Josh: Hey everyone, welcome back to the show! You know, it's kind of wild to think that so much of our world – from city layouts to even the meds we take – might be designed for only half of us. Drew: Hold on, Josh. Half the population? Are we talking about... men being the default? Josh: Exactly! There's this eye-opening book, "Invisible Women: Exposing Data Bias in a World Designed for Men", by Caroline Criado Perez, and it basically argues that, yeah, the world’s often designed with men in mind. Women's needs, their safety, their contributions – they're often overlooked simply because the data influencing these systems often just…ignores them. Drew: Ignoring women? That sounds a bit extreme, no? What exactly does "data invisibility" even “mean” in practice? Josh: It basically means that designers, policymakers, scientists – they're not considering women's unique perspectives, experiences, or needs. They're just assuming that male data is universally applicable. Remember that snow-clearing fiasco in Sweden? Where they prioritized clearing roads for cars and ignored pedestrians? Turned out it led to more injuries, especially for women. That’s a perfect, albeit frustrating, example. Drew: Snowplows becoming instruments of sexism… who would've thought? Josh: Oh, it's way more widespread than you’d imagine, Drew. Today, we’re going to dig into three main areas. First, the gender data gap itself – where it comes from and why it flies under the radar. Second, the domino effect this has on things like healthcare, workplace dynamics, and even how our cities are planned. And finally, we'll explore some inclusive solutions, showing how closing these gaps isn’t just beneficial for women; it really benefits everyone. Drew: Alright, Josh. I’m intrigued, and maybe a little skeptical. Ready to show me just how "male default" my world actually is? Josh: Buckle up, Drew. I'm ready to blow your mind. Let’s dive in!

Josh: Okay, Drew, so let's dive into the gender data gap. I mean, it's not just some minor oversight, right? It's a really deep-seated, systemic issue that's been around for decades. It basically boils down to this idea that men's experiences are the “default” for all humans. Everything else? Well, it's either an afterthought or just completely ignored. Drew: Right, okay, Josh, but hold on a sec. How did we even get here? How did something as fundamental as data get so skewed in the first place? Josh: Exactly! Historically, data collection has really focused on what they considered the "average" person—and surprise, surprise, that meant men. This all started in areas like medicine, criminal justice, even urban planning, where the male perspective was just seen as universal. Take early medical studies, for instance. Clinical trials were almost entirely done on men, usually white men, because they were considered a "neutral" baseline, right? Women's bodies, with all their hormonal stuff and reproductive systems, were seen as too "complicated," so they were just left out. Drew: Which is kind of ridiculous, isn't it? You come up with treatments based on one group and then just hope they'll magically work for the other half of the population? It's like designing a shoe for one foot and assuming the other one will somehow just... adapt. Josh: Exactly, and the impact is huge. Healthcare is where you really see the consequences play out. Think about heart attacks. The "classic" symptoms we all know—you know, crushing chest pain, pain down the left arm—those are based on male patients. But women often have totally different symptoms, like nausea, fatigue, or shortness of breath. And guess what happens when doctors aren’t trained to spot those signs? Drew: I'm guessing they don't get treated quickly enough? Josh: Exactly! Women are 50% more likely to be misdiagnosed during a heart attack because their symptoms just don't fit that male textbook pattern. And that's not even all of it! This bias exists across tons of other conditions, from autoimmune diseases to mental health. Drew: So the system basically tells women, "Sorry, we didn't think of you when we made these treatments. Good luck!" Josh: Unfortunately, that's pretty much it. And it's not just healthcare, either. Urban planning? Totally biased. When cities design everything from public transport to infrastructure, they focus on the needs of the stereotypical male worker. Drew: Wait, you mean like the guy who commutes straight from his suburban house to his downtown office? Josh: Precisely! That "standard commuter" idea completely ignores how women actually travel. They often have more complex routines because of caregiving—taking kids to school, running errands, looking after older family members. These trips are shorter, happen more often, and usually involve more walking or public transport. But city planning hardly ever considers these differences. Drew: So it makes things harder for women, but no one notices because men are still cruising through their morning commutes? Josh: Exactly! Look at those snow-clearing programs