Untangling the Worldwide VAT Web on Digital Supplies
Bibliographic record
Abstract
The imposition of value‑added tax (VAT) style consumption taxes on so‑called digital supplies is a challenge that has been made necessary by general trends of globalisation and the change in patterns of supply and consumption in the modern economy. Part of this challenge relates to the complexities associated with the supply of intangibles, and this complexity is exacerbated by the fact that many such supplies take place across international borders. Tax systems have long been attempting to meet such challenges and these efforts have been redoubled as a result of the OECD/G20 motivated base erosion and profit shifting (BEPS) initiatives aimed at reducing opportunities to minimise taxation using cross‑border structures and arrangements. At the same time, the OECD has been proactive in developing guidelines, for application internationally among OECD members, affecting the imposition of the VAT laws. The most common recent efforts to deal with cross‑border supplies of intangibles have been the burgeoning examples of the so‑called “Netflix tax”. This is a tax on consumption that might initially be thought to tax consumption of movies, electronic games and similar forms of entertainment. This article will review the VAT laws applicable to cross‑border provision of so‑called digital supplies in a selection of jurisdictions, namely, Australia, South Africa, New Zealand and Canada. The authors will critically analyse the taxes on digital supplies in those jurisdictions and determine the extent to which they comply with, or depart from, the OECD guidelines.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".