When you read the words “average person,” who do you picture? If you’re like most people, you probably picture a man. In Invisible Women, feminist campaigner Caroline Criado Perez argues that this is because most humans operate under a male-as-default mindset: We consider the average person to be male. This mindset is particularly visible in gendered languages—for example, in Spanish, the masculine el doctor technically means “male doctor” but is often used to refer to a doctor of any gender.
(Shortform note: Perez notes that gendered languages often default to the male forms of words, but some speakers of these languages are pushing back against these gendered words—which suggests that the male-as-default mindset may be waning. For example, some Spanish speakers use gender-neutral endings to words, like -e or -x.)
Perez contends that this male-as-default mindset causes a gender data gap—a lack of information about the female experience—that harms women’s health, safety, and economic standing. This is because we don’t consider women the “average person,” so we don’t collect data on them.
(Shortform note: Although Perez didn’t coin the term “gender data gap,” data suggests that she did introduce it to the public consciousness. Google searches for “gender data gap” remained relatively low for years but spiked in February 2019 and have remained relatively high since then. Given that Invisible Women was published in March 2019 and started to release preview chapters shortly before that, this spike appears to be related to the book’s popularity—especially since the top related queries to the term include both the author and the book’s name.)
In turn, this gender data gap reinforces the male-as-default mindset. When there is no data about women, people (especially men) assume that the experience of the average man represents the experience of the average woman, so they continue to make choices that reflect this mindset—but ultimately harm women. In this way, the male-as-default mindset and the gender data gap create a vicious, mutually reinforcing cycle.
How the Gender Data Gap Reinforces Itself
It’s not always the male-as-default mindset that causes or is reinforced by the gender data gap: Sometimes, one gender data gap sets off a self-reinforcing cycle or engenders another gender data gap—as is the case with Wikipedia. One of the world’s most popular websites, Wikipedia is ostensibly a gender-neutral encyclopedia, but further analysis demonstrates that it suffers from a gender data gap problem. Wikipedia is open source, meaning that anybody can edit it—but most of its editors are white American men.
Because these men don’t have firsthand information about the female experience, Wikipedia’s editing team suffers from a gender data gap. As a result, it makes decisions that discourage women from joining this team—like calling rape scenes in movies “sex scenes” because the term rape was “not neutral.” In this way, having mostly men on the editing team ensures that mostly men remain on the editing team—in other words, the gender data gap reinforces itself. And this lack of women on the editing team can be harmful to women’s health and safety: Several women on the editing team have been subject to harassment from other editors.
But that’s not all. Critics suggest that the lack of women on the editing team explains why Wikipedia’s data is skewed: Only 18% of the content on Wikipedia is about women, and articles about women are far more likely to be deleted despite meeting the company’s standards regarding whether they’re deserving of a Wikipedia entry. In this way, the gender data gap on the editing team engenders a gender data gap in which Wikipedia has insufficient information on women. And it’s possible that this harms women’s economic standing, given that someone’s recognizability is often relevant to how much they’re paid, and having a Wikipedia page is an easy way to demonstrate that they’re a semi-public figure.
In this guide, we’ll first discuss how our male-as-default mindset leads us to create products that put women’s health at risk. Then, we’ll discover how this mindset both results in and stems from a gender data gap that harms women’s economic standing. Finally, we’ll learn how our lack of consideration for gender-specific concerns harms women’s safety—and ultimately reinforces the male-as-default mindset. Along the way, we’ll provide recent data that sheds further light on the gender data gap and examine how countries and institutions have tried to remedy it.
According to Perez, our male-as-default mindset results in a gender data gap that harms women’s health. In this section, we’ll discuss how this manifests in everyday products—namely cars—and in our healthcare system—namely medicines.
Perez contends that we can see how our male-as-default mindset results in a gender data gap by looking at how everyday products—specifically cars—are created. Perez explains that our male-as-default mindset makes us believe that products that work for men must work for everybody. This leads us to not collect data on women and thus create products that harm women’s health. Notably, our cars don’t properly protect women because we don’t test car safety using female crash-test dummies.
To illustrate, Perez points to how the European Union determines whether a car is safe. In the EU, a car must undergo five regulatory crash tests. These tests determine whether the car is safe for all people, but they require the usage of a crash-test dummy based on the “fiftieth percentile male,” which demonstrates a male-as-default mindset. Since these tests don’t use female crash-test dummies, they ensure that the EU lacks information on whether these cars are safe for women.
