The association between excise taxes and smoking and vaping transitions–Findings from the 2016–2020 ITC United States surveys
Bibliographic record
Abstract
BACKGROUND: While a growing number of studies examined the effect of e-cigarette (EC) excise taxes on tobacco use behaviors using cross-sectional surveys or sales data, there are currently no studies that evaluate the impact of EC taxes on smoking and vaping transitions. METHODS: Using data from the US arm of the 2016-2020 International Tobacco Control Four Country Smoking and Vaping Survey (ITC 4CV), we employed a multinomial logit model with two-way fixed effects to simultaneously estimate the impacts of cigarette/EC taxes on the change in smoking and vaping frequencies. RESULTS: Our benchmark model suggests that a 10 % increase in cigarette taxes led to an 11 % reduction in smoking frequencies (p < 0.01), while EC taxes did not have a significant effect on smoking frequencies. CONCLUSION: Our findings suggest that increasing cigarette taxes may serve as an effective means of encouraging people who smoke to cut back on smoking or quit smoking. The impact of increasing EC taxes on smoking transitions is less certain at this time.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".