Too Young to Die: Regression Discontinuity of a Two-Part Minimum Legal Drinking Age Policy and the Causal Effect of Alcohol on Health
Bibliographic record
Abstract
This study examines the impact of Sweden's unique two-part Minimum Legal Drinking Age (MLDA) policy on alcohol consumption and health using regression discontinuity design. In Sweden on-licence purchasing of alcohol is legalised at 18 and off-licence purchasing is legalised later at 20 years of age. We find an immediate and significant 6% jump in participation and a larger increase in number of days drinking at age 18 of about 16% but no large jumps at age 20. No discernible increases in mortality at age 18 or 20 are found but hospital visits due to external causes do see an increase at both 18 and 20 years. Compared to previous findings for single MLDAs the alcohol impacts we find are smaller and the health impacts less severe. The findings suggest that a two-part MLDA can help young adults in their transition to unrestricted alcohol and help contain the negative health impacts that have been observed elsewhere.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".