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	Spatial Event Study Design	Spatial Regression Discontinuity Design (Spatial RDD)
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2000s–2010s	2010s
Upphovsperson	Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies	Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015)
Typ	Quasi-experimental causal inference with spatial structure	Quasi-experimental causal inference
Ursprungskälla	Autor, D. H., Dorn, D., & Hanson, G. H. (2013). The China Syndrome: Local Labor Market Effects of Import Competition in the United States. American Economic Review, 103(6), 2121-2168.	Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903.
Alias	spatial event study, geographic event study, spatial dynamic DiD, place-based event study	Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design
Närliggande	5	4
Sammanfattning	Spatial event study design estimates the dynamic causal effects of a geographically concentrated shock or policy by plotting how outcomes in affected locations evolve relative to unaffected locations across time periods, while explicitly accounting for spatial spillovers and autocorrelation across geographic units. It is widely used in regional and urban economics to evaluate place-based policies, trade shocks, and local labour market interventions.	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.