Improving the Synthetic Longitudinal Business Database

19 Pages Posted: 12 Apr 2014

See all articles by Satkartar K. Kinney

Satkartar K. Kinney

RTI International

Jerome Reiter

Duke University

Javier Miranda

US Census Bureau — Economy-Wide Statistics Division

Date Written: February 1, 2014

Abstract

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 designed 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 census 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.

Suggested Citation

Kinney, Satkartar K. and Reiter, Jerome and Miranda, Javier, Improving the Synthetic Longitudinal Business Database (February 1, 2014). US Census Bureau Center for Economic Studies Paper No. CES-WP- 14-12, Available at SSRN: https://ssrn.com/abstract=2423400 or http://dx.doi.org/10.2139/ssrn.2423400

Satkartar K. Kinney (Contact Author)

RTI International ( email )

PO Box 12194
Research Triangle Park, 27709
United States

Jerome Reiter

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Javier Miranda

US Census Bureau — Economy-Wide Statistics Division ( email )

Washington, DC
United States

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