Investment behavior of Canada egg producers
Bibliographic record
Abstract
Purpose The purpose of this paper is to document the level of risk in the Québec egg sector (conventional and specialty eggs) and analyze the optimal choices of Québec egg producers that must allocate limited resources to production of different types of eggs. Design/methodology/approach A quadratic programming approach applied to expected mean-variance models is used to analyze the impact of risk on decision to invest when the resources must be allocated to different type of production that have different risk levels. The model is calibrated using monthly data from 2009 to 2016. Findings Results indicated multiple uncertainty sources (technological, cost of production, price of eggs) that vary according to the types of eggs. Given risk aversion parameters, producer would favor production modes with the lowest producers’ price variance, which correspond to free-run eggs. Results also indicated that in response to a greater intensity of risk aversion, the course of action producers may choose is to increase the relative production of free-run eggs. Research limitations/implications The empirical limits of this research are found in the lack of quality data on producer prices and costs for specialty eggs. Future research could explore the relationship between the growing impact of egg for processing, which price is based on the US price, and its relationship with specialty eggs. Practical implications The findings of the study will be useful for policy makers and managers of eggs supply chain. This is important, given the recent announcement by Canadian’s large retailers and fast food companies to increase cage free eggs offering and, in some cases, eventually only selling these types of eggs. Originality/value This study adds to the understanding of the role of risk and uncertainty in the investment decision of egg producers and different mode of production, as well as in the development of the growing production of specialty eggs in Canada. It fills a gap in the literature regarding the impact of risk in Canadian egg production. This gap is likely explained by the perception of a lack of risk in this supply managed sector in Canada and its small size relative to other supply managed sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".