Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

When seeking help, women and racial/ethnic minorities benefit from explicitly stating their identity


Receiving help can make or break a career, but women and racial/ethnic minorities do not always receive the support they seek. Across two audit experiments—one with politicians and another with students—as well as an online experiment (total n = 5,145), we test whether women and racial/ethnic minorities benefit from explicitly mentioning their demographic identity in requests for help, for example, by including statements like “As a Black woman…” in their communications. We propose that when a help seeker highlights their marginalized identity, it may activate prospective helpers’ motivations to avoid prejudiced reactions and increase their willingness to provide support. Here we show that when women and racial/ethnic minorities explicitly mentioned their demographic identity in help-seeking emails, politicians and students responded 24.4% (7.42 percentage points) and 79.6% (2.73 percentage points) more often, respectively. These findings suggest that deliberately mentioning identity in requests for help can improve outcomes for women and racial/ethnic minorities.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Reply rates to emails across conditions in Study 1.
Fig. 2: Reply rates to emails from women and/or racial/ethnic minorities (relative to white male help seekers) across conditions in Study 1.
Fig. 3: Percentage of emails that yielded volunteers across conditions in Study 2.

Data availability

De-identified participant data from Studies 1, 2 and 3 are permanently and publicly available in an OSF folder at There are no restrictions on data availability. These data include information about participant responses, condition assignment and any preregistered control variables for each of our studies. The OSF folder also includes preregistrations for Studies 1, 2 and 3 as well as a copy of our Supplementary Information, which contains further details about our experimental methods and results (for example, stimulus language, screenshots of surveys shared with participants in Study 3 and results of robustness checks). Figures that have associated raw data include Figs. 1, 2 and 3. The raw data for these figures are also included in the OSF folder. Source data are provided with this paper.

Code availability

The code to replicate the analyses in the manuscript and our Supplementary Information is available permanently and publicly in an OSF folder at


  1. Coury, S. et al. Women in the workplace 2020. McKinsey & Company (2020).

  2. The NCES Fast Facts Tool provides quick answers to many education questions. National Center for Education Statistics (2020).

  3. Butler, D. M. & Broockman, D. E. Do politicians racially discriminate against constituents? A field experiment on state legislators. Am. J. Pol. Sci. 55, 463–477 (2011).

    Article  Google Scholar 

  4. Giuliano, L., Levine, D. I. & Leonard, J. Racial bias in the manager-employee relationship an analysis of quits, dismissals, and promotions at a large retail firm. J. Hum. Resour. 46, 26–52 (2011).

    Google Scholar 

  5. Keeves, G. D. & Westphal, J. D. From help to harm: increases in status, perceived underreciprocation, and the consequences for access to strategic help and social undermining among female, racial minority, and white male top managers. Organization Sci. 32, 909–1148 (2021).

    Article  Google Scholar 

  6. Lavy, V. & Sand, E. On the origins of gender gaps in human capital: short-and long-term consequences of teachers’ biases. J. Public. Econ. 167, 263–279 (2018).

    Article  Google Scholar 

  7. McDonald, M. L., Keeves, G. D. & Westphal, J. D. One step forward, one step back: White male top manager organizational identification and helping behavior toward other executives following the appointment of a female or racial minority CEO. Acad. Manag. J. 61, 405–439 (2018).

    Article  Google Scholar 

  8. Milkman, K. L., Akinola, M. & Chugh, D. Temporal distance and discrimination: an audit study in academia. Psychol. Sci. 23, 710–717 (2012).

    Article  PubMed  Google Scholar 

  9. Milkman, K. L., Akinola, M. & Chugh, D. What happens before? A field experiment exploring how pay and representation differentially shape bias on the pathway into organizations. J. Appl. Psychol. 100, 1678–1712 (2015).

    Article  PubMed  Google Scholar 

  10. Price, J. & Wolfers, J. Racial discrimination among NBA referees. Q. J. Econ. 125, 1859–1887 (2010).

    Article  Google Scholar 

  11. White, A. R., Nathan, N. L. & Faller, J. K. What do I need to vote? Bureaucratic discretion and discrimination by local election officials. Am. Polit. Sci. Rev. 109, 129–142 (2015).

    Article  Google Scholar 

  12. Kalla, J., Rosenbluth, F. & Teele, D. L. Are you my mentor? A field experiment on gender, ethnicity, and political self-starters. J. Politics 80, 337–341 (2018).

