The purchase sources of and price paid for cigarettes in sixEuropean countries: Findings from the EUREST-PLUS ITCEurope Surveys
Bibliographic record
Abstract
INTRODUCTION: Tobacco tax policies have been proven to be effective in reducing tobacco consumption, but their impact can be mitigated through price-minimizing behaviours among smokers. This study explored the purchase sources of tobacco products and the price paid for tobacco products in six EU member states. METHODS: Data from Wave 1 of the EUREST-PLUS ITC Europe Survey collected from nationally representative samples of adult smokers in Germany, Greece, Hungary, Poland, Romania and Spain (ITC 6E Survey) were used. The ITC 6E Survey sample, conducted in 2016, randomly sampled 6011 adult cigarette smokers aged 18 years or older. Information on purchase sources of tobacco was examined by country. The difference in reported purchase price by purchase location (store vs non-store/other) was analysed using linear regression for each country. RESULTS: Tobacco purchasing patterns and sources varied widely between countries. Non-store/other purchases were very rare in Hungary (0.1%) while these types of purchases were more common in Germany (5.1%) and Poland (8.6%). Reported prices of one standard pack of 20 cigarettes were highest in Germany (4.80€) and lowest in Hungary (2.45€). While non-store purchases were only made by a minority of smokers (>10% in all countries), the price differential was considerable between store and non-store/other sources, up to 2€ per pack in Greece and in Germany. CONCLUSIONS: The results suggest a huge variation of purchasing sources and price differentials between store and non-store purchasing sources across the six EU member states examined. While the cross-sectional data precludes any causal inference, supply chain control through licensing as introduced in Hungary and the lack of such measures in the other countries might nevertheless be a plausible explanation for the large differences in the frequency of non-store purchases observed in this study.
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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) | 0.000 | 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) | 0.000 | 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".