Improving Measurement of Productivity in Higher Education (2012) / Chapter Skim
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1 The Importance of Measuring Productivity in Higher Education
1 The Importance of Measuring Productivity in Higher Education
Pages 9-18
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From page 9... ...
To accomplish this, the Carnegie Foundation Report created a key unit of measure called the student hour, defined as "one hour of lectures, of lab work, or recitation room work, for a single pupil" (Barrow, 1990:70)
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From page 10... ...
One lesson drawn from this effort is that we may be too sanguine about the accuracy or relevance of measures of productivity in other sectors, having seen how daunting they can be in a setting with which we are more intimately familiar. The conceptual and practical problems surrounding this effort raise additional concerns because it is known that measurements create incentives, incentives change practices, and those practices have the potential to affect people and institutions we care deeply about.
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From page 11... ...
During the current recession, characterized by high and persistent unemployment, analyses of evidence such as online job postings and real-time jobs data reveal a mismatch between job openings and the educational credentials of the workforce. Higher education institutions themselves have become increasingly concerned about improving their own performance, competing with peer institutions on cost and quality, and providing a degree of public accountability.
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From page 12... ...
We then develop a more appropriate approach to productivity measurement -- one that can serve as a key component in the set of information from which to base resource and other policy decisions. However, even the productivity measure developed in this report, which expresses outputs in terms of quantities of credits and degrees, cannot explicitly take account of quality variation and change.
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From page 13... ...
At the conclusion of its study, the panel will issue a report with findings and rec ommendations for developing the conceptual framework and data infrastructure and that provides an assessment of the strengths and limitations of alternative approaches to productivity measurement in higher education. The report will be written for a broad audience that includes national and state policy makers, system and institution administrators, and higher education faculty.
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From page 14... ...
into the concepts and techniques of productivity measurement is indicative of the ongoing process and continuing progress but also of the fact that measurement and conceptual barriers remain.4 Additionally, as described in the next chapter, more than one paradigm exists for constructing productivity models. 5 It is especially worth distinguishing between aggregate models of the kind developed here, which are designed to measure long-term trends, and structural models aimed more specifically at operational improvement and accountability concerns.
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From page 15... ...
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.
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From page 16... ...
" They conclude that colleges "can conceivably become more productive by leveraging technology, reallocating resources, and searching for cost-effective policies that promote student success." Indeed, many industries that formerly were believed to be stagnant have been able to improve productivity dramatically. Even in the quintessential example of Baumol's cost disease (noted above)
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From page 17... ...
The analysis and recommendations in this study attempt to balance these interests. This report has been written for a broad audience including national and state policy makers, system and institution administrators, higher education faculty, and the general public.
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From page 18... ...
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.
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