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Testing for Processor Market Power in the Markets for Cattle and Hogs in Canada

2003· article· en· W1983625482 on OpenAlexaffvenueabout
Kwamena K. Quagrainie, James R. Unterschultz, Michele M. Veeman, Scott R. Jeffrey

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsAgriculture Food and Rural DevelopmentUniversity of Alberta
Fundersnot available
KeywordsLivestockMarket powerProfit (economics)Feeder cattleBusinessAgricultural economicsBeef industryProductivityFed cattleEconomicsAgricultural scienceMarket economyAnimal scienceMicroeconomicsBiology

Abstract

fetched live from OpenAlex

Deviations of livestock input prices from processor marginal value product are usually interpreted as an indication of the application of market power by the meat packing industry. However, market power depends on economic conditions that can influence the behavior of meat packers in the market for cattle and hogs. An industry‐level translog profit function is applied to data on the Canadian finished cattle and hog markets and industry‐wide oligopsony market power functions are estimated. The estimates suggest beef packers exercised a small but sustained amount of market power in the Canadian finished cattle market from 1978 to 1997. This is not the case in the market for hogs, which was competitive from 1960 to 1997. Application of market power in packers’purchases of farm animals decreased with increases in the utilization of domestic supply of slaughter animals and with increased levels of livestock exports. Livestock productivity increases appear to have significantly enhanced oligopsony power in packers’purchases of farm animals. The analysis suggests that beef processors may exert market power when cattle prices are relatively higher.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.157
Teacher spread0.132 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2003
Admission routes3
Has abstractyes

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