The Health Gains, Financial Risk Protection Benefits, and Distributional Impact of Increased Tobacco Taxes in Armenia
Bibliographic record
Abstract
—The majority of Armenian adult males smoke, yet tobacco taxes in Armenia are among the lowest in Europe and Central Asia. Increasing taxes on tobacco is one of the most cost-effective public health interventions, but many opponents often cite regressivity as an argument against tobacco taxation. We use a mixed-methods approach to study the potential regressivity of tobacco taxation and the extent to which the regressivity argument hindered increases in tobacco taxation in Armenia. First, we pursued an extended cost-effectiveness analysis (ECEA) to assess the health, financial, and distributional consequences (by consumption quintile) of increases in the excise tax on cigarettes in Armenia. We simulated a hypothetical price hike leading to a tax rate of about 75% of the retail price of cigarettes, which would be fully passed on to consumers. Second, we conducted a series of stakeholder interviews to examine the importance of the regressivity argument and identify the factors that allowed tobacco tax increases to be adopted as public policy in Armenia. We show that increased excise taxes would bring large health and financial benefits to Armenian households. Half of tobacco-related premature deaths and 27% of associated poverty cases averted would be concentrated among the bottom 40% of the population. Though regressivity was raised as a concern at the initial stages of the policy adoption process, our qualitative stakeholder analysis indicates that the recent accession to the Eurasian Economic Union and the fiscal constraints faced by the government created a window of opportunity for tobacco taxation to be placed on the policy agenda and adopted as government policy, and the ECEA findings were an important input into the process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".