Why the Democrats’ New ‘Debt-Free’ College Plan Won’t Really Make College Debt-Free

This article was originally published at The Conversation and is co-authored with Dennis Kramer II of the University of Florida.

Rising student loan debt and concerns about college affordability got considerable attention from Democrats in the 2016 presidential campaign. Those issues are bound to get renewed attention since House Democrats recently introduced the Aim Higher Act – an effort to update the Higher Education Act, the federal law that governs federal higher education programs.

The bill promises “debt-free” college to students. As scholars who focus on higher education finance and student aid, we believe the bill actually falls well short of that promise.

What ‘free’ really means

In its current form, the bill guarantees two years of tuition-free community college to students. However, the Democratic bill does not address the fact that tuition is only about one-fifth of the total cost of attending community college. Rent, food, books and transportation make up the rest of the cost of attendance and are not covered by this plan.

The “debt-free” label is problematic for other reasons. For instance, the maximum Pell Grant – $6,095 for the 2018-2019 school year – already covers community college tuition in nearly all states. This means the neediest students likely already have access to federal grant funds to cover tuition. Although the bill would increase Pell awards by $500 each year and reduce debt somewhat for the neediest students, many needy students will still need to take out loans to attend college.

States may not cooperate

Another reason the Democrats’ “debt-free” college plan does not live up to its name is the fact that its tuition-free provision requires states to maintain their funding for public colleges in order qualify for more federal funds under the proposed bill. This approach is similar to the state-federal partnership that was part of the recent Medicaid expansion, which led 16 conservative states to decline to expand Medicaid. Many conservative-leaning states might push back against the Aim Higher Act’s tuition-free provision because it restricts states’ ability to cut higher education spending.

Slim chance of becoming law

The ConversationIt is unlikely that either the PROSPER Act or the Aim Higher Act become law in the near future given the lack of comprehensive support within the Republican Party and Democrats’ minority status in Congress. But there are a few parts of both bills that could get bipartisan support, such as simplifying the process for applying for federal financial aid, creating better data systems to help track students’ outcomes, and allowing Pell Grants to be used for shorter-term training programs. Although neither the Republican nor the Democratic bills appear likely to pass, expect both parties to use their proposals in the upcoming midterm elections.

A Look at Federal Student Loan Borrowing by Field of Study

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

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

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

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

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.]

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.

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.

A Poor Way to Tie the Pell Grant to Performance

“Groan” is a word that is typically used to describe something that is unpleasant or bad. But in the language of student financial aid, “groan” has a second meaning—a grant that converts to a loan if students fail to meet certain criteria. The federal TEACH Grant to prospective teachers and New York’s Excelsior Scholarship program both have these clawback requirements, and a 2015 GAO report estimated that one-third of TEACH Grants had already converted into loans for students who did not teach in high-need subjects in low-income schools for four years.

Republican Reps. Francis Rooney (FL) and Ralph Norman (SC) propose turning the Pell Grant into a groan program through their Pell for Performance Act, which would turn Pell Grants into unsubsidized loans if students fail to graduate within six years. While I understand the representatives’ concerns about students not graduating (and thus reducing—but not eliminating—the return on investment to taxpayers), I see this bill as a negative for students and taxpayers alike.

Setting aside the merits of the idea for a minute, I’m deeply skeptical that the Department of Education and student loan servicers can accurately manage such a program. With a fair amount of difficulty managing TEACH Grants and income-driven repayment plans, I would expect a sizable number of students to incorrectly have Pell Grants convert to loans (and vice versa). I appreciate these two representatives’ faith in Federal Student Aid and servicers to get everything right, but that is a difficult ask.

Moving on to the merits of the idea, I am concerned about the implications of converting Pell Grants to a loan for students who left college because they got a job. Think about this for a minute—a community college student who has completed nearly all of her coursework gets a job offer with family-sustaining wages. She now faces a tough choice: forgo a good, solid job until she completes (and hope she can get another one) or take the job and owe an additional $10,000 to the federal government? If one of the purposes of higher education is to help students move up the economic ladder, this is a bad idea.

