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13-05-2025
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The Myth of the Poverty Trap
Subscribe here: Apple Podcasts | Spotify | YouTube | Overcast | Pocket Casts We used to be trapped. And by 'we,' I really do mean all of us. A few hundred years ago, the majority of the world lived in extreme poverty, and even in recent decades, people lucky enough to clear the $2.15-per-day threshold were living lives that others in the developed world would find unrecognizable. Death is inevitable. Living in poverty is not. From 1981 to 2019, the share of the global population living in extreme poverty fell from 44 percent to just 9 percent—an astronomical achievement. On this episode of Good on Paper, we're going to talk about how this all happened. Today's guest is Paul Niehaus, an economist and co-founder of the NGO GiveDirectly. His new paper details what actually happened in the lives of people who escaped extreme poverty since the early 1980s. As he and his co-authors write, by 'how' they mean: 'Did they plant a new cash crop on their farm? Find work in a factory? Start their own business? Move to a city?' And further, what happened across the life of one person, versus what happened between cohorts or generations? The answers provide insight into what a real 'success sequence' looks like, and challenge some foundational ideas within development. 'There's no one story,' Niehaus tells me. 'As an author, it would've been nice if there was a very simple story to tell, which is, Well, the key thing is everybody's gotta move to the city or whatever it is. But you see people getting out of poverty while moving to the city. You see a lot of people getting out of poverty while staying where they are. You see a lot of people getting out of poverty while not switching from agriculture into nonagriculture. And also, the stories are different in different countries.' The following is a transcript of the episode: Jerusalem Demsas: For centuries, mass poverty seemed inevitable. Starvation, disease, death. As late as the 1700s, roughly half of children globally would die before reaching adulthood. This was the natural order of things. And then everything began to change. Looking at a graph of development measures over the past two hundred years is to witness the miracle of human development: On any measure you can think of—child mortality, nutrition, poverty—more and more people are able to live significantly better lives than their ancestors could even dream of. Just 35 years ago, 2 billion people lived in extreme poverty. Today, that number is just under 700 million. That's still a lot of people, but this staggering improvement proves that mass poverty isn't preordained. [] Demsas: My name's Jerusalem Demsas. I'm a staff writer at The Atlantic, and this is Good on Paper, a policy show that questions what we really know about popular narratives. Why did extreme poverty fall so fast, and can we finish the job? Loads of research and debate has gone into the question of why extreme poverty fell, but today we're going to talk about how. Paul Niehaus is an economist at UC San Diego and the co-founder of GiveDirectly, an NGO focused on getting cash into the hands of the global poor. Few have thought harder—academically and practically—about these questions. Today we're going to talk about his work with GiveDirectly and a new paper he co-authored, titled 'How Poverty Fell,' that details what happened between 1981 and 2019 in the lives of those living in extreme poverty. Before we jump into this conversation, one last note from me: This will be my final episode with you all. I have loved my time here hosting Good on Paper and feel so lucky to have been able to explore all of my curiosities with you and the brilliant guests who lent us their time. And I want to thank all of you—those of you who listened, emailed, left a review, and engaged with this show in any way. It has been amazing to realize how many fellow wonks there are out there, excited to dig deep into how and why we know things. And don't worry. The show isn't going away—just taking a break. Let's dive in. Paul, welcome to the show. Paul Niehaus: Thank you, Jerusalem. Great to be here. Demsas: I think because people remain rightly concerned about continuing deprivations, we often don't take a step back to take in just how remarkable the global decline in poverty—in extreme poverty—has been. Can you give me a sense of how much things have changed? Niehaus: Exactly. And so we'll talk about the paper I think that you wanted to talk about, which is 'How Poverty Fell.' But in some sense, I sort of feel like for maybe most people listening, the key thing that they need to take away is actually just the premise that it fell. And so we start the paper with that observation, that over the course of the last four decades or so, from around 1980 to around 2020, the share of the world's population living in extreme poverty—so people living on less than (currently we measure that as) $2.15 per person per day—that fell from about 40 percent to under 10 percent around the start of the pandemic. And that is, in my mind, just one of the most remarkable episodes in human history, and just an achievement to celebrate and to try to understand, which is what the paper's about. Demsas: And extreme poverty. I mean, $2.15 a day—I mean, if folks remember, it used to be $1.90 a day until that 2022 update for inflation. It's not the threshold that people are living what we would consider good lives, right? People will starve at this level. They probably lack access to electricity and other important goods. Why is it important to track this number, versus other metrics of poverty? Niehaus: It's not the only one, and we'll do this in the paper, look at other lines as well. And any line has fundamental issues with it, which people have rightly pointed out. Some people are going to have a greater ability to translate $2.15 a day into the sorts of things that really matter in life—health and relationships and things—than others. So it's just one indicator. But what I think it has done very effectively is to sort of galvanize attention around the world through the process—the Millennium Development Goals and the Sustainable Development Goals and the World Bank's advocacy for that number—to a sort of simple metric that we can track and say, Are we making progress or not? And that matters, right? Because it lets us quantify whether we're seeing the kind of progress that we'd like to see. Demsas: So 'How Poverty Fell' is a very straightforward title, which I appreciate. And I want to start by asking you to explain that question. What does it mean to investigate how poverty fell? Like, what are you looking at? What are you trying to describe? Niehaus: Yeah. Great. The first thing I'd just say is poverty fell, right? As I said, that premise itself is very important. So literally just that fact, that premise, I think is an important takeaway. The next bit is the how, with just a lot of emphasis on how as opposed to why. And so a lot of the movement in development economics over the last couple of decades, which has been tremendous, has been towards trying to understand causality: the why. So why did poverty fall? And of course, many of the great debates that we have about the global development process are about the why. Was it because India liberalized in the early 1990s? How much did that contribute? But this is a paper about how. Descriptively, if we look at all these people who moved out of extreme poverty, what happened in their lives, right? Is it that kids were able to start out life much better than their parents did, because they had access to better schooling or other early-childhood investments? Is it that people moved out of agriculture; they moved to the city and were able to get a manufacturing job as they moved off the farm? These are all sorts of things that we know happened, but how important, quantitatively, were they for all these people that made that step over the poverty line? Demsas: I'd love for you to walk us through how you did this paper, because a big part of why I wanted to talk to you about this is because it's a pretty ambitious attempt to collect data over time from so many different people and families across several countries. So you focus on five countries. What are those countries, and then what did you do to get this information? Niehaus: We're looking here at, as you say, five countries: India, China, Indonesia, South Africa, Mexico. And actually, most of the work—you mentioned all the hard work, so enormous amount of hard work—but actually, most of it was done by people other than ourselves, the people who went out and collected these original survey data sets that let us do this. And so our filter for the project, when we decided which countries to look at, are the countries that have some of the highest-quality household-survey data sets available. That let us really drill into, What are people's standards of living? and also, Where are they getting their income from? so that we can understand how that's changing. And that's a really hard task. And so one piece of context I want to set is that if you're used to thinking about, say, poverty in the United States, we can measure that pretty reliably—you know, issues and so forth—but looking at sort of data that people report to the government automatically through tax reporting and so forth. And we want to supplement that with surveys and so forth, but there's third-party reporting. There's all this machinery that exists. And so in the countries we're studying, that's not the case. And the data that we're looking at are going to come from people that are going out into remote corners of the country because we sampled a village there—and going to that village and trying to track down some people that we sampled that we want to interview, and then asking them if they'd be willing to sit for a multi-hour interview, and asking them really detailed questions like, How much rice did you and your family eat last week? And how much money did you make from your vegetable farm? And how much money did you make from doing some casual labor for other people in the village? And it's an incredibly painstaking and laborious effort. What we're trying to do is capitalize on all of that hard work that other people have done and say, If we now put it all together—because for a bunch of these countries, we have data that are really sort of the best, that adhere to the highest standards of data collection in fieldwork in development—what can we learn from that? Demsas: And, I mean, I think a lay person hearing those five countries would think there's something important missing. I mean, India, China, South Africa, Mexico, Indonesia: It really doesn't include some of the countries where most people think about extreme poverty being the biggest issue. Like, it doesn't include much of sub-Saharan Africa, where we do see the most deprivation. Are you worried about that in terms of—I know you said you picked these countries based on what the best data allowed you to study—but are you concerned that it's not going to extrapolate to the places that are of most concern today? Niehaus: Yeah, there are two parts to that. One is: It is backwards looking, and so these are actually the countries that contributed the most to extreme-poverty reduction during the period that we're looking at, especially India, China, Indonesia. So South Africa, Mexico—relatively small. And we may get into this, but there they're different economically in a bunch of other ways as well. So actually, during the time period that we're looking at, these countries are pretty attractive and may be the ones that you'd want to prioritize. For today, I think you're totally right: If you wanted to sort of look at what's happening in the last five years or think about what might happen in the next 10, you'd probably want to be looking elsewhere in the world. That said, there are also examples of smaller-scale studies—a few regions in Uganda or in Tanzania, for example—that track migrants. And so one of the things we try to do in the paper is to also tip our cap to those and point out some similarities in terms of the findings from those as well. But yeah, those are places where we do face this very deep constraint that the same kinds of data, and especially panel data—meaning, data where we follow the same people over long periods of time—are much scarcer. Demsas: Yeah. It's a hard problem because the very places that are most deprived are the ones that are most difficult to study. Niehaus: Yes. Demsas: So how did poverty fall, Paul? What did you find? Niehaus: I distill three things. So the first is: We look at this intergenerational aspect of it. And probably you've heard language like breaking the cycle of poverty or [breaking] the intergenerational cycle of poverty. So really sort of interested in: To what extent, as poverty fell, was it because one generation was sort of stuck at where they were, because they never had the chance to get a good education or whatever it is, but then they're able to make the sacrifices so that the next generation can have a better life? And so what was really interesting to me—I think I would've expected a lot of that. Actually, what it looks much more like is: When we see a new generation entering into the workforce, they're starting out about as poor as their parents' generation—so much less poor than their parents' generation was when their parents' generation entered the workforce 25 years earlier, let's say. But their parents have made a lot of progress in the meantime. So overall, what seems to be happening is people are making a lot of progress during their lifetimes in parallel to the improvements that they're then able to pass along to their kids. And so we take away from that that it's important to understand what is happening during people's lives, because it's certainly true that, you know, what you get at the start in terms of nutritional investments your parents are able to make, looking after your health, vaccination, schooling—all that stuff's important, but it's not like that's the end of the story, right? We see that there's much more after that, and a lot of people are making progress during their lifetimes as well. So that's sort of fact No. 1. Fact No. 2, we're now going to turn to these panel data sets I mentioned that let us track the same people over time. And so those are even more scarce. You can imagine the difficulties involved, especially when we want to track people as they—you know, maybe they're moving to a different part of the country for better opportunities and so forth. But we have data for these five countries, and what you really see in all of those is an enormous amount of churn, of movement in both directions. So if the first thing I wanted you to know about this paper is that a lot of people got out of poverty, the second might be that even more people got out of poverty in gross, but then on net, poverty rates fell less fast than they might have, because a lot of those people fell back into poverty. And that's a really important fact. We may come to it in terms of how we frame the way we think about poverty and poverty reduction. You hear a lot of conversation talk about poverty traps, people being stuck in poverty. I think that contributes to this mindset that's like, Unless we come in and intervene in some way, unless we kind of find the magic key to unlock this situation, people are going to stay stuck where they are. And what the data actually looked like is much more that, like, people are getting out of poverty all the time at a very fast rate. The biggest problem is they're facing a lot of headwinds, things that knock them back. And we can get into what some of those things are. It might be illness, droughts, agricultural shocks, things like this, but it's that getting knocked back that I think is really important. There's some caveats to that finding. There's going to be some measurement error in our data, right? And that's going to generate more of this. It's going to look like somebody got out of poverty, when actually they didn't. So it's a little tricky, to be completely frank, to say just how much of this is churn versus measurement error. But it's so strong, it's so pronounced in the data, and it lines up with a lot of other research. I just think that's a very important reality to kind of get your head around. Demsas: And you mean measurement error that you're losing people as they move, or you just can't find them again? Niehaus: More just that, like, when I come in and I try to do these surveys and figure out your living standards, it's hard, right? And we don't always get the answer exactly right. And so that's just going to create some noise in the data, and it will look like somebody fell back into poverty when maybe they didn't. So a little bit of it's going to be that. Demsas: And then you had a third stylistic fact for us. Niehaus: So the third is—now we look at these people as they're moving out of poverty and get to some of these questions I posed earlier about what's happening in their lives. And I'd really say there's no one story. As an author, it would've been nice if there was a very simple story to tell, which is, Well, the key thing is everybody's gotta move to the city, or whatever it is. But— Demsas: Yeah. Niehaus: —you see people getting out of poverty while moving to the city. You see a lot of people getting out of poverty while staying where they are. You see a lot of people getting out of poverty while not switching from agriculture into nonagriculture. And also, the stories are different in different countries in interesting ways. So maybe a good sort of broad way to think about it is: It's good to not be looking for, sort of, the solution and saying, What is the path that people need to walk? and more thinking about, like, What are the right paths for a given person in a given context, and how can we accelerate that and help them along that? As opposed to coming in expecting there to be one thing that'll work well for everybody. Demsas: So now I have to talk about my priors here, because you pushed against them in ways that made me uncomfortable. So economists, urbanists, immigrants—we tend to see migration as a huge part of the story for modernization, poverty reduction, increasing the quality of life. I mean, I'm obviously going to be biased, also, because of my own life. My family immigrated here from Addis Ababa when I was very young, and my dad moved from Asmara to Addis Ababa in order to get an education. And it's just, like, this is ingrained in not just personal experiences but also, the economic literature really pushes that moving towards cities—higher-productivity cities—is the key way to improve both economic growth and productivity, but also giving people access to the good life, like access to higher standards of living and other things that they care about. It's not that your paper invalidates this entirely, but it does push back against this dominant view, in some ways. That classic story of rural peasants moving towards cities and towards factories, it's only a part of the picture, and it's not even the dominant one. In some ways, I would think it's not really the movers that are the stars of your stories; it's the stayers, both in terms of geography but also in terms of staying within specific sectors. Is that an accurate kind of read of a takeaway, and did that surprise you? Niehaus: Yeah, that's about how I read it. A few things there. One is: International migration, which you brought up, we're not going to see here. And it's probably not relevant for getting people over $2.15. I'm guessing that your family, when they moved, had access to much better opportunities but that you're moving from a much higher starting point than that poverty line. And so we are really kind of zooming in here on the left tail of the income distribution and trying to understand that. Point two is—and this is where the economics comes in—that when a few people move to the city, let's say, that's going to change wages and labor-market outcomes and other things for the people who stay behind. And so one of the things that economists need to really unpack, and this is something that's been important in other papers that I've worked on, is that bit of not just the direct effect on the person who leaves but also the indirect effects and how that changes things for everybody else. And so that's not something we can see very easily in these kinds of data, but that's one of the questions that we should be asking of them, is how much of that was playing a role. But, you know, all that having been said, yeah, I think it's very clear that a lot of people have been able to make significant progress while staying in place. And, you know, to me, one of the most interesting cases was Indonesia, because we have really good migrant tracking in Indonesia, and there were a lot of people who got out of poverty while moving, but while moving from one rural area to another rural area. So that, again, as you say, it's a little bit different from the standard story. It's an interesting twist. I think that's a good thing to drill into. Demsas: Yeah. I mean, regional economic convergence is something that has been studied at a different level—not really that I haven't seen in the extreme-poverty space. But one of the editors at The Atlantic, Yoni Appelbaum, has written about this, and it starts with Peter Ganong and Daniel Shoag's really great paper about declining regional convergence, which just kind of shows, you know, exactly what you were saying, that when people leave lower-productivity, lower-wage areas towards cities that are higher wage, higher productivity, the places they leave are net better off because there are fewer people there or because the average poverty level declines because the people who remain are often those who are in the good jobs. But I want to tease apart something that you pointed out in response to my question, which is: We're talking about people escaping extreme poverty, not people like my family and others who were not at, like, $2.15 a day or below that level. Do you think that these answers that you're finding—these facts that you're finding—are really just about extreme poverty, and that questions about how to move people into the middle class, into higher levels, are going to be quite different? That industrialization will play a much larger role, urbanization will play a much larger role? How do you think about that? Niehaus: Yeah, possibly. And I think most of the answer to that is: That's something that I think we'll keep doing as we continue with the project and look more at that, so probably better to just look at the data than to speculate too much. Demsas: The second part of this is that it's not just about geographical moving; it's also about within sectors. So you talked a little about this with agriculture, but what is happening within agriculture that is allowing people to get themselves out of extreme poverty? The traditional story, as you said, is that they have to leave that sector to make money. Niehaus: Well, one of the things we can look at with the data is whether you're self-employed or not. And so the sector you choose to work in is a big one, as you say. We tell a lot of stories in economic development about the role of self-employment as opposed to wage employment. And so one of the things that I thought was very interesting here, and one of the ways in which, as I said earlier, some of these countries look very different from each other, is that in the lower-income countries—the countries that started out poorer at the beginning of this period, which are India, China, Indonesia—you see that people who switch into self-employment are making a lot of progress. That accounts for a fair share of the poverty decline, and sort of the rates at which people who make that change exit poverty are relatively high. And then in Mexico and in South Africa, it's the exact opposite, which is that the people who seem to be doing best are people who get a job. And I think that also maybe relates a little bit to your question about the ladder and sort of what the later stages in the ladder look like as well. Because Mexico, South Africa are more developed countries—they're more industrialized countries—agriculture plays a smaller role to begin with. And so that is a place where people who 'become entrepreneurs' look to me a lot like these sort of entrepreneurs of necessity. They're sort of doing it because they couldn't get a wage job, which is what they really wanted. And the best thing they can do is to be a small vendor, sell something at the roadside, try to scrape by until they can get back into wage employment—as opposed to these other countries where I think it is actually: For many people to own their own land or to start their own non-ag business, that would actually be a really high return and exciting thing to do if you could get the capital to do it. Demsas: You hinted at this, but I think the nonlinearity of the stories underlying this research kind of speaks to a lot of people's personal experiences. It's the slippery slope where people are making their way out of poverty to a slightly better position than falling back. Can you just describe what's going on there and maybe give us some of those facts within countries? I think Indonesia, for instance, was a place where you looked into this. Niehaus: Yeah, what we do in our paper is, you know, we're trying to give you the kind of broad-stroke facts here. I think for this question, I would go to other sources. I really like, for example, Anirudh Krishna's book One Illness Away, which sounds like it's a book about health, but it's really a book about poverty dynamics and this dynamic of getting knocked back into poverty. And he called it One Illness Away because for many people, that is the thing that does it. It's like: A primary breadwinner gets sick. You have to spend a lot of money on their health care. Hopefully that works, but maybe it doesn't. Maybe you spend all that money and then they still pass away. In the meantime, they're not earning. And that's just a huge shock, right? And during that period, maybe you're selling off assets and so forth to try to cover their medical bills and to make ends meet. And so I think it's things like that we should have in mind when you think about these people who get knocked back. Demsas: And the numbers were really shocking to me. I mean, in Indonesia, you guys find that 37 percent of households who were poor at the start remained poor at the end of a 15-year panel. But only 16 percent were poor in every survey round, meaning that loads of people are falling in and out. Similarly, in South Africa, you find that only roughly, like, a quarter of initially poor households stayed consistently poor throughout the panel. I think that level of churn is something that we're familiar with, even in higher levels of poverty in developed nations. In the housing context, people are finding a place to stay, and they feel safe, and then divorce happens or an illness happens or a job loss happens, and then they fall back into homelessness. I mean, that kind of churn is really well documented. But, you know, it's interesting because it doesn't feel like that's how people talk about the poverty trap, right? They talk about it as if you're stuck there and waiting for an intervention, and until that intervention happens, there's really nothing to be done. You're just, like, waiting there, and it really stagnates. Why do you think that idea persists? Niehaus: I'll be honest: I think that the somewhat skeptical part of my nature thinks that it's attractive to us. It sort of depicts a scenario in which a hero is needed. And look—I got into development economics hoping to be a hero of some sorts, I guess, and so I should be very honest and self-critical about this. But I think that story sells, right? And it's effective at getting people to donate money. And so you sort of say to people, Yeah, like, people are stuck in this trap, and if we come in, we can get them out. And so I think that's very compelling, maybe a little bit easier to pitch to people than the story that, like, people are making enormous amounts of progress on their own, largely without help. Our role is to come in and think about how we can accelerate that, how we can make it faster, and also how we can provide them with some degree of a safety net so that when they get hit by these shocks, they don't get knocked too far back. It's a bit more nuanced. But I think that's true to the data. Demsas: After the break: Who should receive cash transfers, and who gets to decide? [] Demsas: I want to move a little bit into takeaways for policy makers, for NGOs, for individuals who care about reductions in poverty and want to make a difference. So you are at GiveDirectly, which is an organization that sends cash transfers directly to people living in extreme poverty. Can you broadly describe how you decide where to direct those transfers into which people? Niehaus: Yeah. So first I should say I am at UC San Diego. I'm there today, and they're the ones who pay my paycheck. But I'm also a co-founder and a board member at GiveDirectly. And so: super happy to talk about the work that we do there. GiveDirectly is, I think, the largest global nonprofit focused on direct transfers to people living in extreme poverty. We're currently in around a dozen countries, mostly in sub-Saharan Africa, to your point earlier. And you asked specifically about how we choose who to direct the transfers to. Pretty simple: We're generally looking for people living, you know—the poorest people that we can find, the poorest communities that we can find. We've tended to err on