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| Térbeli eseménytanulmány-tervezés× | Térbeli különbség-különbségek (Spatial Difference-in-Differences)× | |
|---|---|---|
| Tudományterület | Oksági következtetés | Oksági következtetés |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2000s–2010s | 2015 |
| Megalkotó≠ | Developed across applied spatial economics literature; canonical applications in Autor, Dorn & Hanson (2013) and related regional economics studies | Delgado & Florax |
| Típus≠ | Quasi-experimental causal inference with spatial structure | Quasi-experimental estimator |
| Alapmű≠ | 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. DOI ↗ | 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. DOI ↗ |
| Alternatív nevek | 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 |
| Kapcsolódó≠ | 5 | 3 |
| Összefoglaló≠ | 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. |
| ScholarGateAdatkészlet ↗ |
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