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Record W4244015950 · doi:10.3138/cpp.36.2.215

Péréquation et comportement stratégique des provinces bénéficiaires : un contre-exemple intrigant

2010· article· en· W4244015950 on OpenAlexaffvenueabout
Jean‐Thomas Bernard, Soufiene Ben Mabrouk

Bibliographic record

VenueCanadian Public Policy · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEqualization (audio)PaymentTreasuryGovernment (linguistics)EconomicsLiberian dollarTransfer paymentWork (physics)Welfare economicsGeographyFinanceEngineeringMathematicsStatisticsWelfare

Abstract

fetched live from OpenAlex

The work of Boadway and Hayashi (2001) and Smart (2007) tends to confirm the hypothesis that provinces who are the beneficiaries of equalization payments adopt strategic behaviours that reduce their tax capacity and thus increase these payments. In this study, we analyze the impact that a new hydroelectricity royalty paid by Hydro-Quebec to the Quebec Treasury has on total equalization payments received by the province; to do this, we consider the equalization formulas used before 2004 and after 2007. This royalty, which generates approximately $600 million each year, reduces Quebec's equalization payments by just over $100 million, using either formula. Under the terms of the current equalization formula, Quebec loses 38 cents in equalization rights for each additional dollar of income received from natural resources. The new hydroelectricity royalty and the increase in the dividend rates applied to this government-owned corporation have made it possible for the Quebec government to benefit from a transfer of $1.15 billion; on the other hand it loses $437 million in equalization payments. In this study, we provide and describe an important counter example to the hypothesis concerning the strategic behaviour of provinces that benefit from an equalization scheme.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.019
GPT teacher head0.299
Teacher spread0.280 · 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 designTheoretical or conceptual
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

Citations0
Published2010
Admission routes3
Has abstractyes

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