Essays on Structural Labour Supply and GovernmentPolicies
Bibliographic record
Abstract
This thesis uses a structural modeling approach to assess labour supply and evaluate policy programs. The first chapter compares labour market outcomes for high school dropouts to graduates in Quebec, Ontario, Alberta, and British Columbia. Results show that dropouts face worse outcomes across all provinces, with Quebec having a significantly higher proportion of male dropouts. Simulations aimed at boosting employment incentives for low-skilled individuals emphaise the importance of long-term strategies that enhance skill acquisition and reduce financial barriers. Current welfare eligibility criteria offer limited incentives to transition from welfare to work at modest wages. The second chapter focuses on modeling individual heterogeneity, particularly unobserved characteristics, using random coefficients. It uses Monte Carlo simulations across six scenarios with varying shapes and variances for the distribution of unobserved characteristics. Findings reveal that methods accounting for heterogeneity perform well when variances are small, but become sensitive to distribution shapes as variances increase, indicating the need for more flexible models in high-variance contexts. The final chapter examines the labour supply of single mothers, with a focus on childcare utilisation and social assistance participation. Contrary to traditional views, the study finds that childcare costs are no longer a significant barrier to employment, with access to childcare being a more critical issue. Policies targeting direct employment incentives may be more effective in increasing labour force participation. The chapter also highlights the role of unobserved preferences in shaping work decisions, suggesting that current programs may be limited by not fully addressing these behavioural factors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".