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Effectiveness of data correction rules in process-produced data : the case of educational attainment

Author

Listed:
  • Kruppe, Thomas

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

  • Matthes, Britta

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

  • Unger, Stefanie

    () (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])

Abstract

"The use of process-produced data plays a large and growing role in empirical labor market research. To address data problems, previous research have developed deductive correction rules that make use of within-person information. We test data reliability and the effectiveness of different correction rules for information about educational degrees as reported in German register data. Therefore we use the unique dataset ALWA-ADIAB, which combines interview data and process-produced data from exactly the same individuals. This approach enables us to assess how effective the existing correction rules are and whether they manage to eliminate structural biases. In sum, we can state that simple editing rules based on logic assumptions are suitable for improving the quality of process-produced data, but they are not able to correct for structural biases." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Kruppe, Thomas & Matthes, Britta & Unger, Stefanie, 2014. "Effectiveness of data correction rules in process-produced data : the case of educational attainment," IAB Discussion Paper 201415, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  • Handle: RePEc:iab:iabdpa:201415
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    References listed on IDEAS

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    Cited by:

    1. Stephan, Gesine & Uthmann, Sven, 2014. "Akzeptanz von Vergeltungsmaßnahmen am Arbeitsplatz : Befunde aus einer quasi-experimentellen Untersuchung," IAB Discussion Paper 201427, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Stüber, Heiko & Seth, Stefan & Stegmaier, Jens, 2020. "The Administrative Wage and Labor Market Flow Panel Extension for the IAB Establishment Panel 1993 - 2014," FDZ Datenreport. Documentation on Labour Market Data 202007_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.
    4. Kreickemeier, Udo, 2018. "Distance and the Multinational Wage Premium," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181618, Verein für Socialpolitik / German Economic Association.
    5. Seth, Stefan & Stüber, Heiko, 2018. "The Administrative Wage and Labor Market Flow Panel," FAU Discussion Papers in Economics 01/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    6. Schmucker, Alexandra & Seth, Stefan & Ludsteck, Johannes & Eberle, Johanna & Ganzer, Andreas, 2016. "Betriebs-Historik-Panel 1975-2014 (Establishment History Panel 1975-2014)," FDZ Datenreport. Documentation on Labour Market Data 201603_de, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    7. Schmucker, Alexandra & Seth, Stefan & Ludsteck, Johannes & Eberle, Johanna & Ganzer, Andreas, 2016. "Establishment History Panel 1975-2014," FDZ Datenreport. Documentation on Labour Market Data 201603_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    8. Stüber, Heiko & Seth, Stefan & Lochner, Benjamin, 2020. "The Administrative Wage and Labor Market Flow Panel Extension for the IAB Job Vacancy Survey 2010 - 2014," FDZ Datenreport. Documentation on Labour Market Data 202008_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Eberle, Johanna & Schmucker, Alexandra, 2017. "The establishment History Panel : Redesign and update 2016," FDZ Methodenreport 201703_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    More about this item

    Keywords

    prozessproduzierte Daten; Datenaufbereitung; Datenqualität; IAB-Datensatz Arbeiten und Lernen; Interview; Imputationsverfahren;

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • I2 - Health, Education, and Welfare - - Education

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