Parenthood, Gender, and the Risks and Consequences of Job Loss
Bibliographic record
Abstract
Abstract Job loss can be difficult to navigate for individuals and their families. However, we know very little about the relationship between parental status and job loss. Drawing on rich data from Statistics Canada’s Workplace and Employee Survey, we analyse differences across gender and parental status groups in both risks of job loss and its consequences: re-employment, unemployment, and quality of new jobs relative to those that were lost. We find that parenthood reduces the probability of job loss for prime-age men with young children, but only when employer discretion is involved. This advantage is not shared by otherwise similar mothers or fathers of school-aged children, suggesting that employers are particularly sympathetic to risks to men’s breadwinning role in the early years of fatherhood. Despite similar risks of job loss relative to other groups, mothers of young children are the least likely to be re-employed in the subsequent year, mainly because of their higher levels of labour market withdrawal rather than unemployment. Holding out for “family friendly” work arrangements does not seem to account for this pattern. Overall, our results show that the risks and consequences of job loss strengthen connection to employment for fathers of young children while weakening connection for mothers. Job loss dynamics thus not only reflect but also reinforce asymmetrical breadwinning and caring roles for mothers and fathers of preschool-aged children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".