Survey data tends to be biased toward the middle class. Often it fails to adequately cover the highly relevant group of multi-millionaires and billionaires, which in turn results in biased estimates for aggregate wealth and top wealth shares. In order to overcome the under coverage and obtain more reliable measurements of wealth inequality, researchers are simulating the top tail of wealth distributions using Pareto distributions both with and without information on high-net-worth-individuals from rich lists. In a series of Monte Carlo experiments, this study analyzes what assumptions need to be fulfilled in order for such an exercise to yield reliable results. If survey weights are uninformed about the relationship between non-response and wealth, as is to be expected empirically, the former case will underestimate top wealth shares and the latter may overestimate it, while both methods yield estimates of aggregate wealth that are still inherently biased downwards. In an application using German survey wealth data, it is shown that re-weighting the provided frequency weights based on exogenous information possibly affects the estimates more severely than choosing the right parameters of the Pareto distribution. However, empirically the three separate assumptions on the non- response yield wildly different estimates.The validity of exogenous dataâand the rich list dataâremains a matter of trust on the part of the empiricist.