The Changing Structure of Inequality in Canada: A Multi-Level Analysis of Licensing and Education Effects on Wages Within and Across Occupations
Bibliographic record
Abstract
This dissertation investigates the claim that occupational licensing is social closure, creating barriers to entry and generating rent for its existing members, and that licensing has thus contributed to increasing wage inequality. Using Statistics Canada’s Labour Force Survey and a database of occupational regulations collected from public data sources, I explore the wage effects of licensing and it interacts with other occupational characteristics. This dissertation includes three primary chapters. Chapter 3 compares a human capital approach with a social closure model using two-level hierarchical models. I find that the entire wage premium associated with licensing can be explained by education and skill differences between occupations, suggesting licensing does not have independent effects on wages. Chapter 4 explores the question of how education shapes wages; is it just human capital or can it function as social closure? I use factor analysis to measure the presence of different labour allocation mechanisms, reflecting the influence of human capital, internal labour markets, and social closure. I also explore the extent of over-qualification and returns to over-qualification to determine how the three mechanisms shape the effect education on wages. I conclude that education does function differently based on the three mechanisms, and that it can function as social closure. Finally, chapter 5 employs growth curve models to investigate the wage effects of new licenses enacted between 1997 and 2019. I am able to show that new licenses do have wage effects, contributing to an acceleration in wage growth after enactment, an effect that is more significant if the occupation achieves high levels of coverage, and that education plays only a small part in this process. Overall, I conclude that occupational licensing can have wage effects independent of education, but this effect is modified by other occupational characteristics like education, the number of years since the license was enacted, and the coverage of licensing achieved in the occupation. While licensing does appear to contribute to wage inequality, it is likely that licensing is only one part of a larger societal process of institutional transformation in the Canadian labour market.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".