the side of simplicity. When we get into a village, let's say we're going to enroll most of the people living in that village. We might try to exclude a few wealthy landowners or people that are sort of absentee landowner, landlords, people like that. But generally, the goal of it is to try to find the poorest people we can and then get large amounts of money into their hands, no strings attached. Let them decide what they want to do with it. But for everybody listening, I think there's a bit of context that's very important, which is that this show is called Good on Paper. I would say that, like, most of global-development work was, like, up until around 2000, good on paper—in the sense that we had a lot of theories of what ought to work, a lot of intuitions and things like that. There wasn't all that much rigorous testing of anything, let alone the very specific questions about the right way or the best ways to do cash transfers and so forth, which are very good. And so it was only in the last 20 years or so, I'd say, not just because of experimental testing—[randomized controlled trials] (RCTs) and the RCT movement—but especially because of that, I'd say we started to get a lot of really good causal evidence on what works. So that's why we're in a world where (A) I can tell you a lot already about what happens when we give money to people living in extreme poverty, which has generally been very good, and (B) we can get into some of those more nuanced questions that you're asking. But I just want people to have that context. Demsas: And how much research do you do towards things like anticipatory cash transfers or seasonal cash transfers or targeting specifically at women or different groups? How do you think about those aspects? Niehaus: I just wrote a review paper on some of these questions with Tavneet Suri, and the gender one I think is very interesting. We have a few studies that compare what happens if you give money to a husband versus to a wife, let's say. We have fewer of those than you might think. It's obviously a very important question, and the reason is that the overwhelming view has been that you should give it to the wife, and so almost all cash transfers are run that way. GiveDirectly, by the way, it tends to be around 70–80 percent of the time in a typical program that we'll give money to a wife, but we actually let the household decide how they want to do that, and so there's been some variation there. I'd say that if you look at the studies that have varied this, there isn't an obvious winner. It's not like women are good and men are bad, or vice versa. There are differences. There are cases where there are significant differences. There are cases where you see more investment in kids when you give transfers to the mom, maybe more investment in a business when you give transfers to men. That's a pattern that I think fits people's priors. But by the way, there are also cases where there's bigger impacts on kids' nutrition when you give transfers to the men. And there's, I think, good evidence that many women have enterprises that they could, in principle, invest in, but it's harder for them to keep the money safe and keep other people from getting their hands on it, sort of pressuring them for it. So I think it's just a very nuanced story. I don't think there's a clear This is the right person, in terms of the impacts. For me, this one is much more just about, like, on a priori grounds, we look at everything we know about empowerment, about who has the sort of rights to decide how household resources are used. It's very inequitable, right? Women generally have much less say in a lot of things. So to me, this is one where I don't think that the right way to go about it is to go do a bunch of these things and try to show in the data somehow that you get a bigger treatment effect on some particular outcome. It's more like, I think equity really matters, and so I think it's good to give resources to people who have less control over them to begin with. Demsas: How do you think about the balance there? Because a big thing that you laid out for us is that the global NGO community was not sufficiently concerned with outcomes, and it required sort of the randomized controlled trial (RCT) revolution and work by you and organizations like GiveWell that focused on effectiveness and, really, ranking and charity navigators that really tried to force NGOs and force global aid communities to think more deeply about the impact there. How do you balance just focusing on sort of the raw benefits that you can measure and quantify in the spreadsheet versus values that are more difficult to show up in development statistics? Niehaus: Great question. And first I would say, I think you mentioned the NGOs. Certainly true, I think, that many NGOs are much more evidence focused these days than they were in the past, and that's been a good thing. But hey, I mean, let's remember: NGOs are going to follow the money. I think that the process—and I'm a big fan of the whole evidence revolution and of outcome measurement and all of that—but it is still a very top-down, technocratic process where somebody in a position of power who has the money says, Here's the outcome that I want to achieve. And then people go out and try to figure out the best way of doing it, and then they come back and say, Here are the results. And then they get to decide which thing seems most attractive, given the impacts, as opposed to a process where the people that we are ultimately trying to benefit have a meaningful say in what gets done. Cash transfers are obviously an example of that. That's, like, an extreme case where we're going to say, We're going to give all the money directly to these people and let them say what they want to get done, what they want to prioritize. But there are other less-extreme ways that it happens or that you could imagine doing it where people have not just some sort of participatory process—in the sense that we'll talk to people, but then, at the end of the day, we're going to retain power and decide what to do—but people are given real control over how resources get used. So that, to me, is a very important gut check on everything else, because if I come in and say, I really care about health—I'm a health guy, let's say—that's great. Obviously, people living in poverty also care a lot about health and about the health of their family members, but it's not the only thing. And so there has to be some sort of gut check or process check that says, Am I really still kind of pursuing things in a way that's consistent with their values and their priorities? Demsas: Returning a little bit to your paper: Your paper doesn't directly say what one should do in order to reduce extreme poverty. It's a descriptive paper. It's not one that's looking at causation directly, but it does indicate that large reductions in extreme poverty are not really about transfers. Is that because they were insufficient over the time period you're studying, or is it because these are just dwarfed by things like economic growth and other sorts of changes in people's lives? Niehaus: Yeah. Transfers in our data were small, for the most part. If you look in almost all the countries at the people who got out of poverty, after getting out of poverty, transfers are a pretty negligible share of their income, of a couple of percentage points. And so what that means is just that we weren't sort of trying to get people out of poverty with a program—a very ambitious, large-scale program—of transfers at that time. What we were doing in most of those countries is trying to use it to offset some of these shocks that we talked about, to kind of make the slope a little bit less slippery. And so you see that the people who are getting a larger share of their income from transfers are the people that were making negative progress, the people that were falling back into poverty. There's one exception to that fact, which is interesting, which is South Africa, which has historically had extremely generous social transfers—pensions and others, child-support grants, and so forth. And so you'll see in South Africa much larger shares of people's earnings coming from these public transfers. But even there, the people who got out of poverty are going to see that share decline by a lot. They're not getting out of poverty because the government is ramping up transfers. They're getting out of poverty in other ways. Demsas: Does that indicate to you that transfers don't play a large role in reducing extreme poverty? Niehaus: Well, I mean, transfers are going to reduce extreme poverty very mechanically, right? If you give somebody $1 a day, they're going to have $1 more a day. So no. I think what it indicates is that kind of the way we've thought about the role of transfers has been social protection. And that's exactly the language that's used for most cash-transfer programs, which I think is a very good thing. I think that now just in the last five, 10 years, we're starting to think about transfers in a more ambitious way. Which is, (A): Could they be graduative? Could we give people enough money to really push them over the line? And then (B) the question that I'm excited about and would love for us to talk about a little bit is: If we wanted to design a program that would end extreme poverty using transfers, how much would that cost? Because I think we're actually quite close to it. That's not a question that we've asked before, but I think we can now. Demsas: I want to get to that, but the thing I'm trying to press on here is: If you're an individual looking at this paper—and I know you mentioned it's one paper; we don't want to just say this is the end all, be all of descriptive statistics on extreme poverty—but it indicates that the most important thing to focus on is economic growth, right? I think the thing that most people think about when they see the logic of economic growth being so vast in comparison to other interventions is: Is it just that we don't actually know as individuals and NGOs and governments how to actually spur that sort of change? And so we're doing the second-best thing, which is, Alright, let's just send people money and do charity and do other kinds of forms of aid. Or is it that you think it's possible to get these massive reductions, like what you described at the beginning of this episode, roughly 40 percent of people living in extreme poverty to 10 percent. Is it really possible to get those kinds of levels of changes through programs like GiveDirectly? Niehaus: For sure. The way I would think about the role of the paper is: It's certainly not telling us what the high-return things are to do—certainly not. And that's what most of development economics over the last decade or more has been about. And so, obviously, no one paper is going to achieve that for all of these different things. I think what it can do is give us some clues as to where to look. And as economists, we really want to be thinking about what the investments are that people might like to be able to make that they can't, because they don't have the resources, or they face some other constraint. Because that's what's going to drive—you talk about economic growth. We really care about, sort of, growth. But within this small subset—the subset of people who are, kind of, in the left tail of the income distribution—so, you know, economic growth for them might mean paying the cost to migrate or taking the risks to migrate. Or it might mean investing the capital required to start your own business, which we saw was a driver of poverty reduction for a lot of people. So the way we can use results in a paper like this is to ask ourselves where to look and what sorts of things seem like they might be high-return pathways for people that not many of them are able to take, because they can't get the money to do it in the first place. Demsas: And you mentioned that you think it's possible. I mean, this has been a UN development goal for a while: to end extreme poverty by, I think, 2030, which people are now projecting, due to the pandemic's effect on increasing the number of people in extreme poverty, is likely not going to happen. Do you think that 2030 deadline is unlikely to happen? And how would you design a program to actually end extreme poverty? Niehaus: I think it's unlikely to happen under the status quo, just looking at the world as it is and at what's happening. I think it's very doable. And what I would do—in terms of recommendations, what we could do: I'd sort of split it into two parts. I think there are extraordinarily high-return ways of helping people in extreme poverty, which we should do and which we can do for a limited number of people. So you mentioned, like, GiveWell, for example. They'll recommend things like bed nets for people who live in areas with a lot of malaria, with a high prevalence of malaria. Or they'll recommend deworming for kids who live in areas with a high worm load. And so those are going to be extremely high-return things that we can do to help that set of people who face that one particular issue. If we want to do something really ambitious, like end extreme poverty by 2030, we're going to need maybe a portfolio of strategies or maybe something that works everywhere and for everybody. And so part of what I find very compelling about direct transfers is that they do that, right? Cash is relevant, whatever your issue is, wherever you live, whatever your problem is, right? Money's the most flexible thing that we can give people to help them. And also, the numbers on cash transfers and poverty, I think, are very compelling. Like, the global extreme-poverty gap—the total amount by which people who are poor in the world today are below that poverty line—estimates range between maybe $100–$150 billion a year. Demsas: That's not that much. Niehaus: And you put that in global context—that's like 0.1 percent or 0.15 percent of global GDP. So if we could find everybody and give them the exact amount of money they need to get over the line, to finance that, you and I, everybody, we'd all need to give up 0.1 percent or 0.15 percent of our income, which I think is a bargain. I think most of us would be willing to make that. You take, like, the average American sort of making $50,000 a year. What does that mean? That means 75 bucks. So if I ask, Would you be willing to give up 75 bucks, and that would be your part for ending extreme poverty? I think the answer is, in my experience, absolutely. And so I don't think people realize how close we are. The problem is we don't know exactly how to go out and find everybody and give them exactly the amount of money they need to get over the line. And so I'm actually actively working on that right now, and we'll get you an answer, which is: Maybe it's, like, 0.2 percent or 0.3 percent because there's going to be some buffer, because we don't know exactly how much money different people need. But I think that, undoubtedly, the answer from this is going to be that there is a feasible, shovel-ready way of ending extreme poverty that would cost much less than you think and is something you'd feel really good about ethically—that, like, I did my part to end this thing. Demsas: Yeah. So you brought up GiveWell, and there was something really interesting that happened within the GiveWell world. It's an organization that directs charitable contributions, and importantly for this conversation, they evaluate charities on their effectiveness. And GiveWell and others have often used cash-transfer programs and, in their case, particularly GiveDirectly's cash-transfer program as the benchmark with which to evaluate other charities. That is, like: How much better is your program than just giving that money directly to people in need? Like, you need to prove that it'd be better for us to pay to set up this whole organization to do anything—vaccinate people, whatever it is—that would be better for people's outcomes than just giving them cash to do whatever they need to do, including getting vaccinations. So in 2022, they updated their most effective charities to exclude GiveDirectly, pointing instead to a couple of malaria-prevention programs, a vitamin-A supplementation program, and a vaccine-incentive program in northern Nigeria. At the end of last year, they actually also evaluated the impact of unconditional cash transfers again and found that GiveDirectly was three to four times as cost-effective than they previously estimated, but they still think that those other four charities are significantly more cost-effective than GiveDirectly. Did this evaluation change or affect how you think about GD's work? Niehaus: That's exactly what we wanted to do. I think when we set out to start GiveDirectly, we said cash transfers surely are not the only thing that we should be spending global-development money on, but what's missing when you look at the sector is a little bit of this gut check of, like, Okay, I think I have a good idea. I think I have evidence. I have all this stuff. Am I confident that I'm better at spending this money than the person who's actually there, living it, dealing with it, and knows what they need, perhaps better than I do? And so I just really appreciate that GiveWell have sort of baked that into the way they now evaluate other organizations and sort of the way they think about the world. And I think it's really good and really important to have an organization like GiveWell that's out there saying, What can we do that's actually better than what people living in extreme poverty could do for themselves? Because they can do a lot of really good things for themselves. And the update that you mentioned—the three to four times more effective—I think reflects some of that, as well as taking into account some of the aggregate impacts of the transfers, like the macroeconomic impacts of the transfers. So in any given year, I think we might make the cut in terms of being on their very top list, or we might not. But I think that way of thinking about the world, which is, like, Yeah, there is some stuff where you need some coordination right there, externalities and public goods and problems like this we need to solve. But, like, our default should always be transfers. I think that's exactly the right way to think about it. Demsas: Do you think that more money in the global aid community should be going towards these kinds of public-health programs that GiveWell is doing over cash transfers? Do you agree with their ranking? Niehaus: It's a question of: If I were to think about where to give $1—sort of a given finite amount of money—then there's a strong case for it. I think that the part that they don't price that we've always felt at GiveDirectly is that—I think it's true that GiveDirectly has contributed, to some extent, to helping to really shift perceptions in the sector more broadly. Cash transfers have now become a big part of how a lot of global-development work is done. And I think there are a lot of other people that now ask this question of, like, Well, we could come in and design a program, try to move outcomes, all this stuff. But are we sure that's better than just giving money? So I think if there's, like, an unpriced part of GiveDirectly's work that I think isn't reflected in the GiveWell score, it's that. And that's hard to price, and I think we'd all agree with that. So I'm okay with it. But, you know, I don't think it's either-or. We should be doing both of these things, and we are. Demsas: I feel like I've cited this, like, five times on the show already, so apologies, listeners who know this already. But it just reminds me a lot of Amartya Sen's arguments about development as freedom, and that it's obviously very important to center the literal metrics. Like, are people better off, and can you measure that? And also to realize that there are specific things about—the reason we care about development to begin with is because it gives people access to freedoms. Like, are you free to choose how you want to live your life? Are you free to make decisions for your children, for your family, and be free from discrimination and free from abuse? And that it can't always be measured, those democratic freedoms. Like, do you feel free to speak your mind about things? It's hard to show that on a spreadsheet, but that's important. And I asked you this earlier because I struggle with this, too, because a lot of the things that I care about, whether it's gender egalitarianism or the rights of various marginalized groups worldwide, you can see how sometimes a focus on that can move people away from being rigorous about whether their work is actually helping people, because it's hard to measure those things. Like, you can do surveys, but even those sorts of things are often really riddled with error, whether it's just, like, people's perceptions shift or whatever it is. Like, how do you measure whether people are happy on gender-egalitarian grounds? It's just open questions. And I wonder, like, how do you wrestle with that problem and make sure that you're able to balance both of those things? Niehaus: I love what you said. It reminds me of some of the encounters we had in the very earliest days starting GiveDirectly. I remember, in particular, we had a great conversation with Duncan Green, who was at Oxfam at the time, talking about some of the cash-transfer programming they've done and talking about some of the things people did with the money that would not show up in any of the metrics. But that really sort of provoked deep thought about: What is the point of global development? And Amartya Sen, right? Am I really bought into that? Am I okay with that? They found, for example, in one of these programs that a lot of people—there's a program in Vietnam, and a lot of people use transfers to purchase coffins because for them, culturally, religiously, it was very important to be buried in a coffin and not in an open grave. And that's not something that I think anybody ever measures in our surveys, right? But what if that's very important to you? Am I okay with that? And in my own experiences with GiveDirectly fieldwork, I remember: We interviewed a guy who was part of the basic-income project that we were doing and had worked as a security guard in a town to earn money and send money back to his family, and then who lived in the village. And then when he started getting transfers, he quit that job and moved back, going to live in the village, earn less money but see his kids more often. Again, that's going to look like an income reduction in the data that we typically collect. It's because we aren't great at measuring things like the quality of your relationships with your kids, which are what I think actually matters in life. So I just think that it's super, super important to take all of the measurements, the outcome stuff, with a grain of salt and with that humility, and sort of trying to remember that people's own views on what they want out of their lives are very important if we really take that Senian perspective. Demsas: It's funny because there's a subset of people where I'm like, Okay, yeah, you should take this more seriously. But broadly, I find that when I'm talking to people in the charitable-giving space, it's like, maybe they're not taking the effectiveness seriously enough. And it's like, which message does any individual need to receive? And kind of on that, we briefly touched on the UN's goal of ending extreme poverty by 2030. When you look at the global aid and the work that different governments are doing, is the international community largely focused on doing the right thing? What would you change—maybe setting aside what's going on in the US for a moment—what would you change about how countries, intergovernmental organizations, etcetera are doing to try to actually attack this problem? Niehaus: Well, it's a really interesting moment, and I think that actually taking the institutions as given might be a mistake. We might want to actually sort of reimagine the institutions themselves to some extent. Demsas: You mean, like, the UN? Niehaus: The whole architecture for aid. So if you take, for example, the UN architecture for humanitarian response, it's built around these silos that say we're going to have some organizations that are focused on shelter, some organizations that are focused on food, some organizations that are focused on education, and so forth. And so in a world where service provision is done by NGOs, then you need that specialization, right? Because you need to kind of pick something and be good at it. In a world where we're going to give money to individual people and let them decide what they want to do with it, and more of the provision—the service provision—is going to be done through markets, maybe we don't need that same institutional structure. And in fact, seeing the way that's played out within the United Nations system as it's become, in humanitarian work especially, much more receptive to it, I think people now agree that cash transfer should be a much bigger part of humanitarian response. It doesn't sort of fit very neatly right within that system. So I think it would be—and John McArthur and Homi Kharas at Brookings sort of suggested this, as well, that it might be a good time for a new multilateral that's focused on direct transfers. And that would let us think a bit differently, as opposed to trying to fit new wine into old wineskins, so to speak. But in terms of the things that we would be doing, look—we have 20 years now of really rigorous evidence on cost-effectiveness and impactfulness. And so there are a bunch of things that are fantastic. If you're wondering, like, Do we know good things to do with money? Yes. We know great things to do in education that are really effective at improving learning outcomes. We know great things to do in public health that reduce disease burdens and improve people's lives for long periods after that. And we have cash transfers, which are just a great way of helping people do whatever it is they want to do, and have an enormous evidence base people are using money responsibly. They're generally not wasting it. They're generally not using it in self-harm ways, like drinking it away or smoking it away. And in many cases, they're making investments that have long-lasting impacts on their lives. So the point is, like, we have so much good stuff to do. And we, up until now, have been doing a lot of stuff that's really based on old thinking, right? This sort of good-on-paper-type reasoning, 'Teach a man to fish' type thinking. And seeing USAID essentially evaporate overnight, obviously it's sort of a gut-check moment, but I think it's also an opportunity to rethink how we do the whole thing. Demsas: Well, I think that's a great time for our last and final question. Paul, what is something that you once thought was a good idea but ended up being just good on paper? Niehaus: So I would say—and I sort of said this earlier, but—I think that idea of teaching a man to fish is something that when I first heard it made a lot of sense and seemed good on paper. But, like, as we started to test impact over the last 20 years and say, like, What impact do our fishing lessons actually have? I don't think it works very well; the data don't support it. Demsas: We're bad at teaching people how to fish. Niehaus: Unpack that a little bit, right? I mean, if you take it a little bit too literally than it's meant to be, it presumes that the guy doesn't know how to fish in the first place. Maybe, actually, he did know, and what he needed was a fishing rod. It presumes that the lake isn't getting overfished, right? Maybe there are tons of people out there fishing, and the big issue is sort of overextraction of natural resources, and we definitely should not be teaching more people to fish, right? It presumes, as you said, right, that we're good at teaching people how to fish. Maybe we're not. Maybe it's hard, and it's not something that we know how to do well. So there are all these sorts of assumptions baked into it, and that's why it's important to test. And you go out and test it, it actually doesn't. The other thing that I think is really interesting—I'll just riff on this a little bit about teaching a man to fish—is the origins of it. So today you hear it, and the way we interpret it is it's saying, like, Don't just give people money, because they're not going to use it in ways that have a lasting benefit. It's important to kind of help them in these other ways, which I think is just empirically untrue. But actually, if you trace it back, the first place that I've been able to find it, it shows up in this Victorian novelist Anne Thackeray Ritchie, and she has this ironic character in one of her novels saying that the reason that we don't do these things is because affluent people really don't want it. They said you could really help somebody make progress, but affluent people would feel uncomfortable with that—it would upend the social order. So it's funny that the origins of the term are actually this critique of inequality and of people's unwillingness to— Demsas: Wow. I've never heard that. Niehaus: But somehow, over time, it's completely changed. And now the interpretation is: You can't trust people to make financial choices for themselves. That's not what it originally meant. Demsas: Yeah. Well, Paul, thank you so much for coming on the show. Niehaus: Pleasure to be here. Thank you so much. [] Demsas: Good on Paper is produced by Rosie Hughes, edited by Dave Shaw, fact-checked by Ena Alvarado, and engineered by Erica Huang. Our theme music is composed by Rob Smierciak. Claudine Ebeid is the executive producer of Atlantic audio and Andrea Valdez is our managing editor. I'm Jerusalem Demsas. Thanks again for listening. And keep an ear on this space for when to expect new episodes from Good on Paper. Article originally published at The Atlantic
