Bibliographic record
Abstract
This paper examines how the corporate income tax base is to be shared with the proposed global corporate minimum tax, which has been agreed to by more than 130 countries. The aim of the global minimum tax is to reduce the incentive for profit shifting by putting a floor on corporate tax rates so that they do not fall below 15 percent of adjusted accounting profits. However, the global minimum tax itself will introduce new capital market inefficiencies. Foreign-owned capital can be taxed more heavily than domestic capital. The minimum tax distorts capital allocation by favouring labour-intensive projects over capital-intensive projects when the tax is paid. It also distorts the accounting decisions of corporations when they seek to avoid paying the tax. The corporate tax gains to Canada, net of personal tax revenue losses, are small, between $170 million and $645 million annually, depending on whether host countries adopt a top-up tax or not. After adjustments are made for economic losses, the annual net gain to the Canadian economy ranges from $95 million to $360 million, exclusive of administrative and compliance costs. Overall, it is not clear that the global minimum tax will work any better than other policies aimed at reducing corporate profit shifting.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".