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Record W6884618720 · doi:10.11575/prism/37700

Reaching a Better Fiscal Balance: Alternative Spending and Revenue Arrangements Between the Federal Government and the Provinces

2019· other· en· W6884618720 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueFiscal federalismTransfer paymentRevenue sharingGovernment spendingGovernment revenueContext (archaeology)Government (linguistics)Fiscal imbalance

Abstract

fetched live from OpenAlex

Over the years, Canada’s equalization program has frequently attracted attention and controversy. Pointing to significant differences in equalization payments received by provinces, several premiers have publicly expressed their dissatisfaction with the program and have proposed various modifications to the current formula. But while equalization amounts to almost $20 billion for the 2019-2020 fiscal year, it represents only a part of Canada’s fiscal landscape. There are numerous ways by which hundreds of billions in spending and revenue dollars flow between the federal government and the provinces. The diversity in spending needs and tax capacities across regions contributes to the uneven distribution of federal expenditures and revenues collected. Taking stock of the entire fiscal landscape, rather than focusing on a single component like equalization, provides the necessary context when considering changes to Canada’s fiscal regime. This Capstone examines alternative fiscal arrangements between the federal government and the provinces and measures the potential impacts of these arrangements on provincial budgets and on overall parity in the federation. Arrangements investigated include modifying the equalization program, shifting more income and consumption tax revenues from the federal government to provincial governments in exchange for lower federal transfers, and adjusting the allocation of federal transfers based on the notion that provinces with larger populations potentially benefit from economies of scale. If economies of scale exist, more populated provinces are able to provide the same level of public service at a lower cost and their governments will receive a lower federal transfer amount per person. This paper finds that modifications to equalization leads to greater parity but places tremendous burden on regions that have lower fiscal capacities and need equalization the most. [vi] Assigning a greater share of government revenues to the provinces in exchange for lower federal transfers results in more manageable impacts to provincial budgets as well as conveying greater fiscal accountability on provincial governments. However, regions that have lower tax capacities still face greater impacts relative to others. Incorporating potential economies of scale in the allocation of federal transfers strikes a balance of desirable outcomes: the impacts to regions most in need of transfers are minimized, provinces with larger populations still receive a larger share of transfers, and the federal government keeps a significant portion of its transfer programs and influence on ensuring provincial governments deliver the level of public service Canadians expect. Since federal spending obligations will be lower, there is an opportunity to lower the federal tax burden on Canadians or to shift a greater share of the revenue base to provincial governments and further enhance fiscal accountability.

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)0.005
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0110.006
Scholarly communication0.0120.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.031
GPT teacher head0.301
Teacher spread0.271 · 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
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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