Atlantic
13-05-2025
- General
- Atlantic
The Myth of the Poverty Trap
We used to be trapped. And by 'we,' I really do mean all of us. A few hundred years ago, the majority of the world lived in extreme poverty, and even in recent decades, people lucky enough to clear the $2.15-per-day threshold were living lives that others in the developed world would find unrecognizable. Death is inevitable. Living in poverty is not. From 1981 to 2019, the share of the global population living in extreme poverty fell from 44 percent to just 9 percent—an astronomical achievement. On this episode of Good on Paper, we're going to talk about how this all happened. Today's guest is Paul Niehaus, an economist and co-founder of the NGO GiveDirectly. His new paper details what actually happened in the lives of people who escaped extreme poverty since the early 1980s. As he and his co-authors write, by 'how' they mean: 'Did they plant a new cash crop on their farm? Find work in a factory? Start their own business? Move to a city?' And further, what happened across the life of one person, versus what happened between cohorts or generations? The answers provide insight into what a real 'success sequence' looks like, and challenge some foundational ideas within development. 'There's no one story,' Niehaus tells me. 'As an author, it would've been nice if there was a very simple story to tell, which is, Well, the key thing is everybody's gotta move to the city or whatever it is. But you see people getting out of poverty while moving to the city. You see a lot of people getting out of poverty while staying where they are. You see a lot of people getting out of poverty while not switching from agriculture into nonagriculture. And also, the stories are different in different countries.' The following is a transcript of the episode: Jerusalem Demsas: For centuries, mass poverty seemed inevitable. Starvation, disease, death. As late as the 1700s, roughly half of children globally would die before reaching adulthood. This was the natural order of things. And then everything began to change. Looking at a graph of development measures over the past two hundred years is to witness the miracle of human development: On any measure you can think of—child mortality, nutrition, poverty—more and more people are able to live significantly better lives than their ancestors could even dream of. Just 35 years ago, 2 billion people lived in extreme poverty. Today, that number is just under 700 million. That's still a lot of people, but this staggering improvement proves that mass poverty isn't preordained. [ Music ] Demsas: My name's Jerusalem Demsas. I'm a staff writer at The Atlantic, and this is Good on Paper, a policy show that questions what we really know about popular narratives. Why did extreme poverty fall so fast, and can we finish the job? Loads of research and debate has gone into the question of why extreme poverty fell, but today we're going to talk about how. Paul Niehaus is an economist at UC San Diego and the co-founder of GiveDirectly, an NGO focused on getting cash into the hands of the global poor. Few have thought harder—academically and practically—about these questions. Today we're going to talk about his work with GiveDirectly and a new paper he co-authored, titled 'How Poverty Fell,' that details what happened between 1981 and 2019 in the lives of those living in extreme poverty. Before we jump into this conversation, one last note from me: This will be my final episode with you all. I have loved my time here hosting Good on Paper and feel so lucky to have been able to explore all of my curiosities with you and the brilliant guests who lent us their time. And I want to thank all of you—those of you who listened, emailed, left a review, and engaged with this show in any way. It has been amazing to realize how many fellow wonks there are out there, excited to dig deep into how and why we know things. And don't worry. The show isn't going away—just taking a break. Let's dive in. Paul, welcome to the show. Paul Niehaus: Thank you, Jerusalem. Great to be here. Demsas: I think because people remain rightly concerned about continuing deprivations, we often don't take a step back to take in just how remarkable the global decline in poverty—in extreme poverty—has been. Can you give me a sense of how much things have changed? Niehaus: Exactly. And so we'll talk about the paper I think that you wanted to talk about, which is 'How Poverty Fell.' But in some sense, I sort of feel like for maybe most people listening, the key thing that they need to take away is actually just the premise that it fell. And so we start the paper with that observation, that over the course of the last four decades or so, from around 1980 to around 2020, the share of the world's population living in extreme poverty—so people living on less than (currently we measure that as) $2.15 per person per day—that fell from about 40 percent to under 10 percent around the start of the pandemic. And that is, in my mind, just one of the most remarkable episodes in human history, and just an achievement to celebrate and to try to understand, which is what the paper's about. Demsas: And extreme poverty. I mean, $2.15 a day—I mean, if folks remember, it used to be $1.90 a day until that 2022 update for inflation. It's not the threshold that people are living what we would consider good lives, right? People will starve at this level. They probably lack access to electricity and other important goods. Why is it important to track this number, versus other metrics of poverty? Niehaus: It's not the only one, and we'll do this in the paper, look at other lines as well. And any line has fundamental issues with it, which people have rightly pointed out. Some people are going to have a greater ability to translate $2.15 a day into the sorts of things that really matter in life—health and relationships and things—than others. So it's just one indicator. But what I think it has done very effectively is to sort of galvanize attention around the world through the process—the Millennium Development Goals and the Sustainable Development Goals and the World Bank's advocacy for that number—to a sort of simple metric that we can track and say, Are we making progress or not? And that matters, right? Because it lets us quantify whether we're seeing the kind of progress that we'd like to see. Demsas: So 'How Poverty Fell' is a very straightforward title, which I appreciate. And I want to start by asking you to explain that question. What does it mean to investigate how poverty fell? Like, what are you looking at? What are you trying to describe? Niehaus: Yeah. Great. The first thing I'd just say is poverty fell, right? As I said, that premise itself is very important. So literally just that fact, that premise, I think is an important takeaway. The next bit is the how, with just a lot of emphasis on how as opposed to why. And so a lot of the movement in development economics over the last couple of decades, which has been tremendous, has been towards trying to understand causality: the why. So why did poverty fall? And of course, many of the great debates that we have about the global development process are about the why. Was it because India liberalized in the early 1990s? How much did that contribute? But this is a paper about how. Descriptively, if we look at all these people who moved out of extreme poverty, what happened in their lives, right? Is it that kids were able to start out life much better than their parents did, because they had access to better schooling or other early-childhood investments? Is it that people moved out of agriculture; they moved to the city and were able to get a manufacturing job as they moved off the farm? These are all sorts of things that we know happened, but how important, quantitatively, were they for all these people that made that step over the poverty line? Demsas: I'd love for you to walk us through how you did this paper, because a big part of why I wanted to talk to you about this is because it's a pretty ambitious attempt to collect data over time from so many different people and families across several countries. So you focus on five countries. What are those countries, and then what did you do to get this information? Niehaus: We're looking here at, as you say, five countries: India, China, Indonesia, South Africa, Mexico. And actually, most of the work—you mentioned all the hard work, so enormous amount of hard work—but actually, most of it was done by people other than ourselves, the people who went out and collected these original survey data sets that let us do this. And so our filter for the project, when we decided which countries to look at, are the countries that have some of the highest-quality household-survey data sets available. That let us really drill into, What are people's standards of living? and also, Where are they getting their income from? so that we can understand how that's changing. And that's a really hard task. And so one piece of context I want to set is that if you're used to thinking about, say, poverty in the United States, we can measure that pretty reliably—you know, issues and so forth—but looking at sort of data that people report to the government automatically through tax reporting and so forth. And we want to supplement that with surveys and so forth, but there's third-party reporting. There's all this machinery that exists. And so in the countries we're studying, that's not the case. And the data that we're looking at are going to come from people that are going out into remote corners of the country because we sampled a village there—and going to that village and trying to track down some people that we sampled that we want to interview, and then asking them if they'd be willing to sit for a multi-hour interview, and asking them really detailed questions like, How much rice did you and your family eat last week? And how much money did you make from your vegetable farm? And how much money did you make from doing some casual labor for other people in the village? And it's an incredibly painstaking and laborious effort. What we're trying to do is capitalize on all of that hard work that other people have done and say, If we now put it all together —because for a bunch of these countries, we have data that are really sort of the best, that adhere to the highest standards of data collection in fieldwork in development— what can we learn from that? Demsas: And, I mean, I think a lay person hearing those five countries would think there's something important missing. I mean, India, China, South Africa, Mexico, Indonesia: It really doesn't include some of the countries where most people think about extreme poverty being the biggest issue. Like, it doesn't include much of sub-Saharan Africa, where we do see the most deprivation. Are you worried about that in terms of—I know you said you picked these countries based on what the best data allowed you to study—but are you concerned that it's not going to extrapolate to the places that are of most concern today? Niehaus: Yeah, there are two parts to that. One is: It is backwards looking, and so these are actually the countries that contributed the most to extreme-poverty reduction during the period that we're looking at, especially India, China, Indonesia. So South Africa, Mexico—relatively small. And we may get into this, but there they're different economically in a bunch of other ways as well. So actually, during the time period that we're looking at, these countries are pretty attractive and may be the ones that you'd want to prioritize. For today, I think you're totally right: If you wanted to sort of look at what's happening in the last five years or think about what might happen in the next 10, you'd probably want to be looking elsewhere in the world. That said, there are also examples of smaller-scale studies—a few regions in Uganda or in Tanzania, for example—that track migrants. And so one of the things we try to do in the paper is to also tip our cap to those and point out some similarities in terms of the findings from those as well. But yeah, those are places where we do face this very deep constraint that the same kinds of data, and especially panel data—meaning, data where we follow the same people over long periods of time—are much scarcer. Demsas: Yeah. It's a hard problem because the very places that are most deprived are the ones that are most difficult to study. Niehaus: Yes. Demsas: So how did poverty fall, Paul? What did you find? Niehaus: I distill three things. So the first is: We look at this intergenerational aspect of it. And probably you've heard language like breaking the cycle of poverty or [breaking] the intergenerational cycle of poverty. So really sort of interested in: To what extent, as poverty fell, was it because one generation was sort of stuck at where they were, because they never had the chance to get a good education or whatever it is, but then they're able to make the sacrifices so that the next generation can have a better life? And so what was really interesting to me—I think I would've expected a lot of that. Actually, what it looks much more like is: When we see a new generation entering into the workforce, they're starting out about as poor as their parents' generation—so much less poor than their parents' generation was when their parents' generation entered the workforce 25 years earlier, let's say. But their parents have made a lot of progress in the meantime. So overall, what seems to be happening is people are making a lot of progress during their lifetimes in parallel to the improvements that they're then able to pass along to their kids. And so we take away from that that it's important to understand what is happening during people's lives, because it's certainly true that, you know, what you get at the start in terms of nutritional investments your parents are able to make, looking after your health, vaccination, schooling—all that stuff's important, but it's not like that's the end of the story, right? We see that there's much more after that, and a lot of people are making progress during their lifetimes as well. So that's sort of fact No. 1. Fact No. 2, we're now going to turn to these panel data sets I mentioned that let us track the same people over time. And so those are even more scarce. You can imagine the difficulties involved, especially when we want to track people as they—you know, maybe they're moving to a different part of the country for better opportunities and so forth. But we have data for these five countries, and what you really see in all of those is an enormous amount of churn, of movement in both directions. So if the first thing I wanted you to know about this paper is that a lot of people got out of poverty, the second might be that even more people got out of poverty in gross, but then on net, poverty rates fell less fast than they might have, because a lot of those people fell back into poverty. And that's a really important fact. We may come to it in terms of how we frame the way we think about poverty and poverty reduction. You hear a lot of conversation talk about poverty traps, people being stuck in poverty. I think that contributes to this mindset that's like, Unless we come in and intervene in some way, unless we kind of find the magic key to unlock this situation, people are going to stay stuck where they are. And what the data actually looked like is much more that, like, people are getting out of poverty all the time at a very fast rate. The biggest problem is they're facing a lot of headwinds, things that knock them back. And we can get into what some of those things are. It might be illness, droughts, agricultural shocks, things like this, but it's that getting knocked back that I think is really important. There's some caveats to that finding. There's going to be some measurement error in our data, right? And that's going to generate more of this. It's going to look like somebody got out of poverty, when actually they didn't. So it's a little tricky, to be completely frank, to say just how much of this is churn versus measurement error. But it's so strong, it's so pronounced in the data, and it lines up with a lot of other research. I just think that's a very important reality to kind of get your head around. Demsas: And you mean measurement error that you're losing people as they move, or you just can't find them again? Niehaus: More just that, like, when I come in and I try to do these surveys and figure out your living standards, it's hard, right? And we don't always get the answer exactly right. And so that's just going to create some noise in the data, and it will look like somebody fell back into poverty when maybe they didn't. So a little bit of it's going to be that. Demsas: And then you had a third stylistic fact for us. Niehaus: So the third is—now we look at these people as they're moving out of poverty and get to some of these questions I posed earlier about what's happening in their lives. And I'd really say there's no one story. As an author, it would've been nice if there was a very simple story to tell, which is, Well, the key thing is everybody's gotta move to the city, or whatever it is. But— Demsas: Yeah. Niehaus: —you see people getting out of poverty while moving to the city. You see a lot of people getting out of poverty while staying where they are. You see a lot of people getting out of poverty while not switching from agriculture into nonagriculture. And also, the stories are different in different countries in interesting ways. So maybe a good sort of broad way to think about it is: It's good to not be looking for, sort of, the solution and saying, What is the path that people need to walk? and more thinking about, like, What are the right paths for a given person in a given context, and how can we accelerate that and help them along that? As opposed to coming in expecting there to be one thing that'll work well for everybody. Demsas: So now I have to talk about my priors here, because you pushed against them in ways that made me uncomfortable. So economists, urbanists, immigrants—we tend to see migration as a huge part of the story for modernization, poverty reduction, increasing the quality of life. I mean, I'm obviously going to be biased, also, because of my own life. My family immigrated here from Addis Ababa when I was very young, and my dad moved from Asmara to Addis Ababa in order to get an education. And it's just, like, this is ingrained in not just personal experiences but also, the economic literature really pushes that moving towards cities—higher-productivity cities—is the key way to improve both economic growth and productivity, but also giving people access to the good life, like access to higher standards of living and other things that they care about. It's not that your paper invalidates this entirely, but it does push back against this dominant view, in some ways. That classic story of rural peasants moving towards cities and towards factories, it's only a part of the picture, and it's not even the dominant one. In some ways, I would think it's not really the movers that are the stars of your stories; it's the stayers, both in terms of geography but also in terms of staying within specific sectors. Is that an accurate kind of read of a takeaway, and did that surprise you? Niehaus: Yeah, that's about how I read it. A few things there. One is: International migration, which you brought up, we're not going to see here. And it's probably not relevant for getting people over $2.15. I'm guessing that your family, when they moved, had access to much better opportunities but that you're moving from a much higher starting point than that poverty line. And so we are really kind of zooming in here on the left tail of the income distribution and trying to understand that. Point two is—and this is where the economics comes in—that when a few people move to the city, let's say, that's going to change wages and labor-market outcomes and other things for the people who stay behind. And so one of the things that economists need to really unpack, and this is something that's been important in other papers that I've worked on, is that bit of not just the direct effect on the person who leaves but also the indirect effects and how that changes things for everybody else. And so that's not something we can see very easily in these kinds of data, but that's one of the questions that we should be asking of them, is how much of that was playing a role. But, you know, all that having been said, yeah, I think it's very clear that a lot of people have been able to make significant progress while staying in place. And, you know, to me, one of the most interesting cases was Indonesia, because we have really good migrant tracking in Indonesia, and there were a lot of people who got out of poverty while moving, but while moving from one rural area to another rural area. So that, again, as you say, it's a little bit different from the standard story. It's an interesting twist. I think that's a good thing to drill into. Demsas: Yeah. I mean, regional economic convergence is something that has been studied at a different level—not really that I haven't seen in the extreme-poverty space. But one of the editors at The Atlantic, Yoni Appelbaum, has written about this, and it starts with Peter Ganong and Daniel Shoag's really great paper about declining regional convergence, which just kind of shows, you know, exactly what you were saying, that when people leave lower-productivity, lower-wage areas towards cities that are higher wage, higher productivity, the places they leave are net better off because there are fewer people there or because the average poverty level declines because the people who remain are often those who are in the good jobs. But I want to tease apart something that you pointed out in response to my question, which is: We're talking about people escaping extreme poverty, not people like my family and others who were not at, like, $2.15 a day or below that level. Do you think that these answers that you're finding—these facts that you're finding—are really just about extreme poverty, and that questions about how to move people into the middle class, into higher levels, are going to be quite different? That industrialization will play a much larger role, urbanization will play a much larger role? How do you think about that? Niehaus: Yeah, possibly. And I think most of the answer to that is: That's something that I think we'll keep doing as we continue with the project and look more at that, so probably better to just look at the data than to speculate too much. Demsas: The second part of this is that it's not just about geographical moving; it's also about within sectors. So you talked a little about this with agriculture, but what is happening within agriculture that is allowing people to get themselves out of extreme poverty? The traditional story, as you said, is that they have to leave that sector to make money. Niehaus: Well, one of the things we can look at with the data is whether you're self-employed or not. And so the sector you choose to work in is a big one, as you say. We tell a lot of stories in economic development about the role of self-employment as opposed to wage employment. And so one of the things that I thought was very interesting here, and one of the ways in which, as I said earlier, some of these countries look very different from each other, is that in the lower-income countries—the countries that started out poorer at the beginning of this period, which are India, China, Indonesia—you see that people who switch into self-employment are making a lot of progress. That accounts for a fair share of the poverty decline, and sort of the rates at which people who make that change exit poverty are relatively high. And then in Mexico and in South Africa, it's the exact opposite, which is that the people who seem to be doing best are people who get a job. And I think that also maybe relates a little bit to your question about the ladder and sort of what the later stages in the ladder look like as well. Because Mexico, South Africa are more developed countries—they're more industrialized countries—agriculture plays a smaller role to begin with. And so that is a place where people who 'become entrepreneurs' look to me a lot like these sort of entrepreneurs of necessity. They're sort of doing it because they couldn't get a wage job, which is what they really wanted. And the best thing they can do is to be a small vendor, sell something at the roadside, try to scrape by until they can get back into wage employment—as opposed to these other countries where I think it is actually: For many people to own their own land or to start their own non-ag business, that would actually be a really high return and exciting thing to do if you could get the capital to do it. Demsas: You hinted at this, but I think the nonlinearity of the stories underlying this research kind of speaks to a lot of people's personal experiences. It's the slippery slope where people are making their way out of poverty to a slightly better position than falling back. Can you just describe what's going on there and maybe give us some of those facts within countries? I think Indonesia, for instance, was a place where you looked into this. Niehaus: Yeah, what we do in our paper is, you know, we're trying to give you the kind of broad-stroke facts here. I think for this question, I would go to other sources. I really like, for example, Anirudh Krishna's book One Illness Away, which sounds like it's a book about health, but it's really a book about poverty dynamics and this dynamic of getting knocked back into poverty. And he called it One Illness Away because for many people, that is the thing that does it. It's like: A primary breadwinner gets sick. You have to spend a lot of money on their health care. Hopefully that works, but maybe it doesn't. Maybe you spend all that money and then they still pass away. In the meantime, they're not earning. And that's just a huge shock, right? And during that period, maybe you're selling off assets and so forth to try to cover their medical bills and to make ends meet. And so I think it's things like that we should have in mind when you think about these people who get knocked back. Demsas: And the numbers were really shocking to me. I mean, in Indonesia, you guys find that 37 percent of households who were poor at the start remained poor at the end of a 15-year panel. But only 16 percent were poor in every survey round, meaning that loads of people are falling in and out. Similarly, in South Africa, you find that only roughly, like, a quarter of initially poor households stayed consistently poor throughout the panel. I think that level of churn is something that we're familiar with, even in higher levels of poverty in developed nations. In the housing context, people are finding a place to stay, and they feel safe, and then divorce happens or an illness happens or a job loss happens, and then they fall back into homelessness. I mean, that kind of churn is really well documented. But, you know, it's interesting because it doesn't feel like that's how people talk about the poverty trap, right? They talk about it as if you're stuck there and waiting for an intervention, and until that intervention happens, there's really nothing to be done. You're just, like, waiting there, and it really stagnates. Why do you think that idea persists? Niehaus: I'll be honest: I think that the somewhat skeptical part of my nature thinks that it's attractive to us. It sort of depicts a scenario in which a hero is needed. And look—I got into development economics hoping to be a hero of some sorts, I guess, and so I should be very honest and self-critical about this. But I think that story sells, right? And it's effective at getting people to donate money. And so you sort of say to people, Yeah, like, people are stuck in this trap, and if we come in, we can get them out. And so I think that's very compelling, maybe a little bit easier to pitch to people than the story that, like, people are making enormous amounts of progress on their own, largely without help. Our role is to come in and think about how we can accelerate that, how we can make it faster, and also how we can provide them with some degree of a safety net so that when they get hit by these shocks, they don't get knocked too far back. It's a bit more nuanced. But I think that's true to the data. Demsas: After the break: Who should receive cash transfers, and who gets to decide? [ Break ] Demsas: I want to move a little bit into takeaways for policy makers, for NGOs, for individuals who care about reductions in poverty and want to make a difference. So you are at GiveDirectly, which is an organization that sends cash transfers directly to people living in extreme poverty. Can you broadly describe how you decide where to direct those transfers into which people? Niehaus: Yeah. So first I should say I am at UC San Diego. I'm there today, and they're the ones who pay my paycheck. But I'm also a co-founder and a board member at GiveDirectly. And so: super happy to talk about the work that we do there. GiveDirectly is, I think, the largest global nonprofit focused on direct transfers to people living in extreme poverty. We're currently in around a dozen countries, mostly in sub-Saharan Africa, to your point earlier. And you asked specifically about how we choose who to direct the transfers to. Pretty simple: We're generally looking for people living, you know—the poorest people that we can find, the poorest communities that we can find. We've tended to err on the side of simplicity. When we