Lead-lag between female employment and economic growth: evidence from Canada
Bibliographic record
Abstract
Based on estimations by (Aguirre, Hoteit, Rupp, & Sabbagh, 2012) there are 865 million women who have the potential to participate in their countries economic development worldwide. Thus, it is a matter of concern to see the effect of their contribution to the economy and how this contribution can be enhanced. In the recent years, there have been numerous studies on the issue of women labor force participation in the economy and economic growth. however, there is a limited number of studies focusing on the casual relation between the mentioned variables. This article is looking into the issue of the causal relationship of women labor force participation in the economy, gender equality in education, and economic growth by using the standard time series techniques such as, VECM and VDC. The results of the paper tend to indicate that there is a bilateral causality between women employment and economic growth where in the short-run GDP is the leading variable but in the long-run it is women employment which is the leader.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".