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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".