No 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.

We analyze gender differences in expected starting salaries along the wage expectations distribution of prospective university students in Germany, using elicited beliefs about both own salaries and salaries for average other students in the same field. Unconditional and conditional quantile regressions show 5–15% lower wage expectations for females. At all percentiles considered, the gender gap is more pronounced in the distribution of expected own salary than in the distribution of wages expected for average other students. Decomposition results show that biased beliefs about the own earnings potential relative to others and about average salaries play a major role in explaining the gender gap in wage expectations for oneself.