Political economy of tobacco tax in Bangladesh: understanding policy context and implementation challenges
Bibliographic record
Abstract
BACKGROUND: Tobacco taxes are an effective method of reducing consumption, but Bangladesh's tax policy is weak and needs improvement in many areas. OBJECTIVE: This study aimed to analyse the political economy of tobacco tax design and implementation in Bangladesh. METHODS: We reviewed 45 tobacco tax documents, conducted stakeholder mapping, 12 key informant interviews and stakeholder analysis. We used thematic and content analysis approaches. RESULTS: In Bangladesh, the tobacco tax structure is complex and difficult to implement and not designed to curb tobacco consumption. The dual role of the Bangladesh government as regulator of and shareholder in the largest tobacco company weakens the formulation and implementation of tobacco taxes as health policy instruments. Tobacco is one of Bangladesh's highest revenue sectors. As such, tobacco companies yield significant indirect influence on the government and media to shape the conversation around tobacco taxes and to weaken those taxes' design and implementation. Despite significant challenges, a strong antitobacco network (including academia, civil society organisations, non-government organisations, the WHO and media actors) continues to advocate for the use of tobacco taxes to reduce tobacco consumption. CONCLUSION: Our findings illustrate a complex interplay of political economy factors that challenge the design and implementation of tobacco taxes in Bangladesh. Creating opportunities for consultation and collaboration between the Ministries of Finance and Health regarding the formulation of tobacco taxes will be very important. National action plans with multi-stakeholder involvement are essential to achieving tobacco control goals through actionable steps.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".