We examine the sensitivity of estimates and inequality indices to extreme values, in the sense of their robustness properties and of their statistical performance. We establish that these measures are very sensitive to the properties of the income distribution. Estimation and inference can be...

Drawing on recent work concerning the statistical robustness of inequality statistics we examine the sensitivity of poverty indices to data contamination using the concept of the influence function. We show that poverty and inequality indices have fundamentally different robustness properties,...

Distributional dominance criteria are commonly applied to draw welfare inferences about comparisons, but conclusions drawn from empirical implementations of dominance criteria may be influenced by data contamination. We examine a non-parametric approach to refining Lorenz-type comparisons and...

Inequality measures are powerful tools of applied welfare analysis. However, to use the tools effectively one has to take into account the characteristics of the data with which one usually has to work. These raise a number of common statistical problems which are addressed here for both...

mobility measures; robustness; data contamination

The economic analysis of income distribution and related topics makes extensive use of dominance criteria to draw inferences about welfare comparisons. However it is possible that - just as some inequality statistics can be very sensitive to extreme values - conclusions drawn from empirical...

We examine the performance of measures of mobility when allowance is made for the possibility of data contamination. We find that 'single-stage' indices - those that are applied directly to a sample from a multivariate income distribution - usually prove to be non-robust in the face of...