Extreme data are known to be highly influential when measuring income inequality from microdata. Similarly, Lorenz curves and dominance criteria are sensitive to data contamination in the tails of the distribution. In this presentation, I intend to introduce a set of user-written packages that implement robust statistical methods for income distribution analysis. These methods are based on the estimation of parametric models (Pareto, Singh–Maddala) with "optimal B-robust" estimators rather than maximum likelihood. Empirical examples show how robust inequality estimates and dominance checks can be derived from these models.
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