get into a village, let's say we're going to enroll most of the people living in that village. We might try to exclude a few wealthy landowners or people that are sort of absentee landowner, landlords, people like that. But generally, the goal of it is to try to find the poorest people we can and then get large amounts of money into their hands, no strings attached. Let them decide what they want to do with it. But for everybody listening, I think there's a bit of context that's very important, which is that this show is called Good on Paper. I would say that, like, most of global-development work was, like, up until around 2000, good on paper—in the sense that we had a lot of theories of what ought to work, a lot of intuitions and things like that. There wasn't all that much rigorous testing of anything, let alone the very specific questions about the right way or the best ways to do cash transfers and so forth, which are very good. And so it was only in the last 20 years or so, I'd say, not just because of experimental testing—[randomized controlled trials] (RCTs) and the RCT movement—but especially because of that, I'd say we started to get a lot of really good causal evidence on what works. So that's why we're in a world where (A) I can tell you a lot already about what happens when we give money to people living in extreme poverty, which has generally been very good, and (B) we can get into some of those more nuanced questions that you're asking. But I just want people to have that context. Demsas: And how much research do you do towards things like anticipatory cash transfers or seasonal cash transfers or targeting specifically at women or different groups? How do you think about those aspects? Niehaus: I just wrote a review paper on some of these questions with Tavneet Suri, and the gender one I think is very interesting. We have a few studies that compare what happens if you give money to a husband versus to a wife, let's say. We have fewer of those than you might think. It's obviously a very important question, and the reason is that the overwhelming view has been that you should give it to the wife, and so almost all cash transfers are run that way. GiveDirectly, by the way, it tends to be around 70–80 percent of the time in a typical program that we'll give money to a wife, but we actually let the household decide how they want to do that, and so there's been some variation there. I'd say that if you look at the studies that have varied this, there isn't an obvious winner. It's not like women are good and men are bad, or vice versa. There are differences. There are cases where there are significant differences. There are cases where you see more investment in kids when you give transfers to the mom, maybe more investment in a business when you give transfers to men. That's a pattern that I think fits people's priors. But by the way, there are also cases where there's bigger impacts on kids' nutrition when you give transfers to the men. And there's, I think, good evidence that many women have enterprises that they could, in principle, invest in, but it's harder for them to keep the money safe and keep other people from getting their hands on it, sort of pressuring them for it. So I think it's just a very nuanced story. I don't think there's a clear This is the right person, in terms of the impacts. For me, this one is much more just about, like, on a priori grounds, we look at everything we know about empowerment, about who has the sort of rights to decide how household resources are used. It's very inequitable, right? Women generally have much less say in a lot of things. So to me, this is one where I don't think that the right way to go about it is to go do a bunch of these things and try to show in the data somehow that you get a bigger treatment effect on some particular outcome. It's more like, I think equity really matters, and so I think it's good to give resources to people who have less control over them to begin with. Demsas: How do you think about the balance there? Because a big thing that you laid out for us is that the global NGO community was not sufficiently concerned with outcomes, and it required sort of the randomized controlled trial (RCT) revolution and work by you and organizations like GiveWell that focused on effectiveness and, really, ranking and charity navigators that really tried to force NGOs and force global aid communities to think more deeply about the impact there. How do you balance just focusing on sort of the raw benefits that you can measure and quantify in the spreadsheet versus values that are more difficult to show up in development statistics? Niehaus: Great question. And first I would say, I think you mentioned the NGOs. Certainly true, I think, that many NGOs are much more evidence focused these days than they were in the past, and that's been a good thing. But hey, I mean, let's remember: NGOs are going to follow the money. I think that the process—and I'm a big fan of the whole evidence revolution and of outcome measurement and all of that—but it is still a very top-down, technocratic process where somebody in a position of power who has the money says, Here's the outcome that I want to achieve. And then people go out and try to figure out the best way of doing it, and then they come back and say, Here are the results. And then they get to decide which thing seems most attractive, given the impacts, as opposed to a process where the people that we are ultimately trying to benefit have a meaningful say in what gets done. Cash transfers are obviously an example of that. That's, like, an extreme case where we're going to say, We're going to give all the money directly to these people and let them say what they want to get done, what they want to prioritize. But there are other less-extreme ways that it happens or that you could imagine doing it where people have not just some sort of participatory process—in the sense that we'll talk to people, but then, at the end of the day, we're going to retain power and decide what to do—but people are given real control over how resources get used. So that, to me, is a very important gut check on everything else, because if I come in and say, I really care about health— I'm a health guy, let's say—that's great. Obviously, people living in poverty also care a lot about health and about the health of their family members, but it's not the only thing. And so there has to be some sort of gut check or process check that says, Am I really still kind of pursuing things in a way that's consistent with their values and their priorities? Demsas: Returning a little bit to your paper: Your paper doesn't directly say what one should do in order to reduce extreme poverty. It's a descriptive paper. It's not one that's looking at causation directly, but it does indicate that large reductions in extreme poverty are not really about transfers. Is that because they were insufficient over the time period you're studying, or is it because these are just dwarfed by things like economic growth and other sorts of changes in people's lives? Niehaus: Yeah. Transfers in our data were small, for the most part. If you look in almost all the countries at the people who got out of poverty, after getting out of poverty, transfers are a pretty negligible share of their income, of a couple of percentage points. And so what that means is just that we weren't sort of trying to get people out of poverty with a program—a very ambitious, large-scale program—of transfers at that time. What we were doing in most of those countries is trying to use it to offset some of these shocks that we talked about, to kind of make the slope a little bit less slippery. And so you see that the people who are getting a larger share of their income from transfers are the people that were making negative progress, the people that were falling back into poverty. There's one exception to that fact, which is interesting, which is South Africa, which has historically had extremely generous social transfers—pensions and others, child-support grants, and so forth. And so you'll see in South Africa much larger shares of people's earnings coming from these public transfers. But even there, the people who got out of poverty are going to see that share decline by a lot. They're not getting out of poverty because the government is ramping up transfers. They're getting out of poverty in other ways. Demsas: Does that indicate to you that transfers don't play a large role in reducing extreme poverty? Niehaus: Well, I mean, transfers are going to reduce extreme poverty very mechanically, right? If you give somebody $1 a day, they're going to have $1 more a day. So no. I think what it indicates is that kind of the way we've thought about the role of transfers has been social protection. And that's exactly the language that's used for most cash-transfer programs, which I think is a very good thing. I think that now just in the last five, 10 years, we're starting to think about transfers in a more ambitious way. Which is, (A): Could they be graduative? Could we give people enough money to really push them over the line? And then (B) the question that I'm excited about and would love for us to talk about a little bit is: If we wanted to design a program that would end extreme poverty using transfers, how much would that cost? Because I think we're actually quite close to it. That's not a question that we've asked before, but I think we can now. Demsas: I want to get to that, but the thing I'm trying to press on here is: If you're an individual looking at this paper—and I know you mentioned it's one paper; we don't want to just say this is the end all, be all of descriptive statistics on extreme poverty—but it indicates that the most important thing to focus on is economic growth, right? I think the thing that most people think about when they see the logic of economic growth being so vast in comparison to other interventions is: Is it just that we don't actually know as individuals and NGOs and governments how to actually spur that sort of change? And so we're doing the second-best thing, which is, Alright, let's just send people money and do charity and do other kinds of forms of aid. Or is it that you think it's possible to get these massive reductions, like what you described at the beginning of this episode, roughly 40 percent of people living in extreme poverty to 10 percent. Is it really possible to get those kinds of levels of changes through programs like GiveDirectly? Niehaus: For sure. The way I would think about the role of the paper is: It's certainly not telling us what the high-return things are to do—certainly not. And that's what most of development economics over the last decade or more has been about. And so, obviously, no one paper is going to achieve that for all of these different things. I think what it can do is give us some clues as to where to look. And as economists, we really want to be thinking about what the investments are that people might like to be able to make that they can't, because they don't have the resources, or they face some other constraint. Because that's what's going to drive—you talk about economic growth. We really care about, sort of, growth. But within this small subset—the subset of people who are, kind of, in the left tail of the income distribution—so, you know, economic growth for them might mean paying the cost to migrate or taking the risks to migrate. Or it might mean investing the capital required to start your own business, which we saw was a driver of poverty reduction for a lot of people. So the way we can use results in a paper like this is to ask ourselves where to look and what sorts of things seem like they might be high-return pathways for people that not many of them are able to take, because they can't get the money to do it in the first place. Demsas: And you mentioned that you think it's possible. I mean, this has been a UN development goal for a while: to end extreme poverty by, I think, 2030, which people are now projecting, due to the pandemic's effect on increasing the number of people in extreme poverty, is likely not going to happen. Do you think that 2030 deadline is unlikely to happen? And how would you design a program to actually end extreme poverty? Niehaus: I think it's unlikely to happen under the status quo, just looking at the world as it is and at what's happening. I think it's very doable. And what I would do—in terms of recommendations, what we could do: I'd sort of split it into two parts. I think there are extraordinarily high-return ways of helping people in extreme poverty, which we should do and which we can do for a limited number of people. So you mentioned, like, GiveWell, for example. They'll recommend things like bed nets for people who live in areas with a lot of malaria, with a high prevalence of malaria. Or they'll recommend deworming for kids who live in areas with a high worm load. And so those are going to be extremely high-return things that we can do to help that set of people who face that one particular issue. If we want to do something really ambitious, like end extreme poverty by 2030, we're going to need maybe a portfolio of strategies or maybe something that works everywhere and for everybody. And so part of what I find very compelling about direct transfers is that they do that, right? Cash is relevant, whatever your issue is, wherever you live, whatever your problem is, right? Money's the most flexible thing that we can give people to help them. And also, the numbers on cash transfers and poverty, I think, are very compelling. Like, the global extreme-poverty gap—the total amount by which people who are poor in the world today are below that poverty line—estimates range between maybe $100–$150 billion a year. Demsas: That's not that much. Niehaus: And you put that in global context—that's like 0.1 percent or 0.15 percent of global GDP. So if we could find everybody and give them the exact amount of money they need to get over the line, to finance that, you and I, everybody, we'd all need to give up 0.1 percent or 0.15 percent of our income, which I think is a bargain. I think most of us would be willing to make that. You take, like, the average American sort of making $50,000 a year. What does that mean? That means 75 bucks. So if I ask, Would you be willing to give up 75 bucks, and that would be your part for ending extreme poverty? I think the answer is, in my experience, absolutely. And so I don't think people realize how close we are. The problem is we don't know exactly how to go out and find everybody and give them exactly the amount of money they need to get over the line. And so I'm actually actively working on that right now, and we'll get you an answer, which is: Maybe it's, like, 0.2 percent or 0.3 percent because there's going to be some buffer, because we don't know exactly how much money different people need. But I think that, undoubtedly, the answer from this is going to be that there is a feasible, shovel-ready way of ending extreme poverty that would cost much less than you think and is something you'd feel really good about ethically—that, like, I did my part to end this thing. Demsas: Yeah. So you brought up GiveWell, and there was something really interesting that happened within the GiveWell world. It's an organization that directs charitable contributions, and importantly for this conversation, they evaluate charities on their effectiveness. And GiveWell and others have often used cash-transfer programs and, in their case, particularly GiveDirectly's cash-transfer program as the benchmark with which to evaluate other charities. That is, like: How much better is your program than just giving that money directly to people in need? Like, you need to prove that it'd be better for us to pay to set up this whole organization to do anything—vaccinate people, whatever it is—that would be better for people's outcomes than just giving them cash to do whatever they need to do, including getting vaccinations. So in 2022, they updated their most effective charities to exclude GiveDirectly, pointing instead to a couple of malaria-prevention programs, a vitamin-A supplementation program, and a vaccine-incentive program in northern Nigeria. At the end of last year, they actually also evaluated the impact of unconditional cash transfers again and found that GiveDirectly was three to four times as cost-effective than they previously estimated, but they still think that those other four charities are significantly more cost-effective than GiveDirectly. Did this evaluation change or affect how you think about GD's work? Niehaus: That's exactly what we wanted to do. I think when we set out to start GiveDirectly, we said cash transfers surely are not the only thing that we should be spending global-development money on, but what's missing when you look at the sector is a little bit of this gut check of, like, Okay, I think I have a good idea. I think I have evidence. I have all this stuff. Am I confident that I'm better at spending this money than the person who's actually there, living it, dealing with it, and knows what they need, perhaps better than I do? And so I just really appreciate that GiveWell have sort of baked that into the way they now evaluate other organizations and sort of the way they think about the world. And I think it's really good and really important to have an organization like GiveWell that's out there saying, What can we do that's actually better than what people living in extreme poverty could do for themselves? Because they can do a lot of really good things for themselves. And the update that you mentioned—the three to four times more effective—I think reflects some of that, as well as taking into account some of the aggregate impacts of the transfers, like the macroeconomic impacts of the transfers. So in any given year, I think we might make the cut in terms of being on their very top list, or we might not. But I think that way of thinking about the world, which is, like, Yeah, there is some stuff where you need some coordination right there, externalities and public goods and problems like this we need to solve. But, like, our default should always be transfers. I think that's exactly the right way to think about it. Demsas: Do you think that more money in the global aid community should be going towards these kinds of public-health programs that GiveWell is doing over cash transfers? Do you agree with their ranking? Niehaus: It's a question of: If I were to think about where to give $1—sort of a given finite amount of money—then there's a strong case for it. I think that the part that they don't price that we've always felt at GiveDirectly is that—I think it's true that GiveDirectly has contributed, to some extent, to helping to really shift perceptions in the sector more broadly. Cash transfers have now become a big part of how a lot of global-development work is done. And I think there are a lot of other people that now ask this question of, like, Well, we could come in and design a program, try to move outcomes, all this stuff. But are we sure that's better than just giving money? So I think if there's, like, an unpriced part of GiveDirectly's work that I think isn't reflected in the GiveWell score, it's that. And that's hard to price, and I think we'd all agree with that. So I'm okay with it. But, you know, I don't think it's either-or. We should be doing both of these things, and we are. Demsas: I feel like I've cited this, like, five times on the show already, so apologies, listeners who know this already. But it just reminds me a lot of Amartya Sen's arguments about development as freedom, and that it's obviously very important to center the literal metrics. Like, are people better off, and can you measure that? And also to realize that there are specific things about—the reason we care about development to begin with is because it gives people access to freedoms. Like, are you free to choose how you want to live your life? Are you free to make decisions for your children, for your family, and be free from discrimination and free from abuse? And that it can't always be measured, those democratic freedoms. Like, do you feel free to speak your mind about things? It's hard to show that on a spreadsheet, but that's important. And I asked you this earlier because I struggle with this, too, because a lot of the things that I care about, whether it's gender egalitarianism or the rights of various marginalized groups worldwide, you can see how sometimes a focus on that can move people away from being rigorous about whether their work is actually helping people, because it's hard to measure those things. Like, you can do surveys, but even those sorts of things are often really riddled with error, whether it's just, like, people's perceptions shift or whatever it is. Like, how do you measure whether people are happy on gender-egalitarian grounds? It's just open questions. And I wonder, like, how do you wrestle with that problem and make sure that you're able to balance both of those things? Niehaus: I love what you said. It reminds me of some of the encounters we had in the very earliest days starting GiveDirectly. I remember, in particular, we had a great conversation with Duncan Green, who was at Oxfam at the time, talking about some of the cash-transfer programming they've done and talking about some of the things people did with the money that would not show up in any of the metrics. But that really sort of provoked deep thought about: What is the point of global development? And Amartya Sen, right? Am I really bought into that? Am I okay with that? They found, for example, in one of these programs that a lot of people—there's a program in Vietnam, and a lot of people use transfers to purchase coffins because for them, culturally, religiously, it was very important to be buried in a coffin and not in an open grave. And that's not something that I think anybody ever measures in our surveys, right? But what if that's very important to you? Am I okay with that? And in my own experiences with GiveDirectly fieldwork, I remember: We interviewed a guy who was part of the basic-income project that we were doing and had worked as a security guard in a town to earn money and send money back to his family, and then who lived in the village. And then when he started getting transfers, he quit that job and moved back, going to live in the village, earn less money but see his kids more often. Again, that's going to look like an income reduction in the data that we typically collect. It's because we aren't great at measuring things like the quality of your relationships with your kids, which are what I think actually matters in life. So I just think that it's super, super important to take all of the measurements, the outcome stuff, with a grain of salt and with that humility, and sort of trying to remember that people's own views on what they want out of their lives are very important if we really take that Senian perspective. Demsas: It's funny because there's a subset of people where I'm like, Okay, yeah, you should take this more seriously. But broadly, I find that when I'm talking to people in the charitable-giving space, it's like, maybe they're not taking the effectiveness seriously enough. And it's like, which message does any individual need to receive? And kind of on that, we briefly touched on the UN's goal of ending extreme poverty by 2030. When you look at the global aid and the work that different governments are doing, is the international community largely focused on doing the right thing? What would you change—maybe setting aside what's going on in the US for a moment—what would you change about how countries, intergovernmental organizations, etcetera are doing to try to actually attack this problem? Niehaus: Well, it's a really interesting moment, and I think that actually taking the institutions as given might be a mistake. We might want to actually sort of reimagine the institutions themselves to some extent. Niehaus: The whole architecture for aid. So if you take, for example, the UN architecture for humanitarian response, it's built around these silos that say we're going to have some organizations that are focused on shelter, some organizations that are focused on food, some organizations that are focused on education, and so forth. And so in a world where service provision is done by NGOs, then you need that specialization, right? Because you need to kind of pick something and be good at it. In a world where we're going to give money to individual people and let them decide what they want to do with it, and more of the provision—the service provision—is going to be done through markets, maybe we don't need that same institutional structure. And in fact, seeing the way that's played out within the United Nations system as it's become, in humanitarian work especially, much more receptive to it, I think people now agree that cash transfer should be a much bigger part of humanitarian response. It doesn't sort of fit very neatly right within that system. So I think it would be—and John McArthur and Homi Kharas at Brookings sort of suggested this, as well, that it might be a good time for a new multilateral that's focused on direct transfers. And that would let us think a bit differently, as opposed to trying to fit new wine into old wineskins, so to speak. But in terms of the things that we would be doing, look—we have 20 years now of really rigorous evidence on cost-effectiveness and impactfulness. And so there are a bunch of things that are fantastic. If you're wondering, like, Do we know good things to do with money? Yes. We know great things to do in education that are really effective at improving learning outcomes. We know great things to do in public health that reduce disease burdens and improve people's lives for long periods after that. And we have cash transfers, which are just a great way of helping people do whatever it is they want to do, and have an enormous evidence base people are using money responsibly. They're generally not wasting it. They're generally not using it in self-harm ways, like drinking it away or smoking it away. And in many cases, they're making investments that have long-lasting impacts on their lives. So the point is, like, we have so much good stuff to do. And we, up until now, have been doing a lot of stuff that's really based on old thinking, right? This sort of good-on-paper-type reasoning, 'Teach a man to fish' type thinking. And seeing USAID essentially evaporate overnight, obviously it's sort of a gut-check moment, but I think it's also an opportunity to rethink how we do the whole thing. Demsas: Well, I think that's a great time for our last and final question. Paul, what is something that you once thought was a good idea but ended up being just good on paper? Niehaus: So I would say—and I sort of said this earlier, but—I think that idea of teaching a man to fish is something that when I first heard it made a lot of sense and seemed good on paper. But, like, as we started to test impact over the last 20 years and say, like, What impact do our fishing lessons actually have? I don't think it works very well; the data don't support it. Demsas: We're bad at teaching people how to fish. Niehaus: Unpack that a little bit, right? I mean, if you take it a little bit too literally than it's meant to be, it presumes that the guy doesn't know how to fish in the first place. Maybe, actually, he did know, and what he needed was a fishing rod. It presumes that the lake isn't getting overfished, right? Maybe there are tons of people out there fishing, and the big issue is sort of overextraction of natural resources, and we definitely should not be teaching more people to fish, right? It presumes, as you said, right, that we're good at teaching people how to fish. Maybe we're not. Maybe it's hard, and it's not something that we know how to do well. So there are all these sorts of assumptions baked into it, and that's why it's important to test. And you go out and test it, it actually doesn't. The other thing that I think is really interesting—I'll just riff on this a little bit about teaching a man to fish—is the origins of it. So today you hear it, and the way we interpret it is it's saying, like, Don't just give people money, because they're not going to use it in ways that have a lasting benefit. It's important to kind of help them in these other ways, which I think is just empirically untrue. But actually, if you trace it back, the first place that I've been able to find it, it shows up in this Victorian novelist Anne Thackeray Ritchie, and she has this ironic character in one of her novels saying that the reason that we don't do these things is because affluent people really don't want it. They said you could really help somebody make progress, but affluent people would feel uncomfortable with that—it would upend the social order. So it's funny that the origins of the term are actually this critique of inequality and of people's unwillingness to— Demsas: Wow. I've never heard that. Niehaus: But somehow, over time, it's completely changed. And now the interpretation is: You can't trust people to make financial choices for themselves. That's not what it originally meant. Demsas: Yeah. Well, Paul, thank you so much for coming on the show. Niehaus: Pleasure to be here. Thank you so much. [ Music ] Demsas: Good on Paper is produced by Rosie Hughes, edited by Dave Shaw, fact-checked by Ena Alvarado, and engineered by Erica Huang. Our theme music is composed by Rob Smierciak. Claudine Ebeid is the executive producer of Atlantic audio and Andrea Valdez is our managing editor. I'm Jerusalem Demsas. Thanks again for listening. And keep an ear on this space for when to expect new episodes from Good on Paper.
