Valuing productivity loss due to absenteeism: firm-level evidence from a Canadian linked employer-employee survey
Bibliographic record
Abstract
In health economic evaluation studies, to value productivity loss due to absenteeism, existing methods use wages as a proxy value for marginal productivity. This study is the first to test the equality between wage and marginal productivity losses due to absenteeism separately for team workers and non-team workers. Our estimates are based on linked employer-employee data from Canada. Results indicate that team workers are more productive and earn higher wages than non-team workers. However, the productivity gap between these two groups is considerably larger than the wage gap. In small firms, employee absenteeism results in lower productivity and wages, and the marginal productivity loss due to team worker absenteeism is significantly higher than the wage loss. No similar wage-productivity gap exists for large firms. Our findings suggest that productivity loss or gain is most likely to be underestimated when valued according to wages for team workers. The findings help to value the burden of illness-related absenteeism. This is important for economic evaluations that seek to measure the productivity gain or loss of a health care technology or intervention, which in turn can impact policy makers' funding decisions.
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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 | 0.033 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".