The 2019 Net Price Madness Tournament

Ever since 2013, I have taken 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). While the winners are not known for on-court success (see my 2018 bracket and older brackets along with my other writing on net price), it’s still great to highlight colleges that are affordable for their students. (Also, as UMBC’s win on the court last year over Virginia—which my bracket did call—shows, anything is theoretically possible!)

I created a bracket using 2016-17 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 or 7/10 game that you’ll probably get wrong anyway.

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

East: Northern Kentucky ($9,338)

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

South: Purdue ($12,117)

Midwest: Washington ($9,443)

Kudos to Northern Kentucky for having the lowest net price for all students ($9,338), 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 ($11,100). Not to be forgotten, UNC’s Tobacco Road rivals Duke deserve a shoutout for having net prices below $1,000 for students with family incomes below $48,000 per year even as the overall net price is high.

As a closing note, this is the first NCAA tournament for which gambling is legal in certain states (including New Jersey). I can’t bring myself to wager on games in which student-athletes who are technically amateurs are playing. If a portion of gambling revenues went to trusts that players could activate after their collegiate careers are over (and they do not benefit from a particular outcome of a game), I might be interested in putting down a few dollars. But until then, I will use this bracket for bragging rights and educating folks about available higher education data.

New Data on Pell Grant Recipients’ Graduation Rates

In spite of being a key marker of colleges’ commitments to socioeconomic diversity, it has only recently been possible to see institution-level graduation rates of students who begin college with Pell Grants. I wrote a piece for Brookings in late 2017 based on the first data release from the U.S. Department of Education and later posted a spreadsheet of graduation rates upon the request of readers—highlighting public interest in the metric.

ED released the second year of data late last year, and Melissa Korn of The Wall Street Journal (one of the best education writers in the business) reached out to me to see if I had those data handy for a piece she wanted to write on Pell graduation rate gaps. Since I do my best to keep up with new data releases from the Integrated Postsecondary Education Data System, I was able to send her a file and share my thoughts on the meaning of the data. This turned into a great piece on completion gaps at selective colleges.

Since I have already gotten requests to share the underlying data in the WSJ piece, I am happy to post the spreadsheet again on my site.

Download the spreadsheet here!

A few cautions:

(1) There are likely a few colleges that screwed up data reporting to ED. For example, gaps of 50% for larger colleges are likely an error, but nobody at the college caught them.

(2) Beware the rates for small colleges (with fewer than 50 students in a cohort).

(3) This graduation rate measure is the graduation rate for first-time, full-time students who complete a bachelor’s degree at the same institution within six years. It excludes part-time and transfer students, so global completion numbers will be higher.

(4) As my last post highlighted, there are some legitimate concerns with using percent Pell as an accountability measure. However, it’s the best measure that is currently available.

Some Thoughts on Using Pell Enrollment for Accountability

It is relatively rare for an academic paper to both dominate the headlines in the education media and be covered by mainstream outlets, but a new paper by economists Caroline Hoxby and Sarah Turner did exactly that. The paper, benignly titled “Measuring Opportunity in U.S. Higher Education” (technical and accessible versions) raised two major concerns with using the number or percentage of students receiving federal Pell Grants for accountability purposes:

(1) Because states have different income distributions, it is far easier for universities in some states to enroll a higher share of Pell recipients than others. For example, Wisconsin has a much lower share of lower-income adults than does California, which could help explain why California universities have a higher percentage of students receiving Pell Grants than do Wisconsin universities.

(2) At least a small number of selective colleges appear to be gaming the Pell eligibility threshold by enrolling far more students who barely receive Pell Grants than those who have significant financial need but barely do not qualify. Here is the awesome graph that Catherine Rampell made in her Washington Post article summarizing the paper:

hoxby_turner

As someone who writes about accountability and social mobility while also pulling together Washington Monthly’s college rankings (all opinions here are my own, of course), I have a few thoughts inspired by the paper. Here goes!

(1) Most colleges likely aren’t gaming the number of Pell recipients in the way that some elite colleges appear to be doing. As this Twitter thread chock-full of information from great researchers discusses, there is no evidence nationally that colleges are manipulating enrollment right around the Pell eligibility cutoff. Since most colleges are broad-access and/or are trying to simply meet their enrollment targets, it follows that they are less concerned with maximizing their Pell enrollment share (which is likely high already).

