  Linearization methods are customarily adopted in sampling surveys to obtain
approximated variance formulae for estimators of nonlinear functions of finite
population totals - such as ratios, correlation coefficients or measures of
income inequality - which can be usually rephrased in terms of statistical
functionals. In the present paper, by considering the Deville (1991) approach
stemming on the concept of design-based influence curve, we provide a general
result for linearizing large families of inequality indexes. As an example, the
achievement is applied to the Gini, the Amato, the Zenga and the Atkinson
indexes, respectively.
