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Record W3121629385

Through the Roof: The High Cost of Barriers to Building New Housing in Canadian Municipalities

2018· article· en· W3121629385 on OpenAlexaboutno aff
Benjamin Dachis, Vincent Thivierge

Bibliographic record

VenueC.D. Howe Institute Commentary · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueFiscal yearBusinessLegislatureGovernment (linguistics)AccountabilityGovernment revenueEconomic policyEconomicsFinancePublic economicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In 2017, Canada’s senior governments spent some $782 billion on program expenditures and interest payments, amounting to 36 percent of gross domestic product. Control of public money is fundamental to democratic government, so it is natural to wonder how much of this activity – and the taxes, fees and borrowing that support it – reflects deliberate choices by voters and legislators. Formal accountability exists. Governments typically present budgets before or shortly after the start of the fiscal year, and budget votes are votes of confidence on which governments stand or fall. Legislatures and their committees play a key role in authorizing many specific expenditures. Governments table their public accounts, which present the audited results for actual revenues and expenses, after the end of the fiscal year. But comparing the expenses and revenues projected in the budgets of Canada’s federal, provincial and territorial governments at the beginning of the year with the results reported in their public accounts after the end of the year reveals that governments routinely miss their budget targets by economically meaningful amounts. More significant, they miss their targets in predictable ways: expenses and revenue typically come in above what the budgets promised. Over the past 15 years, senior governments’ cumulative spending overshoot adds up to $69 billion, with the Prairie Provinces and the Territories showing the biggest overruns. Even larger is the cumulative revenue overshoot: $104 billion. Governments in Canada are spending and taxing far more this year than they would have if they had delivered on their budget commitments. Comparing the annual patterns of overshoots and undershoots over time raises a further concern. Rather than overshoots of expenses coinciding with undershoots of revenue, or vice versa, as would happen if government finances were responding to economic cycles, overshoots on either side of the ledger tend to coincide – which suggests that governments are spending “windfalls” and/or managing the bottom line. Encouragingly, however, the tendency to overshoot and miss budget targets more generally, and the troubling annual patterns, seem to have become less pronounced over the past 15 years. Several steps, including estimates that are more timely and presented in the context of the government’s fiscal plan, a stronger role for legislative committees that authorize spending, and faster and more frequent publication of actual results, could further improve the record. Canada’s senior governments should improve the quality of their budget forecasts and their adherence to those forecasts, and legislators and voters should hold them accountable for doing so.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.178
Threshold uncertainty score0.953

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0280.005
Scholarly communication0.0090.003
Open science0.0040.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.041
GPT teacher head0.327
Teacher spread0.287 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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