This piece takes a more in-depth look at the current reporting of individuals who identify as non-binary or who are unclassified by sex. The take-home, summarized in Tables 1 and 2 below, is that there are few states explictly collecting data on trans and nonbinary people, and that the ways in which states report “unknown” gender/sex lack transparency and are highly discordant across states.

Our findings are stark. Black men are far more likely to die than any other group; but Black women have over 3 times higher mortality rates than white or Asian/Pacific Islander men (Figure 1). Further, the sex disparity within race varies widely.

The GenderSci Lab is looking for two undergraduate research assistants to assist with the lab’s current project analyzing sex & gender disparities in COVID-19 outcomes.

Highlights from December’s Report Card

From September to December, the average state score increased from 6.65 (D grade) to 6.78 (D grade) on a scale of 0-10.

Both Montana and New Mexico saw score increases of 3. This takes Montana’s score to a B grade, and New Mexico’s score to a D grade.

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Following the initial release of the GenderSci Lab’s Health Affairs Blog, Socially Relevant Variables in US State COVID-19 Surveillance Reporting: A Report Card, at the end of June, the lab has continued to track changes in state reporting of socially relevant variables for COVID-19 cases and fatalities. Data availability is becoming increasingly critical as many states experience a second wave of infections.

The “Gender/Sex Disparities in COVID-19 Outcomes” guide and toolkit is an open-access Google Slides presentation offered by the Harvard GenderSci Lab for adoption in introductory-level gender studies, feminist science studies, and health sciences courses. The presentation helps students develop a critical and intersectional understanding of sex disparities in COVID-19 outcomes.

Following the initial release of the GenderSci Lab’s Health Affairs Blog, Socially Relevant Variables in US State COVID-19 Surveillance Reporting: A Report Card, at the end of June, the lab has continued to track changes in state reporting of socially relevant variables for COVID-19 cases and fatalities. Data availability is becoming increasingly critical as many states experience a second wave of infections.

As governments and private companies take up the charge of deep space travel and occupation, a question arises. If humans are to live away from Earth, how will they reproduce themselves? Here, the GenderSci Lab’s Jonathan Galka examines the evidence for human reproduction in Space, and what the state of the data tells us about who, according to governments and research establishments, gets imagined as a rightful future inhabitant of Space.

This is the July update to the US State COVID-19 Report Card. The Report Card tracks how US States are reporting socially relevant variables including race/ethnicity, sex, age, and interactions between these variables in COVID cases and outcomes. Our goal is to provide a source of transparent data accountability. The increased reporting of socially relevant variables is critical to understanding health inequities in the COVID-19 pandemic.

How well is your state reporting on socially relevant COVID data? At the most basic level, to understand the nature and extent of COVID outcome disparities, we must have data on gender/sex, age, race/ethnicity, and comorbidity status, and the interactions between them. Here, the GenderSci Lab releases a US State COVID-19 Report Card.

In most places, men are dying at higher rates than women of COVID-19. In this post, accompanying our Op-Ed in the NYT and the launch of our US Gender/Sex in COVID-19 Data Tracker, we explain how the explanation for this trend is not all biology. In fact, our findings strongly suggests that gender/sex differences in COVID-19 vulnerabilities mediated by social context.

As a part of its COVID Project, the GenderSci Lab began collecting weekly data on cases and deaths for the fifty U.S. States, Washington, DC, Puerto Rico, and the U.S. Virgin Islands on April 13, 2020, with data published today on our website. Here, we offer key take-aways from this first data roll-out. Stay tuned in future weeks for more insights!

Last week was big news for LGBTQ+ rights in the US. Two major pieces of law came out just days apart, changing the landscape of sex-based anti-discrimination law and the way sex is understood in federal law. In this post, we briefly outline these new legislative policies, consider the implications for LGBTQ+ rights in the US, and think about how this changes legal reliance on biological claims about sex.

In the Gender Equality Paradox, gender equality is assumed to imply gender neutrality. In this post, I explain why this assumption is unfounded, drawing on social psychological research. When we recognize that gender-equal is not synonymous with gender-neutral in terms of stereotypes and attitudes, the Gender Equality Paradox falls apart.

The goal of this post is to identify the assumptions underlying an evolutionary explanation of the Gender Equality Paradox and to offer some equally strong, if not stronger, hypotheses that could be tested.

Here, we situate the Gender Equality Paradox in the larger field of understanding gender segregation in STEM fields by talking to renowned scholar Professor Maria Charles, Professor of Sociology, Director of the Broom Center for Demography, and Feminist Studies affiliate at the University of California, Santa Barbara. Professor Charles has worked for decades to understand why postindustrial countries have greater segregation in STEM fields, and she draws on her broad expertise on the persistence of gender inequalities in gender-progressive societies and global variation in gender equality to help us understand the Gender Equality Paradox.

According to the Gender Equality Paradox, the more gender equal a country, the fewer women in that country participate in STEM. But how is a country's gender equality measured? In this post, we show how looking carefully at measurement choices might lead us to re-think scientific claims about the so-called Gender Equality Paradox.

This post is an explainer and supplement to our Psychological Sciences Commentary. We discuss five key problems with data and inferences that we identified in Stoet and Geary’s study. In places it’s a bit of a wonky read, but we unpack some serious issues, including issues with replicating the findings, spurious correlations, study design, and the ecological fallacy.

Is the feminist project to bring about parity for women and men in traditionally male fields doomed? In this blog post series, we expand on these contributions and offer a thorough consideration of the “Gender Equality Paradox” hypothesis and its theoretical and methodological underpinnings and the assumptions required for it to operate.