JSM 2011 Online Program

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Abstract Details

Activity Number: 290
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract - #302117
Title: Synthetic Data Generation for Small-Area Estimation in the American Community Survey
Author(s): Joseph Sakshaug*+
Companies: University of Michigan
Address: 426 Thompson Street, Ann Arbor, MI, 48104, USA
Keywords: synthetic data ; small area estimation ; disclosure ; microdata
Abstract:

Demand for small area estimates is growing among a variety of stakeholders who use these data to study issues affecting local communities. Statistical agencies regularly collect data from small geographic areas but are prevented from releasing small area identifiers due to disclosure concerns. Several disclosure control methods are used to disseminate microdata, including summary tables, suppression of geographical details, and Research Data Centers, but none of these methods is ideal for meeting the growing demand for small area datasets. This research tests a new method for disseminating public-use microdata that contains more geographical details than are currently being released. Specifically, the method replaces the observed microdata with fully-synthetic, or imputed, microdata generated from a posterior predictive distribution. A hierarchical Bayesian model is used to preserve the small area inferences and simulate the synthetic data. Confidentiality protection is enhanced because no actual values are released. The synthetic data is evaluated by comparing inferences obtained from the synthetic data with observed data from the 2005-2007 American Community Survey.


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