When the truth hurts: ordinary selling price regulation in a monopoly
Bibliographic record
Abstract
How does restricting firms to communicate a truthful ordinary selling price affect pricing and profits when some consumers are uncertain about product quality? In this thesis we analyze a two-period monopoly model to study the welfare effect of ordinary selling price (OSP) regulation. In the model, quality is observed by informed consumers who buy in the first period. However, consumers who arrive in market in the second period are not able to discern quality, but must infer it indirectly through prices. We first characterize the necessary conditions for OSP regulation to make the first-period price informative for second-period consumers. We show that OSP regulation has no effect when the proportion of uninformed consumers is high. This means regulation is ineffective when it would be most useful. We then compare the equilibrium outcome when OSP is effective to the equilibrium outcome in an unregulated environment. A simple welfare measure indicates that restricting firms to communicate a truthful first-period price has no effect on the uninformed consumers' expected surplus, but does create a deadweight loss from deceptive pricing in the first period. This deceptive pricing occurs because OSP regulations provide incentives for a low-quality firm to charge a high initial price when doing so enables it to earn excess profits in the second period.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".