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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.

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Presentation on theme: "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."— Presentation transcript:

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, Santanu Pramanik, 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 2

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 3

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

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. 5

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

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8 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

9 Typical Publication Table *PS = Primary Suppression *CS = Complementary Suppression Loss of Data Utility 9

10 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

11 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

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13 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 13

14 Performance of Cells With 20 or Fewer Employees 14

15 Question for Discussion Are modeled data (synthetic or noise perturbed) considered safe and thus require no further protection? 15

16 Contact Information Shail Butani


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