Bibliographic record
Abstract
In this paper we examine the optimal taxation of corporate profits in a multi–period limit pricing model where a dominant firm faces expansion by a competitive fringe. The optimal policy requires tax rates to vary both intertemporally and across firm sizes, and balances the benefit of fringe growth in eroding the market power of the dominant firm and the cost of displacing the dominant firm’s output with the higher cost output of the fringe. The results are relevant for assessing the policy of giving preferential tax treatment to small firms, as practised by several OECD countries. JEL Classification: H32, L11 Impôts sur les profits et croissance des entreprises périphériques. Ce texte examine la fiscalité optimale des profits des sociétés dans un modèle de tarification limite à plusieurs périodes quand une entreprise dominante fait face à l’expansion d’entreprises périphériques qui la concurrencent. La politique optimale requiert des taux d’imposition qui varient à la fois dans le temps et selon la taille des entreprises, et cherche un équilibre entre les avantages d’une croissance à la périphérie qui entame le pouvoir de l’entreprise dominante, et le coût d’un déplacement de la production de l’entreprise dominante vers des entreprises périphériques dont les coûts de production sont plus élevés. Les résultats de l’analyse sont pertinents pour l’évaluation des politiques accordant un traitement fiscal préférentiel aux petites entreprises, comme c’est le cas dans plusieurs pays de l’OCDE.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".