Policy and Fertility, a Case Study of the Quebec Parental Insurance Plan
Bibliographic record
Abstract
In 2006, the Quebec government implemented a parental leave program more generous than the scheme available through the Canadian federal Employment Insurance (EI) program. It was aimed at maintaining the personal disposable income after a birth, especially for women whose income exceeds the maximum insurable earnings of EI. In this article, we assess whether the implementation of the Quebec Parental Insurance Plan (QPIP) was associated with an increase in the fertility in Quebec, especially for highly educated women. We use data from the rotating panels of the Canadian Labor Force Survey. We test the effect of the implementation of the QPIP on fertility by comparing Quebec and Ontario, which kept the federal EI scheme, before and after the implementation of the QPIP. We adapt the difference in differences method (DiD) to the modeling of the fertility schedule using Poisson regression. We estimate fertility by educational levels within each of the four groups of the DiD design by integrating the estimated fertility schedules. Our results show that the implementation of the QPIP was associated with an increase in fertility in Quebec. The magnitude of the increase varies by educational levels: 17% for women who did not complete secondary education, 46% for those who completed it, and 27% for women who earned a university diploma.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".