The 2018 Net Price Madness Bracket

Every year, I take the 68 teams in the NCAA Division I men’s basketball tournament and fill out a bracket based on colleges with the lowest net price of attendance (defined as the total cost of attendance less all grant aid received). My 2017, 2016, 2015, 2014 and 2013 brackets are preserved for posterity—and often aren’t terribly successful on the hardwood. My 2015 winner (Wichita State) won two games in the tournament, while prior winners New Orleans (2017), Fresno State (2016), Louisiana-Lafayette (2014), and North Carolina A&T (2013) emerged victorious for having the lowest net price but failed to win a single game. But at least West Virginia (a regional champion last year) won two games, so maybe there is some hope for this method.

I created a bracket using 2015-16 data (the most recent available through the U.S. Department of Education for the net price of attendance for all first-time, full-time students receiving grant aid I should note that these net price measures are far from perfect—the data are now three years old and colleges can manipulate these numbers through the living allowance portion of the cost of attendance. Nevertheless, they provide some insights regarding college affordability—and they may not be a bad way to pick that tossup 8/9 game that you’ll probably get wrong anyway.

The final four teams in the bracket are the following, with the full dataset available here:

East: Cal State-Fullerton ($8,170)

West: UNC-Chapel Hill ($10,077)

South: Wright State ($14,464)

Midwest: New Mexico State ($10,213)

Kudos to Cal State-Fullerton for having the lowest net price for all students ($8,170), with an additional shout-out to UNC-Chapel Hill for having the lowest net price among teams that are likely to make it to the final weekend of basketball ($10,077). (Also, kudos to the North Carolina system for having two universities in the last eight.)

Additionally, although I didn’t do a bracket for students in the lowest family income category (below $30,000) this year, the University of Michigan has the lowest net price in that category (at $2,660). Although Michigan doesn’t serve that many low-income students, a new program (designed in part by all-star Michigan economist Susan Dynarski) guarantees four years of free tuition for in-state students with family incomes below $65,000. That’s a good step for a wealthy public university to take.

New Higher Education Policy Voice: Kelly Rosinger

Kelly Rosinger (@kelly_rosinger) is a first-year assistant professor in the Department of Education Policy Studies at Penn State University. Before that, she was an Institute of Education Sciences postdoc at the University of Virginia, where she worked with Ben Castleman’s Nudge4 team applying behavioral interventions to improve the college choice process. An expert in experimental and quasi-experimental research methods, Kelly’s work focuses on the barriers students face on the way to and through college and the impact of policies and interventions aimed at helping students navigate college decisions. Her work influenced by her experience working in admissions at the University of Georgia.

Kelly has a new article in press at Education Finance and Policy that reports findings from a field experiment and quasi-experiment examining the impact of a recent federal policy effort to simplify financial aid award offers on borrowing. The study shows that the informational intervention reduced borrowing at colleges enrolling high shares of Pell recipients and underrepresented minority students, suggesting such interventions may be particularly salient to students who face greater informational barriers to college. She also co-authored an article in Educational Evaluation and Policy Analysis (which was just republished in a high-profile book) examining whether test-optional admissions practices at elite liberal arts colleges actually result in a more diverse student body. You can hear Kelly discuss the research on the Matt Townsend Show. They found that at those particular colleges, test-optional practices did not increase diversity.

New Research on Equity Provisions in State Performance Funding Policies

Previous versions of state performance-based funding (PBF) policies were frequently criticized for encouraging colleges to simply become more selective in order to get more state funding (see a good summary of the research here). This has potential concerns for equity, as lower-income, first-generation, adult, and racial/ethnic minority students often need additional supports to succeed in college compared to their more advantaged peers.

With the support of foundations and advocacy organizations, the most recent wave of state PBF policies has often included provisions that encourage colleges to enroll traditionally underrepresented students. For example, Indiana now gives $6,000 to a college if a low-income student completes a bachelor’s degree; while this is far less than the $23,000 that the college gets if a student completes their degree in four years, it still provides an incentive for colleges to change their recruitment and admissions practices. Today, at least sixteen states provide incentives for colleges to serve underrepresented students.

Given the growth of these equity provisions, it is not surprising that researchers are now turning their attention to these policies. Denisa Gandara of SMU and Amanda Rutherford of Indiana University published a great article in Research in Higher Education last fall looking at the effects of these provisions among four-year colleges. They found that the policies were at least somewhat effective in encouraging colleges to enroll more racial/ethnic minority and lower-income students.

As occasionally happens in the research world, multiple research teams were studying the same topic at the same time. I was also studying the same topic, and my article was accepted in The Journal of Higher Education a few days before their article was released. My article is now available online (the pre-publication version is here), and my findings are generally similar—PBF policies with equity provisions can at the very least help reduce incentives for colleges to enroll fewer at-risk students.

The biggest contribution of my work is how I define the comparison group in my analyses. The treatment group is easy to define (colleges that are subject to a PBF policy with equity provisions), but comparison groups often combine colleges that face PBF without equity provisions with colleges that are not subject to PBF. By dividing those two types of colleges into separate comparison groups, I can dig deeper into how the provisions of performance funding policies affect colleges. And I did find some differences in the results across the two comparison groups, highlighting the importance of more nuanced comparison groups.

Much more work still needs to be done to understand the implications of these new equity provisions. In particular, more details are needed about which components are in a state’s PBF system, and qualitative work is sorely needed to help researchers and policymakers understand how colleges respond to the nuances of different states’ policies. Given the growing group of scholars doing research in this area, I am confident that the state of PBF research will continue to improve over the next few years.

Why Accountability Efforts in Higher Education Often Fail

This article was originally published at The Conversation.

