Farm Income Stabilization and Risk Management: Some Lessons from AgriStability Program in Canada
Bibliographic record
Abstract
This paper analyzes the effectiveness and efficiency of farm income stabilization program such as AgiStability in Canada. This program intends to mitigate farm income fluctuations, which is seemingly more neutral to the farming decision than the payments that are countercyclical with price or revenue, or that are commodity specific. However reduction of income variability generate responses in farmers’ risk management strategies, and most often generate crowding out effects of other strategies such as insurance or diversification. Stochastic analysis of risks and payments is combined with a micro economic model of endogenous risk management decision under uncertainty to explore the interactions between them. The part of the program that is triggered with small margin reductions of 15-30% (frequent normal risk) is found to have the strongest crowding out effects; the most catastrophic part of the payments (when margins are negative) is paid too late for being an effective disaster assistance; the middle range part of the program enters in competition with AgriInsurance, the subsidized insurance program. In all, this program is a socially more acceptable form of supporting farmers, rather than an efficient risk management tool.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".