, and previous research by me, Sara Goldrick-Rab of Temple University, and Braden Hosch of Stony Brook University shows a large amount of variation in living allowances among colleges in the same geographic area. This means that similar students can access different amounts of financial aid—and that colleges with the same tuition price can look much different in a range of accountability measures.

As the U.S. Department of Education currently does not provide guidance for colleges in setting these allowances (and Higher Education Act reauthorization looks increasingly unlikely in 2018), it is worth exploring whether states should step in and provide some assistance for their public colleges and universities. In the two blog posts below, I teamed up with David Tandberg of the State Higher Executive Officers Association and Sarah Pingel of Education Commission of the States to further examine the topic.

Detailed post (with data on variations within and across states)

Summary post

We would love to hear your thoughts on this issue, so send them along!

Comments on Accountability and the Higher Education Act

As the Senate works on its version of a Higher Education Act reauthorization bill, the Health, Education, Labor and Pensions Committee has made accountability one of its key areas of discussion in recent hearings. Committee chairman Lamar Alexander asked the higher education community for comments regarding accountability, so I sent my comments along to the committee. They are reprinted below.

February 15, 2018

The Honorable Lamar Alexander

The Honorable Patty Murray

Senate Health, Education, Labor and Pensions (HELP) Committee

Dear Senators:

My name is Robert Kelchen and I am an assistant professor of higher education at Seton Hall University.[i] I have been closely following the Higher Education Act reauthorization hearings in the Senate HELP Committee and am pleased to see the committee beginning to work on writing a comprehensive, bipartisan piece of legislation. Accountability is a crucial issue for both protecting students and taxpayers alike, and is such it is essential to design a system that encourages institutional improvement while discourages colleges from trying to game the system to remain eligible to receive federal Title IV financial aid dollars.

I have spent the last several years researching higher education accountability in all of its forms, including efforts by the federal government, state governments, accrediting agencies, the private sector, and colleges themselves.[ii] In this letter, I share four key points from my research on how to design accountability systems that have the highest likelihood of success and provide the best possible information for students, their families, and policymakers.

 

Point 1: Avoid all-or-nothing accountability systems. Three key federal accountability policies—cohort default rates (CDRs), the 90/10 rule, and gainful employment—grant institutions access to federal Title IV financial aid if they pass a certain threshold. Although the gainful employment metrics are too new to actually take away programs’ financial aid, it is clear from CDRs and the 90/10 rule that very few colleges are affected. In the most recent year of data, only ten small colleges faced the loss of either federal student loans or all Title IV aid for high CDRs and no colleges were subject to the loss of federal student aid for failing the 90/10 rule in two consecutive years.[iii]

Setting minimum performance floors sounds like an appealing idea, and the idea of ‘bright line’ standards has been proposed with respect to recognizing accreditors.[iv] But actually following through and pulling the plug on the lowest-performing colleges by denying them access to federal financial aid is a much more difficult task. As the majority staff’s white paper notes, just eleven colleges have lost access to Title IV funds due to high CDRs since 1999—even though approximately ten colleges per year should have lost aid if federal laws were strictly followed. Congress and the Department of Education have shown a lack of willingness to effectively close colleges (particularly public or private nonprofit institutions), as shown by the Senate Majority Leader’s recent efforts to exempt a Kentucky technical college from losing Title IV aid and the previous administration’s alteration of CDR calculations just prior to release in 2014 that protected an unknown number of colleges.[v]

A more effective way to hold a larger number of colleges accountable for their outcomes is to use a gradually increasing set of sanctions for lower-performing institutions. In theory, risk-sharing proposals for federal student loan repayment can provide that sort of incentive. However, the PROSPER Act’s loan repayment metric would create the same all-or-nothing incentive that would be subject to both institutional gaming and intense lobbying efforts. The Student Protection and Success Act creates a sliding scale to some extent (although retaining a minimum eligibility threshold), and academics’ risk-sharing proposals also are based on sliding scales.[vi] It is also important to note that risk-sharing proposals should include incentives for institutional improvement as well as sanctions, similar to what the ASPIRE Act would do.

