International apportionment mechanisms for VAT inputs - Is the turnover basis the best mechanism for all retail industries in South Africa?
Bibliographic record
Abstract
Apportionment of input VAT and the mechanisms used to calculate apportionment have been a challenging issue since the inception of the Value-Added Tax Act No. 89 of 1991 in South Africa. This requirement to apportion input VAT has particular relevance to the retail industry due to the increase in the extension of credit which results in the receipt of taxable supplies (ordinary sales) and exempt supplies (interest income). As retailers are therefore making mixed supplies, they are required to apportion the input VAT paid on expenses. At present the standard method for input VAT apportionment in South Africa is the turnover basis however this method is not perceived as equitable by credit retailers. After an in-depth analysis of the retail industry in South Africa, its relevance to the South African economy and the impact of the requirement to apportion input VAT using the turnover method on listed companies within the South African retail industry, this paper analyses the treatment of VAT apportionment by the South African Revenue Service within the context of the Value-Added Tax Act No. 89 of 1991 and relevant South African case law. Recommendations for South Africa are then sought by studying the mechanisms for input VAT apportionment used in countries with VAT systems similar to that of South Africa. Included in this study are those countries which employ traditional VAT systems such as European Union member states and Mexico; and those countries which have implemented modern VAT systems such as New Zealand, Singapore, Australia and Canada. In addition, alternative approaches to address the root cause of the requirement to apportion input VAT used internationally are researched to the extent that these mechanisms have application to the retail industry in South Africa.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".