Optimal price regulation in a growth model with monopolistic suppliers of intermediate goods
Bibliographic record
Abstract
Abstract. In this paper we investigate the trade‐off faced by regulators who must set a price for an intermediate good somewhere between the marginal cost and the monopoly price. We utilize a growth model with monopolistic suppliers of intermediate goods. Investment in innovation is required to produce a new intermediate good. Marginal cost pricing deters innovation, while monopoly pricing maximizes innovation and economic growth at the cost of some static inefficiency. We demonstrate the existence of a second‐best price above the marginal cost but below the monopoly price, which maximizes consumer welfare. Simulation results suggest that substantial reductions in consumption, production, growth, and welfare occur where regulators focus on static efficiency issues by setting prices at or near marginal cost. JEL Classification: D42, D61, D92, O38 Régulation du prix optimal dans un modèle de croissance où existent des fournisseurs monopolistes de biens intermédiaires. Dans ce mémoire, on enquête sur la relation d’équivalence à laquelle les régulateurs doivent faire face au moment de définir le prix quelque part entre le niveau du coût marginal et le niveau du prix de monopole. On utilise un modèle de croissance dans le cas où existent des fournisseurs monopolistes de biens intermédiaires. Des investissements dans l’innovation sont nécessaires pour produire un nouveau produit intermédiaire. La tarification au coût marginal décourage l’innovation alors que la tarification au niveau du prix de monopole maximise l’innovation et la croissance au prix d’une certaine inefficacité statique. On montre que l’existence d’un prix qui est un optimum de second ordre et se situe au‐dessus du coût marginal mais au dessous du prix de monopole maximise le niveau de bien‐être des consommateurs. Des résultats de simulation suggèrent que des réductions substantielles dans la consommation, la production, la croissance, et le niveau de bien‐être se produisent quand les régulateurs sont focalisés sur les problèmes d’efficacité statique et fixent les prix au niveau (ou près du niveau) du coût marginal.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".