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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 OpenAlexaboutno aff
Farah Omran, William B. P. Robson

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

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.849
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.013
Science and technology studies0.0060.003
Scholarly communication0.0150.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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