An economic evaluation of a crop insurance programme for small-scale commercial farmers in South Africa
Bibliographic record
Abstract
Hail insurance is provided by the private sector in South Africa but crop insurance (drought insurance) programmes, after a promising start, failed to attract customers. A crop insurance programme (drought) for small-scale commercial farmers, who are not yet paying tax, has been recommended to government. The purpose in this research is to study the economic viability of such a programme drawing on the US experience. The US programme is well developed but heavily subsidised. During 1998 US growers paid $900 million in premiums while during 1995- 98 the US government spent $1.2 billion per year on subsidies. An area insurance plan (farmers are insured as a group) is shown to be more appropriate for small farmers growing dryland field crops such as maize because risk is systemic (drought related) while adverse selection, moral hazard etc are overcome. Individual crop insurance will not be viable due to the cost of farm visits (verification of claims) and the non-availability of information. As a large part of the cost to government goes to administration of crop insurance it is recommended that an Income Equalisation Deposit (IED) scheme for small growers receive serious consideration with the government making a contribution as for example in Canada.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".