Rainfall Shocks, Livelihood Diversification and Welfare: Evidence from Rural Togo
Bibliographic record
Abstract
Abstract Theory suggests that rural farm households exposed to greater risk should diversify their income portfolios to reduce variation in welfare caused by adverse events such as rainfall shocks. Rainfall shocks, however, can also degrade asset stocks and make diversification more costly. Using a panel dataset of small-scale cattle farmers from rural Togo and long-term historical rainfall data, we first examine whether and in what direction rural portfolio diversification is related to historical rainfall shocks. Second, we test whether diversification is associated with stabilised welfare in the face of recent rainfall shocks. Our results show that historical rainfall shock exposure reduces income diversification. In terms of mitigation, we find that diversification is generally not effective in insulating against welfare losses. We conclude that there is a need to stimulate rural diversification as a means of building resilient livelihoods to cope with increasing weather variability by strengthening credit, agricultural and market institutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".