presentation - VirtualRDC @ Cornell - Cornell University

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Distorting flows ■ Distorted net job flow (JF ) is computed at the aggregate (k = geography, industry, or combination of the two for the appropriate age and sex categories) level as the product of the aggregated, undistorted rate of growth and the aggregated distorted employment: JF ∗ kt = G kt × Ē∗ kt = JF kt × Ē∗ kt Ē kt . ■ The formulas for distorting gross job creation (JC) and job destruction (JD) are similar. ■ Same logic is used to distort wage changes for subgroups August 10, 2005 - p. 13/31
Item suppression ■ Some disclosure risk remains for counts based on very few entities in a cell (fewer than three individuals or employers) ■ Variables affected are: B, E, M, F , A, S, H, R, FA, FH, FS, JC, JD, JF , FJC, FJD, FJF . ➜ item suppression based on the number of either workers or the number of employers that contribute data for that item in a cell k in time period t, where a cell represents a particular combination of geography × industry × age × sex. ■ Because of noise infusion, no complementary suppressions are needed ■ Some denominators may be zeroes - the ratio or rate cannot be computed. August 10, 2005 - p. 14/31
Page 1 and 2: GoBack
Page 3 and 4: Introduction ■ We will describe t
Page 5 and 6: Protection provided ■ In general,
Page 7 and 8: Three-layer confidentiality protect
Page 9 and 10: Three-layer confidentiality protect
Page 11 and 12: Distribution of Fuzz Factors 0 0 2
Page 13: Distorting ratios Ratios are distor
Page 17 and 18: Protection by Weighting ■ QCEW 1,
Page 19 and 20: Protection Table 1: Small Cells: B,
Page 21 and 22: Protection Table 3: Small Cells: B,
Page 23 and 24: Distribution of the Error in the Fi
Page 29 and 30: Cross-sectional unbiasedness: B Fra
Page 31 and 32: Conclusion ■ First large-scale im

presentation - VirtualRDC @ Cornell - Cornell University

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