that potentially creates a more thorough and balanced accounting framework. If costs were the only concern, the obvious solution would be to substitute cheap teachers for expensive ones, to increase class sizes, and to eliminate departments that serve small numbers of students unless they offset their boutique major with a substantial grant-generating enterprise.18 Valid productivity and efficiency measures needed for accountability require integration of additional information—for example, the extent to which use of nontenure track faculty affects learning, pass rates, and preparation for later courses relative to the use of expensive tenured professors. The implication is that analysts should be concerned about quality when analyzing statistics that purport to measure productivity and efficiency. Different input-output ratios and unit costs at differing quality levels simply are not comparable.

Finally, it is important to remember that even valid measures of cost and productivity are designed to answer different questions. A productivity metric, for example, is needed to assess whether changes in production methods are enabling more quality-adjusted output to be generated per quality-adjusted unit of input. That this is an important question can be seen by asking whether higher education is indeed subject to Baumol’s cost disease (see Chapter 1)—the question of whether, in the long run, it is a “stagnant industry” where new technologies cannot be substituted for increasingly expensive labor inputs to gain efficiencies. Unit cost data cannot answer this question directly, but they are needed for other purposes, such as when legislatures attempt to invest incremental resources in different types of institutions to get the most return in terms of numbers of degrees or graduation rates. This kind of resource-based short-run decision making responds to funding issues and institutional accountability, but addresses productivity only indirectly and inadequately.

A critical asymmetry also exists in the way productivity and cost-based measures are constructed. Current period price data can be combined with the physical (quantity) data to calculate unit costs, but it is impossible to unpack the unit cost data to obtain productivity measures. The fact that most measurement effort in higher education is aimed at the generation of unit cost data has inhibited the sector’s ability to assess and improve its productivity.

2.2.2. Other Performance Metrics

Many other performance measures have been proposed for higher education. The most prominent of these are graduation rates, completion and enrollment ratios, time to degree, costs per credit or degree, and student-faculty ratios. These kinds of metrics are undeniably useful for certain purposes and if applied correctly. For example, Turner (2004) uses time-to-degree data to demonstrate the

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18To the credit of Texas A&M University, it did not respond to the findings of its faculty assessment in any of the above-mentioned ways.



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