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Changing demographics of scientific careers: The rise of the temporary workforce

Author

Listed:
  • Staša Milojević

    (Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47401)

  • Filippo Radicchi

    (Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47401)

  • John P. Walsh

    (School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332)

Abstract

Contemporary science has been characterized by an exponential growth in publications and a rise of team science. At the same time, there has been an increase in the number of awarded PhD degrees, which has not been accompanied by a similar expansion in the number of academic positions. In such a competitive environment, an important measure of academic success is the ability to maintain a long active career in science. In this paper, we study workforce trends in three scientific disciplines over half a century. We find dramatic shortening of careers of scientists across all three disciplines. The time over which half of the cohort has left the field has shortened from 35 y in the 1960s to only 5 y in the 2010s. In addition, we find a rapid rise (from 25 to 60% since the 1960s) of a group of scientists who spend their entire career only as supporting authors without having led a publication. Altogether, the fraction of entering researchers who achieve full careers has diminished, while the class of temporary scientists has escalated. We provide an interpretation of our empirical results in terms of a survival model from which we infer potential factors of success in scientific career survivability. Cohort attrition can be successfully modeled by a relatively simple hazard probability function. Although we find statistically significant trends between survivability and an author’s early productivity, neither productivity nor the citation impact of early work or the level of initial collaboration can serve as a reliable predictor of ultimate survivability.

Suggested Citation

  • Staša Milojević & Filippo Radicchi & John P. Walsh, 2018. "Changing demographics of scientific careers: The rise of the temporary workforce," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(50), pages 12616-12623, December.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:12616-12623
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    Citations

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    Cited by:

    1. Alexey Lyutov & Yilmaz Uygun & Marc-Thorsten Hütt, 2021. "Machine learning misclassification of academic publications reveals non-trivial interdependencies of scientific disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1173-1186, February.
    2. Yanmeng Xing & An Zeng & Ying Fan & Zengru Di, 2019. "The strong nonlinear effect in academic dropout," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 793-805, August.
    3. Yajie Zhang & Qiang Yu, 2020. "What is the best article publishing strategy for early career scientists?," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 397-408, January.
    4. Lu, Wei & Ren, Yan & Huang, Yong & Bu, Yi & Zhang, Yuehan, 2021. "Scientific collaboration and career stages: An ego-centric perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    5. Yuki Yamada & Jaime A. Teixeira da Silva, 2022. "A psychological perspective towards understanding the objective and subjective gray zones in predatory publishing," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4075-4087, December.
    6. Song, Le & Ma, Yinghong, 2022. "Evaluating tacit knowledge diffusion with algebra matrix algorithm based social networks," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    7. Zhu, Wanying & Jin, Ching & Ma, Yifang & Xu, Cong, 2023. "Earlier recognition of scientific excellence enhances future achievements and promotes persistence," Journal of Informetrics, Elsevier, vol. 17(2).
    8. Li Hou & Qiang Wu & Yundong Xie, 2022. "Does early publishing in top journals really predict long-term scientific success in the business field?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6083-6107, November.
    9. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    10. Martínez, Catalina & Parlane, Sarah, 2023. "Academic scientists in corporate R&D: A theoretical model," Research Policy, Elsevier, vol. 52(5).
    11. Azoulay, Pierre & Greenblatt, Wesley H. & Heggeness, Misty L., 2021. "Long-term effects from early exposure to research: Evidence from the NIH “Yellow Berets”," Research Policy, Elsevier, vol. 50(9).
    12. Mike Thelwall, 2020. "Mid-career field switches reduce gender disparities in academic publishing," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(3), pages 1365-1383, June.
    13. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).

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