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Production of International Trade Statistics in Eurostat

Production of International Trade Statistics in Eurostat. J án Plánovský Eurostat Prague, 15 September 2009. Eurostat's M ission High-quality statistical service Producing comparable and relevant statistics covering the EU’s areas of competence

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Production of International Trade Statistics in Eurostat

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  1. Production of International Trade Statistics in Eurostat Ján Plánovský Eurostat Prague, 15 September 2009

  2. Eurostat's Mission • High-quality statistical service • Producing comparable and relevant statistics covering the EU’s areas of competence • Disseminating Community statistics to the general public, businesses and decision makers free of charge • Supporting non-EU countries within the cooperation framework with EU

  3. Trade Statistics Overview (1) • Basics of international trade statistics • Intrastat: Intrastat declarations • Extrastat: customs (SAD) declarations • Data reception via eDAMIS • Validation (plausibility checks, missing data) • Data transformation, confidentiality treatment, aggregation, loading to Comext

  4. Trade Statistics Overview (2) • Data dissemination: • Detailed data: Easy Comext, Analytical Comext • Aggregated data: Data Explorer, Tables/Graphs/Maps (TGM) interface • User support (e.g. XT-NET/Assist) • Analysis and Quality • Quality framework • Quality monitoring (Mirror Outlier Detection)

  5. Comext Trade Statistics Production in Eurostat FTP server Confidential Environment 27 Member States Publication database Monthly updates Daily loading eDAMIS Validation Transformation Data Loading Daily loading Monthly updates Standard Environment Easy Comext Monthly updates • Third countries: • EFTA • EU candidates • - West Balkan • Mediterranean Data Explorer Customised reports, user support

  6. Data Processing: Visual Validation

  7. Detailed Data Dissemination: Easy Comext

  8. Detailed Data Dissemination: Analytical Comext

  9. 9 Aggregated Data Dissemination

  10. User Support: XT-NET/Assist

  11. Quality Framework in European Trade Statistics

  12. Quality Monitoring: Mirror Outlier Detection

  13. Future Developments • Eurostat vision for the next decade • ESS now - stovepipe model • Burden on respondents • Not flexible for multidimensional phenomena • Production - inefficient and costly • ESS in the future – integrated model: • Common data warehouses (registers) • Harmonised methods / quality / medatadata • Sharing tools (validation, user support)

  14. Regulations incl. Agreed methodology 27 Member States with very different systems EUROSTAT Quality Management / Metadata Management REFERENCE & DISSEMINATION ENVIRONMENTS Quality management / Metadata Management Quality management / Metadata Management ESS now – stovepipe model User needs User needs Develop and Design Develop and Design Build Build Collect Collect Process Process Analyse Analyse Dissemi-nate Dissemi - nate User needs User needs User needs Collect Collect Collect Process Process Process Analyse Analyse Analyse Dissemi - nate Dissemi - nate Dissemi - nate Develop And Design Develop And Design Develop And Design Data transmission Data collection Data dissemination Quality management / Metadata Management Quality management / Metadata Management Data collection User needs User needs Develop and Design Develop and Design Build Build Collect Collect Process Process Analyse Analyse Dissemi - nate Dissemi-nate Data transmission Data collection Quality management / Metadata Management Quality management / Metadata Management Data transmission User needs User needs Develop and Design Develop and Design Build Build Collect Collect Process Process Analyse Analyse Dissemi-nate Dissemi - nate

  15. Regulations incl. Agreed methodology 27 Member States sharing tools, methodologies 1 unique Business Register EUROSTAT Quality management / Metadata Management Quality Management / Metadata Management ESS in the future Consistency Consistency REFERENCE & DISSEMINATION ENVIRONMENTS User needs EU Develop and Design Build 27 MS Datawarehouses = Input Databases for ESTAT DATAWAREHOUSE Process Analyse Dissemi - nate Collect (Surveys) User needs Develop And Design Collect Process Analyse Dissemi- nate Data collection Data dissemination Analyse Dissemi- nate Process Data flow Process Analyse Dissemi - nate Data collection Collect (Other sources) National needs Analyse Dissemi - nate Analyse Dissemi- nate Process Process Data collection Grouping by e.g. statistical unit Same tools, methodologies are used

  16. Thank you for your attention! Any questions / comments? Jan.Planovsky@ec.europa.eu

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