Tobacco and alcohol excise taxes for improving public health and revenue outcomes: marrying sin and virtue ?
Bibliographic record
Abstract
Excise taxes on alcohol and tobacco have \n long been a dependable and significant revenue source in \n many countries. More recently, considerable attention has \n been paid to the way in which such taxes may also be used to \n attain public health objectives by reducing the consumption \n of products with adverse health and social impacts. Some \n have gone further and argued that explicitly earmarking \n excise taxes on alcohol and tobacco to finance public health \n expenditures—marrying sin and virtue as it were—will make \n increasing such taxes more politically acceptable and \n provide the funding needed to increase such expenditures, \n especially for the poor. The basic idea—tax “bads” and do \n “good” with the proceeds—is simple and appealing. But \n designing and implementing good “sin” taxes is a \n surprisingly complex task. Earmarking revenues from such \n taxes for health expenditures may also sound good and be a \n useful selling point for new taxes. However, such earmarking \n raises difficult issues with respect to budgetary rigidity \n and political accountability. This note explores these and \n other issues that lurk beneath the surface of the attractive \n concept of using increased sin excises on alcohol and \n tobacco to finance “virtuous” social spending on public health.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".