  Missing data is pervasive in econometric applications, and rarely is it
plausible that the data are missing (completely) at random. This paper proposes
a methodology for studying the robustness of results drawn from incomplete
datasets. Selection is measured as the squared Hellinger divergence between the
distributions of complete and incomplete observations, which has a natural
interpretation. The breakdown point is defined as the minimal amount of
selection needed to overturn a given result. Reporting point estimates and
lower confidence intervals of the breakdown point is a simple, concise way to
communicate the robustness of a result. An estimator of the breakdown point of
a result drawn from a generalized method of moments model is proposed and shown
root-n consistent and asymptotically normal under mild assumptions. Lower
confidence intervals of the breakdown point are simple to construct. The paper
concludes with a simulation study illustrating the finite sample performance of
the estimators in several common models.
