Allocating taxable income for provincial corporate income taxation in Canada, 2015-2017: practice and analysis
Bibliographic record
Abstract
Canada is one of the few countries in the world where an intra-country formula allocation (FA) mechanism plays an important role in the taxation of corporate income by sub national governments, that is provinces. This paper presents and analyses information prepared for its authors by CRA on the allocation of taxable income between provinces for three years: 2015, 2016 and 2017. Profits allocated using the FA approach account for about one third of taxable income. The Income tax regulations contain one general formula and nine sector specific formulas, the general one is used to allocate more than 85% of revenues between provinces. The second most important one pertains to the banking sector. The formulae used by the CRA date back mainly to 1947 and have applied to all provinces since 1960.They have not been the object of much analysis. In particular the use of a two factor formula with weights of 50% for each of payroll and revenues has not been much discussed. The paper thus presents both the existing distribution of taxable revenues between provinces and simulates what it would be if one of alternative formulas either used in the United States or under examination in Europe was used. The three factor formula would change the distribution of taxable income significantly between provinces
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".