Too many students make too little after graduating from college to repay the loans they took out. That’s the basic premise behind the Biden administration’s student loan forgiveness promises, which may or may not become reality soon. It’s also a primary motivation behind the administration’s push to more tightly regulate for-profit colleges and universities, which they say are more likely to lead students into debt they cannot repay.

This relationship between graduates’ debt and earnings is the linchpin in the Gainful Employment regulation that was enacted, repealed, and looks likely to be enacted again in successive presidential administrations, and it is vital to any conversation about forgiving student loans. But a new AEI volume edited by Jason Delisle, which provides novel insights into recently available data broken down by each academic program offered by a college or university, challenges past regulatory regimes and offers fresh light about a path forward. The authors he has invited show us that the move from institution-level to program-level data will have impacts both broad and deep across most areas of higher education policy.

These data can tell us more than ever before about which
majors at a given institution have the best return on investment, which
institutions offer the strongest programs within a given course of study, and
much more. Readers of this volume will learn more about why this type of data
is used, how it might be used in the future, implications for various audiences
from students to policymakers, and what limitations or caveats may apply.

The volume first provides a history of federal efforts to
support higher education and attempt to ensure such funds are spent effectively,
with Nicole Ifill and Amy Laitinen focusing on the history of the Department of
Education’s efforts broadly while calling for more consideration of students
who do not graduate. Michael Itzkowitz then dives deeper into the College
Scorecard, the department’s public-facing tool for exploring these data, and
highlights the stunning number of post-secondary programs that leave graduates
earning less than the typical high school graduate. Stephanie Huie tells her
story of leading the University of Texas System’s embrace of program-level
indicators, which strongly influenced subsequent efforts at the federal level.

In two other chapters, one by Diego Briones and Sarah E.
Turner, the other by Kristin Blagg, the authors explore the limitations of program-level
data systems and the caution that we should employ in using them. Jorge Klor de
Alva notes that outcomes often are strongly correlated with the demographics of
the students served and suggests that we should not expect similar outcomes
from highly selective institutions as those that are less selective or serve
more non-traditional learners. Carrie Warick and Sara Melnick hope that
program-level data will change how students select a college or major and
provide examples of where this is already happening. Kevin James and Barry
Cynamon explore the use of earnings data to bolster innovative new financing
options such as income share agreements. Finally, Trace Urdan and Paul Fain suggest
that more transparency around outcomes can drive improvement in for-profit
offerings by helping investors make smarter choices about which companies to
back.

Together, these chapters provide timely insights into the major higher education topics of the day. Discussion of issues as varied as loan forgiveness, gainful employment, college access and completion, the relevancy of college courses to later employment, and student financing models all require insights that only good data can provide. Jason Delisle ties these various threads together and offers a vision for more informed higher education policymaking driven by program-level outcomes information.

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