LEAP

     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 identication 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 condentiality protection. Detailed aggregate statistics by geography or detailed industry classes are rare, public-use microdata on businesses are virtually inexistant, and access to condential 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 datasets 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 dierent countries: Canada (Longitudinal Employment Analysis Program (LEAP)) and Germany (Establishment History Panel (BHP)). We assess utility and protection, and provide an assessment of the feasibility of extending such an approach in a cost-eective way to other data.

Outline

Accessing business microdata for research purposes at the Canadian Centre for Data Development and Economic Research (CDER) at Statistics Canada

• CDER basics
• Data sets available for access to CDER
• Application process
• Future directions
• Other information