We consider treatment effect estimation via a difference-in-difference approach for data with local spatial interaction such that the outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark difference-in-differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations.
# 15-091/VIII (2015-07-30)
- Michael S. Delgado, Purdue University, United States; Raymond J.G.M. Florax, VU University Amsterdam, the Netherlands, and Purdue University, United States
- Difference-in-differences, Monte Carlo simulation, program evaluation, spatial autocorrelation, spatial interaction
- JEL codes:
- C21, C53