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29-04-2025
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The Problem of Finding a Marriageable Man
Subscribe here: Apple Podcasts | Spotify | YouTube | Overcast | Pocket Casts Marriage isn't dying, but it is stratifying. Dating and marriage markets have transformed as more women have gone to college and the share of college graduates has skewed more female. Some observers have concluded that this imbalance has left highly educated women unable to find men to marry. Not so. In a new paper cleverly titled 'Bachelors without Bachelor's,' the economists Clara Chambers, Benjamin Goldman, and Joseph Winkelmann find that 'the share of marriages where the wife has a four-year degree but the husband does not has quadrupled.' Contrary to popular narratives, marriage rates for educated women have remained remarkably stable. So who isn't getting married? Well, a growing share of non-college-educated women. On today's episode of Good on Paper, Goldman, an assistant professor of economics and public policy at Cornell University, joins me to discuss what his findings reveal about the state of American marriage. One clue as to why marriage rates for non-college-educated women declined so steeply over the 20th century is revealed when you look at a map of marriage rates. In areas where men have the lowest rate of bad outcomes such as incarceration or unemployment, the marriage gap between college-educated and non-college-educated women is 50 percent smaller. But what—if anything—is to be done? Although some commentators urge people to 'just get married,' Goldman remains skeptical (as do I): Say we had some technology to put the marriage rate from 60 percent to 70 percent. 'Would that be 'good'? Goldman wondered. 'People still say they really want to be married and it's an important thing they want to achieve in life. But when they're not doing it, I think there's serious questions we have to ask about why, and are folks able to find the right match?' The following is a transcript of the episode: Jerusalem Demsas: Women are so picky. We're gold diggers who want to marry up and would never deign to marry someone less educated than us. If you're on the internet or just a human being alive today, you've heard something along the lines of this narrative: College-educated women refuse to date 'down,' and it's creating a crisis of marriagelessness. There's just one problem with this narrative: It's not true. My name's Jerusalem Demsas. I'm a staff writer at The Atlantic, and this is Good on Paper, a policy show that questions what we really know about popular narratives. Joining me today is Benny Goldman. He's a professor of economics at Cornell and the co-author of a fascinating paper chock-full of narrative violations about the dating and marriage markets. Benny shows that rates of marriage for college-educated women, as they've faced difficulties finding a partner at the same education level, have remained relatively stable. How? Because they're marrying men without college degrees. But what's happening to women with the least education? If you look at the 25th percentile of the education distribution, roughly three-quarters of women born in 1930 were married. Zoom forward to women born in 1980, and just over half of them are married now. That's a big drop. What's causing declining marriage rates among non-college-educated women? Well, a big part of the answer seems to be the difficulty in finding a suitable partner. Benny's research shows that in places where men have the lowest rates of joblessness and incarceration, the marriage gap between college and non-college-educated women shrinks. In these places, that marriage gap is 50 percent smaller. Let's dive in. Benny, welcome to the show! Benny Goldman: Thank you so much for having me, Jerusalem. I appreciate you taking the time. Demsas: Yeah, I'm excited to talk about your paper. I mean, not only does it have a great name, which we'll get into, but I think this is just like a hot topic in general. So let's start extremely broad and in a place where I'm sure you can give us definitive answers: What do people look for in a partner? Let's put aside the spark or whatever. When you look at marriage markets from an economist's lens, what variables explain which people end up together? Goldman: Great. So the first thing I'd say here is definitely don't turn to economists for dating advice. I think the economics view on marriage and couples, it's nothing you need a fancy degree to understand. I think in many ways it will be intuitive to most folks, right? So you can think about gains from scale. So just something like rent or housing—it's certainly not twice as expensive to get a home for two people as it is for one person. You don't need twice the size. You can think about housework. You can think about cooking and preparing meals. So I think there's gains to scale from being in a couple. There's also, I think, an important insurance element. So, you know, if someone falls on hard times, if your partner or spouse loses work or has a decline in their income, the other partner can help them kind of shelter or smooth over that period. And finally, as I think many folks, including Melissa Kearney and others, have documented, there's benefits to raising a child in a two-parent household where you can split up some of the work. Of course, historically, much of that has fallen on women and remains a big issue for gender equality in the labor market. But it certainly seems much, much easier to raise a kid with two people rather than one. Demsas: So I think that's why a lot of people want to get married. But I think one thing that I find interesting is just how deterministic it feels, who you end up with. I mean, this is something you find in your paper, too, but there are certain characteristics that make you more likely to end up with someone, right? Goldman: Absolutely. So in econ parlance, this is what, going back to [Gary] Becker, one would call 'assortative mating.' And at first, it was a puzzle. So you can think about this along many dimensions. You can think about income. You could think about education. You could even think about race. And it is, in some sense, a theoretical puzzle. For instance, would we expect folks to partner with individuals at the same level of income? It's not, ex ante, so obvious, right? Because you might think that for folks who have higher income, they might want to partner with someone who has less to gain from being in the labor market and might be able to spend more time with children or, you know, working— Demsas: Or might just care less. Goldman: Might just care less. It might be a less important part of their life and how they view themselves. But I think in practice, time and time again, when one goes to the data, you really see what we would call 'positive assortative matching,' which is people tend to match with likes. You know, so educated folks tend to be married to other educated folks. Higher-earning folks tend to be married to other higher-earning folks. And I think in addition to just marriage rates overall, this notion of who matches with whom is an important feature for the landscape of inequality in the U.S., both within and across generations, right? If all the haves, if you will, are matching up with each other from the perspective of kids, you're in a situation where either you have two parents who are both high income, and so you're growing up in a really well-resourced environment, or you end up with two lower-income parents or even one parent. And so you can see how these matches could end up propagating inequality across generations in this way. Demsas: Yeah, there was a paper I read several months ago—I'm blanking on who wrote it, but we'll put it in the show notes—that showed that the collective influence of partner selection on household-income inequality led to a three-point increase in the Gini coefficient, which is a pretty significant impact on inequality. Because if you see increased numbers of rich people marrying rich people, and you don't see rich people marrying middle-income people or marrying poor people, then you don't see that kind of income inequality declining over time. You don't see that wealth spreading around. I had no idea that it had such a big impact, though. Goldman: Of course, and I think, you know—who knows. Three percent, that might be hard to get some intuition on, but another way to come at that same problem is to ask, Well, how has inequality changed in the U.S. you know, in the past 40 or 50 years in terms of the Gini coefficient? And I think when you take that frame, there's a lot of folks working on this question of what percentage of the rise in overall inequality in the U.S. can be attributed to the fact that people tend to match with likes or people are increasingly matching with likes over time. Demsas: And do you happen to know how that's changed? Goldman: I think the estimates vary a lot. So one challenge conceptually in this space is the fact that often, you know, if you have a very high-income spouse, especially as is more common for women, you'll drop out or retreat from the labor force after the birth of a child. That makes it difficult to measure total household potential income, if you will, because you might see a lower level of income that reflects choices, in a sense. And it's important to think about that in computing the overall accounting, in terms of how much of this type of matching contributes to inequality. Demsas: Yeah. So I've held a pretty unpopular opinion for a while that dating apps are net positive, and part of why I believe that is, theoretically, they expand the dating pool significantly, and it means that you're more likely to run into more people. It's not just, you know, Do you go to the same church or synagogue as me, or your dad and my dad or friends? And a few pieces of evidence bolstered my opinion that dating apps were increasing partnerships across unexpected lines. So in 1967, roughly 3 percent of newlyweds were in interracial marriages. That's 17 percent in 2015. The census says that in 2022, 19 percent of married opposite-sex couples were interracial. Similarly, you have another story with interfaith marriages. Eighty-one percent of couples married before 1960 were to someone of the same religion. And in 2010 to 2014, that was down to 61 percent. And to me, these trends kind of felt like a proxy for people being willing to date those who were different than them—whether that meant considering someone of different race or national origin or religion. But it seems like this was, like, missing something important about how dating apps—and, also, just how dating is changing, in general—were facilitating matching on other metrics more easily. Can you tell us about that? Why do you think there's this divergence between what you measure, which is mostly kind of class markers, versus these other markers? Goldman: Yeah, absolutely. I think one caveat to have in mind when thinking about trends in those interracial-marriage statistics is how the population of mixed-race individuals is evolving over time. So the U.S. is becoming more diverse. In some sense, you would expect an increase in interracial marriage just because there's now more people. It's harder, in some sense—if you take it from the perspective of white Americans, it's more difficult to marry someone who's exclusively white these days, because it's just a smaller share of the overall population. And so I think there's some nuance there in terms of how much of that is kind of a true change in people's attitudes versus just a constraint in terms of who's around. And in many ways, I think it varies by the particular pairing you're looking at. So for instance, there's been substantial increases in white–Black interracial marriage in the U.S., but the overall rate remains persistently pretty low. So I think at something like age 35, it would be about only 3 percent of Black Americans are married to a white spouse, which is still pretty low relative to the size of the Black and white populations in the U.S. In terms of dating apps themselves, I think the jury is still out. As you mentioned, a lot of these trends in interracial pairings are long-running trends. And to the extent they sharply increased around the time that dating apps got introduced, I think, is still an open question. One reason I might be a little skeptical of the fact that dating apps have generated a substantial increase in marrying across types, if you will, is that geography actually plays a really important role in who you see on the app. And we just have a ton of segregation geographically. So one anecdotal story about this I like to tell is: I recently was living in Cambridge, Massachusetts, which is just about a mile and a half away from Boston, which is right across the Charles River. And many of my friends would say, Oh, I don't go on dates in Boston. It's simply not worth it. The marginal date is just not good enough to justify it. So in practice, if you're dating only amongst the pool of Cambridge residents, where Harvard and MIT are located, you're going to draw from an extremely educated pool of folks who are likely to be similar to you in many dimensions. So I'm deeply interested in that question of what the dynamics introduced by dating apps have done to some of these matching patterns. But I'm not sure we have the answer quite yet. Demsas: Well, I could have this whole conversation with you just about dating apps, but I want to get to your paper. And before we get into the details, I want to pull out a statistic that you cite: Among women born in 1930, roughly 78 to 79 percent were married by age 45, regardless of education. Can you trace us through the past hundred years or so? What has changed over that time period with respect to marriage rates? Goldman: Absolutely. So I should say the reason we're doing this at age 45 here is because part of what's gone on in the past a hundred years is increasing age at marriage, especially for folks with more education. And so when we look at the data over that period, what we see is that the entire decline in marriage rates in the U.S.—and I should be clear that this is a well-documented fact that doesn't just come from our paper—is concentrated among Americans without a four-year college degree. So in particular, if we were to focus on women and ask what has happened to marriage rates for college-educated women at age 45, they've declined slightly, from about 78 percent to about 71 percent. But for non-college women, there's been this huge and steep decline from about 79 percent to now just about 52 percent. And so when one asks what has happened to family and marriage in the U.S., it's really important to kind of have this class lens, since the decline itself is really focused on Americans without college degrees. Demsas: And one of my initial questions when I first read your paper was: Is this just being made up for in cohabitation without getting married? And you look into that. So what do you find? Goldman: Yeah, that's a great question. Some have this intuition that instead of being married, folks are now more likely to be in these kind of committed, long-term but unmarried partnerships. I think that's actually true in other places, especially in Europe, but less true in the U.S. So if we were to include the folks who are in these cohabiting arrangements in these statistics, you would effectively find the same pattern, where things have been very stable for college-educated women in the U.S. and declining for women without four-year degrees. Demsas: So I think a lot of people have been hearing this discourse, not just from your paper but also books like Brad Wilcox's Get Married, Melissa Kearney's research, which you referenced earlier. And a lot of people just ask: Why do you care? Why are economists so obsessed with this topic? Why does it matter that a lot of men and women aren't marrying? What are the tangible consequences for individuals, children, or communities when marriage rates drop? And why do we think that's causal? Goldman: Absolutely. So why do we care? I think bucketing the causal element of this for now, one can just come back to this discussion about the role that marriage and matching plays in overall levels of inequality in the U.S. And so regardless of any causal effect of marriage on kids or anything like that, just plain inequality—what fraction of income, or household income, is concentrated in the top 10 or 20 percent of the income distribution—statistics like this are strongly impacted by the extent to which (1) people marry and (2) whom they end up matching and marry to. And so then you can kind of take the next step and say, Well, if marriage and who one marries matters for overall levels of inequality, it ought to matter in a dynamic sense for the next generation, right? If kids born to, say, lower-income or less-educated mothers are less likely to grow up in married two-parent households, they're also less likely to grow up in households with enough income to get by, and so on. And so I think that's the sense in which these patterns can have kind of a first-order impact on both overall levels of economic inequality, but also differences in outcomes between kids who grow up in high- or low-income households. Demsas: I think some people might say—and, you know, I don't have to put it in someone else's mouth. I could just say to that, though, the question is then: How do we get kids into households that have access to earnings? Like, why the focus on marriage? Goldman: I think that's absolutely right. I think there's much we still have to learn about how important the second person and marriage itself as an institution are— versus, you rightly point out, these overall levels of resources. But I do think, just from first principles, there's good reason to think that having a second person around who has a direct interest in the child's well-being is likely to be important just from a time perspective, from a mental-sanity perspective, of course. But you're right to point out, Jerusalem, that identifying causal effects here is a real challenge, because what one needs to do that is some change that shocks, in some random or exogenous way, marriage rates—while both holding fertility levels effectively where they are and not impacting kids' outcomes through some other channel. So there's been prior research, for instance, on something like the 'China shock,' where, when certain areas of the U.S. were exposed to international trade, there was a large decline in employment rates and, in turn, a decline in marriage rates. The issue with that type of shock for studying impacts of kids is, of course, this trade exposure can impact kids' outcomes in many other ways. And so I do think a kind of frontier in this space is thinking about ways we can learn about the effects on kids of growing up in different types of household arrangements, isolating that resource channel from the kind of married, two-parent channel. But I don't think we know enough yet. But the descriptive data are super compelling. There's just huge differences in outcomes between kids growing up in single-parent households and married households. Demsas: I guess the IRB isn't going to let you randomly assign children to have their parents divorce. (Laughs.) Goldman: Certainly not to date. Demsas: So I have teased the fun name of your paper. It's called 'Bachelors Without Bachelor's.' And for people who are not able to hear the difference from what I'm saying, it's bachelors, as in single men, without bachelor's degrees. Did you come up with that title? Who did that? Goldman: I did that. I came up with the title. Demsas: Wow. Goldman: But I should say, anything intelligent in this paper, I attribute to my excellent co-authors, Clara Chambers, who's at Yale, and Joe Winkelmann at Harvard, who are both two rising stars in this area—but the title was certainly my big contribution. Demsas: They'll have to learn about marketing because that's what got me, honestly. So your study shows that marriage rates have stayed pretty high for college grads but plunged for non-college folks, as we've already talked about. Can we talk more granularly about, you know—let's say you're a 45-year-old man without a high-school diploma versus one with a college degree? What are the rates of marriage that we're expecting for that cohort? Goldman: Yeah, so it used to be for Americans born around 1930, there was hardly any difference. In some sense, one way to describe the data is that marriage was not a kind of high- or low-status thing to do 50 or 60 years ago. It was just simply something everyone did. So for members of that cohort born around 1930, something like 80 percent of them would end up married at age 45, regardless of education. Whereas when we go out today, you're seeing rates closer to 50 percent for Americans without a four-year degree, even lower for folks who don't have a high-school diploma, and then substantially higher rates still closer to around 71 percent for Americans who do have a four-year degree. Demsas: Another thing I was thinking about is, like, the common critique of papers like yours or theses like yours is about composition, right? So it's difficult to compare non-college as an umbrella across decades. There are huge compositional changes going on under the hood there. As more and more people have gone to college, the umbrella non-college has gone from basically everyone, as you said, including high-earning professionals, to meaning you are likely to be working a low-wage job in the service sector. So the 1930s non-college is not the same as 1980 non-college. But you work to address this in your paper. Can you explain how? Goldman: Absolutely. So perhaps a different way to state that hypothesis, Jerusalem, is: What we see is that for Americans born in 1930, about 10 percent of women went on to attend a four-year college. Today, that's closer to 45 or 50 percent. And so one can say, Well, maybe marriage rates are declining for non-college Americans simply because the pool of folks who are no longer going to college are the types of people who never would've kind of gotten married in the first place. But what you can do is, rather than think about college and non-college, which have these labels and these compositional effects, you could simply rank Americans by their education status and, say, focus on marriage outcomes for the person who's at the 25th percentile. So in the 1930 cohort, that would've really been someone with just a high-school degree or even without a degree. Whereas today, that's someone with a high-school degree or even some college. And so when you take that view and ask, Well, how have marriage rates evolved for the relatively most-educated versus relatively least-educated Americans, you see exactly this divergence, where it's for the relatively high-educated Americans where the rates have stayed stable, and for the relatively least-educated Americans—whether we call that, you know, no high-school degree in 1930 or some college today—it's for those folks marriage rates have declined. And I think the simplest way to get intuition on this process is just the fact that, you know, for Americans born in 1930, there was just no difference in marriage rates across these education groups. So then it's difficult to argue that the subsequent decline in marriage rates for non-college Americans comes from the fact that now there's more of these folks who used to just be high-school-degree holders in the college group. The reality is that those folks who used to just go on to high school were very likely to end up married in any case. Demsas: Yeah, in your paper, there's a really great figure where you can see the percentiles. The 25th-percentile woman in 1930 was really likely to get married, but now, even though the 25th-percentile woman is different—like, the characteristics she has, her educational background, etcetera—she's much less likely to get married. I think that visual was really helpful. So I'm actually getting married this year, and it's very funny I'm telling you this story. Goldman: Congratulations. Demsas: Thank you. I was telling this male relative of mine that I was getting married. And I guess he was kind of joking, and, you know, it's across translation—he's Eritrean. But he was like, Oh, it's funny. I didn't think educated women would get married. I thought they just weren't getting married anymore. And he was kind of joking with me. But it was like, there's something that you hear on the internet all the time, right? Like, Oh, like, college-educated women, they're so picky. They won't get married. Why do you think that is such a prevailing narrative despite what you're finding here? Goldman: Yeah, so that, to be frank, Jerusalem, is really how we started on this project, which is we—not only from, I think, what one reads online or in the media, but even being in these kind of social circles where folks have very high levels of education, you get this kind of quote-unquote 'vibe,' if you will, that it's really difficult, that something's kind of going wrong in the dating market for highly educated folks right now. And I think the flavor of these stories in the media tend to focus on two sets of facts to make this point. The first is that there's now these huge gender gaps in college enrollment. And then also there's this growing political divergence between the two genders. So the story goes: For left-leaning, educated women, it's really difficult to find a partner, simply because there are not enough educated, left-leaning men around. And, you know, when we were seeing this story, you know, talking to folks about these trends, it gave us a substantial amount of pause because we know this fact that it's precisely the most educated Americans for whom marriage rates have remained stable over this period. Demsas: Yeah, and I want to repeat what you said earlier—that your paper and your research only goes up to people born in 1985. Did you look at anything for younger cohorts? I know that you can't have the age married at 45; they're not old enough yet. But were you able to see if these trends persist or are likely to persist, or if things are maybe even getting worse and accelerating? Goldman: Absolutely. So one thing we're able to do there is effectively forecast marriage rates at age 45 using data on relationship status earlier in life. So whether someone has a long-term cohabiting partner at age 30, whether they're already married at 30, is, of course, quite predictive of whether they're also likely to be married at 45. And doing that, one can effectively carry these results out through Americans born in 1995. And what you'd see is that these gaps are expected to continue widening, so things continue to look very stable for Americans, for women with more education. And you see a continued decline in marriage rates for Americans without a four-year college degree, such that by the time we get out to the 1995 birth cohort, fewer than one in two non-college women in that cohort are expected to be married at age 45. And that will be the first time in this time series where marriage rates for that group have dropped below 50 percent. Demsas: Wow. You mentioned the finding that there are way more women on college campuses than men on campuses. But you are also saying that the rate of marriage for college-educated women is pretty stable. So who are those women marrying? Goldman: Absolutely. And so just to give a sense of the magnitudes here: For Americans born in 1930, there was 1.8—you know, almost two—men on four-year-college campuses per woman. And by the time we get out to the 1980 cohort, this has dipped below one. And so in many ways, this kind of vibe we were talking about earlier—why is it difficult for highly educated women to date?