Canada's Alcohol Deficit, 2007–2020: Social Cost, Public Revenue, Magnitudes of Alcohol Use, and the Per-Drink Net Deficit for a Fourteen-Year Period
Bibliographic record
Abstract
OBJECTIVE: Governments generate substantial revenue from the distribution and sale of alcoholic beverages. However, the use of this alcohol results in considerable public costs for health care, criminal justice, and economic loss of production. Because comparisons of these two sides of the same coin are limited, this study aims to estimate this net alcohol surplus or deficit in Canada and each province/territory for a 14-year study period. METHOD: Net government revenue from alcohol sales and net social costs of alcohol use were estimated for Canada and each province/territory for all years of study from 2007 to 2020, and all dollar figures were Consumer Price Index-adjusted to 2020 Canadian dollars (CAD). The net alcohol surplus/deficit was estimated as the difference. Per capita recorded alcohol sold was from administrative sources and used as proxy to calculate alcohol used by adding an estimate of unrecorded use and converting to Canadian standard drinks (CSDs). The per-drink net deficit was the net deficit divided by CSDs. RESULTS: In Canada in 2020, governments generated CAD $13.3 billion in revenue from alcohol sales, but this was offset by $19.7 billion in social costs attributable to alcohol use. This "alcohol deficit" increased by 122.0% in real-dollar terms over the study period and reached a high of $6.4 billion in 2020. In 2020, the magnitude of the alcohol used in Canada was 16.8 billion CSDs. Each of these drinks resulted in a public net deficit of $0.379. CONCLUSIONS: Both alcohol use and the resulting public alcohol deficit are high in Canada. To mitigate these losses to the well-being of Canadians and their economy, government planners, regulators, and policymakers must urgently deploy evidence-based alcohol policies toward reducing the magnitude of alcohol used in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".