  We analyze the challenges for inference in difference-in-differences (DID)
when there is spatial correlation. We present novel theoretical insights and
empirical evidence on the settings in which ignoring spatial correlation should
lead to more or less distortions in DID applications. We show that details such
as the time frame used in the estimation, the choice of the treated and control
groups, and the choice of the estimator, are key determinants of distortions
due to spatial correlation. We also analyze the feasibility and trade-offs
involved in a series of alternatives to take spatial correlation into account.
Given that, we provide relevant recommendations for applied researchers on how
to mitigate and assess the possibility of inference distortions due to spatial
correlation.
