• The measure is a multi-factor productivity index. It captures output in physical units (credit hours, degrees) and, unlike cost studies, measures direct labor inputs in terms of full-time equivalents (FTEs). Labor productivity can be derived from the multi-factor results if desired.
  • Outputs include credit hour production and degree attainment, both of which have been shown to be important in labor market studies. Most if not all the measures currently in use (e.g., credit hour production alone or graduation rates) depend on one or the other but not both, and therefore miss a critical output dimension.
  • The measure does not vary along with the proportion of part-time students, except to the extent that being part-time might require different student services or contributes to wasting credits or dropping out. This feature sidesteps the problem of comparing graduation rates and average times to degree among schools with different numbers of part-time students.
  • Credits not on the mainline path to a degree, including those due to changes in major and dropouts, are counted and thus dilute the degree completion effect. In other words, programs with a heavy dropout rate will have more enrollments per completion, which in turn will boost resource usage without commensurate increases in degrees. Productivity could thus increase with the same number of credit hours if more students actually complete their degrees. Credit earned, however, is not treated as entirely wasted just because a degree was not awarded.
  • The measure allows differentiation of the labor and output categories, although doing this in a refined way will require significant new data.
  • The measure readily lends itself to segmentation by institutional type, which is important given the heterogeneity of the higher education sector.
  • The measure can in principle be computed for institutions within a state, or even single institutions. However, the incentives associated with low-aggregation level analyses carry the risk of serious accuracy degradation and misuse unless it is coupled with robust quality assurance procedures. Until quality adjustment measures are developed, the panel advises against using the productivity metric described in this chapter for institution-to-institution comparisons (as opposed to more aggregate level, time series, or perhaps state-by-state or segment analyses).
  • Data collection, including data beyond the Integrated Postsecondary Education Data System (IPEDS) and the proposed special studies, appears to be feasible.

We emphasize again that the proposed measure follows the paradigm of aggregate productivity measurement, not the paradigm for provision of institution-level incentives and accountability. As stressed in Chapter 3, institutions should



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