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Record W2150947619 · doi:10.3138/cpp.34.4.441

The Grants are Falling! The Grants are Falling! How Municipal Governments Changed Taxes in Response to Provincial Support in New Brunswick, 1983–2003

2008· article· en· W2150947619 on OpenAlexaffvenueabout
Craig Brett, Christina Tardif

Bibliographic record

VenueCanadian Public Policy · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsMount Allison University
Fundersnot available
KeywordsProperty taxEconomicsValue-added taxAd valorem taxTax rateIndirect taxPublic economicsTax competitionTax reformDirect taxRevenueTax revenueFalling (accident)Labour economicsBusinessMonetary economicsFinance

Abstract

fetched live from OpenAlex

The real value of grants to New Brunswick municipal councils from the provincial government fell dramatically from 1983 to 2003. At the same time, municipal property tax rates increased, especially among municipalities with comparatively low tax rates in 1983. This study uses an econometric model of the joint determination of local property tax rates and local property tax bases to examine the hypothesis that municipal responses to falling grants were constrained by tax competition. After controlling for observable characteristics, there is little evidence of spatial interaction among jurisdictions, suggesting that tax competition was not a major factor in municipal decisions. The grant cuts themselves appear to be a far more important determinant of changes in property tax rates. There is also some evidence that the tax base is sensitive to a municipality's own tax rates. However, the elasticity of the tax base with respect to the tax rate is small enough that there remains scope for municipalities to increase tax revenue by increasing their tax rates.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.277
Teacher spread0.235 · 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 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".

Quick stats

Citations7
Published2008
Admission routes3
Has abstractyes

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