(2) How are elite colleges manipulating Pell enrollment? This could be happening in one or more of three possible ways. First, if these colleges are known for generous aid to Pell recipients, more students just on the edge of Pell eligibility may choose to apply. Second, colleges could be explicitly recruiting students from areas likely to have larger shares of Pell recipients toward the eligibility threshold. Finally, colleges could make admissions and/or financial aid decisions based on Pell eligibility. It would be ideal to see data on each step of the process to better figure out what is going on.

(3) What other metrics can currently be used to measure social mobility in addition to Pell enrollment? Three other metrics currently jump out as possibilities. The first is enrollment by family income bracket (such as below $30,000 or $30,001-$48,000), which is collected for first-time, full-time, in-state students in IPEDS. It suffers from the same manipulation issues around the cutoffs, though. The second is first-generation status, which the College Scorecard collects for FAFSA filers. The third is race/ethnicity, which tends to be correlated with the previous two measures but is likely a political nonstarter in a number of states (while being a requirement in others).

(4) How can percent Pell still be used? The first finding of Hoxby’s and Turner’s work is far more important than the second finding for nationwide analyses (within states, it may be worth looking at regional differences in income, too). The Washington Monthly rankings use both the percentage of Pell recipients and an actual versus predicted Pell enrollment measure (controlling for ACT/SAT scores and the percentage of students admitted). I plan to play around with ways to take a state’s income distribution into account to see how this changes the predicted Pell enrollments and will report back on my findings in a future blog post.

(5) How can social mobility be measured better? States can dive much deeper into social mobility than the federal government can thanks to their detailed student-level datasets. This allows for sliding scales of social mobility to be created or to use something like median household income instead of just percent Pell. It would be great to have a measure of the percentage of students with zero expected family contribution (the neediest students) at the national level, and this would be pretty easy to add onto IPEDS as a new measure.

I would like to close this post by thanking Hoxby and Turner for provoking important conversations on data, social mobility, and accountability. I look forward to seeing their next paper in this area!

How Colleges’ Carnegie Classifications Have Changed Over Time

Right as the entire higher education community was beginning to check out for the holiday season last month, Indiana University’s Center on Postsecondary Research released the 2018 Carnegie classifications. While there are many different types of classifications based on different institutional characteristics, the basic classification (based on size, degrees awarded, and research intensity) always garners the most attention from the higher education community. In this post, I look at some of the biggest changes between the 2015 and 2018 classifications and how the number of colleges in key categories has changed over time. (The full dataset can be downloaded here.)

The biggest change in the 2018 classifications was about how doctoral universities were classified. In previous classifications, a college was considered a doctoral university if it awarded at least 20 research/scholarship doctoral degrees (PhDs and a few other types of professional doctorates such as EdDs). The 2018 revisions counted a college as being a doctoral university if there were at least 30 professional practice doctorates (JDs, MDs, and other related fields such as in health sciences). This resulted in accelerating the increase in the number of doctoral universities that has existed since 2000:

2018: 423

2015: 334

2010: 295

2005: 279

2000: 258

This reclassification is important to universities because college rankings systems often classify institutions based on their Carnegie classification. U.S. News and Washington Monthly (the latter of which I compile) both base the national university category on the Carnegie doctoral university classification. The desire to be in the national university category (instead of regional or master’s university categories that get less public attention) has contributed to some universities developing doctoral programs (as Villanova did prior to the 2015 reclassification).

The revision of the lowest two levels of doctoral universities (which I will call R2 and R3 for shorthand, matching common language) did quite a bit to scramble the number of colleges in each category, with a number of R3 colleges moving into R2 status. Here is the breakdown among the three doctoral university groups since 2005 (the first year of three categories):

Year R1 R2 R3
2018 130 132 161
2015 115 107 112
2010 108 98 89
2005 96 102 81

Changing categories within the doctoral university group is important for benchmarking purposes. As I told Inside Higher Ed back in December, my university’s moving within the Carnegie doctoral category (from R3 to R2) affects its peer group. All of the sudden, tenure and pay comparisons will be based on a different—and somewhat more research-focused—group of institutions.

