Blog (Kelchen on Education)

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.

New Higher Education Policy Voice: Amy Li

Amy Li (@AmyLiphd) is a second-year assistant professor in the Department of Leadership, Policy, and Development at the University of Northern Colorado. Given that her research interests are in the areas of higher education finance and policy, it is no surprise that our paths quickly crossed on the conference circuit. Amy then reached out to me as she was finishing her PhD at the University of Washington to talk about future plans after graduation. We had a nice Skype conversation, and then a day later we received a call for paper proposals from The ANNALS of the American Academy of Political and Social Science for a special issue on student loan debt. We decided to put in a proposal for a paper comparing factors affecting student loan default and repayment rates, which was accepted. I enjoyed working with Amy on the resulting article, which was recently cited in Senator Lamar Alexander’s white paper on Higher Education Act reauthorization. We also have more follow-up research in progress on this topic.

Amy has written several articles on state performance-based funding policies using both qualitative and quantitative methods (an unusual skill for a researcher). This includes topics such as what leads states to adopt performance funding, the policies’ implications on equity, and how institutions are interpreting these policies. She has also received two research grants: one to examine the equity implications of tuition-free college programs and one examining the price sensitivity of law school students.

Examining Average Student Loan Balances by State

In a blog post last month, I used newly-available data from the U.S. Department of Education’s Office of Federal Student Aid to look at the amount of student loan dollars in income-driven repayment plans by amount of debt. In that post, I showed that students with more debt were far more likely to use IDR than students with less debt, with students having over $60,000 in debt being about twice as likely to use IDR as those with between $20,000 and $40,000 in debt.

In this post, I want to highlight some other new data that provides interesting insights into the federal student aid portfolio. I looked at state-level data (based on current residence, not where they went to college) that shows outstanding balances and the number of borrowers for both all Direct Loans (the vast majority of federal student loans at this point) and for those enrolled in income-driven plans. I then estimated the average loan value by dividing the two. The data are summarized in the table below.

 All Direct Loans  Loans in IDR plans
State  Balance ($bil) Borrowers (1000s)  Avg loan  Balance ($bil) Borrowers (1000s)  Avg loan
AL 15.9 522.2        30,400 5.4 100.0        54,000
AK 1.7 59.9        28,400 0.6 10.3        58,300
AZ 21.2 711.9        29,800 7.7 137.1        56,200
AR 8.5 312.9        27,200 3.0 62.0        48,400
CA 102.8 3307.3        31,100 36.7 600.2        61,100
CO 20.4 662.1        30,800 7.7 133.6        57,600
CT 12.1 414.6        29,200 3.4 59.6        57,000
DE 3.1 101.2        30,600 1.0 17.4        57,500
DC 5.0 102.2        48,900 2.4 25.7        93,400
FL 65.7 2063.1        31,800 26.4 473.1        55,800
GA 45.8 1350.2        33,900 16.6 279.2        59,500
HI 3.1 104.5        29,700 1.1 18.2        60,400
ID 5.3 191.7        27,600 2.1 41.6        50,500
IL 45.7 1439.7        31,700 14.9 247.9        60,100
IN 21.6 794.7        27,200 7.3 152.3        47,900
IA 10.4 405.8        25,600 3.3 67.8        48,700
KS 9.2 339.5        27,100 2.9 57.6        50,300
KY 13.8 507.1        27,200 4.9 102.6        47,800
LA 14.1 499.1        28,300 4.9 95.2        51,500
ME 4.4 158.8        27,700 1.5 30.0        50,000
MD 25.1 707.2        35,500 8.3 123.5        67,200
MA 23.1 783.7        29,500 7.0 114.3        61,200
MI 37.9 1262.4        30,000 13.2 243.4        54,200
MN 19.9 709.9        28,000 6.7 124.4        53,900
MS 10.6 360.7        29,400 3.8 75.2        50,500
MO 21.0 707.2        29,700 7.6 143.5        53,000
MT 3.0 106.5        28,200 1.2 23.1        51,900
NE 5.7 216.9        26,300 1.9 37.8        50,300
NV 7.5 262.9        28,500 2.8 51.6        54,300
NH 4.7 165.2        28,500 1.4 26.2        53,400
NJ 29.7 999.5        29,700 8.4 145.1        57,900
NM 5.3 189.3        28,000 2.1 39.7        52,900
NY 67.9 2113.1        32,100 24.0 387.8        61,900
NC 32.7 1065.5        30,700 11.8 213.6        55,200
ND 1.8 75.1        24,000 0.6 12.0        50,000
OH 45.4 1577.1        28,800 16.0 313.8        51,000
OK 10.3 383.0        26,900 3.6 71.5        50,300
OR 14.9 475.8        31,300 6.1 107.7        56,600
PA 46.1 1539.3        29,900 15.1 275.3        54,800
RI 3.3 119.6        27,600 1.0 18.8        53,200
SC 18.2 584.7        31,100 6.8 123.5        55,100
SD 2.6 98.9        26,300 0.9 17.9        50,300
TN 21.3 700.7        30,400 7.8 146.0        53,400
TX 76.5 2772.1        27,600 26.1 516.4        50,500
UT 6.9 256.8        26,900 2.7 47.3        57,100
VT 2.1 66.4        31,600 0.8 13.1        61,100
VA 29.9 913.8        32,700 10.1 166.0        60,800
WA 20.1 674.8        29,800 7.4 128.0        57,800
WV 5.4 200.3        27,000 1.8 37.0        48,600
WI 17.0 646.6        26,300 5.7 114.5        49,800
WY 1.2 45.3        26,500 0.4 8.0        50,000