in Sweden, right? At first, they focused on roads, which mostly helped male drivers. But sidewalks and walking paths – used more by women – were left covered in snow. Once they saw this in the data, cities like Karlskoga changed their plans to clear walkways first. And guess what? Fewer injuries, lower healthcare costs, a better system for everyone. Drew: So, just by thinking about what women actually need, they made a better system for the whole city. It's such a simple fix. Why isn't this normal? Josh: That's what's so frustrating, Drew. This gender data gap is invisible to most people making decisions until they actually break down the data to show these inequalities. And if you don’t know what to look for, you can’t fix it. Drew: Okay, but Josh, this isn’t just about snow-covered sidewalks and heart attacks, right? You mentioned cars earlier, didn’t you? How is that an example of the same issue? Josh: Oh, that’s a classic example. For decades, car safety standards were based on crash test dummies that were designed to represent an "average" male - about 175 cm tall and 78 kg. But women and men are built differently. We have different muscle distribution, different centers of gravity, even different preferred seating positions. You can probably guess what that means in a crash, can’t you? Drew: Let me guess—women get injured more often? Josh: Absolutely. Women are 47% more likely to suffer severe injuries in car accidents than men. And get this: for years, female crash test dummies didn't even exist. When they finally showed up in the U.S. in 2011, they were just smaller versions of the male dummies, not accurate representations. And even now, they're often put in the passenger seats for testing, not the driver's seat, where they're most at risk. Drew: Wait, are you saying we still don't test cars for women drivers in most safety tests? Josh: That's right. It's a perfect example of how focusing only on the male perspective can “really” put lives in danger. It's not just a small problem or an oversight; these biases have real-world consequences for women. Drew: So, we’re seeing a knock-on effect here. One oversight—using male data as the norm—leads to poorly designed medical treatments, unsafe cars, cities that exclude half the population… What else is there? Josh: It shows up in the workplace, too. Policies are rarely designed with caregiving in mind, which impacts women more. Workplace flexibility is often about "overtime" or being available "on-demand." That's based on a model where workers don’t have to deal with unpaid work like childcare or elder care. Drew: Right, and that unpaid work is another huge problem that gets ignored, right? Economists don’t even include it in GDP, which is crazy when you think about it—it’s like invisible labor that's holding up entire economies. Josh: Exactly. Studies show that if unpaid labor was included, it could add trillions to some countries' GDPs. But it's completely ignored in economic calculations, which keeps alive the idea that caregiving is somehow less important than paid work. Drew: Alright, Josh, I have to admit, this gender data gap isn't just "bad." It's downright dangerous. From medicine to daily commutes to, apparently, how safe I am in my car, not fixing this is costing lives. I’m almost afraid to ask, but what does it take to bridge it?

Josh: So, the first step is quite straightforward, really—awareness. Policymakers, designers, researchers, everyone needs to recognize that their data isn't neutral, and that their systems aren't universally applicable, right? You have to start by asking: "Who is missing here?" Who isn't represented in the data, in the decision-making process, during the system's testing phase? Drew: And I’m guessing the answer is often… women? Josh: Often, yes, exactly. But it could also mean other marginalized groups. I mean, the point is to proactively collect disaggregated data—data that can be broken down by gender, race, socioeconomic status—so you can spot the blind spots before they lead to negative consequences, you know? Drew: Okay, but let's be real here. "Collect better data" sounds good in theory, but how does that translate into real-world solutions? Josh: Exactly, that’s a fantastic point. So, once you have inclusive data, you need to actually apply it to design and policy decisions. For example, in urban planning, when Swedish cities changed their snow-clearing schedules, they weren't just looking at numbers; they were embedding those insights into their everyday systems to make communities more equitable and safer for everyone. It's a great example. Drew: Right, snow-clearing policies. It blows my mind that such a small change—prioritizing sidewalks instead of roads—led to less injuries and lower healthcare costs. Why isn’t everyone doing this already? Josh: Well, it's partly about resistance to change. Established systems, even if flawed, are hard to change because people get used to them. Plus, there's this persistent myth that designing for inclusivity is somehow a "special