5900.0.00.003 - University output measures in the Australian National Accounts: experimental estimates, 2008 to 2017  
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 28/09/2020  First Issue
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Next steps – where to from here?

The experimental output index derived in this paper grows at a similar rate to the current ABS index. However, the proposed index has two main advantages. It uses:

    1. a direct measure of research output; and
    2. applies expenditure weights to enrolment data to implicitly capture some aspects of quality change between universities.

In addition, teaching provided by universities has continued to evolve, and this evolution is continuing as a result of COVID-19. The output streams proposed in this paper provide greater scope for incorporating future compositional and quality change.

Following a peer review process, and resulting revisions to this work, the ABS will consider incorporating measures based on the experimental index into the Australian National Accounts. There are also a number of possible extensions to this work, which we now turn to.

Refinements to the relative weights of teaching versus research

Information about how academics engaged to undertake both teaching and research activities split their time would provide a basis for calculation of the research and teaching weights. In the absence of a detailed collection to obtain such information, consultation with a number of universities could be undertaken. Additionally, incorporation of data regarding the academic level of staff members of different designations would improve the teaching and research weights, but this data is not available.

The relative weights could also be improved by incorporating intermediate and capital inputs. The method for determining the division of expenditure into teaching and research components using the staff research/teaching designation is simplistic, and assumes that all non-academic operating costs are split according to academic designation. Separate models could be developed to identify the research components of intermediate and capital inputs.

Stratifying teaching output data by discipline

Consideration was given to stratifying teaching activity by discipline, because educating a medical student is likely to be more expensive than educating a humanities student, and therefore should carry greater weight. While the Department of Education publishes enrolments statistics by 'broad field of education', operating costs by discipline do not exist in published form. Individual universities would be likely to collect expenditure by faculty in their management accounts, and this could be an avenue for future inquiry.17 The Department of Education has recently commissioned a project on estimating expenditure differences across various disciplines, which could prove to be useful.18

Use of price information

Despite being classified as non-market activity, the public university sector in Australia engages in significant amounts of market-based activity, particularly educating international students. Since these students pay different fees across different universities and courses, this variation could serve as a means by which teaching output could be quality-adjusted. At the current time, this data is not centrally collected. An alternative approach could be to use differences in academic staff salaries across universities, which would reflect differences in the ability of universities to attract higher quality academics, which in turn is an indicator of quality. This approach would have applications in measuring research output as well as teaching. However, once again, such data is not centrally collected or available at this time.

Incorporating research publications within research output measures

Research output is measured using deflated data on research grant funding and higher degree completions. Ideally, research publications should be included in the measure of university research output. One possibility would be to use data from the Australian Research Council (ARC) collected through the Excellence for Research in Australia program (ERA); however, data is not reported on an individual university basis. Efforts to coordinate, extract and compile information from the ARC would help improve the estimates, though the ERA program is only conducted every three years.

Incorporating university ranking data

Some thought has been given to the possibility of weighting each university according to its 'ranking', so that more highly ranked universities carry greater weight.19 While rankings contain information about which universities are 'perceived to be better', they don’t indicate by how much, which poses difficulties for the construction of weights. This could be an avenue for future research.

Quality adjustment by degree types

While this study has focused on quality differences across universities, it captures them implicitly through cost weights. An alternative quality-adjustment, which is prominent in academic literature, involves explicit outcome-based quality adjustment through differences in the rate of return to education across different qualifications. An example is Gu and Wong (2015). This approach accounts for the fact that a medical degree is likely to reward the student with a higher lifetime salary than some other types of qualification, and therefore should carry greater weight. Such explicit quality adjustments would require additional work to establish the strength of the link between the outcome (lifetime salary) and the output (completion of a degree). It is worth noting that while explicit quality adjustment of non-market economic activity in this manner is an engaging research topic, the ABS is not aware of any country that has implemented such an approach in their national accounts.

Incorporation of private universities

This analysis is restricted to the 37 public universities operating in Australia, but could be extended to include universities and other higher educational institutions owned and controlled by the private sector. This will be considered prior to implementation in the national accounts.

Producing experimental multi-factor productivity statistics

Volume measures of inputs used in the production of outputs, such as labour, capital and intermediate goods and services, would facilitate the derivation of experimental measures of multi-factor productivity for public universities. The ABS will investigate the feasibility of this during 2020/21.


17 https://www.education.gov.au/student-data
18 https://docs.education.gov.au/documents/2018-transparency-higher-education-expenditure-publication
19 https://www.universityrankings.com.au/qs-australian-rankings.html

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