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Committees with implicit biases promote fewer women when they do not believe gender bias exists

Nature Human Behaviour volume 3, pages 1171–1179 (2019)Cite this article

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Abstract

Whether gender bias contributes to women’s under-representation in scientific fields is still controversial. Past research is limited by relying on explicit questionnaire ratings in mock-hiring scenarios, thereby ignoring the potential role of implicit gender bias in the real world. We examine the interactive effect of explicit and implicit gender biases on promotion decisions made by scientific evaluation committees representing the whole scientific spectrum in the course of an annual nationwide competition for elite research positions. Findings reveal that committees with strong implicit gender biases promoted fewer women at year 2 (when committees were not reminded of the study) relative to year 1 (when the study was announced) if those committees did not explicitly believe that external barriers hold women back. When committees believed that women face external barriers, implicit biases did not predict selecting more men over women. This finding highlights the importance of educating evaluative committees about gender biases.

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Fig. 1: Gender asymmetry within each academic discipline in the year before the present study, and a timeline of the study.

Data availability

Most of the data supporting the findings of this study at the committee level are available within the paper (and its Supplementary Information files). The entire dataset that supports the findings of this study is available from the Open Science Framework repository (https://osf.io/umf62/). Individual-level data are available on request from the authors.

Code availability

The code used to perform the primary analyses of the study is available from the corresponding authors upon request.

References

  1. Gibney, E. Women under-represented in world’s science academies. Nature News http://www.nature.com/news/women-under-represented-in-world-s-science-academies-1.19465 (29 February 2016).

  2. Gender in research and innovation. Statistics and Indicators https://ec.europa.eu/research/swafs/pdf/pub_gender_equality/she_figures_2015-leaflet-web.pdf (She Figures, European Union, 2015).

  3. Science and Engineering Indicators 2016 https://www.nsf.gov/statistics/2016/nsb20161/#/ (National Science Foundation, 2016).

  4. Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. Science faculty’s subtle gender biases favor male students. Proc. Natl Acad. Sci. USA 109, 16474–16479 (2012).

    Article  CAS  Google Scholar 

  5. Reuben, E., Sapienza, P. & Zingales, L. How stereotypes impair women’s careers in science. Proc. Natl Acad. Sci. USA 111, 4403–4408 (2014).

    Article  CAS  Google Scholar 

  6. Steinpreis, R., Anders, K. & Ritzke, D. The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: a national empirical study. Sex Roles 41, 509–528 (1999).

    Article  Google Scholar 

  7. Williams, W. M. & Ceci, S. J. National hiring experiments reveal 2:1 faculty preference for women on STEM tenure track. Proc. Natl Acad. Sci. USA 112, 5360–5365 (2015).

    Article  CAS  Google Scholar 

  8. Breda, T. & Hillion, M. Teaching accreditation exams reveal grading biases favor women in male-dominated disciplines in France. Science 353, 474–478 (2016).

    Article  CAS  Google Scholar 

  9. Williams, W. M. & Ceci, S. J. Academic science isn’t sexist. The New York Times https://www.nytimes.com/2014/11/02/opinion/sunday/academic-science-isnt-sexist.html (31 October 2014).

  10. Baumeister, R. F., Vohs, K. D. & Funder, D. C. Psychology as the science of self-reports and finger movements: whatever happened to actual behavior? Perspect. Psychol. Sci. 2, 396–403 (2007).

    Article  Google Scholar 

  11. Bernstein, R. Scientific community. No sexism in science? Not so fast, critics say. Science 346, 798 (2014).

    Article  CAS  Google Scholar 

  12. Stewart, A. & Valian, V. An Inclusive Academy: Achieving Diversity and Excellence (MIT Press, 2018).

  13. Devine, P. G. Stereotypes and prejudice: their automatic and controlled components. J. Pers. Soc. Psychol. 56, 5–18 (1989).

    Article  Google Scholar 

  14. Devine, P. G. et al. A gender bias habit-breaking intervention led to increased hiring of female faculty in STEMM departments. J. Exp. Soc. Psychol. 73, 211–215 (2017).

    Article  Google Scholar 

  15. Greenwald, A. G., McGhee, D. E. & Schwarz, J. L. K. Measuring individual differences in implicit cognition: the implicit association test. J. Pers. Soc. Psychol. 74, 1464–1480 (1998).

    Article  CAS  Google Scholar 

  16. Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L. & Banaji, M. R. Understanding and using the implicit association test: III. Meta-analysis of predictive validity. J. Pers. Soc. Psychol. 97, 17–41 (2009).

    Article  Google Scholar 

  17. Nosek, B. A. et al. National differences in gender–science stereotypes predict national sex differences in science and math achievement. Proc. Natl Acad. Sci. USA 106, 10593–10597 (2009).

    Article  CAS  Google Scholar 

  18. Rudman, L. A. Implicit Measures for Social and Personality Psychology (Sage Publications, 2011).

  19. Nosek, B. A., Banaji, M. R. & Greenwald, A. G. Math = male, me = female, therefore math not = me. J. Pers. Soc. Psychol. 83, 44–59 (2002).

    Article  Google Scholar 

  20. Miller, D. I., Eagly, A. H. & Linn, M. C. Women’s representation in science predicts national gender–science stereotypes: evidence from 66 nations. J. Educ. Psychol. 107, 631–644 (2015).

    Article  Google Scholar 

  21. Fazio, R. H. & Olson, M. A. in Dual-Process Theories of the Social Mind 155–172 (eds Gawronski, B., Trope, Y. & Sherman, J. W.) (Guilford Press, 2014).

