Before revision:  We consider a setting in which the spatial correlation follows a linear factor model, which allows for a rich variety of spatial correlation structures. In this setting, we show that inference ignoring spatial correlation is more problematic when both (i) the second moment of the difference between the pre- and post-treatment averages of common factors is large, and (ii) the distribution of factor loadings has different expected values for treated and control groups. The choice among different estimators also affect the relevance of spatial correlation for inference  . Simulations with real datasets corroborate these conclusions.
After revision:  We consider a setting in which the spatial correlation follows a linear factor model, which allows for a rich variety of spatial correlation for inference . Simulations with real datasets corroborate these conclusions.