An introduction and meta-analysis of the association between income inequality and intergenerational mobility
Bibliographic record
Abstract
Our aim is to understand associations between income inequality and intergenerational mobility in the United States, Canada, and eight European countries (Denmark, Finland, France, Germany, Italy, Norway, Sweden, and the United Kingdom). We analyze whether this correlation is observed across and within countries over time. Developed countries have been experiencing increases in inequality in recent decades, mostly due to a steep concentration of income at the top of the distribution. We investigate Great Gatsby curves and perform meta-regression analyses based upon several papers on this topic. Results suggest that countries with high levels of inequality tend to have lower levels of mobility. Intergenerational income elasticities have stronger associations with Gini coefficient, compared to associations with the top one percent income share. Once models are controlled for methodological variables, country indicators, and paper indicators, correlations of mobility with Gini lose significance, but not with the top one percent income share. This result is an indication that recent increases in inequality at the top of the distribution (captured by the top one percent income share) might be negatively affecting mobility on a greater magnitude, compared to variations across the income distribution (captured by Gini).
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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 | 0.022 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".