Farm Support Payments and Risk Balancing: Implications for Financial Riskiness of Canadian Farms
Bibliographic record
Abstract
Risk balancing refers to the balancing of business risk (BR) and financial risk (FR) by firms through their investment and borrowing decisions. Assuming the concept holds, a decrease in income variability (BR) prompts the firm to incur greater debt levels thereby increasing FR. Reducing (BR), which continues to be the central objective of Canadian agricultural policy through programs such as Canadian Agricultural Income Stabilization Program (CAIS)/AgriStability, may lead farmers to take on more FR than they would take otherwise, which, in turn, increases the risk of equity loss. However, it is not known whether Canadian business risk management (BRM) programs offset BR as intended, and whether any potential reduction leads to increased FR (risk balancing) and possibly higher levels of overall risk for individual farm operations. This paper represents the first attempt to shed light on whether Canadian BRM programs fail to reduce farm risk as a result of farmers’ risk balancing behavior using a longitudinal farm‐level data set from Ontario. Results are mixed: (1) BRM payments reduce BR for beef farms but not for field crops farms (though the latter result may be due to the lack of data on Crop Insurance payments); (2) risk balancing holds particularly for the larger farms, and (3) BRM programs overall have no significant effect on the likelihood of increased debt use for either sector, on average; however, participation in CAIS/AgriStability increases the probability that farms take on more debt than they would take otherwise for both sectors. Further analysis is needed to determine whether BRM programs increase the probability of default for farms.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".