Abstract
Recently, Abadie et al. (Am J Polit Sci 59:495–510, 2015) have expanded synthetic control methods by the so-called cross-validation technique. We find that their results are not being reproduced when alternative software packages are used or when the variables’ ordering within the dataset is changed. We show that this failure stems from the cross-validation technique relying on non-uniquely defined predictor weights. While the amount of the resulting ambiguity is negligible for the main application of Abadie et al. (Am J Polit Sci 59:495–510, 2015), we find it to be substantial for several of their robustness analyses. Applying well-defined, standard synthetic control methods reveals that the authors’ results are particularly driven by a specific control country, the USA.
Highlights
As a tool for policy evaluation, Abadie and Gardeazabal (2003) have introduced so-called synthetic control methods (SCM)
These results are very similar to those obtained when using the cross-validation technique: after the reunification, Germany suffered from a significant loss in GDP per capita which amounted to roughly 11% in 2003
When using the standard SCM approach, the US data is essential for detecting a Conclusions The synthetic control method is an important tool in policy evaluation which has been expanded by Abadie et al (2015), who introduce the cross-validation technique for selecting predictor weights
Summary
As a tool for policy evaluation, Abadie and Gardeazabal (2003) have introduced so-called synthetic control methods (SCM). For estimating the development of the treated unit in absence of the treatment, the basic idea of SCM is to find suitable donor weights which describe how the treated unit is synthesized by a weighted mix of unaffected control units. In this context, “suitable” means that treated and synthetic unit should resemble each other as closely as possible prior to the treatment, both with respect to the outcome of interest and with respect to so-called economic predictors. Properties of the SCM estimator, like (asymptotic) unbiasedness, have been developed by Abadie et al (2010), while Gardeazabal and VegaBayo (2017) find that the SCM estimator performs well as compared to alternative panel approaches
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