  Respondent-Driven Sampling (RDS) is a form of link-tracing sampling, a
sampling technique used for `hard-to-reach' populations that aims to leverage
individuals' social relationships to reach potential participants. While the
methodological focus has been restricted to the estimation of population
proportions, there is a growing interest in the estimation of uncertainty for
RDS as recent findings suggest that most variance estimators underestimate
variability. Recently, Baraff et al. (2016) proposed the \textit{tree
bootstrap} method based on resampling the RDS recruitment tree, and empirically
showed that this method outperforms current bootstrap methods. However, some
findings suggest that the tree bootstrap (severely) overestimates uncertainty.
In this paper, we propose the \textit{neighbourhood} bootstrap method for
quantifiying uncertainty in RDS. We prove the consistency of our method under
some conditions and investigate its finite sample performance, through a
simulation study, under realistic RDS sampling assumptions.
