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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.

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
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
the integrated employment biographies ieb
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
literature
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
identification method
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
approach framework
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

processing rules
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
rules of priority
Rules of Priority
  • Differences:
  • Model V1 prefers employment-spells to benefit-spells compared to V0
  • Model V2 downgrades participation in programmes
results before starting the estimation
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

summary prospects
Summary/Prospects
  • differences are significant
  • further descriptive analysis of the different datasets
  • matching
  • comparison of the estimations
  • conclusions
back up
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.

results before starting the estimation1
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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