Assessing Patterns of Alcohol Taxes Produced by Various Types of Excise Tax Methods—A Simulation Study
Bibliographic record
Abstract
AIM: To examine patterns of tax burdens produced by specific, ad valorem, and various types of combination taxations. METHOD: One hundred unique hypothetical alcoholic beverages were mathematically simulated based on the amount of ethanol and perceived-qualities contained. Second, beverages were assigned values of various costs and tax rates, and third, patterns of tax burden were assessed per unit of ethanol produced by each type of tax method. RESULT: Different tax methods produced different tax burdens per unit of ethanol for different alcoholic beverages. The tax burden produced by the ad valorem tax resulted in a lower tax burden for low perceived-quality alcoholic beverages. The specific tax method showed the same tax burden for both low and high perceived-quality alcoholic beverages. However, high perceived-quality beverages benefited from a lower tax burden per beverage price. Lastly, the combination tax method resulted in a lower tax burden for medium perceived-quality alcoholic beverages. CONCLUSION: Under the oligopoly market, ad valorem taxation encourages consumption of low perceived-quality beverages; specific taxation encourages consumption of high perceived-quality beverages; and combination tax methods encourage consumption of medium perceived-quality beverages.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".