Stochastic dominance criteria are commonly used to draw welfare-theoretic inferences about comparisons of income distribution as well as ranking probability distributions in the analysis of choice under uncertainty. However, just as some measures of location and dispersion can be catastrophically sensitive to extreme values in the data it is also possible that conclusions drawn from empirical implementations of dominance criteria are unduly influenced by data contamination. We show the conditions under which this may occur for a number of standard dominance tools used in welfare analysis.

Inequality measures are often used to summarize information about empirical income distributions. However the resulting picture of the distribution and of changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general depend upon the underlying properties of the inequality measure. This issue is investigated theoretically using a technique based on the influence function, and the magnitude of the effect is illustrated using a simulation. Both direct nonparametric estimation from the sample, and indirect estimation using a parametric model are considered; in the latter case the application of a robust estimation procedure is demonstrated. The results are applied to two micro-data examples. Copyright 1996 by The Econometric Society.