  This paper develops a theory and methodology for estimation of Gini index
such that both cost of sampling and estimation error are minimum. Methods in
which sample size is fixed in advance, cannot minimize estimation error and
sampling cost at the same time. In this article, a purely sequential procedure
is proposed which provides an estimate of the sample size required to achieve a
sufficiently smaller estimation error and lower sampling cost. Characteristics
of the purely sequential procedure are examined and asymptotic optimality
properties are proved without assuming any specific distribution of the data.
Performance of our method is examined through extensive simulation study.
