Modeling Aggregates for Reliability and Confidentiality of Output with Application to the QCEW Program of BLS

• Activity Number: 625
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Section on Survey Research Methods
• Author(s): Santanu Pramanik*+ and Avinash C. Singh and Michael Yang and Fritz Scheuren
• Companies: NORC at the The University of Chicago and NORC at the The University of Chicago and NORC at the The University of Chicago and NORC at the The University of Chicago
• Keywords: Disclosure Control ; Small Area Estimation ; Synthetic ; Perturbation ; output treatment

Abstract

The Quarterly Census of Employment and Wages (QCEW) program of the Bureau of Labor Statistics publishes tabulations of certain attributes (employment, wages etc.) by industry and geography. The publication of data collected from BLS surveys (or census) should not lead to identification of cooperating respondents. The BLS has been concerned for some time about the current cell suppression method that can compromise the quality and utility of the QCEW data. To address such concerns, BLS has been conducting research on the random noise method toward extending that model for application to QCEW. The goal is to release significantly more data and to respond to new disclosure vulnerabilities. In this paper, we propose an alternative based on small area modeling techniques for output treatment for disclosure. In this new application of small area modeling we would like to exploit the built-in p