National Trends

SHEF compiles national averages across each of our primary metrics (education appropriations, public student aid, net tuition revenue, student share, total education revenues).

National trends are adjusted for inflation over time and often presented on a per-full-time equivalent (FTE) enrollment basis. Data for prior years are adjusted to represent the equal amount in today’s dollars. The raw unadjusted data is available for download.

The U.S. per-FTE averages are the sum of all revenue divided by the sum of all FTE enrollment, not simple averages of state per-FTE figures. For this reason, trends in the most populous states have a strong impact on the national metrics.

Many readers may look to interstate financial analysis and comparisons to determine “appropriate” or “sufficient” funding for higher education, but these decisions should always be made in the context of a state’s objectives and circumstances.

State Comparisons

SHEF is a collection of 50 very different states. The state comparison section illustrates the variability across states and over time concerning the primary SHEF metrics. The states are shown relative to one another to provide context for the national trends.

Many factors affect the relative positions of states in their levels of funding for higher education.

Although no analysis can account for all of these factors, SHEF makes two adjustments to reflect differences in cost of living and enrollment levels at various institution types across the states. These adjustments tend to draw states closer to the national average; for example, states with a high cost of living also often support higher education at above average levels, and the cost of living index reduces the extent of their above average revenue per student. Only the cost of living index is applied to sector-level data.

Data Uses and Cautions

The SHEF report seeks to provide reliable data and methods to examine state funding for higher education. While making finance data cleaner, consistent, and more comparable, SHEF’s analytic methods also add complexity. Readers should be cognizant of inherent limitations.

1. Comparing states is a difficult task. States vary in climate, energy costs, housing costs, population densities, growth rates, areas of poverty, resource bases, and the mix of industries driving their local economies. Additionally, the extent and rate at which these factors are changing vary across states.
2. State higher education systems differ. Differences in the number and size of institutions, the proportion of students attending independent institutions, and varying combinations of institutional types add complexity to the data.
3. In addition to these differences, technical factors can distort interstate comparisons. For example, states differ in how they finance employee retirement. Some pay all retirement costs to employee accounts when the benefits are earned, while others defer part of the costs until the benefits are paid. Some pay benefit costs through a state agency, while others pay from institutional budgets.