Three Essays On The Economics of Education
Bibliographic record
Abstract
This thesis examines heterogeneity in human capital outcomes across race and gender. Using administrative data from British Columbia, the first chapter investigates the income-achievement gap in provincial test scores among Grade 4 and 7 students of different racial backgrounds. The second chapter estimates the impact of switching post-secondary majors on labour market earnings for men and women. Finally, using university application data from Ontario, the third chapter investigates gender gaps in applications, offers and acceptances to engineering and computer science programs. In Chapter 1, I show that there is considerable variation in test score gaps between children from families of high- and low-socioeconomic status (SES) across racial backgrounds. In particular, the gap in mean test scores between Indigenous children of high- and low SES is 0.7 standard deviations, while it is only 0.37 standard deviations for East Asian children. Further investigation into the gap among Indigenous students reveals a potential connection to broader socio-economic issues impacting Indigenous communities. In Chapter 2, I study the impact of switching post-secondary majors on earnings. To address the endogeneity of switching, I employ a doubly-robust matching estimator to create a credible counterfactual group for switchers. Switching has a greater impact on the earnings of women, with women experiencing gains (losses) as large as $15,500 ($23,000) conditional on initial major. These results highlight the importance of major-choice as it relates to labour market earnings. Finally, Chapter 3 investigates the gender gaps throughout the application process to undergraduate engineering and computer science programs. While we observe large gender gaps in applications to both programs, we also observe gender gaps in offers to engineering programs and acceptances to computer science programs. This suggests that both programs may face unique challenges in achieving gender parity in enrollment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".