The impact of tobacco taxation on illicit cigarette trade in selected low- and middle-income countries
Bibliographic record
Abstract
Objective The importance of monitoring and deterring the illicit trade of tobacco is illuminated by the recent ratification of the WHO’s Protocol to eliminate illicit trade of Tobacco Products. While the correlatory relationship between tobacco taxation and illicit trade of cigarettes have been measured globally, there is a paucity of research on the impact that a change in tax and price policy measures will have on illicit trade. Measuring this impact objectively is crucial to countering biased estimates aimed at influencing public opinion and policy makers. The objective of the project was to use the exogeneity of tobacco taxation and price policy changes in four low- and middle-income countries (the Gambia, Georgia, Mongolia and South Africa) as natural experiments to measure this impact. Methods Using primary-collected household survey data and the discarded pack collection methodology, we measured illicit trade before and after policy changes. Results In Mongolia, South Africa and Georgia, the policy had no impact on illicit trade. Mongolia observed a decline in their illicit trade during this period, from 15.4% (April 2017) to 6.3% (May/June 2018). In the Gambia, last minute changes to planned policy changes meant that we could only collect one wave of cross-sectional data. However, the data revealed that illicit trade is low at 0.9%-7.3%. We will also relate methodological lessons and recommendations from the studies, specifically recommendations for rapid response research by policymakers on this topic. Conclusions The evidence indicates that tobacco taxation does not have an impact on the illicit trade of cigarettes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".