Bibliographic record
Abstract
Traditionally, the Laffer effect has been discussed in context of macroeconomic endogenous growth models or in the situation of labor market whether a tax cut on wages would persuade people to work more and also increase income tax revenues of the government. In this paper, we are firstly interested in the Laffer effect in a single product¡¯s market rather than a general macroeconomic situation. Secondly, we provide a general formula mathematically in that particular commodity market for the optimal tax amount of the government in the case of a specific tax using non-linear demand and supply curves, which is the most possible extension. It turns out that the optimal tax amount depends on after-tax demand elasticity and not on before-tax elasticity as it is commonly assumed in the economics literature and also on after-tax demand price and on consumers¡¯ share of burden of tax. Some important novel concepts such as the proportional increase in equilibrium price relative to initial equilibrium price ... are defined and discussed. Finally, we believe that any government should consider the issues discussed in this paper before taking a fiscal step in a micro market!
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".