Credence Goods, Consumer Misinformation, and Quality
Bibliographic record
Abstract
For certain products, consumers' misinformation about quality is more endemic at intermediate levels of the quality spectrum rather than at the top or the bottom levels of quality. Using an oligopoly model of vertical product differentiation with three quality levels - green, natural, and brown - we examine the consequences of consumers' overestimation of the quality of the natural (i.e. intermediate quality) product. There are three firms in the market, with each type of firm producing the corresponding type of the product. The firms choose the quality level of their product before choosing its price (Bertrand case) or quantity (Cournot case). Irrespective of the nature of second stage competition, we find that quality overestimation by consumers increases profit of the natural firm, and motivates it to raise its product’s quality. In response, the green firm improves its quality even further, but ends up with lower profit. Overall, average quality of the vertically differentiated product improves, which raises consumer surplus. Social welfare increases when firms compete in prices but falls when they compete in quantities.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".