  In this paper, we draw attention to a promising yet slightly underestimated
measure of variability - the Gini coefficient. We describe two new ways of
defining and interpreting this parameter. Using our new representations, we
compute the Gini index for a few probability distributions and describe it in
more detail for the negative binomial distribution. We also suggest the latter
as a tool to measure overdispersion in epidemiology.
