The Economic Geography of Kenyan Tobacco Farmers’ Livelihood Decisions
Bibliographic record
Abstract
INTRODUCTION: The narrative of prosperous economic livelihood of tobacco farmers in Kenya as alleged by the tobacco industry deserves challenge as evidence increasingly suggests that smallholder tobacco farmers are making little or no profits. Article 17 of the World Health Organization Framework Convention on Tobacco Control encourages viable alternative livelihoods for tobacco farmers. There is little evidence, however, on how tobacco farmers make livelihood choice decisions. METHODS: A total of 527 purposefully selected smallholder tobacco farmers in Kenya from three main tobacco-growing regions participated in a 2017 economic livelihood survey. Geo-economic data were matched to surveyed farmers' Global Positioning System coordinates to estimate each farmer's access to nearby economic centers. Ownership of cell phones or radios was also used to estimate farmers' virtual access to nearby economic activities to understand better the role of information. Multivariate logistic regressions were used to control socioeconomic status and self-reported activity in nearby economic centers. RESULTS: Tobacco farmers rarely live within 10 km of an economic center. Results suggest that the further away farmers live from economic centers, the less likely they are to grow tobacco, but more likely to grow tobacco under contract. Also, farmers owning a cell phone or radio are not only less likely to grow tobacco, but also to not engage in farming under contract if they do grow tobacco. CONCLUSIONS: Physical and virtual access to nearby economic activities is significantly associated with tobacco farmers' livelihood choice decision and should be taken into consideration by decision makers while developing interventions for FCTC Article 17. IMPLICATIONS: Smallholder tobacco farmers in lower-income countries are making little or no profits, but few studies have been conducted to illuminate what perpetuates tobacco production, with such studies urgently needed to support governments to develop viable alternative livelihoods for tobacco farmers. This study suggests that geographic and technological factors that shape farmers' economic decisions can help policy makers tailor alternative livelihood policies to different regional contexts and should be a focus of future research in this area.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".