Mitigating Tax Evasion by improving the organizational structure of VAT on Digital Imports into South Africa
Bibliographic record
Abstract
The South African Value-Added Tax (VAT) Act exhibits an illogical structure for digital imports. The complexity of digital import taxation creates uncertainty and has an impact on compliance, resulting in tax avoidance and diminished tax revenues. This study analysed the organisational structure of digital imports in the VAT Act as a legally complex element. This study established that the organisation of the VAT on digital imports complicates legislation and introduces ambiguity, leading to increased tax evasion and compliance, as well as administrative expenses. This study employed existing guidelines to simplify the VAT Act and improve the organisational structure regarding the VAT implications of digital imports. The methods used included a qualitative research technique utilising a doctrinal approach, as well as applied research. This study is the first to apply Hassan, Bornman and Sawyer’s VAT simplification framework to South African digital imports. The guidelines developed by these authors encompass section grouping, headings and subheadings, and explicit signposting, which were implemented in this article to effectively demonstrate and simplify the VAT consequences for digital imports. A logically structured VAT framework will improve clarity in digital import compliance, thereby reducing tax evasion. Therefore, this study contributes to tax compliance theory by proposing that a reduction in complexity and improvement in transparency mitigate tax evasion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".