Bibliographic record
Abstract
The 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. A sequential decision was considered. An initial decision regarding the willingness to purchase and a subsequent decision on the willingness to pay amount conditional on a positive initial decision was determined. The study employed descriptive statistical techniques to analyse primary data obtained from 208 sampled farmers in the region. Out of the sampled farmers, 52.9% expressed interest in crop insurance. The Probit model was used to estimate the mean willingness to pay (WTP) for crop insurance. The results revealed that farmers were willing to pay approximately GHc 66.2 per cropping season. A Heckman two stage approach was employed to estimate the factors influencing the WTP for crop insurance. The empirical results of the Probit model revealed that marital status, education, crop type, access to extension service, borrowing, savings and awareness of crop insurance influenced farmers’ willingness to purchase insurance. Farmers WTP amount estimated with the Ordinary Least Square regression model was shown to be influenced by variables such as marital status, other occupation, access to credit, borrowing and savings. The study recommends that with adequate and detailed information and affordable premiums, farmers will be willing to purchase insurance. Appropriate distribution channels are also recommended to incite demand for crop insurance.
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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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".