(Shortform note: According to Perez, we believe that our cars protect men because we use male crash-test dummies—but it’s possible that modern cars don’t properly protect men either, given that the “fiftieth percentile male” crash-test dummy may not provide accurate information about today’s drivers. Most car manufacturers today use the Hybrid III, a crash-test dummy based on a man who’s 5 foot 9 and 171 pounds—but the average 5 foot 9 man today weighs about 198 pounds.)
Moreover, although the EU has a separate regulatory test that requires the use of a crash-test dummy based on the “fifth-percentile female,” Perez argues that this test still doesn’t provide enough information on whether a car sufficiently protects women. This is because there are practically no anatomically correct female crash-test dummies that account for all the biological differences between the sexes that might be relevant in a car crash—like the fact that each sex’s muscle mass is distributed differently.
Therefore, Perez argues, cars are created to work for the average male—not the average female—and thus do not sufficiently protect women’s bodies. As evidence, Perez points to the fact that, even though a woman is less likely to be in a car crash than a man, she is far more likely to be seriously injured or die from one.
(Shortform note: Experts admit that there are factors other than car design that contribute to women’s risk in car crashes: Notably, it’s possible that men tend to drive bigger cars than women and are thus more protected in a car crash. However, like Perez, the researchers contend that the car’s design is a major factor: In fact, one 2019 study found that even when driving the same model car, women were 73% more likely to experience serious injury than men if that car crashed.)
In this way, Perez contends, our male-as-default mindset leads to a lack of data regarding whether a car is safe for women. This, in turn, leads to unsafe cars on the road—and ultimately harms women’s health.
The World Since Invisible Women: Updated Safety Regulations and New Learnings
Since Invisible Women was published in 2019, the world has updated its safety regulations and learned more about how car crashes affect men and women differently. Notably, the EU has updated its safety regulations to take into account the reality that cars that are safe for men are not necessarily safe for others: In July 2022, new regulations went into effect that require new cars to have frontal impact protection “which does not disadvantage women and older people."
Moreover, we’ve also learned why using the fifth-percentile female crash-test dummy doesn’t provide sufficient information on whether a car protects women. Safety agencies argue that testing a car on both a fifth-percentile female dummy and a fiftieth-percentile male dummy makes it clearer whether it is safe for the widest possible range of people than testing with a fiftieth-percentile female dummy would.
However, UK researchers inspired by Perez’s work discovered that women were more frequently trapped than men after car crashes—partly because women have wider pelvises than men so they were more likely to have pelvic injuries that made escaping from the car difficult. Tests that use fifth-percentile female dummies don’t reveal women’s risk of pelvic injuries—because, as the researchers note, these dummies are based on men and thus don’t accurately represent the width of an adult woman’s pelvis.
Our assumption that products that work for men must work for everybody doesn’t just harm women in automobiles. Perez argues that our male-as-default mindset also creates a harmful gender data gap in the drug creation process: Specifically, we don’t know how medicines affect women because we don’t test them on women.
Perez explains that many pharmaceutical companies operate on a male-as-default mindset: They test their drugs exclusively on men and assume they’ll also work on women. Why not include women? Women’s hormones fluctuate throughout their menstrual cycle—and these companies worry that introducing this additional variable would make their test results less clear.
How the Male-as-Default Mindset Contributed to Vaccine Hesitancy in Women
In her weekly newsletter about the gender data gap, Perez argues that ignoring gender-specific concerns when creating medicines contributed to some women’s Covid-19 vaccine hesitancy. The vaccine developers demonstrated a male-as-default mindset by not studying the menstrual effects of the vaccine—despite these applying to half the people who would take the vaccine. So although women reported changes to their menstrual cycles following their vaccines, their complaints were not taken seriously until September 2021, when the US National Institutes of Health agreed to research them.
This lack of information, Perez feels, led women to be wary of getting the Covid-19 vaccine because they weren’t sure how it would affect their cycles. Women did get updated information by January 2022, when study results demonstrated that while the Covid-19 vaccine does affect the menstrual cycle, these changes are likely only temporary. However, had vaccine developers started by assuming that the vaccine would affect men and women differently, they could have provided more accurate information to women earlier.
