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

Understand

We investigate whether generating synthetic data can be a viable strategy for providing 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 Employment Research (IAB) in Germany that linked exact geocodes to the Integrated Employment Biographies (IEB), a large administrative database containing several million records.
• We evaluate the performance of three synthesizers regarding the trade-off between preserving analytical validity and limiting disclosure risks: One synthesizer employs Dirichlet Process mixtures of products of multinomials (DPMPM), while the other two use different versions of Classification and Regression Trees (CART).
• In terms of preserving analytical validity, our proposed synthesis strategy for geocodes based on categorical CART models outperforms the other two.