The Uneasy Case Against Discriminatory Excise Taxation: Soft Drink Taxes in Ireland
Bibliographic record
Abstract
This study uses an empirical case study to investigate the revenue implications of reducing a discriminatory excise tax. The case study is Ireland, which provides a natural experiment because it has both imposed and removed such a discriminatory tax (on soft drinks) in the past two decades. The authors find that soft drink consumption is price elastic, income elastic, and sensitive to weather. They estimate that 30% of the amount of surrendered excise tax revenue is recaptured by the value-added tax and income tax. The remaining 70% loss is further reduced by a small reduction in welfare costs, elimination of administration costs, and reduced compliance costs. The rate-revenue curve has a negative slope, even though demand is price elastic, presumably because marginal costs are rising and the tax reduction is not fully captured in the price reduction. In effect, the authors find undershifting and no evidence of a Laffer effect.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".