interest" cause, as if meeting women's needs is optional. But the data clearly shows that inclusive design actually benefits everyone—it's more efficient, cost-effective, and, of course, ethical. Drew: So, inclusivity isn't just a nice-to-have; it's good for business and solid public policy. You'd think that alone would be a wake-up call for people. Josh: One would hope, right? But there’s still so much to be done. And then, there’s the matter of accountability, Drew. Even when governments or industries gather better data, they don’t always act on it. Like in Mumbai, where the government allocated money to build safe toilets for women after a legal mandate—but to this day, many neighborhoods saw no results. So that’s where political will and sustained pressure come into play. Drew: It's not just about identifying the problem; it's about following through. Now, take me back to the workforce, because this labor idea is still nagging at me. How does unpaid caregiving even begin to get the recognition it deserves? Josh: A really powerful example comes from Quebec, Canada. They introduced a universal childcare program that didn’t just help women—it transformed the economy. Women with young children could re-enter the workforce more easily, maternal employment skyrocketed, and all that economic activity fed back into tax revenues. It was such a success that it actually paid for itself in the long term. Drew: Wait—you're saying supporting unpaid labor has measurable financial returns? Josh: Exactly! When you support caregiving systems—like affordable childcare, elder care policies, or flexible work hours—women aren’t forced to leave the workforce. And when more people are working and contributing to the economy, GDP grows, benefiting everyone. Drew: It's like the economy's been running on one engine, but adding these policies brings the other engine online. Feels like a no-brainer. Josh: Right? It is, but again, entrenched systems resist change. The myth of meritocracy is hard to dismantle. People still believe that the hardest workers succeed, ignoring the fact that for many women, success comes with layers of unpaid labor at home. Until we start valuing that invisible work, inequality will persist, you know? Drew: Which makes me wonder—what about the future? Is AI going to make all of this better, or are we just baking these biases into the algorithms of tomorrow? Josh: That's a valid concern. AI is only as impartial as the data it’s fed. If that data is biased—let’s say, male-dominated resumes for a hiring algorithm—the same inequalities just get automated, right? The potential to embed systemic bias on an even larger scale is massive. Drew: So, we’re playing a high-stakes game. Either AI becomes a tool that corrects those gender gaps by using inclusive data, or it reinforces them even further. Josh: Mmmhmm, exactly. And the difference depends on what we do now—how much we push for representative data, intentional design, and accountability at every level.

Josh: So, having figured out why these gender gaps exist, let's talk about how inclusive systems can actually fix them. We're not talking quick fixes here, but real changes to how things work that tackle inequality head-on. Think of it in three buckets: gathering data that shows differences between genders, creating policies that include everyone, and making sure women are leading the way. Drew: Right, so we're not just acknowledging the problem, but seeing these solutions in action, yeah? Josh: Exactly. Let's start with data. It’s about breaking down data across all demographics—gender, race, age, everything. Why? Because if you don't, you miss out on what's really happening with different groups of people. And then, the systems we create only work for one type of person. Drew: This isn't just a thought experiment, is it? There are stories out there where this has worked? Josh: Totally. Take Karlskoga, Sweden. They used to clear snow based on what they thought was most "efficient"—roads for cars. But when they started breaking down the data by gender, they realized something: women were more likely to be walking, using public transport, or on sidewalks, often because of childcare responsibilities. Drew: Ah, like taking kids to school or running errands. So snow-covered sidewalks mess up their whole day. Josh: Exactly. Once the town switched their snow-clearing plan to prioritize walkways and transit routes, injuries from slips went way down, which also saved money on healthcare. It's a perfect example—solving a problem for women ended up helping “everyone.” Drew: It makes you wonder how much cities around the world could save just by paying attention to details like that. Josh: Seriously. When you break down the data, you see who's being impacted the most. And once you see it, you can't ignore it. Whether it's safer walkways or better urban planning, it's not about making "special" changes. It's about planning for reality. Drew: Okay, I get how data helps us see the full picture. But how do you actually turn that