Inheritances and wealth inequality: a machine learning approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper explores the relationship between received inheritances and the distribution of wealth (financial, non-financial and total) in four developed countries: the United States, Canada, Italy and Spain. We follow the inequality of opportunity (IOp) literature and − considering inheritances as the only circumstance− we show that traditional IOp approaches can lead to non-robust and arbitrary measures of IOp depending on discretionary cut-off choices of a continuous circumstance such as inheritances. To overcome this limitation, we apply Machine Learning methods (‘random forest’ algorithm) to optimize the choice of cut-offs and we find that IOp explains over 60% of wealth inequality in the US and Spain (using the Gini coefficient), and more than 40% in Italy and Canada. Including parental education as an additional circumstance −available for the US and Italy− we find that inheritances are still the main contributor. Finally, using the S-Gini index with different parameters to weight different parts of the distribution, we find that the effect of inheritances is more prominent at the middle of the wealth distribution, while parental education is more important for the asset-poor.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it