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The Incentive Effects of Equalization Grants on Fiscal Policy

2017· article· en· W6941094668 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveEqualization (audio)Government (linguistics)Fiscal policyFiscal federalismUnderpinningTax incentiveFiscal imbalance

Abstract

fetched live from 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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 designObservational
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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