How Do Workplace Smoking Laws Work? Quasi-Experimental Evidence from Local Laws in Ontario, Canada
Bibliographic record
Abstract
There are very large literatures in public health and economics on the effects of workplace smoking bans, with most studies relying on cross-sectional variation.We provide new quasi-experimental evidence on the effects of workplace bans by using the differential timing of adoption of over 100 very strong local smoking by-laws in Ontario, Canada over the period 1997-2004.We employ restricted-use repeated cross section geocoded outcome data to estimate reduced form models that control for demographic characteristics, year fixed effects, and county fixed effects.We first show that the effects of the local laws on actual worksite smoking policy (i.e. the "first stage") were not uniform; specifically, local laws were only effective at increasing ban presence among blue collar workers.Among blue collar workers, adoption of a local by-law significantly reduced the fraction of worksites without any smoking restrictions (i.e.where smoking is allowed anywhere at work) by over half.The differential effect of local policies also improved health outcomes: we find that adoption of a local by-law significantly reduced SHS exposure among blue collar workers by 25-30 percent, and we confirm that workplace smoking laws reduce smoking.We find plausibly smaller and insignificant estimates for white collar and sales/service workers --the vast majority of whom worked in workplaces with privately initiated smoking bans well before local by-laws were adopted.Overall our findings advance the literature by confirming that workplace smoking bans reduce smoking, documenting the underlying mechanisms through which local smoking by-laws improve health outcomes, and showing that the effects of these laws are strongly heterogeneous with respect to occupation.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".