Price pass‐through in the U.S. beef industry: Implications of feedlot capacity utilization
Bibliographic record
Abstract
Abstract Transmission of prices, profits, and more generally, economic well‐being across vertically connected sectors of agriculture have a long history of interest—arguably of most current interest in meat and livestock markets. Disruptions in live animal harvesting, especially from COVID‐19, have corresponded with substantial market adjustment and hence elevated interest in inner‐industry relationships, including from policymakers. This paper's main contribution is assessing how price changes in the U.S. feedlot industry manifest in feeder cattle markets. We use Ricardian rent theory as a framework to quantify price transmission by testing how price fluctuations actually pass through the supply chain versus theoretical expectations. We posit that the capacity utilization of feedlots changes because of market shocks, impacting price relationships. In the empirical model, when feedlot capacity utilization rates are below the 65% critical point, we find that both fed to feeder cattle and corn to feeder cattle pass‐through rates are higher than hypothesized. When feedlot capacity utilization rates are high (>65%), estimated pass‐through rates are lower and not statistically different from Ricardian rent theory. Understanding how prices pass through in the beef industry can help inform policy discussions about beef market competitiveness and promote efficient resource allocation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".