Linganisha mbinu

	Tofauti za Maeneo-ndani-ya-Tofauti	Tofauti-katika-Tofauti (Diff-in-Diff)
Nyanja	Uhitimisho wa Kisababishi	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2015	1994
Mwanzilishi	Delgado & Florax	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Aina	Quasi-experimental estimator	Causal inference / panel regression
Chanzo asilia	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Majina mbadala	Spatial DiD, Geo-DiD, Difference-in-Differences with Spatial Autocorrelation, Mekansal Fark-içinde-Farklar	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Zinazohusiana	3	5
Muhtasari	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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.