Review of Agricultural Economics—Volume 24, Number 1—Pages 196–207 Belief in Disaster Relief and the Demand for a Public–Private Insurance Program
Bibliographic record
Abstract
Producers ’ demand for a public–private crop insurance program in the Netherlands is sur-veyed. A novel aspect was the inclusion of the producer’s belief in disaster relief. Despite emphatic assertions that future governmental involvement would only be directed at a hy-pothetical insurance program, the participation decision was negatively and significantly associated with the producer’s belief about the availability of disaster relief in the future. So, if governments continue to provide (free) ad hoc disaster relief, an important incentive to participate would be severely undermined. However, the conditional decision about the amount was not affected. Agricultural production is usually considered as rather volatile due to a wholeseries of stochastic factors. This is because, among other factors, climatic con-ditions have an important impact on production. Throughout the years, various risk transfer tools have been used to cope with these production risks. For ex-ample, various forms of crop insurance and ad hoc disaster relief programs exist in the United States and Canada to cover losses as a result of adverse weather conditions (Goodwin and Smith; Barnett). In Europe, Spain has a comprehen-sive, multiperil crop insurance scheme. A form of disaster relief program is ap-plied in most other European Union (EU) member states whereby governments compensate farmers for serious and widespread losses. In general, large govern-mental subsidies are provided to support these programs. World Trade Organi-zation (WTO) agreements (increasingly) restrict the amount of subsidies that is allowed (Ritson and Harvey; Swinbank). The current insurance programs seem legitimate in the green box (i.e., “the allowed forms of support”) of the WTO Marcel van Asseldonk and Miranda Meuwissen are researchers at the Institute for
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".