Hey, folks. Welcome back. In this video, we're going to be talking about a pretty important topic, and that is doing design in the context of socioeconomic differences in your user base. So before we get started talking about design in the context of socioeconomic status, let's talk about socioeconomic status in general. This is a concept that most people are somewhat familiar with, maybe implicitly. But it's important to talk about it explicitly and provide some data points with regard to the tremendous diversity that exists in this context. So a definition is always helpful when we're talking about socioeconomic status. A lot of people have different, but broadly overlapping definitions. The best one that I think more or less represents a standard is the one from the APA, and they say that education, income, and occupation are the three key variables to consider when we're talking about socioeconomic status. But oftentimes, you also see people add in some additional things, like for instance, related factors, say whether someone lives in a rural or urban or suburban area, and these types of things. Very important point you'll often see socioeconomic status abbreviated as SES, and I'll be using that abbreviation here. And an even more important point is that the world is sadly full of tremendous socioeconomic status differences. And I just want to give you a quick tour of these just within the United States of relatively high-income country, overall we have a tremendous variation in overall incomes. So for instance, the highest income county, counties are secondary administrative units. So below the state, right. Each state has a bunch of counties. The highest income county in the United States is this county in Virginia. It has a median per household income of over $115,000. So take all the households in this county in Virginia, half of them have an income greater than this. Half of them have an income less than this and this is a pretty large amount of money, right. To give you an idea of where Silicon Valley falls here, Silicon Valley being where a lot of the technologies we use today are designed. Silicon Valley is the 14th richest county in the United States. So not very far behind at least in terms of rank with respect to that county in Virginia. I'm using Santa Clara county here as a proxy for Silicon Valley. It's where the heart of Silicon Valley is. The median per household income there is $91,000. Again, a great deal of money, I think most people would be pretty happy to have a salary for instance. A single person salary of $91,000. Okay, so all the way down to the end of the spectrum, the lowest income county in the United States is Buffalo County in South Dakota has a median income of 21,000, right. So we have 21,000 median household income, $21,000 to $117,000, so we're talking about roughly a factor of 5 or 6. And Silicon Valley where most of the technologies we use today are designed way up towards the rich end, right. But there are a lot of people there are poorer than the folks who are around the people who are designing and who are the people who are designing these technologies. Moving to a global scale here, we see even greater variation. This is data from the World Bank. You can see in this map right that out there are some countries that have a median, excuse me, this is per capita, this is per mean per capita income. So this is per person in the United States it's over $44,000 in Northern Europe, Western Europe that's pretty similar but if you look at for instance, Sub-Saharan Africa you'll see that the per capita income in thousands, as you can see on this map, much, much, much less. So we're talking about less than, for instance, 6 to $7,000 a year. So tremendous variation in income. So what does all this mean for design? Well, there are a couple of key points that we're going to cover in this video. And these are two of them, first research is showing that socioeconomic status is tremendously important to consider when designing technologies and that very few existing technologies adequately account for socioeconomic status in their design. So let's walk through a couple of examples here. One is very very recent, this is extremely hot off the presses research, I just got this from my students actually last night. And this could change, of course, it's hot off the presses, but it gives you a sense of what's going on. This is the number of pokestops per square kilometer in the United States. Pokestops being a critical component of the game Pokemon Go which is currently a big deal as I'm recording this. Over here, on this graph, you can see, let's get that arrow there. Over here on this graph, right, you can see these are counties that are, as the US government defines them, the most urban. And these are counties that are the most rural. And we want to pay attention here to this green line. And you can see that there in the most urban counties right there are two pokestops per square kilometer. And over here in the most rural counties we can see that there are many, many, many fewer. So when we're talking about design, what are the implications here? Well the implications are that Pokemon Go was not designed to take into account the 15 to 20% of the American population that lives in rural areas. If you know anything about the game Pokemon Go, you have to go to these Pokemon stops to do well in the game. It's a lot easier to get to a pokestop with two per square kilometer in these urban counties than it is in these rural counties with many, many, many, many fewer per square kilometer. You have to travel a lot longer distance, and I wouldn't be surprised if we looked at Pokemon Go's data behind the scenes, if they basically have no usage from people who live in these types of rural counties, right. So this product was not designed to take into account a variable that is typically consider, or is often considered when considering socioeconomic status, this urban versus rural spectrum, right. This is a clear case of failure. Another clear case of failure here is foursquare. This is a chart from a paper I published with a colleague a couple years ago. You can see here that foursquare has 25 times