The Influence of Market Returns and Government Payments on Canadian Farmland Values
Bibliographic record
Abstract
This study estimates the impact of changes in market returns and government payments on farmland values across Canada using data from 1959 to 2009. A recursive simultaneous equation model is estimated to account for the counter-cyclical relationship between market returns and government payments. The results indicate that farmland values are more responsive to changes in market returns than in government payments, but both are important drivers of land values. The elasticity of land values with respect to government payments is lower than has been observed in the United States. In addition, the partial decoupling of government payments has not reduced their impact on farmland values.--------------------------------------------Revised and published as: Vyn, Richard J., Zahoor Ul Haq, Jeevika Weerahewa and Karl D. Meilke. 2012. “The Influence of Market Returns and Government Payments on Canadian Farmland Values. Journal of Agricultural and Resource Economics 37(2):199-212.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".