—there's really some truth there. If you go to a four-year college as a woman and you're intent on finding a partner who also has a four-year degree, it has indeed gotten much more difficult over this period. There's now way fewer men relative to women to choose from. So I think that sets up this question, Jerusalem: Well, if that's true, how have marriage rates for college women remain stable, given they tend to marry college-educated men? And what we see in the data is that they've effectively begun to substitute, to marry non-college men, but really not any non-college guy. They tend to marry the relatively well-off non-college men. And so one way to see that is if you look, over time, at the earnings of the non-college men married to college-educated women, they've been doing pretty well. So now today they have, on average, earnings of around $65,000. Whereas if you look at all the other non-college men, in some sense, the ones left in the dating pool, there's just been a huge collapse in outcomes for those folks. Demsas: And does this mean that college-educated men are basically getting married at, like, 99 percent likelihood or what? Goldman: Yeah. So in some sense, this would lead one to believe college-educated men have it very good, right? Because they're in really high demand. I think their marriage rates are not quite at 100 percent. It's hard to ever get up that high, just because you have divorce and other factors here, but they are indeed really high. So we're talking about rates upwards of 70 percent still, so very similar to how it looked for Americans born in the 1930 cohort. Demsas: Why is it so stable for them if they're in such high demand? Is there just a stable number of men who either don't want to get married or are 'unmarriageable' for various other reasons? Goldman: I think there's some natural kind of attrition in and out of marriage due to divorce. There might be some men who, you know, are waiting an especially long time to settle down. But what I'll say is: When you look at these data, the key thing that kind of comes out is just this fact that on the female side of the market, they have kind of remained marrying college men at similar rates to what they used to, which wasn't 100 percent. It's not like every college person marries a college person. It's really that the college–college matches have been relatively stable. But what's happened is that in response to the growing shortage of college men, these kinds of marginal matches are between college-educated women and non-college men. And one way to see that is just to ask: For Americans born in 1930, what are the odds you end up in a marriage where the wife has a four-year degree and the husband does not? This used to be a type of couple that effectively did not exist. So for Americans born in 1930, that was just 2 percent of folks. By the time we get out to the 1980 birth cohort, closer to 10 percent of Americans are going to end up in marriages where the woman has a four-year degree and the husband does not. If one were just to extrapolate those trends to around kids born today, we're going to expect something like a third of all marriages in the U.S. to be this kind of new type of couple that didn't used to exist. Demsas: After the break: one economist's point of view on what makes men marriageable. [] Demsas: There's an implication when you say the kind of difference between a college-educated woman and a non-college-educated man—that matchup—that the woman is maybe earning more money or is in a different class bracket than a non-college-educated man. But one thing that a lot of researchers have pointed out is that there are employment industries that are predominantly made up of men that lead to higher-income opportunities then for women which are highly professionalized. So for instance, if you have a teacher who's married to, you know, let's say a tradesman, and who are maybe making similar salaries—or maybe, you know, the tradesman is actually making more than his teacher spouse. Are those really indicating class difference, or is it just that there are a lot of feminized industries and workplaces that require college degrees at rates higher than for men? Goldman: So that could certainly be true, Jerusalem. What I'll say, though, is that it is still true in the 1930 cohort that 10 percent of women got a four-year degree. And it was pretty rare at that time—the number of marriages between college women and non-college men was quite low, in part because there were just not as many college women to go around. But I think you're right: This notion of class is relatively loose, and it doesn't seem so foreign to think of a marriage, say, between a nurse and someone who might own, you know, a local landscaping business. What I will say is, if that feels familiar, why do we think these trends are important? I think it's useful to think about the implication of this for the marriage prospects of non-college women. And so one way to come at this is with a very crude kind of economist definition of marriageable. So don't try this at home. Demsas: (Laughs.) Goldman: But one can ask, for the non-college men—let's say you have to meet two criteria to be quote-unquote 'marriageable.' One is that your earnings need to be above the national median. So for much of this period, this would mean something like earning above $30,000 per year—so nothing crazy, but a relatively stable, well-paying job. And of course, you can't already be married to a college-educated woman. And if one takes that view, if you go back to the 1930 birth cohort, roughly 70 percent of non-college men in that cohort were quote-unquote 'marriageable,' according to this view. Whereas when we go out to the 1980 cohort, this has dropped remarkably. It's now much closer to 35 percent of non-college men that fit the bill today. Demsas: Wow. Goldman: And that's both because earnings have declined for this group of men, but, also, college women are now marrying the kind of HVAC technician, the person who owns the local electrician business, and so on. And that's kind of eaten into the pool of relatively well-earning non-college men. And so this trend has had really, I think, important implications for the marriage prospects of Americans without college degrees. Demsas: So let's talk about the men who are left out. Your paper documents that many men without college degrees, especially those with lower incomes, are ending up unmarried, as we've discussed. What are the main factors holding these men back from marriage? Is it just a question of relative status? Is it their earnings, cultural attitudes? Or is there something else that's going on here? Goldman: I think that's the right question to ask, because I think the way in which we're having this discussion can give the impression where it's kind of the non-college women making the decision and choosing not to be married to the non-college men anymore. But in practice, of course, it's a joint decision. And these broad trends don't tell you, really, which side of the market is driving the decline in marriage rates between non-college Americans. And so I think it is important, exactly as you say, Jerusalem, to think about what is going on with this pool of men. What I'll say is that we're really using earnings here as an aggregate or summary to describe what's happened to these folks. So at age 45, if you look at the pool of non-college men who are not married to college women, they used to earn in the 1930 cohort about $56,000 per year, on average. Now they earn about $46,000 per year, on average, for the 1980 cohort. This is remarkable, the fact that earnings have declined over this 50-year period at a time when the U.S. economy has grown substantially. So the fact that you're able to find any group that's had a decline in real earnings is remarkable and, I think, a signal of what is going on with this group, but I don't want to give the impression that it's all about earnings. So I think Richard Reeves and other folks in this space have done a good job to document some of the trouble with working-class men in recent years and decades. And you see, you know, for instance, overdose deaths tend to be concentrated on non-college men. When we think about things like addictions to sports-gambling technologies, again, it tends to be concentrated in this group. We have factors like incarceration and so on. And so, certainly, I don't mean to give the impression this is all about economics—rather that there's something going on with this pool of folks that we ought to understand better because it has important implications for women, and especially non-college women. Demsas: Are you finding that predominant—I mean, one thing that you do is you look at employment-to-population ratio and incarceration rates in various areas to see how much is being driven by those factors. And you do find that in areas where there's more employment and less incarceration, non-college men are significantly more likely to be married than not. So what proportion of the lack of marriage at this part of the dating market is because of something other than, Do you have a job? and, Have you been to jail? Goldman: Yeah, absolutely. So I think thus far we've focused on these patterns over time. And what you see in the data is that in a period where (1) men are going to college at lower rates than women and (2) non-college men have been struggling economically, that's been characterized by a period in which marriage rates have been relatively stable for college women and decline sharply for non-college women. But one can ask that same question in present data, but instead of looking over time, look across areas in the U.S. And what you see when you do that is exactly the fact you mentioned, Jerusalem, where if we zoom in on the areas where non-college men have the lowest employment ratios, you see those are precisely the parts of the U.S. that have just these massive gaps in marriage rates between college and non-college women. And instead, when we go to the areas where almost all of the non-college men are working, you see those gaps are substantially smaller. In some sense, those areas, which often are concentrated, for instance, in the upper [Great] Plains states—suburban Minneapolis is one example—kind of look like marriage outcomes looked in the U.S. 40 years ago, in a sense, where there was not a ton of education polarization in marriage. You asked about, you know, is this about employment or incarceration? I think it's really hard to tease these different measures apart, because they tend to move together. The areas where non-college men are not working are also the areas where many of them fail to graduate from high school. A higher share of them might end up incarcerated. There might be issues around even early-life mortality. And so it's difficult to diagnose: Is it about earnings? Is it about incarceration? And so on. It's more of this broad pattern, which is: Where in the U.S. are the areas where these education gaps in marriage for women are the largest? And they are really concentrated in the subset of places where men who didn't go to college are struggling. Demsas: Yeah, and I was just struck by the finding that 'marriage rates between college and non-college women are 50 percent smaller in commuting zones where men have the lowest incidence of [what economists refer to as] 'left-tail' outcomes,' whether it's joblessness and incarceration or something else. And I think that's a huge, important finding for policy makers who care about this issue and want to make it increase the life outcomes for these men, and also for women who are searching for a partner, unable to find one who meets their needs. You know, there is actually a lot of policy-making that goes into whether the employment-to-population ratio is high and whether incarceration is high, and attacking those things is actually really within policy makers' control. This isn't just a question of, Do you think that we should regress to the 1950s cultural norms or not? if you care about this issue. Goldman: Absolutely. Demsas: I wanted to ask you about race in your paper, because we started this conversation talking about interracial marriage, and we know, of course, that race correlates with class and education. What do you find when you look at racial outcomes here? Goldman: Yeah. So in terms of these geographic differences, you see a very similar pattern within each racial group. So what you see is for white Americans, for white women, for instance, these education disparities in marriage are concentrated in the areas where non-college white men have the lowest employment rates. And that relationship between employment-to-population ratio of non-college men and marriage rates for college and non-college women is actually very similar for white Americans, for Black Americans, for Hispanic Americans. Demsas: This feels like it really mirrors a lot of what's going on in our political discourse, where you see increasing racial depolarization and increasing class and education polarization. It's just remarkable to see that across so many different planes. It's not just marriage markets. It's also happening in labor markets and education markets and in political ideology. So it's not just in this one place. Do you see all these as connected? Goldman: Yeah, absolutely. I think I have other work looking at recent trends in intergenerational mobility in the U.S., and you see exactly this racial depolarization you're mentioning, Jerusalem, but it tends to be perniciously coupled with a growing importance of class. And obviously, that's been very salient politically recently in the U.S. But we're starting to see the same thing happen to inequality and intergenerational mobility. So in particular, what I mean by that is gaps in outcomes between lower-income Black and white Americans who are born to lower-income parents have shrunk by about 30 percent in the past 15 or 20 years. Yet the gaps in outcomes between white children born to high- versus low-income parents are growing over time. And so that's the sense in which class is becoming a bit more salient than race. We know it's happening politically in the U.S. but, as you rightly point out, it's happening in these other domains, too, and marriage is certainly not exempt. Demsas: So there's an implicit assumption latent in a lot of the 'just get married' discourse—particularly, I think, coming out of Brad Wilcox's book, but in other places as well—that if unmarried men and women would just pull the trigger, outcomes across a variety of variables would be better off. And this is kind of getting back to the question I asked you earlier about causality. And I think this is kind of, like, a weird prior for people to have. I mean, I think that many people want to be married, and they understand the financial benefits of marriage. And if they were able to find a partner that fit their needs on a bunch of metrics, some of which you can measure and some of which you can't, they would do that. And what we're seeing is actually that the people who are not getting married are, I think, likely, on average, making a rational choice about what would make life better for them and their families. And I wonder how you think about this, because there is a ton of research descriptively that you've walked us through that talks about the benefits of marriage to children, to communities. But I think that's always going to be skewed towards marriage among people with college degrees, good jobs, given all we've talked about. So how do you think about this? Goldman: This is the key puzzle in this space. You've completely hit the nail on the head here, which is: Marriage rates, albeit what they are in present-day U.S.—so at age 45, you know, something like 60 percent of Americans are married—the key question is, well, say we had some technology to put that from 60 percent to 70 percent. Would that be 'good'? And I really agree with your intuition that marriage is an awfully important decision folks make in their life. And so it's a bit strange to come at it from this view of, Well, folks are making a mistake. They would actually be better off, you know, if they tried harder to be married or if they ended up tying the knot with someone who they have major reservations about. And I think we should be cautious when bringing that view to the data. And I think, in some sense, part of what's driving that is when you do look at survey data of folks, people still say they really want to be married and it's an important thing they want to achieve in life. But when they're not doing it, I think there's serious questions we have to ask about why, and are folks able to find the right match in person? One important feature of this conversation is, I think, a discussion of people's expectations. It's kind of a new phenomenon that they expect their partner to be this all-encompassing best friend, thought partner, physically attractive, and so on. And maybe some of this is related to social media. I'm, of course, speculating now. This is not for my own research. But I think these things matter, but it might be helpful to split this into two parts. One is, like, why are adults not getting into these marginal marriages? Who are these people? If we did boost marriages from 60 to 70 percent, who are those people kind of on the edge of tying the knot, and would it be good for them personally to make that decision? And I think that's where you get into these tougher questions of, you know, does it make sense to question people's own intuition about this important decision in their life? But there's a separate question, which is: If folks are going to have kids anyway, what is the impact on their kids of the two parents who are marginal to being married, whether or not they tie the knot? And there, I think, we just need more evidence. So there's some early work looking at the introduction of unilateral divorce in the U.S. by Jon Gruber that suggests there might be benefits to some of these marginal marriages staying together. But there's much more we need to understand. And, of course, no one wants to be in the position of recommending someone stay in a relationship with someone who might be abusive, physically or emotionally, just for the benefit of the kid. I think we need to understand why people are not getting married and then, you know, if there are ways we can help along that margin that might be supportive both to adults and to children. Demsas: Benny, this has been a great conversation. I think it's time for our last and final question, which is: What is something that you once thought was a good idea but ended up being only good on paper? Goldman: Well, Jerusalem, you'll have to let me know if this is too on the nose. We've already talked about this a bit, but I'm going to go with dating apps here. Demsas: Really? Okay. Goldman: And this is going to be, in part, hypocritical because I met my girlfriend of two years on a dating app. Demsas: Okay. We won't have her listen to this. Goldman: (Laughs.) Yeah, exactly. The sense in which I mean it's good on paper but not in reality is: I do think it could make a lot of sense from an individual level to join a dating app, especially when many other folks are doing that. But I think, in many ways, this kind of algorithmic approach to dating has had odd effects on equilibrium behavior, whether or not folks are accountable when they go on these dates, the dynamics and inequality it's introduced, especially for men. So we know a small number of men are getting almost all the matches, and that might impact their incentive to settle down. And, you know, it might impact just literally how people are interacting with each other on these dates themselves. So I think that's something that we need to think more about as a society. Demsas: Yeah, I mean, as I mentioned before, I have very uncommon views on dating apps. I also met my fiancé on a dating app, and I think it's hard for me to believe that it is worse to date now than it was when your only options were, like, the people that your parents or friends introduced you to. I believe that there are some serious problems that would need to be addressed. I think you've mentioned a lot of them already. But just, like, in term of relative benefits, like, I would never have met my partner. You know what I mean? So I don't know. You're right—like, it's probably worse for some people; it's better for other people. And it's hard for me to fully disaggregate what's going on here. But it would be hard to prove to me that things have gotten worse, on average. Goldman: I think I share your intuition, but I think there are psychological effects of having all this choice, as well as how quickly folks give up on each other. And, you know, of course, if anyone from Hinge or Match Group is listening and wants to give Benny and team access to the data so we can answer these questions once and for all, I know me and many other very competent researchers would be excited about that opportunity. Demsas: Okay. Hinge, Match, if you do this, we will have Benny on the show again because I'm very curious. Goldman: Bring it on. We're ready for the data. Demsas: Thanks, Benny. Thanks for coming on. Goldman: Thank you guys so much for having me. Great conversation. Demsas: Demsas: Good on Paper is produced by Rosie Hughes. It was edited by Dave Shaw, fact-checked by Ena Alvarado, and engineered by Erica Huang. Our theme music is composed by Rob Smierciak. Claudine Ebeid is the executive producer of Atlantic audio. Andrea Valdez is our managing editor. And hey, if you like what you're hearing, please leave us a rating and review on Apple Podcasts. I'm Jerusalem Demsas, and we'll see you next week. Article originally published at The Atlantic
Atlantic
29-04-2025
- General
- Atlantic
The Problem of Finding a Marriageable Man
