The inclusive synthetic control method
Roberta Di Stefano () and
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Roberta Di Stefano: Department of Methods and Models for Economics, Territory and Finance, Sapienza University of Rome
No 21/20, Working Papers from Sapienza University of Rome, DISS
The Synthetic Control Method (SCM) estimates the causal effect of a policy intervention in a panel data setting with only a few treated units and control units. The treated outcome in the absence of the intervention is recovered by a weighted average of the control units. The latter cannot be affected by the intervention, neither directly nor indirectly. We introduce the inclusive synthetic control method (iSCM), a novel and intuitive synthetic control modification that allows including units potentially affected directly or indirectly by an intervention in the donor pool. Our method is well suited for applications with multiple treated units where including treated units in the donor pool substantially improves the pre-intervention fit and/or for applications where some of the units in the donor pool might be affected by spillover effects. Our iSCM is very easy to implement, and any synthetic control type estimation and inference procedure can be used. Finally, as an illustrative empirical example, we re-estimate the causal effect of German reunification on GDP per capita allowing for spillover effects from West Germany to Austria.
Keywords: Synthetic Control Method; spillover e ects; causal inference. (search for similar items in EconPapers)
JEL-codes: C21 C23 C31 C33 (search for similar items in EconPapers)
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Working Paper: The inclusive synthetic control method (2020)
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