An Empirical Investigation of Private Label Supply by National Label Producers
Bibliographic record
Abstract
Private labels (PLs) are ubiquitous in several categories, including groceries, apparel, and appliances. However, existing empirical work has not examined the differential impact of various upstream supply arrangements for PL products or the strategic motives for PL supply. To do so requires one to model the interaction between private and national label (NL) products both upstream and downstream while accounting for strategic behavior on the part of manufacturers and retailers and retaining essential differences between NL and PL products. We build a model that satisfies these requirements and lets us answer our two research questions: First, can an NL firm profit from being an outsourced PL supplier? Second, what are the upstream and downstream impacts of different PL supply arrangements? We answer these questions by modeling private labels as homogenous products at wholesale, but as differentiated products at retail. In contrast, national label products are differentiated at both wholesale and retail levels. Using structural model estimates for fluid milk in a major metropolitan area, we conduct three counterfactual experiments. We find that both NL producers and retailers profit from adding private labels. We also find that a vertically integrated supply of PL leads to lower prices for end consumers.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".