Impact of Weather Index Insurance on Groundnut Farmers’ Technical Efficient in Senegal: A Propensity Score Matching Approach
Bibliographic record
Abstract
Irregular and low rainfall levels and drought have become important sources of low agricultural yields and agricultural incomes in sub-Saharan Africa. Weather index insurance is a financial product for climate risk management aimed at securing farmers' incomes. This paper aims to evaluate the impact of a weather index insurance project piloted with groundnut farmers in Senegal in 2015-2016 agricultural season on farmer’s technical efficiency (TE). A Stochastic Production Frontier model was used to estimate the TE scores. A matched group of beneficiaries and control farmers was determined using propensity score matching techniques to mitigate biases stemming from observed variables. The results showed that average TE is consistently higher for control farmers than the beneficiary group. Age, gender and education were found to be significantly related to technical efficiency, while membership in farmers’ association, credit, improved seeds and extension contact were not significantly related to technical efficiency. From a policy perspective, we suggest that weather index insurance programs targeting smallholder farmers in developing countries, and particularly in sub-Saharan Africa, be accompanied with education services, provision of new technologies such as high yield seeds and other best farm management practices and credit to help farmers better adapt to weather shocks and secure their production and income.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".