Indeed, if the CBO is right, then student loans are now a profit center for the government; the government no longer subsidizes them. Only “subsidized” student loans are subsidized, and only starting in 2016. Until then, even “subsidized” student loans are unsubsidized. Note that that these numbers are [...]]]> The Congressional Budget Office (CBO) released the May 2013 baseline cost estimates for the Pell grant and student loan programs. One of the things that jumped out at me was that for 2013 and 2014, the government estimates that it will make more money on student loans than it spends on Pell grants.

Indeed, if the CBO is right, then student loans are now a profit center for the government; the government no longer subsidizes them. Only “subsidized” student loans are subsidized, and only starting in 2016. Until then, even “subsidized” student loans are unsubsidized. Note that that these numbers are based on the Fair Credit Reporting Act (FCRA) estimates, whereas the fair value method would provide a more realistic estimate, which would show lower profits and perhaps even losses. Also note that these estimates assume current law, meaning the interest rate on these loans will increase to 6.8 percent this summer. See Jason Delisle and Clare McCann’s insightful piece for the latest on the interest rate wars.

The fair value vs. FCRA issue aside, I still have major reservations about the accuracy of the CBO’s subsidy rate estimate for GradPlus loans. They state that in present value terms, the government will earn a profit of $0.636 on every dollar loaned out under GradPlus. This strikes me as borderline fantasy, given that most students who enter IBR would have much of their principal (let alone interest) forgiven. Presumably, the current subsidy rate estimates are based on the currently low take-up rate for IBR, but as Jason Delilse and Alex Holt noted:

“Working together, Grad PLUS and the new IBR are set to provide massive subsidies to graduate students, graduate schools, and employers who no longer need to pay salaries that justify the debt incurred to obtain a graduate or professional education. Should the Grad PLUS windfall under new IBR go unnoticed and unused as some skeptics claim, it will be the first time in history that the federal government offers $41,000 or $100,000 checks to the most educated segment of society and nobody shows up to claim them.”

What is the policy angle on all of this? First, we could use some more accurate numbers. The current FCRA estimates “are not comprehensive measures of the costs of the federal student loan programs, for two main reasons: They do not take into account the cost of some of the risks that student loans impose on taxpayers, and they omit most administrative costs (which are recorded elsewhere in the budget).” In other words, FCRA systematically overestimates the amount of profit and underestimates the losses the government will realize on student loans. Second, once we have more complete estimates, the country can decide if it wants to subsidize student loans or not, and if so, by how much.

The blue line shows how many Pell and Perkins recipients colleges would have if Perkins loans were distributed on the basis of need (roughly 5 Perkins loans for every 100 Pell grants), and the red line shows the best fit line for the actual [...]]]> According to the U.S. Department of Education, the Perkins Loan program “provides lowinterest [sic] loans to help needy students finance the costs of postsecondary education.” Pell grants target needy students as well, so the chart below shows the number of students receiving Perkins loans and the number of students receiving Pell grants at a sample of colleges.

The blue line shows how many Pell and Perkins recipients colleges would have if Perkins loans were distributed on the basis of need (roughly 5 Perkins loans for every 100 Pell grants), and the red line shows the best fit line for the actual distributions. Since both programs are targeting needy students, there should be a much stronger relationship between the number of recipients in each program. (The red line should be more similar to the blue line, and the dots should be more clustered around the blue line.)

If Perkins loans were evenly distributed among needy students, differences in the number of needy students at a college would explain 100 percent of the differences in the number of students receiving Perkins loans. The actual number is 2.5 percent. In other words, there is very little relationship between the number of needy students at a college and the number of Perkins loans awarded.

Indeed, if Perkins loans were distributed based on need, each college would have about 20 times as many Pell grant recipients as Perkins loan recipients. Yet incredibly, some colleges manage to give more students Perkins loans than there are Pell grant students at the college. For example:

• Washington University in St Louis awarded 2,217 Perkins loans, yet only had 542 Pell students.
• Duke awarded 1,731 Perkins loans, yet only had 961 Pell students.
• Harvard awarded 2,149 Perkins loans, yet only had 1,307 Pell students.

