เปรียบเทียบวิธี

	การออกแบบการศึกษาเหตุการณ์เชิงพื้นที่	ความแตกต่างเชิงพื้นที่ในความแตกต่าง (Spatial Difference-in-Differences)
สาขาวิชา	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2000s–2010s	2015
ผู้ริเริ่ม	Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies	Delgado & Florax
ประเภท	Quasi-experimental causal inference with spatial structure	Quasi-experimental estimator
แหล่งต้นตำรับ	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.	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 126, 35–40.
ชื่อเรียกอื่น	spatial event study, geographic event study, spatial dynamic DiD, place-based event study	Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar
ที่เกี่ยวข้อง	5	3
สรุป	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 Difference-in-Differences (Spatial DiD) extends the classical DiD estimator to settings where observations are geo-referenced and outcomes may be spatially autocorrelated or subject to spillover effects. Introduced by Delgado and Florax (2015), the method augments the standard two-way fixed-effects DiD regression with a spatial lag or spatial error term, yielding unbiased treatment-effect estimates even when policy shocks propagate across geographic units. It is used by economists, regional scientists, and urban planners evaluating place-based interventions such as infrastructure investment, environmental regulations, or zoning reforms.