  We review the fuzzy approach to poverty measurement by comparing poverty
indices using different membership functions proposed in the literature. We put
our main focus on the issue of estimation of the mean squared errors of these
fuzzy methods showing which indices can be more accurately estimated using
sample data. By means of simulations, we also investigate the role of
parameters of the membership function when it comes to estimating mean squared
errors via a robustness analysis.
