take. Failure to implement a credible measure may indefinitely defer the benefits achievable from a better understanding of quantitative productivity, even in the absence of a viable method of quality adjustment. We emphasize again the essential idea that effective and transparent quality assurance systems should be maintained to supplement productivity and other performance measures. This will allow progress to be made in measuring the quantitative aspects of productivity while containing the risk of triggering institutional competition that results in lowering educational quality. Progress on the development of quantitative productivity measures may also boost the priority for developing a serviceable quality adjustment index.

DEVELOPING THE DATA INFRASTRUCTURE

While progress can be made to develop and implement productivity measures using existing information, full implementation of the recommendations in this report will require new or improved data capabilities as well. One significant change required for enhancement of the baseline model involves standardizing the capacity to link credit hours to degree or field. To move in this direction, institutions should collect credit-hour data in a way that follows students, and not only the departments that teach them. Indeed, the necessary information already exists in many institutions’ student registration files. To fully exploit the potential from this kind of information, the Integrated Postsecondary Education Data System (IPEDS) produced by the National Center for Education Statistics could report these data along with the numbers of degrees awarded.

Detailed productivity measurement will require other kinds of information as well, such as comprehensive longitudinal student databases (to better calculate graduation rates and estimate the cost and value of degrees) and more accessible administrative sources. The potential of administrative data sources—maintained at various levels, ranging from institutions’ accounting and management systems to those of the federal statistical agencies—depends heavily on the ability of researchers and policy analysts to link records across state boundaries and across elementary, secondary, postsecondary, and workforce boundaries (Prescott and Ewell, 2009). Standardization and coordinated linkage of states’ student record databases should be a priority. Another example of useful administrative data is unemployment insurance records kept by all states. As with individual state unit record data resources for postsecondary education, it is now often difficult to assemble multi-state or national datasets. This makes it difficult to track cohorts of graduates (or nongraduates) across state lines. The Bureau of Labor Statistics should continue to do what it can to facilitate multi-state links of unemployment insurance wage records and education data which would create new opportunities for research on issues such as return on investment from postsecondary training or placement rates in various occupations.



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