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Explaining Gender Differences in Academics' Career Trajectories Explaining Gender Differences in Academics’ Career Trajectories Aniko Hannak,1, ∗ Kenneth Joseph,2, y Andrei Cimpian,3, z and Daniel B. Larremore4, 5, x 1Department of Computer Science, University of Zurich,¨ Zurich,¨ Switzerland{ 2Department of Computer Science and Engineering, University at Buffalo, SUNY, Buffalo, NY, USA{ 3Department of Psychology, New York University, New York, NY, USA{ 4 Department of Computer Science, University of Colorado, Boulder, CO, USA 5BioFrontiers Institute, University of Colorado, Boulder, CO, USA{ Academic fields exhibit substantial levels of gender segregation. To date, most attempts to explain this persis- tent global phenomenon have relied on limited cross-sections of data from specific countries, fields, or career stages. Here we used a global longitudinal dataset assembled from profiles on ORCID.org to investigate which characteristics of a field predict gender differences among the academics who leave and join that field. Only two field characteristics consistently predicted such differences: (1) the extent to which a field values raw in- tellectual talent (“brilliance”) and (2) whether a field is in Science, Technology, Engineering, and Mathematics (STEM). Women more than men moved away from brilliance-oriented and STEM fields, and men more than women moved toward these fields. Our findings suggest that stereotypes associating brilliance and other STEM- relevant traits with men more than women play a key role in maintaining gender segregation across academia. Gender segregation in academia—and the workplace more formative at a broad temporal scale, which creates additional generally—remains substantial well into the 21st century [1– challenges—most notably, data availability. 6], undermining gender equity in earnings and status [7–9]. Here we provide the first investigation of gender segrega- Although understanding the causes of this phenomenon, es- tion in academia that simultaneously satisfies all of the above pecially as it concerns STEM fields, is a priority for many criteria, in that (i) it takes into account individuals movements governmental and international agencies [10–12], a clear between fields; (ii) it encompasses as many as 30 fields across view is complicated by several factors. First, the reasons why STEM, social sciences, and the humanities; and it includes women and men differentially leave or join certain fields information on individuals (iii) from over 200 different coun- are best understood in the context of their broader career tries (iv) across more than 6 decades. trajectories—what they did after leaving the field in question To accomplish this goal, we created a unique dataset, or before joining it [13]. Yet, most analyses of gender seg- which we have now made freely available. This dataset was regation in academia to date have not examined individual- compiled from two independent sources. One source con- level longitudinal information of this sort. Second, academic sisted of publicly available author profiles from ORCID.org fields vary considerably in their levels of gender segregation, (Open Researcher and Contributor ID), a not-for-profit orga- regardless of whether they are in STEM, the social sciences, nization that maintains a global database of scholars, their or the humanities [14, 15]. Although the issue of womens un- educational and employment history, and their published re- derrepresentation in STEM has received substantial attention, search (see Section1 in the Supplementary Text and Sup- few investigations to date have considered a wide enough plementary Fig.1). The ORCID profiles allowed us to com- range of fields to be able to identify cross-cutting explana- prehensively compare how women and men move between tory factors underlying gender segregation across academia— fields.1 The second source of data consisted of a survey of or heterogeneity in such factors (for some important ex- academics from 30 different fields, in which they rated their ceptions, see [15–18]). Third, there is systematic variability own fields along several dimensions [15]. When combined in the gender composition of academic fields across coun- with the ORCID profiles, these data offered unprecedented arXiv:2009.10830v1 [cs.SI] 22 Sep 2020 tries [6, 19, 20], yet investigations of this phenomenon have insight into the processes underlying gender segregation in predominantly focused on a specific cultural context, poten- academia. tially missing explanatory factors that emerge at broader lev- We seek to understand gender segregation in academia by els of analysis (for some important exceptions, see [19–22]). explaining how and why academics move between fields. Fourth, there are secular trends in the extent to which men and This approach provides an update to the common metaphor women differ in the skills needed for various careers [23] and of a “leaky pipeline.” Most pipeline analyses compare the are subject to stereotypes relevant to these careers [24, 25], as proportion of women (or men) at consecutive stages in the well as in other factors that may contribute to gender segre- professional trajectory of a fields members (e.g., bachelors gation [19]. Thus, analyses of this phenomenon are most in- degrees vs. PhD degrees; [17, 18]). In these analyses, a fields pipeline is said to be leaking women (or men) if the propor- tion of women (or men) in the field declines from one career ∗ hannak@ifi.uzh.ch y [email protected] z [email protected] x [email protected] 1 Throughout, we use the terms move, switch, and transition interchangeably { The four authors contributed equally to this work. Author order is random. to refer to an observed change in an ORCID user’s academic field. 