  Empirical work often uses treatment assigned following geographic boundaries.
When the effects of treatment cross over borders, classical
difference-in-differences estimation produces biased estimates for the average
treatment effect. In this paper, I introduce a potential outcomes framework to
model spillover effects and decompose the estimate's bias in two parts: (1) the
control group no longer identifies the counterfactual trend because their
outcomes are affected by treatment and (2) changes in treated units' outcomes
reflect the effect of their own treatment status and the effect from the
treatment status of 'close' units. I propose conditions for non-parametric
identification that can remove both sources of bias and semi-parametrically
estimate the spillover effects themselves including in settings with staggered
treatment timing. To highlight the importance of spillover effects, I revisit
analyses of three place-based interventions.
