There is a well-documented gap between the observed number of works produced by women and by men in science, with clear consequences for the retention and promotion of women1. The gap might be a result of productivity differences2–5, or it might be owing to women’s contributions not being acknowledged6,7. Here we find that at least part of this gap is the result of unacknowledged contributions: women in research teams are significantly less likely than men to be credited with authorship. The findings are consistent across three very different sources of data. Analysis of the first source—large-scale administrative data on research teams, team scientific output and attribution of credit—show that women are significantly less likely to be named on a given article or patent produced by their team relative to their male peers. The gender gap in attribution is present across most scientific fields and almost all career stages. The second source—an extensive survey of authors—similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source—qualitative responses—suggests that the reason that women are less likely to be credited is because their work is often not known, is not appreciated or is ignored. At least some of the observed gender gap in scientific output may be owing not to differences in scientific contribution, but rather to differences in attribution.

This paper examines international and domestic collaborations using data from an original survey of corresponding authors and Web of Science data of articles that had at least one US coauthor in the fields of Particle and Field Physics, Nanoscience and Nanotechnology, and Biotechnology and Applied Microbiology. The data allow us to investigate the connections among coauthors and the views of corresponding authors about the collaboration. We have four main findings. First, we find that US collaborations have increased across US cities as well as across international borders, with the nature of collaborations across cities resembling that across countries. Second, face-to-face meetings are important in collaborations: most collaborators first met working in the same institution and communicate often through meetings with coauthors from distant locations. Third, the main reason for most collaborations is to combine the specialized knowledge and skills of coauthors, but there are substantial differences in the mode of collaborations between small lab-based science and big science, where international collaborations are more prevalent. Fourth, for biotech, we find that citations to international papers are higher compared to papers with domestic collaborators only, but not for the other two fields. Moreover, in all three fields, papers with the same number of coauthors had lower citations if they were international collaborations. Overall, our findings suggest that all collaborations are best viewed from a framework of collaborations across space broadly, rather than in terms of international as opposed to domestic collaborative activity.

This paper examines the behavior of job seekers and recruiters in the labor market for software engineers. I obtained data from a recruiting platform where individuals can self-report their computer programming skills and recruiters can message individuals they wish to contact about job opportunities. I augment this data set with measures of each individual’s previous programming experience based on analysis of actual computer source code they wrote and shared within the open-source software community. This novel data set reveals that candidates’ self-reported technical skills are quantitatively important predictors of recruiter interest. Consistent with social psychology and behavioral economics studies, I also find female programmers with previous experience in a programming language are 11.07% less likely than their male counterparts to self-report knowledge of that programming language on their resume. Despite public pronouncements, however, recruiters do not appear more inclined toward recruiting female candidates who self-report knowing programming languages. Indeed, recruiters are predicted to be 6.47% less likely to express interest in a female candidate than a male candidate with comparable observable qualifications even if those qualifications are very strong. Ultimately, a gender gap in the self-reporting of skills on resumes exists; but recruiters do not appear to be adjusting their response to such signals in ways that could increase the representation of women among software engineering recruits. This paper was accepted by Yan Chen, behavioral economics and decision analysis.

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