 

Point 2: Both institution-level and program-level accountability policies are important. Policy conversations are rapidly moving toward holding individual programs within colleges accountable for their performance. Gainful employment regulations already seek to do that for a subset of programs, but the PROSPER Act would use program-level loan repayment rates for all institutions of higher education. This proposal makes intuitive sense, but program-level data collection efforts have some important limitations.

In general, programmatic outcomes data make the most sense when students enter a college or university with a particular field of study in mind. This is less of a concern for vocationally-oriented programs at the undergraduate level and for graduate and professional education in which students are generally admitted to study in particular programs. But not all students enter associate or baccalaureate degree programs with a declared major, and roughly one-third of first-time college students changed their declared major at least once within three years of starting college.[vii] This means that attributing student outcomes to a particular program becomes a concern. Additionally, if undeclared students are ignored for the purposes of program-level accountability metrics, colleges suddenly have an incentive to restrict when students can officially declare majors. Waiting until two years into a bachelor’s degree program to declare a major restricts the pool of students to those with a higher likelihood of success, meaning that dropouts are less likely to be counted.

Another potential option is to restrict program-level accountability only to students who graduate, as is the case with the current gainful employment regulations. But this obscures important data for students, since only 54% of students at four-year colleges and 32% of students at two-year colleges graduated from that same institution within eight years of initial enrollment.[viii] It is likely the case that reported program-level outcomes are far better for graduates than dropouts, thus providing an overly rosy picture of lower-performing programs.[ix]

Given the interest in program-level outcomes data alongside the difficulty in fully relying on program-level accountability measures in certain sectors of higher education, a more reasonable solution would be to use a combination of program-level and institution-level data for accountability purposes. It may be worthwhile to consider tougher performance measures for entire institutions than individual programs due to the difficulty in accurately measuring programmatic outcomes in non-vocational fields and due to concerns about small cell sizes for certain programs of study.

 

Point 3: Defining the loan repayment rate is perhaps the most important accountability-related issue in HEA reauthorization. Very few academics consider CDRs to be a tremendously valuable measure of institutional performance due to their ability to be manipulated by colleges, the presence of income-driven repayment programs, and their relatively short time horizon. The student loan repayment rate that was included in the 2015 release of the College Scorecard represented a more comprehensive look at how former students are managing their loans, and painted a completely different pictures than CDRs (especially after an unfortunate coding error was finally fixed in early 2017).[x]

Both the definition of student loan repayment rates in the College Scorecard and the types of loans included are decisions that have substantial accountability implications. The Scorecard definition (repaying at least $1 in principal at 1, 3, 5, and 7 years after entering repayment) includes federal subsidized and unsubsidized loans, omitting Parent and Grad PLUS loans. Approximately $21 billion of the $94 billion in federal loans during the 2016-17 academic year was in the form of PLUS loans, yet this is entirely missing from the Scorecard repayment rate (and CDRs, as well).[xi] It may be worth considering a separate loan repayment rate metric for Parent PLUS loans, but Grad PLUS loans should be included with other student loans for accountability purposes.

There are two other potential definitions of loan repayment rates that are worth considering. The first is the percentage of dollars that are repaid during a certain period of time. This is similar to the initial definition of repayment rates that was used in the 2010 negotiations regarding gainful employment.[xii] This is a more taxpayer-focused metric, as it captures overall risk of nonrepayment instead of the percentage of borrowers who are struggling to pay down principal. The second definition is the percentage of students who are on track to repay their loans within a fixed window of time. The challenge with this definition is that students can choose from a menu of loan repayment plans, with extended payment plans being particularly common among students with larger amounts of debt.

