Measuring income-related inequalities in health using a parametric dependence function
Attention has been given recently to the Concentration Index; specifically, corrected versions have been generated that supersede the original with properties such as transform invariance, reversal invariance and transfer invariance. While previous studies have promoted a transformed or normalised index to overcome these problems, I propose, in this paper, two novel approaches to a direct parametric model for dependence as a measure of inequality in the distributions of health and income. These are the copula and quantile regression using jackknifed samples. As well as accommodating any form of health or income, and being robust to invariance criteria, both methods parameterise the measure of inequality directly, rather than indirectly through functions on one of the marginals. Results from an illustrating example using the Survey of Health, Retirement and Ageing in Europe suggest that such inequality in these countries is not explained well by covariates on age, gender, education and lifestyles.
Health Inequality, Non-Continuous data, Copulas, Quantile Regression
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