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The IAB-INCHER project of earned doctorates (IIPED): A supervised machine learning approach to identify doctorate recipients in the German integrated employment biography data

Author

Listed:
  • Heinisch, Dominik

    (University of Kassel and INCHER-Kassel (Germany))

  • Koenig, Johannes
  • Otto, Anne

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

Abstract
"Only scarce information is available on doctorate recipients' career outcomes in Germany (BuWiN 2013). With the current information base, graduate students cannot make an informed decision whether to start a doctorate (Benderly 2018, Blank 2017). Administrative labour market data could provide the necessary information, is however incomplete in this respect. In this paper, we describe the record linkage of two datasets to close this information gap: data on doctorate recipients collected in the catalogue of the German National Library (DNB), and the German labour market biographies (IEB) from the German Institute of Employment Research. We use a machine learning based methodology, which 1) improves the record linkage of datasets without unique identifiers, and 2) evaluates the quality of the record linkage. The machine learning algorithms are trained on a synthetic training and evaluation dataset. In an exemplary analysis we compare the employment status of female and male doctorate recipients in Germany." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Heinisch, Dominik & Koenig, Johannes & Otto, Anne, 2019. "The IAB-INCHER project of earned doctorates (IIPED): A supervised machine learning approach to identify doctorate recipients in the German integrated employment biography data," IAB-Discussion Paper 201913, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:201913
    as

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    File URL: https://doku.iab.de/discussionpapers/2019/dp1319.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bundesrepublik Deutschland ; beruflicher Verbleib ; Berufserfolg ; Berufsverlauf ; Bibliothek ; Datengewinnung ; Dissertation ; Frauen ; Hochschulabsolventen ; Datenfusion ; Integrierte Erwerbsbiografien ; künstliche Intelligenz ; Lernen ; Männer ; Promotion ; 1975-2015;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • I20 - Health, Education, and Welfare - - Education - - - General

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