A novel approach to estimating the prevalence of untaxed cigarettes in the USA: findings from the 2009 and 2010 international tobacco control surveys
Bibliographic record
Abstract
BACKGROUND: Increases in tobacco taxes are effective in reducing tobacco consumption, but because of the addictive nature of cigarettes, smokers often seek out less expensive sources of cigarettes. The objective of this study is to estimate the prevalence of cigarette packs that are untaxed by the state in which the participant resides in a sample of US smokers at two time points. METHODS: Data for this study were taken from the 2009 and 2010 waves of the International Tobacco Control United States Survey. Members of this nationally representative cohort of smokers were invited to send us an unopened pack of their usual brand of cigarettes. RESULTS: In 2009, 318 packs were received from 401 eligible participants (79%). In 2010, 366 packs were received from 491 eligible participants (75%). In total, 20% of the packs in 2009 and 21% in 2010 were classified as untaxed by the participant's state of residence. The prevalence of untaxed cigarettes was higher in states with higher-excise taxes. Smokers who do not have a plan to quit were significantly more likely to have sent back a pack that was classified as untaxed by the participant's state of residence. CONCLUSIONS: One in five packs were untaxed with rates higher in states with higher-excise taxes. It is unclear whether these estimates differ from the actual prevalence of cigarettes that are untaxed by a smoker's state of residence. Harmonisation of excise tax rates across all 50 US states might be one method of reducing or eliminating the incentive to avoid or evade these taxes.
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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 | 0.002 | 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) | 0.000 | 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".