Regression modelQuasi-experimental / causal inference
Spatial Regression Discontinuity Design (Spatial RDD)
Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border.
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Sources
- Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI: 10.3982/ECTA8121 ↗
- Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI: 10.1093/pan/mpu014 ↗
Related methods
Referenced by
Spatial Coarsened Exact MatchingSpatial Event Study DesignSpatial Fuzzy Regression DiscontinuitySpatial Instrumental VariablesSpatial Interrupted Time SeriesSpatial Matching EstimatorSpatial Panel Event StudySpatial Placebo TestSpatial Propensity Score MatchingSpatial Propensity Score WeightingSpatial Synthetic Control Method