Bibliographic record
Abstract
We study how market structure within a product category varies across retail formats. Building on the literature on internal market structure, we estimate a joint store and brand choice model where the loading matrix of brand attributes are allowed to be retail format specific. The approach allows us to recover brand maps for different retail formats while controlling for the short-term marketing mix activities at these stores and the self-selection of households that frequent a particular format. The model is applied to consumer panel data from two product categories, where households are observed to make purchases across three store types: high-end grocery store, traditional supermarket, and large everyday low pricing (EDLP) formats. Our results show strong correlations between the marketing mix sensitivities, store format preference, and unobserved brand attributes. These correlations translate into significant differences in market structure across retail formats and in the direction and size of preference vectors for unobservable brand attributes. We find a tight clustering of brands at the EDLP format, whereas brands are found to compete in distinct subgroups at other stores. Results show that failure to account for retail format effects can substantially bias the understanding of underlying market structure and could lead to incorrect implications in applications such as new product entry.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".