Downloadable Dataset of Pell Recipient Graduation Rates

Earlier this week, my blog post summarizing new data on Pell Grant recipients’ graduation rates at four-year colleges was released through the Brookings Institution’s Brown Center Chalkboard blog. I have since received several questions about the data and requests for detailed data for specific colleges, showing the interest within the higher education community for better data on social mobility.

I put together a downloadable Excel file of six-year graduation rates and cohort sizes by Pell Grant receipt in the first year of college (yes/no) and race/ethnicity (black/white/Hispanic). One tab has all of the data, while the “Read Me” tab includes some additional details and caveats that users should be aware of. Hopefully, this dataset can be useful to others!

A Look at Pell Grant Recipients’ Graduation Rates

This post originally appeared on the Brookings Institution’s Brown Center Chalkboard blog.

The federal government provides nearly $30 billion in grant aid each year to nearly eight million students from lower-income families (mainly with household incomes below $50,000 per year) through the Pell Grant program, which can give students up to $5,920 per year to help pay for college. Yet in spite of research showing that the Pell Grant and similar need-based grant programs are effective in increasing college completion rates, there are still large gaps in graduation rates by family income. For example, among students who began college in the fall 2003 semester, Pell recipients were seven percentage points less likely to earn a college credential within six years than non-Pell students.

In spite of the federal government’s sizable investment in students, relatively little has been known about whether Pell recipients succeed at particular colleges. The last Higher Education Act reauthorization in 2008 required colleges to disclose Pell graduation rates upon request, but two studies have shown that colleges have been unable or unwilling to disclose these data. This means that before now, little has been known about whether colleges are able to graduate their students from lower-income families.[1]

The U.S. Department of Education recently updated its Integrated Postsecondary Education Data System (IPEDS) to include long-awaited graduation rates for Pell Grant recipients, and I focus on graduation rates for students at four-year colleges (about half of all Pell recipients) in this post. I examined the percentage of Pell recipients and non-Pell recipients who graduated with a bachelor’s degree from the same four-year college within six years of entering college in 2010.[2] After limiting the sample to four-year colleges that had at least 50 Pell recipients and 50 non-Pell recipients in their incoming cohorts, my analysis included 1,266 institutions (504 public, 747 private nonprofit, and 15 for-profit).

The average six-year graduation rate for Pell recipients in my sample was 51.4%, compared to 59.2% for non-Pell recipients. The graphic below shows the graduation rates for non-Pell students on the horizontal axis and Pell graduation rates on the vertical axis, with colleges to the left of the red line having higher graduation rates for Pell recipients than non-Pell recipients. Most of the colleges (1,097) had non-Pell graduation rates higher than Pell graduation rates, but 169 colleges (13.3%) had higher Pell graduation rates.

Table 1 below shows five colleges where Pell students graduate at the highest and lowest rates relative to non-Pell students.[3] For example, the University of Akron (which had 3,370 students in its incoming class of first-time, full-time students) reported that just 8.8% of its 1,505 Pell recipients in its incoming class graduated within six years compared to 70.1% of its 1,865 non-Pell students—a yawning gap of 61.3% and the second-largest in the country. Assuming the Pell and non-Pell graduation rates are not the result of a data error that the university made in its IPEDS submission, this is a serious concern for institutional equity. On the other hand, some colleges had far higher graduation rates for Pell recipients than non-Pell students. An example is Howard University, where 79.4% of Pell recipients and just 46.1% of non-Pell students graduated.

Table 1: Colleges with the largest Pell/non-Pell graduation rate gaps.
Name State Number of new students Pell grad rate Non-Pell grad rate Gap Pct Pell
Saint Augustine’s University NC 440 2.7 92.2 -89.5 76.8
University of Akron OH 3370 8.8 70.1 -61.3 44.7
St. Thomas Aquinas College NY 290 20.7 78.3 -57.6 31.7
Southern Virginia University VA 226 20.7 54.3 -33.6 64.2
Upper Iowa University IA 201 27.9 60.8 -32.9 51.7

Ninety-seven of the colleges with at least 50 Pell and 50 non-Pell recipients had graduation rates of over 80% for both Pell and non-Pell students. Most of these colleges are highly selective institutions with relatively low percentages of Pell recipients, but six institutions had Pell and non-Pell graduation rates above 80% while having at least 30% of students in their incoming class receive Pell Grants. All six are in California, with five in the University of California system (Davis, Irvine, Los Angeles, San Diego, and Santa Barbara) and one private institution (Pepperdine). This suggests that it is possible to be both socioeconomically diverse and successful in graduating students.

As a comparison, I also examined the black/white graduation rate gaps for the 499 colleges that had at least 50 black and 50 white students in their graduation rate cohorts. The average black/white graduation rate gap at these colleges was 13.5% (59.0% for white students compared to 45.5% for black students). As the figure shows below, only 39 colleges had higher graduation rates for black students than for white students while the other 460 colleges had higher graduation rates for white students than black students.

Fourteen colleges had higher graduation rates for Pell recipients than non-Pell students and for black students than white students. This group includes elite institutions with small percentages of Pell recipients and black students such as Dartmouth, Duke, and Yale as well as broader-access and more diverse colleges such as CUNY York College, Florida Atlantic, and South Carolina-Upstate. Table 2 shows the full list of 14 colleges that had higher success rates from Pell and black students than non-Pell and white students.

