A Risk Management Tool or an Investment Strategy? Understanding the Unstable Farm Insurance Demand via a Gain-Loss Framework
Bibliographic record
Abstract
Risk management tools are at the core of farm policy in many developed countries, and their effectiveness relies on the appropriate mechanism design. This study developed a gain-loss framework based on prospect theory to examine the reasons for the declining use of the main risk management tool offered to farmers despite growing volatility in returns. Using the administrative Ontario Farm Income Database (OFID) 2003 to 2013 and taking the beef sector as the example, this study found that the gain-loss framework predicts and explains the dynamic program participation pattern better than the conventional expected utility framework. Farms were found to be more likely to stay enrolled in the program when they experienced either larger gains or losses in revenue compared to previous years, suggesting that they were using the insurance programs both as an investment strategy (to seek government subsidies) and as a risk management tool (to protect against business risks), though the effects of revenue losses and hence risk management needs were stronger than gains. In addition, the program payment history and farm characteristics also shape the dynamic participation patterns. The findings increased the understanding of the drivers of withdrawal behavior associated with government-sponsored business risk management programs.
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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 | 0.001 | 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) | 0.000 | 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".