1. ICES III Montreal, June 18-21, 2007 A new Approach for Disclosure Control in the IAB Establishment Panel Multiple Imputation for Better Data Access Jörg Drechsler Institute for Employment Research (IAB)

2. 2 Overview Background Statistical disclosure control with fully synthetic data sets Application to the IAB-Establishment Panel First results Proceedings/open questions

3. 3 The IAB Establishment Panel Annually conducted Establishment Survey Since 1993 in Western Germany, since 1996 in Eastern Germany Population: All establishments with at least one employee covered by social security Source: Official Employment Statistics Response rate of repeatedly interviewed establishments more than 80% Sample of more than 16.000 establishments in the last wave Contents: employment structure, changes in employment, business policies, investment, training, remuneration, working hours, collective wage agreements, works councils

4. 4 Overview Background Statistical disclosure control with fully synthetic data sets Application to the IAB-Establishment Panel First results Proceedings/open questions

5. 5 Y synthetisch Generating Synthetic Data Sets (Rubin 1993) Advantages: - Data are fully synthetic - no re-identification of single units possible - all variables are still fully available Y observed X Y not observed Y synthetic

6. 6 Overview Background Statistical disclosure control with fully synthetic data sets Application to the IAB-Establishment Panel First results Proceedings/open questions

7. 7 Generating synthetic data sets for the IAB Establishment Panel Create a synthetic data set for selected variables from the wave 1997 from the Establishment Panel Imputation for the whole population is not feasible Draw a new sample from the Official Employment Statistics using the same sampling design as for the Establishment Panel (Stratification by economic branch, size, and region) Each stratum cell contains the same number of observations as the wave 1997 from the Establishment Panel Additional Information from the German Social Security Data (GSSD) for the imputation

8. 8 The German Social Security Data (GSSD) Contains information on all employees covered by social security Since 1973 all employers are required to notify the social security agencies about all employees covered by social security. The GSSD represents about 80% of the German workforce Information from the GSSD is aggregated on the establishment level and is matched to the IAB Establishment Panel via establishment identification number Information on: number of employees by gender, schooling, mean of the employees age, mean of the wages of the employees…

9. 9 Y synthetisch Synthetic Establishment Panels The IAB Establishment Panel GSSD EP synthetic

10. 10 Imputation Procedure For simplicity new founded establishments are excluded from the sampling frame and from the panel 10 new samples are drawn The number of observations in each sample equals the number of observations in the panel n s =n p =7332 Every sample is imputed ten times using chained equations Number of variables from the GSSD: 24 Number of variables from the establishment panel: 48 Imputations are generated using IVEware by Raghunathan, Solenberger and Hoewyk (2001)

11. 11 Overview Background Statistical disclosure control with fully synthetic data sets Application to the IAB-Establishment Panel First results Proceedings/open questions

12. 12 First Results Compare regression results from the original data with results from the synthetic data Zwick (2005) analyses the productivity effects of different continuing vocational training forms in Germany Results: vocational training is one of the most important measures to gain and keep productivity Probit regression to explain, why firms offer vocational training 13 Explanatory variables including: Share of qualified employees, establishment size, region, collective wage agreement, high qualification needs expected… 2 variables, based on the 1998 wave of the panel, are dropped for the evaluation

13. 13 Descriptive comparison of the original and in the synthetic data set Variable survey mean synthetic data mean Deviation Training Yes/No0.70690.71090.55% Redundancies expected0.22390.2223-0.75% Many employees are expected to be on maternity leave0.06440.073714.34% High qualification needs expected0.1551 0.02% Establishment size 20-1990.39730.40431.77% Establishment size 200-4990.13480.14396.78% Establishment size 500-9990.07450.07693.30% Establishment size 1000+0.09420.09773.71% Collective wage agreement0.76430.7535-1.41% Apprenticeship training reaction on skill shortages0.36320.36551.00% Training reaction on skill shortages0.44900.46784.10% State-of-the-art technical equipment0.65130.68615.35% Apprenticeship training0.61410.62471.73% Share of qualified employees0.67400.6271-6.96% number of employees365.6238350.5626-4.12%

14. 14 Results from the regression original data setsynthetic data set Exogenous variablescoeff.p-valuecoeff.p-value Redundancies expected0.2503***0.0000 0.2513***0.0001 Emp. exp. on maternity leave0.2657**0.0050 0.2445*0.0172 High qual. needs expected0.6480*** 0.00000.6245***0.0000 Appr. tr. react. on skill shortages0.1130*0.0390 0.1471*0.0130 Tr. reaction on skill shortages0.5273***0.0000 0.5233***0.0000 Establishment size 20-1990.6859*** 0.00000.6450***0.0000 Establishment size 200-4991.3554*** 0.00001.2031***0.0000 Establishment size 500-9991.3473*** 0.00001.3402***0.0000 Establishment size 1000+1.9641*** 0.00001.7775***0.0000 Share of qualified employees0.7776*** 0.00000.8204***0.0000 State-of-the-art tech. equipment0.1690*** 0.00000.1678***0.0001 Collective wage agreement0.2541*** 0.00000.3131***0.0000 Apprenticeship training0.4838***0.00000.4058***0.0000 *** significant on the 0.1% level, ** significant on the 1% level, * significant on the 5% level

15. 15 Overview Background Statistical disclosure control with fully synthetic data sets Application to the IAB-Establishment Panel First results Proceedings/open questions

16. 16 Proceedings/open questions More detailed evaluation Replace only selected variables Generate weights for the synthetic sample Imputation of more than one wave maintaining the panel structure References Drechsler, J., Dundler, A., Bender, S., Rässler, S., Zwick, T. (2007). A New Approach for Disclosure Control in the IAB Establishment Panel - Multiple Imputation for a Better Data Access, IAB Discussion Paper No.11/2007 Reiter, J. und Drechsler, J. (2007). Releasing Multiply-Imputed, Synthetic Data Generated in Two Stages To Protect Confidentiality, submitted

17. 17 Thank you for your attention

18. 18 Information from the two data sets

19. 19 Disclosure is possible, if… An establishment is included in the original data set and in at least on of the newly drawn samples The original values and the imputed values for this establishment are nearly the same

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21. 21 How often are establishments included in the IAB- Establishment Panel drawn in the new samples? Occurrence in … sample(s) NumberPercentage 04,46961.0% 11,09114.9% 25357.3% 33624.9% 42753.8% 51992.7% 61442.0% 7891.2% 8530.7% 9320.4% 10831.1% Total7,332100%

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23. 23 Comparing original and imputed values Binary variables: probability of identical values: 60-90% Multiple response questions: - with four categories: 57% - with 13 categories:6% Numerical variables: - average relative difference: 21% - outliers