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The productivity of science & engineering PhD students hired from supervisors’ networks

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  • Baruffaldi, Stefano
  • Visentin, Fabiana
  • Conti, Annamaria

Abstract

We compare the scientific productivity of PhD students who are hired from a fine-grained set of mutually exclusive affiliation types: a PhD supervisor's affiliation, an external affiliation from which the supervisor derives her coauthors, and an external affiliation with which the supervisor has no coauthorship ties. Using a novel dataset of science and engineering PhD students who graduated from two major Swiss universities, we find that the most productive PhD category is the one made of students who are affiliated with universities other than their supervisors’ affiliation, but from which the PhD supervisors derive their coauthors. This result suggests an inverted U-shaped relationship between PhD students’ productivity and the social distance from their supervisors. Additionally, we find evidence consistent with the role of supervisors’ coauthor networks in resolving information asymmetries regarding PhD talent.

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  • Baruffaldi, Stefano & Visentin, Fabiana & Conti, Annamaria, 2016. "The productivity of science & engineering PhD students hired from supervisors’ networks," Research Policy, Elsevier, vol. 45(4), pages 785-796.
    • Handle: RePEc:eee:respol:v:45:y:2016:i:4:p:785-796
    DOI: 10.1016/j.respol.2015.12.006
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    Cited by:

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    2. Baruffaldi, Stefano H. & Di Maio, Giorgio & Landoni, Paolo, 2017. "Determinants of PhD holders’ use of social networking sites: An analysis based on LinkedIn," Research Policy, Elsevier, vol. 46(4), pages 740-750.
    3. Plantec, Quentin & Cabanes, Benjamin & le Masson, Pascal & Weil, Benoit, 2023. "Early-career academic engagement in university–industry collaborative PhDs: Research orientation and project performance," Research Policy, Elsevier, vol. 52(9).
    4. Lu, Xiao & McInerney, Paul-Brian, 2016. "Is it better to “Stand on Two Boats” or “Sit on the Chinese Lap”?: Examining the cultural contingency of network structures in the contemporary Chinese academic labor market," Research Policy, Elsevier, vol. 45(10), pages 2125-2137.
    5. Corsini, Alberto & Pezzoni, Michele & Visentin, Fabiana, 2022. "What makes a productive Ph.D. student?," Research Policy, Elsevier, vol. 51(10).
    6. Wuestman, Mignon & Wanzenböck, Iris & Frenken, Koen, 2023. "Local peer communities and future academic success of Ph.D. candidates," Research Policy, Elsevier, vol. 52(8).
    7. Quentin Plantec & Benjamin Cabanes & Pascal Le Masson & Benoit Weil, 2021. "Market-Pull Or Research Push? Effects Of Research Orientations On University-Industry Collaborative Ph.D. Projects' Performances," Post-Print halshs-03190142, HAL.
    8. Dominik P. Heinisch & Guido Buenstorf, 2018. "The next generation (plus one): an analysis of doctoral students’ academic fecundity based on a novel approach to advisor identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 351-380, October.
    9. John Manuel Barrios & Laura Giuliano & Andrew J. Leone, 2020. "In Living Color: Does In-Person Screening Affect Who Gets Hired?," Working Papers 2020-38, Becker Friedman Institute for Research In Economics.
    10. Rose, Michael E. & Shekhar, Suraj, 2023. "Adviser connectedness and placement outcomes in the economics job market," Labour Economics, Elsevier, vol. 84(C).
    11. Michael E. Rose & Suraj Shekhar, 2021. "Indirect Contacts in Hiring: The Economics Job Market," Working Papers 55, Ashoka University, Department of Economics.
    12. Mignon Wuestman & Koen Frenken & Iris Wanzenböck, 2020. "A genealogical approach to academic success," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-16, December.

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    More about this item

    Keywords

    Scientific productivity; PhD students; Supervisors’ networks;
    All these keywords.

    JEL classification:

    • I2 - Health, Education, and Welfare - - Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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