I saw a Fortune article that immediately annoyed me: “Gen Z women are the new face of unemployment—and it’s not because they’re too choosy: Low grades and bad health are to blame for these NEETs (not in education, employment, or training), new research warns.” Gen Z women make up the generation of women born between 1997 and 2012, making this age group 14-29 this year. A picture of a young Black woman on her phone accompanied the article as its sole image.
The article itself doesn’t discuss race, rather, it uses a Black woman’s picture with “low grades and bad health are to blame…” to play on negative stereotypes, situating Black women as the responsible party for being pushed out of the labor market. This is a textbook example of media priming, the psychological process where images activate racial and ethnic stereotypes in readers to influence perceptions of racial and ethnic in-groups and out-groups that the text never has to state explicitly. As an economic researcher and Black Gen Z woman, I hate to see my generation’s experience in the workforce being reduced to low grades and poor health. To me, it sounds like another version of “Gen Z doesn’t want to work” and “Black women are undeserving.”
Its premise reaffirms the opinions of many people. The thing is, this is an American Global Business Magazine based in NYC, writing about UK NEET women, as though it won’t be applied to U.S. women. Whether intentional or not, this framing is reductive to the dynamics driving unemployment, like job losses in the federal government, the professional and business sectors, and manufacturing. Instead of discussing the reduction in jobs, the Fortune article reflects a broader pattern of attributing structural unemployment to individual deficiency, one that U.S. data directly contradict. Although Gen Z and Black women more broadly are on the receiving end, this dynamic extends to Hispanic, Asian, and Indigenous women, especially when there’s a lack of data documenting their experiences.
Nothing New Under the Sun
For at least the last decade, young women aged 16 to 24 have had the highest unemployment rates by age group (See Figure 1). Gen Z is just the youngest working-age generation. Before them, it was Millennials, and after them will be Gen Alpha. This persistence is structural inequality, the disparities in wealth, resources, and other outcomes that result from discriminatory practices of institutions. 16-24 year olds occupy a unique position in the labor market where they have not settled into a career or employer as older workers, and their lack of relevant work experience makes their employment more volatile in times of economic growth and distress. This type of persistence is what stratification economics, a field developed by economist William “Sandy” Darity Jr., would predict: unemployment gaps are not random or personal, but the product of institutionalized patterns of resource protection.
Figure 1: Women’s unemployment rate by age group, 2015–2025
Note: This is based on seasonally adjusted unemployment rates for ages 16–24, 25–34, 35–44, and 45–54 and non-seasonally adjusted for 55-65+. Seasonal adjustment controls for seasonal changes in employment, like how high school students might only work in the summer.
Source: Calculations by the Women’s Institute for Science, Equity and Race using U.S. Bureau of Labor Statistics, Labor Force Statistics from the Current Population Survey, 2015–2025.
When we look within Gen Z at women who are typically out of school, we see this dynamic persist across racial groups. Rather than a new dynamic explained by an individual’s inability to perform well, unemployment gaps are evidence that the labor market is racially stratified, producing structurally unequal outcomes regardless of the credentials or effort workers bring to it (see Figure 2). Over the last decade, the gap between Black, Asian, and White women has been between 1.2 and 5.4 percentage points. Black Gen Z women remain among the highest across growth years, recession years, and recovery years. A hierarchy this durable across economic cycles is consistent with structural forces, not individual choices.
Figure 2: Unemployment Rate for Women ages 25-29 by Race
Note: This uses seasonally unadjusted unemployment rates. 2025 figures are Q1–Q3 averages. Q4 2025 not produced due to federal government shutdown. Indigenous and Multiracial women are excluded due to insufficient sample sizes in the CPS.
Source: Calculations by the Women’s Institute for Science, Equity and Race using U.S. Bureau of Labor Statistics, Labor Force Statistics from the Current Population Survey, 2015–2025.
In 2024, women and men ages 16-24 in the U.S. had almost identical percentages of the population classified as NEET (13% and 12% respectively). With disaggregation, this 13% hides the realities among different racial groups: 15% of Black and Hispanic women, 11% White women and 9% Asian women are classified as NEET in the U.S. The NEET rate for young women has actually declined over the last 30 years while the rate for young men has increased. For this age group, the world opens up after high school, where based on performance, students can continue their education in college or a trade, join the labor force, or fall into NEET status. If bad grades and poor health close doors to the workforce and education, then we would see rising NEET classifications and declines in enrollment.
High school grades are central to how access to college works, where higher grades are tied to enrolling and graduating. While the article points to poor grades, U.S. women are outpacing men in college enrollment and completion. But enrollment alone doesn’t explain the unemployment gap; many enrolled women are also working. Among those actively in the labor market, the racial disparities persist regardless of whether they are in school. So, if U.S. women aren’t NEET, if they’re in school, getting credentials, doing what the labor market asks, the real question becomes why that investment isn’t paying off equally for everyone.
Education is NOT the Great Equalizer
If grades and education were behind Gen Z unemployment, we’d expect that gap to close as credentials increase. It doesn’t. The unemployment rate for Hispanic women with an associate’s degree (3.4%) is nearly identical to White women with a high school diploma (3.5%) (See Table 1). Black women with a high school diploma face nearly identical unemployment to White women without one (6.6% vs 6.1%). A Black woman with an associate’s degree still faces higher unemployment than a White woman with only a high school diploma (3.8% vs. 3.5%). By contrast, Asian women experience volatile unemployment rates, where Asian women with a bachelor’s have lower unemployment than Asian women with only a high school diploma (2.5% vs 2.9%), but Asian women with some college have a higher unemployment rate than Asian women with an associate’s degree (2.9% vs 2.3%).
Table 1: Unemployment Rates of Women Ages 25+ by Education Level and Race/Ethnicity, Averages 2022–2024.
Note: Indigenous and Multiracial women are excluded due to insufficient sample sizes in the U.S. Bureau of Labor Statistics
This is stratification economics in practice. The credential costs the same tuition, the same years, the same opportunity cost, and returns a different outcome based on race. The labor market applies a racial discount to Black and Hispanic women’s credentials that individual effort alone cannot reliably overcome.
Notice what the table shows for White women: at every credential level in Table 1, they have the lowest unemployment rate of any group. White women with a bachelor’s degree or higher face 2.0% unemployment, the floor of the entire table (See Table 1). For White women, each additional credential reliably reduces unemployment. That same return does not hold consistently for Black and Hispanic women. Why would Black and Hispanic women make the investment in higher education if the labor market does not afford them the same returns as their peers? That disparity, within the population of women alone, rarely gets named, let alone explained.
More than a Headline
Framing determines policy responses and dictates who gets blamed and who gets support. When we reduce Gen Z women’s unemployment to “low grades” and “bad health,” we let institutions off the hook and put the burden back on the very workers facing the steepest odds. At the Women’s Institute for Science, Equity and Race, we are committed to using data to correct narratives and dispel myths. But we need your help.
As a member of Gen Z, I encourage you to look beyond the articles, regardless of whether they confirm or contradict your beliefs. Look for the human. We have more in common than the news describes and more to lose when data gets weaponized to tell stories that protect the status quo. If we are serious about fixing Gen Z’s employment opportunities, the answer is not another lecture about personal responsibility and grit. It means investigating beyond the headline, and in this case, treating unemployment as what it really is: a failure to match willing, qualified workers with jobs that value their skills. Only then can we start dismantling the barriers that keep Gen Z women from the equitable employment outcomes that disaggregated data already reveal.
Lily S. Johnson is a former research assistant. She worked on projects focused on financial well-being and menstrual health.
