Why do drinkers earn more? Job characteristics as a possible link
Bibliographic record
Abstract
After some initial controversy, an inverted U-shape relationship between the consumption of alcohol and earnings seems to be an established result, at least in North America. It has been dubbed a "drinking premium", at least in the lower portion of the consumption curve. It is still unclear, perhaps even counter-intuitive, why such a drinking premium exists and the literature suggests it is not causal but results rather from selection effects. We suggest here that part of the premium is linked to occupation: some occupations pay better, controlling for the usual human capital determinants, and also attract drinkers or induce workers to drink more. Using a sample of full-time employed or self-employed individuals aged 25-64 and not in poor health from the 2015-16 Canadian Community Health Survey (CCHS), we confirm the existence of a drinking premium and a positive return to the quantity or frequency of drinking up to high levels of consumption. Using information on jobs held by respondents, linked to a data set of job characteristics, we find that controlling for job characteristics reduces the premium or return to drinking by approximately 30% overall, and up to 50% for female workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".