This paper proposes the use of the bootstrap for the most commonly applied procedures in inequality, mobility and poverty measurement. In addition to simple inequality index estimation the scenarios considered are inequality difference tests for correlated data, decompositions by sub-group or income source, decompositions of inequality changes, and mobility index and poverty index estimation. Besides showing the consistency of the bootstrap for these scenarios, the paper also develops simple ways to deal with longitudinal correlation and panel attrition or non-response. In principle, all the proposed procedures can be handled by the #delta#-method, but Monte Carlo evidence suggests that the simplest possible bootstrap procedure should be the preferred method in practice, as it achieves the same accuracy as the #delta#-method and takes into account the stochastic dependencies in the data without explicitly having to deal with its covariance structure. If a variance estimate is available, then thestudentized version of the bootstrap may lead to an improvement in accuracy, but substantially so only for relatively small sample sizes. All results incorporate the possibility that different observations have different sampling weights.
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