Essays on Labour Economics
Bibliographic record
Abstract
The first chapter studies the labour force participation of older individuals during COVID-19. COVID-19 significantly changed the labour participation rates of older Canadians, leading to substantial flows among employment, unemployment, marginal attachment, and non-attachment. Using the Canadian Labour Force Survey (LFS), this paper examines the impact of these flows on the participation rates of older individuals and explores whether COVID-19 prompted early retirements. Unlike the Great Recession, the pandemic caused significant direct separations from employment to non-participation. Additionally, older women experienced slower participation rate recovery than men due to higher outflows and lower inflows. Notably, many individuals who initially became non-attached to the labour force in early 2020 transitioned back to employment in the following months of the same year. Generally, the pandemic did not increase older individuals' self-reported retirement transitions and reduced their probability of staying non-attached to the labour market. The second chapter examines the cyclicality of worker flows across experience levels in Canada. Using the LFS, I estimate individual monthly transition probabilities over business cycles conditional on labour-market experience and job tenure. The job-finding rate and separation rate are relatively more cyclical for the youth. I find that experience is a major contributor to the cyclical fluctuations in employer-to-employer probabilities, whereas tenure is a major contributor to the cyclicality of employment-to-nonemployment. The third chapter studies the evolution of the gender unemployment gap in Canada. The gender unemployment gap - defined as women's unemployment rates minus men's unemployment rates - was positive before 1990 but has remained negative since then. I decompose the gender unemployment gap into contributions from gender differences in transition flows between employment, unemployment, and non-participation. The results show that gender differences in flows between employment and non-participation have been positive contributors to the gap over time, while gender differences in employment-to-unemployment flows have been a significant negative contributor. Over the decades, the contribution of flows between employment and non-participation has been decreasing. As employment-to-unemployment flows continue to contribute negatively to the gap, the diminishing contribution of flows between employment and non-participation explains the flip of the gender unemployment gap from positive to negative. Furthermore, I find that differences in industry and occupation composition play a significant role in explaining the gender difference in employment-to-unemployment transition rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".