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Eurostat Secondary data: collection and use Presented by Arnout van Delden Methodologist Statistics Netherlands.

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Presentation on theme: "Eurostat Secondary data: collection and use Presented by Arnout van Delden Methodologist Statistics Netherlands."— Presentation transcript:


2 Eurostat Secondary data: collection and use

3 Presented by Arnout van Delden Methodologist Statistics Netherlands

4 Secondary data


6 Secondary Sources Secondary Sources Registers Base registers Statistical registers specific


8 Official Statistics Post-war II  Identifiers  Concepts: variable, units, time  Population registers  Administrative Census –Denmark (1981), Finland (1991), Netherlands (2001)

9 Use ( EU/EFTA Survey 2010 )  Frame  Observations  Auxiliary data  Model parameters  Data quality admin data only admin and survey data survey data only not specifiednon responseTotal BR12,016,02,030 SBS10,511,54,70,72,730 STS4,011,014,00,01,030 Prodcom0,010,013,01,02,026

10 In sum  Many types of data sources  Long history  Potentially very useful

11 Collection Existence Access

12 Existence Data protection act Organisation registers data under DPA

13 Existence Data protection act Organisation registers data under DPA

14 Access ElementExplanation LegislationNational Statistics Act Public approvalInformed consent Identification codesBase registers (business, dwellings, …) Reliable dataObliged to report errors; multi users CooperationContacts with administration authorities

15 In Sum  Explore potential data sources  Access: legal uses and public consent

16 Proper use


18 Exploration phase Source Meta

19 Processing phase: data useful? March ‘04Dec ‘04 Turnover Sample Survey

20 Data patterns UnitPeriodValueUnitPeriodValue 2022253Q13000222201Q12000 2022253Q23000222201Q22500 2022253Q33000222201Q30 2022253Q44561333301Q42200

21 Issues to consider DimensionIssuesMethods TimeReporting delaysNow casting, imputation Reporting Statistical period Harmonisation (time series) RepresentationAdministrative unitsLinkage Coverage errorsBusiness register MeasurementData patternsModel/time series CorrectionsUpdates Different meaningAnalyse


23 Access

24 Set of base registers data re-used report errors 1 contact person in NSI large dependency users

25 Properties of Administrative data 1 Collected externally 2 Administrative goal 3 Different objectives 4 Subject to changes

26 2 Can I use of a specific data source? What ‘steps’ are needed? Existence Access Fitness for use Fall back scenario’s Processing

27 Processing: data integration Linkage Micro-integration Imputation/weighting Macro-integration

28 Fall back scenarios Quarterly turnover from Survey en Admin data –Risk only data from month 1 and 2 –Model: missing units predicted from respondents –Indicator: how many and which units to call

29 Fall back scenarios Risk analyses Strategy fall back scenario –Obtain missing data elsewhere? –Model-based approach –Inform users –Postpone publication

30 Processing: robust estimation Medical expenses (volume, prices) Coding system for medical treatments First coding in 2008 Coding slightly revised 2009 New coding system 2010

31 Fitness for use DimensionDescription Technical ChecksTechnical usability of file and data Accuracy1) Closeness to true values, 2) Correctness, reliability CompletenessDescribe the corresponding set of real-world objects and variables Time-related dimensionRime and/or stability related IntegrabilityCapable of undergoing integration or of being integrated. Data

32 Use Type of useExampleSource type Population frameChamber of Commerce data for Business Register Base register Source for observations VAT data for quarterly turnover estimatesPublic admin source Auxiliary dataInternet data to verify the NACE code of enterprises Organic source Estimation of model parameters Energy supplier data for average energy consumption for CPI Private admin source Audit quality of statistical data Social security data to assess quality employment position based on sampling data Public admin source

33 Concluding remarks Merits –Reduction response burden –Detailed & Longitudinal –Longitudinal data Consequences –Relations with administrative data holder –Prone to changes

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