1. Len Cook: Hero or Zero of the 2001 Census? OR A look at the impact of disclosure control on aggregate census outputs

2. 1. The need for SDC v

3. 2. The pre-2001 ‘consensus’

4. Imputation (District level)

5. Imputation (ward level)

6. Item Imputation rate Professional qualifications17.2 Hours worked 8.0 Workplace postcode 7.8 Religion 7.6 Employment status 6.6 Qualifications 6.2 Long-term illness 3.9 Tenure 3.4 Ethnic group 2.9 Age 0.5 Sex 0.4 Imputation (item level)

7. Other protective measures Thresholding (of spatial output) Aggregation and top-coding Barnardisation Data access barriers Time Imperfect local knowledge

9. 0 1 2 3 … 3. The 2001 Census controversy

11. 4. SDC approaches Exact details not published, but: UK 2001: ‘Small Cell Adjustment  rounding to 0 or 3 Australia/New Zealand: Round to Base 3 Canada: Round to Base 5 UK 1991: ‘Barnadisation’  prob = 0.1 (or 0.02?)

12. 5. SDC Confidence Intervals Bnd_0.02  0.2(n-1) 0.5 Bnd_0.1  0.5(n-1) 0.5 SCA_3  1.0(n-1) 0.5 Base_3  1.2(n-1) 0.5 Base_5  2.0(n-1) 0.5

13. 5. SDC Confidence Intervals

14. 6. Impacts of SDC? Unknown! Solution? Summarise impact across a range of tables/counts/types of analysis

21. 6. Impacts of SDC? Solution: Summarise impact of SDC by table/count type population rarity level of geographical aggregation method of SDC type of analysis

22. All counts

24. Internal counts

25. Marginal counts

26. Percentages

27. Ranking

29. Correlation across 30 areas [wrong signs]

30. Bivariate regression across 30/100 areas [coefficient error > 1 SE]

31. Multivariate regression across 30 areas [Difference in r 2 ]

32. 7. Responses to SDC Univariate counts  marginals Largest possible building blocks Minimise spatial units used Can estimate context-specific CI, given number of 1s and 2s in data being analysed

33. 8. Len Cook, Hero or Zero? 0 1 2 3 …

34. Ward level variability in tabular counts

35. 8. Len Cook, Hero or Zero? 0 1 2 3 …