This is astounding. Perkins is supposedly a program for needy students. Yet many non-Pell students at Washington University, Duke, Harvard, and many other colleges are receiving Perkins loans, while more than 9 million Pell grant students at other colleges—who are even more needy—do not receive a Perkins loan.

This incredible state of affairs is possible because Perkins loans are one of the campus-based award programs where funding is based largely on past allocations rather than the actual need of students, meaning that older colleges and those that aggressively lobby receive more funding.

There is something very wrong when the amount of money awarded by a college from a program for needy students is based more on its political power than the need of its students. It is time to recognize that the Perkins loan program is not an aid program for needy students; rather, it is a slush fund for America’s aristocratic colleges.

One of my strangest experiences as a researcher is the period of time between when work on a study is finished and when it is released. For me, this timing issue greatly complicates public outreach efforts like blogging—by the time a study is edited and released, I’ve mentally moved on and mostly want to blog about some new/ongoing research rather than the just-published study. Fortunately, this problem does not apply [...]]]> Our new study, Degrees of Value, explores the returns to investing in college and finds that much more attention should be paid in the vast ranges of outcomes that students experience.

One of my strangest experiences as a researcher is the period of time between when work on a study is finished and when it is released. For me, this timing issue greatly complicates public outreach efforts like blogging—by the time a study is edited and released, I’ve mentally moved on and mostly want to blog about some new/ongoing research rather than the just-published study. Fortunately, this problem does not apply to our new study as many other related papers have been released; it’s hard to keep up.

First, Brookings released a report by Stephanie Owen and Isabel Sawhill that nicely complements our study. We take a more consumer-focused (micro) look at the issue, while they focus on the big picture (macro) issue:

“We all know that, on average, college graduates make significantly more money over their lifetimes than those with only a high school education. What gets less attention is the fact that not all college degrees or college graduates are equal…

…it is a mistake to unilaterally tell young Americans that going to college—any college—is the best decision they can make. If they choose wisely and attend a school with generous financial aid and high expected earnings, and if they don’t just enroll but graduate, they can greatly improve their lifetime prospects. The information needed to make a wise decision, however, can be difficult to find and hard to interpret.”

Second, the College Measures data for Texas, which show the average earnings by college and major, were just released. The accompanying report by Mark Schneider notes that:

“…graduates do not earn just a bachelor’s degree; they earn a degree from a specific institution and in a specific field of study. And these choices have consequences as graduates enter the labor market… Students should know these data before they attend a school or choose a program.”

Third, we are starting to see evidence that more discerning students are affecting the business model of colleges (for mid-tier, private non-profits anyway). For the past few years, anecdotes have swirled that private colleges are starting to feel the pressure of heightened consumer attention to price and value. These anecdotes are now backed up by evidence: Some private colleges are facing lower enrollment and must offer steep discounts. Indeed, half of the colleges that “reported a drop in enrollment attributed it to ‘price sensitivity.’”

Stay tuned for more on this very important topic.

Last summer’s interest rate “crisis” was a real eye-opener for me. Regardless of political orientation, financial aid analysts regarded the ultimately successful effort [...]]]> The interest rate “crisis” is heating up again, so now is a good time to revisit the last time this played out (last summer). To refresh your memory, a 2007 law gradually reduced the interest rates on some student loans to 3.4 percent by 2011-12. But rates were scheduled to return to 6.8 percent last summer. The “crisis” began in late April 2012, as proposals to keep rates low began to emerge, and was resolved by late June 2012.

Last summer’s interest rate “crisis” was a real eye-opener for me. Regardless of political orientation, financial aid analysts regarded the ultimately successful effort to keep interest rates at 3.4 percent as ill-advised; and yet just about everyone else portrayed the issue as a “crisis” (and continue to do so) that politicians “saved” us from.

