Ignoring spatial interdependence in outcomes can lead to biased estimates, even with randomly assigned instruments. This bias worsens when the instrument is spatially clustered, like rainfall or economic shocks. Addressing only one type of bias can increase overall bias. A new method, S-2SLS, considers both outcome interdependence and predictor endogeneity, providing more accurate estimates of predictor effects.