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

Author

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  • Kruppe, Thomas

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Matthes, Britta

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Unger, Stefanie

    (Institute for Employment Research (IAB), 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. Stüber, Heiko & Seth, Stefan, 2019. "The FDZ sample of the Administrative Wage and Labor Market Flow Panel 1976 - 2014," FDZ Datenreport. Documentation on Labour Market Data 201901_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
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    3. Hartmut Egger & Elke J. Jahn & Udo Kreickemeier, 2018. "Distance and the Multinational Wage Premium," CESifo Working Paper Series 7347, CESifo.
    4. 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].
    5. 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].
    6. repec:iab:iabfda:202008(en is not listed on IDEAS
    7. 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.
    8. 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].
    9. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2015. "Generalised partially linear regression with misclassified data and an application to labour market transitions," ZEW Discussion Papers 15-043, ZEW - Leibniz Centre for European Economic Research.
    10. Reichelt, Malte & Vicari, Basha, 2014. "Ausbildungsinadäquate Beschäftigung in Deutschland: Im Osten sind vor allem Ältere für ihre Tätigkeit formal überqualifiziert (Job-qualification mismatch in Germany: In East Germany, especially older ," IAB-Kurzbericht 201425, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    11. 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].
    12. Gathmann, Christina & Berlingieri, Francesco & Quinckhardt, Matthias, 2022. "College Openings and Local Economic Development," CEPR Discussion Papers 17374, C.E.P.R. Discussion Papers.
    13. repec:iab:iabfda:202007(en is not listed on IDEAS
    14. 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].
    15. Eberle Johanna & Schmucker Alexandra, 2017. "The Establishment History Panel – Redesign and Update 2016," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(6), pages 535-547, December.
    16. 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, revised 2018.
    17. 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].
    18. repec:iab:iabfda:201901(en is not listed on IDEAS

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    Keywords

    Bundesrepublik Deutschland ; Datenaufbereitung ; Datenqualität ; IAB-Datensatz Arbeiten und Lernen ; Imputationsverfahren ; Interview ; prozessproduzierte Daten;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • I20 - Health, Education, and Welfare - - Education - - - General

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