Improving The Synthetic Longitudinal Business Database
In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments’ confidentiality. Agencies potentially can manage these risks by releasing synthetic microdata, i.e., individual establishment records simulated from statistical models de- signed to mimic the joint distribution of the underlying observed data. Previously, we used this approach to generate a public-use version—now available for public use—of the U. S. Census Bureau’s Longitudinal Business Database (LBD), a longitudinal cen- sus of establishments dating back to 1976. While the synthetic LBD has proven to be a useful product, we now seek to improve and expand it by using new synthesis models and adding features. This article describes our efforts to create the second generation of the SynLBD, including synthesis procedures that we believe could be replicated in other contexts.
|Date of creation:||Feb 2014|
|Date of revision:|
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- Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011.
"Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database,"
International Statistical Review,
International Statistical Institute, vol. 79(3), pages 362-384, December.
- Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," Working Papers 11-04, Center for Economic Studies, U.S. Census Bureau.
- Miranda, Javier & Lars Vilhuber, 2014. "Looking Back On Three Years Of Using The Synthetic Lbd Beta," Working Papers 14-11, Center for Economic Studies, U.S. Census Bureau.
- Ron S. Jarmin & Thomas A. Louis & Javier Miranda, 2014. "Expanding The Role Of Synthetic Data At The U.S. Census Bureau," Working Papers 14-10, Center for Economic Studies, U.S. Census Bureau.
- Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
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