When we bootstrap an inconsistent estimator, its bootstrap analogs are concentrated more and more around the probability limit of the estimator, and thus the estimate of the bias becomes smaller and smaller as the sample size grows. That is, bootstrapping is able to correct the bias caused by nite sample nonsymmetry of the distribution, but not the asymptotic bias (dierence between the probability limit of the estimator and the true parameter value).