The demand for processed meat in Canada: an application of the almost ideal demand system
Bibliographic record
Abstract
The objective of this research is to conduct an econometric analysis of Canada’s demand for processed meat based on panel data. The four meat categories included in the system of equations used in the estimation are fresh and frozen beef, pork, and poultry as well as processed meat. The random effect panel method was estimated by generalized least squares (GLS) and the complete demand system with a linear approximation of an almost ideal demand system (LA/AIDS). The expenditure elasticity of processed meat was estimated to be positive but not with much confidence which suggests processed meat (as defined by Statistics Canada’s Survey of Household Spending) may a normal good. The Marshallian own-price elasticities estimated using two methods were at -.568 and -.976 with some confidence suggesting a tax to lower processed meat consumption may need to be relatively high to reduce consumption. Some evidence was identified that the consumption of processed meat causes health costs. Processed meat, beef and pork are estimated to be mild substitutes as measured by their substitution elasticities. This study shows that the Canadian consumption of processed meat is comparable to other meats but the demand needs to be further investigated before recommendations regarding a processed meat tax are made.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".