There was certainly no shortage of folks advocating for keeping rates at 3.4 percent. But advocacy shouldn’t be confused with wisdom. I have yet to find anyone of any political persuasion who actually studies financial aid (as opposed to advocating for it) and who supported the effort to keep rates at 3.4 percent. For example, Jason Delisle (and here and here), Matthew Chingos, and Rick Hess cover just about the entire political spectrum. (They are at New America, Brookings, and AEI, respectively.) And yet they all opposed efforts to keep interest rates at 3.4 percent.

There were plenty of news stories on the various proposals, but missing from virtually all of them was any context. There was no discussion of what interest rates should be and why; if the government is/should be making money on student loans; what the optimal rate is in light of social benefits from higher education; or whether subsidizing interest rates is the best use of increasingly scarce financial aid dollars. Any of these would have provided readers with some context to evaluate the various proposals. But we didn’t get any of that, and as a result, the various proposals all took it for granted that rates needed to stay at 3.4 percent and were evaluated on Pavlovian political affiliation.

Now that there is no longer a presidential election to complicate things, perhaps we will get a better debate this year. It is already looking that way too—with sensible proposals from President Obama and House Republicans.

Photo Credit: Trulia

We explore this important issue by examining the economics behind college returns in light of recent changes in student attitudes:

“College graduates earn more and are less [...]]]> We are pleased to release Degrees of Value today. This paper aims to provide information on one important aspect of college—the return on investment. While there is certainly more to a college education than the financial payoff, the fact remains that this is an increasingly dominant concern for students. More tahn 80 percent of students now cite “to be able to get a better job” as a very important reason for attending college.

We explore this important issue by examining the economics behind college returns in light of recent changes in student attitudes:

“College graduates earn more and are less likely to be unemployed than those with only high school diplomas. But with the onset of the Great Recession in 2008, the perimeters of the value debate in higher education began to shift. College prices continued to climb even as average household wealth declined… leading prospective students and their families to increasingly ask the value question: What will we get in return for our investment in college, especially if we are taking on significant debt? Often it’s not that students and families are questioning the value of college per se, just the value of attending certain colleges.”

We also review new consumer-friendly tools such as the U.S. Department of Education’s College Scorecard, College Measures, and College Reality Check, each of which provides evidence that students are right to become more discerning. Where you go to college and what you do while there can have a dramatic impact on a student’s return on investment.

Yet these are, at best, partial solutions—students and parents need a system that measures college outputs much more comprehensively in order to be able to make truly informed decisions.

The first map below shows each state’s average four-year graduation rate (enrollment-weighted, meaning schools with only a few students don’t skew the average). Nevada, New Mexico, and Alaska (not shown) have enrollment-weighted, four-year graduation rates of less than 15 percent, which is so low I’m pretty much speechless.

On a separate note, the second map recreates the original map of college locations, but varies the size of each college’s dot based on the enrollment at the college, with larger colleges getting bigger dots.

This chart emphasizes two somewhat [...]]]> Recent commenters have asked for maps showing graduation rates and enrollment. Ask and ye shall receive.

The first map below shows each state’s average four-year graduation rate (enrollment-weighted, meaning schools with only a few students don’t skew the average). Nevada, New Mexico, and Alaska (not shown) have enrollment-weighted, four-year graduation rates of less than 15 percent, which is so low I’m pretty much speechless.

On a separate note, the second map recreates the original map of college locations, but varies the size of each college’s dot based on the enrollment at the college, with larger colleges getting bigger dots.

This chart emphasizes two somewhat contradictory points. First, the huge number of dots indicates that there are a massive number of colleges in this country indicating wide (institutional) diversity within higher education. But the second point is that higher education is less diverse than the first point leads us to believe because the big colleges have absolutely massive enrollments. Indeed, just 106 colleges enroll one out of every four college students. (In case you’re curious, the blob obscuring most of Alabama is the Community College of the Air Force. Also, the big dot in Arizona is Arizona State University, not the University of Phoenix, which distributes its massive enrollment among various branch campuses).

There is a lot of interesting information buried in these maps, but three things jumped out at me.

First, tuition tends to be highest in the Northeast, particularly at the four-year level, and lowest in the non-coastal West.

