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Quality of administrative data - bringing out the best.

Quality of administrative data - bringing out the best. Testing data corrections for overlaps and inconsistencies. Patrycja Scioch (Research Data Centre of the BA at the IAB, Germany). European Conference on Quality in Official Statistics, 10.07.2008. Motivation.

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Quality of administrative data - bringing out the best.

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  1. Quality of administrative data - bringing out the best. Testing data corrections for overlaps and inconsistencies Patrycja Scioch (Research Data Centre of the BA at the IAB, Germany) European Conference on Quality in Official Statistics, 10.07.2008

  2. Motivation • increasing importance of using administrative data for research • in Germany we have two types of such data: • collected for official statistical purposes • by-product of administration (e.g. federal employment services) • administrative data: • not collected for research • different and independent sources of data • merging may cause contradictions in information

  3. The Integrated Employment Biographies - IEB • combination of four different sources: • Employee History • Benefit Recipient History • Applicants Pool Data • Participants in Measure Dataset • subsample: • 2.2% random sample • latest update 2006 • characteristics: • daily records • splitted into episodes • quality depends on source of information

  4. Literature • previous findings: • concentrate on the analysis of overlaps - qualitative and quantitative • (Jaenichen et. al (2005), Bernhard et. al (2006)) • correction of single variables (Waller, M. (2007), Kruppe et. al (2007)) evidence: • need for data processing in the IEB • the way heavily depends on the research question open issues: • impact on estimates • data processing by transformation of structure of dataset

  5. Identification/Method • assumptions: dataset → processing → method → result • within the Case: Wunsch/Lechner (2007) • evaluation of labour market programmes in West Germany • analyses by comparing matching-estimates • time-dependent employment opportunities as outcome • step: replication of the data processing and variations of the analysis sample • step: replication of the evaluation study • 3. step: analyses of the effects of the variations on the results

  6. Approach/Framework analysis- sample V0 outcome V0 IEB - data set analysis- sample V1 outcome V1 outcome analysis- sample V2 outcome V2 Processing - variable ‚Matching-estimatior‘ - fix Comparison

  7. Processing rules • time windows of two weeks • multiple possibilities of spells (different sources, overlaps) • goal: exact one state for each period • Sort by duration and priority of source • Choose the two with capital importance • Select one final state using more priority-rules • different analysis samples

  8. Rules of Priority • Differences: • Model V1 prefers employment-spells to benefit-spells compared to V0 • Model V2 downgrades participation in programmes

  9. Results before starting the estimation programme – benefit – employment – applicant analysis- sample V0 programme – employment – benefit – applicant IEB- data set analysis- sample V1 employment – benefit – applicant – programme analysis- sample V2

  10. First results – V0 vs. V1

  11. Summary/Prospects • differences are significant • further descriptive analysis of the different datasets • matching • comparison of the estimations • conclusions

  12. Thank you for your attention! Patrycja.Scioch@iab.de http://fdz.iab.de/

  13. Back-Up References Bernhard, S., Dressel, C., Fitzenberger, B. und Schnitzlein, D. (2006): Überschneidungen in der IEBS: Deskriptive Auswertung und Interpretation, FDZ Methodenreport 4/2006, Nürnberg. Jaenichen, U., Kruppe, T., Stephan, G., Ullrich, B. und Wießner, F. (2005): You can split it if you really want: Korrekturvorschläge für ausgewählte Inkonsistenzen in IEB und MTG, FDZ Datenreport 4/2005, Nürnberg. Kruppe, T., Müller, E., Wichert, L. und Wilke, R. (2007): On the Definition of Unemployment and ist Implementation in Register Data – The Case of Germany, FDZ Methodenreport 3/2007, Nürnberg. Waller, M. (2007): Do Reported End Dates of Treatments Matter for Evaluation Results?, FDZ Methodenreport 1/2007, Nürnberg. Wunsch, C. und Lechner, M. (2007): What Did All the Money Do? On the General Ineffectiveness of Recent West German Labour Market Programmes, University of St. Gallen Department of Economics working paper series 2007 2007-19, Department of Economics, University of St. Gallen.

  14. Results before starting the estimation programme – benefit – employment – applicant analysis- sample V0 programme – employment – benefit – applicant IEB- data set analysis- sample V1 employment – benefit – applicant – programme analysis- sample V2

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