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Record W3125083388 · doi:10.55016/ojs/sppp.v7i1.42479

Canada’s Equalization Formula: Peering Inside the Black Box.. and Beyond

2014· article· en· W3125083388 on OpenAlexaffabout
Jim Feehan

Bibliographic record

VenueThe School of Public Policy Publications · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPeeringBlack boxEqualization (audio)Environmental scienceTelecommunicationsComputer scienceThe InternetArtificial intelligenceWorld Wide Web

Abstract

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Ontario only started receiving equalization payments, for the first time in its history, in 2009. As soon as Ontario slipped into that “have-not” status, the federal government imposed a cap on the growth of equalization payouts. That led to substantial federal savings, but has cost Ontario and other recipients what would have been much larger payments since then. The federal government’s move to rein in the potential ballooning cost of equalization may have been understandable, from a cost-control perspective, but it ultimately defied the very purpose of equalization. The fixed-growth rule imposed by the federal government is just one of several elements within the current equalization arrangement that should be corrected. The federal government should end that practice and absorb any resulting increase in cost. However, if that cost is onerous, then it could consider adjustments of its other major transfers to the provinces – Canada Health Transfer and the Canada Social Transfer – and reduce those per-capita transfers to provinces that are well ahead of the equalization norm. That would be better than shifting the entire burden to the those below the norm. Another flaw in the current equalization arrangement is the inclusion of Crown-owned hydro corporations’ remittances of earnings to their provincial owners in the natural resources category of equalization calculations. Many of these corporations are not simply energy producers, but are also vertically integrated, with transmission and retail sales operations, and some have no resources at all, but rely instead on fuel purchased in the marketplace. Moreover, taxes paid by private energy corporations are not considered part of the natural resource category but are included in the business income tax category. This means the formula is essentially inconsistent, discriminating based on the ownership profile. Hydro remittances should be removed from the natural resource revenue category in the formula that calculates equalization. They should go in the business income tax category, just as do the earnings of other commercial Crown corporations and taxes paid by private businesses. Going beyond the formula, it is time to re-consider the practice of exempting commercial Crown corporations from corporate income taxation. A more fundamental and long-recognized problem is the incentive for provinces receiving equalization payments to underprice the water-rental rates they charge for hydro production. Lowering water-rental rates has the effect of reducing provincial hydro revenues, which can entitle those provinces to larger equalization payments, while benefitting residents with cheaper hydro rates. Looked at empirically, “have-not” provinces do charge lower average rates for hydro than do “have” provinces, lending credence to the criticism that non-recipient provinces subsidize cheaper energy for residents of recipient provinces. The increased development of competitive North American wholesale electricity markets in recent decades has made it more feasible to assess what a fair market price for water-rental rates could be. Updating the equalization formula to consider not water-rental revenue, but water-rental fiscal capacity, should be the highest priority of all in reforming Canada’s equalization formula to align it more closely to the principles behind its creation. It is also time to include municipal government revenues from user fees in the formula. Those revenues are significant and it makes little sense to exclude them when municipal property tax revenues are included. Equalization is not out of control but reform is needed. Action on these fronts should be the priorities. These insidethe-box issues should be resolved before going beyond and considering the more complex task of extending the formula to account for provincial governments’ different expenditure needs and costs.

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 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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.294
Teacher spread0.266 · 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 teacher head, not a consensus.

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

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

Citations6
Published2014
Admission routes2
Has abstractyes

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