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Record W7074661838

An economic analysis of product quality within the Canadian dairy industry

2004· other· en· W7074661838 on OpenAlexfundaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2004
Typeother
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
FundersForeign Affairs and International Trade CanadaCanadian Dairy Commission
KeywordsDairy industryRationalization (economics)Quality (philosophy)Product (mathematics)Production (economics)Consolidation (business)Economies of scaleWork (physics)Milk production
DOInot available

Abstract

fetched live from OpenAlex

Dairy production often occurs in distorted, highly protected environments, which affect both the price and quality of the end product. Current Canadian regulations serve to protect the local dairy industry with production quotas and high import tariffs. By raising the price of milk, these regulations may induce Canadian cheese manufacturers to find ways to substitute away from Canadian milk inputs to lower costs and maximize profit. Given that some alternative ingredients can be imported without tariffs, it is natural to examine the link between protective policies for Canadian milk and the quality of processed milk products such as cheese. Along with the regulations, other changes have occurred in the Canadian cheese processing industry. The structure of the Canadian dairy processing industry has undergone a significant rationalization process in the last decade. The shift into fewer and larger plants has been necessary to achieve the efficiency level and economies of scale to remain competitive. Over the past decade, technological change has induced processors to substitute alternative inputs for the traditional ingredients, which in turn has affected the quality of final products such as cheese. There has been a great deal of research on the price and efficiency effects of Canada's supply management system for dairy production, but little (or no) work has been done on the effects of supply management on the quality of processed milk products at the consumer level. I develop a theoretical and empirical model to examine the effect of Canadian regulations, technological change and industry consolidation on cheese quality. The analysis helps explain current quality trends in the Canadian cheese and dairy industry, as well as the social cost of Canadian supply management in milk products. This thesis uses theoretical and empirical analysis to examine the effects of supply management on quality in the dairy industry, thereby filling a gap in the literature. Specifically, for the theoretical model I examine the effect of supply management on cheese quality, quantity produced and number of firms in the industry using some comparative statics analysis. For the empirical model, I use the derived demand equation for casein as a function of the reduced form demand for the outputs (cheddar, specialty cheese, yogurt and ice cream), the marginal costs (milk price, wage and metal) and the number of plants to determine the effect of high levels of protection on product quality. Most of the empirical results match the theoretical ones. In both models, evidence suggests that supply management negatively affects cheese quality. In the theoretical model, supply management, by increasing marginal costs, increases the quantity of extended cheese produced. In the empirical model, the quantity of casein imports increases in response to an increase in the price of milk. The results of the empirical model suggest that approximately 9.8% of specialty cheese is produced using casein. Furthermore, I estimate the amount of milk displaced by casein if milk price increases one dollar, 722.65 hi of milk per month; and the implied spillover effect on Canadian dairy farmers, which, on average, decreases their rents by 33,033 dollars per month.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations0
Published2004
Admission routes2
Has abstractyes

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