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Initial analyses on comparable dissemination from the Essnet project on SDC harmonization

Initial analyses on comparable dissemination from the Essnet project on SDC harmonization. Luisa Franconi and Laura Corallo Istat. ESSnet on common tools and harmonised methodology for SDC in the ESS TIME: December 2010 – April 2012

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Initial analyses on comparable dissemination from the Essnet project on SDC harmonization

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  1. Initial analyses on comparable dissemination from theEssnet project on SDC harmonization Luisa Franconi and Laura Corallo Istat

  2. 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 Project on SDC harmonisation

  3. Disclosure risk SDL methods Original microdata Anonimized microdata Utility Dissemination strategy Apply SDL to reduce risk maintaining some utility Evaluate utility Microdata  risk assessment R U

  4. Comparability:HOW to achieve it? • 1. Definition of benchmarking statistics • 2. Assessmentof 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. 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 Structure of Earning Survey

  6. 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. 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. SES2006: minimum requirements Hierarchical classification “Independent” SDC sampling relationship formula

  9. Studies • Wage differentials/wage dispersion • Labour market policy • Determinants/decomposition • “classical” average gross earnings per enterprise or employee • Low(high)-pay dynamics • Bargaining regimes

  10. Models and methods • Linear models • Mixed-effects, multi-level, ANOVA, quantile • Log(earnings) as response variable • Assumption of normal distributions on error • Method: Ordinary least squares • Descriptive statistics/tabulations • Sometimes in two stages (enterprise and employee)

  11. Conclusions from deliverable 1 • Usage of some SDL method is necessary  some information loss is unavoidable! • 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. 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. THANK YOU! Comments are welcome! Contacts: franconi@istat.it ichim@istat.it Matthias.Templ@statistik.gv.at

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