Effect of e-cigarette taxes on e-cigarette and cigarette retail prices and sales, USA, 2014–2019
Bibliographic record
Abstract
OBJECTIVE: To use a standardised e-cigarette tax measure to examine the impact of e-cigarette taxes on the price and sales of e-cigarettes and cigarettes in the USA. DESIGN: We used State Line versions of NielsenIQ Retail Scanner data from quarter 4 of 2014 through quarter 4 of 2019 to calculate e-cigarette and cigarette prices and sales in 23 US states. We then estimated how these outcomes are associated with standardised state-level e-cigarette taxes, controlling for state fixed effects, quarter-by-year fixed effects, cigarette taxes, other tobacco control policies and other state-level time-varying characteristics. RESULTS: A real $1 increase in the e-cigarette standardised tax increases the price of 1 mL of e-liquid between $0.43 and $0.59 depending on specification. Controlling for fixed effects and cigarette taxes, a 10% increase in e-cigarette taxes is estimated to reduce e-cigarette sales by 0.5% and increase cigarette sales by 0.1%, though both results are attenuated and statistically insignificant in a model with full controls. CONCLUSIONS: Our study finds that e-cigarette taxes increase e-cigarette retail prices by approximately half of the tax. Further, e-cigarette taxes are associated with reduced sales of e-cigarettes and increased sales of cigarettes in some specifications. Our estimates are sizably lower than from other studies using sales and survey data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".