There has also been an increase in the number of two-year colleges offering at least some bachelor’s degrees, driven by the growth of community college baccalaureate efforts in states such as Florida and a diversifying for-profit sector. Here is the trend in the number of baccalaureate/associate colleges since 2005:

2018: 269

2015: 248

2010: 182

2005: 144

Going forward, Carnegie classifications will continue to be updated every three years in order to keep up with a rapidly-changing higher education environment. Colleges will certainly be paying attention to future updates that could affect their reputation and peer groups.

Announcing a New Data Collection Project on State Performance-Based Funding Policies

Performance-based funding (PBF) policies in higher education, in which states fund colleges in part based on student outcomes instead of enrollment measures or historical tradition, have spread rapidly across states in recent years. This push for greater accountability has resulted in more than half of all states currently using PBF to fund at least some colleges, with deep-blue California joining a diverse group of states by developing a PBF policy for its community colleges.

Academic researchers have flocked to the topic of PBF over the last decade and have produced dozens of studies looking at the effects of PBF both on a national level and for individual states. In general, this research has found modest effects of PBF, with some differences across states, sectors, and how long the policies have been in place. There have also been concerns about the potential unintended consequences of PBF on access for low-income and minority students, although new policies that provide bonuses to colleges that graduate historically underrepresented students seem to be promising in mitigating these issues.

In spite of the intense research and policy interest in PBF, relatively little is known about what is actually in these policies. States vary considerably in how much money is tied to student outcomes, which outcomes (such as retention and degree completion) are incentivized, and whether there are bonuses for serving low-income, minority, first-generation, rural, adult, or veteran students. Some states also give bonuses for STEM graduates, which is even more important to understand given this week’s landmark paper by Kevin Stange and colleagues documenting differences in the cost of providing an education across disciplines.

Most research has relied on binary indicators of whether a state has a PBF policy or an incentive to encourage equity, with some studies trying to get at the importance of the strength of PBF policies by looking at individual states. But researchers and advocacy organizations cannot even agree on whether certain states had PBF policies in certain years, and no research has tried to fully catalog the different strengths of policies (“dosage”) across states over time.

Because collecting high-quality data on the nuances of PBF policies is a time-consuming endeavor, I was just about ready to walk away from studying PBF given my available resources. But last fall at the Association for the Study of Higher Education conference, two wonderful colleagues approached me with an idea to go out and collect the data. After a year of working with Justin Ortagus of the University of Florida and Kelly Rosinger of Pennsylvania State University—two tremendous assistant professors of higher education—we are pleased to announce that we have received a $204,528 grant from the William T. Grant Foundation to build a 20-year dataset containing detailed information about the characteristics of PBF policies and how much money is at stake.

Our dataset, which will eventually be made available to the public, will help us answer a range of policy-relevant questions about PBF. Some particularly important questions are whether dosage matters regarding student outcomes, whether different types of equity provisions are effective in reducing educational inequality, and whether colleges respond to PBF policies differently based on what share of their funding comes from the state. We are still seeking funding to do these analyses over the next several years, so we would love to talk with interested foundations about the next phases of our work.

To close, one thing that I tell often-skeptical audiences of institutional leaders and fellow faculty members is that PBF policies are not going away anytime soon and that many state policymakers will not give additional funding to higher education without at least a portion being directly tied to student outcomes. These policies are also rapidly changing, in part driven by some of the research over the last decade that was not as positive toward many early PBF systems. This dataset will allow us to examine which types of PBF systems can improve outcomes across all students, thus helping states improve their current PBF systems.

New Research on the Relationship between Nonresident Enrollment and In-State College Prices

Public colleges and universities in most states are under increased financial stress as they strain to compete with other institutions while state appropriations fail to keep up with increases in both inflation and student enrollment. As a result, universities have turned to other revenue sources to raise additional funds. One commonly targeted source is out-of-state students, particularly in Northeastern and Midwestern states with declining populations of recent high school graduates. But prior research has found that trying to enroll more out-of-state students can reduce the number of in-state students attending selective public universities, and this crowding-out effect particularly impacts minority and low-income students.

