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Shail Butani U.S Bureau of Labor Statistics (BLS)

Adaptation of Evans, Zaytaz, and Slanta (EZS) Disclosure Method to Quarterly Census of Employment and Wages (QCEW). Shail Butani U.S Bureau of Labor Statistics (BLS) Michael Buso*, Shail Butani, David Hiles , Spencer Jobe (BLS)

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Shail Butani U.S Bureau of Labor Statistics (BLS)

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  1. Adaptation of Evans, Zaytaz, and Slanta (EZS) Disclosure Method to Quarterly Census of Employment and Wages (QCEW) Shail Butani U.S Bureau of Labor Statistics (BLS) Michael Buso*, Shail Butani, David Hiles, Spencer Jobe (BLS) Ali Mushtaq, SantanuPramanik, Fritz Scheuren**, and Michael Yang (NORC)

  2. Quarterly Census of Employment & Wages (QCEW) • Unemployment Insurance (U.I.) tax reports from 50 States, D.C., Puerto Rico, Virgin Islands • Virtual Census – 97% employment – 9 Million establishments – 130 Million employment – $6 Trillion wages

  3. QCEW Data Stratification Elements • Geography – County – Metropolitan Statistical Areas (MSAs) – State • Industry – 6 digit North American Industry Classification System (NAICS) • Ownership – Private, local government, State government, Federal Government

  4. QCEW Key Data Elements • Employment – Month 1, Month 2, Month 3 of the quarter • Total Quarterly Wages

  5. QCEW Administrative Data Elements • Employer Identification Number (EIN) – Federal Tax ID • Unemployment Insurance (UI) number • Worksite location for multi- establishment employers • Name, addresses, phone numbers, etc.

  6. Current Publication Cells • Most Detailed Cells County by 6 – digit Industry by Ownership • Publication Cells Most Detailed Cells and Aggregates

  7. Current Publication Status • Sensitivity tests– P – percent rule, multiple variables • Complementary Suppression - - for many specific purposes • Total of 3.6 Million Publication Cells – 50% primary suppression (N < 3 or a dominant unit) – 10% complementary suppression

  8. Typical Publication Table *PS = Primary Suppression *CS = Complementary Suppression • Loss of Data Utility

  9. EZS Noise Method Developed in the 1990s by Evans, Zaytaz, and Slanta (EZS) • Magnitude variables • All responses are noise perturbed with some minimum level • Multiplicative factors, randomly selected, symmetrical • All worksites for a given company are noise perturbed either positive or negative • No complementary suppression; thus some perturbed values for sensitive cells can be derived arithmetically • No calibration to higher level aggregates

  10. Challenges for QCEW • Employment of over 60% of the establishments is < 5 ─ Multiplicative noise factors do not work well for small employment levels since many of them round to zero ─ There are a large number of zeros (15%) in the population • Protection for small values requires some alternative to EZS.

  11. Mixed Approach with Both Synthetic and Noise Treated Data • Synthetic for small establishments - Predicted values from statistical models, e.g., logistic regression • EZS for large establishments • Both synthetic and EZS values may be equal to reported values on a probability basis • Raking to control some key marginals • No complementary suppression – 10% more publication cells – Reduced processing time

  12. Performance of Cells With 20 or Fewer Employees

  13. Question for Discussion Are modeled data (synthetic or noise perturbed) considered safe and thus require no further protection?

  14. Contact Information Shail Butani butani.shail@bls.gov 202-691-6347

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