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2012 Annual Global Tax Competitiveness Ranking – A Canadian Good News Story

2017· article· en· W6940942342 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
KeywordsValue-added taxTax reformAd valorem taxCorporate taxSales taxIndirect taxTax revenueTax rate

Abstract

fetched live from OpenAlex

Since 2000, Canada has been remarkably successful in building a more competitive corporate tax system, principally by lowering tax rates and broadening the tax base. Canada’s marginal effective tax rate (METR) is now the lowest, and hence the most tax-competitive among the G-7, the 20th most tax-competitive in the 34-member OECD, and 57th among the 90 countries surveyed in this paper. The result has been greater investment and improved economic growth despite recessionary pressures. In particular, provincial sales tax harmonization with the GST has heightened Ontario’s competitiveness and promises to do the same for PEI, the latest convert to the cause. However, progress has not been uniform. Some provincial governments have lost focus by raising rates or introducing tax preferences that narrow the base, inevitably harming business efficiency. British Columbia’s decision to replace the new Harmonized Sales Tax with the old retail sales tax will cost it dearly, especially when it comes to public spending. On the other hand, corporate tax rate reductions of more than 30 percent (since 2000) have, contrary to the critics’ cries, failed to make an appreciable dent in tax revenues thanks to multinationals’ habit of shifting profits to Canada to take advantage of lower rates. This paper, in providing a candid snapshot of Canadian taxation measured against 89 other nations, serves as an invaluable foundation for understanding how far this country has come, and what its next steps should be.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.184
Teacher spread0.175 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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