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Record W2901956891 · doi:10.1186/s12992-018-0413-2

From transit hub to major supplier of illicit cigarettes to Argentina and Brazil: the changing role of domestic production and transnational tobacco companies in Paraguay between 1960 and 2003

2018· article· en· W2901956891 on OpenAlexafffund
Roberto Iglesias, Benoît Gomis, Natalia Carrillo Botero, Philip L. Shepherd, Kelley Lee

Bibliographic record

VenueGlobalization and Health · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsSimon Fraser University
FundersNational Cancer InstituteNational Institutes of HealthSimon Fraser University
KeywordsSocial policyBusinessPublic healthProduction (economics)Tobacco industryEnvironmental healthEconomic growthPolitical scienceInternational tradeEconomicsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Paraguay has reportedly been a major transit hub for illicit tobacco products since the 1960s, initially to supply markets in Argentina and Brazil and, more recently, other regional markets and beyond. However, to date there has been no systematic analysis, notably independent of the tobacco industry, of this trade including the roles of domestic production and transnational tobacco companies (TTCs). This article fills that gap by detailing the history of Paraguay's illicit cigarette trade to Brazil and Argentina of TTC products and Paraguayan production between 1960 and 2003. The effective control of illicit cigarette flows, under Article 15 of the World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC) and the Protocol to Eliminate the Illicit Trade in Tobacco Products, requires fuller understanding of the changing nature of the illicit trade. METHODS: We systematically searched internal industry documents to understand the activities and strategies of leading TTCs in Paraguay and subregion over time. We also mapped illicit trade volume and patterns using US government and UN data on the cigarette trade involving Paraguay. We then estimated Paraguay's cigarette production from 1989 to 2003 using tobacco leaf flows from the United Nations Commodity Trade Statistics Database (UN Comtrade). RESULTS: We identify four phases in the illicit tobacco trade involving Paraguay: 1) Paraguay as a transit hub to smuggle BAT and PMI cigarettes from the U.S. into Argentina and Brazil (from the 1960s to the mid-1970s); 2) BAT and PMI competing in north-east Argentina (1989-1994); 3) BAT and PMI competing in southern and southern-east Brazil (mid to late 1990s); and 4) the growth in the illicit trade of Paraguayan manufactured cigarettes (from the mid- 1990s onwards). These phases suggest the illicit trade was seeded by TTCs, and that the system of supply and demand on lower priced brands they developed in the 1990s created a business opportunity for manufacturing in Paraguay. Brazil's efforts to fight this trade, with a 150% tax on exports to Latin American countries in 1999, further prompted supply of the illicit trade to shift from TTCs to Paraguayan manufacturers. CONCLUSION: This paper extends evidence of the longstanding complicity of TTCs in the illicit trade to this region and the consequent growth of Paraguayan production in the 1990s. Our findings confirm the need to better understand the factors influencing how the illicit tobacco trade has changed over time, in specific regional contexts, and amid tobacco industry globalization. In Paraguay, the changing roles of TTC and domestic production have been central to shifting patterns of illicit supply and distribution since the 1960s. Important questions are raised, in turn, about TTCs efforts to participate as legitimate partners in global efforts to combat the problem, including a leading role in data gathering and analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.324
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2018
Admission routes2
Has abstractyes

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