What You See is Not What You Get: Budgets versus Results in Canada’s Major Cities, 2019
Notice bibliographique
Résumé
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 projections for spending and the bottom line (revenues minus expenses) in the budgets of 31 of Canada’s largest municipalities over the period from 2010 to 2018, and the results reported in those municipalities’ year-end financial statements. 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 would conclude when comparing the budget to the results. In most of the municipalities we look at, simply finding informative numbers about spending plans in budgets is a challenge: less than one-third of their budget documents contain numbers using the same public sector accounting standards (PSAS) used in the year-end 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. Comparing plans in cities’ budget documents to results in cities’ financial statements, users would find that the difference between spending growth as projected in budgets and expenses growth published after year-end averaged 8 percent annually. A key contributor to these discrepancies is the fact that cities typically budget using different accounting practices than the PSAS-consistent rules they follow in publishing their results. Critically, municipal budgets show investments in capital assets like buildings, sewers and transit on a cash, upfront basis while the financial statements amortize the cost over years. Comparing budgets on a PSAS basis to results yields an average annual gap between plans and outcomes of 4 percent, and suggests that cities have a tendency to undershoot their budgeted spending. As for the bottom line, the budget debate in most municipalities, and the assumptions of most councillors, citizens and journalists, emphasize the need to “balance the operating budget, ” and downplays the separate capital budget. PSAS do not separate “operating” and “capital” – accrual accounting writes capital down as it delivers its services (amortization), and produces a single statement of revenue and expense with a bottom line that represents a change in a government’s net worth and capacity to deliver services. A city’s “operating budget” balance is nevertheless typically the subject of serious anxiety, culminating in council voting a budget with a bottom line very close to zero. In these municipalities, the revelation of substantial surpluses in the year-end financial statements is completely at variance with peoples’ understanding, and the anxiety of the budget debate. Most Canadians would be amazed to learn that Canada’s cities routinely record large surpluses, and – in contrast to many senior governments – have positive net worth. The 31 municipalities we look at ran aggregate budget surpluses of $11 billion in 2018, $8 billion over budget expectations. Improving this situation is partly a matter of presenting budgets using the same PSAS-consistent revenue, expense, and bottom-line numbers that municipalities already use in their financial statements. Ideally, provinces that mandate municipal budgets prepared in other ways – splitting operating and capital budgets, with the latter prepared on an antiquated cash basis – would stop doing so. Councillors, ratepayers, and voters should insist on better numbers from their municipalities, and on the improved fiscal accountability the better numbers will make possible.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,013 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,015 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».