However, Perez argues, this decision to not test drugs on women ultimately harms women’s health. Men and women are biologically different, so these drugs affect them differently: For example, women tend to metabolize drugs faster than men. So by giving women drugs that haven’t been tested on women, these companies are not supporting women’s health at best and actively harming it at worst. As evidence, Perez points to the fact that women are far more likely to experience an adverse drug reaction than men are. One of the most common is that the drug fails to treat the condition it’s supposed to.
(Shortform note: A year after Invisible Women was published, a study more specifically revealed the risks of not testing drugs on women. It examined 86 medications and found that when men and women took the same dosage of a particular medicine, women systematically experienced a higher number and greater frequency of adverse drug reactions and retained more of the drug in their bodies for a longer period of time than men did.)
Perez writes that in this way, our lack of data on how these drugs affect women leads to women taking drugs that don’t work for them and thus harms their health.
Perez asserts that the gender data gap, in addition to harming women’s health, also harms women’s economic standing. In this section, we’ll learn how our male-as-default mindset causes a gender data gap by leading us to not collect data separately on men and women—a failure that ultimately results in discriminatory systems that harm women’s finances. Then, we’ll learn how the gender data gap contributes to a male-as-default mindset that ultimately results in workplaces that don’t consider women’s needs and thus harm their ability to succeed.
Previously, we learned how our failure to collect data on women harms their health by contributing to the creation of cars that don’t protect them and medicines that don’t treat them. However, Perez argues that even when we do collect data on women, we still have a male-as-default mindset that ultimately harms women.
Perez specifically contends that due to our male-as-default mindset, we collect data on people and assume that it represents the average life experience. However, women have gender-specific concerns—and by not collecting and using sex-disaggregated data (data that is separated by sex) we create systems that ultimately discriminate against women.
(Shortform note: Experts suggest that if we did collect and use sex-disaggregated data, we could create systems that not only don’t discriminate against women but also actively improve their standing in society. These experts divide governmental policies into categories ranging from “gender-unaware,” which don’t acknowledge how decisions affect each gender differently, to “gender-transformative,” which seek to combat discriminatory practices against women so they become more equal to men. For example, a gender-transformative policy might try to reduce women’s care responsibilities.)
To illustrate how this happens, Perez points to how modern governments that don’t gender-analyze their budgets spend and save money. These governments often try to foster economic growth by cutting taxes on their highest earners. However, almost every country has a gender pay gap: Globally, men earn nearly 38% more than women. Since high earners are more likely to be men, these tax cuts are more likely to benefit men—not women. Therefore Perez argues that by not gender-analyzing their taxation systems, modern governments pass tax policies that disproportionately benefit men and thus discriminate against women.
Why Gender-Analyzing Budgets Matters in a Post-Covid World
Gender-analyzing tax budgets may be particularly important in light of the Covid-19 pandemic. The World Economic Forum found that the pandemic set women’s equality back by 36 years. Their report didn’t specifically analyze how the pandemic affected the gender pay gap, but the conclusions it did make suggest that the pandemic caused women to earn less money. For example, the report found that pandemic-induced lockdowns were more likely to affect female-dominated industries (like hospitality).
To address the gendered effects of the pandemic, a United Nations report recommends altering the tax system so it benefits women and thus puts them back on an equal playing field with men. For example, the report suggests that, instead of cutting taxes on high earners, governments create benefits for its lowest earners, who are more likely to be women. It also suggests that governments consider decreasing taxes on industries more likely to employ women.
Moreover, Perez argues that when these governments need to save money, their failure to consider how policy changes might impact men and women differently results in policies that disproportionately disadvantage women—and thus discriminate against them. Notably, governments often try to save money by closing public services—a move that, according to Perez, disproportionately affects women.
Why is this so? The public services the government closes often provide care work: After the 2008 financial crisis, the UK cut funding for nearly 300 children’s centers. But even if the government doesn’t provide this care, someone still has to—and usually, that burden is passed onto women, who do 75% of the world’s unpaid care work. However, as Perez notes, doing unpaid labor takes time away from a woman’s ability to do paid labor—and therefore, the closure of these public services often causes women to lose potential income.
