Optimal taxation in a free‐entry Cournot oligopoly: The average cost function approach
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Bibliographic record
Abstract
Abstract This study examines the optimal taxation in a free‐entry Cournot oligopoly using the average cost function and aggregative games approach when either a specific tax, an ad valorem tax, or both, is imposed. When either a specific or an ad valorem tax is imposed, we obtain the following results. First, the business‐stealing (business‐augmenting) effect in the free‐entry equilibrium makes the optimal tax rate positive (negative). Second, social welfare under the optimal specific tax is lower than that under the optimal ad valorem tax. Third, when both taxes are imposed, marginally increasing a positive ad valorem tax (negative specific tax) improves social welfare when the optimal specific (ad valorem) tax is initially imposed. Finally, the optimal combination of a specific subsidy and ad valorem tax depends on the shape of the average cost function.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it