  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.
We present numerical illustrations to demonstrate finite-$n$ implications.
These results suggest, in practice, using listwise deletion may mean using few
of the variables available to the researcher even when $n$ is very large.
