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Record W2144151537 · doi:10.3138/cpp.2013-062

Milked and Feathered: The Regressive Welfare Effects of Canada's Supply Management Regime

2015· article· en· W2144151537 on OpenAlexaffvenueabout
Ryan Cardwell, Chad Lawley, Di Xiang

Bibliographic record

VenueCanadian Public Policy · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWelfareEconomicsProduction (economics)TariffCounterfactual thinkingAgricultural economicsMarket accessAlmost ideal demand systemIndex (typography)International economicsBusinessGeographyMarket economyMicroeconomics

Abstract

fetched live from OpenAlex

The production and trade of dairy and poultry products in Canada are controlled by a system of supply management (SM). Output is regulated with production quotas, and imports are restricted through a system of tariff-rate quotas. Many of Canada's trading partners are seeking better access to Canadian dairy and poultry markets in negotiations over proposed preferential trade agreements. These pressures have renewed debate about the future of SM in Canada. We investigate one criticism of SM: that high prices for dairy and poultry products impose regressive distributional effects on Canadian consumers. We apply the Exact Affine Stone Index demand model to data from the Canadian Food Expenditure Survey to estimate consumer responses to price changes for dairy and poultry products. Parameters from the demand model are used to generate welfare comparisons between the current SM regime and a counterfactual liberalized market. Canada's SM policies are highly regressive, imposing a burden of approximately 2.3 percent ($339) of income per year on the poorest households, compared to 0.5 percent ($554) for the richest households. The burden is larger for households with children.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.534

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 head0.011
GPT teacher head0.171
Teacher spread0.159 · 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 designNot applicable
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

Citations39
Published2015
Admission routes3
Has abstractyes

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