In late 2025, the Australian National University (ANU) found itself in the spotlight for reasons less flattering than its academic reputation. After only two years at the helm, Professor Genevieve Bell resigned as Vice Chancellor. There were months of speculation, debate, and even criticism.
Do Economies of Scale Exist in Australian Universities?
INSIGHTS & RESOURCES
Do Economies of Scale Exist in Australian Universities?
by KEITH HOUGHTON
November 2025
Australia has, on average, some of the largest universities in the English-speaking world. Some might speculate that this is an idiosyncratic outcome of the Australian education funding approach. While this is possible, even likely, the effect of global ranking algorithms may also factor into this. Those who have attempted to reverse engineer the three or four largest rankings appear to recognise that controlling for scale is a near-impossible task.
The scale of operations of a company or organisation is often seen to impact productivity performance. Does it also impact university productivity performance?
Assessing the effect of scale on productivity outcomes has been challenging because measuring productivity performance by individual universities is difficult. The Australian Bureau of Statistics once provided estimates of this productivity for both education and research, but only at the whole-of-sector level. Fortunately, Higher Education and Research Group’s (HERG’s) measure of university productivity performance, the Research and Education Efficiency Frontier (REEF) Index, is measured at the individual institution level.
Australia’s universities have outputs principally focused varying intensities of teaching and research. This diversity makes for challenges in the choice of a measure of size. Some universities have a large number of students and a more modest level of research. Others, including the Group of Eight, are more research-intensive with a proportionally smaller student population. To develop a valid measure of scale, a complex integrated measure is needed.
Table 1 shows the measures used to examine the relationship between size and outputs in this document. The aim is to explore the relationship between size and productivity to gauge economies or diseconomies of scale.
Principal Components Analysis is one way to measure size and provide results across the range of Australian universities.
TABLE 1 | Individual Measures of Size |
Variable | Definitions |
Academic Staff | Number of EFT Academic Staff as reported to the Federal government (2021) |
Research Publications | Number of Research Publications attributed to researchers with affiliations to an Australian university (2021). Measured as the greater of Scopus and Web of Science. |
Undergraduate Student Population | Number of EFT Equivalent undergraduate students (EFTSL) as shown in the Federal Government Department of Education listing for 2021 |
Graduate Coursework Population | Number of EFT Equivalent graduate coursework students (EFTSL) as shown in the Federal Government Department of Education listing for 2021 |
Research Student Population | Number of EFT Equivalent research higher degree candidates (EFTSL) as shown in the Federal Government Department of Education listing for 2021 |
Several different measures of efficiency are shown in the analysis:
- Cost efficiency using unweighted total EFTSL for measures of student populations.
- Cost efficiency using weighted total EFTSL with weights (UG=1, GC= 2 and RHD= 4);
- Academic staff efficiency using unweighted total EFTSL (36 universities)
- Academic staff efficiency using weighted total EFTSL as in 2 above (36 universities).
One Australian university is omitted since it has contractual arrangements that result in more than 20% of the student population being educated by an outsourced supplier.
Charts 1 to 4 show the results of the four efficiency measures across scale, with the smoothed line calculated using LOESS (Local weighted Estimated Scatterplot Smoothing).
This smoothing ensures that the trend in efficiency versus scale is easily visualised. This smoothing is non-parametric (that is, it does not assume a pre-determined formula). It uses a high degree of smoothing to clearly illustrate the trend between efficiency and size (span 2).
Chart 1 shows a peak of the cost efficiency curve that resembles an inverted U-shape. This implies that there are economies of scale in the middle range of Australian universities. It also implies that there are diseconomies of scale at both the lower and upper areas of the size measure (as measured using the first principal component of the size individual size measures).
Chart 1. Efficiency using Unweighted EFTSL
Chart 2 shows the scale efficiency with EFTSL weighted as described above. This weighting is, arguably, a more valid measure as the cost of undergraduate education is likely greater than graduate coursework education, which in turn is less than the training of candidates enrolled in research higher degrees.
The pattern shown in Chart 2 clearly shows an inverted U shape.
Chart 2. Cost Efficiency using Weighted EFTSL
Expenditure on academic activities is not the only way to measure the efficient use of resources; academic staff utilisation can also be used to assess efficiency across the range of academic staff resources. The results are shown in Charts 3 and 4 and parallel those in Charts 1 and 2.
Chart 3. Academic Staff Utilisation Unweighted EFTSL (36 universities)
Chart 4 Academic Staff Utilisation Weighted EFTSL (36 universities)
Comparing Charts 1 and 2 with Charts 3 and 4 reveals a stark difference in results. The inverted U shape so apparent in Charts is absent in Charts 3 and 4. The result using academic staff utilisation shows a largely flat line with a modest increase in economies of scale as universities increase in size.
This is consistent with the conclusion that effective management of academic staff workloads successful deals with any possible diseconomies of both large and small scale.
One could also reasonably conclude that, while there are economies and diseconomies of scale, it is the ‘other’ (i.e., non-academic activities and operations such as non-academic staff, equipment or simply the real estate, among others) that explain these diseconomies. This points to where productivity improvement might be more easily found, at least within some (particularly large or small) universities.
This intriguing, and potentially unexpected, outcome underscores the success by universities in using academic workload models to manage academic staff resources, solving a key element in the problem of economies and diseconomies of scale.
Earlier this year, the Higher Education and Research Group released its latest annual analysis of the productivity of the Australian university sector, including, for the first time, the three large private universities.
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.