Information Provision in a Vertically Differentiated Competitive Marketplace
Bibliographic record
Abstract
This paper examines the interaction of information provision, product quality, and pricing decisions by competitive firms to explore the following question: in a competitive market where consumers face uncertainty about product quality and/or their preference for quality, which firms—those that sell higher- or lower-quality products—have the higher incentive to provide what type of information? We find that while the higher-quality firm should always provide information resolving consumer uncertainty on product quality, the lower-quality firm under certain conditions will have the higher incentive to and will be the one to provide information resolving consumer uncertainty about their quality preferences. In the analysis, we trace the latter result to competition and to free-riding on the information provision. Specifically, in a monopoly market or when consumer free-riding is restricted by the costliness of store visits, the lower-quality firm would have a lower incentive to provide information resolving consumer preference uncertainty than otherwise. The model is also adapted to examine product returns as a possible strategy of information provision.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".