(Shortform note: During the Covid-19 pandemic, governments were forced to close public services for health reasons: In the UK, several children’s centers provided solely virtual services during lockdowns. So when these services closed, who provided the unpaid care and potentially lost income as a result? One study found that the results varied significantly by country, even if these countries were close to each other, had the same number of Covid-19 cases, and were of similar size. In the UK, men took on a greater share of the childcare, but in Germany, the number of partnered women providing all of the childcare increased.)
In this way, the government’s failure to review sex-disaggregated data—in other words, a gender data gap caused by a male-as-default mindset—results in the creation of budgets and taxation systems that ultimately harm women’s economic standing.
When governments don’t sex-disaggregate data, this is a clear and deliberate gender data gap: They could analyze how their budgetary and taxation systems affect each gender separately; they just don’t. However, as Perez writes, most gender data gaps aren’t deliberate refusals to consider the needs of women: They’re often unintentional—but they still harm women, as is frequently the case in the business world.
Perez explains that most business leaders are men who operate on a male-as-default mindset: They assume that if something works for them, it must also work for women. This mindset stems from the reality that these men don’t have the life experience of women. That gap in experience is a form of a gender data gap. Because they’re not women, they don’t have the data regarding what women might need. As a result, these men create workplace cultures that don’t take women’s needs into account simply because women’s needs don’t occur to them.
Data Gaps in the Workplace Also Harm Black People
In Biased, Jennifer Eberhardt describes another way in which workplace policies inadvertently harm people—namely, that American workplaces are implicitly biased against Black people: For example, people with Black-sounding names have a harder time getting jobs than equally qualified people with white-sounding names.
On the one hand, Eberhardt’s description of how this bias plays out suggests that it’s not due to a data gap: If it were, this bias should be present at all times because non-Black workers have never had the experience of being Black. But most people act in biased ways in specific situations—like if they have to make decisions about people very quickly. On the other hand, Eberhardt explains that having strong interracial relationships with coworkers mitigates the impact of racial bias, which suggests that this bias is a data gap: People are less likely to be biased if they have data that leads them to question these biases.
To illustrate, Perez points to the standard modern workplace culture, which she argues is designed for unencumbered workers—people who can focus solely on work because they’re not responsible for domestic work or care. As she points out, the standard modern worker has fixed hours (9 a.m. to 5 p.m.). The longer you stay, the more productive you’re considered, and sudden shifts to your schedule are frowned upon.
Perez asserts that as a result, standard modern workplaces disproportionately disadvantage women. Since most of the world's unpaid care is done by women, women are more likely to be encumbered—and thus can’t succeed as well as men in a culture designed for the unencumbered. If a child gets sick, his mother might need to suddenly shift her work schedule—but this damages her standing at work. And if a company’s policy is to distribute raises based on how much overtime someone works, this policy disadvantages women—who are more likely to need to go home at exactly 5 p.m. to cook dinner for their families.
(Shortform note: Perez’s criticisms of modern workplace culture focus on the expectation that unencumbered workers can spend a lot of time at work. However, other researchers criticize modern workplace culture for not paying some of their unencumbered workers enough money. Men who work while their wives take care of the children are considered unencumbered. However, back when this arrangement was the norm, workplaces paid these men “family wages” so they could support their families. Today, workplaces expect both men and women to work for wages (while women still do most of the care work)—and so they both make less than married, unencumbered men did in the past.)
As Perez points out, several companies do try to consider women’s unpaid care responsibilities—like IBM, which offers on-site childcare. But these companies are the exceptions, not the rule—and modern workplaces regularly make decisions that don’t take women’s needs into account. For example, in 2017, Apple announced plans to create the “best office in the world.” These plans included several fantastic amenities like a dentist—but not a daycare center, which Perez feels would likely be more appreciated by many women.
How Companies Can Make Workplaces Better for Women
As a result of the Covid-19 pandemic, many offices moved away from the standard workplace culture Perez describes and began offering fully or partially remote roles. However, experts suggest that even these workplaces may still disadvantage women. This is partly because, even if both parents are working remotely, the woman still likely takes on more childcare. One study found that during the Covid-19 pandemic, both men and women spent more time on unpaid childcare than they did before—but women took on three times as much as men did. Additionally, experts warn that in hybrid environments, remote workers are less likely to be promoted than in-person workers—and this disadvantages women, who are more likely to take on remote work because it allows them more flexibility to cover their unpaid care responsibilities.
