The impact of e-cigarette and cigarette prices on e-cigarette and cigarette sales in California
Bibliographic record
Abstract
Although numerous studies have examined the impact of cigarette prices on cigarette demand, research examining the impact of e-cigarette and cigarette prices on e-cigarette demand is relatively limited. This study estimated the impact of e-cigarette and cigarette prices on e-cigarette and cigarette sales in California. Using the 2012-2017 Nielsen Retail Scanner Data, we constructed e-cigarette prices, cigarette prices, and per capita e-cigarette and cigarette sales by year, quarter, and Nielsen scantrack market in California. E-cigarettes were categorized as disposable or reusable. Separate fixed-effects models estimated the impact of e-cigarette and cigarette prices on per capita disposable e-cigarette, reusable e-cigarette, and cigarette sales controlling for year, quarter, market, and smoke-free air law coverage. Average prices were $5.86 per pack of 20 cigarettes, $9.80 per disposable e-cigarette, and $19.11 per reusable e-cigarette. When prices of disposable e-cigarettes, reusable e-cigarettes, and cigarettes increased by 1%, per capita sales of the products decreased by 0.37%, 0.20%, and 0.21% respectively. Cigarette prices were positively associated with per capita sales of reusable e-cigarettes, indicating reusable e-cigarettes are substitutes for cigarettes. Reusable e-cigarette prices were positively associated with per capita sales of disposable e-cigarettes, indicating disposable e-cigarettes are substitutes for reusable e-cigarettes. No statistically significant association was found between disposable/reusable e-cigarette prices and cigarette sales. Our results suggest that raising prices of disposable e-cigarettes, reusable e-cigarettes, and cigarettes such as via tobacco excise tax increases would result in reduced sales for the products. Policymakers should consider the substitution between e-cigarettes and cigarettes when designing tobacco control policies.
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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 | 0.001 |
| 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 | 0.001 |
| 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".