I have long been interested in studying how colleges use their revenue, so I began sketching out a paper looking at whether public universities appeared to use additional revenue from out-of-state students to improve affordability for in-state students. Since I am particularly interested in prices faced by students from lower-income families, I was also concerned that any potential increase in amenities driven by out-of-state students could actually make college less affordable for in-state students.

I started working on this project back in the spring of 2015 and enjoyed two and a half conference rejections (one paper submission was rejected into a poster presentation), two journal rejections, and a grant application rejection during the first two years. But after getting helpful feedback from the journal reviewers (unfortunately, most conference reviewers provide little feedback and most grant applications are rejected with no feedback), I made improvements and finally got the paper accepted for publication.

The resulting article, just published in Teachers College Record (and is available for free for a limited time upon signing up as a visitor), includes the following research questions:

(1) Do the listed cost of attendance and components such as tuition and fees and housing expenses for in-state students change when nonresident enrollment increases?

(2) Does the net price of attendance (both overall and by family income bracket) for in-state students change when nonresident enrollment increases?

(3) Do the above relationships differ by institutional selectivity?

After years of working on this paper and multiple iterations, I am pleased to report…null findings. (Seriously, though, I am glad that higher education journals seem to be willing to publish null findings, as long as the estimates are precisely located around zero without huge confidence intervals.) These findings suggest two things about the relationship between nonresident enrollment and prices faced by in-state students. First, it does not look like nonresident tuition revenue is being used to bring down in-state tuition prices. Second, it also does not appear that in-state students are paying more for room and board after more out-of-state students enroll, suggesting that any amenities demanded by wealthier out-of-state students may be modest in nature.

I am always happy to take any questions on the article or to share a copy if there are issues accessing it. I am also happy to chat about the process of getting research published in academic journals, since that is often a long and winding road!

How Financial Responsibility Scores Do Not Affect Institutional Behaviors

One of the federal government’s longstanding accountability efforts in higher education is the financial responsibility score—a metric designed to reflect a private college’s financial stability. The federal government has an interest in making sure that only stable colleges receive federal funds, as taxpayers often end up footing at least part of the bill when colleges shut down and students may struggle to resume their education elsewhere. The financial responsibility score metric ranges from -1.0 to 3.0, with colleges scoring between 1.0 and 1.4 being placed under additional oversight and those scoring below 1.0 being required to post a letter of credit with the Department of Education.

Although these scores have been released to the public since the 2006-07 academic year and there was a great deal of dissatisfaction among private colleges regarding how the scores were calculated, there had been no prior academic research on the topic before I started my work in the spring of 2014. My question was simple: did receiving a poor financial responsibility score induce colleges to shift their financial priorities (either increasing revenues or decreasing expenditures) in an effort to avoid future sanctions?

But as is often the case in academic research, the road to a published article was far from smooth and direct. Getting rejected by two different journals took nearly two years and then it took another two years for this paper to wind its way through the review, page proof, and publication process at the Journal of Education Finance. (In the meantime, I scratched my itch on the topic and put a stake in the ground by writing a few blog posts highlighting the data and teasing my findings.)

More than four and a half years after starting work on this project, I am thrilled to share that my paper, “Do Financial Responsibility Scores Affect Institutional Behaviors?” is a part of the most recent issue of the Journal of Education Finance. I examined financial responsibility score data from 2006-07 to 2013-14 in this paper, although I tried to get data going farther back since these scores have been calculated since at least 1996. I filed a Freedom of Information Act request back in 2014 for the data, and my appeal was denied in 2017 on the grounds that the request to receive data (that already existed in some format!) was “too burdensome and expensive.” At that point, the paper was already accepted at JEF, but I am obviously still a little annoyed with how that process went.

Anyway, I failed to find any clear evidence that private nonprofit or for-profit colleges changed their fiscal priorities after receiving an unfavorable financial responsibility score. To some extent, this result made sense among private nonprofit colleges; colleges tend to move fairly slowly and many of their costs are sticky (such as facilities and tenured faculty). But for-profit colleges, which generally tend to be fairly agile critters, the null findings were more surprising. There is certainly more work to do in this area (particularly given the changes in higher education that have occurred over the last five years), so I encourage more researchers to delve into this topic.