    Article  Google Scholar 

  13. Eby, L. T., Allen, T. D., Evans, S. C., Ng, T. & DuBois, D. L. Does mentoring matter? A multidisciplinary meta-analysis comparing mentored and non-mentored individuals. J. Vocat. Behav. 72, 254–267 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kaas, L. & Manger, C. Ethnic discrimination in Germany’s labour market: a field experiment. Ger. Econ. Rev. 13, 1–20 (2012).

    Article  Google Scholar 

  15. Seibert, S. E., Kraimer, M. L. & Liden, R. C. A social capital theory of career success. Acad. Manag. J. 44, 219–237 (2001).

    Google Scholar 

  16. Bohren, J. A., Imas, A. & Rosenberg, M. The dynamics of discrimination: theory and evidence. Am. Econ. Rev. 109, 3395–3436 (2019).

    Article  Google Scholar 

  17. Doleac, J. L. & Stein, L. C. The visible hand: race and online market outcomes. Econ. J. 123, F469–F492 (2013).

    Article  Google Scholar 

  18. Edelman, B., Luca, M. & Svirsky, D. Racial discrimination in the sharing economy: evidence from a field experiment. Am. Econ. J. Appl. Econ. 9, 1–22 (2017).

    Article  Google Scholar 

  19. Kang, S. K., DeCelles, K. A., Tilcsik, A. & Jun, S. Whitened résumés: race and self-presentation in the labor market. Adm. Sci. Q. 61, 469–502 (2016).

    Article  Google Scholar 

  20. Bertrand, M. & Mullainathan, S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am. Econ. Rev. 94, 991–1013 (2004).

    Article  Google Scholar 

  21. Dovidio, J. F. & Gaertner, S. L. Aversive racism and selection decisions: 1989 and 1999. Psychol. Sci. 11, 315–319 (2000).

    Article  CAS  PubMed  Google Scholar 

  22. Plant, E. A. & Devine, P. G. Internal and external motivation to respond without prejudice. J. Pers. Soc. Psychol. 75, 811–832 (1998).

    Article  Google Scholar 

  23. Apfelbaum, E. P., Sommers, S. R. & Norton, M. I. Seeing race and seeming racist? Evaluating strategic colorblindness in social interaction. J. Pers. Soc. Psychol. 95, 918 (2008).

    Article  PubMed  Google Scholar 

  24. Bodner, R. & Prelec, D. in The Psychology of Economic Decisions Vol. 1 (eds Brocas, I. & Carillo, J.) (Oxford Univ. Press, 2003).

  25. Paluck, E. L. & Green, D. P. Prejudice reduction: what works? A review and assessment of research and practice. Annu. Rev. Psychol. 60, 339–367 (2009).

    Article  PubMed  Google Scholar 

  26. Plant, E. A. & Devine, P. G. The active control of prejudice: unpacking the intentions guiding control efforts. J. Pers. Soc. Psychol. 96, 640–652 (2009).

    Article  PubMed  Google Scholar 

  27. Rokeach, M. Long-range experimental modification of values, attitudes, and behavior. Am. Psychol. 26, 453–459 (1971).

    Article  Google Scholar 

  28. Pope, D. G., Price, J. & Wolfers, J. Awareness reduces racial bias. Manag. Sci. 64, 4988–4995 (2018).

    Article  Google Scholar 

  29. Sommers, S. R. & Ellsworth, P. C. White juror bias: an investigation of prejudice against Black defendants in the American courtroom. Psychol. Public. Policy. Law. 7, 201–229 (2001).

    Article  Google Scholar 

  30. Banaji, M. R. & Hardin, C. D. Automatic stereotyping. Psychol. Sci. 7, 136–141 (1996).

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Taylor, S. E., Fiske, S. T., Etcoff, N. L. & Ruderman, A. J. Categorical and contextual bases of person memory and stereotyping. J. Pers. Soc. Psychol. 36, 778–793 (1978).

    Article  Google Scholar 

  33. Ai, C. & Norton, E. C. Interaction terms in logit and probit models. Econ. Lett. 80, 123–129 (2003).

    Article  Google Scholar 

  34. Gomila, R. Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J. Exp. Psychol. Gen. 150, 700–709 (2021).

    Article  PubMed  Google Scholar 

  35. Butler, D. M. & Crabtree, C. Moving beyond measurement: adapting audit studies to test bias-reducing interventions. J. Exp. Pol. Sci. 4, 57 (2017).

