Are E‐Cigarettes Substitutes or Complements to Combustible Cigarettes Among Youths? Evidence From Canada
Bibliographic record
Abstract
Existing evidence on whether e-cigarettes are substitutes or complements to combustible cigarettes is limited and mixed. We revisit this question using nationally-representative Canadian survey data over 14 years (2004-2017) and difference-in-differences methods that exploit the staggered adoption of e-cigarette Minimum Legal Age (MLA) laws in Canadian provinces between 2015 and 2017. We study the laws' effects not only on youth smoking but also on smoking initiation and cessation to shed light on the mechanisms through which these laws affect youth smoking. We find that the relationship between e-cigarette use and combustible cigarette use depends on smoking status of youths. While the MLA laws reduced smoking initiation among youth non-smokers, they made existing youth smokers less likely to quit smoking. Our results highlight the tradeoffs between lower smoking initiation and lower smoking cessation associated with policies that aim to reduce youth e-cigarette use.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".