Farm Income Variability and Off-Farm Diversification in Canadian Agriculture
Bibliographic record
Abstract
For a majority of farm families and operators in OECD countries, off-farm or non-farm occupations have become a significant source of income and a major determinant of their well being. This study investigates the use of off-farm employment as a risk management tool by farm operators. A two-part model is developed to estimate the impact of farm income risk on the decision to participate in the off-farm labour market and the level of off-farm employment income. Longitudinal farm level data for about 30, 000 Canadian farms from 2001 to 2006 are used for this study. The variability of farm market revenue is found to positively affect the likelihood of off-farm work and the level of off-farm employment income, in particular for operators of larger commercial farms. The apparent ability of a significant number of operators of large farms to increase their resilience and coping capacity through off-farm employment income suggest the presence of substantial interactions between off-farm income and farm income stabilization policies. Consequently, the focus of agricultural policies on risk management and income stabilization reinforces the linkages between rural and agricultural policies. In particular, it appears that policies designed to facilitate access to off-farm work or to enhance off-farm opportunities, such as rural development programs, could contribute to achieve some objectives underlying agricultural income stabilization programs. These results reinforce the need for coherent rural and agricultural policies, and raises questions about the desirable balance between placed based rural policies and sector specific agricultural policies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".