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Improved input data quality from administrative sources though the use of quality indicators

Improved input data quality from administrative sources though the use of quality indicators

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Improved input data quality from administrative sources though the use of quality indicators

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  1. Improved input data quality from administrative sourcesthoughtheuseofqualityindicators Use of Administrative Registers in Production of Statistics Group work Oslo, 14 - 17 October 2014 Coen Hendriks Division for Statistical Populations Statistics Norway

  2. Topics • The three C’s of register based statistics • Measure quality • Analyse quality

  3. Co-operation, Communication and Co-ordination • The three C’s of register based statistics contribute strongly to the quality of register based statistics • How is this done in Statistics Norway?

  4. Co-operation on registers within SN • SN has taken several measures to professionalise and develop the co-operation, o.a. • Improve the quality in the administrative sources • Develop ways to measure, document and communicate on quality • Professionalise the contact with the register owners

  5. Rather then repairing, errors should be avoided in the source • Co-operation between SN and the register owners • SN reports errors • The register owner makes corrections in the source • Single source approach • Multiple source approach – agreements on data processing • Feed-back at micro level • Errors within the source can be reported • SN make a complaint on the data quality • Agreements on cooperation

  6. Single sourceapproach • Feed-back at micro level • Errors within the source can be reported • SN make a complaint on the data quality

  7. Multiple source approach: Agreements on data processing • Errors which appear after linking two sources • General rule: aggregated reporting • An agreement on data processing allows reporting at micro level from SN to the register owner • Provided the register owner can use both registers for administrative reasons • E.g. population registration uses information from the Cadastre to improve quality in the CPR • SN can do these checks “in batch” on behalf of the register owner

  8. Agreements on co-operation • Co-ordination in SN • Involves the Director of the Division for Data Capture, SN’s legal adviser, experts on quality (CoP, methodology), statistical departments and the Department for Statistical Populations • Drafted an agreement • Developed quality reports • SN invites major register owners into an agreement • Very positive receipt by the register owners • A win-win situation • The agreement is supported by a quality report • Based on the quality indicators from the Blue-ets WP 4 • A descriptive approach, highlighting the problem areas

  9. Managingstatisticalpopulations • Three administrative baseregisters and thestatisticalversions • The Central Coordinating Register for Legal Units (LU) - The Register for Businesses and Enterprises • Cadaster - The statisticalCadaster • Central Population Register (CPR) - The Statistical Population Register • Dailyupdates, integrated data in a common database • Othersourcesareintegrated, newsourcesarebeingadded • New information, new units, bettercoverage, more (actual) addresses, bettercontactinformation • Purpose: providequalityassured and updated registers withqualityindicators, which cover all statisticalpopulations

  10. Qualityindicators from Blue–ets WP 4 • The group leaders determined which units to measure for quality and operationalized the indicators • CPR: registered person, family and residential address • Cadastre: address, building, land property and functional unit in a building (dwelling) • LU: legal entities and LKAU • The quality indicators where reviewed and coordinated • Programming in SAS • Counted up all the positive indicators (P) • Reporting (Q)

  11. The indicator file Numberof positive indicators: P= A general qualityindicator: Q = (P/(N*M))*1000 Extracts: Indicators with many occurrences (e.g. Ind7) Units with many positive indicators (e.g. Unit1)

  12. Quality report for registered persons in the CPR, 2012-2014

  13. The practicalcooperationwiththe data owners (registerred persons in the CPR) Municipalitieswithhighestvaluesof Q, the major cities and Norway, 1.1. 2014 Analysis shows: - manyinconsistentvalues (PIN ofmother, fatherand/or spouce/partner is invalid) - manymeasurementerrors (missingdwellingnumber, invalid address) - trouble in thecountyof Nordland (18xx) Suspicious units aretransferred to the CPR

  14. Otherexamplesofanalysis • Whatkindof positive indicatorsarefound for newlyregistered persons? • Measurementerrors (missingdwellingnumber, invalid addresses) • Dubiousobjects (toomanyregisterred persons in a dwelling) • Refer to Appendix, tabel 3 • Why do previouslyregistered persons show an increase in thenumberof positive indicators? • Inconsistent units and values due to immigration • Measurementerrors (missingdwellingnumber, invalid addresses) • Referto Appendix, tabel4

  15. The principles for thepracticalcooperationwiththe data owners • Positive indicators are identified within a source: • SN complain on the quality of the deliverables • SN return individual based information with positive indicators • Positive indicators are found by matching to another source: • SN give feedback at aggregate level – main rule • Assuming a data processing agreement: We can supply individual data with positive indicators • The data owner has the legal authority to use the second source • The data owner has a copy of the other source available

  16. Qualityacross registers • SN has a long record of matching sources for quality control and improvement • The approachworks: «Improvedquality» in theCadastregives «fewermistakes» in the CPR • Indicators for quality across registers need to be developed. We are just starting • A cluster with employees in an enterprise without business (LKAU) in reasonable distance, might indicate under coverage in the Business Register (missing LKAU)

  17. Final remarks • There is a differencebetweengoodquality data from registers and goodquality register basedstatistics • Statistical inference • Definition errors – changes in the register due to politicaldecisions