Market Concentration in Canadian Beef Packing: The Wrong Target for Competition Policy
Bibliographic record
Abstract
During the COVID-19 pandemic, market concentration within the Canadian beef packing industry was highlighted as a policy issue that should be addressed using competition policy. This paper finds though the market is indeed highly concentrated, concentration alone is unlikely to give packers the ability to exert oligopsony power in live cattle markets nor is it maintained through anti-competitive conduct. Beef packing has reached this level of concentration primarily due to the strong influence of economies of scale, whereby firms are incentivized to increase scale in order to remain efficient. Yet, during normal market conditions firm concentration does not allow Canadian packers to exert market power because of competition from U.S.-based processors for live cattle inputs. Literature analyzing market power during the BSE-crisis, as well as analysis of beef sector Lerner Indices during the COVID-19 pandemic, do suggest that Canadian packers will exert oligopsony when market conditions allow. In terms of competition policy, market concentration alone is neither a violation of the law nor a necessarily undesirable outcome. To ensure Canadian beef packers cannot exert market power in live cattle markets, policymakers would be better served to ensure the U.S.-Canada border remains open to trade in cattle.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".