Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in OnThe Map
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We report results from the first comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI): total employment, beginning-of-quarter employment, full-quarter employment, total payroll, and average monthly earnings of full-quarter employees. Beginning-of-quarter employment is also the main tabulation variable in the LEHD Origin-Destination Employment Statistics (LODES) workplace reports as displayed in OnTheMap (OTM). The evaluation is conducted by generating multiple threads of the edit and imputation models used in the LEHD Infrastructure File System. These threads conform to the Rubin (1987) multiple imputation model, with each thread or implicate being the output of formal probability models that address coverage, edit, and imputation errors. Design-based sampling variability and finite population corrections are also included in the evaluation. We derive special formulas for the Rubin total variability and its components that are consistent with the disclosure avoidance system used for QWI and LODES/OTM workplace reports. These formulas allow us to publish the complete set of detailed total quality measures for QWI and LODES. The analysis reveals that the five publication variables under study are estimated very accurately for tabulations involving at least 10 jobs. Tabulations involving three to nine jobs have quality in the range generally deemed acceptable. Tabulations involving zero, one or two jobs, which are generally suppressed in the QWI and synthesized in LODES, have substantial total variability but their publication in LODES allows the formation of larger custom aggregations, which will in general have the accuracy estimated for tabulations in the QWI based on a similar number of workers.
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Acknowledgements: Portions of Appendix A are based on an unpublished technical memo dated February 1, 2011 by John Abowd, Henry Hyatt, Mark Kutzbach, Erika McEntarfer, Kevin McKinney, Michael Strain, Lars Vilhuber, and Chen Zhao. Liliana Sousa was an important contributor to the work documented in the Appendix. We received helpful comments from Erika McEntarfer and John Eltinge. Sara Sullivan edited the final manuscript. Abowd and Vilhuber acknowledge direct support from the U.S. Census Bureau (prior to Abowd's appointment) and NSF Grants SES-0922005, BCS 0941226, TC-1012593, and SES-1131848. This research uses data from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics Program, which was partially supported by the following National Science Foundation Grants: SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. Kevin McKinney (kevin.l.mckinney@census.gov) is Senior Economist, U.S. Census Bureau. Andrew Green (asg248@cornell.edu) is Economist, U.S. Census Bureau, and Economics Ph.D. student at Cornell University, Lars Vilhuber (lars.vilhuber@cornell.edu) is Senior Research Associate and Executive Director of the Labor Dynamics Institute at Cornell University and Economist (IPA), U.S. Census Bureau. John Abowd (john.maron.abowd@census.gov) is Associate Director for Research and Methodology and Chief Scientist, U.S. Census Bureau, Edmund Ezra Day Professor of Economics, Statistics and Information Science, Cornell University (ILR School), and Director, Labor Dynamics Institute, Cornell University ILR School.
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2017-12-16
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Multiple imputation; Total quality measures; Employment statistics; Earnings statistics; Total survey error
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The data produced by the analysis described in this paper have been released for public use and may be found at http://doi.org/10.3886/E100590V1.