Multiproduct firm behaviour in a differentiated market
Bibliographic record
Abstract
Abstract In this paper we offer a generalization of the circular model of product differentiation by introducing a multiproduct firm facing monoproduct competitors. We prove existence and explicitly characterize equilibrium when transportation costs are quadratic. We exhibit interesting equilibrium features for price policy, market shares, and profits. In equilibrium, the multiproduct firm uses its connected market shares to build asymmetric pricing schemes that allow a fraction of its product line (brands, stores or firms) to be shielded from outside competition and hence extracts maximum consumer surplus. Our results shed some light on the link between product differentiation and mergers and acquisitions activity (M&As). JEL Classification: D21, L11, L13 Le comportement de l’entreprise multi‐produits dans un marché différencié Cet article propose une généralisation du modèle circulaire de différenciation des produits en considérant le cas d’une firme multi‐produits en concurrence avec des firmes mono‐produit. Nous montrons l’existence d’un équilibre en prix, que nous caractérisons dans le cas de coûts de transports quadratiques. Nous exposons les propriétés de cet équilibre en terme de parts de marchés et de profits obtenus par les entreprises. A l’équilibre, la firme multi‐produits utilise ses niches de marché pour mettre en place une tarification asymétrique permettant à une partie de sa ligne de produits d’être protégée de la concurrence et d’extraire le maximum de surplus aux consommateurs. Nous proposons ensuite une discussion sur le lien entre différenciation des produits et activité de fusion et acquisition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".