This study investigates the effect of research independence during the PhD period on students' career outcomes. We use a unique and detailed dataset on the French population of STEM PhD students who graduated between 1995 and 2013. To measure research independence, we compare the PhD thesis content with the supervisor's research. We employ advanced neural network text analysis techniques evaluating the similarity between the student's thesis abstract and supervisor's publications during the PhD period. After exploring which characteristics of the PhD training experience and supervisor explain the level of research similarity, we estimate how similarity associates with the likelihood of pursuing a research career. We find that the student thesis's similarity with her supervisor's research work is negatively associated with starting a career in academia and patenting probability. Increasing the PhD-supervisor similarity score by one standard deviation is associated with a 2.1 percentage point decrease in the probability of obtaining an academic position and a 0.57 percentage point decrease in the probability of patenting. However, conditional on starting an academic career, PhD-supervisor similarity is associated with a higher student's productivity after graduation as measured by citations received, network size, and probability of moving to a foreign or US-based affiliation.
|Number of pages||45|
|Publication status||Published - 2021|
|Series||UNU-MERIT Working Papers|
- d22 - Firm Behavior: Empirical Analysis
- o30 - "Technological Change; Research and Development; Intellectual Property Rights: General"
- o33 - "Technological Change: Choices and Consequences; Diffusion Processes"
- o38 - Technological Change: Government Policy
- research independence
- early career researchers
- scientific career outcomes
- neural network text analysis