LOOKING BACK ON THREE YEARS OF USING THE SYNTHETIC LBD BETA
February 2014
Working Paper Number:
CES-14-11
Abstract
Distributions of business data are typically much more skewed than those for household or individual data and public knowledge of the underlying units is greater. As a results, national statistical offices (NSOs) rarely release establishment or firm-level business microdata due to the risk to respondent confidentiality. One potential approach for overcoming these risks is to release synthetic data where the establishment data are simulated from statistical models designed to mimic the distributions of the real underlying microdata. The US Census Bureau's Center for Economic Studies in collaboration with Duke University, the National Institute of Statistical Sciences, and Cornell University made available a synthetic public use file for the Longitudinal Business Database (LBD) comprising more than 20 million records for all business establishment with paid employees dating back to 1976. The resulting product, dubbed the SynLBD, was released in 2010 and is the first-ever comprehensive business microdata set publicly released in the United States including data on establishments employment and payroll, birth and death years, and industrial classification. This pa- per documents the scope of projects that have requested and used the SynLBD.
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:
data,
researcher,
payroll,
statistical,
enterprise,
database,
data census,
industrial,
microdata,
survey,
disclosure,
aggregate,
agency,
employee,
establishment,
business data,
establishments data,
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identified to contain references to specific institutions, datasets, and other organizations.
:
Service Annual Survey,
National Science Foundation,
Center for Economic Studies,
County Business Patterns,
Company Organization Survey,
Longitudinal Business Database,
Cornell University,
Research Data Center,
North American Industry Classification System,
Business Register,
Census Bureau Disclosure Review Board,
Duke University,
Business Dynamics Statistics
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One major criticism against the use of synthetic data has been that the efforts necessary to generate useful synthetic data are so in- tense that many statistical agencies cannot afford them. We argue many lessons in this evolving field have been learned in the early years of synthetic data generation, and can be used in the development of new synthetic data products, considerably reducing the required in- vestments. The final goal of the project described in this paper will be to evaluate whether synthetic data algorithms developed in the U.S. to generate a synthetic version of the Longitudinal Business Database (LBD) can easily be transferred to generate a similar data product for other countries. We construct a German data product with infor- mation comparable to the LBD - the German Longitudinal Business Database (GLBD) - that is generated from different administrative sources at the Institute for Employment Research, Germany. In a fu- ture step, the algorithms developed for the synthesis of the LBD will be applied to the GLBD. Extensive evaluations will illustrate whether the algorithms provide useful synthetic data without further adjustment. The ultimate goal of the project is to provide access to multiple synthetic datasets similar to the SynLBD at Cornell to enable comparative studies between countries. The Synthetic GLBD is a first step towards that goal.View Full Paper PDF
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