So what can companies do to meet the needs of their female employees? If they have an in-person office but can’t provide on-site childcare, experts recommend partnering with nearby childcare facilities. However, this isn’t always a popular decision: Many Nike employees were upset when the company switched from on-site childcare to partnering with a nearby facility, partly because having their children so close was especially useful to some parents—like nursing mothers. That said, if companies want to discourage a work-life balance, not providing childcare may be better: Apple’s lack of a daycare center was arguably a deliberate message to their employees to prioritize their work over everything else.
Perez contends that in these ways, the gender data gap contributes to a male-as-default mindset that ultimately harms women’s ability to succeed at work.
We’ve now learned how the male-as-default mindset results in and stems from a gender data gap—but how does the gender data gap reinforce the male-as-default mindset? Perez writes that we can see this in the failure of society to sufficiently protect women’s safety in both daily life and in our responses to disasters.
To illustrate how the gender data gap affects women’s safety in daily life, Perez points to a ubiquitous feature of many women’s lives: public transit.
As we’ve seen, Perez argues that thanks to the gender data gap, many products work for the average man but not the average woman. The same is true of systems. Notably, many public transit systems don’t adequately protect women: Several statistics indicate that these systems aren’t equally safe for men and women because women are far more likely to be sexually harassed on public transit than men are.
But why don’t public transit systems adequately protect women? Perez believes this is because of a gender data gap that reinforces the male-as-default mindset: Women who are sexually harassed on public transit don’t report the crime.
Perez asserts that women don’t report harassment on public transit for two main reasons. First, women don’t know how to report it: Few transit systems make clear what to do if someone harasses you while you’re using their service. Second, women who do report often have poor experiences. She cites the case of one Indian woman whose bus driver kicked her off for disrupting other riders when she shouted at the man who groped her.
In this way, Perez contends public transit systems’ failures to provide adequate reporting procedures results in a gender data gap: Public transit agencies don’t have data indicating that women are less safe on public transit than men are—in fact, official statistics suggest that men are more likely to be victimized in public. Moreover, the people who run these agencies are usually men, so they believe these statistics: They’ve likely never been harassed on public transit, so they don’t have the life experience that suggests these statistics might be wrong. This lack of knowledge is also a manifestation of a gender data gap.
Therefore, because the men who run public transit systems can’t see the need for measures that protect women from harassment, they don’t implement them. In other words, they continue to believe that their transit system is safe for the average person (both male and female) because they never get data that indicates otherwise. In this way, the gender data gap ultimately reinforces the male-as-default mindset—and with it, transit systems that harm women’s safety.
Why Public Transit Responses Don’t Work: Examining Japan’s Harassment Response
Even when people accept that women aren’t as safe on public transit as men are, the response of public transit agencies and the public still reflects several of the issues that Perez pinpoints. We can see how this manifests by examining a country that’s been dealing with a specific sexual harassment issue on public transit for decades: Japan.
In Japan, unlike many other countries, public transit agencies did have data that they had a harassment problem—specifically chikan, or people who grope or take explicit photos of women on the train. In 2004, police received over 2,000 complaints regarding chikan and recommended that the transit agencies take action. The agencies complied, and many train lines now have women-only cars. Despite these changes, studies indicate that 75% of Japanese women have encountered chikan.
Why might this be? It’s possible that chikan who don’t get caught continue assaulting women—and the reality is that women still face the difficulties Perez points out when reporting assaults despite cultural acceptance of the chikan as a problem. The Japanese system makes it relatively clear how to report chikan; however, in a crowded environment, it’s often difficult to pinpoint who has groped you and to keep track of him. One rail company did introduce an app so that victims could report the assault by tapping a button and have staff at the next station catch their assaulter—but not until 2020, six years after it published its main app that showed everything from train schedules to air conditioning information on train cars.
Similarly, as Perez suggests, Japanese women don’t report chikan because when they do report, they have poor experiences: In fact, 90% of Japanese women who encounter chikan don’t report their assault because they worry they won’t be believed. And while you won’t get kicked off a train for reporting chikan in Japan, these poor experiences can be extreme in other ways. In 2017, two men accused of assault tried to escape onto the train tracks and died as a result. In response to these deaths, one insurance company introduced a policy against “false groping accusations for men”—suggesting a cultural mindset that argued that these men’s deaths were the fault of their sexual assault victims.
