An Empirical Analysis of Assortment Similarities Across U.S. Supermarkets
Bibliographic record
Abstract
This paper examines pairwise assortment similarities at U.S. supermarkets to understand how assortment composition and size are related to underlying factors that describe local store clientele, local competitive structure, and the retail outlets' characteristics. The top-selling items, which cumulatively make up 50% of sales, are sold at nearly every store, but other items are viewed as optional. We find that, within states, supermarkets owned by the same chain carry similar assortments and that the composition of their clientele and the presence of competing stores have effects on assortment similarity that are an order of magnitude smaller than ownership structure. In contrast, we find that, across states, supermarkets owned by the same chain do version their assortment. We explain this difference using extant work on the minimal efficient scale of supermarkets and on local demand effects. Furthermore, we investigate the distribution and role of regional brands. We find that regional brands are primarily distributed by small regional chains or independent stores. “Value” regional brands are primarily distributed by supermarket firms without store brands, whereas the distribution of “premium” regional brands is unrelated to the presence of store brands. We discuss our findings in the context of modeling assortment decisions and manufacturers designing distribution policies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".