Abstract
I began reading Disparate Measures: Feminist and Economic Perspectives on the STEM Gender Gap during summer break, a time when I, like many working parents, was caring for a school-aged child while also supporting aging parents. Although I’m a feminist sociologist who studies racial and gender inequality in Silicon Valley, these personal experiences renewed my proximity to a long-standing feature of the U.S. labor system: the systemic exploitation of women’s care work. Like many others, I was once drawn in by cultural narratives that equated white-collar careers with stability, upward mobility, and prestige. Today I find myself, like so many women, sustaining some of the very institutions that promised those rewards—through the often invisible, uncompensated, or undercompensated reproductive labor on which they depend.
In Disparate Measures, Mary Armstrong and Susan Averett take up an urgent and important topic: how marginalized women workers, promised economic mobility by the STEM industrial complex, are actually faring. As the authors put it, “Seen as part economic driver and part social remedy, STEM jobs are commonly understood to benefit everyone, especially women and particularly women from underrepresented groups” (p. 2). Deploying a mix of census and labor force data, Armstrong and Averett assess the outcomes for women ages 25 to 64 who have imbibed these promises. Grounded in intersectionality and a critical data studies framework, the book poses two central questions: “What do diverse women actually find when they work in STEM occupations?” and “What do STEM jobs really deliver, and for whom?” (p. 2).
Using American Community Survey data from 2012 to 2019 and public datasets from the Center for Economic and Policy Research, among other sources, the authors find that while women in STEM generally earn more than women in non-STEM occupations, they remain second-class citizens when compared to white men in the same fields. The book features eight intersectional case studies, with each chapter centering a different demographic identity and the wage/status gaps associated with it. Chapters Three through Ten focus on Black women; American Indian and Alaska Native women; Asian and Pacific Islander women; Hispanic/Latina women; foreign-born women; women with disabilities; LGBTQ+ populations (including lesbians, bisexual women, trans women, and gender-nonbinary individuals); and mothers.
A notable structural feature of the book is the way each chapter begins with a critical unpacking of the category (or categories) under analysis. The authors contextualize each identity within a history of measurement, exploring how that group has been constructed and represented in federal data over time. For instance, Chapter Seven on “foreign-born” women opens by showing how the U.S. Census uses “foreign-born” as a catchall, flattening important internal distinctions—between naturalized citizens, lawful permanent residents, temporary visa holders, refugees, and asylum-seekers. This move not only signals the limitations of state-generated data but also models reflexive feminist social science. The authors use these introductory sections to signal the partiality of quantitative endeavors and to caution against taking statistical categories at face value. These framing choices are among the book’s most innovative and pedagogically useful contributions, and I expect to draw on several of these chapters in my own teaching on research methods, race, and gender.
Armstrong and Averett define STEM broadly—beyond just computer science and engineering—to include professional health care occupations (diagnosing and treating practitioners, therapists, nurses, and medical support workers). This expanded definition is crucial for surfacing the often-overlooked STEM contributions of women workers. As they show, more than 70 percent of “STEM-related” jobs in 2019 were held by women, yet women in this sector earned just 75 percent of what white men earned. The book carefully illustrates how wage gaps persist across and within STEM domains and how racial and gender hierarchies structure even those jobs held up as beacons of opportunity.
While the book’s strengths are numerous, there are limitations worth noting. Despite its commitment to intersectionality, Disparate Measures leans heavily on traditional economic tools—regression analysis, wage decomposition, and labor force statistics. Though rigorous, these approaches can feel reductive, especially in areas where cultural, organizational, and relational dynamics are central to understanding inequality. For instance, Armstrong and Averett largely sidestep the extensive sociological literature on how gender and race operate within organizations—how workplace culture, role expectations, managerial norms, and interpersonal dynamics reproduce inequality in ways not captured by census data.
Notably absent are insights from scholars who have long studied the cultural and institutional reproduction of inequality in STEM settings (including my own work). Likewise, the book engages only minimally with sociology of race scholarship on the failures of corporate diversity initiatives. This omission means that Disparate Measures often explains what is happening (e.g., wage gaps and occupational clustering) without fully addressing why—particularly in terms of organizational culture, ideology, or the relational dynamics of work and homelife. In this way, the book underplays how inequality is sustained through mechanisms that show up later in economic indicators.
For example, Chapter Nine, which addresses LGBTQ+ women in STEM, draws on National Health Interview Survey and ACS data but, as the authors acknowledge, cannot fully analyze the experiences of trans women and nonbinary people due to gaps in available data. This is not a flaw of the authors so much as a limitation of quantitative data collection and analysis, but it nonetheless highlights the constraints of an approach that relies exclusively on what can be mathematically measured.
In certain places, the book edges toward an additive model of disadvantage—gender plus race plus disability, and so on—without fully embracing the relational insights of intersectional theory. As recent work by scholars like Pallavi Banerjee and Sharla Alegria and France Winddance Twine demonstrate, immigration status and racialized categories interact to produce stratified workplace hierarchies that can resemble caste-like systems. These dynamics are difficult to capture using the kinds of statistical methods employed in Disparate Measures, but they are essential for understanding how inequality is lived and organized on the ground.
Finally, while Armstrong and Averett acknowledge the significance of the reproductive economy—care work, emotional labor, and household labor—it remains secondary in the analysis. For a book that foregrounds feminist commitments, this is a missed opportunity. A deeper engagement with reproductive labor would have brought needed attention to how capitalist labor markets rely on women’s unpaid work to reproduce labor power itself.
At times, Disparate Measures feels like two distinct projects: one is a feminist history of how population categories have been constructed through census data, and the other is a study of wage disparities between white men and marginalized women in STEM. The former is particularly innovative and important, offering a rare resource for sociologists interested in the politics of measurement, feminist methods, and the construction of empirical categories. The latter offers vital insight into how inequality persists in domains often presumed to be meritocratic.
Despite its limitations, Disparate Measures is an important contribution to feminist economics, critical data studies, and the sociology of work. It is especially useful for scholars and instructors who wish to critically examine STEM inclusion narratives, reflect on the limits of large-scale data, or teach students how to interrogate the categories they so often take for granted. I plan to incorporate this text into my upper-division undergraduate courses on Race and Racism and Women and Work, where students will benefit from its methodological transparency and its persistent challenge to data-driven optimism about women’s progress.