  The categorical Gini correlation, $\rho_g$, was proposed by Dang et al. to
measure the dependence between a categorical variable, $Y$ , and a numerical
variable, $X$. It has been shown that $\rho_g$ has more appealing properties
than current existing dependence measurements. In this paper, we develop the
jackknife empirical likelihood (JEL) method for $\rho_g$. Confidence intervals
for the Gini correlation are constructed without estimating the asymptotic
variance. Adjusted and weighted JEL are explored to improve the performance of
the standard JEL. Simulation studies show that our methods are competitive to
existing methods in terms of coverage accuracy and shortness of confidence
intervals. The proposed methods are illustrated in an application on two real
datasets.
