An Empirical Investigation of Private Label Supply by National Label Producers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it