The choice of discount brand cigarettes: a comparative analysis of International Tobacco Control surveys in Canada and the USA (2002–2005)
Bibliographic record
Abstract
BACKGROUND: Increasing tobacco taxes to increase price is a proven tobacco control measure. This article investigates how smokers respond to tax and price increases in their choice of discount brand cigarettes versus premium brands. OBJECTIVE: To estimate how increase in the tax rate can affect smokers' choice of discount brands versus premium brands. METHODS: Using data from International Tobacco Control surveys in Canada and the USA, a logit model was constructed to estimate the probability of choosing discount brand cigarettes in response to its price changes relative to premium brands, controlling for individual-specific demographic and socioeconomic characteristics and regional effects. The self-reported price of an individual smoker is used in a random-effects regression model to impute price and to construct the price ratio for discount and premium brands for each smoker, which is used in the logit model. FINDINGS: An increase in the ratio of price of discount brand cigarettes to the price of premium brands by 0.1 is associated with a decrease in the probability of choosing discount brands by 0.08 in Canada. No significant effect is observed in case of the USA. CONCLUSIONS: The results of the model explain two phenomena: (1) the widened price differential between premium and discount brand cigarettes contributed to the increased share of discount brand cigarettes in Canada in contrast to a relatively steady share in the USA during 2002-2005 and (2) increasing the price ratio of discount brands to premium brands-which occurs with an increase in specific excise tax-may lead to upward shifting from discount to premium brands rather than to downward shifting. These results underscore the significance of studying the effectiveness of tax increases in reducing overall tobacco consumption, particularly for specific excise taxes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".