A further complicating factor is the growth of income-driven repayment plans, which now represent about 40% of all outstanding federal student loan dollars.[xiii] These plans often result in outstanding balances rising in early years of repayment (when incomes are low) before principal is paid down later. An analysis of recent bachelor’s degree recipients found that only about 25% of students in income-driven plans paid off any principal within five years of entering repayment, while about 75% of students not in income-driven repayment had repaid principal within one year of entering repayment.[xiv] How to address students in income-driven repayment plans is a key concern regarding student loan repayment rates, as the federal government could simultaneously encourage students to enroll in income-driven plans while penalizing colleges where students take up such plans.

 

Point 4: Free the higher education data! Students and their families are currently being asked to make one of the biggest financial decisions of their lives based on relatively little objective information. The College Scorecard was a helpful step forward, as was including part-time and Pell recipient graduation rates in the Integrated Postsecondary Education Data System in 2017. A student unit record data system would certainly be helpful in making better data available in the college choice process, but there are things that the Department of Education can do (with the support of Congress) without overturning the ban. A few of the most important data points are the following:

  • Program-level outcomes for all Title IV institutions. Regardless of how the gainful employment negotiated rulemaking panel turns out, HEA reauthorization should encourage data to be released for all programs of study (with the caveats as noted earlier in this letter). Some programmatic accreditors are starting to require institutions to release this sort of information, but ED can do so fairly easily for all students receiving federal financial aid. It would be nice to include all students attending Title IV-participating institutions, but program-level data for federal aid recipients would be a good start.
  • Separate data for undergraduate and graduate students. Although graduate and professional students represent just 17% of all federal student loan borrowers, they make up 38% of all federal student loan dollars.[xv] Yet loan repayment and debt data are not presented separately for graduate students—and current data do not even include Grad PLUS loans.
  • Include outcomes for Parent PLUS loans. Although only five percent of students had a Parent PLUS loan in the 2011-12 academic year, this is still an important financing source for families; borrowing rates are higher (13%) at HBCUs and average loan amounts among borrowers are over $12,000.[xvi] Yet the only information available on PLUS loan outcomes is a set of sector-level default rates that ED released following a negotiated rulemaking panel in 2014.[xvii] It is more difficult to envision a high-stakes accountability policy based on parent outcomes instead of student outcomes, but making institution-level data public would be a valuable service to families.
  • Provide data on incomedriven repayment plan usage by institution. Because repayment rates are affected by income-driven repayment plans, it would be helpful to provide information on the percentage of borrowers from each institution who are enrolled in income-driven repayment plans, ideally at the undergraduate and graduate level. A lower repayment rate at a college that graduates a large percentage of students into public service may be more acceptable due to income-driven repayment plans, while a similar repayment rate at a college where students are enrolled in more lucrative majors would be a greater cause for concern.[xviii]

I would like to thank the HELP Committee for holding a series of hearings on Higher Education Act reauthorization and for actively engaging with the research community throughout the process. As the committee is drafting the bill over the next few months, so I encourage Senators and staff members to continue reaching out to researchers while considering potential policy proposals and legislative text. I am more than happy to talk with any committee members during this process and I wish you all the best of luck in working on a much-needed overhaul of the Higher Education Act.

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[i] All views and opinions expressed in this letter are mine alone and do not necessarily reflect my employer.

[ii] See my new book Higher Education Accountability, which is now available through Johns Hopkins University Press.

[iii] Author’s calculations using Federal Student Aid data on cohort default rates (FY14 cohort) and proprietary school revenue percentages (2015-16). Additionally, I appreciate the majority staff referencing one of my blog posts on the topic—that made my day!

[iv] Lederman, D. (2017, June 21). Where winds are blowing on accreditation. Inside Higher Ed. https://www.insidehighered.com/news/2017/06/21/bright-line-indicators-student-outcomes-dominate-discussion-federal-accreditation.

