New data dissemination approaches in old Europe -- synthetic datasets for a German establishment survey

Disseminating microdata to the public that provide a high level of data utility, while at the same time guaranteeing the confidentiality of the survey respondent is a difficult task. Generating multiply imputed synthetic datasets is an innovative statistical disclosure limitation technique with the potential of enabling the data disseminating agency to achieve this twofold goal. So far, the approach was successfully implemented only for a limited number of datasets in the U.S. In this paper, we present the first successful implementation outside the U.S.: the generation of partially synthetic datasets for an establishment panel survey at the German Institute for Employment Research. We describe the whole evolution of the project: from the early discussions concerning variables at risk to the final synthesis. We also present our disclosure risk evaluations and provide some first results on the data utility of the generated datasets. A variance-inflated imputation model is introduced that incorporates additional variability in the model for records that are not sufficiently protected by the standard synthesis.