Social Mobility Trends in Canada: Going up the Great Gatsby Curve
Bibliographic record
Abstract
While cross-sectional increases in inequality are a cause for concern, the study of the intergenerational transmission of socioeconomic status is perhaps more relevant. How is social status reproduced from one generation to the next? Recent work has highlighted the relationship, if not causal then correlational, between inequality and measures of social mobility in a cross-country setting. This relationship is dubbed the Great Gatsby Curve (Corak 2013): places with higher inequality during one's childhood are correlated with lower intergenerational income mobility between the child and his or her parents. In this paper, newly developed administrative Canadian tax data are exploited to compute measures of intergenerational income mobility at the national and provincial levels. This work provides detailed descriptive evidence on the trends in social mobility. Results show that mobility has steadily declined over time, and that there has been an increase in the inequality of the parental income distribution, as measured by the Gini coefficient. Hence Canada, and all its provinces, have been "going up" the Great Gatsby Curve. The cross sectional, cross country relationship thus also holds within a same country over time, leading credence to the more causal than correlational nature of the relationship, though causality is not formally tested here. The decrease in mobility, particularly for children born in the bottom quintile of the income distribution, should be of concern to federal and provincial policymakers alike and highlights the need for additional research in order to provide equal opportunities to all children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".