Bibliographic record
Abstract
This dissertation applies statistical methods to understand labour market issues in China and Canada. The first chapter uses an instrumental variable method to identify the causal effect of migrant networks on the probability of rural-urban labour migration in China. It uncovers a substantial heterogeneity in migrant network effects by gender, age groups, and between people with and without migration experience. Evidence shows that migrant networks affect migration decisions through increasing job tenure and improving work environments of migrants. The second and third chapters answer research questions related to gender wage gap and returns to postgraduate education in Canada. Using a broader set of occupational characteristics than previous studies, the second chapter adopts a quantile decomposition method to reveal that women with different educational levels experience the gender gap for different reasons. DOT-skills used in previous studies are important in explaining the gender gap for most workers in Canada, but not for high-school dropouts and for the top 10% of wage earners among the university-educated workers. For the latter, men working in more competitive jobs and taking more managerial responsibilities are the explanations underlying Canada’s “glass-ceiling” phenomenon. By applying imputation techniques in a novel way, this chapter quantitatively demonstrates that correcting for selection into work makes little difference in estimating the gender gap for individuals with post-secondary education. For individuals without post-secondary education, the use of observed characteristics is sufficient to capture the selection rule. The third chapter documents up-to-date evidence on the decline in returns to postgraduate education relative to four-year university degrees from 1995 to 2010. The return has declined in all major fields of study except engineering and computer science in which workers with postgraduate education have experienced a substantial gain over the same period of time. By focusing on the supply side of the labour market, this paper provides an explanation for the decline in returns to postgraduate education by exploring changes over time in the occupational composition of workers with postgraduate education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".