Nationwide, the average outstanding Direct Loan balance was right at $30,000, with significant variation across states (ranging from $24,000 in North Dakota to $48,900 in Washington, DC). The average outstanding balance in IDR was $55,800, which suggests that many borrowers in IDR attended graduate school in order to accumulate that amount of debt. State-level average IDR balances ranged from $47,800 in Kentucky to an impressive $93,400 in Washington, DC. California, Hawaii, Illinois, Maryland, Massachusetts, New York, Vermont, and Virginia all had average balances over $60,000—and they are all high cost of living states with high percentages of adults obtaining graduate or professional degrees.

Once again, kudos to the Department of Education for slowly releasing more data on the federal student loan portfolio. But there are still quite a few important data points (such as school-level data or anything on PLUS loans) that still aren’t available to the public.

New Higher Education Policy Voice: Oded Gurantz

Oded Gurantz (@odedgurantz) earned his Ph.D. in education policy from the Stanford Graduate School of Education, and will begin as an assistant professor at the Truman School of Public Affairs at the University of Missouri in Fall 2018. He is currently working as an associate policy research scientist at the College Board, where he uses the College Board’s great datasets to help answer important policy questions regarding the choices students make in high school and how they impact student success.

Oded is another doctoral candidate whose CV far exceeds those of most doctoral candidates of even five years ago. Oded has engaged in a number of projects evaluating the educational and labor market impacts of financial aid. Along with his dissertation mentor Eric Bettinger and a team of researchers, Oded worked on a rare long-term evaluation of a state financial aid program: the merit and need-based Cal Grant program. They were able to use tax data to examine long-term earnings of students, finding a modest but significant effect of receiving the grant on earnings around age 30. Work in progress is examining the impacts of aid for a variety of groups, including: older, non-traditional students; high school students offered “free” community college through the Oregon Promise; and students attending for-profit colleges.

He also conducted a fascinating study on course availability in California community colleges, which was published in The Journal of Higher Education. He found that many students waited to enroll for required classes until the very last minute, which can affect the likelihood of completing college. One implication of the study is that policies that shift registration priorities – determining which students get to select courses first – may not produce large differences in the population of students who ultimately enroll in community college courses.

New Higher Education Policy Voice: Ellie Bruecker

Fridays in the higher education policy world have a little extra meaning thanks to Ellie Bruecker (@elliebruecker), a PhD student in the Department of Education Leadership and Policy Analysis at the University of Wisconsin-Madison. Every Friday, she eagerly awaits Federal Student Aid’s release of the number of high school students who file the FAFSA by state so she can share the results via Twitter. She also works with Nick Hillman at Madison to help share the data via Nick’s great blog.

