Price discrimination and quality improvement
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
This paper models quality improvements when multiple quality levels can sell, owing to differences in consumers' valuations of quality improvements. Firms can collude to price discriminate, so that consumers with high valuations pay a price premium, while others receive a quality level below the highest available. Imposing minimum quality standards or price ceilings can ensure that only the highest quality level of each product is sold. Such intervention reduces the quality‐adjusted price paid by consumers but also reduces the incentives for firms to innovate. When enough consumers have high valuations, such intervention must be welfare reducing, owing to reduced innovation. JEL Classification: O31, L16 Discrimination par les prix et amélioration de la qualité. Ce mémoire présente un modèle d'amélioration de la qualité quand on peut vendre des produits à divers niveaux de qualitéà cause des différences dans les évaluations d'amélioration de qualité par les consommateurs. Les entreprises peuvent entrer en collusion pour faire de la discrimination par les prix de manière à ce que les consommateurs qui apprécient davantage la qualité paient une prime pendant que les autres consommateurs reçoivent une qualité au‐dessous de ce qui est la meilleure qualité disponible. Si on impose des normes de qualité minimale ou des plafonds aux prix, on peut s'assurer que seuls les produits de la plus haute qualité seront vendus. De telles interventions réduisent le niveau de prix ajusté pour la qualité payé par les consommateurs, mais réduisent aussi les incitations des entreprises à innover. Quand un nombre suffisant de consommateurs apprécient beaucoup la qualité, de telles interventions peuvent réduire le niveau de bien‐être à cause des innovations moins importantes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it