2012 Annual Global Tax Competitiveness Ranking – A Canadian Good News Story
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".