This could also have additional negative ramifications for students who stop out of college due to family issues, the need to support a family, or simply realizing that they weren’t college ready at the time. Asking a 30-year-old adult to repay additional student loans (when he may have left in good standing) under this groan program would probably reduce the number of working adults who go back and finish their education.

If the representatives’ concern is that students make very slow progress through college and waste taxpayer funds, a better option would be to gradually ramp up the current performance requirements for satisfactory academic progress. These requirements, which are typically defined as a 2.0 GPA and completing two-thirds of attempted credits, already trip up a significant share of students. But on the other hand, research by Doug Webber of Temple University and his colleagues finds significant economic benefits to students who barely keep a 2.0 GPA and are thus able to stay in college.

Finally, although I think this proposal is shortsighted, I have to chuckle at a take going around on social media noting that one of the representatives owns a construction company that helped build residence halls. Wouldn’t that induce a member of Congress to support policies that get more students into college (and create demand for his company’s services)? It seems like he is going against his best interest if this legislation scares students away from attending college.

Is There Evidence of the Bennett Hypothesis in Legal Education?

“If anything, increases in financial aid in recent years have enabled colleges and universities blithely to raise their tuitions, confident that Federal loan subsidies would help cushion the increase…Federal student aid policies do not cause college price inflation, but there is little doubt that they help make it possible.”

In what year was the above quote first printed in The New York Times? Given concerns about college affordability and the ever-rising price tag of a college education, it’s reasonable to assume that the quote comes from the last few years. Yet this quote came from William Bennett (who was President Reagan’s Secretary of Education) way back in 1987. Bennett is now a conservative commentator and occasional advisor to the Trump administration, and his higher education views likely get traction in key federal policy circles.

Since 1987, what came to be known as the Bennett Hypothesis has been vigorously debated in the research and policy communities. As I detailed in two previous blog posts, the evidence to support the Bennett Hypothesis is generally modest among undergraduate students—with stronger evidence at private nonprofit and for-profit colleges than community colleges. However, prior research often looks at small changes in student loan borrowing limits or Pell Grant award amounts since there have been no large-scale changes in financial aid for undergraduate students over the past several decades.

Many graduate and professional students, on the other hand, saw a large increase in their federal student loan limits in 2006 (from $18,500 per year up to the full cost of attendance) due to the creation of the Grad PLUS loan program. This increase, which could be in the tens of thousands of dollars for students, provides a rare opportunity to test how colleges responded to a large change in potential federal revenue. This is particularly salient for students in master’s and professional degree programs, as institutional financial aid is far less common than in PhD programs.

Thanks to support from the AccessLex Institute and the Association for Institutional Research, I have spent much of the last year examining whether professional programs responded to the creation of the Grad PLUS program and the following expansion of income-driven repayment programs by increasing tuition and fees and/or living allowances. I also looked at whether student debt burdens of graduates increased. Today, I am releasing a SSRN working paper examining these questions for law schools, with additional analyses for business and medical schools to come at some point in the future.

In the seven months of tedious data compilation, coding, and cleaning that preceded any analyses (a big thanks to my sharp research assistants Joe Fresco and Olga Komissarova for their hard work!), I fully expected to find a great deal of evidence to support the Bennett Hypothesis due to the entrepreneurial nature of law schools and the sheer amount of federal student loan dollars that became available in 2006. Yet as the graphics below show, there was no immediate smoking gun in the descriptive data (focus on the red line at 2006).

