Minimum wages and alcohol consumption: Evidence from Canadian longitudinal microdata
Bibliographic record
Abstract
The objective is to estimate the effect of provincial minimum wage increases in Canada on heavy drinking, binge drinking and average daily alcohol consumption. We estimate standard regression models by gender-age group with drinking behaviours as the dependent variables and the minimum wage among the independent variables. We employ the Canadian National Population Health Survey which began in 1994 and ended in 2011, a period comparable to that used by many U.S. studies. The longitudinal feature of the Canadian microdata is an advantage over most U.S. datasets, allowing control for individual fixed effects, including unobserved propensities regarding alcohol. As in U.S. studies, estimation relies on differences in timing and size of minimum wage changes across jurisdictions. We find no consistent evidence that minimum wage increases increase drinking overall. Indeed, for less-educated males ages 26–64, we estimate that a $1 increase (about 15 %) in the real minimum wage would have reduced the prevalence of heavy drinking by 2.2 percentage points and average daily alcohol consumption by 0.15 standard drinks, with wild bootstrap 95 % confidence intervals (-4.3, −0.1) and (-0.28, −0.07) respectively. Our estimates for females are less consistent but some point towards modest increases in drinking. Besides our strongest finding of no evidence that minimum wages increase drinking overall, our findings can also be seen as consistent with earlier research on this same Canadian dataset that found minimum wage increases reduced stress in less-educated male workers. • The effect of minimum wages on drinking is investigated using Canadian panel data. • There is substantial variation in minimum wage changes across Canadian provinces. • No evidence is found that minimum wages increase drinking/problem drinking overall. • There is some evidence of reduced drinking by less educated working age males. • Results are consistent with research that minimum wage increases can reduce stress.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".