Three Lenses to View Risk-Sharing Program Data

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In yesterday’s post, I mentioned that the risk-sharing reimbursement portion is calculated by academic program and then aggregated to the institution level. But we haven’t seen much analysis on that program data shared by the House Committee on Education & Workforce (CEW). There are over 48,000 programs listed in the data set, which can be hard to visualize.

In this post, I’ll share new views of this program data using three different lenses into the risk-sharing payments where colleges and universities would have to reimburse a portion of the student loan debt for each academic program in non-repayment status. The PROMISE grants are calculated at the institution level and are not included in these views. All stick today, no carrot.

Lens 1: Different Results Within Each Institution

As a reminder, the risk-sharing reimbursements would be based on each annual cohort within each academic program, based on value-added earnings (aka the College Premium), total program price (tuition + fees + expenses excluding grants for the expected life of the program), and the amount of the cohort’s debt that is not current on payments (but excluding defaults for some reason).

This means that each institution will have different results for different programs. For example, consider Simmons University, a private nonprofit university in Massachusetts.

That Masters of Social Work program is going to cost Simmons a lot of money - over $10,000 per student per year in reimbursements - due to low earnings in that field. The Literature program will cost a lot per student, but it’s small and will not cost as much in total. Library Science, however . . .

But also note that the Bachelor’s degree programs in blue have the highest program price (due to four years duration) but very little in reimbursements due.

Also note that the programs tend to group horizontally. Bachelor’s programs tend to have the same price, clinical and business master’s programs at one price, and all other master’s at another price.

Now let’s look at Arizona State University.

Again, a lot of separate groupings horizontally. Again, master’s of Social Work is going to be a costly program if this proposal succeeds. We also see doctoral programs in green on the right - fairly high priced due to duration. On the left, there are several master’s programs that will trigger significant reimbursements per graduate despite low program price. History and Curriculum and Instruction in particular.

What about looking at an entire system, such as the 23 universities of the Cal State system?

My biggest takeaway here is that Cal State’s entry into graduate programs is going to prove costly if this bill passes.

Lens 2: Different Results Across Program Types

The Master’s of Social Work, as seen above, can be problematic due to higher program prices and low earnings of graduates. What if we looked at there programs across all private nonprofit universities?

That is quite a spread of program prices within one sector, and without a significant difference in risk-sharing reimbursements due. And those three outliers are facing a possible payment of more than $30,000 per graduate. Per year.

The other primary example that gets called out in institutional accountability is for Cosmetology programs. Let’s look at all less-than-2-year (nondegree) programs, color-coded by specific programs.

The pink bubbles are Cosmetology, and you can see that this is the most common type of <2-year program and that as a single program type it fares the worst in the House Risk-Sharing bill. The orange bubbles are for Allied Health programs.

Lens 3: Program Trends On Primary Risk-Sharing Metrics

Theoretically, the House Risk-Sharing proposal should reward programs with higher value-added earnings and harm programs with high student debt levels. And you would expect that investing in expensive programs (leading to higher debt) would lead to higher earnings. But not always. What if we plot these two variables for specific program types? First, let’s look for undergraduate computer science degree programs.

The three trend lines are for Public 4-year (blue), Private 4-year (orange), and For-profit 4-year (red). Not only are the public and for-profit trend lines flat (similar earnings regardless of debt levels), but the private program trend line is reversed. Higher debt levels leading to lower earnings (leading, correlating, you decide). A poor investment, at least based on CEW data and assumptions.

And Harvard having higher value-added earnings than MIT? Go figure.

Master’s of Business Administration (MBAs) is a very different story, however.

This makes more sense. Public and private MBAs show a relationship where programs with higher debt lead to higher earnings, meaning the investment in a Cornell or NYU MBA is looking pretty good. But Duke doesn’t just produce one-and-done teenage athletes (what is your cohort’s graduation rate, Mr. Scheyer?), it also produces even higher MBA earnings with lower debt.

There are so many ways to look at the data, but hopefully these three lenses will help give insight into some of the program-level dynamics of the proposed House Risk-Sharing proposal.

Disclosure: I have provided a declaration in the lawsuit against the FVT & GE regulations, analyzing the data provided. I am also providing visualizations and data analysis of the new proposals to clients.

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