Table 2: Colleges with higher graduation rates for Pell and black students.
Name State Pell grad rate Non-Pell grad rate Black grad rate White grad rate
U of South Carolina-Upstate SC 50.4 34.0 47.3 38.8
CUNY York College NY 31.5 27.3 32.7 28.0
Agnes Scott College GA 71.1 68.3 72.4 62.1
Clayton State University GA 34.0 31.5 33.2 31.0
Duke University NC 96.6 94.3 95.1 95.0
Florida Atlantic University FL 50.6 49.0 50.1 48.5
Wingate University NC 54.5 53.1 60.0 51.4
UMass-Boston MA 45.8 44.7 50.0 40.6
U of South Florida FL 68.1 67.1 68.7 65.5
CUNY City College NY 47.2 46.3 52.8 45.6
Dartmouth College NH 97.2 96.5 97.3 97.1
CUNY John Jay College NY 44.1 43.4 43.5 42.4
Yale University CT 98.2 97.7 100.0 97.6
Stony Brook University NY 72.5 72.3 71.3 70.5

The considerable variation in Pell recipients’ graduation rates across colleges deserves additional investigation. Colleges with similar Pell and non-Pell graduation rates should be examined to see whether they have implemented any practices to support students with financial need. The less-selective colleges that have erased graduation rate gaps by race and family income could potentially serve as exemplars for other colleges that are interested in equity to emulate. Meanwhile, policymakers, college leaders, and the public should be asking tough questions of colleges with reasonable graduation rates for non-Pell students but abysmal outcomes for Pell recipients.

Finally, the U.S. Department of Education deserves credit for the release of Pell students’ graduation rates, as well as several other recent datasets that provide new information on student outcomes. This includes new data on students’ long-term student loan default and repayment outcomes and the completion rates of students who were not first-time, full-time students, along with an updated College Scorecard that now includes a nifty college comparison tool. Though the Pell graduation rate measure fails to cover all students and does not credit institutions if a student transfers and completes elsewhere, it is still a useful measure of whether colleges are effectively educating students from lower-income families. In the future, student-level data that includes part-time and transfer students would be useful to help examine whether colleges are helping all of their students succeed.

[1] Focusing on Pell Grant recipients undercounts the number of lower-income students because a sizable percentage of lower-income students do not file the Free Application for Federal Student Aid, which is required for students to be eligible to receive a Pell Grant.

[2] I calculated the number of non-Pell recipients by subtracting the number of Pell recipients from the total graduation rate cohort in the IPEDS dataset.

[3] This excludes two colleges that reported a 0% or 100% graduation rate for their Pell students, which is likely a data reporting error.

A Peek Inside the New IPEDS Outcome Measures Dataset

Much of higher education policy focuses on “traditional” college students—those who started college at age 18 after getting dropped off in the family station wagon or minivan, enrolled full-time, and stayed at that institution until graduation. Yet although this is how many policymakers and academics experienced college (I’m no exception), this represents a minority of the current American higher education system. Higher education data systems have often followed this mold, with the U.S. Department of Education’s Integrated Postsecondary Education Data System (IPEDS) collecting some key success and financial aid metrics for first-time, full-time students only.

As a result of the 1990 Student Right-to-Know Act, all colleges were required to start compiling graduation rates (and disclosing them upon request) for first-time, full-time students and a smaller group of colleges were also required to collect transfer-out rates. Colleges were then required to submit the data to IPEDS for students who began college in the 1996-97 academic year so information would be available to the public. This was a step forward for transparency, but it did little to accurately represent community colleges and less-selective four-year institutions. Some groups, such as the Student Achievement Measure, have developed to voluntarily provide information on completion rates for part-time and transfer students. These data have shown that IPEDS significantly understates overall completion rates even among students who initially fit the first-time, full-time definition.

After years of technical review panels and discussions about how to best collect data on part-time and non-first-time students along with a one-year delay to “address data quality issues,” the National Center for Education Statistics released the first year of the new Outcome Measures survey via College Navigator earlier this week. This covers students who began college in 2008 and were tracked for a period of up to eight years. Although the data won’t be easily downloadable via the IPEDS Data Center until mid-October, I pulled up data on six colleges (two community colleges, two public four-year colleges, and two private nonprofit colleges in New Jersey) to show the advantages of more complete outcomes data.

Examples of IPEDS Outcome Measures survey data, 2008 entering cohort.
Institution 6-year grad rate 8-year grad rate Still enrolled within 8 years Enrolled elsewhere within 8 years
Community colleges
Atlantic Cape Community College
First-time, full-time 26% 28% 3% 27%
Not first-time, but full-time 41% 45% 0% 29%
First-time, part-time 12% 14% 5% 20%
Not first-time, but part-time 23% 26% 0% 38%
Brookdale Community College
First-time, full-time 33% 35% 3% 24%
Not first-time, but full-time 36% 39% 2% 33%
First-time, part-time 17% 18% 3% 25%
Not first-time, but part-time 25% 28% 0% 28%
Public four-year colleges
Rowan University
First-time, full-time 64% 66% 0% 20%
Not first-time, but full-time 82% 82% 1% 7%
First-time, part-time 17% 17% 0% 0%
Not first-time, but part-time 49% 52% 5% 21%
Thomas Edison State University
Not first-time, but part-time 42% 44% 3% 29%
Private nonprofit colleges
Centenary University of NJ
First-time, full-time 61% 62% 0% 4%
Seton Hall University
First-time, full-time 66% 68% 0% 24%
Not first-time, but full-time 67% 68% 0% 18%
First-time, part-time 0% 0% 33% 33%
Not first-time, but part-time 38% 38% 0% 38%

There are several key points that the new data highlight:

(1) A sizable percentage of students enrolled at another college within eight years of enrolling in the initial college. The percentages at the two community colleges in the sample (Atlantic Cape and Brookdale) are roughly similar to the eight-year graduation rates, suggesting that quite a few students are transferring without receiving degrees. These rates are lower in the four-year sector, but still far from trivial.

