The illicit cigarette market in six South African townships
Bibliographic record
Abstract
BACKGROUND: We estimate the size of the illicit cigarette market in low socioeconomic areas in South Africa before and after a tax increase. In 2018, the real excise tax increased by 3% and the value-added tax (VAT) rate increased from 14% to 15%. Thus, the real tax on cigarettes increased by 4%. METHODS: A total of 2427 smokers were interviewed over two rounds of data collection (1234 before the tax increase and 1193 after). Data were collected in six townships across four of South Africa's nine provinces. Smokers were asked about their most recent cigarette purchase. Cigarettes purchased for R1 (US$0.08) or less per stick are presumed illicit, based on a threshold price, which includes production costs and taxes. RESULTS: In 2017 and 2018 respectively, 34.6% and 36.4% of smokers in the sample purchased illicit cigarettes. The increase in the proportion of illicit purchases was not statistically significant. Smokers with relatively low socioeconomic status, those who have low levels of education and those who are older or unemployed are most likely to purchase illicit cigarettes. CONCLUSIONS: The illicit cigarette trade in South African townships is widespread. The government should implement an independent track and trace system to curb tax evasion. This would reduce the availability of illicit cigarettes, improve public health and increase excise tax collection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".