Copula-based estimation of health inequality measures

Bouezmarni, T, Doukali, M ORCID: 0000-0001-9315-2326 and Taamouti, A ORCID: 0000-0002-1360-8803 (2025) Copula-based estimation of health inequality measures Journal of the Royal Statistical Society Series A Statistics in Society. qnaf039-. ISSN 0964-1998, 1467-985X

	Text Accepted_Manuscript.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (1MB) | Preview
	Text qnaf039.pdf - Open Access published version Download (580kB) | Preview

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.

Item Type:	Article
Uncontrolled Keywords:	49 Mathematical Sciences, 38 Economics, 4905 Statistics, 3802 Econometrics, Generic health relevance, 10 Reduced Inequalities
Divisions:	Faculty of Humanities & Social Sciences Faculty of Humanities & Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	20 Mar 2025 10:42
Last Modified:	23 Jan 2026 15:27
DOI:	10.1093/jrsssa/qnaf039
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3190895
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.