The Price You Pay for College: Why It’s so Difficult to Predict Merit Scholarships

The Price You Pay for College: Why It’s so Difficult to Predict Merit Scholarships

This article is adapted from “The Price You Pay for College: An Entirely New Road Map for the Biggest Financial Decision Your Family Will Ever Make,” by Ron Lieber. Reprinted here with permission of Harper, an imprint of HarperCollins Publishers

You want some merit aid, and that’s understandable. But here’s what you probably don’t realize: There is a system that operates behind the scenes to distribute these discounts that itself requires about $1 billion of annual care and feeding.

It works in part by manipulating you emotionally. And because you may not be expecting a sly, soft sell, you probably don’t know to look out for it.

 

What is Enrollment Management?

If you spend any amount of time on college admissions and financial aid websites trying to figure out who the gatekeepers are and whom they report to, you’ll often notice someone overseeing something called enrollment management.

Perhaps this rings a bell.

Maybe you know something about the airline industry, where American Airlines used mainframe computers to popularize what came to be known as yield management. The big idea was to use historical data to try to predict which seats on which flights would sell out by what date. Then the airline would price seats accordingly, changing fares constantly right up until takeoff in order to maximize revenue.

Enrollment management, which emerged in the 1980s but really came into its own a decade or so later, isn’t all that different. Just think about an empty classroom seat or a dorm bed the same way you would an airline seat: Both are useless when a plane pushes back from the gate or a semester begins.

Enrollment management calls for using data to figure out which students to recruit and how to woo them. To do this, schools draw in part on information that students supply when taking the PSAT. Test takers pay for the privilege of taking the test in most instances; then the test administrator performs the nifty trick of turning around and selling colleges access to the data so they can use it for marketing.

 

How Colleges Use Data Mining and Algorithms

The merit aid strategizing extends to figuring out what to say to the students during the courtship and finding out whether they need incentives, such as application fee waivers.

Once high school seniors apply, schools often use algorithms and software to predict what sort of discount to offer, if any, to get them to say yes. The wooing continues over the summer lest incoming students change their minds during what is known as the “summer melt” period. Finally, schools track the under- graduates themselves once they arrive, since any single one who drops out or transfers can represent lost revenue of well over $100,000.

This is where the $1 billion comes in. Given the stakes, most colleges have long since stopped trying to manipulate the data themselves. Instead, most of them spend piles of money on consultants who suck in the figures and put the numbers through proprietary algorithms.

The software spits out custom-crafted, head-spinning offer grids that help dictate who gets what amount of merit aid. You’ve probably never heard of these companies, and they and their clients are fine with that. When they market themselves to colleges, they refer to what they do as “financial aid optimization.” That sounds rather nice, with its hints of smoothing things out and spreading the discounts around so all students get just what they need. But eventually the firms get around to using the word “leverage,” as in using brute force, involving large amounts of money to maximum advantage.

The consultants help schools leverage information about your family to optimize your discount, if any, and the process can get quite granular.

Each year, colleges decide where their priorities lie, and they mostly have similar goals. They hope to attract a larger pool of applicants who are more racially and geographically diverse than the previous year’s pool and who have even better credentials.

They would also like their net tuition revenue per student—the money they take in after all need-based and merit grant aid—to rise. And they want to lose even fewer students to their rivals.

To try to win—or not to lose ground, at least—the consultants help schools keep track of which students are coming to their websites, how much time they spend on which page, and how quickly they open emails.

The schools tap into proprietary databases that the consultants maintain in order to track the quality of thousands of high schools and the sorts of colleges that their graduates have attended in the past. Then the consultants pull in as much data from each college as it has, including what sorts of students responded to earlier marketing campaigns and the numbers and demographics of all the people who applied, who got in, and who matriculated at what price point.

Once the latest applicant pool is in place, the consultants provide algorithms that suggest what size discount, if any, to offer to whom.

 

Why It’s So Difficult To Predict Merit Scholarships

The challenge here, which is an extension of the one we saw with need-based financial aid, is that it can be very difficult to predict if a school will offer you merit aid and how much.

It bears repeating, and I’ll say it a few more times before this book is done: Schools ask families to jump through several application hoops just to put themselves in the running to write some of the biggest checks they’ll ever write, but you often don’t get to find out the amount until several months into the process. That takes some nerve.

The most reasonable defense that the schools offer up of the system’s opaqueness goes something like this: “How would you propose we make this more predictable? Rely on test scores that wealthier high school students tend to do better on?” (This is ironic, given that some schools use merit aid specifically to make sure they have enough affluent students.)

“And if not test scores, then we’d need to predict and hand out merit aid on the basis of high school grades. Be careful what you wish for there, because grades can mean wildly different things in different schools. Some schools use curves, while others use cutoffs. Plus, you probably want us to adjust for the degree of difficulty of the classes an applicant takes or correct any overly optimistic recalculation that a school does itself when it adjusts its own averages. And we also need to be fair to applicants from high schools that don’t offer as many higher-level classes.”

Objections to a merit aid prediction calculator are all quite sensible, but it’s also quite convenient for the schools that don’t provide one. Colleges and universities that operate this way get to shield their discounting formulas from the view of competitors. They also preserve the ability to offer whatever merit aid discounts they need to favor violinists and the deeply religious over French scholars and teenage entrepreneurs, depending on where an institution might feel depleted in any given year.

Maintaining flexibility and the resulting campus-wide balance of talents is a reasonable goal; any accompanying lack of clarity for price- conscious potential consumers of the education is a feature, not a bug! But it leads naturally to the following logic: I’d better apply to as many schools as possible in the hope that one of them will offer outsized merit aid. After all, I can’t possibly guess which school might do that.

This is how the system creates more work for everyone, with high school juniors and seniors tearing their hair out and admissions officers up all night for months each year scouring an increasing number of applications and running thousands and thousands of numbers.

CONNECT WITH OTHER PARENTS TRYING TO FIGURE OUT

HOW TO PAY FOR COLLEGE

JOIN ONE OF OUR FACEBOOK GROUPS:

PAYING FOR COLLEGE 101

HOW TO FIND MERIT SCHOLARSHIPS


Ron Lieber

Ron Lieber is the "Your Money" columnist for The New York Times. Before joining The Times in 2008, he wrote The Wall Street Journal's "Green Thumb" personal finance column, was part of the start-up team at the paper's "Personal Journal" section, and worked at Fortune and Fast Company magazines. He is the author or coauthor of three books, including The New York Times bestseller Taking Time Off and The Opposite of Spoiled. He lives in Brooklyn with his wife, fellow New York Times reporter Jodi Kantor, and their daughters.
CATEGORIES