(2) New colleges show up in the graduation rate data! Thomas Edison State University is well-known for focusing on adult students (they only accept students age 21 or older). So, as a result, they didn’t have a first-time, full-time cohort for the traditional graduation rate. But TESU has a respectable 42% graduation rate of part-time students within six years, and another 29% enrolled elsewhere within eight years. On the other hand, residential colleges may just have a first-time, full-time cohort (such as Centenary University) or small cohorts of other students for which data shouldn’t be trusted (such as Seton Hall’s tiny cohort of first-time, part-time students).

(3) Not first-time students graduate at similar or higher rates compared to first-time students. To some extent, this is not surprising as students enter with more credits. For example, at Rowan University, 82% of transfer students who entered full-time graduated within six years compared to 64% of first-time students.

(4) Institutional graduation rates don’t change much after six years. Among these six colleges, graduation rates went up by less than five percentage points between six and eight years and few students are still enrolled after eight years. It’s important to see if this is a broader trend, but this suggests that six-year graduation rates are fairly reasonable metrics.

Once the full dataset is available in October, I’ll return to analyze broader trends in the Outcome Measures data. But for now, take a look at a few colleges and enjoy a sneak peek into the new data!

Beware OPEIDs and Super OPEIDs

In higher education discussions, everyone wants to know how a particular college or university is performing across a range of metrics. For metrics such as graduation rates and enrollment levels, this isn’t a big problem. Each freestanding college (typically meaning that they have their own accreditation and institutional governance structure) has to report this information to the U.S. Department of Education’s Integrated Postsecondary Education Data System (IPEDS) each year. But other metrics are more challenging to use and interpret because they can cover multiple campuses—something I dig into in this post.

In the 2015-16 academic year, there were 7,409 individual colleges (excluding administrative offices) in the 50 states and Washington, DC that reported data to IPEDS and were uniquely identified by a UnitID number. A common mistake that analysts make is to assume that all federal higher education (or even all IPEDS) data metrics represent just one UnitID, but that is not always the case. Enter researchers’ longtime nemesis—the OPEID.

OPEIDs are assigned by the U.S. Department of Education’s Office of Postsecondary Education (OPE) to reflect each postsecondary institution that has a program participation agreement to participate in federal student aid programs. However, some colleges within a system of higher education share a program participation agreement, in which one parent institution has a number of child institutions for financial aid purposes.

Parent/child relationships can generally be identified using OPEID codes; parent institutions typically have OPEIDs ending with “00,” while child institutions typically have OPEIDs ending in another value. These reporting relationships are fairly prevalent, with there being approximately 5,744 parent and 1,665 child institutions in IPEDS in the 2015-16 academic year based on OPEID values. For-profit college chains typically report using parent/child relationships, while a number of public college and university systems also aggregate institutional data to the OPEID level. For example, Penn State and Rutgers have parent/child relationships while the University of Missouri and the University of Wisconsin do not.

In this case of a parent/child relationship, all data that come from the Office of Federal Student Aid or from the National Student Loan Data System are aggregated up across a number of colleges. This includes all data on student loan repayment rates, earnings, and debt from the College Scorecard as well as student loan default rates that are currently used for accountability purposes. Additionally, some colleges report finance data out at the OPEID level on a seemingly chaotic basis—which can only be discovered by combing through data to see if child institutions do not have values. For example, Penn State always reports at the parent level, while Rutgers has reported at the parent level and the child level on different occasions over the last 15 years. Ozan Jaquette and Edna Parra have pointed out in some great research that failing to address parent/child issues can result in estimates from IPEDS or Delta Cost Project data being inaccurate (although trend data are generally reasonable).

If UnitIDs and OPEIDs were not enough, the Equality of Opportunity Project (EOP) dataset added a new term—super-OPEIDs—to researchers’ jargon. This innovative dataset, compiled by economists Raj Chetty, John Friedman, and Nathaniel Hendren, uses federal income tax records to construct social mobility metrics for 2,461 institutions of higher education based on pre-college family income and post-college student income. (I used this dataset last month in a blog post looking at variations in marriage rates across four-year colleges.) However, the limitation of this approach is that the researchers have to rely on the names of the institutions on tax forms, which are sometimes aggregated beyond UnitIDs or OPEIDs. Hence, the super-OPEID.

The researchers helpfully included a flag for super-OPEIDs that combined multiple OPEIDs (the variable name is “multi” in the dataset, for those playing along at home). There are 96 super-OPEIDs that have this multiple-OPEID flag, including a number of states’ public university systems. The full list can be found in this spreadsheet, but I wanted to pull out some of the most interesting pairings. Here are a few:

–Arizona State And Northern Arizona University And University Of Arizona

–University Of Maryland System (Except University College) And Baltimore City Community College

–Minnesota State University System, Century And Various Other Minnesota Community Colleges

–SUNY Upstate Medical University And SUNY College Of Environment Science And Forestry

–Certain Colorado Community Colleges

To get an idea of how many colleges (as measured by UnitIDs) have their own super-OPEID, I examined the number of colleges that did not have a multiple-OPEID flag in the EOP data and did not have any child institutions based on their OPEID. This resulted in 2,143 colleges having their own UnitID, OPEID, and super-OPEID—meaning that all of their data across these sources is not combined with different institutions. (This number would likely be higher if all colleges were in the EOP data, but some institutions were either too new or too small to be included in the dataset.)