more users per capita in urban areas, and 23 times more check-ins per capita in urban areas than in rural areas. Again, a lot foursquare's design assumes, for instance, that you have a lot of restaurant selection, and assumes that you would want to compete with other people to be the mayor of individual restaurants and individual locations. None of these are true in rural areas. So, the designers of foursquare failed to consider the needs of rural users as they went about designing their product. Since this is not atypical, right, you see cases where products work very well with a certain socioeconomic group and just dramatically badly with other SES groups. So, let's continue our list of key points here. These days this was not the case I said five to ten years ago, these days the points I just made, in part because of the research that's getting done in the research side of user interface design. These days, it is pretty widely recognized that we have a problem here and that we need to figure out what to do. We need to figure out ways to design technologies for a wider range of various socioeconomic spectra. That said, I'll say that is very complex and nuanced and often you need to take things on a case by case basis, so you'll see a research papers and products that sort of bite off a chunk here and there. We're slowly trying to bite off all the chunks. I do want to cover three strategies that I think are relevant across many of these chunks, many of these problem, many of these use cases, or many of these, I should say, socioeconomic status contexts. These strategies work in pluralities here or minorities there. But there are three ideas, three things to think about that at least can get you started designing your products for a wider range on the socioeconomic spectrum. Okay, let's talk about strategy number 1. And this is probably the most important of the three I'm going to talk about. You really have to consider all technological contexts for your user base. All the technological context that your users will be considering. And this is something that those of us who are designing technologies very, very often forget about. So, those of us who are designing these technologies, your median household income's $91,000. Your company's super rich, doing well. You got all these sweet things that you're designing your technologies for and that you're using in your everyday life. You got your MacBook. You got your iPhone 7. Who knows, you maybe even have your iPhone 8, if you work at Apple. You got your smartwatch. You have maybe a tablet to keep at home and a tablet for the road, these types of things. This is what you're using to develop your software, this is what you're using to test your software and these types of things. Very common situation. The bad news is, that the vast majority of your users do not exist in this technological context, especially if your user base includes a significant proportion of the lower income population. Which if you're developing a mass market technology like for instance, almost any of these, tablets, computers, smartphones, smartwatches, I'd say, aren't mass market yet. This situation is not at all representative of the majority of what your users experience. Let me tell you what might be. You might be dealing with users who have low end phones that are older that have smaller screens and critically that have limited or no data plan, right. So the people might just use Wi-Fi, go to get free Wi-Fi at the library and these types of things. These are all technological context that if you're designing a mass market technology you really need to consider. On the desktop side, you have similar deals, right. You might be dealing with people who are using under-powered desktops, using, for instance, even Windows 98 or Windows XP, older machines, right, and also people who don't have computers, to use a computer have to go to a nearby school or library. And all of these have just fundamental design implications, right? If you're designing for people with low end phones, you have to support those old Android operating systems, those old iOS operating systems, right? If you're designing technology for people with no data plan. You've gotta make sure that offline modes work well. You've gotta support the use case, where someone goes to a library, goes to a school, downloads what they need, and then uses that offline. I'm consistently surprised that how many sort of broad mass market technology, mass market apps don't support this well, weather apps, these types of things. Same type of deal with desktops when systems are designed for desktops, right? So if someone is just going to the library to use it, you'll have to realize that, if a larger users are doing that, you have to design for that context, right? I mentioned earlier that a lot of technology companies do recognize this problem and are making progress on it, although slowly. Facebook is one of those companies, and I think this is pretty interesting. When I was at Facebook, I guess a year and a half ago to give a talk, I noticed that they had something called their empathy lab. And their empathy lab had a lot of different components. But one key component was it was sitting right there, smack in the middle of one of their main eateries, and it encouraged employees to come in and use Facebook on devices that they themselves probably had never used. Devices with very small screens devices that are much older than the phone they probably have in their pocket. But these are the devices that are being used by a huge percentage of Facebook users in Africa and Southeast Asia, and these types of places. So the company was sort of publicly saying we need to take into account all of our users' technological contacts, not just the technological contacts that exist in Facebook, in this sort of Candy Land. High income Candy Land that all of these very fortunate people and very intelligent people are lucky enough to work in. Okay, second strategy I want to point out that is very important but usually for a specific class of products is that geography matters. So when we're talking about socioeconomic status, we're also talking about geographic