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Copula-based estimation of health inequality measures with an application to COVID-19

Author

Listed:
  • Taoufik Bouezmarni

    (Universite de Sherbrooke)

  • Mohamed Doukali

    (School of Economics, University of East Anglia)

  • Abderrahim Taamouti

    (University of Liverpool)

Abstract

This paper aims to use copulas to derive alternative estimators of Health Concentration Curve, hereafter CH, and Gini coefficient for health distribution. We motivate the importance of expressing health inequality measures in terms of copula, which we in turn use to build copula-based semi and non-parametric 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 health concentration curve and Gini health coe¢ cient. A Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that 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 CH and Gini health coe¢ cient estimators to show that the inequalities across U.S. states socioeconomic variables like income/poverty and race/ethnicity explain the observed inequalities in the U.S. COVID-19s infections and deaths.

Suggested Citation

  • Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2023. "Copula-based estimation of health inequality measures with an application to COVID-19," University of East Anglia School of Economics Working Paper Series 2023-01, School of Economics, University of East Anglia, Norwich, UK..
  • Handle: RePEc:uea:ueaeco:2023-01
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Health concentration curve; Gini health coe¢ cient; inequality; copula; semi- and non-parametric estimators; COVID-19 infections and deaths;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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