It’s also possible that Japanese women continue to encounter chikan despite the transit agencies’ efforts because these efforts are inadequate. Many women don’t use women-only train cars because they’re usually at the far ends of the train and are thus hard to get to. Moreover, these cars are only reserved for women during rush hour—and even then, men are not expressly forbidden from boarding them: They’re just asked not to board them.
In fact, while the men who run transit agencies clearly admit there’s a harassment problem because they’ve implemented women-only cars, men who ride the cars are less receptive. Many contend that having women-only cars is a form of discrimination against them and ride them anyway, leading several Japanese women to request a change in policy that allowed train staff to remove men from women-only cars. Moreover, these men have asked for male-only cars to protect them against false accusations of groping, although transit companies have yet to oblige.
The gender data gap doesn’t just reinforce the male-as-default mindset and harm women’s safety in public transit. According to Perez, it does the same in the field of disaster response. To demonstrate, she points to the sexual violence experienced by female refugees.
As Perez notes, female refugees have a gender-specific concern: Reports from several humanitarian agencies and news organizations suggest that women experience rampant sexual violence at the hands of the male authority figures they encounter—like the aid workers in refugee camps.
Perez attributes this violence to a cycle caused by the male-as-default mindset of the institutions that hire these workers. These institutions design facilities for the average displaced person—in other words, the average displaced man. As such, they don’t consider gender-specific concerns and hire male authorities without considering the possibility that they might violate their female charges.
However, these male authorities do violate their female charges—and it continues, according to Perez, because of a gender data gap: Women who experience sexual violence from male authorities usually don’t report it due to various concerns, such as a cultural taboo against discussing sex with men. Perez contends that due to this lack of data, the institutions that hire male predators don’t see how hiring them damages women—and so they continue to keep them in power.
In this way, this gender data gap reinforces the male-as-default mindset that assumes that these institutions work for the average woman as well as they do for the average man—and ultimately puts the safety of female refugees at risk.
How Hiring Female Authorities Reduces Violence
Official reports support Perez’s contention that institutions are generally unbelieving of the gender-based violence women may experience at the hands of authority figures in refugee camps. The UN found that rates of gender-based violence against refugee women increased from their already-high levels in 2020 but provided no information specifically regarding acts of violence committed by authorities against refugees. Additionally, the UN’s handbook on gender-responsive police services for women and girls subject to violence, published in 2021, suggests several ways to help solve the gender-based violence women experience in general (at the hands of other refugees) but presents extremely limited recommendations regarding how to deal with male authorities who commit violent acts against women.
The UN presents hiring female authorities as a solution to the general violence women face in refugee camps (from other refugees). This is the same solution Perez presents to the violence women face from male authorities specifically.
Why might hiring female authorities reduce violence against women from both other refugees and authority figures? The UN explains that having female police officers regularly patrol refugee camps signals to others that women are strong—and implies that this perception may discourage men from assaulting women they might otherwise have thought were weak. Additionally, the UN notes that hiring more female police officers in refugee camps reduces the data gap surrounding gender-based violence because women are often more comfortable reporting assaults to other women. This is both for cultural reasons and because they think other women will be less likely to victim-blame or not believe them.
Throughout this guide, we’ve learned how various institutions harm women—both deliberately and inadvertently—because they operate on a male-as-default mindset. In this exercise, we’ll examine the ways in which you might be operating in a male-as-default mindset—and how you might start to question those assumptions.
As you read this guide, what examples of a male-as-default mindset did you recognize from your own thoughts and behaviors? For example, perhaps you regularly complain about a female coworker who leaves early to take care of her kids or you’ve always questioned whether women are harassed on public transportation because the statistics suggest otherwise. (Remember that since the male-as-default mindset is societal, you likely have it even if you’re not a man.)
How has reading this guide affected how you feel now about these thoughts and behaviors? Do you still stand by them, or are there extenuating circumstances you may not have considered?
After reading this guide, what steps can you take to actively combat the male-as-default mindset in your life? For example, if you’re a manager, maybe you can be more understanding the next time a female employee needs to take an emergency day off because her child is sick.