A return on investment analysis for the 2017 increase in alcohol excise taxation in Lithuania
Bibliographic record
Abstract
AIMS: To conduct a return on investment analysis of Lithuania's 2017 increase in alcohol excise taxation of 112% for beer, 111% for wine, and 23% for ethyl alcohol (spirits), resulting in a marked decrease in alcohol affordability. METHODS: Economic analyses based on costs of the increased taxation and economic benefits derived from a societal perspective. Costs were measured according to World Health Organization standards, based on Lithuanian public data. Benefits were derived from the difference of direct (healthcare, childcare, legal) and indirect costs between 12 months pre- and post-enactment of the policy. All costs and benefits were expressed in 2023 Euros (€). RESULTS: Overall, there were net benefits from reductions in productivity losses and increases in tax revenue. Tax revenue increased by 20%, or more than €100 million, in the first-year post enactment, and productivity losses decreased over the same time period by about €35.3 million (95% confidence interval [CI]: -51.9 to -17.1; proportionally -7%; 95% CI: -11.0% to -4.0%), the latter based on marked reductions in premature mortality in all alcohol-attributable causes of death. In addition, healthcare costs decreased by about €3.8 million (95% CI: -8.4 to +0.1; proportionally -5%; 95% CI: -11.0% to +0.1%). On the other hand, childcare and legal costs increased compared with the year before, by €5.3 million (no 95% CI possible; proportionally: +7%) and €4.6 million (95% CI: +0.2 to +8.0; proportionally +5%; 95% CI: +0.3 to +8.7%), respectively. The final return on investment was 420 to 1, i.e. for each Euro invested, the return was €420. In the sensitivity analyses, the return on investment varied between 292 to 1 and 530 to 1, meaning that all assumptions resulted in a very positive return. CONCLUSIONS: The increase in excise taxation for alcohol on March 1, 2017 in Lithuania created a large return on investment and reduced alcohol-attributable mortality and hospitalizations.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".