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Improving Measurement of Productivity in Higher Education (2012) / Chapter Skim
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Summary
Pages 1-8

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From page 1...
... Metrics ranging from graduation rates to costs per student have been developed to serve this purpose. However, the capacity to assess the performance of higher education institutions and systems remains incomplete, largely because the inputs and outputs in the production process are difficult to define and quantify.
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From page 2...
... inform individual consumers and communities to whom colleges and universities are ultimately accountable for private and public investments in higher education. Though it should be noted that the experimental measure developed in this report does not directly advance all of these objectives -- particularly that pertaining to measurement of individual institution perfomance -- the overall report pushes the discussion forward and offers first steps.
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From page 3...
... joint production -- colleges and universities generate a number of outputs (such as educated and credentialed citizens, research findings, athletic events, hospital services) , and the labor and other inputs involved cannot always be neatly allocated to them; (2)
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From page 4...
... In the meantime, while accounting is incomplete, it is essential to monitor when apparent increases in measurable output arise as a result of quality reduction. For the foreseeable future, this will have to be done through parallel tracking of additional information generated independently by universities and third party quality assurance methods.
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From page 5...
... as a greater sheepskin effect -- that is, an added value assigned for degree completion. Similarly, high student and teacher quality at selective private institutions may offset high input costs by creating an environment conducive to high throughput (graduation)
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From page 6...
... Therefore, in the context of resource allocation or other high stakes decisions, the marginal success effect attributable to this input quality effect should ideally be taken into consideration in performance assessments. Because heterogeneity leads to measurement complications even within institutional categories, it is also important to account for differences in factors such as the mix of degrees and majors.
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From page 7...
... 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.
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Key Terms

  • productivity measures
  • graduation rates
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  • quantitative productivity
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  • postsecondary education
  • education data
  • quality differences
  • quality adjustment
  • administrative data

  • social value
  • credit hours
  • baseline model
  • input costs
  • measure developed

  • unemployment insurance
  • implement productivity
  • productivity models
  • valid productivity
  • supplement productivity

  • adequate productivity
  • implementing productivity
  • instructional productivity
  • accurate productivity
  • productivity statistic

  • detailed productivity
  • resulting productivity
  • proposed productivity
  • student input
  • educational quality


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