In a post last week, I suggested that a chart on average teaching costs compared to funding rates in the Tehan reform discussion paper, which was causing concern and confusion, and was later amended by the Department of Education, might make things look worse than was the case.
This turned on what kind of ‘average’ we were looking at. There are multiple versions of average 2018 estimated costs by field of education floating around – an average of averages (all institution average costs by field added up/number of institutions), a median of averages (institutional average cost in the middle of the range for each field), and a true average (total costs for field/EFTSL in field). I thought the chart might use an average of averages, which would over-weight low-EFTSL, high-average cost institutions.
But, having realised that I had the true average numbers when I thought I did not (in a file with a name that did not reveal this aspect of its contents), I now think the Department’s chart is based on true average figures.
Dividing the fields into loss-making, breakeven, and profitable my true average tally is now close to the Department’s chart, and much closer than my count of average of averages or median of averages.
The remaining differences may flow from different assumptions about inflating 2018 costs to 2021 levels (I chose 6 per cent) and different interpretations of breakeven. I classed a field as breakeven in the Department’s chart where the dot marking costs overlapped with the edge of the bar indicating the funding rate. In my own analysis, I classed a field as breakeven if its costs were within $400, up or down, of the funding rate. This minimises the classing of fields as profitable or loss-making based on minor absolute differences in estimated average costs.
On this basis, I get to 13 fields as profitable at the true average, 6 as breakeven, and 5 as loss-making. My reading of the Department’s chart is 10 profitable, 9 breakeven, and also 5 loss making. The chart below has more detail on my figures.
It’s worth noting that both sets of numbers rely on some questionable aggregations and disaggregations of fields of education.
In the original data, society and culture, humanities, law and economics, and English are all combined in ‘society and culture other’. So we don’t actually know their distinct costs. The true average of all fields is attributed to each field.
Economics and law are usually in a different faculty or faculties to other ‘society and culture other’ fields, which led me to omit their assumed average cost data from my original analysis as too unreliable. The chart above puts them back in but with more-plausible management and commerce costs, but either way the Department’s analysis and mine classify them as breakeven.
Medical studies causes particular confusion. Courses other than medicine are classified as medical studies. On my count there were eight medical schools taking undergraduates in 2018, but 24 universities reported cost data in the medical studies field of education. They are likely to be driving the high apparent profits in medical studies (that said, only three institutions have average medical studies costs clearly above the funding rate).
Another problem area is allied health and other health. They are combined in the cost data spreadsheet, but are two distinct groups in the current funding system. There are strong reasons for thinking that the current separation reflects real cost differences, which are driven by pedagogical practices, not broad thematic similarities.
Allied health fields have a clinical component, including physiotherapy, optometry, radiology, speech pathology and podiatry. They need one-on-one or small group training and specialised equipment. ‘Other health’ includes fields that are more like social science such as public health, epidemiology, and OH&S. Combining the two makes the other health fields look artificially expensive to teach, and allied health fields look misleadingly cheap to teach.
All this highlights a key issue with a policy focused on paying teaching costs with limited or no profit margins in each field. To avoid leaving universities with inadequate resources for teaching, especially in highly-regulated fields like health where expenditure may be hard to cut without losing professional accreditation, the underlying cost data needs to be highly accurate. That can’t be done when the cost data collection does not distinguish between fields that have different pedagogical practices.