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Spatial Synthetic Difference-in-Differences

Author

Listed:
  • Renan Serenini

    (University of Rome)

  • Frantisek Masek

    (National Bank of Slovakia)

Abstract

We propose a spatial extension of the Synthetic Difference-in-Differences (Sy- DiD) estimator developed by Arkhangelsky et al. (2021). Our estimator addresses violations of the StableUnit TreatmentValue Assumption (SUTVA) that arisewhen treatment effects spill over to other units. Spillovers to units in the donor pool can lead to biased and inconsistent estimates of the Average Treatment Effect on the Treated (ATT), while spillovers outside the donor pool leave the ATT identi- fiable but prevent identification of the Average Treatment Effect (ATE). Building on the framework of the Spatial Difference-in-Differences estimator introduced by Delgado and Florax (2015), we develop a method that decomposes the ATE into direct (ATT) and indirect treatment effects. We demonstrate that our esti- mator improves the identification of the indirect effect relative to the standard Spatial Difference-in-Differences approach, while retaining the robustness and favorable properties of the Synthetic Difference-in-Differences method for esti- mating the direct effect.

Suggested Citation

  • Renan Serenini & Frantisek Masek, 2025. "Spatial Synthetic Difference-in-Differences," Working and Discussion Papers WP 11/2025, Research Department, National Bank of Slovakia.
    • Handle: RePEc:svk:wpaper:1126
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    References listed on IDEAS

    as
    1. Delgado, Michael S. & Florax, Raymond J.G.M., 2015. "Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction," Economics Letters, Elsevier, vol. 137(C), pages 123-126.
    2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    3. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
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    5. Dubé, Jean & Legros, Diègo & Thériault, Marius & Des Rosiers, François, 2014. "A spatial Difference-in-Differences estimator to evaluate the effect of change in public mass transit systems on house prices," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 24-40.
    6. Marynia Kolak & Luc Anselin, 2020. "A Spatial Perspective on the Econometrics of Program Evaluation," International Regional Science Review, , vol. 43(1-2), pages 128-153, January.
    7. Butts, Kyle, 2023. "JUE Insight: Difference-in-differences with geocoded microdata," Journal of Urban Economics, Elsevier, vol. 133(C).
    8. Roberta Di Stefano & Giovanni Mellace, 2020. "The inclusive synthetic control method," Working Papers 21/20, Sapienza University of Rome, DISS.
    9. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    10. Alberto Abadie & Jérémy L’Hour, 2021. "A Penalized Synthetic Control Estimator for Disaggregated Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1817-1834, October.
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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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