[v] Douglas-Gabriel, D. (2018, February 8). McConnell attempts to protect two Kentucky colleges in budget deal. The Washington Post. https://www.washingtonpost.com/news/grade-point/wp/2018/02/08/mcconnell-attempts-to-protect-two-kentucky-colleges-in-budget-deal/?utm_term=.a5e47c2fb77e. Stratford, M. (2014, September 24). Reprieve on default rates. Inside Higher Ed. https://www.insidehighered.com/news/2014/09/24/education-dept-tweaks-default-rate-calculation-help-colleges-avoid-penalties.

[vi] Kelchen, R. (2015). Proposing a federal risk-sharing policy. Indianapolis, IN: Lumina Foundation. Webber, D. A. (2017). Risk-sharing and student loan policy: Consequences for students and institutions. Economics of Education Review, 57, 1-9.

[vii] Leu, K. (2017). Beginning college students who change their majors within 3 years of enrollment. Washington, DC: National Center for Education Statistics Report NCES 2018-434.

[viii] Ginder, S. A., Kelly-Reid, J. E., & Mann, F. B. (2017). Graduation rates for selected cohorts, 2008-13; outcome measures for cohort year 2008; student financial aid, academic year 2015-16; and admissions in postsecondary institutions, fall 2016. Washington, DC: National Center for Education Statistics Report NCES 2017-150rev.

[ix] There is an argument that students want outcomes of graduates only instead of combining graduates and dropouts, given common complaints about College Scorecard data and the common trend of students overstating their likelihood of graduation. But for an accountability system tied to federal financial aid instead of consumer information, the proper sample may differ.

[x] Kelchen, R., & Li, A. Y. (2017). Institutional accountability: A comparison of the predictors of student loan repayment and default rates. The ANNALS of the American Academy of Political and Social Science, 671, 202-223.

[xi] Baum, S., Ma, J., Pender, M., & Welch, M. (2017). Trends in student aid. New York, NY: The College Board.

[xii] Belfield, C. R. (2013). Student loans and repayment rates: The role of for-profit colleges. Research in Higher Education, 54(1), 1-29.

[xiii] Author’s analysis using Federal Student Aid data.

[xiv] Conzelman, J. G., Smith, N. D., & Lacy, T. A. (2016, July 11). The tension between student loan accountability and income-driven repayment plans. Brown Center Chalkboard. https://www.brookings.edu/blog/brown-center-chalkboard/2016/07/11/the-tension-between-student-loan-accountability-and-income-driven-repayment-plans/.

[xv] Baum, S., & Steele, P. (2018). Graduate and professional school debt: How much students borrow. West Chester, PA: AccessLex Institute.

[xvi] Goldrick-Rab, S., Kelchen, R., & Houle, J. (2014). The color of student debt: Implications of federal loan program reforms for black students and historically black colleges and universities. Madison, WI: Wisconsin HOPE Lab.

[xvii] Stratford, M. (2014, April 3). Default data on Parent PLUS loans. Inside Higher Ed. https://www.insidehighered.com/news/2014/04/03/education-department-releases-default-data-controversial-parent-plus-loans.

[xviii] It would be helpful to have data on Public Service Loan Forgiveness interest by institution, but these data would be incomplete because students do not have to signal any intent to use the program until they officially apply after making 120 qualifying payments.

Examining Long-Term Student Loan Default Rates by Race and Ethnicity

Since the release of long-term student loan default data in the Beginning Postsecondary Students Longitudinal Study last fall, one finding that has gotten a great deal of attention is the large gap in default rates by race and ethnicity. Judith Scott-Clayton of Teachers College, Ben Miller of the Center for American Progress, and I all highlighted the high percentage of African-American students who began college in the 2003-04 academic year and defaulted on their loans by 2015. As the chart below shows, black students were more than twice as likely to default on their loans than white students (49% versus 20%), with some differences by institutional type.

But since some of the difference in default rates could be due to other factors (such as family resources and the type of college attended), I ran logistic regressions using the handy regression tools in PowerStats. The first regression just controls for race/ethnicity, while the second regression adds in other control variables of interest. The results are presented as odds ratios, meaning that coefficients larger than 1 reflect a higher likelihood of default and coefficients smaller than 1 reflect a lower likelihood. (Here’s a good primer on interpreting odds ratios.)

