Taxing customer loyalty programmes : an international perspective
Bibliographic record
Abstract
Customer loyalty programmes (CLPs) have become prevalent in various economies, which is indicative of a change in the nature of the economic transactions occurring between businesses and customers. Interestingly, research indicates that there has been no change in how these transactions are taxed in South Africa that corresponds with the change in business. In general, the South African tax system provides for tax revenue to fund the expenditure incurred by the South African government. Therefore, the taxation of CLP rewards in the hands of customers should increase tax revenues, which South Africa urgently needs as the government has consistently spent more than it has have received in recent years. Furthermore, it is acknowledged that the South African government needs additional tax revenue because of the damages caused by the COVID-19 pandemic, which has caused severe disruptions to economies over the world. This study contributes by using a doctrinal research methodology to analyse the tax treatment of CLPs internationally for both direct and indirect tax from the perspective of the customer and the CLP provider. The findings revealed that although international jurisdictions such as Australia, Canada, New Zealand, the UK and the USA have made headway in taxing CLPs in direct response to their increased prevalence in the commercial environment, the tax provisions established and the administration thereof can be improved. While research has been done on the indirect tax (consumption tax levied on the supply of goods or services under a CLP), employee benefits tax (employees taxed on the value of CLP rewards received from employers) and income tax implications for the provider (sales revenue from the CLP transaction is fully included in the income of the provider) relating to CLPs in Australia, Canada, New Zealand, the UK, and the USA, it does not address the tax implications of CLPs in the hand of the customer (the rewards). Simply stated, the existing legislation focuses on taxing flight rewards and employee benefit rewards and is insufficient. Consequently, further research on the taxation of CLPs is necessary to contribute to this area of taxation. Therefore, using the findings of this study, South Africa has an opportunity to devise an effective, concise and administratively efficient tax reform for CLPs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".