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Record W2257084460 · doi:10.1111/cjag.12057

Revealed Comparative Advantage and Half‐a‐Century Competitiveness of Canadian Agriculture: A Case Study of Wheat, Beef, and Pork Sectors

2014· article· en· W2257084460 on OpenAlexaffvenueabout
Rakhal Sarker, Shashini Ratnasena

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAgricultureFood sectorAgricultural economicsNonmarket forcesIndex (typography)BusinessAgricultural scienceEconomicsGeographyMarket economyFactor market

Abstract

fetched live from OpenAlex

While the competitiveness of the Canadian agri‐food sector attracted significant research attention since the mid 1980s, no study has measured competitiveness using longitudinal data and determined empirically the drivers of competitiveness. This article contributes to the competitiveness literature by measuring the international competitiveness of wheat, beef, and pork sectors in Canada using data from 1961 to 2011 and by determining the drivers of competitiveness. Our results demonstrate that Canada enjoys competitiveness in the wheat sector but not in the beef or pork sectors. Empirical results also suggest that the competitiveness of the Canadian wheat sector can be enhanced if the cost seed in Canada relative to that in the United States is lower. Similarly, if the relative labor cost of meat processing is lower, the competitiveness of both beef and pork sectors in Canada will be enhanced. Exchange rates are important drivers of international competitiveness of beef and pork sectors in Canada. The decoupled farm policies in Canada do not have a significant impact on the competitiveness of wheat and pork sectors in Canada. While our empirical results seem to indicate that major agri‐food policies in Canada may not have delivered the expected benefits, the NRCA index did not allow us to separate the contributions of market and nonmarket failures to competitive performance of Canadian agriculture.Bien que la compétitivité du secteur agroalimentaire canadien ait suscité un vif intérêt de la part des chercheurs depuis le milieu des années 1980, aucune étude ne l'a évaluée à l'aide de données longitudinales et aucune n'en a déterminé de manière empirique les moteurs. Le présent article contribue à la littérature sur la compétitivité en ce sens qu'il présente une évaluation de la compétitivité des secteurs du blé, du bœuf et du porc canadiens sur la scène internationale effectuée à l'aide de données couvrant la période 1961–2011 et en détermine les moteurs. Selon les résultats de notre étude, le Canada jouit d'une compétitivité dans le secteur du blé uniquement. Les résultats empiriques autorisent aussi à penser qu'il serait possible d'accroître la compétitivité du secteur du blé canadien si le coût des semences au Canada était plus faible que celui aux États‐Unis. De façon similaire, une baisse du coût relatif de la main‐d’œuvre dans les usines de transformation de la viande contribuerait à accroître la compétitivité des secteurs du bœuf et du porc canadiens. Les taux de change sont des moteurs importants de la compétitivité des secteurs du bœuf et du porc canadiens sur la scène internationale. Au Canada, les politiques agricoles découplées n'ont pas de répercussions significatives sur la compétitivité des secteurs du blé et du porc canadiens. Bien que les résultats empiriques de notre étude indiquent que les principales politiques agroalimentaires au Canada n'ont peut‐être pas procuré les avantages escomptés, l'indice de l'avantage comparatif révélé normalisé (ACRN) n'a pas permis de distinguer les échecs du marché et ceux de la réglementation quant à la performance de la compétitivité du secteur canadien de l'agriculture.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.175
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations68
Published2014
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicGlobal Trade and CompetitivenessFrench-language works237,207