Income inequality across Canadian provinces in an era of globalization: explaining recent trends
Bibliographic record
Abstract
In this article, I use panel data methods to investigate possible factors influencing recent trends in income inequality across Canadian provinces. The ratio of the income share of the highest‐to‐lowest quintiles and the Gini coefficient of total income are used as measures of inequality. Both point to rising levels of inequality from 1981 to 1999, especially during the 1990s, and the estimation results suggest that several factors have had significant effects on such an increase. In particular, an increase in international trade, technological change, educational heterogeneity, and the unemployment rate are found to contribute to greater inequality. Deindustrialization and declining government transfer payments to persons are also factors explaining the rise in inequality. In contrast, an increase in the female labour force participation rate appears to have dampened inequality. There is also some evidence of a negative association between de‐unionization and inequality while no significant association is found between inequality and other demographic shifts, such as immigration and the share of the population over the age of 65 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".