University casualisation (Australia)

Interactive charts linking casual staff share, QILT student experience, and profitability — with click-through sourcing for each number.

What this is
Institution-level, observational comparisons. Useful for pattern-spotting — not causal claims.
Key caveat
“Casualisation” here is casual FTE share (not headcount share), because headcount isn’t published in this release.
Sourcing
Click any dot to see URLs and spreadsheet sheet/cell references.

Student experience

QILT/SES: ComparED “Had a positive overall experience” (undergraduates), pooled across two most recent SES years.

Profitability

Finance tables for 2023. Net margin is derived as net operating result / total revenue.

Institution details

Click a point (or search) to see values and sourcing.

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Notes

  • Association only: these charts do not establish causal effects.
  • Outlier trimming: charts restrict the x-axis to ≤25% and exclude points above that. This reduces leverage from extreme outliers; excluded counts are shown under each chart.
  • p-values: the p-value shown is for the OLS slope (two-sided t-test, df = n − 2). A small p-value does not imply useful predictive power; check R².
  • FTE vs headcount: staffing is measured as actual staff FTE, including casual FTE.
  • Timing: staff and finance metrics are for 2023; ComparED pools SES years.
  • Transparency: the CSV includes per-value sourcing columns (URLs, sheet/cell refs, and derivation notes).

Baselines (for context)

  • Australian economy overall (ABS, Aug 2025): 20.0% of employees without paid holiday/sick leave (ABS proxy for casual); 19.5% self-identified as casual.
  • Universities / higher ed providers (Dept staff series, 2023): casual staff were 14.4% of total staff FTE (Table A + B), down from 15.3% in 2022 and 17.8% in 2019.
  • Within universities (2023): teaching-only roles were 64.7% casual; Level A / below lecturer academic FTE were 53.5% casual.
  • Education & training industry (ABS, Aug 2025): about 16.3% self-identified as casual (≈200.1k casual employees).

These use different statistical definitions (employee counts vs FTE; proxy vs self-identified). Treat them as rough benchmarks, not apples-to-apples.