But because graphics do not prove that there is (or is not) a relationship between federal student loan availability and law schools’ prices, I used two analytic strategies to try to draw causal inferences. I used an interrupted time series model that compared law schools before and after the 2006 implementation of Grad PLUS and a difference-in-differences model that looked at the difference between law schools and undergraduate institutions before and after 2006. Both of these models showed generally null or small positive coefficients, suggesting that law schools did not react by raising tuition prices or living allowances by massive amounts. (These findings generally match the conclusions from the literature at the undergraduate level, and are robust across a range of model specifications.) Below are the coefficients for tuition and fees, with the coefficients for living allowances and debt burdens available in the paper.

So why was there far less evidence for the Bennett Hypothesis than I expected to see? I offer three potential explanations.

Explanation 1: Law schools didn’t strategically increase prices in response to increased federal financial aid availability. Yes, law school tuition is expensive, and it’s certainly true that colleges have viewed law schools as potential revenue centers. But law schools may have thought that their price increases were already substantial enough to fund their operations.

Explanation 2: Any law school that increased tuition by more than their competitors may have seen a decline in applicants and/or revenue. This is somewhat similar to the classic prisoner’s dilemma in game theory, in which cooperating with other players (to raise prices) would result in a better solution than going alone. But to collude here would be price fixing—and illegal. Thus law schools stick to the norm of sizable (but not absurd) tuition increases.

Explanation 3: Students shifted from private loans to PLUS loans and thus already had access for loans up to the full cost of attendance. There is some evidence to support this logic, as 36% of law students took out private loans in 2003-04 compared to just 5% in 2011-12. Yet this would not hold for the majority of students who didn’t take out private loans.

I would love to get your comments on this working paper before it undergoes the formal peer review process in a few weeks (it’s already been informally reviewed). Send me your thoughts!

New Data on Long-Term Student Loan Default Rates

In recent years, more data have come out on how well students are able to manage repaying their loans beyond the three-year window currently used for federal accountability purposes (via cohort default rates). A great 2015 paper by Adam Looney and Constantine Yannelis used tax records merged with data from the National Student Loan Data System (NSLDS) to show longer-term trends in default in repayment. Two days later, the release of the College Scorecard provided college-level data on student loan repayment rates going out seven years (even though the repayment rates were initially calculated incorrectly).

Thanks to a lot of hard work by the data folks at the U.S. Department of Education and their contractor RTI, there are new data available on long-term student loan default rates. ED and RTI used NSLDS data going through 2015 to match records from the Beginning Postsecondary Students studies of cohorts beginning college in 1995-96 and 2003-04. This allowed a 20-year look at student loan default and payoff rates for the 1995-96 cohort and a 12-year look at the 2003-04 cohort, as detailed in this useful report from the National Center for Education Statistics.

Thanks to NCES’s wonderful PowerStats tool, I took a look at the percentage of students in the 2003-04 entering cohort (my college cohort) who had defaulted on at least one of their federal student loans within 12 years. Many of the news headlines focused on the high default rates of students at for-profit colleges (about 52%!), but this isn’t entirely a fair comparison because for-profit colleges tend to serve more economically-disadvantaged students. So in this post, I focused on racial/ethnic differences in default rates by type of college attended to give a flavor of what the data can do.

As the below chart shows, nearly half of all black students (49%) defaulted on at least one loan within 12 years—more than twice the rate of white students (20%) and more than four times the rate of Asian students (11%). The differentials are still present across sector, with more than one-third of black students defaulting across all sectors while a relatively small percentage of Asian students defaulted across all nonprofit sectors. Default rates at for-profit colleges are high for all racial/ethnic groups, with almost half of white students defaulting alongside nearly two-thirds of black students.

An advantage of the PowerStats tool is that it allows users to run regressions via NCES’s remote server. This allows interested people to analyze the relationship between long-term default rates and attending a for-profit college after controlling for other characteristics. However, PowerStats is overwhelmed by requests by my fellow higher education data nerds at this point, so I gave up on trying to run the regression after several hours of waiting. But if someone wants to run some regressions using the new loan repayment data in the BPS once the server calms down, I’m happy to feature their work on my blog!