Government support, transfer efficiency, and moral hazard within heterogeneous regions in Canadian Agriculture
Bibliographic record
Abstract
This study estimates the transfer efficiency of government payments on Canadian agriculture. Three measures of efficiency are used: (1) the capitalization of support into farmland values, (2) the rate of income stabilization, and (3) the effect of past government support on the variance of income. We derive transfer efficiency estimates by applying panel econometric techniques to provincial time series data. With regard to the capitalization formula, we find that the capitalization of government into farmland values is homogeneous across provinces. We estimate the rate of capitalization at approximately $11.76 for every $1.00 increase in government support. A substantial amount of heterogeneity was found for the stabilization equation. Four homogeneous regions were found for Canada: 1) Maritimes; 2) Central Canada; 3) Western Canada; and 4) British Columbia. Among these heterogeneous regions, substantial differences in the stabilization coefficient estimates were found. There is also evidence of a trend in government payments in the Maritimes, indicating evidence of rent seeking. The variance of income was found to be correlated with past levels of government support, indicating that government support may be causing a moral hazard problem.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".