The political economy of sugar-sweetened beverage taxation in Latin America: lessons from Mexico, Chile and Colombia
Bibliographic record
Abstract
BACKGROUND: In Latin America, total sales of sugar-sweetened beverages (SSBs) continue to rise at an alarming rate. Consumption of added sugar is a leading cause of diet-related non-communicable diseases (NCDs). Coalitions of stakeholders have formed in several countries in the region to address this public health challenge including participation of civil society organizations and transnational corporations. Little is currently known about these coalitions - what interests they represent, what goals they pursue and how they operate. Ensuring the primacy of public health goals is a particular governance challenge. This paper comparatively analyses governance challenges involved in the adoption of taxation of sugar-sweetened beverages in Mexico, Chile and Colombia. The three countries have similar political and economic systems, institutional arrangements and regulatory instruments but differing policy outcomes. METHODS: We analysed the political economy of SSB taxation based on a qualitative synthesis of existing empirical evidence. We identify the key stakeholders involved in the policy process, identified their interests, and assess how they influenced adoption and implementation of the tax. RESULTS: Coalitions for and against the SSB taxation formed the basis of policy debates in all three countries. Intergovernmental support was critical to framing the SSB tax aims, benefits and implementation; and for countries to adopt it. A major constraint to implementation was the strong influence of transnational corporations (TNCs) in the policy process. A lack of transparency during agenda setting was notably enhanced by the powerful presence of TNCs. CONCLUSION: NCDs prevention policies need to be supported across government, alongside grassroots organizations, policy champions and civil society groups to enhance their success. However, governance arrangements involving coalitions between public and private sector actors need to recognize power asymmetries among different actors and mitigate their potentially negative consequences. Such arrangements should include clear mechanisms to ensure transparency and accountability of all partners, and prevent undue influence by industry interests associated with unhealthy products.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".