    Article  Google Scholar 

  36. Einstein, K. L. & Glick, D. M. Does race affect access to government services? An experiment exploring street-level bureaucrats and access to public housing. Am. J. Pol. Sci. 61, 100–116 (2017).

    Article  Google Scholar 

  37. Barron, G. & Yechiam, E. Private e-mail requests and the diffusion of responsibility. Comput. Hum. Behav. 18, 507–520 (2002).

    Article  Google Scholar 

  38. Bullock, J. G., Green, D. P. & Ha, S. E. Yes, but what’s the mechanism? (Don’t expect an easy answer). J. Pers. Soc. Psychol. 98, 550–558 (2010).

    Article  PubMed  Google Scholar 

  39. Imai, K., Keele, L. & Tingley, D. A general approach to causal mediation analysis. Psychol. Methods. 15, 309–334 (2010).

    Article  PubMed  Google Scholar 

  40. Imai, K., Keele, L. & Yamamoto, T. Identification, inference and sensitivity analysis for causal mediation effects. Stat. Sci. 25, 51–71 (2010).

    Article  Google Scholar 

  41. Fishbane, A., Ouss, A. & Shah, A. K. Behavioral nudges reduce failure to appear for court. Science 370, eabb6591 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Tiefenbeck, V. et al. Overcoming salience bias: How real-time feedback fosters resource conservation. Manag. Sci. 64, 1458–1476 (2018).

    Article  Google Scholar 

  43. Zhang, T., Fletcher, P. O., Gino, F. & Bazerman, M. H. Reducing bounded ethicality: how to help individuals notice and avoid unethical behavior. Organ. Dyn. 44, 310–317 (2015).

  44. Butz, D. A. & Plant, E. A. Prejudice control and interracial relations: the role of motivation to respond without prejudice. J. Pers. 77, 1311–1342 (2009).

    Article  PubMed  Google Scholar 

  45. Glover, D., Pallais, A. & Pariente, W. Discrimination as a self-fulfilling prophecy: evidence from French grocery stores. Q. J. Econ. 132, 1219–1260 (2017).

    Article  Google Scholar 

  46. Heilman, M. E. Description and prescription: how gender stereotypes prevent women’s ascent up the organizational ladder. J. Soc. Issues. 57, 657–674 (2001).

    Article  Google Scholar 

  47. Ibarra, H. Homophily and differential returns: sex differences in network structure and access in an advertising firm. Adm. Sci. Q. 37, 422–447 (1992).

    Article  Google Scholar 

  48. Rosette, A. S., Leonardelli, G. J. & Phillips, K. W. The White standard: racial bias in leader categorization. J. Appl. Psychol. 93, 758–777 (2008).

    Article  PubMed  Google Scholar 

  49. Watkins, M. B., Simmons, A. & Umphress, E. It’s not black and white: toward a contingency perspective on the consequences of being a token. Acad. Manag. Perspect. 33, 334–365 (2019).

    Article  Google Scholar 

  50. City and town population totals: 2010–2019. U.S. Census Bureau (2020).

  51. Kirgios, E. L., Rai, A., Chang, E. H. & Milkman, K. L. When seeking help, women and racial/ethnic minorities benefit from explicitly stating their demographic identity. OSF (2021).

Download references


This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1845298 (awarded to E.L.K.) and the Wharton School. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to A. Duckworth, J. Kessler, A. Rees-Jones, I. Silver and attendees at the Society for Judgment and Decision Making conference for their feedback. We also thank K. Shonk and K. Brabaw for providing editorial input on this manuscript and the Wharton Behavioural Lab for their assistance in gathering data for this research. Finally, we are grateful to the many research assistants who helped us make this work possible, particularly K. Herrera, M. Chung, M. Huang, C. Kornicker and G.M. Waldman.

Author information

Authors and Affiliations



E.L.K., A.R., E.H.C. and K.L.M. designed and performed the research. E.L.K. and A.R. analysed the data. E.L.K. wrote the paper. A.R., E.H.C. and K.L.M. provided critical feedback on the paper. E.L.K. prepared the supplementary materials.

Corresponding author

Correspondence to Erika L. Kirgios.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Additional details for Supplementary Methods for Studies 1–3, Tables 1–24 and Fig. 1, and screenshots from surveys used in Studies 2 and 3.

Reporting Summary.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kirgios, E.L., Rai, A., Chang, E.H. et al. When seeking help, women and racial/ethnic minorities benefit from explicitly stating their identity. Nat Hum Behav 6, 383–391 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing