Welfare–maximizing and revenue–maximizing tariffs with a few domestic firms
Bibliographic record
Abstract
In this paper we compare the orthodox optimal tariff formula with the appropriate welfare–maximizing tariff when there are a few producing or importing firms. The welfare–maximizing tariff can be very low, voire negative in some cases, while in others it can even exceed the maximum–revenue tariff. The relationship between the welfare–maximizing tariff and the number of firms need not be monotonically increasing, because the tariff is not strictly used to internalize terms of trade externality. It is also used to manipulate cost asymmetries between producing and importing firms. Welfare–maximizing specific tariffs are never worse than their ad valorem counterparts. JEL Classification F13, L13 Tarif qui maximise le bien être et tarif qui maximise le revenu quand on est en présence de peu de firmes. Nous comparons le tarif optimal orthodoxe au tarif maximisant le bien être lorsqu’il y a peu de firmes productrices ou importatrices. Le tarif qui maximise le bien être est parfois très bas, même négatif, mais il peut excéder le tarif qui maximise le revenu dans d’autres cas. La relation entre le tarif et le nombre de firmes n’est pas nécessairement monotone parce que le tarif n’est pas strictement utilisé pour améliorer les termes d’échange. Il est aussi utilisé pour manipuler les asymétries dans les coûts des firmes productrices et importatrices. Le tarif spécifique n’est jamais dominé par le tarif ad valorem.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 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".