Costs, contracts and the narrative of prosperity: an economic analysis of smallholder tobacco farming livelihoods in Kenya
Bibliographic record
Abstract
BACKGROUND: The tobacco industry has used the alleged negative impacts on economic livelihoods for tobacco farmers as a narrative to oppose tobacco control measures in low/middle-income countries. However, rigorous empirical evidence to support or refute this claim remains scarce. Accordingly, we assess how much money households earn from selling tobacco, and the costs they incur to produce the crop, including labour inputs. We also evaluate farmers' decision to operate under contract directly with tobacco manufacturers and tobacco leaf-buying companies or to operate as independent farmers. METHODS: A stratified random sampling method was used to implement a nationally representative household-level economic survey of 585 farmers across the three main tobacco growing regions in Kenya. The survey was augmented with focus group discussions in all three regions to refine and enrich the context of the findings. RESULTS: Both contract and independent farmers experience small profit margins per acre, with contract farmers operating at a loss. Even when family labour is excluded from the calculation, income levels remain low, particularly considering the typically large households. Generally, tobacco farmers enter into contracts with tobacco companies because they have a 'guaranteed' buyer for their tobacco leaf and receive the necessary agricultural inputs (fertiliser, seeds, herbicides and so on) without paying cash up-front. CONCLUSIONS: Tobacco farming households enter into contract with tobacco companies to realise perceived economic benefits. The narrative that tobacco farming is a lucrative economic undertaking for smallholder farmers, however, is inaccurate in the context of Kenya.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".