Mary A. Armstrong, a professor of Women's Gender and Sexuality Studies, and Susan L. Averett, a professor of Economics, dive deep into the economic realities faced by diverse women in STEM fields. They challenge the myth that STEM work inherently benefits these women, presenting in-depth case studies that reveal stark disparities. The discussion highlights the unique struggles of women of color, the impact of motherhood, and the need for intersectional analysis to address systemic inequities. Armstrong and Averett advocate for substantial changes beyond mere access to foster true equality in STEM.
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insights INSIGHT
Quantitative Data Challenges Intersectionality
Quantitative data freezes identities that are inherently fluid and dynamic in intersectional research.
Despite this, it's possible and necessary to balance quantitative methods with intersectionality's complexity for better understanding.
insights INSIGHT
Diverse Case Studies Illuminate STEM
The book centers on eight in-depth case studies of diverse women in STEM including race, nativity, disability, gender identity, and motherhood.
Each case reveals unique intersectional economic disparities and challenges faced within STEM fields.
insights INSIGHT
Importance of STEM-Related Jobs
STEM-related occupations, especially in healthcare, are often overlooked despite being crucial and predominantly female.
Including STEM-related jobs reveals broader occupational segregation and pay disparities faced by women.
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An exploration of workplace participation and earnings patterns for diverse women in US STEM professions that upends the myth that STEM work benefits women economically.
Seen as part economic driver, part social remedy, STEM work is commonly understood to benefit both the US economy and people—particularly women—from underrepresented groups. But what do diverse women find when they work in US STEM occupations? What do STEM jobs really deliver—and for whom? InDisparate Measures: The Intersectional Economics of Women in STEM Work(MIT Press, 2024), Mary Armstrong and Susan Averett challenge the conventional wisdom that a diverse US STEM workforce will bring about economic abundance for the women who participate in it. Combining intersectionality theory and critical data theory with a feminist economic analysis, the authors explore how different groups of diverse women truly fare in US STEM professions. Disparate Measures is centered on eight unique, in-depth case studies, each of which provides an intersectional economic analysis (a term coined by the authors) of diverse women working in STEM occupations. Four case studies prioritize women of color and examine the STEM participation and earnings of Black women, American Indian and Alaska Native women, Asian and Pacific Islander women, and Hispanic women/Latinas; four additional case studies illuminate intersections that are frequently neglected by the STEM inclusivity literature: foreign-born women, women with disabilities, Queer women, and mothers. What the authors find in their groundbreaking, detailed analysis is that the promises of STEM are only partly true: when compared to women not working in STEM, most women are indeed economically elevated by STEM occupations—yet when compared to white men in the same STEM occupations, women's second-class status is usually reaffirmed. The authors conclude by offering seven “big-picture” recommendations for rethinking STEM equity, showing just how we can successfully confront the entrenched patterns of economic disadvantage faced by diverse women in STEM jobs.
New Books in Economics 