Bibliographic record
Abstract
This paper studies the occurrence of dumping and the implications of anti‐dumping duties in a deterministic price‐setting two‐period duopoly model for differentiated products. When current market shares matter for future demand, cost‐based dumping can be profitable. Dumping thus arises as a form of investment in market shares. This might trigger the application of anti‐dumping law. We further show that correctly anticipated duties do not necessarily hinder firms from selling below costs. The mere existence of anti‐dumping law, however, significantly changes the structure of the game, leading to higher first‐period prices for both firms. JEL Classification: F12, F13 Parts de marché, dumping défini par les coûts, et politiques anti‐dumping. Ce mémoire étudie le phénomène de dumping et les implications des droits compensatoires anti‐dumping dans un modèle de duopole de produits différenciés dans un cadre de deux périodes où le processus de définition des prix est déterministe. Quand la nature présente des parts de marché a des cons´equences sur la demande future, le dumping défini par les coûts peut être profitable. Dans ce cas, le dumping émerge en tant que forme d'investissement dans le renforcement des parts de marché. Voilà qui peut déclencher l'application de la loi anti‐dumping. On montre que des droits compensatoires correctement anticipés n'empêchent pas nécessairement les entreprises de vendre à des prix plus bas que leurs coûts. Cependant, le seul fait de l'existence de la loi anti‐dumping modifie substantiellement la structure du jeu et conduit les deux entreprises àétablir des prix plus élevés dans la première période.
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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 | 0.005 |
| 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) | 0.007 | 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".