Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction

We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assu...

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Bibliographic Details
Published in:Economics letters 2015-12, Vol.137, p.123-126
Main Authors: Delgado, Michael S., Florax, Raymond J.G.M.
Format: Article
Language:English
Subjects:
Benchmarks
Difference-in-differences
Economic theory
Estimating techniques
Monte Carlo simulation
Program evaluation
Regression analysis
Spatial autocorrelation
Spatial data
Spatial interaction
Studies
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Summary:We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential 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. •Develop a difference-in-differences estimator for spatial data.•Allow for local spatial interaction in potential outcomes.•Identify direct and indirect treatment effects.•Monte Carlo simulations show good finite sample performance.
ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2015.10.035