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

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.