Bibliographic record
Abstract
Introduction Specific excise rates for tobacco products increased in Ukraine by 40% in 2016, 40% in 2017 and 29.8% in 2018. The aim of the research is to estimate the impact of these increases on tobacco prices, consumption, and revenue. Methods Monthly data published by Ukrainian official bodies were analyzed. Results In 2016, the average price increased by 7% while inflation was 14%. In 2017 and 2018, the average price increased by 35% and 28% with inflation of 14% and 10%. Cigarette sales increased from 73 billion sticks in 2015 to 76 billion in 2016 but then decreased to 67 billion in 2017 and 55 billion cigarettes in 2018. Tobacco excise revenue increased in 2016 by 59%: from 22 billion to 33 billion UAH, while in 2017, it increased to 40 billion UAH (by 20%) and in 2018 – to 43 billion UAH (by 8%). In 2018, the revenue was 15 billion and 28 billion UAH in January-June and July-December respectively. Conclusions In three years excise increased by 154% and it caused an increase in price by 84%, reduction in sales by 25% and an increase in revenue by 94%. So, in long term, tobacco excise increase did have the expected impact. However, in short term, the impact can vary as the tobacco industry uses sophisticated tactics to distort the results of tobacco taxation: price wars and price over-shifting, forestalling and others. Such tactics should be taken into account when forecasting the possible impacts of proposed tax increases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".