I want to close by noting the limitations of both the EOP and Federal Student Aid/College Scorecard data for analytic purposes, as well as highlighting the importance of the wonky terms UnitID, OPEID, and super-OPEID. Analysts should carefully note when data are being aggregated across separate UnitIDs (particularly when different types of colleges are being combined) and consider omitting colleges where aggregation may be a larger concern across OPEIDs or super-OPEIDs.

For example, earnings data from the College Scorecard would be fine for the University of Maryland-College Park (as the dataset just reflects those earnings), but social mobility data would include a number of other institutions. Users of these data sources should also describe their strategies in their methods discussions to an extent that would allow users to replicate their decisions.

Thanks to Sherman Dorn at Arizona State University for inspiring this blog post via Twitter.

The U.S. Dept. of Education Should Continue to Collect Benefits Costs by Functional Expense

This is a guest post by my colleague and collaborator Braden Hosch, who is the Assistant Vice President for Institutional Research, Planning & Effectiveness at Stony Brook University. He has served in previous positions as the chief academic officer for the Connecticut Department of Education and the chief policy and research officer for the Connecticut Board of Regents for Higher Education. He has published about higher education benchmarking, and has taught about how to use IPEDS data for benchmarking, including the IPEDS Finance Survey. Email: Braden.Hosch@stonybrook.edu | Twitter: @BradenHosch

Higher education finance is notoriously opaque. College students do not realize they are not paying the same rates as the student sitting next to them in class. Colleges and universities struggle to determine direct and indirect costs of the services they provide. And policymakers (sometimes even the institutions themselves) find it difficult to understand how various revenue sources flow into institutions and how these monies are spent.

All of these factors likely contribute to marked increases in the expense of delivering higher education and point toward a need for more information about how money flows through colleges and universities. But quite unfortunately proposed changes to eliminate detail collected in the IPEDS Finance Survey about benefits costs will make it more difficult to analyze how institutions spend the resources entrusted to them. The National Center for Education Statistics should modify its data collection plan to retain breakouts for benefits costs in addition to salary costs for all functional expense categories. If you’re reading this blog, you can submit comments on or before July 25, 2016 telling them to do just that.

Background

Currently, colleges and universities participating in Title IV student financial aid programs must report to the U.S. Department of Education through the Integrated Postsecondary Education Data System (IPEDS) how they spend money in functional areas such as instruction, student services, institutional support, research, etc. and separate this spending into how much is spent on salaries, benefits, and other expenses, with allocations for depreciation, operations and maintenance, and interest charges. This matrix looks something like this, with minor differences for public and private institutions:

hosch_fig1

The proposed changes, solely in the name of reducing institutional reporting burden, will significantly scale back detail by requiring institutions to report only total expenses by function and total expenses by natural classification, but will not provide the detail of how these areas intersect:

hosch_fig2

Elimination of the allocations for depreciation, interest, and operations & maintenance is a good plan because institutions do not use a consistent method to allocate these costs across functional areas. But elimination of reporting actual benefits costs for each area is problematic.

To be clear, under the proposed changes, institutions must still, capture, maintain, and summarize these data (which is where most effort lies); they are simply saved the burden of creating a pivot table and several fields of data entry.

Why does this matter?

For one thing, the Society for Human Resource Management 2016 survey shows that benefits costs have increased across all economic sectors over the past two decades. IPEDS would continue to collect total benefits costs, but without detail about the areas in which these costs are incurred, it will be impossible to determine in what areas these costs may be increasing more quickly. Thus, a valuable resource for benchmarking and diagnosis would be lost.

Additionally, without specific detail for benefits components of function expenses, the ability to control for uneven benefits costs will be lost; it would be impossible for instance to remove benefits costs from standard metrics like education and general costs or the Delta Cost Project’s education and related costs. Further, benefits costs neither are distributed uniformly across functions like instruction, research, and student services nor are distributed uniformly across sectors or jurisdictions. Thus, to understand how the money flows, at even a basic level, breaking out benefits and other expenses is critical.

Here are two quick examples.

Variation at the institution level

First, as a share of spending on instruction, benefits and other items, benefits expenses are widely variable by institution. I have picked just a few well-known institutions to make this point – it holds across almost all institutions. If spending on benefits were evenly distributed across functions, then the difference among these percentages should be zero, but in fact it’s much higher.

 hosch_fig3

Variation by state

Because benefits costs are currently reported separately across functions, it is possible to analyze how the benefits component of the Delta Cost Project education and related costs metric – spending on student related educational activities while setting aside auxiliary, hospital, and other non-core metrics. Overall, the Delta Cost Project also shows that benefits costs are rising, but a deeper look at the data also show wide variation by state, and in some states, this spending accounts for large amounts on a per student basis.

Among 4-year public universities in FY 2014, for instance, spending on benefits comprised 14.1% of E&R in Massachusetts, 20.2% in neighboring New Hampshire to the north, and 30.2% in neighboring New York to the west. The map below illustrates the extent of this variation.

Benefits as a percent of E&R spending, Public, 4-year institutions FY 2014

hosch_fig4

Excludes amounts allocated for depreciation and interest. Source Hosch (2016)

Likewise, on a per student (not per employee) basis these costs ranged from $1,654 per FTE student spent on E&R benefits in Florida, compared to $7,613 per FTE student spent on benefits in Illinois.

