  We consider the properties of listwise deletion when both $n$ and the number
of variables grow large. We show that when (i) all data has some idiosyncratic
missingness and (ii) the number of variables grows superlogarithmically in $n$,
then, for large $n$, listwise deletion will drop all rows with probability 1.
Using two canonical datasets from the study of comparative politics and
international relations, we provide numerical illustration that these problems
may emerge in real world settings. These results suggest, in practice, using
listwise deletion may mean using few of the variables available to the
researcher.
