Explaining Why Farmers Grow Tobacco: Evidence From Malawi, Kenya, and Zambia
Bibliographic record
Abstract
INTRODUCTION: Tobacco production continues to increase in low- and middle-income countries creating complications for tobacco control efforts. There is the need to understand and address the global tobacco leaf supply as a means of decreasing tobacco consumption and improving farmers livelihoods in line with Article 17 of the WHO Framework Convention on Tobacco Control. This study aims to understand the reasons why farmers grow tobacco and identify factors that influence these reasons. METHODS: Primary survey data (N = 1770) collected in Kenya, Malawi, and Zambia in the 2013-2014 farming season. Data analysis uses both descriptive and multinomial logistical regression methods. RESULTS: Majority of farmers started and are currently growing tobacco because they believed it was the only economically viable crop. Compared with Malawi, farmers in Kenya and Zambia have a 0.2 and 0.4 lower probability of growing tobacco, respectively because they perceive it as the only economically viable crop, but a 0.04 and 0.2 higher probability of growing tobacco, respectively because they believe it is highly lucrative. There are district/county differences in the reasons provided with some districts having a majority of the farmers citing the existence of a ready market or incentives from the tobacco industry. Statistically significant factors influencing these reasons are the educational level and age of the household head, land allocated to tobacco and debts. CONCLUSION: There is the need to address the unique features of each district to increase successful uptake of alternative livelihoods. One consistent finding is that farmers' perceived economic viability contributes to tobacco growing. IMPLICATIONS: This study finds that perceived economic viability of tobacco is the dominant factor in the decisions to grow tobacco by smallholder farmers in Malawi, Kenya, and Zambia. There is the need to more deeply understand what contributes to farmers' perceived viability of a crop. Understanding and addressing these factors may increase the successful uptake of alternative livelihoods to tobacco. Furthermore, this study demonstrates that a one-size fits all alternative livelihood intervention is less likely to be effective as each district has unique features affecting farmers' decisions on growing tobacco.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".