Conflicting interests, institutional fragmentation and opportunity structures: an analysis of political institutions and the health taxes regime in Pakistan
Bibliographic record
Abstract
Pakistan is the world's fifth most populous country, with large segments of its population at risk from non-communicable diseases caused by consumption of harmful products, including tobacco and sugar-sweetened beverages. Even though evidence exists that increased taxes on harmful products leads to consumption reductions as well as increased revenues, Pakistan's health taxes remain low. We seek to understand the reasons for the deficient health tax regime. Much of the existing literature emphasises industry tactics, resources and motivations. We take a different approach and instead focus on political institutions in Pakistan which could help explain deficiencies in the health taxes regime. We employed a mixed method design. We conducted: (1) a detailed analysis of media content, (2) semistructured interviews with key stakeholders (and attended relevant meetings) and (3) an analysis of primary and secondary literature, including legal and policy documents. We identify two key aspects of Pakistan's political institutions which may help explain deficiencies in health taxes. First, we identified structural issues in the design and functioning of key institutions responsible for health taxes, including with respect to federalism, intraelite conflict, interagency coordination and intra-agency fragmentation. Second, we found evidence of an entrenchment of industry interests within governmental institutions, which are characterised by weak frameworks for regulating conflicts of interest. We conclude that gaps and conflict within political institutions, owing to weak design, instability and fragmentation, create political opportunity for industry actors to influence the system to advance their interests. The findings of this research indicate towards needed interventions.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".