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Record W3007811528

What You See is Not What You Get: Budgets versus Results in Canada’s Major Cities, 2019

2020· article· en· W3007811528 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueC.D. Howe Institute Commentary · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsTaxpayerRevenueCapital expenditureBusinessQuality (philosophy)AccountingFinanceEconomics
DOInot available

Abstract

fetched live from OpenAlex

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.

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.276
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it