In the biomedical sciences, author order reflects the role people play on articles. The first author has primary responsibility for the work, while the last author runs the lab and/or is the principal investigator who supported the work. Thus, author order affects the credit received for work and conveys information about the stature of authors.We leverage this feature of scholarly publishing to make two interrelated contributions to our understanding of underrepresentation in the sciences. First, studying the probability that a person is the last author on a publication and algorithmically resolving author ambiguities and imputing ethnicity, gender, and race allows us to use massive population-level longitudinal data to study underrepresentation. (West et al. 2013 use a similar approach to study women.)Second, we use these data to look at ethnicity, gender, race, and experience and how they interact in a way that is impossible with sampleddata. This analysis is timely because of serious concerns with underrepresentation of women and minorities in biomedicine and other STEM fields (NIH 2012) and with barriers confronting female and minority scientists (e.g., Cook and Kongcharoen 2010; Ginther et al. 2011; and Lariviere et al. 2013).
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