“We think globally”: the rise of Paraguay’s Tabacalera del Este as a threat to global tobacco control
Bibliographic record
Abstract
BACKGROUND: Leading transnational tobacco companies (TTCs) began to expand their operations in Latin America in the 1960s. This included legally exporting their cigarettes to Paraguay during the 1960s which, in turn, were illegally re-exported to Argentina and Brazil. By the 1990s, competition between BAT and PMI for this lucrative illicit market, focusing on low-priced brands, prompted manufacturing in Paraguay. Paraguayan manufacturing rapidly grew after the introduction of a new cigarette export tax in Brazil in 1999. METHODS: We systematically searched Truth Tobacco Industry Documents (TTID) to understand the activities and strategies of leading TTCs in Paraguay and subregion over time. We applied the analytical framework of Lee and Eckhardt (2017) to understand Tabesa's global business strategy. We searched the websites of TTCs and Tabesa for activities since the mid 2000s to understand how the companies publicly describe these strategies. We used the United Nations Commodity Trade Statistics Database (UN Comtrade) as an independent source to crosscheck statements by Tabesa executives about export markets. We contextualized and triangulated our findings with 42 key informant interviews. RESULTS: Tabesa became the largest cigarette manufacturer in Paraguay, and one of the largest companies in the country, through complicity in the illicit trade. Enabled by market conditions created by leading TTCs, and a permissive regulatory environment in Paraguay, evidence suggests Tabesa had become a major source of illicit cigarettes across Latin America and beyond by the late 2000s. Although Brazil continues to account for the bulk of Tabesa's revenues, findings suggest that the company is aspiring to compete with TTCs in markets worldwide through legal and illegal sales. CONCLUSION: There is a need for fuller understanding of the risks to global tobacco control from local companies aspiring to compete with TTCs. The rise of Tabesa is part of the changing nature of the illicit trade in tobacco products which must be taken into account in implementing the Framework Convention on Tobacco Control (FCTC) and its Protocol to Eliminate Illicit Trade in Tobacco Products. Potential conflicts of interest concerning Tabesa illustrate the importance of FCTC Article 5.3 on industry interference. There is also an urgent need to address the lack of independent and rigorous data on the illicit tobacco trade in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".