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Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in On The Map

Author

Listed:
  • Kevin L. McKinney
  • Andrew S. Green
  • Lars Vilhuber
  • John M. Abowd

Abstract

We report results from the rst 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 nite 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.

Suggested Citation

  • Kevin L. McKinney & Andrew S. Green & Lars Vilhuber & John M. Abowd, 2017. "Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in On The Map," Working Papers 17-71, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:17-71
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    File URL: https://www2.census.gov/ces/wp/2017/CES-WP-17-71.pdf
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    References listed on IDEAS

    as
    1. Benedetto, Gary & Haltiwanger, John & Lane, Julia & McKinney, Kevin, 2007. "Using Worker Flows to Measure Firm Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 299-313, July.
    2. John M. Abowd & Bryce E. Stephens & Lars Vilhuber & Fredrik Andersson & Kevin L. McKinney & Marc Roemer & Simon Woodcock, 2009. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," NBER Chapters,in: Producer Dynamics: New Evidence from Micro Data, pages 149-230 National Bureau of Economic Research, Inc.
    3. Abowd, John M. & Vilhuber, Lars, 2005. "The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 133-152, April.
    4. Li, Qi & Racine, Jeff, 2003. "Nonparametric estimation of distributions with categorical and continuous data," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 266-292, August.
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    Cited by:

    1. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.

    More about this item

    Keywords

    Multiple imputation; Total quality measures; Employment statistics; Earnings statistics; Total survey error;

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