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As If It Weren't Hard Enough Already: Breaking down Hiring Discrimination Following Burnout

Author

Listed:
  • Sterkens, Philippe

    (Ghent University)

  • Baert, Stijn

    () (Ghent University)

  • Rooman, Claudia

    (Ghent University)

  • Derous, Eva

    (Ghent University)

Abstract

Hiring discrimination towards (former) burnout patients has been extensively documented in the literature. To tackle this problem, it is important to understand the underlying mechanisms of such discrimination. Therefore, we conducted a vignette experiment with 425 genuine recruiters and jointly tested the potential stigma against job candidates with a history of burnout that were mentioned earlier in the literature. We found candidates revealing a history of burnout elicit perceptions of requiring work adaptations, likely having more unpleasant collaborations with others as well as diminished health, autonomy, ability to work under pressure, leadership capacity, manageability, and learning ability, when compared to candidates with a comparable gap in working history due to physical injury. Led by perceptions of a reduced ability to work under pressure, the tested perceptions jointly explained over 90% of the effect of revealing burnout on the probability of being invited to a job interview. In addition, the negative effect on interview probability of revealing burnout was stronger when the job vacancy required higher stress tolerance. In contrast, the negative impact of revealing burnout on interview probability appeared weaker when recruiters were women and when recruiters had previously had personal encounters with burnout.

Suggested Citation

  • Sterkens, Philippe & Baert, Stijn & Rooman, Claudia & Derous, Eva, 2020. "As If It Weren't Hard Enough Already: Breaking down Hiring Discrimination Following Burnout," IZA Discussion Papers 13514, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp13514
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    References listed on IDEAS

    as
    1. Baert, Stijn & De Visschere, Sarah & Schoors, Koen & Vandenberghe, Désirée & Omey, Eddy, 2016. "First depressed, then discriminated against?," Social Science & Medicine, Elsevier, vol. 170(C), pages 247-254.
    2. Baert, Stijn & Norga, Jennifer & Thuy, Yannick & Van Hecke, Marieke, 2016. "Getting grey hairs in the labour market. An alternative experiment on age discrimination," Journal of Economic Psychology, Elsevier, vol. 57(C), pages 86-101.
    3. Van Belle, Eva & Caers, Ralf & Cuypers, Laure & De Couck, Marijke & Neyt, Brecht & Van Borm, Hannah & Baert, Stijn, 2020. "What do student jobs on graduate CVs signal to employers?," Economics of Education Review, Elsevier, vol. 75(C).
    4. Baert, Stijn & Lippens, Louis & Moens, Eline & Sterkens, Philippe & Weytjens, Johannes, 2020. "How do we think the COVID-19 crisis will affect our careers (if any remain)?," GLO Discussion Paper Series 520, Global Labor Organization (GLO).
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    6. Baert, Stijn & De Pauw, Ann-Sophie, 2014. "Is ethnic discrimination due to distaste or statistics?," Economics Letters, Elsevier, vol. 125(2), pages 270-273.
    7. Hannah Van Borm & Ian Burn & Stijn Baert, 2019. "What Does a Job Candidate’s Age Signal to Employers?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 19/984, Ghent University, Faculty of Economics and Business Administration.
    8. Ann-Renée Blais & Elke U. Weber, 2006. "A Domain-Specific Risk-Taking (DOSPERT)Scale for Adult Populations," CIRANO Working Papers 2006s-24, CIRANO.
    9. Ann-Renée Blais & Elke U. Weber, 2006. "A Domain-Specific Risk-Taking (DOSPERT) scale for adult populations," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 1, pages 33-47, July.
    10. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-661, September.
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    More about this item

    Keywords

    hiring discrimination; burnout; statistical discrimination; taste-based discrimination;
    All these keywords.

    JEL classification:

    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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