Canada and the United States: Winners or Losers from Pillar One Amount A?
Bibliographic record
Abstract
On 4 February 2021, in a panel on “Digitalisation of the Economy: OECD Blueprints Discussion” at the Canadian Tax Foundation’s virtual Transfer Pricing Conference, I spoke about possible winners and losers from the OECD’s Pillar One “Amount A” proposals. My estimates focused on tax jurisdictions grouped by GDP and region. During the session, the chair Shiraj Keshvani (PwC Canada) asked about the likely impacts of Amount A on Canada, my country of birth. This article found its own birth in my attempt to answer that question. I examine the “tax base receiving” and “tax base relieving” impacts of Amount A on high-income (HI) jurisdictions in the Americas. Since the HI Americas group is dominated the United States and Canada, finer-grained estimates of Amount A impacts are possible. My results show that the HI Americas group, and thus Canada and the United States, are likely to be losing tax jurisdictions under Amount A.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".