Data sources in higher education are now more comprehensive than ever, enabling researchers and policymakers to conduct carefully considered performance assessments.
Summary
Discussions about the productivity and performance of higher education have become more nuanced, reflecting an understanding that one-dimensional measures are insufficient for most policy, operation and consumer information purposes. The limitations of unit cost, graduation rate, time to degree, and similar metrics, while essential components of an information dashboard, are well understood. And efforts to measure the inputs and outputs of higher education production, and to address the quality dimensions of each component alongside quantities, have accelerated. The findings in this paper reflect these developments as well as advances in the modeling of economic concepts applied to the sector.
Key Insights
- Metrics measuring productivity in higher education should be constructed after goals have been identified—otherwise, administrators and policymakers will value something that is measureable rather than measuring something is valuable.
- When attention is myopically focused on one performance dimension—such as unit costs or graduation rates—there's a heightened risk that goals based on that dimension will be pursued at the expense of quality.
- Difficult-to-quantify elements in productivity and performance measurement should not be used as an excuse to ignore these elements.
- In the continuing effort to expand college access and affordability, productivity improvement is seen as the most promising strategy for containing costs while keeping the quality of higher education in the United States at a world-class level.
- Studies suggest more than half of higher education's total benefits to society accrue as positive externalities and public goods. Failure to capture these benefits distorts calculations of value added and return on investment.
- Weighting the different elements of value associated with higher education outcomes (e.g., graduates' learning versus earnings gains) will always require subjectivity and subject matter expertise and will be driven by the specific question being asked.
