Centralized versus Decentralized Taxation of Mobile Polluting Firms
Bibliographic record
Abstract
We consider a world in which a mobile polluting firm must locate in one of two regions. The regions differ in two dimensions: their marginal cost of pollution and the production cost of the firm. It is shown that under incomplete information on regional marginal costs of pollution, fiscal competition may lead to the sub-optimal location of the firm. We also show that under incomplete information, a sub-optimal location is less likely under centralized than under decentralized taxation. Nous étudions un monde dans lequel une firme polluante mobile cherche à se localiser dans une de deux régions données. Les régions présentent des différences quant à leur coût marginal des émissions polluantes et quant à leur coût de production. Il est démontré que dans un contexte d'information incomplète sur les coûts marginaux de pollution des régions, la concurrence fiscale peut mener à une localisation non-optimale de la firme. Il est également démontré que la centralisation de la taxation réduit la probabilité d'une localisation non-optimale.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.002 | 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".