Disentangling regional trade agreements, trade flows and tobacco affordability in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: In principle, trade and investment agreements are meant to boost economic growth. However, the removal of trade barriers and the provision of investment incentives to attract foreign direct investments may facilitate increased trade in and/or more efficient production of commodities considered harmful to health such as tobacco. We analyze existing evidence on trade and investment liberalization and its relationship to tobacco trade in Sub-Saharan African countries. METHODS: We compare tobacco trading patterns to foreign direct investments made by tobacco companies. We estimate and compare changes in the Konjunkturforschungsstelle (KOF) Economic Globalization measure, relative price measure and cigarette prices. RESULTS: Preferential regional trade agreements appear to have encouraged the consolidation of cigarette production, which has shaped trading patterns of tobacco leaf. Since 2002, British American Tobacco has invested in tobacco manufacturing facilities in Nigeria, Kenya and South Africa strategically located to serve different regions in Africa. Following this, British America Tobacco closed factories in Ghana, Rwanda, Uganda, Mauritius and Angola. At the same time, Malawi and Tanzania exported a large percentage of tobacco leaf to European countries. After 2010, there was an increase in tobacco exports from Malawi and Zambia to China, which may be a result of preferential trade agreements the EU and China have with these countries. Economic liberalization has been accompanied by greater cigarette affordability for the countries included in our analysis. However, only excise taxes and income have an effect on cigarette prices within the region. CONCLUSIONS: These results suggest that the changing economic structures of international trade and investment are likely heightening the efficiency and effectiveness of the tobacco industry. As tobacco control advocates consider supply-side tobacco control interventions, they must consider carefully the effects of these economic agreements and whether there are ways to mitigate them.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".