Bibliographic record
Abstract
With recent accessions to the federal-provincial Harmonized Sales Tax, provinces with valueadded taxes (VATs) now comprise over two-thirds of the national economy. While Canadian VATs are economically superior to the taxes they replaced, they are not as well designed as in other countries. An efficient VAT is a uniform tax on all consumer (but not business) purchases. Although the OECD has reported that Canada’s VAT is one of the most efficient in the world, that assessment was based on data shown here to be misleading. In reality, Canada’s VATs have large exemptions, rebates and rate preferences that reduce revenues and hamper productivity. If all these tax preferences were eliminated, government VAT revenues would increase by as much as $39 billion, or more than 50 percent. Moreover, taxing consumer commodities at a single rate reduces opportunities for tax evasion, simplifies tax compliance, and in most cases increases economic productivity. Given the fiscal and productivity challenges currently facing Canadian governments, a new look at VAT design is clearly warranted. This paper offers a detailed assessment of the effects of the tax on the economy, and it proposes a number of specific, feasible reforms to the GST-HST system.
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 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".