Second, Wyoming is the only state that is in the most affordable bracket in every map. Pennsylvania deserves the opposite distinction, since it’s in the most expensive bracket in every [...]]]> One of the comments on last week’s post asked for a map of tuition. Below are maps of the enrollment-weighted, average tuition by state for (1) all colleges, (2) four-year colleges, and (3) two-year colleges.

There is a lot of interesting information buried in these maps, but three things jumped out at me.

First, tuition tends to be highest in the Northeast, particularly at the four-year level, and lowest in the non-coastal West.

Second, Wyoming is the only state that is in the most affordable bracket in every map. Pennsylvania deserves the opposite distinction, since it’s in the most expensive bracket in every map. (Vermont is too, but its two-year figures are heavily swayed by a single, very expensive two-year college.)

Third, there are very different tuition patterns among the states. Some states are consistently very expensive at both the four-year and two-year levels (e.g. Illinois and Pennsylvania). Some are consistently very affordable (e.g. Wyoming, Mississippi, and New Mexico). Some have low tuition at the two-year level, but high tuition at the four-year level (e.g. California and North Carolina), while some have low tuition at the four-year level and high tuition at the two-year level (e.g. Nevada). There’s at least one political science dissertation to be written on whether these differences resulted from deliberate policy decisions or historical accident. Either way, I’m even more curious about whether these different patterns lead to different results in terms of enrollment, attainment, debt, etc.

Specifically, the 1987-88 DAS output for the “mean” value of the “Classes, number of” variable in 1987-88 is 3.6, but this is not the mean teaching load of the typical professor. Moreover, the method by which this value was calculated makes it incomparable to the mean teaching load of the [...]]]> My paper, Selling Students Short, released on March 20, 2013, examined teaching loads among tenured and tenure-track faculty. I relied on teaching load data from the U.S. Department of Education’s Data Analysis System (DAS). After publication of the paper, further investigation revealed that the teaching load data I used was incorrect.

Specifically, the 1987-88 DAS output for the “mean” value of the “Classes, number of” variable in 1987-88 is 3.6, but this is not the mean teaching load of the typical professor. Moreover, the method by which this value was calculated makes it incomparable to the mean teaching load of the typical professor in 2003-04, meaning that we simply cannot determine whether teaching loads have declined, stayed the same, or even increased between 1987-88 and 2003-04.

Here is what happened:

This is the DAS output table in question.

I interpreted the numbers in the “Classes, number of (Mean[0])” column to be the mean number of classes taught. This is the proper interpretation of the corresponding columns for the 1992-93, 1998-99 and 2003-04 surveys. But this is not the correct interpretation for the 1987-88 survey.

For the 1987-88 survey, responses for the number of classes taught question were numerically categorized, and the “Classes, number of (Mean[0])” column in the DAS screenshot above refers to the mean of the numerical categories, rather than the mean of classes taught, and these numerical categories do not match the number of classes taught. A professor who taught zero courses was assigned to numerical category one, and a professor who taught one course was assigned to numerical category two, etc. Since most professors were in a numerical category with a number equal to their actual teaching load plus one, the mean of the numerical categories is approximately equal to the mean teaching load plus one.

In addition, for the 1987-88 survey, professors who did not teach any classes did not answer the number of classes taught question, but did answer that question in the 2003-04 survey. In other words, the 1987-88 “mean” value does not contain any professors who taught zero classes, but the 2003-04 “mean” value does.

The bottom line is that changes in the surveys and the calculations by DAS mean that we cannot determine whether teaching loads for the typical professor declined, stayed the same, or increased. There are subsets of classes for a subset of professors meeting certain criteria where it may be possible to estimate the change in teaching loads by manually adjusting DAS figures, but this subset of classes and professors will not necessarily be representative of the change in teaching loads for the typical professor.

While reporting a “mean” value of a “Classes, number of” taught variable when it is not the mean number of classes taught could be considered misleading on the part of the U.S. Department of Education, the ultimate responsibility for this misunderstanding rests with me. I apologize for any confusion and inconvenience this may have caused. The only consolation I can offer is that, as this experience amply demonstrates, I will continue to faithfully follow the data wherever it may lead.