Factors affecting variability in farm and off‐farm income
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the factors affecting the relative variability in farm and off‐farm income for Canadian farm operators. Design/methodology/approach Variability of farm and off‐farm income is analyzed using a dataset of 17,000 farm operators from 2001 to 2006. Relative ranking of the coefficients of variation (CV) for farm and off‐farm income are compared across farm types and are regressed against factors conditioning the variations. Findings Greater reliance on farm income results in lower (greater) relative variability in farm (off‐farm) income. Larger commercial operations experience larger farm income volatility because they are less risk averse or they can manage more risk. Diversification and off‐farm employment appear to be risk management strategies for commercial operations. Research limitations/implications Government payments have a small, positive effect on farm and off‐farm income variability, indicating this support leads farmers to take on more risky activities and/or reduce the use of self‐insurance activities. Results could also be due to the lag between the time of the income reduction and the time in which the aid is received. Further research is necessary to decipher the effects of government support on farm decisions. Practical implications The results on relative variation in the farm and off‐farm income across farm type raises questions about whether government programs should target specific operations. Originality/value While income variation remains a focus of public policy, factors affecting its variability are not well‐understood. Studies have examined the level of farm income and the decision to participate in off‐farm employment but none has examined the variance in both income sources.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".