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Record W3132611931 · doi:10.22434/ifamr2020.0116

Canadian dairy regulations as a driver of foreign direct investment: the case of Saputo

2021· article· en· W3132611931 on OpenAlexaffabout
James Rude, Ellen Goddard

Bibliographic record

VenueThe International Food and Agribusiness Management Review · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsCompetitor analysisRestrictivenessEconomic rentBusinessInvestment (military)Control (management)CashProduct (mathematics)Cash flowEconomicsFinanceIndustrial organizationMarketingMarket economy

Abstract

fetched live from OpenAlex

Across North America dairy processors are facing financial difficulties, yet a Canadian processor continues to grow through acquisitions of its competitors. This company, Saputo, grew up in a highly regulated marketplace where input raw milk supplies are restricted, input prices are inflated and imports of final product are restricted. This study asks if the Canadian system of dairy supply management prodded and assisted Saputo into making acquisitions at home and abroad. An empirical model estimates the probability of Saputo making acquisitions as a function of factors influenced by supply management and control variables accounting for Saputo’s financial performance. The results indicate that cash flows, which may be increased by regulatory rents, and a measure of restrictiveness of the Canadian milk supply, both are statistically significant positive determinants of the probability that Saputo will make an acquisition. On average over the estimation period removing the Canadian supply managed regime would reduce the probability of acquisitions by 7%. The implication is that the Canadian system is losing investment and employment opportunities by retaining its restrictive regulatory system.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2021
Admission routes2
Has abstractyes

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