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Activity Number: 640
Type: Topic Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #315184
Title: Synthetic Data Generation for Firm Links
Author(s): Satkartar Kinney* and Jerry Reiter
Companies: NISS and Duke University
Keywords: Establishment data ; multiple imputation ; statistical disclosure limitation ; data confidentiality

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 paper describes our efforts to create the second generation of the SynLBD, including synthesis procedures that we believe could be replicated in other contexts.

Authors who are presenting talks have a * after their name.

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