Understanding the Structure of Canadian Farm Incomes in the Design of Safety Net Programs<sup>1</sup>
Bibliographic record
Abstract
The effectiveness of safety net programs in meeting their purpose depends implicitly on the nature of farm profitability distributions. This study provides an empirical characterization of farm operating profit distributions and assesses the implications for Canadian safety net programs. Pooled time series data from the Statistics Canada Tax Data Program and the Farm Financial Survey is queried across a range of farm types and provinces, with quartile distributions of Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) within four farm‐size categories analyzed. The results show that regardless of farm type or province, there is greater variation in operating profit within a sales category than there is across the sales categories, and that the range in operating profit increases with size, revealing some very profitable small farms and unprofitable large farms. Thus, the discussion of the social value of farm stabilization programs ought not to be focused on farm size alone. L'efficacité avec laquelle les programmes de protection du revenu atteignent leurs objectifs dépend implicitement de la nature des distributions de probabilités des fermes. La présente étude établit une caractérisation empirique des distributions du bénéfice d'exploitation agricole et évalue les répercussions sur les programmes de protection du revenu au Canada. Des données chronologiques tirées du Programme des données fiscales (PDF) et de l'Enquête financière sur les fermes (EFF) de Statistique Canada sont totalisées par type de ferme et par province, y compris des distributions par quartile du résultat avant intérêts, impôts et dotations aux amortissements (EBITDA) de quatre catégories de taille de ferme. Les résultats ont montré que, sans égard au type de ferme ou à la province, la variation du bénéfice d'exploitation au sein d'une même catégorie de ventes était supérieure à la variation du bénéfice d'exploitation observée entre les différentes catégories de ventes et que l'étendue du bénéfice d'exploitation augmentait avec la taille, révélant des fermes de petite taille très rentables et des fermes de grande taille non rentables. Par conséquent, la discussion sur la valeur sociale des programmes de stabilisation du revenu agricole ne devrait pas s'appuyer sur la taille de la ferme uniquement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".