Blog (Kelchen on Education)

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.

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.