Bibliographic record
Abstract
Firms often take actions that make it more difficult for consumers to gather information about the prices of products. These practices are widespread in many markets—by both the retailers that sell directly to consumers and, often, by product manufacturers. For instance, manufacturers can obscure prices by imposing different restrictions that limit the information about prices available to consumers or by producing several similar models of their products. We analyze price obscuring practices in markets where a manufacturer sells through retailers. We show that these practices will arise only when retailers have some bargaining power. When the bargaining power lies entirely with the manufacturer, obscuring prices does not occur. This does not imply that consumers are better off, however, because the manufacturer acts as a monopolist and charges monopoly prices to its retailers, which then charge monopoly prices to their consumers. Our findings suggest that regulators should consider the market structure when designing consumer protection policies. For instance, we find that policies that try to limit price obscuring practices may backfire in vertical markets. In addition to the desired effect of making prices more transparent, they also have an undesired effect of encouraging higher wholesale prices. Our findings recommend that policies imposing caps on wholesale prices may be more effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".