The American Association of University Women's "The STEM Gap: Women and Girls in Science, Technology, Engineering and Mathematics" is a periodically updated research synthesis drawing on federal data — primarily the American Community Survey — to document women's earnings and employment in STEM occupations. The 2022 edition, which drew on 2021 ACS data, provided the most recent comprehensive picture of the women-in-STEM earnings landscape and was the relevant source for understanding where the pay and participation gaps were concentrated in the post-pandemic year.
The headline figure — women in STEM earning less than men in STEM — was not new. What made the AAUW report worth reading carefully was its field-level disaggregation: the pay gap in STEM was not uniform across STEM occupations, and the variation in where gaps were largest and smallest contained information about the mechanisms driving them. The aggregate figure obscured more than it revealed.
The Field-Level Disaggregation
The most significant finding of the 2022 AAUW report, viewed from an analytical perspective, was the variation in the gender pay gap across STEM subfields. Engineering and computer science showed the widest pay gaps: in these fields, women earned substantially less than men with equivalent education levels and experience, with gaps in some engineering disciplines exceeding 20 percent. Physical sciences showed similar gaps. Biological and life sciences showed the narrowest gaps, with some subfields approaching parity.
This field-level variation was consistent with what the wider literature on gender and earnings suggested: the pay gap tended to be largest in fields with high negotiation dependence (where individual salary negotiation played a large role), high return to tenure (where long-tenured workers earned substantially more than those who had taken breaks), and strong informal network effects on job access. Engineering and CS fit this profile; biological sciences, where the academic sector was more prominent and salary structures more transparent, fit it less well.
The disaggregated data also showed variation by race and ethnicity within STEM. The intersection of gender and race produced pay gaps for women of colour in STEM that exceeded the gender gap alone. Black women in STEM occupations faced the widest gaps; Asian women in some STEM fields showed a different pattern, with the wage gap relative to white men being smaller in some technical roles. The AAUW report's attention to these intersectional dimensions was one of its analytical strengths relative to reports that considered only gender.
The Earnings Gap Versus the Occupational Representation Gap
The STEM Gap report treated two related but distinct phenomena: the earnings gap (women in STEM earning less than men in STEM) and the occupational representation gap (women being a smaller share of the STEM workforce than their educational attainment would predict). These were related in causation — the earnings gap discouraged women from entering and remaining in some STEM fields, and the representation gap affected women's relative bargaining power in salary negotiations — but they required different analytical and policy responses.
The earnings gap analysis in the 2022 report was primarily descriptive: it documented the gaps and decomposed them into an explained portion (field, experience, hours worked) and an unexplained residual. The unexplained residual — the portion of the gap that persisted after controlling for observable factors — was the portion attributable to discrimination, negotiation differences, and other mechanisms that the ACS data could not directly measure. For most STEM fields, this unexplained residual was significant.
The policy implications of an unexplained residual in pay gap analysis were contested. One interpretation was straightforward: unexplained gaps represented discrimination or bias that should be addressed through pay-transparency requirements, structured pay-setting processes, and legal enforcement. Another interpretation was more cautious: the unexplained residual included factors (workplace flexibility, job stability, benefits beyond salary) that were not in the salary measure but affected employment decisions in ways that produced apparent pay gaps without requiring bias as the explanation. The empirical literature leaned toward the first interpretation for most of the residual, but the debate was not fully resolved.
The AAUW Report and the International Picture
The AAUW's "The STEM Gap" was a US-focused document, drawing on ACS data that covered only US workers. This was appropriate — ACS data was what was available, and the AAUW's mission was primarily directed at US women. The limitation was that the report's findings could not be generalised internationally without additional evidence.
Comparable international data on STEM gender pay gaps existed from OECD labour force surveys and from the European Commission's She Figures reports, but the coverage was less granular than the ACS-based analysis and the field taxonomies were not perfectly comparable. What the international data consistently showed was that STEM gender pay gaps were present across OECD countries and broadly consistent in their field-level pattern — largest in engineering and CS, smallest in biological sciences — suggesting that the mechanisms were not specific to US labour market institutions.
The Attainment Dimension
The AAUW report's coverage of educational attainment — the degree-level and field-level data on women's share of STEM bachelor's and advanced degrees — provided the upstream context for the earnings data. In 2021, women were earning the majority of US STEM bachelor's degrees overall, but this aggregate masked the field-level pattern: women earned a majority of degrees in biological sciences, psychology, and health sciences, but approximately 24 percent of engineering degrees and approximately 22 percent of computer science degrees.
The attainment data was relevant to the earnings data in two ways. First, the fields with the largest pay gaps were exactly the fields with the lowest women's enrollment — a correlation that reflected shared underlying mechanisms rather than coincidence. Second, the attainment data was leading rather than coincident: changes in degree attainment would take years to appear in workforce earnings data. Progress (or regression) in women's STEM enrollment rates would not be visible in the earnings data until the cohorts who had enrolled graduated, entered the workforce, and accumulated enough experience to appear in labour market surveys.
What the 2021 Data Said About the Pandemic's Effects
The 2021 ACS data reflected conditions that included some pandemic-related distortions. Employment disruptions in 2020-2021 had affected the STEM workforce differently across fields — some STEM fields (healthcare, data science) had expanded employment; others (academic research, physical science fieldwork) had contracted. Women were more likely than men to have exited the labour force temporarily in 2020-2021, and the re-entry patterns were not yet fully stabilised by 2021.
The AAUW report was appropriately cautious about treating the 2021 data as fully representative of structural trends rather than pandemic-era conditions. The longitudinal comparison with pre-pandemic figures was the relevant benchmark, and the 2022 report's analysis suggested that most of the structural gender gaps had persisted through the pandemic period without the dramatic worsening that some early-pandemic analyses had predicted.
Frequently Asked Questions
What is AAUW's "The STEM Gap" report?
AAUW's "The STEM Gap" is a periodically updated research synthesis on women's earnings and occupational representation in STEM, drawing primarily on American Community Survey data. The 2022 edition examined 2021 ACS data and covered both the gender pay gap within STEM and women's representation in the STEM workforce.
Where is the gender pay gap in STEM largest?
Engineering and computer science show the widest gender pay gaps among STEM fields, with gaps in some engineering disciplines exceeding 20 percent after controlling for education and experience. Biological and life sciences show the narrowest gaps, with some subfields approaching parity.
What is the "unexplained residual" in pay gap analysis?
The unexplained residual is the portion of the gender pay gap that persists after controlling for observable factors like field, experience, and hours worked. It represents the combined effect of factors not captured in the data, including discrimination, negotiation differences, and workplace characteristics. In most STEM fields, the unexplained residual is significant.
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