A supervised machine learning approach to trace doctorate recipients' employment trajectories

Dominik P. Heinisch, Johannes Koenig, Anne Otto
2019 Quantitative Science Studies  
Only scarce information is available on doctorate recipients' career outcomes (BuWiN, 2013). With the current information base, graduate students cannot make an informed decision whether to start a doctorate or not (Benderly, 2018; Blank, 2017). However, administrative labour market data, which could provide the necessary information, is 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
more » ... 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 evolution of the employment status of female and male doctorate recipients in Germany.
doi:10.1162/qss_a_00001 fatcat:ocsd3566prhz7mln74nk3pv4cq