A Profound Tax Reform: The Impact of Sales Tax Harmonization on Prince Edward Island’s Competitiveness
Bibliographic record
Abstract
Prince Edward Island’s decision to harmonize its provincial sales tax with the federal GST next year will bring Canada’s smallest province huge benefits; consumers and businesses will reap the rewards. While services will be taxed higher, the removal of PST on purchases of goods and services used in operations will keep more money in consumers’ wallets as lower production costs cascade from business to the general public. Businesses, faced with a lower cost of capital, will have the chance to increase investments by around $560 million over seven years. Further, the HST regime’s potentially more neutral treatment of economic activities will minimize distortions by promoting the efficient allocation of capital. By 2021, when the harmonization has been fully implemented, PEI’s effective tax rate on new investments will have fallen by 18 points, making the island one of the most competitive economies in the OECD. Businesses of every size will gain enormously from the changes, as will Prince Edward Islanders themselves, in the form of more jobs, $380 million in additional wages and dramatically improved opportunities. This brief paper models the effects of the HST’s gradual phase-in on all major sectors over the next seven years, and argues forcefully that PEI’s course is the correct one — a major step toward the neutral corporate tax structure Canadian prosperity depends on.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".