From Adjustment to Apportionment in the Goods and Services Tax Act: A Comparative Analysis of the Change-In-Use Rules in Australia, Canada, and New Zealand
Bibliographic record
Abstract
All countries that have adopted Goods and Services Tax (GST) or Value Added Tax (VAT) employ a ‘change-in-use’ mechanism to distinguish consumption from the stages of production and distribution. New Zealand’s former change-in-use rules were unique. Unlike the ‘use’ based apportionment approaches employed in Australia, Canada and the United Kingdom, New Zealand employed an adjustment approach that utilised a ‘principal purpose’ test and deemed supply mechanism. While Canada has also employed an adjustment approach for capital property, the New Zealand rules have operated differently to those in Canada. In response to criticism for being overly complex and confusing, the New Zealand change-in-use rules will adopt a new ‘use’ based apportionment approach, together with a new mechanism to constrain the number of adjustments, from 1 April 2011 for a number of taxpayers. Applying criteria identified by the Tax Working Group the performance of New Zealand’s change-in-use rules are examined, in comparison to those applied in Australia and Canada. In addition, the comparative readability of the change-in-use provisions in all three jurisdictions is examined. The paper concludes that New Zealand should adopt an apportionment approach and that the Goods and Services Tax Act should be rewritten for improved readability.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".