Many granting agencies allow reviewers to know the identity of a proposal’s principal investigator (PI), which opens the possibility that reviewers discriminate on the basis of PI race and gender. We investigated this experimentally with 48 NIH R01 grant proposals, representing a broad range of NIH-funded science. We modified PI names to create separate white male, white female, black male and black female versions of each proposal, and 412 scientists each submitted initial reviews for 3 proposals. We find little to no race or gender bias in initial R01 evaluations, and additionally find that any bias that might have been present must be negligible in size. This conclusion was robust to a wide array of statistical model specifications. Pragmatically, important bias may be present in other aspects of the granting process, but our evidence suggests that it is not present in the initial round of R01 reviews.
Subscribe to Journal
Get full journal access for 1 year
only $8.67 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
All code used in this paper can be accessed at our project page at https://osf.io/c5csm/
Our data and materials have been deposited at https://osf.io/uy7vq/, which also includes our preregistered protocol. The modified grant proposals have not been deposited for confidentiality reasons. Modified grant proposals can be obtained by contacting the corresponding authors, who will seek permission to share these materials from the research teams that prepared the proposals.
Ginther, D. K. et al. Race, ethnicity, and NIH research awards. Science 333, 1015–1019 (2011).
Ceci, S. J. & Williams, W. M. Understanding current causes of women’s underrepresentation in science. Proc. Natl Acad. Sci. USA 108, 3157–3162 (2011).
Pohlhaus, J. R., Jiang, H., Wagner, R. M., Schaffer, W. T. & Pinn, V. W. Sex differences in application, success, and funding rates for NIH extramural programs. Acad. Med. 86, 759–767 (2011).
Lakens, D. Equivalence tests: a practical primer for t tests, correlations, and meta-analyses. Soc. Psychol. Personal. Sci. 8, 355–362 (2017).
Kaatz, A., Magua, W., Zimmerman, D. R. & Carnes, M. A quantitative linguistic analysis of National Institutes of Health R01 application critiques from investigators at one institution. Acad. Med. 90, 69–75 (2015).
Crowne, D. P. & Marlowe, D. A new scale of social desirability independent of psychopathology. J. Consult. Psychol. 24, 349–354 (1960).
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).
Steinpreis, R. E., Anders, K. A. & 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).
Lerner, J. S. & Tetlock, P. E. Accounting for the effects of accountability. Psychol. Bull. 125, 255–275 (1999).
Tomkins, A., Zhang, M. & Heavlin, W. D. Reviewer bias in single- versus double-blind peer review. Proc. Natl Acad. Sci. USA 114, 12708–12713 (2017).
Peterson, D. A. M. Author gender and editorial outcomes at Political Behavior. PS Polit. Sci. Polit. 51, 866–869 (2018).
Samuels, D. Gender and editorial outcomes at Comparative Political Studies. PS Polit. Sci. Polit. 51, 854–858 (2018).
König, T. & Ropers, G. Gender and editorial outcomes at the American Political Science Review. PS Polit. Sci. Polit. 51, 849–853 (2018).
Tudor, C. L. & Yashar, D. J. Gender and the editorial process: World Politics, 2007–2017. PS Polit. Sci. Polit. 51, 870–880 (2018).
Nedal, D. K. & Nexon, D. H. Gender in the International Studies Quarterly Review process. PS Polit. Sci. Polit. 51, 859–865 (2018).
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).
Link, A. M. US and non-US submissions: an analysis of reviewer bias. JAMA 280, 246–247 (1998).
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).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Brauer, M. & Curtin, J. J. Linear mixed-effects models and the analysis of nonindependent data: a unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychol. Med. 23, 389–411 (2018).
Halekoh, U. & Højsgaard, S. A Kenward–Roger approximation and parametric bootstrap methods for tests in linear mixed models—the R package pbkrtest. J. Stat. Softw. 59, 1–32 (2014).
Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE, Los Angeles, CA, USA, 2011).
Neale, M. C. et al. OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81, 535–549 (2016).
Simonsohn, U., Simmons, J. P. & Nelson, L. D. Specification curve: descriptive and inferential statistics on all reasonable specifications. SSRN Electron. J. https://doi.org/10.2139/ssrn.2694998 (2015).
Pordes, R. et al. The Open Science Grid. J. Phys. Conf. Ser. 78, 12057 (2007).
Sfiligoi, I. et al. The Pilot Way to Grid Resources Using glideinWMS. In 2009 WRI World Congress on Computer Science and Information Engineering (eds Burgin, M. et al.) 428–432 (IEEE, 2009).
Tausczik, Y. R. & Pennebaker, J. W. The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29, 24–54 (2009).
We acknowledge E. Brandt, K. Lange, D. (Dianne) Lee, J. Marsh, V. Martinez, C. Mitamura, N. Mohan, Y. Lee, C. Henriques, R. Grzenia, K. Scott, S. Staples, D. Statz and P. Rienke for their help in conducting this research. We also acknowledge M. Carnes, C. Ford, A. Kaatz and J. Raclaw for their help in the design of the research. Finally, we acknowledge J. Westfall for his helpful comments on our analyses and J. Fox for his advice on the car package. This research was supported by NIH grant 5R01GM111002-02 to P.G.D. Part of this research was conducted using technical resources provided by the Open Science Grid25,26, which is supported by the National Science Foundation award 1148698 and the US Department of Energy’s Office of Science. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Forscher, P.S., Cox, W.T.L., Brauer, M. et al. Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. Nat Hum Behav 3, 257–264 (2019) doi:10.1038/s41562-018-0517-y
Science Advances (2019)