As the price tag of a college education continues to rise along with questions about academic quality, skepticism about the value of a four-year college degree has grown among the American public.

This has led both the federal government and many state governments to propose new accountability measures that seek to spur colleges to improve their performance.

This is one of the key goals of the PROSPER Act, a House bill to reauthorize the federal Higher Education Act, which is the most important law affecting American colleges and universities. For example, one provision in the act would end access to federal student loans for students who major in subjects with low loan repayment rates.

Accountability is also one of the key goals of efforts in many state legislatures to tie funding for colleges and universities to their performance.

As a researcher who studies higher education accountability – and also just wrote a book on the topic – I have examined why policies that have the best of intentions often fail to produce their desired results. Two examples in particular stand out.

Federal and state failures

The first is a federal policy that is designed to end colleges’ access to federal grants and loans if too many students default on their loans. Only 11 colleges have lost federal funding since 1999, even though nearly 600 colleges have fewer than 25 percent of their students paying down any principal on their loans five years after leaving college, according to my analysis of data available on the federal College Scorecard. This shows that although students may be avoiding defaulting on their loans, they will be struggling to repay their loans for years to come.

The second is state performance funding policies, which have encouraged colleges to make much-needed improvements to academic advising but have not resulted in meaningful increases in the number of graduates.

Based on my research, here are four of the main reasons why many accountability efforts fall short.

1. Competing initiatives

Colleges face many pressures that provide conflicting incentives, which in turn makes any individual accountability policy less effective. In addition to the federal government and state governments, colleges face strong pressures from other stakeholders. Accrediting agencies require colleges to meet certain standards. Faculty and student governments have their own visions for the future of their college. And private sector organizations, such as college rankings providers, have their own visions for what colleges should prioritize. (In the interest of full disclosure, I am the methodologist for Washington Monthly magazine’s college rankings, which ranks colleges on social mobility, research and service.)

As one example of these conflicting pressures, consider a public research university in a state with a performance funding policy that ties money to the number of students who graduate. One way to meet this goal is to admit more students, including some who have modest ACT or SAT scores but are otherwise well-prepared to succeed in college. This strategy would hurt the university in the U.S. News & World Report college rankings, which judge colleges in part based on ACT/SAT scores, selectivity and academic reputation.

Research shows that students considering selective colleges are influenced by rankings, so a university may choose to focus on improving their rankings instead of broadening access in an effort to get more state funds.

2. Policies can be gamed

Colleges can satisfy some performance metrics by gaming the system, instead of actually improving their performance. The theory behind many accountability policies is that colleges are not operating in an efficient manner and that they must be given incentives in order to improve their performance. But if colleges are already operating efficiently – or if they do not want to change their practices in response to an external mandate – the only option to meet the performance goal may be to try to game the system.

An example of this practice is with the federal government’s student loan default rate measure, which tracks the percentage of borrowers who default on their loans within three years of when they are supposed to start repaying their loans. Colleges that are concerned about their default rates can encourage students to enroll in temporary deferment or forbearance plans. These plans result in students owing more money in the long run, but also they push the risk of default outside the three-year period that the federal government tracks, which essentially lets colleges off the hook.

3. Unclear connections

It’s hard to tie individual faculty members to student outcomes. The idea of evaluating teachers based on their students’ outcomes is nothing new; 38 states require student test scores to be used in K-12 teacher evaluations, and most colleges include student evaluations as a criterion of the faculty review process. Tying an individual teacher to a student’s achievement test scores has been controversial in K-12 education, but it is far easier than identifying how much an individual faculty member contributes to a student’s likelihood of graduating from college or repaying their loans.

For example, a student pursuing a bachelor’s degree will take roughly 40 courses during their course of study. That student may have 30 different professors over four or five years. And some of them may no longer be employed when the student graduates. Colleges can try to encourage all faculty to teach better, but it’s difficult to identify and motivate the worst teachers because of the elapsed time between when a student takes a class and when he or she graduates or enters the workforce.

4. Politics as usual

Even when a college should be held accountable, politics often get in the way. Politicians may be skeptical of the value of higher education, but they will work to protect their local colleges, which are often one of the largest employers in their home states. This means that politicians often act to stop a college from losing money under an accountability system.

The ConversationTake for example Senate Majority Leader Mitch McConnell, R-Ky., who was sympathetic to the plight of a Kentucky community college with a student loan default rate that should have resulted in a loss of federal financial aid. He got a provision added to the recent federal budget agreement that allowed only that college to appeal the sanction.

New Higher Education Policy Voice: Benjamin Skinner

Benjamin Skinner (@btskinner) is a first-year research assistant professor in the Curry School of Education at the University of Virginia. His research interests include quantitative methods, the geography of opportunity, and broad-access colleges. While working on his dissertation at Vanderbilt, he co-authored two great articles with his committee chair Will Doyle. The first, in Economics of Education Review, estimated the economic returns to college using geographic variation in the location of colleges to draw causal inference. The second, in The Journal of Higher Education (and an article that I use in my higher ed finance class), used a similar estimation strategy to look at the relationship between years of education and civic engagement.

Ben is perhaps best known for his incredible work with data—and for his willingness to share his code and materials with the general public. (More scholars should be doing this!) For example, the “code” page of his website includes helpful packages to help download and manage the massive College Scorecard dataset and how to work with LaTeX files. He has also put together some interesting data visualizations of college opportunity that look great and tell a compelling story. There is also quite a bit of material on his GitHub page, which is a great way to work with large data files (and something that I probably should learn at some point).

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.

[v] Douglas-Gabriel, D. (2018, February 8). McConnell attempts to protect two Kentucky colleges in budget deal. The Washington Post. Stratford, M. (2014, September 24). Reprieve on default rates. Inside Higher Ed.

[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.

[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.

[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.