E&R benefits spending per FTE student, public 4-year institutions, FY 2014

hosch_fig5

Excludes amounts allocated for depreciation and interest. Source Hosch (2016)

Bottom line: variation is stark, important, and needs to be visible to understand it.

What would perhaps most difficult about not seeing benefits costs by functional area is that benefits expenses in the public sector are generally covered through states. States do not transfer this money to institutions but rather largely negotiate and administer benefits programs and their costs themselves. Even though institutions do not receive these resources, they show up on their expenses statements, and in instances like Illinois and Connecticut in the chart above, the large amount of benefits spending by institutions really reflects state activity to “catch up” on historically underfunded post-retirement benefits. To see what institutions really spend, the benefits costs generally need to be separated out from the analysis.

What you can do

Submit comments on these changes through regulations.gov. Here’s what you can tell NCES through the Federal Register:

  1. We need to know more about spending for colleges and universities, not less
  2. Reporting of functional expenses should retain a breakout for benefits costs, separate from salaries and other costs
  3. Burden to institutions to continue this reporting is minimal, since a) they report these costs now and b) the costs are actual and do not require complex allocation procedures, and c) they must maintain expense data to report total benefits costs.

Which Factors Affect Student Fees?

Tuition increases tend to get the most focus in discussions about college affordability, but a number of other factors also affect the total price tag of a college education. In addition to researching living allowances for off-campus students, I have looked into the often-confusing world of student fees at public colleges. These fees are used for a variety of purposes, such as supporting core instructional activities, funding athletics, paying for student activities, or even seismic safety. The University of California-Santa Cruz lists over 30 mandatory fees that all undergraduates must pay, ranging from $.75 per year to fund a marine discovery center to $1,020 per year for student services. At the typical four-year public college, student fees were nearly $1,300 in the 2012-13 academic year, roughly 20% of median tuition and nearly double their 1999-2000 rate after adjusting for inflation.

In a new article that was just published in The Review of Higher Education, I used a panel regression framework to explore potential institution-level and state-level factors affecting student fee levels between the 2001-02 and 2012-13 academic years.  For institution-level factors, I included tuition, the percent of nonresident students, measures of selectivity, and per-student athletics expenditures (a proxy for the magnitude of a college’s athletics program). For state-level factors, I considered appropriations and financial aid levels, economic conditions, whether a tuition or fee cap was in place, who had the ability to set tuition or fees (politicians, state or system boards, or the individual college), and partisan political control in the state.

Given that students subsidized athletics at public colleges to the tune of at least $10 billion over five years, I fully expected to find that higher per-student athletics expenditures would be associated with higher student fees. Yet after controlling for other factors, there was no significant relationship between athletics spending and fees. This could be explained by the small number of high-spending colleges in big-time conferences that come close to breaking even on athletics, or it could be due to my data ending in 2012-13 and larger increases in athletics fees occurring since then. The only significant institution-level factor was tuition—as tuition rose, fees fell. This implies that some colleges likely treat tuition and fees as interchangeable.

More of the state-level factors have statistically significant relationships with student fee levels. States that have capped fee levels do have fees about $128 lower than states without fee caps, but I also found evidence that colleges in states with tuition caps have fees $59 higher. This suggests that colleges will substitute fees for tuition where possible. If a state’s governor and/or legislature can set tuition, fees tend to be lower, but if policymakers can set fees, fees tend to be higher. Finally, partisan political control only has a small relationship with fees, as having a Republican governor is associated with slightly lower fee levels and control of the legislature was not significant.

Given the magnitude of student fees and the relatively small body of research in this area, I hope to see more studies (particularly qualitative in nature) digging into how student fees are set and how the money is supposed to be used compared to its actual uses.

Which Colleges Benefit from Counting More Graduates?

The official graduation rate that colleges must report to the U.S. Department of Education has included only first-time, full-time students who graduate from that college within 150% of normal time (three years for a two-year college or six years for a four-year college). Although part-time and non-first-time students were included in the federal government’s Integrated Postsecondary Education Data System (IPEDS) collection for the first time this year, it will still be about another year or so before those data will be available to the public. (Russell Poulin at WICHE has a nice summary of what the new IPEDS outcome measure data will mean.)

In the meantime, the Student Achievement Measure (SAM)—a coalition of organizations primarily representing public colleges and funded by the Gates Foundation and Carnegie Corporation—has developed in response to calls for more complete tracking of student outcomes. SAM has launched a public relations campaign that has been quite visible in the higher education community using the hashtag #CountAllStudents to show the number of students who aren’t captured in the current graduation rate metric. (Barack Obama and Sarah Palin are two well-known examples.)

But what can be learned from a more complete picture of graduation rates? In this blog post, I examined SAM outcome data for 54 participating colleges in four states (California, Maryland, Missouri, and South Carolina) to see the extent to which graduation rates for first-time, full-time students at four-year universities changed by counting students who transferred and graduated elsewhere as a success, as well as looking at the percentage of students still enrolled after six years. I focused on first-time, full-time students here so I could compare the current graduation rate metrics to alternative metrics; completion rates for part-time students can be a topic for another day. The data can be downloaded here, and a summary is below.

