Three essays on wage inequality: Evidence for the role of monopoly power, average city rent, regional growth cluster, and interprovincial migration in Canada (1995-2015)
Bibliographic record
Abstract
This dissertation is an empirical analysis investigating various determinants of real wage inequality in Canada. It includes a general literature review and three empirical studies. The first two empirical essays aim to explore the link between wage inequality and in-dustrial monopoly power, average city rent (ACR), and regional growth clusters (RGCs) within industries, as well as within Census Metropolitan Areas (CMAs). Canadian Cen-sus Microdata (1996, 2001, 2006, 2011, and 2016) will be used as the primary dataset in these essays. In the third essay, the effect of interprovincial migration on national wage inequality is examined using the 2016 Census PUMF. A two-round empirical analysis is conducted to examine the effect of monopoly power (measured by Lerner’s index derived from multifactor productivity dataset) on within-industry wage inequalities in Chapter 3. In the first round, wage inequalities (captured by Theil’s index) are computed for two-digit industries (sectors) to uncover variations of wage inequality within industries. Within-industry T-values (Theil’s index), then, will be used as a dependent variable in the second round in a pooled OLS framework. The same approach as Chapter 3 (i.e. two-round analysis) has been taken in Chapter 4 for CMAs instead of industries to disentangle the effect of ACR (an index representing high/low-pay industry composition of a CMA) and RGCs (representing major industrial clusters of a CMA) on within-CMA wage inequalities. To uncover the effect of interpro-vincial migration on inequality, a semiparametric approach is considered in which coun-terfactual wage densities and inequalities are estimated in the absence of internal migra-tion. The results show that a higher monopoly power is associated with a reduction in wage inequality within industries by 0.19%. With respect to within-CMA inequality, the esti-mated results exhibit that there is a significant relationship between wage inequality and average city rent. More importantly, the growth rate of RGCs also matter to inequality across CMAs. It is estimated that a faster rate of growth of RGCs tends to increase CMA wage (earnings) inequality by 0.51% in Canada. And, finally, the estimated counterfac-tual Theil’s values indicate that interprovincial migration substantially reduces wage in-equality in Canada. Besides, estimated wage densities show that migration exerts large and differing impacts on the lower portion of the wage distribution, whereas the effect sharply reduces and fades away in the upper portion of the wage distribution.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".