Ellie’s research interests span both K-12 and higher education finance (we need more of this!), and the FAFSA filing work is a great example. Higher education folks are really just beginning to grapple with questions of resource adequacy that K-12 people have thought about for a long time. She has worked on examining issues of K-12 voucher funding in Wisconsin and school finance as well as how California community college students afford their education. Ellie also participated in AEI’s Education Policy Academy last summer, which helps to expose graduate students to the policymaking process and how to get their work out to decisionmakers.

New Research on Brain Drain and Recent College Graduates

As I discussed in my previous post, I believe there is value in education scholars using social media in spite of the concerns that being active on venues like Twitter can raise. One example of this occurred last April, when Doug Webber of Temple University ran some numbers from the American Community Survey looking at the percentage of young college graduates who left New York (in the context of the state’s proposed Excelsior Scholarship program). The numbers got quite a bit of attention in a very nerdy portion of higher ed Twitter and led me to encourage Doug to write up the results.

He then reached out to me about working on the paper with him, which ended up being a lot of fun to write. After going through the peer review process (one substantive and one minor round of changes), our resulting article is now online at Educational Researcher. (And a big kudos to the ER reviewers and editorial team for taking the paper from initial submission to appearing online in just eight months!)

We ended up looking at state-level interstate mobility rates among young (age 22-24) bachelor’s degree recipients using ACS data, focusing on the 2005-2015 period to examine pre-recession and post-recession patterns. Overall mobility rates dropped from 12.7% in 2005 to 10.4% in 2015, which is a surprising finding given that people have historically tended to move at higher rates during economic downturns. We found quite a bit of variation across states in net interstate mobility rates both pre-recession (2005-07) and post-recession (2013-15), as summarized in the table below.

State-level changes in the number of young adults with bachelor’s degrees.
  Gain/loss of young adults w/BAs (pct)
State 2005-2007 2013-2015
Alabama -4.0 -4.6
Alaska 3.9 -5.0
Arizona 4.2 -0.5
Arkansas -1.4 -2.7
California 3.9 3.7
Colorado 0.7 8.0
Connecticut -2.3 -4.1
Delaware -17.5 -7.2
District of Columbia 20.0 19.0
Florida 2.6 1.0
Georgia 6.5 -1.0
Hawaii 7.6 8.1
Idaho -3.9 -10.8
Illinois 3.6 3.4
Indiana -12.9 -7.2
Iowa -5.1 -8.1
Kansas -10.3 -4.6
Kentucky -1.2 -2.8
Louisiana -8.3 3.4
Maine -12.5 -8.7
Maryland 4.9 -1.5
Massachusetts 0.7 2.1
Michigan -8.7 -5.6
Minnesota 1.9 -1.2
Mississippi -2.3 -10.8
Missouri -0.7 -2.6
Montana -23.4 -13.3
Nebraska 3.6 -4.3
Nevada 13.3 10.0
New Hampshire -4.6 -10.0
New Jersey 3.0 -3.4
New Mexico 4.3 2.1
New York -0.2 -0.3
North Carolina 3.6 4.2
North Dakota -9.0 -1.8
Ohio -5.9 -3.5
Oklahoma -5.8 -4.4
Oregon -2.1 1.4
Pennsylvania -6.2 -6.1
Rhode Island -19.1 -11.3
South Carolina -2.7 -2.8
South Dakota -8.0 0.0
Tennessee -1.6 1.9
Texas 3.5 3.4
Utah -12.4 -3.7
Vermont -15.4 -10.9
Virginia 3.6 2.8
Washington 6.2 6.8
West Virginia -12.7 -1.9
Wisconsin -3.3 -0.2
Wyoming 6.1 3.5
Notes:
(1) The percentages reflect changes over the number of 22-24 year olds with a bachelor’s degree who were in the state in a given year.
(2) These values represent averages across the years referenced above.

