Use of Parental Benefits by Family Income in Canada: Two Policy Changes
Bibliographic record
Abstract
Objective: This article examines how two recent policy extensions affected the use and sharing of parental benefits in Canada and how this differed by family income. Background: Paid parental benefits positively affect economic and health outcomes. However, not all policy changes increase leave‐taking, especially among low‐income families. Method: Drawing on administrative data from 1998 to 2012, we estimate linear probability models to examine the likelihood of either parent using parental benefits and multinomial logit models to examine patterns in sharing benefits. We stratify models by household income to examine how the two policy changes affected families differently across the income spectrum. Results: Both policies increased use more among low‐income families than those with higher incomes, which is likely due to widening eligibility criteria that affected low‐income families disproportionately. Second, policy design induced different patterns of sharing benefits in response to the two policy changes. In contrast to the 2001 policy that only moderately increased sharing of parental benefits, Quebec's 2006 program explicitly promoted gender equality and increased sharing of benefits across all income groups, but three times as much for middle‐ and high‐income families than low‐income families. Conclusion: We conclude that policy design shapes socioeconomic inequality in newborns' early life parental context.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".