Average graduation rate for first-time, full-time students at the same university within six years: 57%

Average graduation rate for first-time, full-time students anywhere within six years (SAM): 66%

Gain from SAM metric: 9%

Still enrolled anywhere, but no bachelor’s degree: 15%

The first figure below shows the distribution of IPEDS and SAM graduation rates, and it shows that they are pretty strongly related. The correlation between the two graduation rates is 0.966, which is a nearly-perfect relationship.

ipeds_sam_fig1

But colleges with lower IPEDS graduation rates did tend to gain more from the SAM graduation rate than those with higher graduation rates, as shown below. Six colleges with IPDS graduation rates between 35% and 70% had at least 15% of students graduate from another college, including five of the six universities participating in SAM from South Carolina. On the other hand, UCLA (with a 90% graduation rate in IPEDS) gained just 2% from the SAM metric. This suggests that a more complete definition of a graduate will help to at least slightly narrow graduation rate gaps.

ipeds_sam_fig2

It is also stunning to see the percentage of students who were still enrolled in college after six years. While the average college in my sample had 15% of its first-time, full-time students still plugging away somewhere, most of the less-selective colleges with higher percentages of lower-income and minority students still had at least 20% of students still enrolled. The new IPEDS metrics will count students through eight years, which should give a better picture of completion rates. I’m excited to see those metrics come out in the future—and hopefully incorporate them in future versions of the Washington Monthly college rankings.

How Colleges’ Net Prices Fluctuate Over Time

This piece first appeared at the Brookings Institution’s Brown Center Chalkboard blog.

As student loan debt has exceeded $1.2 trillion and many colleges continue to raise tuition prices faster than inflation, students, their families, and policymakers have further scrutinized how much money students pay to attend college. A key metric of affordability is the net price of attendance, defined as the total cost of attendance (tuition and fees, books and supplies, and a living allowance) less all grants and scholarships received by students with federal financial aid. The net price is a key accountability metric used in tools such as the federal government’s College Scorecard and the annual Washington Monthly college rankings that I compile. In this post, I am focusing on newly released net price data from the U.S. Department of Education through the 2013-14 academic year.

I first examined trends in net prices since the 2009-10 academic year for the 2,621 public two-year, public four-year, and private nonprofit four-year colleges that operate on the traditional academic year calendar. I do this for all students receiving federal financial aid (roughly 70% of all college students nationwide), as well as students with family incomes below $30,000 per year—roughly the lowest income quintile of students. Note that students from different backgrounds qualify for different levels of financial aid from both the federal government and the college they attend (and hence face different net prices). Table 1 shows the annual percentage changes in the median net price by sector over each of the five most recent years, as well as the median net price in 2013-14.

netprice_jan16_table1

The net price trends in the most recent year of data (2012-13 to 2013-14) look pretty good for students and their families. The median net price for all students with financial aid increased by just 0.1% at two-year public colleges, 1.4% at four-year public colleges, and 1.7% at four-year private nonprofit colleges—roughly in line with inflation. The lowest-income students saw lower net prices in 2013-14 at two-year public colleges (-1.4%) and four-year private nonprofit colleges (-0.5%) and a small 0.4% increase at four-year public colleges.

Even with one year of good news, net prices are up about 15% at four-year colleges and 10% at two-year colleges since the beginning of the Great Recession in 2009, with a slightly larger percentage increase for lower-income students. Much of this increase in net prices, particularly for lowest-income students, occurred during the 2011-12 academic year.

Although some may blame the lingering effects of the recession or reduced state funding for the increase, in my view the likely culprit appears to be changes made to the federal Pell Grant program. In 2011-12, the income cutoff for an automatic zero EFC (Expected Family Contribution, and hence automatically qualifying for the maximum Pell Grant) was cut from $31,000 to $23,000. This resulted in a 25% decline in the number of automatic zero EFC students and contributed to the average Pell award falling by $278—the first decline in average Pell awards since 2005.

I next examined potential reasons for colleges’ changes in net prices. As colleges are facing incentives to lower their net price, they can do so in three main ways. Lowering tuition prices or increasing institutional grant aid would both benefit students, but they are difficult for cash-strapped colleges to achieve.

If colleges want to lower their net price without sacrificing tuition or housing revenue, the easiest way to do so is to reduce living allowances for off-campus students. Colleges have wide latitude in setting these living allowances, and research that I’ve conducted with Sara Goldrick-Rab at Wisconsin and Braden Hosch at Stony Brook shows a wide range in living allowances within the same county. Here, I looked at whether colleges’ patterns of changing tuition and fees or their off-campus living allowance seemed to be related to their change in net price.

Table 2 shows the change between the 2012-13 and 2013-14 academic years in the total cost of attendance (COA), tuition and fees, and off-campus living allowances (for colleges with off-campus students), broken down by changes in the net price. Colleges with the largest increases in net price (greater than $2,000) increased their COA for off-campus students by $1,398, while colleges with smaller increases (between $0 and $1,999) increased their COA by $829. Both groups of colleges typically increased both tuition and fees and living allowances, which together resulted in the increase in COA.

netprice_jan16_table2

However, colleges with a reported decrease in net price between 2012-13 and 2013-14 had a different pattern of changes. They still increased tuition and fees, but they reduced off-campus living allowances in order to keep the cost of attendance lower. For example, the 131 colleges with a decrease in net price of at least $2,000 had average tuition increases of $310 while living allowances were reduced by $610. Some of these reductions in allowances may be perfectly reasonable (for example, if rent prices around a college fall), but others may deserve additional scrutiny.

