Consumer Misinformation and the Brand Premium: A Private Label Blind Taste Test
Bibliographic record
Abstract
We run in-store blind taste tests with a retailer's private label food brands and the leading national brand counterparts in three large CPG categories.In a survey administered during the taste test, subjects self-report very high expectations about the quality of the private labels relative to national brands.However, they predict a relatively low probability of choosing them in a blind taste test.Surprisingly however, an overwhelming majority systematically chooses the private label in the blinded test.During the week after the intervention, the tested private label product market shares increase by 15 share points, on top of a base share of 8 share points.However, the effect diminishes to 8 share points during the second to fourth week after the test and to 2 share points during the second to fifth month after the test.Using a structural model of demand, we show these effects survive controls for point-of-purchase prices, purchase incidence, and the feedback effects of brand loyalty.We also find that the intervention increases the preference for the private label brands, and that it decreases the preference for the national brands, relative to the outside good.The findings are consistent with a treatment effect of information on demand where the memory for this information decays slowly over time.Alternative explanations to the information treatment are ruled out.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".