This article reflects a great example of how a willingness to share some preliminary data on social media results in a publication that is both (hopefully) policy-relevant and a chance to work with a new collaborator. I can’t say enough great things about working with Doug—and I hope to have more of these types of collaborations in the future!

Who Uses Income-Driven Repayment Plans?

Over the last two years, the U.S. Department of Education’s Office of Federal Student Aid has quietly released additional data on the federal government’s portfolio of nearly $1.4 trillion in student loans. I was on the FSA website today looking up the most recent data on Public Service Loan Forgiveness employment certification forms (up to 740,000 filed as of September 30) for a paper I am currently drafting when a new set of spreadsheets on the income-driven repayment (IDR) programs caught my eye.

Overall, just over $375 billion of the $1.05 trillion in federal Direct Loans is now enrolled across the various types of IDR programs. (The rest of the federal loan portfolio is in the old FFEL program, which does not make new loans.) This is up from $269 billion of loans in IDRs when I last wrote about the topic on my blog in mid-2016, which has implications for both students and taxpayers alike. Here, I summarize some of the new data on the types of borrowers who use IDR, as well as some of the other data elements that would be helpful to have going forward.

It is not surprising that students with more debt are more interested in income-driven repayment plans, as many borrowers with less debt could manage payments under the standard ten-year repayment plan. But I was surprised by how much of the debt is held by a small percentage of borrowers. About 1.9 million of the 35.3 million borrowers (or five percent) have more than $100,000 in debt—and this is primarily due to graduate school attendance (since undergraduates cannot borrow more than $57,500 without resorting to PLUS loans). Yet these borrowers hold $325 billion in Direct Loans, or about 30% of all loans outstanding. About $173 billion of this amount is enrolled in IDR plans—53% of all debt held by those with six-figure debts. On the other hand, less than one-fourth of all debt of borrowers with less than $40,000 outstanding is enrolled in IDR. The table and figure below show the amount of Direct Loans outstanding and the amount enrolled in IDR by debt burden.

(UPDATE 2/1/18: As a commenter noted below, there is a small percentage of loans from the old FFEL program in income-driven repayment plans. But as far as I can tell from the data, this only slightly overstates the percentage of Direct Loans in IDR. I’m confident that the general trends still hold, though.)

Table 1: Direct Loan and IDR volumes by debt burden.
Amount of debt All Direct Loans ($bil) IDR ($bil) Pct of loans in IDR
Less than $5k 16.9 0.9 5.3%
$5k-$10k 45.6 4.0 8.8%
$10k-$20k 110.7 16.7 15.1%
$20k-$40k 220.6 52.9 24.0%
$40k-$60k 154.6 50.9 32.9%
$60k-$80k 110.5 48.2 43.6%
$80k-$100k 71.3 30.3 42.5%
$100k-$200k 191.7 90.5 47.2%
More than $200k 133.5 82.3 61.6%
Total 1055.4 376.7 35.7%

I also examined new FSA data on Direct Loan and IDR volumes by age (Table 2 below) and institution type (Table 3). The data show that younger borrowers (between ages 25 and 49) have a higher percentage of dollars in IDR than older borrowers and that students who attended private nonprofit and for-profit colleges rely on IDR more heavily than students who went to public colleges. The finding by sector matches general patterns in tuition and fees, but it does not suggest that for-profit college students disproportionately turn to IDR to manage their loan burdens.

Table 2: Direct Loan and IDR volumes by age of borrower.
Age All Direct Loans ($bil) IDR ($bil) Pct of loans in IDR
24 or younger 128 9.2 7.2%
25-34 418.3 177.4 42.4%
35-49 337.9 139.8 41.4%
50-61 140.5 39 27.8%
62 or older 30.6 11.3 36.9%
Total 1055.3 376.7 35.7%

 

Table 3: Direct Loan and IDR volumes by institutional type.
Sector All Direct Loans ($bil) IDR ($bil) Pct of loans in IDR
Public 464.2 143.3 30.9%
Private nonprofit 337 126.5 37.5%
For-profit 176.6 63.6 36.0%
Total 977.8 333.4 34.1%

There are two additional data elements that would be extremely useful in considering the implications of income-driven repayment plans. Ideally, data on IDR takeup would be available at the institutional level (as I have politely requested in the past). But at the very least, a breakdown by undergraduate/graduate student status would be useful information.