Marriage isn't dying, but it is stratifying. Dating and marriage markets have transformed as more women have gone to college and the share of college graduates has skewed more female. Some observers have concluded that this imbalance has left highly educated women unable to find men to marry. Not so. In a new paper cleverly titled 'Bachelors without Bachelor's,' the economists Clara Chambers, Benjamin Goldman, and Joseph Winkelmann find that 'the share of marriages where the wife has a four-year degree but the husband does not has quadrupled.' Contrary to popular narratives, marriage rates for educated women have remained remarkably stable. So who isn't getting married? Well, a growing share of non-college-educated women. On today's episode of Good on Paper, Goldman, an assistant professor of economics and public policy at Cornell University, joins me to discuss what his findings reveal about the state of American marriage. One clue as to why marriage rates for non-college-educated women declined so steeply over the 20th century is revealed when you look at a map of marriage rates. In areas where men have the lowest rate of bad outcomes such as incarceration or unemployment, the marriage gap between college-educated and non-college-educated women is 50 percent smaller. But what—if anything—is to be done? Although some commentators urge people to 'just get married,' Goldman remains skeptical (as do I): Say we had some technology to put the marriage rate from 60 percent to 70 percent. 'Would that be 'good'? Goldman wondered. 'People still say they really want to be married and it's an important thing they want to achieve in life. But when they're not doing it, I think there's serious questions we have to ask about why, and are folks able to find the right match?' The following is a transcript of the episode: Jerusalem Demsas: Women are so picky. We're gold diggers who want to marry up and would never deign to marry someone less educated than us. If you're on the internet or just a human being alive today, you've heard something along the lines of this narrative: College-educated women refuse to date 'down,' and it's creating a crisis of marriagelessness. There's just one problem with this narrative: It's not true. My name's Jerusalem Demsas. I'm a staff writer at The Atlantic, and this is Good on Paper, a policy show that questions what we really know about popular narratives. Joining me today is Benny Goldman. He's a professor of economics at Cornell and the co-author of a fascinating paper chock-full of narrative violations about the dating and marriage markets. Benny shows that rates of marriage for college-educated women, as they've faced difficulties finding a partner at the same education level, have remained relatively stable. How? Because they're marrying men without college degrees. But what's happening to women with the least education? If you look at the 25th percentile of the education distribution, roughly three-quarters of women born in 1930 were married. Zoom forward to women born in 1980, and just over half of them are married now. That's a big drop. What's causing declining marriage rates among non-college-educated women? Well, a big part of the answer seems to be the difficulty in finding a suitable partner. Benny's research shows that in places where men have the lowest rates of joblessness and incarceration, the marriage gap between college and non-college-educated women shrinks. In these places, that marriage gap is 50 percent smaller. Let's dive in. Benny, welcome to the show! Benny Goldman: Thank you so much for having me, Jerusalem. I appreciate you taking the time. Demsas: Yeah, I'm excited to talk about your paper. I mean, not only does it have a great name, which we'll get into, but I think this is just like a hot topic in general. So let's start extremely broad and in a place where I'm sure you can give us definitive answers: What do people look for in a partner? Let's put aside the spark or whatever. When you look at marriage markets from an economist's lens, what variables explain which people end up together? Goldman: Great. So the first thing I'd say here is definitely don't turn to economists for dating advice. I think the economics view on marriage and couples, it's nothing you need a fancy degree to understand. I think in many ways it will be intuitive to most folks, right? So you can think about gains from scale. So just something like rent or housing—it's certainly not twice as expensive to get a home for two people as it is for one person. You don't need twice the size. You can think about housework. You can think about cooking and preparing meals. So I think there's gains to scale from being in a couple. There's also, I think, an important insurance element. So, you know, if someone falls on hard times, if your partner or spouse loses work or has a decline in their income, the other partner can help them kind of shelter or smooth over that period. And finally, as I think many folks, including Melissa Kearney and others, have documented, there's benefits to raising a child in a two-parent household where you can split up some of the work. Of course, historically, much of that has fallen on women and remains a big issue for gender equality in the labor market. But it certainly seems much, much easier to raise a kid with two people rather than one. Demsas: So I think that's why a lot of people want to get married. But I think one thing that I find interesting is just how deterministic it feels, who you end up with. I mean, this is something you find in your paper, too, but there are certain characteristics that make you more likely to end up with someone, right? Goldman: Absolutely. So in econ parlance, this is what, going back to [Gary] Becker, one would call 'assortative mating.' And at first, it was a puzzle. So you can think about this along many dimensions. You can think about income. You could think about education. You could even think about race. And it is, in some sense, a theoretical puzzle. For instance, would we expect folks to partner with individuals at the same level of income? It's not, ex ante, so obvious, right? Because you might think that for folks who have higher income, they might want to partner with someone who has less to gain from being in the labor market and might be able to spend more time with children or, you know, working— Demsas: Or might just care less. Goldman: Might just care less. It might be a less important part of their life and how they view themselves. But I think in practice, time and time again, when one goes to the data, you really see what we would call 'positive assortative matching,' which is people tend to match with likes. You know, so educated folks tend to be married to other educated folks. Higher-earning folks tend to be married to other higher-earning folks. And I think in addition to just marriage rates overall, this notion of who matches with whom is an important feature for the landscape of inequality in the U.S., both within and across generations, right? If all the haves, if you will, are matching up with each other from the perspective of kids, you're in a situation where either you have two parents who are both high income, and so you're growing up in a really well-resourced environment, or you end up with two lower-income parents or even one parent. And so you can see how these matches could end up propagating inequality across generations in this way. Demsas: Yeah, there was a paper I read several months ago—I'm blanking on who wrote it, but we'll put it in the show notes—that showed that the collective influence of partner selection on household-income inequality led to a three-point increase in the Gini coefficient, which is a pretty significant impact on inequality. Because if you see increased numbers of rich people marrying rich people, and you don't see rich people marrying middle-income people or marrying poor people, then you don't see that kind of income inequality declining over time. You don't see that wealth spreading around. I had no idea that it had such a big impact, though. Goldman: Of course, and I think, you know—who knows. Three percent, that might be hard to get some intuition on, but another way to come at that same problem is to ask, Well, how has inequality changed in the U.S. you know, in the past 40 or 50 years in terms of the Gini coefficient? And I think when you take that frame, there's a lot of folks working on this question of what percentage of the rise in overall inequality in the U.S. can be attributed to the fact that people tend to match with likes or people are increasingly matching with likes over time. Demsas: And do you happen to know how that's changed? Goldman: I think the estimates vary a lot. So one challenge conceptually in this space is the fact that often, you know, if you have a very high-income spouse, especially as is more common for women, you'll drop out or retreat from the labor force after the birth of a child. That makes it difficult to measure total household potential income, if you will, because you might see a lower level of income that reflects choices, in a sense. And it's important to think about that in computing the overall accounting, in terms of how much of this type of matching contributes to inequality. Demsas: Yeah. So I've held a pretty unpopular opinion for a while that dating apps are net positive, and part of why I believe that is, theoretically, they expand the dating pool significantly, and it means that you're more likely to run into more people. It's not just, you know, Do you go to the same church or synagogue as me, or your dad and my dad or friends? And a few pieces of evidence bolstered my opinion that dating apps were increasing partnerships across unexpected lines. So in 1967, roughly 3 percent of newlyweds were in interracial marriages. That's 17 percent in 2015. The census says that in 2022, 19 percent of married opposite-sex couples were interracial. Similarly, you have another story with interfaith marriages. Eighty-one percent of couples married before 1960 were to someone of the same religion. And in 2010 to 2014, that was down to 61 percent. And to me, these trends kind of felt like a proxy for people being willing to date those who were different than them—whether that meant considering someone of different race or national origin or religion. But it seems like this was, like, missing something important about how dating apps—and, also, just how dating is changing, in general—were facilitating matching on other metrics more easily. Can you tell us about that? Why do you think there's this divergence between what you measure, which is mostly kind of class markers, versus these other markers? Goldman: Yeah, absolutely. I think one caveat to have in mind when thinking about trends in those interracial-marriage statistics is how the population of mixed-race individuals is evolving over time. So the U.S. is becoming more diverse. In some sense, you would expect an increase in interracial marriage just because there's now more people. It's harder, in some sense—if you take it from the perspective of white Americans, it's more difficult to marry someone who's exclusively white these days, because it's just a smaller share of the overall population. And so I think there's some nuance there in terms of how much of that is kind of a true change in people's attitudes versus just a constraint in terms of who's around. And in many ways, I think it varies by the particular pairing you're looking at. So for instance, there's been substantial increases in white–Black interracial marriage in the U.S., but the overall rate remains persistently pretty low. So I think at something like age 35, it would be about only 3 percent of Black Americans are married to a white spouse, which is still pretty low relative to the size of the Black and white populations in the U.S. In terms of dating apps themselves, I think the jury is still out. As you mentioned, a lot of these trends in interracial pairings are long-running trends. And to the extent they sharply increased around the time that dating apps got introduced, I think, is still an open question. One reason I might be a little skeptical of the fact that dating apps have generated a substantial increase in marrying across types, if you will, is that geography actually plays a really important role in who you see on the app. And we just have a ton of segregation geographically. So one anecdotal story about this I like to tell is: I recently was living in Cambridge, Massachusetts, which is just about a mile and a half away from Boston, which is right across the Charles River. And many of my friends would say, Oh, I don't go on dates in Boston. It's simply not worth it. The marginal date is just not good enough to justify it. So in practice, if you're dating only amongst the pool of Cambridge residents, where Harvard and MIT are located, you're going to draw from an extremely educated pool of folks who are likely to be similar to you in many dimensions. So I'm deeply interested in that question of what the dynamics introduced by dating apps have done to some of these matching patterns. But I'm not sure we have the answer quite yet. Demsas: Well, I could have this whole conversation with you just about dating apps, but I want to get to your paper. And before we get into the details, I want to pull out a statistic that you cite: Among women born in 1930, roughly 78 to 79 percent were married by age 45, regardless of education. Can you trace us through the past hundred years or so? What has changed over that time period with respect to marriage rates? Goldman: Absolutely. So I should say the reason we're doing this at age 45 here is because part of what's gone on in the past a hundred years is increasing age at marriage, especially for folks with more education. And so when we look at the data over that period, what we see is that the entire decline in marriage rates in the U.S.—and I should be clear that this is a well-documented fact that doesn't just come from our paper—is concentrated among Americans without a four-year college degree. So in particular, if we were to focus on women and ask what has happened to marriage rates for college-educated women at age 45, they've declined slightly, from about 78 percent to about 71 percent. But for non-college women, there's been this huge and steep decline from about 79 percent to now just about 52 percent. And so when one asks what has happened to family and marriage in the U.S., it's really important to kind of have this class lens, since the decline itself is really focused on Americans without college degrees. Demsas: And one of my initial questions when I first read your paper was: Is this just being made up for in cohabitation without getting married? And you look into that. So what do you find? Goldman: Yeah, that's a great question. Some have this intuition that instead of being married, folks are now more likely to be in these kind of committed, long-term but unmarried partnerships. I think that's actually true in other places, especially in Europe, but less true in the U.S. So if we were to include the folks who are in these cohabiting arrangements in these statistics, you would effectively find the same pattern, where things have been very stable for college-educated women in the U.S. and declining for women without four-year degrees. Demsas: So I think a lot of people have been hearing this discourse, not just from your paper but also books like Brad Wilcox's Get Married, Melissa Kearney's research, which you referenced earlier. And a lot of people just ask: Why do you care? Why are economists so obsessed with this topic? Why does it matter that a lot of men and women aren't marrying? What are the tangible consequences for individuals, children, or communities when marriage rates drop? And why do we think that's causal? Goldman: Absolutely. So why do we care? I think bucketing the causal element of this for now, one can just come back to this discussion about the role that marriage and matching plays in overall levels of inequality in the U.S. And so regardless of any causal effect of marriage on kids or anything like that, just plain inequality—what fraction of income, or household income, is concentrated in the top 10 or 20 percent of the income distribution—statistics like this are strongly impacted by the extent to which (1) people marry and (2) whom they end up matching and marry to. And so then you can kind of take the next step and say, Well, if marriage and who one marries matters for overall levels of inequality, it ought to matter in a dynamic sense for the next generation, right? If kids born to, say, lower-income or less-educated mothers are less likely to grow up in married two-parent households, they're also less likely to grow up in households with enough income to get by, and so on. And so I think that's the sense in which these patterns can have kind of a first-order impact on both overall levels of economic inequality, but also differences in outcomes between kids who grow up in high- or low-income households. Demsas: I think some people might say—and, you know, I don't have to put it in someone else's mouth. I could just say to that, though, the question is then: How do we get kids into households that have access to earnings? Like, why the focus on marriage? Goldman: I think that's absolutely right. I think there's much we still have to learn about how important the second person and marriage itself as an institution are— versus, you rightly point out, these overall levels of resources. But I do think, just from first principles, there's good reason to think that having a second person around who has a direct interest in the child's well-being is likely to be important just from a time perspective, from a mental-sanity perspective, of course. But you're right to point out, Jerusalem, that identifying causal effects here is a real challenge, because what one needs to do that is some change that shocks, in some random or exogenous way, marriage rates—while both holding fertility levels effectively where they are and not impacting kids' outcomes through some other channel. So there's been prior research, for instance, on something like the 'China shock,' where, when certain areas of the U.S. were exposed to international trade, there was a large decline in employment rates and, in turn, a decline in marriage rates. The issue with that type of shock for studying impacts of kids is, of course, this trade exposure can impact kids' outcomes in many other ways. And so I do think a kind of frontier in this space is thinking about ways we can learn about the effects on kids of growing up in different types of household arrangements, isolating that resource channel from the kind of married, two-parent channel. But I don't think we know enough yet. But the descriptive data are super compelling. There's just huge differences in outcomes between kids growing up in single-parent households and married households. Demsas: I guess the IRB isn't going to let you randomly assign children to have their parents divorce. (Laughs.) Goldman: Certainly not to date. Demsas: So I have teased the fun name of your paper. It's called 'Bachelors Without Bachelor's.' And for people who are not able to hear the difference from what I'm saying, it's bachelors, as in single men, without bachelor's degrees. Did you come up with that title? Who did that? Goldman: I did that. I came up with the title. Demsas: Wow. Goldman: But I should say, anything intelligent in this paper, I attribute to my excellent co-authors, Clara Chambers, who's at Yale, and Joe Winkelmann at Harvard, who are both two rising stars in this area—but the title was certainly my big contribution. Demsas: They'll have to learn about marketing because that's what got me, honestly. So your study shows that marriage rates have stayed pretty high for college grads but plunged for non-college folks, as we've already talked about. Can we talk more granularly about, you know—let's say you're a 45-year-old man without a high-school diploma versus one with a college degree? What are the rates of marriage that we're expecting for that cohort? Goldman: Yeah, so it used to be for Americans born around 1930, there was hardly any difference. In some sense, one way to describe the data is that marriage was not a kind of high- or low-status thing to do 50 or 60 years ago. It was just simply something everyone did. So for members of that cohort born around 1930, something like 80 percent of them would end up married at age 45, regardless of education. Whereas when we go out today, you're seeing rates closer to 50 percent for Americans without a four-year degree, even lower for folks who don't have a high-school diploma, and then substantially higher rates still closer to around 71 percent for Americans who do have a four-year degree. Demsas: Another thing I was thinking about is, like, the common critique of papers like yours or theses like yours is about composition, right? So it's difficult to compare non-college as an umbrella across decades. There are huge compositional changes going on under the hood there. As more and more people have gone to college, the umbrella non-college has gone from basically everyone, as you said, including high-earning professionals, to meaning you are likely to be working a low-wage job in the service sector. So the 1930s non-college is not the same as 1980 non-college. But you work to address this in your paper. Can you explain how? Goldman: Absolutely. So perhaps a different way to state that hypothesis, Jerusalem, is: What we see is that for Americans born in 1930, about 10 percent of women went on to attend a four-year college. Today, that's closer to 45 or 50 percent. And so one can say, Well, maybe marriage rates are declining for non-college Americans simply because the pool of folks who are no longer going to college are the types of people who never would've kind of gotten married in the first place. But what you can do is, rather than think about college and non-college, which have these labels and these compositional effects, you could simply rank Americans by their education status and, say, focus on marriage outcomes for the person who's at the 25th percentile. So in the 1930 cohort, that would've really been someone with just a high-school degree or even without a degree. Whereas today, that's someone with a high-school degree or even some college. And so when you take that view and ask, Well, how have marriage rates evolved for the relatively most-educated versus relatively least-educated Americans, you see exactly this divergence, where it's for the relatively high-educated Americans where the rates have stayed stable, and for the relatively least-educated Americans—whether we call that, you know, no high-school degree in 1930 or some college today—it's for those folks marriage rates have declined. And I think the simplest way to get intuition on this process is just the fact that, you know, for Americans born in 1930, there was just no difference in marriage rates across these education groups. So then it's difficult to argue that the subsequent decline in marriage rates for non-college Americans comes from the fact that now there's more of these folks who used to just be high-school-degree holders in the college group. The reality is that those folks who used to just go on to high school were very likely to end up married in any case. Demsas: Yeah, in your paper, there's a really great figure where you can see the percentiles. The 25th-percentile woman in 1930 was really likely to get married, but now, even though the 25th-percentile woman is different—like, the characteristics she has, her educational background, etcetera—she's much less likely to get married. I think that visual was really helpful. So I'm actually getting married this year, and it's very funny I'm telling you this story. Goldman: Congratulations. Demsas: Thank you. I was telling this male relative of mine that I was getting married. And I guess he was kind of joking, and, you know, it's across translation—he's Eritrean. But he was like, Oh, it's funny. I didn't think educated women would get married. I thought they just weren't getting married anymore. And he was kind of joking with me. But it was like, there's something that you hear on the internet all the time, right? Like, Oh, like, college-educated women, they're so picky. They won't get married. Why do you think that is such a prevailing narrative despite what you're finding here? Goldman: Yeah, so that, to be frank, Jerusalem, is really how we started on this project, which is we—not only from, I think, what one reads online or in the media, but even being in these kind of social circles where folks have very high levels of education, you get this kind of quote-unquote 'vibe,' if you will, that it's really difficult, that something's kind of going wrong in the dating market for highly educated folks right now. And I think the flavor of these stories in the media tend to focus on two sets of facts to make this point. The first is that there's now these huge gender gaps in college enrollment. And then also there's this growing political divergence between the two genders. So the story goes: For left-leaning, educated women, it's really difficult to find a partner, simply because there are not enough educated, left-leaning men around. And, you know, when we were seeing this story, you know, talking to folks about these trends, it gave us a substantial amount of pause because we know this fact that it's precisely the most educated Americans for whom marriage rates have remained stable over this period. Demsas: Yeah, and I want to repeat what you said earlier—that your paper and your research only goes up to people born in 1985. Did you look at anything for younger cohorts? I know that you can't have the age married at 45; they're not old enough yet. But were you able to see if these trends persist or are likely to persist, or if things are maybe even getting worse and accelerating? Goldman: Absolutely. So one thing we're able to do there is effectively forecast marriage rates at age 45 using data on relationship status earlier in life. So whether someone has a long-term cohabiting partner at age 30, whether they're already married at 30, is, of course, quite predictive of whether they're also likely to be married at 45. And doing that, one can effectively carry these results out through Americans born in 1995. And what you'd see is that these gaps are expected to continue widening, so things continue to look very stable for Americans, for women with more education. And you see a continued decline in marriage rates for Americans without a four-year college degree, such that by the time we get out to the 1995 birth cohort, fewer than one in two non-college women in that cohort are expected to be married at age 45. And that will be the first time in this time series where marriage rates for that group have dropped below 50 percent. Demsas: Wow. You mentioned the finding that there are way more women on college campuses than men on campuses. But you are also saying that the rate of marriage for college-educated women is pretty stable. So who are those women marrying? Goldman: Absolutely. And so just to give a sense of the magnitudes here: For Americans born in 1930, there was 1.8—you know, almost two—men on four-year-college campuses per woman. And by the time we get out to the 1980 cohort, this has dipped below one. And so in many ways, this kind of vibe we were talking about earlier—why is it difficult for highly educated women to date?