How to predict financial attrition by calculating the unmet need cliff

Unmet need is on the rise

For most institutions, budget constraints make it impossible to meet every student’s full financial need. In 2012, only 61 out of 1,130 respondents that supplied financial aid data to the U.S. News & World Report Annual Survey reported meeting students’ full financial need (with and without loans). For the rest of the institutions, financial aid gapping is a necessary practice. The consequence of gapping is unmet need—the cost of attendance (COA) a student is responsible for after expected family contribution (EFC) and all merit scholarships, need-based aid, and loans are accounted for.

While news headlines have focused on the climbing sticker price for higher education, net price has grown slowly, in particular for students from the lowest income quartile—approximately 25% over the past two decades. However, since 1990, average unmet need has more than doubled for those same students to $8,221, according to the Pell Institute. The mismatch between slow price growth and higher unmet need suggests that investments in financial aid have merely offset increases in sticker price, not reduced unmet need.

New indicator of financial risk: The “unmet need cliff”

Institutional surveys indicate that finances are playing a major role in students’ decision to leave their studies. While some argue financial problems might mask underlying engagement or academic issues, data suggest inadequate finances do directly cause some students to drop out. The difficulty is accurately identifying students for whom financial aid counseling or additional aid could be the difference between staying and leaving.

Some institutions are monitoring financial risk indicators, including EFC, private loan levels, and, in particular, levels of unmet need. Retention falls as unmet need rises, but EAB research has uncovered a simple indicator for assessing an excessive level of unmet need: the unmet need cliff, a level of unmet need beyond which persistence sharply declines.

The need cliff is surprisingly common across institutions. Several public institutions observed cliffs at ≈$10,000, while higher-priced privates saw retention drop after unmet need exceeded $15,000 to $25,000.

This is particularly troublesome, as students with high unmet need levels are likely to have substantial loans—the relationship between dropping out and loan default is well documented. An institution-specific unmet need cliff analysis provides a clear marker to guide support to students at the highest risk of financial attrition and loan default.

How to calculate the unmet need cliff

Calculating the unmet need cliff is a relatively simple four-step exercise:

1. Calculate unmet need for each student in a cohort. Public institutions may want to split students based on residency.
2. Based on the distribution of unmet need across a cohort, create groupings or unmet need “bands.” At most institutions the highest unmet need band will have very few students.
3. Calculate the average retention rate for each unmet need band. For example, at the representative public institution below, ~963 students with no unmet need were retained, for an overall 77% retention rate.
4. The band where retention drops sharply is the unmet need cliff. For instance, at the representative public institution, retention rates for in-state students drop sharply to 58% once unmet need surpasses $10,000. While retention rates at the representative private institution are higher overall, the same noticeable drop in retention is evident once unmet need exceeds $10,000.

*Calculations are based on real data, rounded and blinded to protect institutional identity.

Applying the unmet need cliff

The unmet need cliff examples provided here can be the basis for more sophisticated analyses that pair unmet need with other student characteristics. For instance, does the unmet need cliff change when first-year GPA is taken into consideration? One could also segment unmet need cliff calculations by different student populations, such as first-generation or out-of-state students.

The unmet need cliff analysis on its own, or segmented by different student characteristics, can help institutions proactively identify the most financially at-risk students. This determination drives targeted interventions like aid incentives or financial aid counseling. By intervening early, institutions can prevent the most financially vulnerable from dropping out.