Socioeconomic and country variations in cross-border cigarette purchasing as tobacco tax avoidance strategy. Findings from the ITC Europe Surveys
Bibliographic record
Abstract
BACKGROUND: Legal tobacco tax avoidance strategies such as cross-border cigarette purchasing may attenuate the impact of tax increases on tobacco consumption. Little is known about socioeconomic and country variations in cross-border purchasing. OBJECTIVE: To describe socioeconomic and country variations in cross-border cigarette purchasing in six European countries. METHODS: Cross-sectional data from adult smokers (n=7873) from the International Tobacco Control (ITC) Surveys in France (2006/2007), Germany (2007), Ireland (2006), The Netherlands (2008), Scotland (2006) and the rest of the UK (2007/2008) were used. Respondents were asked whether they had bought cigarettes outside their country in the last 6 months and how often. FINDINGS: In French and German provinces/states bordering countries with lower cigarette prices, 24% and 13% of smokers, respectively, reported purchasing cigarettes frequently outside their country. In non-border regions of France and Germany, and in Ireland, Scotland, the rest of the UK and The Netherlands, frequent purchasing of cigarettes outside the country was reported by 2-7% of smokers. Smokers with higher levels of education or income, younger smokers, daily smokers, heavier smokers and smokers not planning to quit smoking were more likely to purchase cigarettes outside their country. CONCLUSIONS: Cross-border cigarette purchasing is more common in European regions bordering countries with lower cigarette prices and is more often reported by smokers with higher education and income. Increasing taxes in countries with lower cigarette prices, and reducing the number of cigarettes that can be legally imported across borders could help to avoid cross-border purchasing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".