Analysing the trend of illicit tobacco in the Philippines from 1998 to 2018
Bibliographic record
Abstract
Tobacco taxation is the most effective measure to reduce cigarette consumption and consequently improve public health outcomes. It is also an important source of government revenue. The presence of an illicit tobacco market diminishes the public health and fiscal gains of cigarette levies by making cheaper non-taxed cigarettes available. To date, the research on the extent of illicit tobacco trade in the Philippines, despite its potential to inform policies for controlling the supply of illicit cigarettes, has been limited. This study provides an estimate of the size of the illicit tobacco market in the Philippines from 1998 to 2018. It employs gap analysis comparing an estimate of the survey-based adult cigarette consumption with legally sold cigarettes in the Philippines. The illicit trade estimates are contrasted with the evolution of tax changes. The results show that the illicit cigarette market share dropped by 42% from 2003 to 2008 and by an additional 79% from 2008 to 2013. In spite of the large tax increases by the Philippine government through the Sin Tax Law starting from 2013 until 2018, the illicit share in 2018 remains similar to its 1998 level of 16% of the total market. Hence, our study finds no evidence of a positive relationship between tobacco taxes and size of illicit cigarette market in the Philippines.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".