Re‐exploring the early relationship between teenage cigarette and e‐cigarette use using price and tax changes
Bibliographic record
Abstract
In 2016, the Surgeon General used longitudinal cohort studies to conclude that youth e-cigarette use is strongly associated with cigarette use. We re-evaluate data from the period of time before the writing of the Surgeon General report, using quasi-experimental methods, and reach the opposite conclusion. We study contemporaneous and intertemporal effects of e-cigarette and cigarette price and tax changes. Our price variation comes from 35,000 retailers participating in the Nielsen Retail Scanner data system. We match price and tax variation to survey data on current use of e-cigarettes and cigarettes for over 94,000 students between grades 6 and 12 in the National Youth Tobacco Survey (NYTS) for years 2011-2015. We find evidence that e-cigarettes and cigarettes are same-period economic substitutes. Coefficient estimates (while imprecisely estimated) also suggest potentially large positive effects of past e-cigarette prices on current cigarette use, indicating intertemporal economic substitution. Our findings raise doubts about the conclusion of government-sponsored reports that e-cigarettes and cigarettes are strongly positively associated. We recommend revisiting and possibly amending this conclusion.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".