The jackknife is a resampling method that uses subsets of the original database by leaving
out one observation at a time from the sample. The paper develops fast algorithms for jackknifing
inequality indices with only a few passes through the data. The number of passes is
independent of the number of observations. Hence, the method provides an efficient way to
obtain standard errors of the estimators even if sample size is large. We apply our method
using micro data on individual incomes for Germany and the US.