Quantile means and quantile share standard errors and a toolbox of distributional statistics
Bibliographic record
Abstract
This article derives the (asymptotic) variances and covariances – and hence standard errors – of quantile means and quantile shares in terms of explicit formulas that are distribution-free and easily computable. The article then develops a toolbox of quantile-based disaggregative inequality measures, based on the means and shares, which allow for detailed inferential analysis of income distributions in a straightforward unified framework. The analytical formulas are applied to Canadian Census public-use microdata files on workers’ earnings for 2000 and 2005. The results highlight the statistical significance of how upper-earnings levels have advanced beyond middle earnings, how much the share of mid-range earnings has eroded over even a five-year period, and how decile mean growth rates for women were everywhere higher than for men – except at the top decile, where the opposite phenomenon was highly significant.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".