Australian university fee reform — an interactive optimiser

Solving…

Current vs optimised

Current (2026) Old (pre-JRG) New optimum ✦ Protected field Enrolments (EFTSL)

Field-by-field results

How the optimiser works

For each of 27 fields the model chooses a new student contribution S and government contribution G. University revenue per student is R = S + G. Your settings become hard constraints: aggregate government spending, the university revenue rule, the fee caps and the protected fields. Enrolments are held fixed (about 237,000 EFTSL, imputed from 2022 UAC composition scaled to 2024 national totals) — defensible because estimated demand elasticities are small (≈ −0.1), so fee reform mainly reallocates costs rather than students.

Within the feasible set, your objectives are minimised in strict priority order (lexicographic goal programming): a lower-priority objective can never trade away a higher one. Everything is a linear program, solved in your browser in milliseconds — there is no goal-seeking and no hidden target schedule. If your constraints are jointly impossible, the tool says so and suggests what to relax.

This tool and the accompanying working paper now share one set of inputs: the published statutory 2026 amounts, including the official grandfathered (pre-JRG) rates that set several of the fee caps. Examples B and C reproduce the paper’s figures exactly. Example A is the one deliberate difference: the paper’s version adds a fee target for the broad science category, which this tool omits because it contains no target schedules of any kind, so its largest increase is $3,443 here against $3,676 in the paper.

Data and assumptions. Coverage is Australia-wide: all Commonwealth-supported bachelor students, about 237,000 EFTSL across 27 fields of study. The 27 fields are the unique combinations of field of study and funding cluster, so study areas that share both — politics and sociology, for example — are merged, and fields labelled “(broad)” are generalist programs recorded at a broader classification level, whose rates blend more than one funding band. Fees and subsidies are the official 2026 statutory rates (Department of Education, Indexed rates — amounts for 2026), with blended broad categories re-weighted to those rates. Field-level enrolments are an imputation: 2022 UAC (NSW & ACT) applicant composition is scaled to 2024 national Commonwealth-supported bachelor totals, as in the accompanying working paper and Yong, Coelli & Kabátek (2023). Amounts are annual dollars per EFTSL in 2026-equivalent terms. The optimiser holds enrolments fixed (estimated demand elasticities are small), so results show who pays under a re-priced schedule, not enrolment forecasts — a policy exploration tool, not financial or costing advice.

About this tool

The Australian Government sets a student contribution and a government contribution for every field of study; their sum is what universities receive per student. That identity creates a trilemma: cut student fees, and either taxpayers pay more, universities receive less, or other fees must rise.

This tool solves for the best available fee schedule under constraints you choose, using lexicographic goal programming over 27 fields of study (about 237,000 EFTSL), calibrated to the 2026 fee schedules. Everything runs as a linear program in your browser, live. There is no goal-seeking: no target fee schedule is baked in anywhere — you set constraints and priorities, and the optimiser does the rest.

It accompanies a working paper by the author on constrained reform of the Job-ready Graduates fee settings.

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