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Enregistrement W6941094668 · doi:10.11575/sppp.v7i0.42478.g30369

The Incentive Effects of Equalization Grants on Fiscal Policy

2017· article· en· W6941094668 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Calgary · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueMycorrhizal Fungi and Plant Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIncentiveEqualization (audio)Government (linguistics)Fiscal policyFiscal federalismUnderpinningTax incentiveFiscal imbalance

Résumé

récupéré en direct d'OpenAlex

The equalization system has long been considered a vital underpinning of the Canadian federation: a means to create some purported fairness or justice among the provinces, by redistributing the wealth of provinces with larger fiscal capacities to allow those with weaker fiscal capacities to provide roughly equivalent services to their citizens. However, the mechanics of the equalization formula have long been suspected of being flawed. Since grant-receiving provinces can adjust the way their fiscal capacities are calculated and reflected in the equalization formula — by adjusting tax rates and spending, for instance — governments are confronted with incentives to design their fiscal regimes in ways that maximize the size of the grants they receive, even if the fiscal policies are designed for less-than-optimal economic efficiency. The incentive for grant-receiving governments to “game” the formula, even unconsciously, is apparent; what has remained largely unresolved is to what extent is it actually occurring. This analysis shows that indeed it is occurring, and to a measurable degree. It finds that equalization grants provide recipient provinces with incentive to raise their business and personal tax rates. This is because when a government raises its own tax rate, it raises the national standard average tax rate, which is used in the equalization allocation formula. That, in turn, raises the individual “have-not” province’s equalization-grant entitlement. Exacerbating the problem is that the tax-raising provincial governments tend to underestimate the deadweight cost that the tax hikes will have, potentially worsening the fiscal situation of a province that already faces difficult economic challenges. This analysis also finds that the equalization-grant allocation system encourages spending among recipient provinces, particularly on health-care services, resource conservation, industrial assistance, environment and housing. Results show that for every $1.00 increase in equalization grants, recipient provinces further increase spending by an additional $0.64 in total expenditure. Neither effect necessarily furthers the equalization program’s idealistic intent. The promotion of higher tax rates especially would seem to work at odds with the program’s conceptualization of a federal redistribution model. By potentially further repelling business and taxpayers from “have-not” provinces, the result could be making those provinces increasingly needy while continually reducing their citizens’ wealth. The equalization formula is not unfixable. The arrangement can be made to work even better, in a way that maintains the principle of redistributing wealth from more privileged provinces to less privileged ones, while avoiding the perverse incentives that motivate “have-not” provinces to raise taxes. If equalization grants were substituted with block grants that are unrelated to taxing capacity, taxes in grant-receiving provinces may actually decline. A $100 per capita increase in block grants is potentially associated with an up to 2.6 percentage points drop in business tax and an up to 0.26 percentage point drop in personal income tax. The result would be an equalization arrangement that could help increase, rather than decrease, competitiveness in the very “have-not” provinces that most urgently need to attract investment. Switching to block grants would not only keep the integrity of the principles behind equalization in tact, it would actually make equalization work better for all provinces.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,723
Score d'incertitude au seuil0,534

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,202
Écart entre enseignants0,194 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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