The net price data provide useful insights regarding trends in college affordability, but students and their families should not necessarily expect the posted net price to reflect how much money they will need to pay for tuition, fees, and other necessary living expenses during the academic year. These metrics tend to be more accurate for on-campus students (as a college controls room and board prices), but everyone should also look at colleges’ net price calculators for more individualized price estimates as the net price for off-campus students in particular may not reflect their actual expenses.

To Reduce Debt, Give Students More Information to Make Wise College Choice Decisions

This article was originally published at The Conversation.

Higher education has gotten a lot of attention during the early stages of the 2016 presidential campaign. All three major candidates for the Democratic nomination – former New York Senator Hillary Clinton, former Maryland Governor Martin O’Malley and Vermont Senator Bernie Sanders – have proposed different plans to reduce or eliminate student loan debt at public colleges.

However, the price tags of these plans (at least $350 billion over 10 years for Clinton’s proposal) will make free college highly unlikely. Republicans, including leading presidential candidates, have already made their opposition quite clear.

But student loan debt is unlikely to go away anytime soon. What is important for now is that students and their families get better information about tuition costs and college outcomes so they can make more informed decisions, especially as the investments are so large.

What colleges will reveal

Although colleges are required to submit data on hundreds of items to the federal government each year, only a few measures that are currently available are important to most students and their families:

First, colleges must report graduation rates for first-time, full-time students. This does a good job reflecting the outcomes at selective colleges, where most students go full-time.

But full-time students make up only a small percentage of students at some colleges, and data on the graduation rates of part-time students will not be available until 2017.

The price tag of Hillary Clinton’s college plan is too steep.
Marc Nozell, CC BY

Colleges must also report net prices (the cost of attendance less all grant aid received) by different family income brackets. The cost of attendance (defined as tuition and fees, room and board, books and supplies, and other living expenses such as transportation and laundry) and the resulting net price are important measures of affordability.

Because financial aid packages can vary across colleges with similar sticker prices, net prices are important to give students an idea of what they might expect to pay.

Colleges that offer their students federal loans must report the percentage of students who defaulted on their loans within three years of leaving college. This measure reflects whether students are able to make enough money to repay their loans. Colleges must also report average student loan debt burdens, so students can see what their future payments might look like.

In addition, vocationally oriented programs must report debt and earnings metrics under new federal “gainful employment” regulations. This provides students in technical fields a clear idea of what they might expect to make.

The Obama administration has promised that additional information on student outcomes will be made available “later this summer”, although they have not said what will be made available.

What don’t we know?

Despite the availability of information on some key outcomes, more can still be done to help students make wise decisions about which college to attend.

Below are some example of outcomes that would be helpful for students and their families to know about.

Although enormous gaps in college completion rates exist by family income, students and their families cannot currently access data on the graduation rates of low-income students receiving federal Pell Grants. (The federal government is purchasing data from the National Student Clearinghouse to fix this going forward.)

Colleges are required to report the percentage of minority students and the percentage of students receiving Pell Grants, but nothing is known about the percentage of first-generation students.

This is of particular interest given the key policy goal of improving access to American higher education; without this information, it is harder to tell which colleges are engines of social mobility.

Students need to have more information.
Lynda Kuit, CC BY-ND

Private-sector organizations such as PayScale and LinkedIn work to fill this gap, but they can only provide a limited amount of information.

How could we know more?

The data needed to answer many of the questions above are already held by the federal government, but in multiple databases that are not allowed to communicate with each other.

The greatest barrier to better information from the federal government is due to a provision included in the 2008 reauthorization of the Higher Education Act which banned the federal government from creating a “student unit record” data system that would link financial aid, enrollment and employment outcomes for students receiving federal financial aid dollars. This ban was put in place in part due to concerns over data privacy, and in part due to an intense lobbying effort from private nonprofit colleges.

States, in contrast, are allowed to have unit record data systems, and a few of them make detailed information available to anyone at the click of a mouse.

For example, Virginia makes a host of student loan debt information available in a series of convenient tables and graphics.

Senator Rubio has teamed with Democratic Senators Ron Wyden of Oregon and Mark Warner of Virginia to introduce legislation overturning the ban on unit record data, although no action has yet been taken in Congress.

A bipartisan push to make more information available to students and their families has the potential to help students make better decisions.

But getting data is only one part of the challenge. The other is getting that into the hands of students at the right time. For that, it is important for the federal government to work with college access organizations and guidance counselors.

Students should be able to access this information as they begin considering attending college. Although additional information may not allow a student to graduate debt-free, it will help him or her to make a more informed decision about where to attend college and if the price tag is worth paying.

The Conversation

Read the original article.

Examining Trends in Living Allowances for College

The National Center for Education Statistics released a new report and data on trends in the cost of attendance for different types of colleges, including data from the 2012-13 to 2014-15 academic years. The report shows that, among colleges operating on a traditional academic year basis (excluding most vocationally-oriented colleges), tuition and fees generally increased at a rate faster than inflation among public and private nonprofit colleges over the last two years. However, tuition failed to keep up with inflation in the for-profit sector and allowances for other living expenses (such as transportation and laundry) declined over the past two years after taking inflation into account.

I dug deeper into the data, looking at the percentage of colleges by sector that increased, decreased, or held constant each of the cost of attendance components (tuition/fees, room and board, books and supplies, and other living expenses) between 2013-14 and 2014-15—without adjusting for inflation. I focused on students living off-campus without their family, as colleges have the ability to determine the room and board allowance but do not directly receive any housing revenue for off-campus students. (My blog post on the topic last year ended up connecting me to Braden Hosch at Stony Brook and Sara Goldrick-Rab at Wisconsin-Madison, and we’ve dug deeper into the accuracy and consistency of these estimates in a working paper.)

