In this paper we examine the use of Efron's (1992) jackknife-after-bootstrap to assess the accuracy of the bootstrap. We consider the possibility of using the bootstrap to estimate the finite sample variability of some simple linear statistical models and feasible generalized least squares estimator applied to the seemingly unrelated regressions model. We find that the jackknife-after-bootstrap usually overestimates the variability of the bootstrap standard error by a substantial amount, but that the amount of error declines with increasing numbers of bootstrap samples. Thus the jackknife-after-bootstrap can serve to estimate a comfortable upper bound for the bootstrap standard-error.