with sufficient information, a more granular adjustment factor could be devised that allows for variation by academic field, institution, and possibly region.4 Estimates of the market value of degrees should be adjusted as warranted by ongoing research, and regularly updated as the values change with labor market trends. Longitudinal surveys (e.g., the Wisconsin Longitudinal Study) designed to determine the average salary for degree and nondegree earners at various points after graduation (starting, after five years, etc.) are essential for this kind of research and may serve as a model.5

Adjusting Output Measures to Reflect Institution Type and Mission

Appropriate specification of a productivity measure varies by context and by intended use. For many purposes, it is misleading to compare performance measures across institutional types unless adjustments are made.6 Community colleges, which are likely to continue to contribute to any increases in the nation’s production of postsecondary education, provide a clear example of the need for flexibility in the measurement concept. In this case, the unit of output should be defined to reflect a student body mix dominated by individuals pursuing (at least as a first step) an objective other than a four-year degree. The method of establishing point totals in the numerator of the productivity measure for community colleges can use the baseline framework presented in Chapter 4; however, it will have to be modified to reflect different mission objectives. Specifically, certifi-


4At this point, it may be asking too much to provide the differentials by field, but it warrants further research along the lines of Arum and Roksa (2010). Credit hours for students who have not declared a major would be prorated over the declared majors for that degree level. There may be credit hours that cannot be assigned to specific majors. If a residual category of “nonmatriculated students” is needed, a weighting procedure could be devised. Also, in some fields, such as nursing, the credentialing effect is strong and credits themselves less so; in other cases, such as the student with two years in liberal arts at a prestigious institution, credits themselves may be quite valuable. One possible benefit of applying a single degree bonus to all degrees is that it may avoid unhelpful attacks on academic areas (such as the humanities) for which social benefits are less fully captured by current salary data.

5In addition to the longitudinal data, modeling techniques must control for student characteristics that may affect both the probability of graduating and subsequent earnings levels. Thus far, the literature has mainly addressed earnings differentials between those who attend or do not attend college (see Dale and Krueger, 2002), but the methods would be similar. Techniques should also ensure that the marginal earnings effect of an advanced degree is not attributed to the undergraduate degree. Eliminating those with advanced degrees from the study may introduce a selection bias in either direction. That is, those with the highest earning potential may have a tendency either to enter the labor market directly with a bachelor’s degree or to pursue a graduate degree.

6A good example is the “Brain Gain” initiative of the Oklahoma Board of Regents, which employs a statistical methodology that estimates the amount an institution deviates from model-predicted graduation rates that takes into account such variables as average admissions test scores, gender, race, and enrollment factors such as full- versus part-time status.

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