In the first regression, the odds ratios for black (3.69), Hispanic (2.09), multiracial (2.56), and American Indian/Alaska Native students (2.45) were all significantly higher than white students at p<.05, while Asian students (0.48) had significantly lower default rates.

Results of logistic regression predicting student loan default rates by 2015 (with controls).
Variable Odds Ratio Lower 95% Upper 95% p-value
Race/ethnicity (reference group: white)
  Black or African American 3.6890 3.0490 4.4620 <0.01
  Hispanic or Latino 2.0870 1.5770 2.7600 <0.01
  Asian 0.4750 0.3170 0.7120 <0.01
  American Indian or Alaska Native 2.4540 1.1220 5.3680 0.03
  Native Hawaiian / other Pacific Islander 0.7170 0.1640 3.1330 0.66
  Other 1.2200 0.7610 1.9560 0.41
  More than one race 2.5640 1.6800 3.9140 <0.01
Source: Beginning Postsecondary Students Longitudinal Study.

After adding in control variables, the coefficients for underrepresented minority students were somewhat smaller. But for African-American (2.56) and multiracial (2.45) students, they were still significantly higher than for white students after adding other controls. This means that black students were about 150% more likely to default than white students—an enormous gap after taking a number of other important factors into account. The coefficients for Hispanic and American Indian students were no longer significant, and Asian students were still less likely to default than white students (an odds ratio of 0.42).

Variable Odds Ratio Lower 95% Upper 95% p-value
Race/ethnicity (reference group: white)
  Black or African American 2.5587 2.0370 3.2139 <0.01
  Hispanic or Latino 1.2606 0.9526 1.6683 0.10
  Asian 0.4249 0.2629 0.6869 <0.01
  American Indian or Alaska Native 1.7371 0.7307 4.1299 0.21
  Native Hawaiian / other Pacific Islander 0.3473 0.0473 2.5505 0.30
  Other 0.8835 0.4458 1.7509 0.72
  More than one race 2.4492 1.5499 3.8704 <0.01
Parents’ highest level of education (reference group: high school grad)
  Did not complete high school 0.7242 0.5085 1.0315 0.07
  Some college or associate degree 0.7883 0.6404 0.9702 0.02
  Bachelor’s degree 0.6181 0.4821 0.7923 <0.01
  Graduate/professional degree 0.5502 0.4242 0.7135 <0.01
Income as percent of poverty level 2003-04 0.9981 0.9976 0.9987 <0.01
Dependency status 2003-04 (reference group: dependent)
  Independent 1.4552 1.0738 1.9719 0.02
Gender (reference group: female)
  Male 1.3553 1.1491 1.5984 <0.01
Age first year enrolled 0.9893 0.9709 1.0081 0.26
First institution sector 2003-04 (reference group: community colleges)
  Public 4-year 0.7858 0.6403 0.9644 0.02
  Private nonprofit 4-year 0.7756 0.6025 0.9985 0.05
  Private nonprofit 2-year or less 1.4838 0.6498 3.3880 0.35
  For-profit 2.1968 1.7624 2.7384 <0.01
Source: Beginning Postsecondary Students Longitudinal Study.

Additionally, the regression also shows the importance of parental education, family income, and sector of attendance in predicting the likelihood of long-term default. Notably, students who began at a for-profit college were about 120% more likely to default on their loans than community college students, while four-year students were less likely. Men were 36% more likely to default on their loans than women, an interesting finding given men typically earn more money than women.

Much more needs to be done to dig deeper into factors associated with long-term student loan default rates. But at this point, it appears clear that other demographic and institutional characteristics available in the BPS do relatively little to explain the large gaps in default rates between black and white students. It would be helpful to have measures of family wealth available given large black-white differences in wealth to see how much of the variation in default rates is due to family resources.