And one final request of any journalists or qualitative researchers who may be reading this blog—I would love to know more about how the PSLF approval process is going now that some borrowers have made the 120 monthly payments necessary to qualify for forgiveness. It’s been strange not to hear anything about that process after applications could be submitted as early as October 2017.

New Higher Education Policy Voice: Denisa Gándara

Denisa Gándara (@GandaraDenisa) is a second-year assistant professor in the Department of Education Policy and Leadership at Southern Methodist University. Her research focuses on state-level higher education finance policies, and she is one of a small number of scholars who can combine expertise in qualitative and quantitative methods with deep knowledge of the state policymaking process.

Much of Denisa’s research has examined state performance funding policies in higher education, which have spread throughout much of the country in the last two decades in spite of (to this point, at least) generally having at most very modest effects. In an article recently published in The Journal of Higher Education, Denisa worked with Jennifer Rippner of the University System of Georgia and Erik Ness of the University of Georgia to interview stakeholders in three states to learn more about how national organizations have helped to foster the spread of performance funding.

She then teamed up with Amanda Rutherford of Indiana University to look at whether states that directly rewarded underrepresented students in their performance funding models–the newest wave of performance funding strategies–were effective in increasing minority and low-income student enrollment. In this article (just out in Research in Higher Education), they found some evidence that the incentives achieved this goal.

Don’t Expect a Wave of Private Nonprofit College Closures

American higher education certainly faces its share of challenges. Overall higher education enrollment has dropped from its post-recession high, students and their families are increasingly skeptical of the value of higher education, and the credit rating agency Moody’s recently downgraded the sector to negative from neutral over revenue concerns. These challenges have led to some doomsday predictions regarding college closures; Clayton Christensen of Harvard predicted back in 2011 that half of all colleges would close within 10 to 15 years and since doubled down on his prediction.

To this point, the data tell a different story. While a sizable number of for-profit colleges merge or close in a given year, nonprofit higher education is remarkably stable (and public colleges rarely ever close). According to the U.S. Department of Education, eight degree-granting private nonprofit colleges closed in 2015-16 (the most recent year of data available). Yet the number of degree-granting private nonprofit colleges increased from 1,672 to 1,701—the largest number in 20 years.

Among private nonprofit colleges, there are a few clear risk factors for closure. Small, less-selective institutions with tiny endowments are at a higher risk of closure, particularly if they are located in parts of the country where the pool of traditional-age students is drying up. But these risk factors have existed for decades, yet there is rarely a year in which ten private nonprofit colleges close. (Moody’s expects the number to rise to about 15 per year going forward.)

A recent article published in The Journal of Higher Education helps to provide some data on how resilient small private colleges can be. Melissa Tarrant of the University of West Georgia led a team of researchers who looked back at a 1972 paper by Alexander Astin and Calvin Lee called “The Invisible Colleges.” In that paper, Astin and Calvin identified 491 private, broad-access institutions with fewer than 2,500 students—exactly the type of college that is of greater risk of closure. Yet Tarrant and colleagues showed that 354 of the colleges (more than 70%) were still operating as standalone private nonprofit institutions and only 80 had closed in the following four decades. A failure rate of less than 20% over 40 years does not bode well for predictions that higher education as we know it is going away anytime soon.

A case can be made that the current number of small private colleges is more than would exist if the higher education system were to be designed from scratch to meet the needs of today’s students. But Christensen misses the loyalty of campus communities and alumni (the saga of Sweet Briar College was a great recent example) and the sheer tenacity of institutions as they face extreme financial difficulties. More colleges may consider mergers and strategic alliances, but the rate of college closures in nonprofit higher education is likely to only tick up slightly in the coming decade.