91社区

TITLE / TITRE
Copula-based estimation of health inequality measures

ABSTRACT / R脡SUM脡

This paper aims to use copulas to derive estimators of the health concentration curve and Gini coefficient for health distribution. We highlight the importance of expressing health inequality measures in terms of a copula, which we in turn use to build copula-based semi and nonparametric estimators of the above measures. Thereafter, we study the asymptotic properties of these estimators. In particular, we establish their consistency and asymptotic normality. We provide expressions for their variances, which can be used to construct confidence intervals and build tests for the health concentration curve and Gini health coefficient. A Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and the latter does better than the empirical estimator in terms of Mean Squared Error. We also run an extensive empirical study where we apply our estimators to show that the inequalities across U.S. states鈥檚 socioeconomic variables like income/poverty and race/ethnicity explain the observed inequalities in COVID-19 infections and deaths in the U.S.

SPEAKER / CONF脡RENCIER聽

Taoufik Bouezmarni received his MSc in 1999 and his PhD in 2004 from the Universit茅 Catholique de Louvain (UCL, Belgium). He spent five years as a postdoctoral fellow at several international universities (Katholieke Universiteit Leuven (KUL, Belgium), HEC Montreal (Business School), Universit茅 de Montr茅al and 91社区 (Departement on Economics)). In 2010, he joined the D茅partement de math茅matiques of the Universit茅 de Sherbrooke as an assistant professor. His research interests include semi- and nonparametric modeling, econometrics, survival analysis, and time series analysis.

Meeting ID: 893 3779 3218

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