The results (below) show that for-profit colleges were far more likely to lower tuition and fees than public or private nonprofit colleges. While 75% of public colleges and 85% of private nonprofits increased tuition, just 42% of for-profit colleges did so. For-profits were also more likely to lower books/supplies and other living expense allowances, although the typical allowance was still higher than for nonprofit colleges. A majority of colleges across sectors increased room and board, while most colleges did not change their allowances for books and supplies.

 

Table 1: Changes in COA components by sector, 2013-14 to 2014-15.
Private nonprofit
Characteristic (2014-15) Public For-profit
Cost of attendance, students living off-campus without family
  Median ($) 18,328 37,900 28,796
  Increased from 2013-14 (pct) 77.8 84.9 56.3
  No change from 2013-14 (pct) 7.2 5.8 8.2
  Decreased from 2013-14 (pct) 15.0 9.3 35.5
Tuition and fees
  Median ($) 4,200 24,670 14,040
  Increased from 2013-14 (pct) 74.9 84.6 42.3
  No change from 2013-14 (pct) 19.5 11.0 38.5
  Decreased from 2013-14 (pct) 5.7 4.4 19.2
Room and board
  Median ($) 8,280 9,000 7,574
  Increased from 2013-14 (pct) 55.1 56.4 59.2
  No change from 2013-14 (pct) 34.6 34.5 28.2
  Decreased from 2013-14 (pct) 10.4 9.2 12.5
Books and supplies
  Median ($) 1,265 1,200 1,380
  Increased from 2013-14 (pct) 37.8 23.1 25.7
  No change from 2013-14 (pct) 54.4 69.3 59.1
  Decreased from 2013-14 (pct) 7.8 7.6 15.2
Other living expenses
  Median ($) 3,742 3,150 5,000
  Increased from 2013-14 (pct) 42.0 35.1 35.5
  No change from 2013-14 (pct) 36.8 48.9 27.4
  Decreased from 2013-14 (pct) 21.2 16.0 37.1
Number of colleges 1,573 1,233 719
SOURCE: Integrated Postsecondary Education Data System.
Note: Limited to colleges reporting costs on an academic year basis.

Yet as was noted in last year’s blog post on this topic, some colleges set room and board allowances that are unreasonably low by any standard. This year, I focused on the 27 colleges that reduced their room and board allowance for off-campus students by at least $3,000 between 2013-14 and 2014-15. Some of the changes may be reasonable, such as Thomas University’s drop from $15,200 to $10,530 for nine months of room and board. But many others are unlikely to meet any standard of reasonableness. For example, Emory & Henry College in Virginia reduced its allowance from $11,800 for nine months to just $3,000, while the College of DuPage in Illinois cut its allowance from $8,257 to $2,462. Good luck trying to rent an apartment and eating ramen on that budget!

Table 2: Colleges with large declines in off-campus room and board allowances, 2013-14 to 2014-15.
Name State 2013-14 2014-15 Change
Emory & Henry College VA 11,800 3,000 -8,800
Atlanta Metropolitan State College GA 10,753 3,160 -7,593
Mount Carmel College of Nursing OH 13,392 6,380 -7,012
Vanguard University of Southern California CA 11,286 4,600 -6,686
Louisiana Delta Community College LA 15,322 8,789 -6,533
Trinity College of Nursing & Health Sciences IL 12,346 5,858 -6,488
Arkansas Northeastern College AR 11,969 6,102 -5,867
College of DuPage IL 8,257 2,462 -5,795
College of the Mainland TX 11,330 5,665 -5,665
Randolph-Macon College VA 9,200 3,650 -5,550
The University of Texas at Brownsville TX 11,495 6,250 -5,245
SAE Institute of Technology-Nashville TN 15,000 10,000 -5,000
Bon Secours Memorial College of Nursing VA 15,000 10,000 -5,000
Thomas University GA 15,200 10,530 -4,670
Davenport University MI 8,692 4,340 -4,352
Southwestern Illinois College IL 8,516 4,280 -4,236
Lee University TN 11,650 7,520 -4,130
Grace School of Theology TX 12,684 8,584 -4,100
Prairie View A & M University TX 11,289 7,197 -4,092
NY Methodist Hospital Center for Allied Health Education NY 17,568 13,496 -4,072
College of Business and Technology-Flagler FL 12,000 8,320 -3,680
College of Business and Technology-Miami Gardens FL 12,000 8,320 -3,680
Anoka Technical College MN 10,356 6,994 -3,362
Central Penn College PA 6,855 3,500 -3,355
St Margaret School of Nursing PA 9,960 6,640 -3,320
Fortis Institute-Port Saint Lucie FL 12,732 9,495 -3,237
Southern California Seminary CA 14,616 11,493 -3,123
SOURCE: Integrated Postsecondary Education Data System.
Note: Limited to colleges reporting costs on an academic year basis.

Why do some colleges feel pressures to cut back living allowances? It’s all about accountability. The amount of loan dollars students can borrow is limited by the cost of attendance, meaning that reducing living allowances (and hence the cost of attendance) reduces borrowing—and potentially the risk of a college facing sanctions for high student loan default rates. The cost of attendance also determines the net price (the COA after grants are applied), an important accountability metric. Since colleges don’t directly benefit financially from a higher off-campus living allowance, they have an incentive to reduce the living allowance while continuing to increase tuition.