2 stage to the next. Although useful, this approach is intrinsi- dicts that women should be relatively more likely than men cally limited by the fact that it cannot provide insight into why to transition toward academic fields with lower on-campus gaps emerge when they do. For instance, are more women workloads and, conversely, that men should be more likely than men leaving the field, or more men than women joining than women to transition toward academic fields with higher it, or both? Where did the women who left go, and where did workloads (see Supplementary Table1 for the measure of a the men who joined come from? What is it about a field that field’s on-campus workload). explains gender-differentiated career transitions into and out A third explanation appeals to differences among academic of it? Traditional pipeline analyses cannot answer questions fields in the extent to which they focus on inanimate objects such as these, which are essential for an adequate understand- vs. living things, including people. Prior work has suggested ing of gender gaps in representation. Our approach, which that men tend to prefer occupations that focus on inanimate engages with the complexities of the “branching pipeline” objects more than women do, whereas women tend to prefer of womens and mens career trajectories [26], may provide a occupations that deal with living things and people more than promising step toward this deeper theoretical understanding. men do [37, 38]. A more recent formulation of this idea ap- With the extensive new dataset we created by enriching the peals to the concepts of systemizing (i.e., analyzing the world ORCID data with field attributes (as rated by academics in as a system of inputs and outputs; see also the notion of sys- these fields; [15]), we were able to compare five distinct ex- tematic self-concepts [2, 39]), which is claimed to be more planations for why women and men in academia might follow common in men, and empathizing (i.e., intuitively under- different paths. We focused on these explanations, which we standing others mental states), which is claimed to be more describe in the next section, for the same reasons they were common in women [40]. From this perspective, the more a included in Leslie, Cimpian, Meyer, and Freeland’s survey field values systemizing relative to empathizing, the less wel- of academics [15]: Although by no means exhaustive, they coming and/or appealing it should be to women and the more represent some of the more prominent and well-supported welcoming and/or appealing it should be to men [41]. This theories in the literature. They also represent a range of per- hypothesis predicts that women should be relatively more spectives on the mechanisms underlying gender segregation, likely than men to transition toward academic fields with a from those that emphasize the culture of a field and gender weaker emphasis on systemizing (vs. empathizing) and, con- stereotypes to those that focus on hypothesized differences versely, that men should be more likely than women to tran- between women’s and men’s preferences and abilities. Com- sition toward academic fields with a stronger emphasis on paring multiple theoretical perspectives with the same data systemizing (vs. empathizing) (see Supplementary Table1 and methods, rather than focusing narrowly on just one (type for the measure of a field’s emphasis on systemizing vs. em- of) perspective, is arguably more likely to lead to theoretical pathizing). progress. Fields in which empathizing is valued may also be more Five potential explanations. One explanation for gender likely to facilitate their members’ pursuit of communal goals segregation in academia appeals to differences among fields (e.g., working with others, helping others). If so, this test in the extent to which their members believe that success of the systemizing–empathizing hypothesis may also bear depends on innate intellectual ability (“brilliance”). Because on the proposal that women are underrepresented in fields cultural stereotypes associate men more than women with whose pursuit is typically seen as inconsistent with the pur- this trait [29–31], fields that value brilliance—which include suit of communal goals (such as many fields in STEM; [42]). many STEM fields—may be more welcoming to men than On the assumption that the systemizing–empathizing variable women.
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