resource allocation decisions at the system, state, and national levels and to assist policy makers who must assess investments in higher education against other compelling demands on scarce resources; (2) provide administrators with better tools for improving their institutions’ performance; and (3) inform individual consumers and communities to whom colleges and universities are ultimately accountable for private and public investments in higher education. Though it should be noted that the experimental measure developed in this report does not directly advance all of these objectives—particularly that pertaining to measurement of individual institution perfomance—the overall report pushes the discussion forward and offers first steps.

While the panel is in no way attempting to design an accountability system, it is important to think about incentives that measures create. Since institutional behavior is dynamic and directly related to the incentives embedded within measurement systems, steps have to be taken to (1) ensure that the incentives in the measurement system genuinely support the behaviors that society wants from higher education institutions, and (2) maximize the likelihood that measured performance is the result of authentic success rather than manipulative behaviors. Clearly, a single high-stakes measure is a flawed approach in that it makes gaming the system simpler; a range of measures will almost always be preferable for weighing overall performance. While not diminishing the weight of these cautions, it should be noted that monitoring productivity trends would not be adding incentives to a world without them. Among the major incentives now in play are to enroll students, get research grants, improve in national rankings, raise money, and win athletic competitions. The panel believes that adding another incentive (and one more worthy than a number of these) will help round out the current set in a positive way.


Improving and implementing productivity metrics begins with recognition of their role in the broader performance assessment picture:

  • Productivity should be a central part of the higher education conversation.
  • Conversations about the sector’s performance will lack coherence in the absence of a well-vetted and agreed-upon set of metrics, among which productivity is essential.
  • Quality should always be a core part of productivity conversations, even when it cannot be fully captured by the metrics.
  • The inevitable presence of difficult-to-quantify elements in a measure should not be used as an excuse to ignore those elements.

The first step is to define key terms by applying the standard economic concept of productivity to higher education. In the model developed in this report, the base-

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