versities from those in the rest of the world, and that the country’s future depends strongly on the continued nurturing of its research-intensive universities (Cole, 2010). Indeed, this is why the federal government and state governments have invested and continue to invest billions of dollars in university-based research.

The decision to limit the report’s focus to measurement of instructional productivity is not intended as a comment on the relative importance of teaching, research, and public service for institutions with multiple missions. However, the focus on instruction does come with the analytical consequence that the resulting productivity measure can provide only a partial assessment of the sector’s aggregate contributions to national and regional objectives. For this reason, just as the performance and progress of the instructional capabilities of institutions must be monitored, measures should also be developed for assessing the value of the nation’s investments in research. Even for a purely instruction-based measurement objective, an improved understanding of faculty resource allocation to research is essential because time use is not fully separable, and because research intensities may affect the quality of teaching.

Quality Variation and Change

Historically, institution or system performance has been assessed using unidimensional measures such as graduation rates, time to degree, and costs per credit. When attention is overwhelmingly focused on completions or costs, the risk is raised that stated goals will be pursued at the expense of quality. For this reason, input and output quantity measures should ideally be adjusted to reflect quality differences; that is, productivity should be defined as the ratio of quality-adjusted outputs to quality-adjusted inputs. However, such measurement is extremely difficult, which means that developing data and methods for doing so is a very long-term project. In the meantime, while accounting is incomplete, it is essential to monitor when apparent increases in measurable output arise as a result of quality reduction. For the foreseeable future, this will have to be done through parallel tracking of additional information generated independently by universities and third party quality assurance methods. And, until adjustments can be made to productivity metrics to account for quality differences, it will be inappropriate to rely exclusively on them when making funding and resource reallocation decisions. To do so would risk incentivizing a “race to the bottom” in terms of quality.

In some ways, the situation has not changed significantly in 100 years. A 1910 Carnegie Foundation report attempted to develop a time-use accounting formula to estimate the costs and outputs of higher education in order to “measure the efficiency and productivity of educational institutions in a manner similar to that of industrial factories.” The authors of that volume struggled with measuring quality and, while forced to confine their observation largely to quantity, did strive “to make quality a background for everything that may appear to

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