(including allocated overheads), then divide by a volume measure of output to produce a ratio such as cost per degree. Under tightly specified conditions, this would produce the same result as a productivity measure. These conditions, however, are rarely if ever realized. The problem is that simple ratios like cost per student or degree does not take into consideration quality and the multiple outputs produced by higher education institutions. Hence, this approach conveys too little information to be able to attribute productivity differences to differences (over time or between institutions) in price and quality.
Efficiency is improved when cheaper inputs are substituted for more expensive ones without damaging quality proportionately. For example, it has become a common trend for institutions to substitute adjunct instructors for tenure-track faculty. Whether this move toward lower-priced inputs has a proportionately negative impact on output quantity and quality (e.g., numbers of degrees and amount learned) is not yet fully known, and surely varies from situation to situation (e.g., introduction and survey classes versus advanced seminars). In reviewing evidence from the emerging literature, Ehrenberg (2012:200-201) concludes that, in a wide variety of circumstances, the substitution of adjuncts and full-time nontenure-track faculty for tenure-track faculty has resulted in a decline in persistence and graduation rates.
Without data tying changes in faculty composition to student outcomes, efforts to implement accountability systems will be made with only partial information and will lead to problematic policy conclusions. For example, in 2010 the office of the chancellor of Texas A&M University published what amounted to a “a profit-and-loss statement for each faculty member, weighing annual salary against students taught, tuition generated, and research grants obtained … the number of classes that they teach, the tuition that they bring in and research grants that they generate” (Wall Street Journal, October 22, 2010). When a metric as simple as faculty salary divided by the number of students taught is used, many relevant factors are omitted. An instructor teaching large survey courses will always come out ahead of instructors who must teach small upper-level courses or who are using a year to establish a laboratory and apply for grants, as is the case in many scientific disciplines.17 These metrics do not account for systematic and sometimes necessary variations in the way courses at different levels and in different disciplines are taught; and they certainly do not account for differences in the educational experience across faculty members and across different course designs.
The value of productivity and efficiency analysis for planning purposes is that it keeps a focus on both the input and output sides of the process in a way
17In recognition of these limitations, administrators did pull the report from a public website to review the data and the university president promised faculty that the data would not be used to “assess the overall productivity” of individual faculty members (see http://online.wsj.com/article/SB10001424052748703735804575536322093520994.html [June 2012]).