CAN ALCOHOL PRICE POLICIES BE USED TO REDUCE DRUNK DRIVING? EVIDENCE FROM CANADA<sup>*</sup>
Bibliographic record
Abstract
Drunk driving is one of the more serious negative consequences of alcohol consumption. Since consumption of alcohol is sensitive to the price of alcohol, and the occurrence of drunk driving is sensitive to the level of alcohol consumption, the possibility exists for alcohol pricing policies to be used to reduce drunk driving in the population. This paper reviews the evidence on this possibility in the literature and adds results based on data from the Canadian province of Ontario. Multiple regression analysis of time series data for Ontario from 1972 to 1990 indicate that, controlling for income, the proportion of young males in the population, changes in the minimum drinking age, and other confounding variables, increasing the price of alcohol has a significant effect in reducing alcohol-related motor vehicle accidents (elasticity = - 1.2, p < .05) and alcohol-related traffic offenses (elasticity = -0.50, p < .05). Overall, the evidence strongly supports the view that alcohol tax and pricing policies can be used to reduce the extent of drunk driving.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".