into real change? What's the process? Josh: That's where inclusive policymaking comes in. Think about tools like gender-responsive budgeting. By looking at budgets with an eye on gender, officials can make sure money is being spent in a way that meets the specific needs of women and men. Drew: Hold on—gender-responsive budgeting? Sounds like that's going to cause some arguments in government. What would that look like? Josh: A great example is Quebec's universal childcare program. Unpaid caregiving, mostly done by women, was a huge issue. By helping families with childcare costs, Quebec made it easier for mothers to get back into the workforce. The result? More women working, and the taxes they paid boosted the economy. Over time, the program paid for itself. Drew: So, they didn't just put a temporary fix on unpaid labor—they created something that helped the economy in the long run. Makes you wonder why more places aren't copying that idea. Josh: Exactly! It's a win-win. But it's also about the smaller things, like public transportation. Some cities in Canada used gender-focused planning to redesign bus stops and improve safety. Better lighting, updated schedules, adding cameras—it really cut down on harassment and made women feel safer using the system. Drew: Creating policies that work in real life, not just on paper. Imagine that. Josh: The effects are amazing. When women feel safe and have the ability to move freely, join the workforce, and chase opportunities, society as a whole does better. Drew: Alright, so data and policies are key. But even the best systems need someone to keep them in check. Who makes sure inclusivity stays a priority? Josh: That's where the third piece comes in—women in leadership. When women have a seat at the table, they share their personal experiences and understand the challenges women face. Their involvement leads to policies that are inclusive from the very beginning. Drew: Okay, but realistically, how much of a difference are we talking about? Josh: A huge one. Look at Rwanda. After the 1994 genocide, they rewrote their constitution to guarantee at least 30% of parliamentary seats for women. Now, women hold over 60% of the seats—the highest in the world. And the result? Groundbreaking laws on education, maternal health, and gender-based violence. These policies have really improved life for communities, setting a high standard for other countries. Drew: So, you're saying more women in power equals better policies, for everyone. Sounds obvious. Josh: Exactly. And it's not just politics. In urban planning, when women are involved, they plan with safety and usability in mind. Malmö, Sweden, changed its public spaces—adding better lighting in parks, improving sports facilities, and creating accessible playgrounds—to address women's needs. The result? Fewer reports of harassment and a more engaged community. Drew: Okay, framing it like that, having women in leadership isn't just about fairness—it's about being efficient. And people still fight against this? Josh: They do, which is why systemic support is so important. It's not just about getting individual policies passed—it's about creating new norms in how we govern, in our everyday actions, and in the way we make decisions. That's how you create lasting change. Drew: So, these three things—data, policies, leadership—are about fixing the cause of the problem. Not just treating the symptoms, but building inclusive systems from the ground up. It's practical, it's effective, and honestly… it should have happened a long time ago.

Josh: Okay, so, to bring everything we've discussed full circle, we’ve really been highlighting the hidden impact of a world designed primarily with men in mind. You know, this gender data gap isn't just about missing information; it's about how these omissions lead to biased systems, unsafe urban environments, disparities in healthcare, and the undervaluing of women's labor. Drew: Right, and it's important to stress—this isn't just a women's issue. These oversights make our systems less efficient and, frankly, less effective for everyone. But the upside is, there are solutions. We're talking about gathering and utilizing more comprehensive data, re-evaluating policies with inclusivity as a core principle, and ensuring women have a seat at the table where decisions are being made. Josh: Precisely! When we incorporate everyone's lived experiences into how we design our systems, we pave the way for a world that's not only safer and fairer but also more responsive to actual needs. So, listeners, here’s something to consider: Take a look around you—your city, your workplace, your community. Whose perspectives might be missing from the equation? And what positive changes could happen if we included them? Drew: That's a brilliant point, Josh. It's evident that by simply acknowledging these blind spots and taking concrete steps to address them, we're not just bridging the gaps, we're constructing more effective systems that benefit us all. Until next time, everyone.