Template-type: ReDIF-Paper 1.0
Author-Name: Patsali, Sofia
Author-Email: Sofia.PATSALI@univ-cotedazur.fr
Author-Workplace-Name: Université Côte d'Azur, GREDEG, and Université de Strasbourg,
	 BETA, CNRS France
Author-Name: Pezzoni, Michele
Author-Email: Michele.PEZZONI@univ-cotedazur.fr
Author-Workplace-Name: Université Côte d'Azur, GREDEG, CNRS, Observatoire des
	 Sciences et Techniques, HCERES, OFCE, Sciences Po, and ICRIOS, Bocconi
	 University, Italy
Author-Name: Visentin, Fabiana
Author-Email: f.visentin@maastrichtuniversity.nl
Author-Workplace-Name: UNU-MERIT, Maastricht University
Title: The impact of research independence on PhD students' careers: Large-scale 
	 evidence from France
Abstract: 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.
Classification-JEL: D22, O30, O33, O38
Keywords: Research independence, Early career researchers, Scientific career
	 outcomes, Neural network text analysis
Series: UNU-MERIT Working Papers
Creation-Date: 20211015
Number: 2021-036
File-URL: https://unu-merit.nl/publications/wppdf/2021/wp2021-036.pdf
File-Format: application/pdf
File-Size: 275 Kb
Handle: RePEc:unm:unumer:2021036