The impact of health information on wine demand : the case of Ontario
Bibliographic record
Abstract
This thesis tests the commonly-held hypothesis that information change concerning the relative health benefits of consuming red and white wine caused the worldwide switch in preferences from white wine to red wine. A significant effect of health information change on consumer choice of red versus white wine is estimated. Approximately one half of the change in demand over the period 1991 to 1998 is estimated to have been caused by a combination of the ageing of the post war "baby boom" generation and the change in health information provided by newspapers during this period. The results are based on the following steps undertaken and described in the thesis: (1) Measurement of the flow of new information about the health impact of consuming wine; (2) Development of Health Information Indicators for all Wine and for Red Wine based on the measured health information flow; and, (3) Estimate of the impact of health information change on consumption of four wine types (red and white domestic and imported wines) using a two-stage translog demand model for wine that incorporates the Health Information Indicators. The approach to measuring information change developed here is novel, however, it is an extension of the method used by others who have previously developed proxy variables for the quantity of health information change based on counts of articles. The new method involves scoring the flow of information. The score reflects both the quantity and the quality of the information provided. The implications of consumer response to information change for governments and producers are not trivial. Research such as this evaluates the consumer response to health information and provides valuable input to decisions about whether or not the government has a role to play in providing additional health information. Also, concerns about food safety have highlighted the impact of consumer purchasing decisions on consumer health, producer profits and government responsibility for reporting on health implications of consumption of certain foods. This research contributes to the refinement of the way information change is measured and incorporated into demand models. Hence, these improved methods can contribute to better-informed policy decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".