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We investigate whether generating synthetic data can be a viable strategy forproviding access to detailed geocoding information for external researchers,without compromising the confidentiality of the units included in the database.Our work was motivated by a recent project at the Institute for EmploymentResearch (IAB) in Germany that linked exact geocodes to the IntegratedEmployment Biographies (IEB), a large administrative database containingseveral million records. We evaluate the performance of three synthesizersregarding the trade-off between preserving analytical validity and limitingdisclosure risks: One synthesizer employs Dirichlet Process mixtures ofproducts of multinomials (DPMPM), while the other two use different versions ofClassification and Regression Trees (CART). In terms of preserving analyticalvalidity, our proposed synthesis strategy for geocodes based on categoricalCART models outperforms the other two. If the risks of the synthetic datagenerated by the categorical CART synthesizer are deemed too high, wedemonstrate that synthesizing additional variables is the preferred strategy toaddress the risk-utility trade-off in practice, compared to limiting the sizeof the regression trees or relying on the strategy of providing geographicalinformation only on an aggregated level. We also propose strategies for makingthe synthesizers scalable for large files, present analytical validity measuresand disclosure risk measures for the generated data, and provide generalrecommendations for statistical agencies considering the synthetic dataapproach for disseminating detailed geographical information.

Synthesizing geocodes to facilitate access to detailed geographical information in large scale administrative data