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.

———————————————————–

[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.

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 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.

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.