Gender Gap in Dropping out of High School: Evidence from the Canadian NLSCY Youth
Bibliographic record
Abstract
This paper exploits the panel features of the Canadian National Longitudinal Survey of Children and Youth (NLSCY) and the large diversity of measures collected on the children and their families over 7 cycles (1994-1995 to 2006-2007) to explain high school graduation (dropout rates) of Canadian youth aged 18 to 23 observed in the most recent wave of the survey. We focus on the gap between females and males which in some provinces is high, particularly in Québec. The econometric approach uses a non-linear Blinder-Oaxaca decomposition technique to identify and quantify the separate contributions of group differences in measurable characteristics (youth attributes and family endowments) to the gender gap in high school graduation rates. We find that the traditional barriers to high school graduation, linked to poverty, are very detrimental for males in Québec. However, we also find that the male-female gap across Canada is very partially explained by differences in endowments such as reading or maths skills in school. Finally, as in other recent studies, our results show that parental expectations about educational attainment are predictors of high school graduation. Public policy approaches for the reduction of the male-female gap are proposed. More radical measures and some experimental approaches (pilot projects) should be adopted in Québec to decrease rapidly the dropout rates and increase high school graduation rates by the age of 18.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".