Family friendly employee benefits: incidence and relationship with
Bibliographic record
Abstract
In this paper I use data from the Workplace and Employee Survey 1999 employee file to determine what factors influence the incidence of family friendly employee benefits and their relationship with wages. Using simple tabulations, I first examine the relationship between benefit incidence and between wages for workers with specific benefits and wages for workers without these benefits, and factors such as occupation, education, gender, and union status. I also report the predicted impact (positive or negative) from a probit equation of employee and employer characteristics on the probability that a benefit is used or available. I then estimate wage equations for workers with and without certain types of benefits and report predicted wages and wage ratios for workers in different education and occupation groups for three industries. The benefits I consider are flexible time, the availability of Employment Insurance supplements, family support benefits, and childcare benefits. Findings reveal complex relationships between employee and employer characteristics and family benefits ’ incidence, and between wages and family benefits. Acknowledgements I gratefully acknowledge funding from Human Resource Development Canada and ongoing support from Statistics Canada in the research for this paper. I would also like to thank Thomas Kochan for his valuable comments. 1 Family friendly employee benefits: incidence and relationship with wages 1.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".