Killing the sacred dairy cow? Consumer preferences for plant‐based milk alternatives
Bibliographic record
Abstract
Abstract We examine the relationship between demand for plant‐based milk alternatives and dairy milk using data from a survey and a discrete choice experiment completed by 902 Canadians. Our survey results show that most individuals who drink milk alternatives also consume dairy milk, and preferences for milk alternatives vary across consumption contexts. Using our experimental data, we estimate consumer preferences via a mixed logit model. These preferences are then used to calculate a set of price elasticities and to predict market shares for dairy milk in counterfactual simulations that exclude select milk alternatives. Although the elasticity of dairy milk with respect to the price of milk alternatives is relatively low, we predict that between 57% and 83% of respondents who purchase milk alternatives would have purchased dairy milk if milk alternatives were not available, depending on the counterfactual. We also show that preferences for milk alternatives are linked to age and food values. Finally, we use our experiment to test the impact of additional information about the impact of dairy milk on animal welfare, the environment, and nutrition on preferences for milk alternatives. The treatment effects are generally statistically insignificant. [EconLit Citations: L66, Q18, D12]
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".