Paternity Leave and Fathers' Responsibility: Evidence From a Natural Experiment in Canada
Bibliographic record
Abstract
Abstract Objective This study uses a natural experiment in Canada to examine whether reserved paternity leave policy can increase fathers' involvement with their children. Background Although a growing body of research suggests that paternal leave‐taking is associated with increased father involvement, the causality of this relationship is unclear. Furthermore, leave‐taking may differently impact multiple dimensions of father involvement, including engagement (direct interaction with children), accessibility (time in children's presence), and responsibility (solo parenting time). Method Using two cross‐sectional waves of time diary data from the 2005 and 2010 Canadian General Social Survey, this study exploits the natural experiment of the reserved paternity leave policy introduced in the province of Quebec in 2006 compared to the shared parental leave benefits offered in the rest of Canada. Difference‐in‐differences methods are used to estimate the causal effect of the policy on multiple measures of father involvement. Results The reserved paternity leave policy led to a direct increase in fathers' responsibility time—2.2 additional hours of solo parenting time per week—but no direct effect on fathers' engagement or accessibility time. The findings also suggest that there may be indirect, contextual effects of the policy that have shifted the norms in Quebec regarding fathering. Conclusion This study concludes that reserved paternity leave can increase fathers' responsibility for children in ways that may benefit family well‐being and gender equality more broadly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".