Copula-based estimation of health inequality measures
Bibliographic record
Abstract
Abstract This paper aims to utilize bivariate copulas for deriving estimators of the health concentration curve and Gini coefficient for health distribution. We highlight the importance of expressing health inequality measures in terms of bivariate copulas, which we in turn use to build copula-based semi- and nonparametric estimators of the above measures. Subsequently, we investigate the asymptotic properties of these estimators, establishing their consistency, and asymptotic normality. We provide formulas for their variances, facilitating the construction of confidence intervals and tests for the health concentration curve and Gini health coefficient with respect to a given socioeconomic variable. Through a Monte–Carlo simulation exercise, we demonstrate the superior performance of the semiparametric estimator over the smoothed nonparametric estimator, with the latter outperforming the empirical estimator in terms of Mean Squared Error. Additionally, an extensive empirical study applies our estimators, revealing that inequalities in US states’ socioeconomic variables, such as income/poverty and race, contribute to observed disparities in COVID-19 infections and deaths in the U.S.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".