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Investigating PhDs' early career occupational outcomes in Italy: individual motivations, role of supervisor and gender differences.

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Title: Investigating PhDs' early career occupational outcomes in Italy: individual motivations, role of supervisor and gender differences.
Authors: Carriero, Renzo1,2 renzo.carriero@unito.it, Coda Zabetta, Massimiliano3, Geuna, Aldo1,2,4, Tomatis, Francesca1
Source: Higher Education (00181560). May2024, Vol. 87 Issue 5, p1375-1392. 18p.
Subject Terms: *Doctor of philosophy degree, *Motivation (Psychology), Gender differences (Psychology), Supervisors
Geographic Terms: Italy
Abstract: The paper examines how individual motivations, the role of the supervisor and gender influence the early career path of doctorate holders. We investigate PhD graduates' occupational outcomes beyond academia in the framework of current literature on the oversupply of PhD holders and labor market constraints. Our analysis relies on two unique datasets. The first, at the national level, includes microdata from the Italian National Institute of Statistics regarding about 41,000 graduates who account for over 70% of the population of 6 cohorts surveyed for the period 2004–2014. The other dataset is from a single university, and resulted from an original survey of 760 PhD holders who earned their doctorates from the University of Turin in 2007–2017. We find that PhD holders' motivation towards science is associated with their subsequent employment in academia or in other research and non-research jobs. Sponsoring support in early career and the supervisor's propensity for basic research also play a role in the future academic career path. Gender differences in type of occupation, however, continue to persist even taking motivations and the supervisor's role into account. [ABSTRACT FROM AUTHOR]
Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Higher+Education+%2800181560%29%22">Higher Education (00181560)</searchLink>. May2024, Vol. 87 Issue 5, p1375-1392. 18p.
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  Data: The paper examines how individual motivations, the role of the supervisor and gender influence the early career path of doctorate holders. We investigate PhD graduates' occupational outcomes beyond academia in the framework of current literature on the oversupply of PhD holders and labor market constraints. Our analysis relies on two unique datasets. The first, at the national level, includes microdata from the Italian National Institute of Statistics regarding about 41,000 graduates who account for over 70% of the population of 6 cohorts surveyed for the period 2004–2014. The other dataset is from a single university, and resulted from an original survey of 760 PhD holders who earned their doctorates from the University of Turin in 2007–2017. We find that PhD holders' motivation towards science is associated with their subsequent employment in academia or in other research and non-research jobs. Sponsoring support in early career and the supervisor's propensity for basic research also play a role in the future academic career path. Gender differences in type of occupation, however, continue to persist even taking motivations and the supervisor's role into account. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Higher Education (00181560) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2024
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