The remainder of the report is organized as follows: In Chapter 2, we define productivity and then characterize the activities of higher education in terms of inputs or outputs. We pay particular attention to the heterogeneity of the sector, including the great range of its products and the changes and variation in the quality of its inputs and outputs. Accounting for all outputs of higher education is particularly daunting, as they range from research findings and production of credentialed citizens to community services and entertainment. Although the panel’s recommendations focus on degree output, research and other scholarly and creative activities must be acknowledged because they are part of the joint product generated by universities, and because they may affect the quality and quantity of teaching. We also contrast productivity with other measurements that have been used as proxies for it and discuss the merits and limitations of proxies currently in use.

In Chapter 3, we articulate why measurement of higher education productivity is uniquely difficult. Colleges and universities produce a variety of services simultaneously. Additionally, the inputs and outputs of higher education production processes are heterogeneous, mix market prices and intangibles, vary in quality, and change over time. Measurement is further impeded by conceptual uncertainties and data gaps. While none of these difficulties is unique to higher education, their severity and number may be. We detail the complexities—not to argue that productivity measurement in higher education is impossible, but rather to indicate the problems that must be overcome or mitigated to make accurate measurements.

This report will be instructive to the extent that it charts a way forward for productivity measurement. Toward this end, in Chapter 4, we provide a prototype productivity measure intended to advance the conceptual framework. The objective here is not to claim a fully specified, ideal measure of productivity, for such does not exist. Rather, we aim to provide a starting point to which wrinkles and qualifications can be added to reflect the complexity of the task, and to suggest a set of factors for analysts and policy makers to consider when using productivity measures or other metrics to inform policy.

In Chapter 5, we offer practical recommendations designed to advance measurement tools and the dialogue surrounding their use. We provide guidance for developing the basic productivity measure proposed in Chapter 4, targeting specific recommendations for the measurement of inputs and outputs of higher education, and discuss how changes in the quality of the range of variables could be better detected. A major requirement for improved measurement is better data. Thus, identifying data needs demanded by the conceptual framework, with due attention to what is practical, is a key part of the panel’s charge. This is addressed in Chapter 6. In some cases, the most useful measures would require data that do not now exist but that could feasibly be collected.

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