Bibliographic record
Abstract
This dissertation consists of three essays. The first essay examines the role of firms in wage dispersion in Senegal, while the other two essays focus on gender earnings inequality in Canada.In the first essay, we collaborate with several Senegalese governmental agencies to build the first longitudinal employer-employee dataset in a Sub-Saharan African country. The resulting linked employer-employee dataset in Senegal covers approximately 10% of total employment and presents an opportunity to extend the existing research on wage-setting in low-income countries. Our findings indicate that the share of wages explained by firms in Senegal is around 30%.The second essay provides evidence of the impact of children on parents' labor market outcomes in Canada, commonly referred to as "child penalties". Using Canadian administrative data, I estimate child penalties for both men across Canadian provinces. I also explore the link between childcare costs and the long-term effects of children on parents' labor market outcomes, using variations of childcare across provinces. Additionally, I use variation introduced by the 2006 Quebec reform to examine the long-run effect of a generous parental leave policy on the earnings and participation of men and women.The third essay examines the role of social interaction in governmental program participation, specifically focusing on the 2006 Quebec Parental Insurance Plan (QPIP). It investigates whether a father's decision to take parental leave is influenced by the choices made by his co-workers regarding paternity leave. We use the Canadian-matched employer-employee datasets to identify new fathers and their co-workers. The findings reveal that fathers are more inclined to take paternity leave when their male colleagues also opt for parental leave
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".