Cigarette tax avoidance and evasion: findings from the International Tobacco Control Policy Evaluation (ITC) Project
Bibliographic record
Abstract
BACKGROUND: Decades of research have produced overwhelming evidence that tobacco taxes reduce tobacco use and increase government tax revenue. The magnitude and effectiveness of taxes in reducing tobacco use provide an incentive for tobacco users, manufacturers and others, most notably criminal networks, to devise ways to avoid or evade tobacco taxes. Consequently, tobacco tax avoidance and tax evasion can reduce the public health and fiscal benefit of tobacco taxes. OBJECTIVES: First, this study aims to document, using data from the International Tobacco Control Policy Evaluation Project (ITC), levels and trends in cigarette users' tax avoidance and tax evasion behaviour in a sample of 16 low-, middle- and high-income countries. Second, this study explores factors associated with cigarette tax avoidance and evasion. METHODS: We used data from ITC surveys conducted in 16 countries to estimate the extent and type of cigarette tax avoidance/evasion between countries and across time. We used self-reported information about the source of a smoker's last purchase of cigarettes or self-reported packaging information, or similar information gathered by the interviewers during face-to-face interviews to measure tax avoidance/evasion behaviours. We used generalised estimating equations to explore individual-level factors that may affect the likelihood of cigarette tax avoidance or evasion in Canada, the USA, the UK and France. FINDINGS: We found prevalence estimates of cigarette tax avoidance/evasion vary substantially between countries and across time. In Canada, France and the UK, more than 10% of smokers reported last purchasing cigarettes from low or untaxed sources, while in Malaysia some prevalence estimates suggested substantial cigarette tax avoidance/evasion. We also found important associations between household income and education and the likelihood to engage in tax avoidance/evasion. These associations, however, varied both in direction and magnitude across countries.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".