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Using the simple random sampling (SRS) for collecting the income data may results poor estimators specially when the sample size is not enough large. Since under this circumstance, it may be difficult to obtain a representative subset from the income population based on SRS. Ranked set sampling (RSS) and its simplified versions overcome to this shortcoming. These sampling schemes work based on judgment ranking of the sample units. Moreover, the judgment post-stratification sampling (JPS) is also another rank-based sampling plan that can be considered as a competitor of RSS. This paper is organized in order to find the most appropriate sampling scheme among the SRS, RSS, JPS and some more, for estimating of some well-known inequality indices. Comparison of the estimators is carried out through a simulation study based on both perfect and imperfect ranking mechanisms. Results show that the suggested scheme is different for each inequality index. Finally, a real data set is analyzed.

Estimation of the inequality indices based on the well-known rank-based sampling schemes