Product line pricing in a vertically differentiated oligopoly
Bibliographic record
Abstract
Abstract This paper examines the joint pricing decision of products in a firm’s product line. When products are distinguished by a vertical characteristic, those with higher values of that characteristic will command higher prices. We investigate whether, holding the value of the characteristic constant, there is an additional price premium for products on the industry and/or the firm frontier, that is, for the products with the highest value of the characteristic in the market or in a firm’s product line. We also investigate the existence of price premia for lower‐ranked products and other product line pricing questions. Using personal computer price data, we show that prices decline with the distance from the industry and firm frontiers, even after holding absolute quality constant. We find evidence that consumer tastes for brands is stronger for the consumers of frontier products (and thus competition between firms weaker in the top end of the market). There is also evidence that a product’s price is higher if a firm offers products with the immediately faster and immediately slower computer chip (holding the total number of a firm’s offerings constant), possibly as an attempt to reduce cannibalization. Finally, a product’s price declines with the time it is offered by a firm, suggesting intertemporal price discrimination.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".