Australian universities make strong productivity gains but regionals lag


Australian universities make strong productivity gains but regionals lag

The Australian
by Julie Hare – Higher Education Editor
20 April 2016

Key Takeaways:

  1. Australian universities experienced an average productivity gain of 15.7% from 2007 to 2013, with the Group of Eight (Go8) universities leading with gains of approximately 25%.
  2. Some regional universities faced challenges, with a small number showing negative productivity growth during the same period.
  3. Technological advancements and system-wide efficiencies were major drivers of productivity improvements, leading to heightened competition among universities.
  4. The Australian National University stood out as the most efficient research-intensive university, while Murdoch, UNSW, and UWA exhibited significant productivity growth.
  5. Regional universities faced constraints in finding efficiencies, like enrolling more students, and while the study tracked efficiency and productivity improvements, it didn’t account for quality factors.

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Full Article:

“Australian universities increased their productivity by an average 15.7 per cent in the six years to 2013, with the Group of Eight ­averaging gains of about 25 per cent. But a small number of ­regional universities slipped into negative territory.

These are the findings of research that seeks to understand how universities responded across a six-year period to factors both within and outside their control.

“The system has improved greatly in terms of productivity at higher levels than may be anticipated,” ….Keith Houghton….said the efficiency of the entire system improved across the period from 2007 to 2013, “possibly driven by ­advances in technologies or process and systems or by efficiencies in staff enterprise agreements among other possible reasons”.

“The efficiencies growth across the Go8 was 25.1 per cent, for the Innovative Research Group 19.4 per cent, the Australian Technology Network of 12.2 per cent, while the average for the non-Go8 was of 13.9 per cent.”

Using data covering 37 universities from 2007 to 2013 using student numbers and total publications as the major outputs of teaching and research, Professor Houghton’s report shows a spike in average productivity of 15.2 per cent, based on student numbers flooding into the system under the demand-driven system, in 2012-13.

He said while the demand ­driven system had heightened competition between universities from 2010 onwards, forcing them to ­accept the need to use their ­resources more efficiently and focus on improving productivity, this report did not focus on ­individual policies and their ­consequences.

“We are in the process of writing a paper that teases out the uncapping of places, so this is the step before that,” Professor Houghton said.

While technological and system-wide efficiencies had been the major contributors to productivity improvements, some universities stood out.

Australian National University was by far the most efficient ­research-intensive university…………..But in terms of change across the timeframe, Murdoch, the University of NSW and the University of Western Australia exhibited the most significant productivity growth. At the other end of the spectrum, Charles Sturt and Southern Cross experienced declines of 4.7 per cent.

Professor Houghton said [regional] …… universities were constrained in their attempts to find efficiencies, such as enrolling more students. However, he agreed that while his work could track efficiency and productivity improvements, it was incapable of putting quality into the equation….

There is growing anticipation that the federal government’s response to the Universities Accord review’s final report will come soon. Given this and the fact that the budget is less than a month away, it is timely to review one of the final report’s key insights.

Recently released analysis finds that one large Group of Eight university outperformed other public universities in its research and education productivity outcomes during the pandemic.

The joint and common cost problem arises where there are two or more outputs that arise from costs that are shared in the production of these outputs. In many situations, the ability to assign costs to these two or more outputs is not complex. But there are instances where it is highly complex. In these situations, there is a need to use advanced analytics to provide a valid and reliable estimate of costs.

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