Farmers willingness to pay for crop insurance: evidence from Eastern Ghana
Bibliographic record
Abstract
Crop insurance is a risk management tool with the potential of dealing with risk more efficiently. This study uses a dichotomous contingent valuation method to elicit the willingness to pay for crop insurance among cereal farmers in the Eastern region of Ghana. The study employed descriptive statistical techniques to analyze primary data obtained from 208 sampled farmers in the region. Approximately, 52.9% of the farmers expressed interest in crop insurance. A Heckman two stage approach was employed to estimate the factors influencing the WTP for crop insurance. The results revealed that farmers were willing to pay approximately $18.36 per cropping season. The demand for insurance was found to be negatively correlated with the premium amounts suggesting that it is a normal good. The Probit model revealed that marital status and awareness of crop insurance had a positive correlation with the willingness to purchase insurance. The coefficient for education was positive and statistically significant at the 5% significance level in relation to farmers’ WTP. Borrowing and savings were, however, found to be negative and significant at the 1% and 10% levels respectively in relation to WTP. Farmers’ WTP amount estimated with the interval regression model was shown to be influenced by key variables such as age, crop type, farm size, farm experience, income, weather variation, savings and access to extension agents. Innovative insurance products and the appropriate distribution channels are also recommended to incite demand for crop insurance.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".