Tobacco taxation, illegal cigarette supply and geography: findings from the ITC Uruguay Surveys
Bibliographic record
Abstract
BACKGROUND: In Uruguay, real tobacco taxes increased significantly during 2005-2010 and 2014-2017 and decreased during 2010-2014. The effects of these tax changes on illegal and legal cigarette usage differed significantly when we compared cities in the middle and south of the country with cities on the border. OBJECTIVE: This paper analyses whether supply side factors such as geographical location, distribution networks and the effectiveness of tobacco control play a significant role in sales and use of illegal cigarettes when tobacco taxes change, particularly given the price gap between legal and lower-priced illegal cigarettes. METHODS: Using the International Tobacco Control Evaluation Project Uruguay Survey data (2008, 2010, 2012 and 2014), choices among illegal, legal and roll-your-own cigarettes are estimated as a function of smokers' geographical location, an indicator of illegal cigarette supply, and controlling for socioeconomic and demographic variables. Smoking behaviours in Montevideo, Durazno and Maldonado were compared with those in two border cities, Salto and Rivera, where illegal cigarette prevalence may differ. FINDINGS: An increase in taxes on manufactured legal and roll-your-own cigarettes increased the odds that smokers in cities near the borders and women switched down to illegal cigarettes. City geographical location, controls effectiveness and distribution networks may play a significant role in accessibility of illegal cigarettes. To improve the effectiveness of increased taxes and prices in reducing smoking, policy-makers may consider specific policies intended to reduce access to illegal cigarettes, such as ratification and effective implementation of the Protocol to Eliminate Illicit Trade in Tobacco Products of WHO.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".