Bibliographic record
Abstract
The purpose of this paper is to integrate into a general model of an open economy the study of optimal wedges on domestic and foreign transactions. While it has been customary in the literature to link the analysis of domestic taxes to the provision of public goods, the model presented here views the imposition of taxes and tariffs in the general context of internal and external monopolies. As such, the paper begins with the idea of a compromised optimality. This means essentially that a modern society, while maximizing the welfare of its members, is constrained by other internal objectives such as the fact that the State shares its monopoly power with several other economic entities (for instance employers' federations, trade-unions). Thus, the mere fact of levying taxes gives a State some monopoly power which, in a sense, is similar to that of a Cournot-type monopolist who "imposes" private taxes. On the other hand, given the possibility that a country with some monopoly power in international trade could improve its situation by imposing tariffs, the analysis lends itself to the study of tariffs and taxes in the broad context of optimal wedges. To allow for this characterization, the paper incorporates into the model of normalization. As a by-product of this, a) it establishes, in terms of generalized inverses of the Slutsky matrix, a link between domestic marginal relative revenues and foreign ones; b) it defines two concepts of optimal tariffs evaluated from f.o.b. prices and c.i.f. prices; c) it suggests some further extensions such as the analysis of transactions costs, the incorporation of market retaliations and cultural characteristics of goods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".