The Effects of Work-Life Benefits on Employment Outcomes in Canada: A Multivariate Analysis
Bibliographic record
Abstract
The aim of this study is to examine the empirical question of how the provision of work-life benefits is associated with wages, promotions, and job satisfaction. This is an important question for industrial relations scholars and one that, as yet, has no definitive answer. In order to answer this question, we employ both economic theory and methods. Specifically, the economic theories being tested are the compensating wage differentials theory and the efficiency wage theory. To test the efficacy of each theory, we use econometric techniques using longitudinal data from the most recent Workplace and Employee Survey of Canada. We use regression to unpack the effects of work-life benefits on various employment outcomes and employ instrumental variables to mitigate against reverse causality. We find broad support for the efficiency wage theory. Alternatively stated, we find that increases in benefits are not associated with decreases in wages and other employment outcomes. If bundled correctly, work-life benefits are positively associated with increased wages, a greater number of promotions, enhanced employee morale in the form of job satisfaction, and improved employee retention. These results suggest that the provision of work-life benefits is not a zero-sum game for employers and employees. On the contrary, it appears that both parties to the employment relationship can benefit from work-life benefits.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".