has been increasing or declining. The model may not reveal why this is so, but at the very least it pushes us to ask additional, useful questions. However, these kinds of models are not typically intended to be used for accountability or incentivizing purposes—especially for applications such as higher education where output prices do not necessarily reflect quality. In contrast, the structural models involve a fairly detailed representation of an entity’s internal structure, and thus require more granular data. Such models also generally focus on marginal revenues and marginal costs, as opposed to the average revenues and costs considered in the aggregate models. As noted above, the panel was not charged with developing a structural model and has not attempted to do so.
At a conceptual level, this report dedicates considerable attention to productivity measurement at different levels of aggregation, including the institution, system, and sector levels. For most purposes, it is necessary to segment the sector by institution type to avoid inappropriate comparisons. However, the measure developed in Chapter 4 is focused on productivity of the sort typically applied to aggregate economic sectors (e.g., autos, steel, higher education), which rests on the methodology used by the BLS. While one can imagine aggregating institution-level data to produce a macro productivity measure, such an approach is not practical at the present time for the higher education sector. As a technical matter, there is nothing to prevent the model developed here from being applied at the level of a state, system, or individual institution, but this opens the way for it to be exploited for performance measurement without the proper support of additional quality measures. The panel generally believes that this risk associated with pushing forward with productivity measurment is worth taking, and that to maintain the “know-nothing” status quo would perpetuate dysfunctional behavior.
It is noteworthy that the panel was not charged with recommending processes to improve productivity, for example, through innovative new methods for designing courses or through online education. Similarly, the panel was not asked to develop models for monitoring departmental, institutional, or system activity; these are applications. One stumbling block to productivity measurement—and indeed, to productivity improvement—has been the widely-held view that, because learning is a service and its production is labor-intensive, colleges and universities suffer from a condition known as Baumol’s cost disease. The underlying theory, which breaks from the notion in classical economics that wage changes are closely tied to labor productivity changes, is that labor costs in some sectors of the economy are affected by productivity gains in other unrelated sectors. Those productivity gains drive an increase in wages across the entire economy. Sectors without productivity gains are nonetheless faced with a higher wage bill, making them appear less efficient.6 Archibald and Feldman (2011) subscribe to
6In their landmark book, Performing Arts: The Economic Dilemma, Baumol and Bowen (1966) use as an example a Mozart string quintet composed in 1787. More than two centuries later, it still requires five musicians and the same amount of time to perform the piece.