To aspiring researchers and those who rely on research in their jobs—I hope this blog post provides some insights into the scholarly publication process and all of the factors that can slow down the production of research. I started this paper during my first year on faculty and it finally came out during my tenure review year (which is okay because accepted papers still count even if they are not yet in print). Many papers move more quickly than this one, but it is worth highlighting that research is a pursuit for people with a fair amount of patience.

Some Good News on Student Loan Repayment Rates

The U.S. Department of Education released updates to its massive College Scorecard dataset earlier this week, including new data on student debt burdens and student loan repayment rates. In this blog post, I look at trends in repayment rates (defined as whether a student repaid at least $1 in principal) at one, three, five, and seven years after entering repayment. I present data for colleges with unique six-digit Federal Student Aid OPEID numbers (to eliminate duplicate results), weighting the final estimates to reflect the total number of borrowers entering repayment.[1]

The table below shows the trends in the 1-year, 3-year, 5-year, and 7-year repayment rates for each cohort of students with available data.

Repayment cohort 1-year rate (pct) 3-year rate (pct) 5-year rate (pct) 7-year rate (pct)
2006-07 63.2 65.1 66.7 68.4
2007-08 55.7 57.4 59.5 62.2
2008-09 49.7 51.7 55.3 59.5
2009-10 45.7 48.2 52.6 57.4
2010-11 41.4 45.4 51.3 N/A
2011-12 39.8 44.4 50.6 N/A
2012-13 39.0 45.0 N/A N/A
2013-14 40.0 46.1 N/A N/A

One piece of good news is that 1-year and 3-year repayment rates ticked up slightly for the most recent cohort of students who entered repayment in 2013 or 2014. The 1-year repayment rate of 40.0% is the highest rate since the 2010-11 cohort and the 3-year rate of 46.1% is the highest since the 2009-10 cohort. Another piece of good news is that the gain between the 5-year and 7-year repayment rates for the most recent cohort with data (2009-10) is the largest among the four cohorts with data.

Across all sectors of higher education, repayment rates increased as a student got farther into the repayment period. The charts below show differences by sector for the cohort entering repayment in 2009 or 2010 (the most recent cohort to be tracked over seven years), and it is worth noting that for-profit students see somewhat smaller increases in repayment rates than other sectors.

But even somewhat better repayment rates still indicate significant issues with student loan repayment. Only half of borrowers have repaid any principal within five years of entering repayment, which is a concern for students and taxpayers alike. Data from a Freedom of Information Act request by Ben Miller of the Center for American Progress highlight that student loan default rates continue to increase beyond the three-year accountability window currently used by the federal government, and other students are muddling through deferment and forbearance while outstanding debt continues to increase.

Other students are relying on income-driven repayment and Public Service Loan Forgiveness to remain current on their payments. This presents a long-term risk to taxpayers as at least a portion of balances will be written off over the next several decades. It would be helpful for the Department of Education to add data to the College Scorecard on the percentage of students by college enrolled in income-driven repayment rates so it is possible to separate students who may not be repaying principal due to income-driven plans from those who are placing their credit at risk by falling behind on payments.

[1] Some of the numbers for prior cohorts slightly differ from what I presented last year due to a change in how I merged datasets (starting with the most recent year of the Scorecard instead of the oldest year, as the latter method excluded some colleges that merged). However, this did not affect the general trends presented in last year’s post. Thanks to Andrea Fuller at the Wall Street Journal for helping me catch that bug.

How to Provide Context for College Scorecard Data

The U.S. Department of Education’s revamped College Scorecard website celebrated its third anniversary last month with another update to the underlying dataset. It is good to see this important consumer information tool continue to be updated, given the role that Scorecard data can play in market-based accountability (a key goal of many conservatives). But the Scorecard’s change log—a great resource for those using the dataset—revealed a few changes to the public-facing site. (Thanks to the indefatigable Clare McCann at New America for pointing this out in a blog post.)

scorecard_fig1_oct18

So to put the above screenshot into plain English, the Scorecard used to have indicators for how a college’s performance on outcomes such as net price, graduation rate, and post-college salary compared to the median institution—and now it doesn’t. In many ways, the Department of Education’s decision to stop comparing colleges with different levels of selectivity and institutional resources to each other makes all the sense in the world. But it would be helpful to provide website users with a general idea of how the college performs relative to more similar institutions (without requiring users to enter a list of comparison colleges).

