Grain Transportation Policy Reform in Western Canada
Bibliographic record
Abstract
Abstract In 2016, the Minister of Transport released a review of the Canada Transportation Act that recommended the eventual removal of the Maximum Revenue Entitlement (MRE) governing the movement of grain by rail. The MRE is sometimes referred to as a revenue cap, but the MRE sets a maximum average allowable freight rate over all tonnes of grain moved. There is no a priori cap to the revenue or amount of grain moved. We argue that the MRE permitted railways to set prices to promote more efficient movement. This paper evaluates the consequences of ending the MRE in the context of Canada's evolving grain supply chain and its interaction with railways. Assuming cartel behavior among railways, we find that the removal of the MRE for grain transportation could increase rates by $42 to $73/t, depending on the market power of grain companies. Farmer prices were lowered by basis shifts larger than $73 in 2013–14, but a long‐term rate increase of this magnitude could generate regulatory or strategic responses that were not part of our model. In our worst case scenario, farmers lose 90% of their current surplus. Even modest amounts of market power can generate significant farm price reductions. En 2016, le ministre du Transport déposait le rapport de l'examen de la Loi sur les transports au Canada. Y était proposée l'éventuelle élimination du Revenu admissible maximal (RAM) gouvernant le transport du grain par chemin de fer. L'on qualifie souvent le RAM de plafond du revenu, mais le RAM établit un maximum taux moyen autorisé de fret sur toutes les tonnes de grain acheminées. Aucun plafond n'est fixé pour le revenu ni la quantité de grain transporté. Nous croyons que le RAM a permis aux lignes ferroviaires d'établir leur prix et de promouvoir un transport plus efficient. Cet article évalue les conséquences de l'élimination du RAM au sein du contexte évolutif de la chaine canadienne d'approvisionnement en grain, et ses interactions avec les lignes ferroviaires. Supposant des comportements anticoncurrentiels parmi les lignes ferroviaires, nous concluons que l'élimination du RAM pour le transport du grain pourrait augmenter les taux de 42 $ à 73 $ par tonne, en fonction du pouvoir des sociétés céréalières sur le marché. Les prix agricoles ont été baissés de marges de base dépassant les 73 $ en 2013–14, mais une augmentation du taux de cette envergure à long terme pourrait générer des réactions réglementaires ou stratégiques ne faisant pas partie de notre modèle. Notre pire scénario verrait les exploitations agricoles perdre 90 % de leurs surplus actuels. Même un modéré pouvoir sur le marché peut générer de considérables réductions des prix agricoles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".