One Size Fits All? Disentangling the Effects of Tobacco Taxes, Laws, and Control Spending on Adult Subgroups in the United States
Bibliographic record
Abstract
Background : To determine the relative impact of each of the 3 state-level tobacco control policies (cigarette taxation, tobacco control spending, and smoke-free air [SFA] laws) on adult smoking rate overall and separately for adult subgroups in the United States. Methods : A difference-in-differences analysis was conducted with generalized propensity scores. State-level policies were merged with the individual-level Behavioral Risk Factor Surveillance System in 1995–2009. Results : State cigarette taxation was the only policy that significantly impacted smoking among the general adult population, with a 1-standard deviation increase in taxes (i.e., $0.68 in constant 2014 dollars) lowering the adult smoking rate by about a quarter of a percentage point. The taxation impact was consistent, regardless of the presence of, or interactions with, other policies. Taxation was also the only policy that significantly reduced smoking for some adult subgroups, including females, non-Hispanic whites, adults aged 51 or older, and adults with more than a high school education. However, other adult subgroups responded to the other 2 types of policies, either by mediating the taxation effect or by reducing smoking independently. Specifically, tobacco control spending reduced smoking among young adults (ages 18–25 years) and Hispanics. SFA laws affected smoking among men, young adults, non-Hispanic blacks, and Hispanics. Conclusions : State cigarette taxation is the single most important policy for reducing smoking among the general adult population. However, adult subgroups’ reactions to taxes are diverse and mediated by tobacco control spending and SFA laws.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".