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Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy

Author

Listed:
  • Katy Börner

    (School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408; Educational Technology/Media Centre, Dresden University of Technology, 01062 Dresden, Germany)

  • Olga Scrivner

    (School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408)

  • Mike Gallant

    (School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408)

  • Shutian Ma

    (School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408; Department of Information Management, Nanjing University of Science and Technology, 210094 Nanjing, China)

  • Xiaozhong Liu

    (School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408)

  • Keith Chewning

    (Burning Glass Technologies, Boston, MA 02110)

  • Lingfei Wu

    (School of Journalism and Communication, Nanjing University, 210008 Nanjing, China; Department of Sociology, University of Chicago, Chicago, IL 60637; Knowledge Lab, University of Chicago, Chicago, IL 60637; Tencent Research Institute, 100080 Beijing, China)

  • James A. Evans

    (Department of Sociology, University of Chicago, Chicago, IL 60637; Knowledge Lab, University of Chicago, Chicago, IL 60637; Santa Fe Institute, Santa Fe, NM 87501)

Abstract

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-)alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for “soft” social skills, like teamwork and communication, increase with greater demand for “hard” technical skills and tools.

Suggested Citation

  • Katy Börner & Olga Scrivner & Mike Gallant & Shutian Ma & Xiaozhong Liu & Keith Chewning & Lingfei Wu & James A. Evans, 2018. "Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(50), pages 12630-12637, December.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:12630-12637
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    Citations

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    Cited by:

    1. Consoli, Davide & Marin, Giovanni & Rentocchini, Francesco & Vona, Francesco, 2023. "Routinization, within-occupation task changes and long-run employment dynamics," Research Policy, Elsevier, vol. 52(1).
    2. repec:hal:spmain:info:hdl:2441/13fti1jo4t8vjpe6ko3qrrv2nv is not listed on IDEAS
    3. Nik Dawson & Marian-Andrei Rizoiu & Benjamin Johnston & Mary-Anne Williams, 2020. "Predicting Skill Shortages in Labor Markets: A Machine Learning Approach," Papers 2004.01311, arXiv.org, revised Aug 2020.
    4. Lyu, Wenjing & Liu, Jin, 2021. "Soft skills, hard skills: What matters most? Evidence from job postings," Applied Energy, Elsevier, vol. 300(C).
    5. Laura S. Zilian & Stella S. Zilian & Georg Jäger, 2021. "Labour market polarisation revisited: evidence from Austrian vacancy data," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 55(1), pages 1-17, December.
    6. Wennberg, Karl & Anderson, Brian S., 2020. "Editorial: Enhancing the exploration and communication of quantitative entrepreneurship research," Journal of Business Venturing, Elsevier, vol. 35(3).
    7. Joel Klinger & Juan Mateos-Garcia & Konstantinos Stathoulopoulos, 2021. "Deep learning, deep change? Mapping the evolution and geography of a general purpose technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5589-5621, July.
    8. Clara Boothby & Staša Milojević, 2021. "An exploratory full-text analysis of Science Careers in a changing academic job market," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4055-4071, May.
    9. Fabian Stephany & Ole Teutloff, 2022. "What is the Price of a Skill? The Value of Complementarity," Papers 2210.01535, arXiv.org, revised Nov 2023.

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