variation because unfortunately, you're socioeconomic status in almost every country on the world, defines typically where you live. People who are richer live in one type of neighborhood, right. And people who are poor tend to live in another type of neighborhood. This here is a map of Chicago and you can see this very visibly. Now the darker orange here indicates areas that are richer. The lighter orange indicates areas that are poorer. And you can see that there's this cluster of wealthy areas in the city of Chicago and then in the suburbs. And this cluster of poor areas on the south side of Chicago. And there is a great deal of clustering in this map. So dark colors are clustered together, light colors are clustered together. Now if you're designing a product that has an explicit geographic component, and often one that has an implicit geographic component as well, you really have to keep this strategy in mind in order to make your product work for all people in all areas. And this map here is a great example. We looked at the base map here. We looked at the colors, right? But now I want to call your attention to the little dots. These are data from a study I ran with some of my students. Where we surveyed people who work for TaskRabbit which is a sharing economy service. You can hire people to do your laundry, to go pick up your groceries for you and these types of things. And these dots indicate where people lived. One thing that's pretty clear, right, is that there are no dots down here. And what does this mean? Well it means that to get someone to come down here, to hire someone for TaskRabbit, to hire, excuse me, someone to do your work, you're going to have to pay someone to travel further. Sometimes they won't go that far, right? That's too far away. I can do tasks that are closer to me. So you're going to have a situation where people are less willing to do the works and it's probably going to cost more as well to do that work relative to the richer areas. So you have a great irony here, if you're living in a poor area TaskRabbit's more expensive and it's also less accessible. And I would say combined, those mean that TaskRabbit is less effective in these lower SES areas. So, this is just a quick example of how these geographic SES factors can affect the effectiveness of a technology. In terms of ways to fix this problem, the first thing is to be aware of it, right? Prior to this research no one was aware of this phenomenon in TaskRabbit and the sharing economy as a whole. In terms of action items, you can take with that knowledge, well you can incentivize people to go down to these areas and maybe hang out for a bit. So there'll be people we're ready to take on tasks. You can provide discounts for the people that live there, and you can take many other steps. But being aware of things is the beginning and in many cases we're not even there yet. Okay, so the third and final strategy is to consider correlates of socioeconomic status that may be important for the technology that you're designing. People at different points in various socioeconomic spectra will they have different interests. They have different constraints, they have different needs, and so on and so forth. And as we'll see here in a second, those can have very important implications for the technologies that you design, if your technology is targeting a wide swath of a given socioeconomic spectrum. So for instance, what we have here is a chart of how people spend their money in the United States. It's from the U.S. Bureau of Labor, and the data's from the U.S. Bureau of Labor, the chart is produced by the National Public Radio. And what you see here is that lower income folks have very different spending patterns from higher income folks. And this is a critical thing to consider for many different technologies. So for instance, lower income folks tend to spend more money on cooking at home, on food for home, relative to high-income folks. If you're designing the many, many, many delivery products and sort of restaurant oriented products that we see today. This is for these people, right? It does not appeal as much to these people. These people need technology to help them cook at home. So if you're designing technology related to food, this is a very important thing to consider, right? Let's go to another one, utilities. Let's say you're designing a technology that helps people save energy. Well, for this population, helping them save money, specifically might be more interesting, way more appealing to them. For this population, helping people, for instance, protect the environment. That might be more appealing to them, right? And then down here, we see transportation and gasoline, same type of thing, right? Developing, for instance, a new routing algorithm that's more ecofriendly. This would appeal to these folks, but the exact same type of idea, but optimized specifically for saving money. You might want to point people to the cheapest gas stations and these types of things. That's going to appeal more to this population. So considering all three of these classes, the entire income spectrum can lead to designed successes considering only a part of this spectrum will essentially cut some people out of the design. And we saw that right in an extreme way in the Pokemon Go and the foursquare examples I presented earlier. I do want to reiterate that what I've talked about today is just a very tip of the iceberg. There are many more strategies and many more factors to consider. I focused in particular on SES factors and strategies for people in the United States. There are a lot of great researchers in industry and academia who are focusing on SES factors globally. A lot of great research coming out in that domain right now. I will say though that the research community can never do enough at this point on this topic. We want to be designing technology so that everyone benefits, not just folks who are lucky enough to be at the high end of the spectrum, which is where most of the focus has tended to be. Again, because many of the people designing technologies tend to be at that end of the spectrum. And with that, I will see you next time.
