Applying Data Synthesis for Longitudinal Business Data across Three Countries

TL;DR

This paper explores creating secure, synthetic microdata for business statistics across Canada and Germany, aiming to balance data utility with confidentiality, and assesses the feasibility of extending this approach internationally.

Contribution

It demonstrates the application of a previously used synthetic data generation model to new countries and evaluates its utility, protection, and scalability.

Findings

Synthetic data maintains analytical validity.

Protection against identification is enhanced.

Method is feasible and cost-effective for multiple countries.

Abstract

Data on businesses collected by statistical agencies are challenging to protect. Many businesses have unique characteristics, and distributions of employment, sales, and profits are highly skewed. Attackers wishing to conduct identification attacks often have access to much more information than for any individual. As a consequence, most disclosure avoidance mechanisms fail to strike an acceptable balance between usefulness and confidentiality protection. Detailed aggregate statistics by geography or detailed industry classes are rare, public-use microdata on businesses are virtually inexistant, and access to confidential microdata can be burdensome. Synthetic microdata have been proposed as a secure mechanism to publish microdata, as part of a broader discussion of how to provide broader access to such data sets to researchers. In this article, we document an experiment to create analytically valid synthetic data, using the exact same model and methods previously employed for the United States, for data from two different countries: Canada (LEAP) and Germany (BHP). We assess utility and protection, and provide an assessment of the feasibility of extending such an approach in a cost-effective way to other data.