Wild Numbers: Getting Better Fiscal Accountability in Canada’s Municipalities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canada’s municipalities deliver services that are critical to quality of life, and require major commitments of resources in taxes, fees and intergovernmental transfers. But their budgeting practices, and people’s ability to measure their municipality’s performance against its budget commitments, are nowhere near the level appropriate to this importance. This report looks at the annual spending projections in the budgets of 31 of Canada’s largest municipalities since 2009, and the results reported in those municipalities’ financial statements at the end of each of those years. It asks what a councillor, or taxpayer, or citizen – a person who is motivated and numerate, but non-expert, would infer from each budget, and how close this same person would judge the municipality had come to its spending targets when inspecting the municipality’s reported expenses. In most municipalities, simply finding numbers that describe spending plans in budgets is a challenge: very few budget documents even contain numbers on the same accounting basis used in the financial statements. Users who do put the time and effort into finding numbers describing their municipality’s operating and capital spending plans, and compare them to the expenses reported after year end, would typically conclude that the municipality did a terrible job of hitting its budget projections. Over the past nine years this study looks at – from 2009, when Canada’s cities began reporting their results using Public Sector Accounting Standards (PSAS), to 2017, the most recent year available – these 31 cities have typically undershot those projections on average over that period; and missed them in one direction or another by an average of 9 percent. Improving this situation is partly a matter of presenting budgets using the same comprehensive PSAS-consistent revenue and expense numbers that municipalities already use in their financial statements. Provinces that mandate municipal budgets prepared in other ways – splitting operating and capital budgets, with the latter prepared on an antiquated cash basis – should stop doing so. Either way, municipalities can show PSAS-consistent numbers as supplementary information on their own, and can take other steps to ensure that their budgets represent the full picture of the municipality’s activities and its claim on citizens’ resources. Better matching of results with budget plans will also require councillors, ratepayers, and voters to demand – and get – timely budgets, regular updates in interim reports, and rapid publication of final results. Those are all key tools to help them compare budget plans to past results, and current results to past plans – and, when circumstances warrant, demand corrective action. Councillors, ratepayers, and voters should insist on better numbers from their municipalities, and on the improved fiscal accountability the better numbers will make possible.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it