  22. Crandall, C. S. & Eshleman, A. A justification–suppression model of the expression and experience of prejudice. Psychol. Bull. 129, 414–446 (2003).

    Article  Google Scholar 

  23. Uhlmann, E. L. & Cohen, G. L. “I think it, therefore it’s true”: effects of self-perceived objectivity on hiring discrimination. Organ. Behav. Hum. Decis. Process 104, 207–223 (2007).

    Article  Google Scholar 

  24. Caplar, N., Tacchella, S. & Birrer, S. Quantitative evaluation of gender bias in astronomical publications from citation counts. Nat. Astron. 1, 0141 (2017).

    Article  Google Scholar 

  25. Hayes, A. F. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach (Guilford Press, 2013).

  26. Miller, D. I. & Halpern, D. F. The new science of cognitive sex differences. Trends Cogn. Sci. 18, 37–45 (2014).

    Article  Google Scholar 

  27. Régner, I. et al. Individual differences in working memory moderate stereotype-threat effects. Psychol. Sci. 21, 1646–1648 (2010).

    Article  Google Scholar 

  28. Schmader, T., Johns, M. & Forbes, C. An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115, 336–356 (2008).

    Article  Google Scholar 

  29. Valian, V. Why So Slow? The Advancement of Women (MIT Press, 1998).

  30. Stewart, A. J., Malley, J. E. & Herzog, K. A. Increasing the representation of women faculty in STEM departments: what makes a difference? J. Women Minor. Sci. Eng. 22, 23–47 (2016).

    Article  Google Scholar 

  31. Cox, W. T. L. Multiple Determinants of Prejudicial and Nonprejudicial Behavior. PhD thesis, Univ. Wisconsin-Madison (2015).

  32. Sady, K. & Aamodt, M. G. in Adverse Impact Analysis: Understanding Data, Statistics, and Risk (eds Morris, S. B. and Dunleavy, E. M.) 216–238 (Taylor & Francis, 2017).

  33. Nosek, B. A., Greenwald, A. G. & Banaji, M. R. Understanding and using the implicit association test: II. Method variables and construct validity. Pers. Soc. Psychol. Bull. 31, 166–180 (2005).

    Article  Google Scholar 

  34. Sommers, S. R. On racial diversity and group decision making: identifying multiple effects of racial composition on jury deliberations. J. Pers. Soc. Psychol. 90, 597–612 (2006).

    Article  Google Scholar 

  35. Klein, K. J. & Kozlowski, S. W. J. From micro to meso: critical steps in conceptualizing and conducting multilevel research. Org. Res. Methods 3, 211–236 (2000).

    Article  Google Scholar 

  36. Chan, D. Functional relations among constructs in the same content domain at different levels of analysis: a typology of composition models. J. Appl. Psychol. 83, 234–246 (1998).

    Article  Google Scholar 

  37. Bell, S. T. Deep-level composition variables as predictors of team performance: a meta-analysis. J. Appl. Psychol. 92, 595–615 (2007).

    Article  Google Scholar 

  38. Bradley, B. H., Klotz, A. C., Postlethwaite, B. E. & Brown, K. G. Ready to rumble: how team personality composition and task conflict interact to improve performance. J. Appl. Psychol. 98, 385–392 (2013).

    Article  Google Scholar 

  39. Devine, D. J. & Philips, J. L. Do smarter teams do better: a meta-analysis of cognitive ability and team performance. Small Group Res. 32, 507–532 (2001)

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. Heine, E. Uhlmann, S. Spencer, W. Hall, J. Nezlek and V. Valian for providing valuable input on an earlier version of the manuscript and/or the analyses. Financial support was provided by the Mission pour la Place des Femmes au CNRS (the CNRS Mission for Women’s Integration) and the Social Sciences and Humanities Research Council (895-2017-1025; Canada). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work benefited greatly from technical support provided by the CNRS Mission for Women’s Integration. We thank A. Greenwald, N. Sriram and the other members of Project Implicit for efficient technical assistance on the IAT.

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Author notes

  1. These authors contributed equally: Toni Schmader, Pascal Huguet.

Authors and Affiliations

  1. Laboratoire de Psychologie Cognitive, Aix Marseille Univ, CNRS, Marseille, France

    Isabelle Régner & Catherine Thinus-Blanc

  2. Institut National des Hautes Etudes de la Sécurité et de la Justice, CNRS, Paris, France

    Agnès Netter

  3. University of British Columbia, Vancouver, British Columbia, Canada

    Toni Schmader

  4. Laboratoire de Psychologie Sociale et Cognitive, Université Clermont Auvergne, CNRS, Clermont-Ferrand, France

    Pascal Huguet

Authors
  1. Isabelle Régner
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  2. Catherine Thinus-Blanc
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  3. Agnès Netter
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  4. Toni Schmader
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  5. Pascal Huguet
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Contributions

I.R., P.H. and C.T.-B. designed the study. I.R., P.H. and C.T.-B. performed the study with technical assistance from A.N.; I.R. and T.S. analysed the data. I.R., C.T.-B. and P.H. supervised the project. I.R., P.H. and T.S. wrote the manuscript. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Isabelle Régner or Pascal Huguet.

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The authors declare no competing interests.

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Peer review information: Primary Handling Editor: Stavroula Kousta.

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Régner, I., Thinus-Blanc, C., Netter, A. et al. Committees with implicit biases promote fewer women when they do not believe gender bias exists. Nat Hum Behav 3, 1171–1179 (2019). https://doi.org/10.1038/s41562-019-0686-3

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