Workplace risks and wages: Canadian evidence from alternative models
Bibliographic record
Abstract
Three alternative models of compensating wage premiums for risk are estimated: the conventional OLS wage regression; an endogenous risk model that accounts for the simultaneity that may occur if workers of high potential earnings prefer safer jobs; and a self‐selection model to account for the possibility that workers sort into jobs based on unobserved tolerance for risk that affects their productivity in dangerous work environments. The results suggest that the existing Canadian estimates, which have been based on the basic model, may seriously underestimate the wage premium for risk and hence the implied cost of fatal and non‐fatal injuries. JEL Classification: J28, J31 Risques au travail et salaires: résultats canadiens pour plusieurs modèles. Les auteurs calibrent trois modèles pour évaluer la compensation salariale pour les risques au travail: l'équation conventionnelle de régression des salaires estimée par la méthode des moindres carrés ordinaires; un modèle de risque endogène qui tient compte de la simultanéité qui peut se produire si les travailleurs dont les revenus potentiels sont élevés préfèrent les emplois où il y a moins de risques; un modèle d'auto‐sélection où les travailleurs se répartissent entre les emplois sur la base d'une tolérance non‐observée pour le risque qui affecte leur productivité dans des environnements de travail dangereux. Les résultats suggèrent que les évaluations canadiennes en vogue, qui sont fondées sur le modèle de base, peuvent sous‐estimer sérieusement la prime salariale de risque et donc les coûts des blessures mortelles ou non que les accidents entraînent.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".