Structural changes in the demand for wine in Canada
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate structural changes in the elasticity of demand for wine in the Province of Ontario and in Canada as a whole. It will provide academic researchers and practitioners with a better understanding about structural changes in the levels of wine demand elasticities over time. It will also help the relevant governments and wine business establishments in developing taxation policy and business decisions. Design/methodology/approach The Kalman filter method, in conjunction with the Chow test, is applied to investigate structural changes in the elasticity of demand for wine. The Chow test is used for primary investigations of structural changes in the elasticity of demand for wine over time, whereas the Kalman filter method provides information about the behavior of the elasticity coefficients over time. Findings The Chow test reveals that structural changes in wine demand for Ontario are more pronounced than those for Canada as a whole. The Kalman filter results indicate that increased (decreased) government taxation could be an effective tool for discouraging (encouraging) consumption of wine. The analysis of own‐price elasticity suggests that the effects of increased government taxation on wine consumption be totally unexpected. Cross‐price elasticity coefficients change their signs over time, suggesting that some of the goods that are considered to be complements may become substitutes. Income and unemployment levels have different effects on wine consumption in Ontario and in Canada as a whole. Research limitations/implications There may be variation in results by wine color (red or white), country of origin (imported or domestic), or price (premium or non‐premium). Practical implications Understanding changes in the elasticity of demand structure for wine over time would help policy makers at the provincial and federal levels come up with effective tools for controlling consumption of wine over time, including their taxation policies. For the wine business establishments, the information on consumer response is important for pricing purposes. Originality/value The Kalman filter has not previously been used to examine structural changes in the demand for wine in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".