1. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Initial analyses on comparable dissemination from the Essnet project on SDC harmonization Luisa Franconi and Laura Corallo Istat

2. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Project on SDC harmonisation ESSnet on common tools and harmonised methodology for SDC in the ESS TIME: December 2010 – April 2012 Partnes: CBS, Istat, Destatis, SCB and University of Vienna http://neon.vb.cbs.nl/casc/ESSNet2index.htm –Task 1: Harmonisation of microdata release in multiple countries –Task 2: Case studies on tabular data –Task 3: Future directions of SDC software tools

3. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Dissemination strategy Microdata  risk assessment U R Apply SDL to reduce risk maintaining some utility Evaluate utility Original microdata Disclosure risk Utility SDL methods Anonimized microdata

4. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Comparability: HOW to achieve it? 1. Definition of benchmarking statistics 2. Assessment of effects of different practices on such statistics 3. Definition of a threshold to define when action is needed 4 setting a process for choosing acceptable practices Bounded utility  comparability

5. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Structure of Earning Survey European Linked Employer-Employee Data Information on Enterprises/local units AND employees Crucial for analysis of European labour market policy EU Microdata file for research Eurostat Safe centre

6. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona SES: Benchmarking Setting of objectives: 1. Production Process (Member States) a) Dissemination policy (Nace, Size, etc.) b) Coherence among variables 2. Users’ needs a) High-priority variables: (eg: NACE, SIZE, region, salary, etc. ) b) Minimum level of detail (NACE 2digits) c) Types of analyses Ratios, Weighted totals, salary change, etc.

7. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona SES-which statistics? Essnet SDC Harmonisation Deliverable 1: Part A. Survey structure a) focus on consequences on SDL b) quality c) relationships between variables d) classifications, etc Part B. Scientific research on SES data a) models b) methods c) breakdowns d) minimum level of detail, etc Input Output

8. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona SES2006: minimum requirements 11.1. Geographical location of the local unit NUTS 1 level 11.2. Size of the enterprise to which the local unit belongs 1-9*, 10-49, 50-249, 250-499, 500-999, 1000 and more employees. *This first band is optional for the 2006 SES. 11.3. Principal economic activity of the local unit 2-digit level of NACE Rev.1.1 for sections C to O. NACE section L is optional for the 2006 SES 22.3. Occupation in the reference month To be coded according to the International Standard Classification of Occupations, 1988 version (ISCO-88 (COM)) at the two-digit level and, if possible, at the three- digit level. 22.5. Highest successfully completed level of education and training Six levels coded according to the International Standard Classification of Education, 1997 version (ISCED 97). 22.7.1. Share of a full-timer’s normal hours For a part-time employee, the hours contractually worked should be expressed as a percentage of the number of normal hours worked by a full-time employee in the local unit 33.1. Number of weeks in the reference year to which the gross annual earnings relate should correspond to the actual gross annual earnings (variable 4.1). 44.2. Gross earnings in the reference month should be re-calculated so that it reflects the exclusion of such employees from the sample. 44.3. Average gross hourly earnings in the reference month average gross earnings per hour paid to the employee in the reference month. 55.1. Grossing-up factor for the local unit Within each sampling stratum, (Variable 5.1) = (Number of local units in the population) / (Number of local units in the sample) 55.2. Grossing-up factor for the employees (Variable 5.2) = (Variable 5.1) * (Number of employees in the local unit / Number of employees in the sample) Hierarchical classification “Independent” relationship formula SDC sampling

9. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Studies 1.Wage differentials/wage dispersion 2.Labour market policy 3.Determinants/decomposition 4.“classical” average gross earnings per enterprise or employee 5.Low(high)-pay dynamics 6.Bargaining regimes

10. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Models and methods 1.Linear models Mixed-effects, multi-level, ANOVA, quantile 2.Log(earnings) as response variable 3.Assumption of normal distributions on error 4.Method: Ordinary least squares 5. Descriptive statistics/tabulations Sometimes in two stages (enterprise and employee)

11. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona Conclusions from deliverable 1 1.Usage of some SDL method is necessary  some information loss is unavoidable! 2.Conclusion from deliverable 1 of Essnet on SDC: By some breakdowns, Weighted means and linear models should be the benchmarking statistics (to disseminate comparable European datasets) (the relationships of earnings should be preserved)

12. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona What’s next Involvement of Member States We will send documentation to SDC experts in MS on a set of methods coupled with the corresponding routines to apply them Interested MS are welcome to test the different methods to their national data and provide feedback/comments Final reports with the findings on the experience and comments on feasibility

13. Joint Eurostat Unece Worksession on Statistical Data Confidentiality 2011, Tarragona THANK YOU! Comments are welcome! Contacts: franconi@istat.it ichim@istat.it Matthias.Templ@statistik.gv.at