Excise tax incidence: the inequity of taxing obesity and beauty
Bibliographic record
Abstract
The estimation and analysis of the distribution of the negative health impacts of certain commodities subject to excise taxes in Belize and the distribution of the burdens of the excise taxes across households of different income levels are the focus of this article. Particular attention is given to the taxation of soft drinks and cosmetics. We examine the income distribution and tax revenue impacts using the commodity data from the household expenditure survey by and the effective tax rates expressed as a percentage of the value of the final consumption of each item. As in many developing countries, taxes on alcoholic beverages and tobacco products are found to be regressive. The most regressive excise taxes are on soft drinks and cosmetics. Households across the economy pay more in excise taxes on cosmetics than they do on either alcoholic beverages or tobacco products. Relative to the level of household expenditures, the burden of the excise taxes on cosmetics is highest for households in the lowest quintile of total expenditures. The impact of soft drinks in creating obesity is likely to be much greater for high income households whose total consumption per household is twice that of low-income households.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".