OP-ED Are We Measuring the Value of Higher Education— or Just What’s Easy to Measure?

By Dr. Stephen Welsh Chair, Integrative Health Policy Consortium

As policymakers seek to bring greater accountability to higher education, new “value-added earnings” frameworks are emerging as a central tool for evaluating whether academic programs deliver meaningful economic outcomes.

The goal is both understandable and widely shared: students should be able to pursue education with confidence that it will lead to opportunity, and public investment should be aligned with demonstrable results. But as these frameworks move closer to implementation, an important question deserves closer attention: are we measuring the value of education—or simply measuring what is easiest to quantify?

At the heart of many current proposals is a reliance on federal datasets that track earnings outcomes for specific subsets of students, most notably Pell Grant recipients. These data are attractive for a simple reason—they are available, consistent, and linkable to tax records. But availability is not the same as representativeness. Pell recipients, by definition, come from lower-income backgrounds and often navigate college under different financial and personal constraints than other students. They may work more hours while enrolled, take longer to complete programs, or make different early career choices based on financial necessity rather than long-termopportunity. These factors can influence earnings outcomes in ways that are independent of the quality or value of the educational program itself.

This raises a fundamental question: Can the outcomes of one distinct population be reliably used to represent all students within a program—particularly those who finance their education through loans with different expectations and timelines?

A similar concern arises when models developed around undergraduate education are applied to post-graduate and professional degree programs. The two are not interchangeable. Students entering professional programs do so with prior degrees, established earning potential, and a more defined career trajectory. Their decision to pursue additional education is not an initial step into the workforce, but a second-stage investment—often involving greater financial commitment and a longer time horizon for returns.

Yet many value-based frameworks appear to treat these pathways as though they share the same economic profile. If the underlying assumptions do not reflect these differences, the resulting conclusions may be incomplete—or worse, misleading.

The issue becomes even more pronounced in professions where income does not follow a linear path. In fields such as chiropractic and other integrative health disciplines, graduates frequently enter practice models that involve self-employment, business ownership, or gradual patient-base development. Earnings in these professions often build over time rather than appearing immediately in the form of standardized W-2 wages. When early-career earnings are used as the primary indicator of value, these pathways can appear less favorable—not because they lack economic viability, but because their value is realized over a longer arc. Measuring them too early is like evaluating an investment before it has had time to mature.

There is also the question of how earnings themselves are captured. Federal datasets tend to emphasize traditional employment income, yet many health professionals operate in mixed or independent revenue models that are not always fully reflected in these systems. If what is being measured does not fully align with how income is actually generated, the resulting picture may be incomplete.

Taken together, these considerations point to a broader methodological challenge: distinguishing the effect of an educational program from the characteristics of the students who enroll in it. Earnings outcomes are shaped by a complex interaction of factors—prior education, socioeconomic background, geography, personal choices, and market conditions. Isolating the true “value added” by a program is not a simple task, and it requires careful attention to how data are selected and interpreted.

None of this diminishes the importance of accountability in higher education. On the contrary, it underscores the need to get it right.

Well-designed evaluation systems can help students make informed choices, guide institutional improvement, and align public policy with long-term societal goals—including the transformation of our health care system toward more preventive, patient-centered models.

But if the metrics used to assess value are based on assumptions that have not been fully tested—or on data that are convenient rather than representative—there is a risk of unintended consequences. Programs that serve important roles in the health system, particularly those built around independent practice and long-term patient relationships, may be undervalued simply because their outcomes do not fit neatly into early, standardized earnings snapshots. Before these frameworks are finalized, there is an opportunity—and a responsibility—to ask a few essential questions. Do the data being used accurately represent the full population of students? Are differences between undergraduate and professional education appropriately accounted for? Do measurement windows reflect the realities of career development across diverse fields? And are we capturing the full range of how earnings are actually generated?

These are not objections to accountability. They are prerequisites for it. Because in the end, the goal is not merely to measure value—but to measure it in a way that reflects the real-world complexity of education, careers, and the evolving needs of the American workforce.

Dr. Stephen Welsh is Chair of the Integrative Health Policy Consortium (IHPC) and a leader in national health policy and professional education advocacy.