Economic Return
Long-term earnings and return relative to cost.
U.S. Colleges & Universities · Pilot edition in development
Durable Value Index is a transparent index of U.S. colleges and universities designed to show where students are most likely to receive durable economic value over time.
No rankings have been published yet. The first edition is being designed as a transparent, public-data-led pilot.
Return + Resilience Methodology
Durable Value asks whether a college’s value still holds up after cost, debt, completion risk, earnings outcomes, and career resilience are considered.
Long-term earnings and return relative to cost.
Net price, debt pressure, and affordability risk.
Whether students are likely to complete and benefit from the credential.
How well the institution’s academic mix aligns with durable labor-market opportunity.
How it works
Start with U.S. colleges and universities with sufficient public outcome evidence.
Use published scoring rules across return, affordability, completion, and resilience.
Show the strength and completeness of evidence instead of overstating precision.
First pilot scope
The first release is planned as a disciplined pilot, not a global ranking. The goal is to validate the model, reduce bias, and build trust before expanding coverage.
Methodology validation, data QA, and sensitivity testing.
Public profiles, score bands, and Data Confidence notes.
Paid peer comparison and score-driver analysis for professional users.
Who it helps
Understand where college value is most likely to hold up over time.
Use clearer evidence for college guidance, stories, and comparison.
Benchmark score drivers, peer position, and data-quality issues.
Important guardrails
Founding partner conversations now open
For institutions, counselors, media, data partners, and policy-adjacent organizations interested in durable college value.
Email hello@durablevalueindex.comQuick answers
Value that still holds up after cost, risk, time, and disruption. For college, that means looking beyond prestige to economic return, affordability, completion, and career resilience.
Yes, but the first release is being designed with score bands and Data Confidence notes to avoid false precision.
No. AI may support evidence organization and quality assurance. The scoring model is intended to rely on structured evidence and published rules.