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Synthetic establishment microdata around the world

Article type: Research Article

Authors: Vilhuber, Larsa; * | Abowd, John M.a | Reiter, Jerome P.b

Affiliations: [a] Labor Dynamics Institute, Cornell University, Ithaca, NY, USA | [b] Department of Statistical Science, Duke University, Durham, NC, USA

Correspondence: [*] Corresponding author: Lars Vilhuber, Labor Dynamics Institute, Cornell University, Ithaca, NY 14853, USA. E-mail:{[email protected]}

Keywords: Business data, confidentiality, international comparison, multiple imputation, synthetic, differential privacy

DOI: 10.3233/SJI-160964

Journal: Statistical Journal of the IAOS, vol. 32, no. 1, pp. 65-68, 2016

Published: 27 February 2016
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Abstract

In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large part, this difficulty in providing access to business microdata is due to the skewed and sparse distributions that characterize business data. Synthetic data are simulated data generated from statistical models. We organized sessions at the 2015 World Statistical Congress and the 2015 Joint Statistical Meetings, highlighting work on synthetic \emph{establishment} microdata. This overview situates those papers, published in this issue, within the broader literature.

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