Biases against women in the workplace have been documented in a variety of studies. This paper presents the largest study to date on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, women's acceptance rates are higher only when they are not identifiable as women. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

Biases against women in the workplace have been documented in a variety of studies. This paper presents a large scale study on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, for contributors who are outsiders to a project and their gender is identifiable, men's acceptance rates are higher. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless. How to cite this article Terrell et al. (2017), Gender differences and bias in open source: pull request acceptance of women versus men. .i08. Howard M, Pincus J, Wing JM. 2005. Measuring relative attack surfaces. In: Computer security in the 21st century. Springer, 109-137. Knobloch-Westerwick S, Glynn CJ, Huge M. 2013. The Matilda Effect in science communication an experiment on gender bias in publication quality perceptions and collaboration interest. Science Communication 35(5):603-625 A, Devanbu P, Filkov V. 2015. Gender and tenure diversity in GitHub teams. In: CHI conference on human factors in computing systems, CHI. ACM, 3789-3798. Yu Y, Wang H, Filkov V, Devanbu P, Vasilescu B. 2015. Wait for it: determinants of pull request evaluation latency on GitHub. In: Mining software repositories (MSR), 2015 IEEE/ACM 12th working conference on. IEEE, 367-371.

Biases against women in the workplace have been documented in a variety of studies. This paper presents the largest study to date on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, when a woman's gender is identifiable, they are rejected more often. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

Biases against women in the workplace have been documented in a variety of studies. This paper presents the largest study to date on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women's contributions tend to be accepted more often than men's. However, women's acceptance rates are higher only when they are not identifiable as women. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

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