24.06.2020 12:52

Newly published IAB-Discussionpaper by Dominik Heinisch, Johannes Koenig and Anne Otto: The IAB-INCHER project of earned doctorates (IIPED)

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, 13/2019), Nürnberg, 30 S.

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 decisionwhether 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 de-scribe the record linkage of two datasets to close this information gap: data on doctorate recipi-ents collected in the catalogue of the German National Library (DNB), and the German labour mar-ket biographies (IEB) from the German Institute of Employment Research. We use a machine learn-ing based methodology, which 1) improves the record linkage of datasets without unique identifi-ers, 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 employ-ment status of female and male doctorate recipients in Germany.