The distributional effects of tobacco tax increases across regions in Mexico: an extended cost-effectiveness analysis
Bibliographic record
Abstract
BACKGROUND: Several studies have shown the beneficial effects of tobacco fiscal policy, but distributional effects have been less examined, especially at the subnational level. The objective of this study is to analyse the distributional effects of a one-peso tobacco tax increase (roughly equivalent to tripling the current excise tax) on health, poverty, and financial outcomes at the subnational level in Mexico. METHODS: We employ an extended cost-effectiveness analysis that estimates life-years gained, smoking attributable deaths averted, treatment costs averted, number of persons avoiding poverty and catastrophic health expenditures, and additional tax revenues by income group across five regions. RESULTS: With the one-peso tax increase (or 44% price increase), about 1.5 million smokers would quit smoking across the five regions, resulting in nearly 630 thousand premature deaths averted and 12.6 million life years gained. The bottom income quintile would gain three times more life years gains than the top quintile (ratio 3:1), and the largest gain for the most deprived would occur in the South (ratio 19:1), the region with the highest poverty incidence. Costs averted and additional tax revenues would reach 44.6 and 16.2 billion pesos, respectively. Moreover, 251 thousand individuals would avoid falling into poverty, including 53.2 in the lowest income quintile, and 563.9 thousand would avoid catastrophic health expenditures. Overall, the bottom income group would obtain 26% of the life years gained and 24% of the cost averted, while only paying 3% of the additional tax revenue. CONCLUSIONS: The most significant gains from a substantial cigarette price increase would be for the poorest 20%, especially in the South, the most impoverished region of Mexico. Therefore, tobacco taxes are an opportunity for governments to advance in equity and towards the achievement of sustainable development goals on non-communicable diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".