For example, here is what the Scorecard data now look like for Cal State—Sacramento (the closest college to me as I write this post). The university sure looks affordable, but the context is missing.

scorecard_fig2_oct18

It would sure be helpful if ED already had a mechanism to generate a halfway reasonable set of comparison institutions to help put federal higher education data into context. Hold on just a second…

scorecard_fig3_oct18

It turns out that there is already an option within the Integrated Postsecondary Education Data System (IPEDS) to generate a list of peer institutions. ED creates a list of similar institutions to the focal college based on factors such as sector and level, Carnegie classification, enrollment, and geographic region. For Sacramento State, here is part of the list of 32 comparison institutions that is generated. People can certainly quibble with some of the institutions chosen, but they clearly do have some similarities.

scorecard_fig4_oct18

I then graphed the net prices of these 32 institutions to help put Sacramento State (in black below) into context. They had the fifth-lowest net price among the set of universities, information that is at least somewhat more helpful than looking at a national average across all sectors and levels.

scorecard_fig5_oct18

My takeaway here: the folks behind the College Scorecard should talk with the IPEDS people to consider bringing back a comparison group average based on a methodology that is already used within the Department of Education.

Beware Dubious College Rankings

Just like the leaves starting to change colors (in spite of the miserable 93-degree heat outside my New Jersey office window) and students returning to school are clear signs of fall, another indicator of the change in seasons is the proliferation of college rankings that get released in late August and early September. The Washington Monthly college rankings that I compile were released the week before Labor Day, and MONEY and The Wall Street Journal have also released their rankings recently. U.S. News & World Report caps off rankings season by unveiling their undergraduate rankings later this month.

People quibble with the methodology of these rankings all the time (I get e-mails by the dozens about the Washington Monthly rankings, and we’re not the 800-pound gorilla of the industry). Yet at least these rankings are all based on data that can be defended to at least some extent and the methodologies are generally transparent. Even rankings of party schools, such as this Princeton Review list, have a methodology section that does not seem patently absurd.

But since America loves college rankings—and colleges love touting rankings they do well in and grumbling about the rest of them—a number of dubious college rankings have developed over the years. I was forwarded a press release about one particular set of rankings that immediately set my BS detectors into overdrive. This press release was about a ranking of the top 20 fastest online doctoral programs, and here is a link to the rankings that will not boost their search engine results.

First, let’s take a walk through the methods section. There are three red flags that immediately stand out:

(1) The writing resembles a “word salad” and clearly was never edited by anyone. Reputable rankings sites use copy editors to help methodologists communicate with the public.

(2) College Navigator is a good data source for undergraduates, but does not contain any information on graduate programs (which they are trying to rank) other than the number of graduates.

(3) Reputable rankings will publish their full methodology, even if certain data elements are proprietary and cannot be shared. And trust me—nobody wants to duplicate this set of rankings!

As an example of what these rankings look like, here is a screenshot of how Seton Hall’s online EdD in higher education is presented. Again, let’s walk through the issues.

(1) There are typos galore in their description of the university. This is not a good sign.

(2) Acceptance/retention rate data are for undergraduate students, not for a doctoral program. The only way they could get these data are by contacting programs, which costs money and runs into logistical problems.

(3) Seton Hall is accredited by Middle States, not the Higher Learning Commission. (Thanks to Sam Michalowski for bringing this to my attention via Twitter.)

(4) In a slightly important point, Seton Hall does not offer an online EdD in higher education. Given that I teach in the higher education graduate programs and am featured on the webpage for the in-person EdD program, I’m pretty confident in this statement.

For any higher education professionals who are reading this post, I have a few recommendations. First, be skeptical of any rankings that come from sources that you are not familiar with—and triple that skepticism for any program-level rankings. (Ranking programs is generally much harder due to a lack of available data.) Second, look through the methodology with the help of institutional research staff members and/or higher education faculty members. Does it pass the smell test? And finally, keep in mind that many rankings websites are only able to be profitable by getting colleges to highlight their rankings, thus driving clicks to these sites. If colleges were more cautious about posting dubious rankings, it would shut down some of these websites while also avoiding embarrassment when someone finds out that a college fell for what is essentially a ruse.