line productivity measure for the instructional component of higher education is estimated as the ratio of (a) changes in the quantity of output, expressed to capture both degrees (or other markers of successful completion) and passed credit hours to (b) changes in the quantity of inputs, expressed to capture both labor and nonlabor factors of production. The assumption embedded in the numerator, consistent with the economics literature on human capital (e.g., Bailey et al., 2004; Barro and Lee, 2010a), is that education adds to a student’s knowledge and skill base, even if it does not result in a degree. Key to the denominator is the heterogeneity of labor and other inputs used in the production of education—and the need to account for it.
The proposed approach should be viewed as a starting point; additional research will be essential for addressing a number of thorny issues that impede full and accurate productivity measurement and, in turn, its value for guiding policy. However, it is not premature to introduce a statistical construct to serve as a foundation for work on the topic. Indeed, having such a construct will guide data collection and research upon which the measures must be based.
A number of complexities characterize higher education production processes. These reflect the presence of (1) joint production—colleges and universities generate a number of outputs (such as educated and credentialed citizens, research findings, athletic events, hospital services), and the labor and other inputs involved cannot always be neatly allocated to them; (2) high variability in the quality and characteristics of inputs, such as teachers and students, and outputs, such as degrees; and (3) outputs (and inputs) of the production process that are nonmarket in nature. As is the case with other sectors of the economy, particularly services, productivity measurement for higher education is very much a work in progress in terms of its capacity to handle these complexities. Because no single metric can incorporate everything that is important, decision makers must appeal to a range of statistics or indicators when assessing policy options—but surely a well-conceived productivity measure is one of these.
Reflecting policy information needs as well as feasibility-of-measurement constraints, this study focuses on the instructional mission. By not directly accounting for other contributions of higher education to society—perhaps most notably research—the baseline model developed in this report omits a central mission of a large subset of institutions. Commentators such as Jonathan Cole have argued that research capacity is the primary factor distinguishing U.S. uni-