be prepared to resist inappropriate initiatives to improve productivity as measured by applying the formula below to their particular data, and to buttress their resistance with their own internal data about quality.
Section 4.2 presents our base model. It is a “multi-factor productivity model” in that it uses output and input quantities and includes all categories of inputs. Section 4.3 proposes a segmentation scheme, which is important because of the heterogeneity of higher education. The section also discusses how the model can be computed at the state and single-institution level but, again, we stress that this will be dangerous without a robust quality assurance system. Section 4.4 enhances the base model by differentiating among labor categories. This is important because of the fundamental difference between academic and nonacademic labor, and the difference between tenure-track and adjunct faculty. Section 4.5 differentiates among output categories, which again is important because of institutional heterogeneity and the fact that production of degrees at different levels and in different fields involves different production functions. Finally, Section 4.6 presents the rationale for using the model in conjunction with quality assurance procedures.
Nearly all the data required for calculating values using the model sketched out here can be obtained from the U.S. Department of Education’s IPEDS or other standard public sources (though this would not be the case for the fully specified “ideal”). Adding the model refinements outlined in Section 4.3 requires a modest amount of additional information. Data requirements for the enhancements described in Section 4.4 can be approximated from IPEDS, but proper implementation will require additional data collection. The panel’s recommended changes to IPEDS are discussed in detail in Chapter 6. The new data that are called for would break useful ground not only for productivity analysis, but also for institutional planning and resource allocation. This is important because an institution’s use of data for its own purposes makes data collection more palatable and improves accuracy.
Following the concepts defined in Chapter 2, the model calculates the ratio of changes in outputs (credit hours and degrees) to inputs (labor, purchased materials, and capital). The focus is on instructional productivity, with inputs being apportioned among instruction, research, and public services prior to calculating the productivity ratio. As emphasized throughout this report, our model involves only quantitative factors. It will be reliable only to the extent that input and output quality remains approximately constant, or at least does not decline materially. Currently, quality measurement—of both inputs and outputs—is largely beyond the capacity of quantitative modeling; but, because quality should never be taken for granted, we return to the issue at the end of the chapter.