The Inclusive Synthetic Control Method

Abstract

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.

Creating and maintaining film clusters: Synthetic control method analysis of the enactment and repeal of US state film incentives

“…Such spillovers could bias the estimated treatment effect of the incentive policy for the state. We address this by implementing the procedure of Di Stefano and Mellace (2020) to eliminate post‐intervention effects from neighbouring donor states so that they can be included in the donor pool without introducing bias.…”

“…One solution would be to restrict the donor pool to only those who are not neighbours (Grossi et al, 2020). Di Stefano and Mellace (2020) instead implement a procedure to eliminate post‐intervention effects from neighbouring donors so that they can be included in the donor pool. The procedure is particularly useful where there is a limited number of potential donors.…”

“…We implement the approach of Di Stefano and Mellace (2020), defining neighbours as those sharing a border with the incentive state. In our base results, there are only five cases where a neighbouring state is part of the synthetic control.…”

Synthetic Control Method: A tool for comparative case studies in economic history

“…Although the focus of our investigation is economic growth over the long historical period, the usefulness of the method for non-economic variables such as demographic changes, election outcomes, institutional and political stability, and human capital stock among many others should not be downgraded. 3 Against this backdrop, Di Stefano and Mellace (2020) introduce the inclusive synthetic control method as a modification of the originally proposed method by Abadie and Gardeazabal (2003). The inclusive synthetic control method allows the inclusion of units potentially affected directly and indirectly by the intervention in the donor pool, which is particularly suitable for the settings with multiple treated units where some of the units might be contaminated by the spillover effect of the intervention.…”

“…33Against this backdrop, Di Stefano and Mellace (2020) introduce the inclusive synthetic control method as a modification of the originally proposed method by Abadie and Gardeazabal (2003). The inclusive synthetic control method allows the inclusion of units potentially affected directly and indirectly by the intervention in the donor pool, which is particularly suitable for the settings with multiple treated units where some of the units might be contaminated by the spillover effect of the intervention.…”