  We study the impact of weak identification in discrete choice models, and
provide insights into the determinants of identification strength in these
models. Using these insights, we propose a novel test that can consistently
detect weak identification in commonly applied discrete choice models, such as
probit, logit, and many of their extensions. Furthermore, we demonstrate that
when the null hypothesis of weak identification is rejected, Wald-based
inference can be carried out using standard formulas and critical values. A
Monte Carlo study compares our proposed testing approach against commonly
applied weak identification tests. The results simultaneously demonstrate the
good performance of our approach and the fundamental failure of using
conventional weak identification tests for linear models in the discrete choice
model context. Furthermore, we compare our approach against those commonly
applied in the literature in two empirical examples: married women labor force
participation, and US food aid and civil conflicts.
