Towards a greater understanding of the illicit tobacco trade in Europe: a review of the PMI funded ‘Project Star’ report
Bibliographic record
Abstract
BACKGROUND: Following a legal agreement with the European Union (EU), Philip Morris International (PMI) commissions a yearly report ('Project Star', PS) on the European illicit cigarette trade from KPMG, the global accountancy firm. METHODS: Review of PS 2010 report. Comparison with data from independent sources including a 2010 pan-European survey (N=18,056). FINDINGS: Within PS, data covering all 27 EU countries are entered into a model. While the model itself seems appropriate, concerns are identified with the methodologies underlying the data inputs and thus their quality: there is little transparency over methodologies; interview data underestimate legal non-domestic product partly by failing to account for legal cross-border sales; illicit cigarette estimates rely on tobacco industry empty pack surveys which may overestimate illicit; and there is an over-reliance on data supplied by PMI with inadequate external validation. Thus, PMI sales data are validated using PMI smoking prevalence estimates, yet PMI is unable to provide sales (shipment) data for the Greek islands and its prevalence estimates differ grossly from independent data. Consequently, comparisons with independent data suggest PS will tend to overestimate illicit cigarette levels particularly where cross-border shopping is frequent (Austria, Finland, France) and in Western compared with Eastern European countries. The model also provides data on the nature of the illicit cigarette market independent of seizure data suggesting that almost a quarter of the illicit cigarette market in 2010 comprised PMI's own brands compared with just 5% counterfeited PMI brands; a finding hidden in PMI's public representation of the data. CONCLUSIONS: PS overestimates illicit cigarette levels in some European countries and suggests PMI's supply chain control is inadequate. Its publication serves the interests of PMI over those of the EU and its member states. PS requires greater transparency, external scrutiny and use of independent data.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".