—there's really some truth there. If you go to a four-year college as a woman and you're intent on finding a partner who also has a four-year degree, it has indeed gotten much more difficult over this period. There's now way fewer men relative to women to choose from. So I think that sets up this question, Jerusalem: Well, if that's true, how have marriage rates for college women remain stable, given they tend to marry college-educated men? And what we see in the data is that they've effectively begun to substitute, to marry non-college men, but really not any non-college guy. They tend to marry the relatively well-off non-college men. And so one way to see that is if you look, over time, at the earnings of the non-college men married to college-educated women, they've been doing pretty well. So now today they have, on average, earnings of around $65,000. Whereas if you look at all the other non-college men, in some sense, the ones left in the dating pool, there's just been a huge collapse in outcomes for those folks. Demsas: And does this mean that college-educated men are basically getting married at, like, 99 percent likelihood or what? Goldman: Yeah. So in some sense, this would lead one to believe college-educated men have it very good, right? Because they're in really high demand. I think their marriage rates are not quite at 100 percent. It's hard to ever get up that high, just because you have divorce and other factors here, but they are indeed really high. So we're talking about rates upwards of 70 percent still, so very similar to how it looked for Americans born in the 1930 cohort. Demsas: Why is it so stable for them if they're in such high demand? Is there just a stable number of men who either don't want to get married or are 'unmarriageable' for various other reasons? Goldman: I think there's some natural kind of attrition in and out of marriage due to divorce. There might be some men who, you know, are waiting an especially long time to settle down. But what I'll say is: When you look at these data, the key thing that kind of comes out is just this fact that on the female side of the market, they have kind of remained marrying college men at similar rates to what they used to, which wasn't 100 percent. It's not like every college person marries a college person. It's really that the college–college matches have been relatively stable. But what's happened is that in response to the growing shortage of college men, these kinds of marginal matches are between college-educated women and non-college men. And one way to see that is just to ask: For Americans born in 1930, what are the odds you end up in a marriage where the wife has a four-year degree and the husband does not? This used to be a type of couple that effectively did not exist. So for Americans born in 1930, that was just 2 percent of folks. By the time we get out to the 1980 birth cohort, closer to 10 percent of Americans are going to end up in marriages where the woman has a four-year degree and the husband does not. If one were just to extrapolate those trends to around kids born today, we're going to expect something like a third of all marriages in the U.S. to be this kind of new type of couple that didn't used to exist. Demsas: After the break: one economist's point of view on what makes men marriageable. [ Break ] Demsas: There's an implication when you say the kind of difference between a college-educated woman and a non-college-educated man—that matchup—that the woman is maybe earning more money or is in a different class bracket than a non-college-educated man. But one thing that a lot of researchers have pointed out is that there are employment industries that are predominantly made up of men that lead to higher-income opportunities then for women which are highly professionalized. So for instance, if you have a teacher who's married to, you know, let's say a tradesman, and who are maybe making similar salaries—or maybe, you know, the tradesman is actually making more than his teacher spouse. Are those really indicating class difference, or is it just that there are a lot of feminized industries and workplaces that require college degrees at rates higher than for men? Goldman: So that could certainly be true, Jerusalem. What I'll say, though, is that it is still true in the 1930 cohort that 10 percent of women got a four-year degree. And it was pretty rare at that time—the number of marriages between college women and non-college men was quite low, in part because there were just not as many college women to go around. But I think you're right: This notion of class is relatively loose, and it doesn't seem so foreign to think of a marriage, say, between a nurse and someone who might own, you know, a local landscaping business. What I will say is, if that feels familiar, why do we think these trends are important? I think it's useful to think about the implication of this for the marriage prospects of non-college women. And so one way to come at this is with a very crude kind of economist definition of marriageable. So don't try this at home. Demsas: (Laughs.) Goldman: But one can ask, for the non-college men—let's say you have to meet two criteria to be quote-unquote 'marriageable.' One is that your earnings need to be above the national median. So for much of this period, this would mean something like earning above $30,000 per year—so nothing crazy, but a relatively stable, well-paying job. And of course, you can't already be married to a college-educated woman. And if one takes that view, if you go back to the 1930 birth cohort, roughly 70 percent of non-college men in that cohort were quote-unquote 'marriageable,' according to this view. Whereas when we go out to the 1980 cohort, this has dropped remarkably. It's now much closer to 35 percent of non-college men that fit the bill today. Demsas: Wow. Goldman: And that's both because earnings have declined for this group of men, but, also, college women are now marrying the kind of HVAC technician, the person who owns the local electrician business, and so on. And that's kind of eaten into the pool of relatively well-earning non-college men. And so this trend has had really, I think, important implications for the marriage prospects of Americans without college degrees. Demsas: So let's talk about the men who are left out. Your paper documents that many men without college degrees, especially those with lower incomes, are ending up unmarried, as we've discussed. What are the main factors holding these men back from marriage? Is it just a question of relative status? Is it their earnings, cultural attitudes? Or is there something else that's going on here? Goldman: I think that's the right question to ask, because I think the way in which we're having this discussion can give the impression where it's kind of the non-college women making the decision and choosing not to be married to the non-college men anymore. But in practice, of course, it's a joint decision. And these broad trends don't tell you, really, which side of the market is driving the decline in marriage rates between non-college Americans. And so I think it is important, exactly as you say, Jerusalem, to think about what is going on with this pool of men. What I'll say is that we're really using earnings here as an aggregate or summary to describe what's happened to these folks. So at age 45, if you look at the pool of non-college men who are not married to college women, they used to earn in the 1930 cohort about $56,000 per year, on average. Now they earn about $46,000 per year, on average, for the 1980 cohort. This is remarkable, the fact that earnings have declined over this 50-year period at a time when the U.S. economy has grown substantially. So the fact that you're able to find any group that's had a decline in real earnings is remarkable and, I think, a signal of what is going on with this group, but I don't want to give the impression that it's all about earnings. So I think Richard Reeves and other folks in this space have done a good job to document some of the trouble with working-class men in recent years and decades. And you see, you know, for instance, overdose deaths tend to be concentrated on non-college men. When we think about things like addictions to sports-gambling technologies, again, it tends to be concentrated in this group. We have factors like incarceration and so on. And so, certainly, I don't mean to give the impression this is all about economics—rather that there's something going on with this pool of folks that we ought to understand better because it has important implications for women, and especially non-college women. Demsas: Are you finding that predominant—I mean, one thing that you do is you look at employment-to-population ratio and incarceration rates in various areas to see how much is being driven by those factors. And you do find that in areas where there's more employment and less incarceration, non-college men are significantly more likely to be married than not. So what proportion of the lack of marriage at this part of the dating market is because of something other than, Do you have a job? and, Have you been to jail? Goldman: Yeah, absolutely. So I think thus far we've focused on these patterns over time. And what you see in the data is that in a period where (1) men are going to college at lower rates than women and (2) non-college men have been struggling economically, that's been characterized by a period in which marriage rates have been relatively stable for college women and decline sharply for non-college women. But one can ask that same question in present data, but instead of looking over time, look across areas in the U.S. And what you see when you do that is exactly the fact you mentioned, Jerusalem, where if we zoom in on the areas where non-college men have the lowest employment ratios, you see those are precisely the parts of the U.S. that have just these massive gaps in marriage rates between college and non-college women. And instead, when we go to the areas where almost all of the non-college men are working, you see those gaps are substantially smaller. In some sense, those areas, which often are concentrated, for instance, in the upper [Great] Plains states—suburban Minneapolis is one example—kind of look like marriage outcomes looked in the U.S. 40 years ago, in a sense, where there was not a ton of education polarization in marriage. You asked about, you know, is this about employment or incarceration? I think it's really hard to tease these different measures apart, because they tend to move together. The areas where non-college men are not working are also the areas where many of them fail to graduate from high school. A higher share of them might end up incarcerated. There might be issues around even early-life mortality. And so it's difficult to diagnose: Is it about earnings? Is it about incarceration? And so on. It's more of this broad pattern, which is: Where in the U.S. are the areas where these education gaps in marriage for women are the largest? And they are really concentrated in the subset of places where men who didn't go to college are struggling. Demsas: Yeah, and I was just struck by the finding that 'marriage rates between college and non-college women are 50 percent smaller in commuting zones where men have the lowest incidence of [what economists refer to as] 'left-tail' outcomes,' whether it's joblessness and incarceration or something else. And I think that's a huge, important finding for policy makers who care about this issue and want to make it increase the life outcomes for these men, and also for women who are searching for a partner, unable to find one who meets their needs. You know, there is actually a lot of policy-making that goes into whether the employment-to-population ratio is high and whether incarceration is high, and attacking those things is actually really within policy makers' control. This isn't just a question of, Do you think that we should regress to the 1950s cultural norms or not? if you care about this issue. Goldman: Absolutely. Demsas: I wanted to ask you about race in your paper, because we started this conversation talking about interracial marriage, and we know, of course, that race correlates with class and education. What do you find when you look at racial outcomes here? Goldman: Yeah. So in terms of these geographic differences, you see a very similar pattern within each racial group. So what you see is for white Americans, for white women, for instance, these education disparities in marriage are concentrated in the areas where non-college white men have the lowest employment rates. And that relationship between employment-to-population ratio of non-college men and marriage rates for college and non-college women is actually very similar for white Americans, for Black Americans, for Hispanic Americans. Demsas: This feels like it really mirrors a lot of what's going on in our political discourse, where you see increasing racial depolarization and increasing class and education polarization. It's just remarkable to see that across so many different planes. It's not just marriage markets. It's also happening in labor markets and education markets and in political ideology. So it's not just in this one place. Do you see all these as connected? Goldman: Yeah, absolutely. I think I have other work looking at recent trends in intergenerational mobility in the U.S., and you see exactly this racial depolarization you're mentioning, Jerusalem, but it tends to be perniciously coupled with a growing importance of class. And obviously, that's been very salient politically recently in the U.S. But we're starting to see the same thing happen to inequality and intergenerational mobility. So in particular, what I mean by that is gaps in outcomes between lower-income Black and white Americans who are born to lower-income parents have shrunk by about 30 percent in the past 15 or 20 years. Yet the gaps in outcomes between white children born to high- versus low-income parents are growing over time. And so that's the sense in which class is becoming a bit more salient than race. We know it's happening politically in the U.S. but, as you rightly point out, it's happening in these other domains, too, and marriage is certainly not exempt. Demsas: So there's an implicit assumption latent in a lot of the 'just get married' discourse—particularly, I think, coming out of Brad Wilcox's book, but in other places as well—that if unmarried men and women would just pull the trigger, outcomes across a variety of variables would be better off. And this is kind of getting back to the question I asked you earlier about causality. And I think this is kind of, like, a weird prior for people to have. I mean, I think that many people want to be married, and they understand the financial benefits of marriage. And if they were able to find a partner that fit their needs on a bunch of metrics, some of which you can measure and some of which you can't, they would do that. And what we're seeing is actually that the people who are not getting married are, I think, likely, on average, making a rational choice about what would make life better for them and their families. And I wonder how you think about this, because there is a ton of research descriptively that you've walked us through that talks about the benefits of marriage to children, to communities. But I think that's always going to be skewed towards marriage among people with college degrees, good jobs, given all we've talked about. So how do you think about this? Goldman: This is the key puzzle in this space. You've completely hit the nail on the head here, which is: Marriage rates, albeit what they are in present-day U.S.—so at age 45, you know, something like 60 percent of Americans are married—the key question is, well, say we had some technology to put that from 60 percent to 70 percent. Would that be 'good'? And I really agree with your intuition that marriage is an awfully important decision folks make in their life. And so it's a bit strange to come at it from this view of, Well, folks are making a mistake. They would actually be better off, you know, if they tried harder to be married or if they ended up tying the knot with someone who they have major reservations about. And I think we should be cautious when bringing that view to the data. And I think, in some sense, part of what's driving that is when you do look at survey data of folks, people still say they really want to be married and it's an important thing they want to achieve in life. But when they're not doing it, I think there's serious questions we have to ask about why, and are folks able to find the right match in person? One important feature of this conversation is, I think, a discussion of people's expectations. It's kind of a new phenomenon that they expect their partner to be this all-encompassing best friend, thought partner, physically attractive, and so on. And maybe some of this is related to social media. I'm, of course, speculating now. This is not for my own research. But I think these things matter, but it might be helpful to split this into two parts. One is, like, why are adults not getting into these marginal marriages? Who are these people? If we did boost marriages from 60 to 70 percent, who are those people kind of on the edge of tying the knot, and would it be good for them personally to make that decision? And I think that's where you get into these tougher questions of, you know, does it make sense to question people's own intuition about this important decision in their life? But there's a separate question, which is: If folks are going to have kids anyway, what is the impact on their kids of the two parents who are marginal to being married, whether or not they tie the knot? And there, I think, we just need more evidence. So there's some early work looking at the introduction of unilateral divorce in the U.S. by Jon Gruber that suggests there might be benefits to some of these marginal marriages staying together. But there's much more we need to understand. And, of course, no one wants to be in the position of recommending someone stay in a relationship with someone who might be abusive, physically or emotionally, just for the benefit of the kid. I think we need to understand why people are not getting married and then, you know, if there are ways we can help along that margin that might be supportive both to adults and to children. Demsas: Benny, this has been a great conversation. I think it's time for our last and final question, which is: What is something that you once thought was a good idea but ended up being only good on paper? Goldman: Well, Jerusalem, you'll have to let me know if this is too on the nose. We've already talked about this a bit, but I'm going to go with dating apps here. Demsas: Really? Okay. Goldman: And this is going to be, in part, hypocritical because I met my girlfriend of two years on a dating app. Demsas: Okay. We won't have her listen to this. Goldman: (Laughs.) Yeah, exactly. The sense in which I mean it's good on paper but not in reality is: I do think it could make a lot of sense from an individual level to join a dating app, especially when many other folks are doing that. But I think, in many ways, this kind of algorithmic approach to dating has had odd effects on equilibrium behavior, whether or not folks are accountable when they go on these dates, the dynamics and inequality it's introduced, especially for men. So we know a small number of men are getting almost all the matches, and that might impact their incentive to settle down. And, you know, it might impact just literally how people are interacting with each other on these dates themselves. So I think that's something that we need to think more about as a society. Demsas: Yeah, I mean, as I mentioned before, I have very uncommon views on dating apps. I also met my fiancé on a dating app, and I think it's hard for me to believe that it is worse to date now than it was when your only options were, like, the people that your parents or friends introduced you to. I believe that there are some serious problems that would need to be addressed. I think you've mentioned a lot of them already. But just, like, in term of relative benefits, like, I would never have met my partner. You know what I mean? So I don't know. You're right—like, it's probably worse for some people; it's better for other people. And it's hard for me to fully disaggregate what's going on here. But it would be hard to prove to me that things have gotten worse, on average. Goldman: I think I share your intuition, but I think there are psychological effects of having all this choice, as well as how quickly folks give up on each other. And, you know, of course, if anyone from Hinge or Match Group is listening and wants to give Benny and team access to the data so we can answer these questions once and for all, I know me and many other very competent researchers would be excited about that opportunity. Demsas: Okay. Hinge, Match, if you do this, we will have Benny on the show again because I'm very curious. Goldman: Bring it on. We're ready for the data. Demsas: Thanks, Benny. Thanks for coming on. Goldman: Thank you guys so much for having me. Great conversation. Demsas: Demsas: Good on Paper is produced by Rosie Hughes. It was edited by Dave Shaw, fact-checked by Ena Alvarado, and engineered by Erica Huang. Our theme music is composed by Rob Smierciak. Claudine Ebeid is the executive producer of Atlantic audio. Andrea Valdez is our managing editor.
Yahoo
15-04-2025
- Politics
- Yahoo
Can We Stop Kids From Watching Porn?
Subscribe here: Apple Podcasts | Spotify | YouTube | Overcast | Pocket Casts Cultural attitudes toward porn may be liberalizing, but the belief that minors shouldn't have unfettered access to it remains broadly shared. Parents are the natural guardians of their children's internet habits, but many report feeling powerless against the innumerable work-arounds and relentless societal pull toward unrestricted internet use. So what can be done to prevent kids from accessing harmful content? Make porn websites check ID? That's exactly what several states have tried—with mixed results. A new study by researchers at Stanford, NYU, the University of Georgia, and Georgia State followed the implementation of a law in Louisiana that required any website publishing a substantial amount of pornographic content to take reasonable steps to verify the age of users before giving them access. The researchers found that while search traffic to Pornhub—which complied with the law—dropped by 51 percent, traffic to its noncompliant rival, XVideos, rose by 48.1 percent. This is a classic tale of tech regulation: lots of friction while the primary aim remains unfulfilled. But one of the researchers, Zeve Sanderson, the executive director of NYU's Center for Social Media and Politics, isn't resigned to defeat. On today's episode of Good on Paper, we discuss what governments can even do to regulate the internet on behalf of minors and what doing so might cost the rest of us. Also, he explains, Louisiana's legislation shows that writing a law can be the beginning, not the end, of a policy process. 'A noncompliant firm that platforms content that we would be more concerned about has risen,' Sanderson laments. 'And it's not clear to me that any laws are gonna change as a result.' The following is a transcript of the episode: Jerusalem Demsas: Thirty years ago, one of the only legal ways to access porn was to walk into a store, show some ID, and purchase a magazine or video. Today, the concept is almost laughable. I don't even think most minors even realize they're doing something illegal when they search for porn online. When something is trivially easy—like jaywalking or setting off fireworks or finding porn on the internet—it feels legal. But over the past three years, legislators in nearly half of U.S. states have passed laws to try to end the porn free-for-all. The goal, they say, is to stop kids from viewing adult content, by forcing porn sites to verify the ages of their users. This episode is about how policy can backfire, and raises questions about how governments can even begin regulating what kids do on the internet. My name's Jerusalem Demsas. I'm a staff writer at The Atlantic, and this is Good on Paper, a policy show that questions what we really know about popular narratives. My guest today is Zeve Sanderson. He's the executive director of the NYU Center for Social Media and Politics, and a research associate at the school's Center on Technology Policy. In a new study, Zeve and his co-authors find that the effect of these laws are not as policy makers intended. While there was a 51 percent reduction in searches for Pornhub, which complied, there was a nearly commensurate increase in searches for the dominant noncompliant platform, XVideos. We wanted to give XVideos an opportunity to respond to this story and the claims that they are not complying with U.S. state laws. We requested a comment from the company but have yet to hear back from them. Let's dive in. Zeve, welcome to the show. Zeve Sanderson: Thanks so much for having me. Demsas: So we're going to start with a noncontroversial question. Do you think porn is bad for kids? Sanderson: So it's a good question. One of the challenges with answering this question and answering many of the questions right now around, sort of, 'insert a particular type of digital media,' including social media, and that effect on kids, is that it's really hard to run, sort of, causal studies on kids. Demsas: You're not going to randomly assign kids to watch porn. Sanderson: Exactly. And which is actually sort of a big issue when it comes to, like, Is porn bad for kids? Demsas: Yeah. Sanderson: And so there's a fair amount of correlational work. I want to flag that my focus is on political communications and on tech policy. So I obviously have a broad understanding of the literature, but I myself am not a psychologist. As I understand the literature, studies have shown that adolescent exposure to online pornography is associated with things that we would consider to be normatively bad—things like body-image concerns, lower self-esteem, and increased acceptance of sort of aggressive sexual scripts, which may normalize sexual aggression more broadly in intimate contexts. However, again, these are purely correlative, and so drawing that sort of causal connection is hard, in general, but especially hard, like you said, when we're not going to run, like, an RCT where we expose kids to pornography. Demsas: Hey, kids. Do you want to join this fun study? (Laughs.) Sanderson: Yeah. I can't imagine any university ethics board that would go along with that. Demsas: Yeah, that's like a 1960s-type study. And if they didn't do it then, we're not going to get it now. Sanderson: Right. Demsas: Okay, but what do you think? Like, you've spent a lot of time in this space, right? I get that it's correlational, but the problem with this kind of research is that we have to make policy even though we don't have RCTs. So do you think it's more plausible that porn is making kids less happy, have body-image issues, express more anxiety, have negative interactions or things that we would consider bad social scripts regarding gender relations? Or do you think it's mostly, if not all, selection—that the kids who already kind of have those traits are the ones more likely to be using porn or admit to using porn in surveys? Sanderson: So I think that it broadly depends on contextual factors, right? So kids with access to comprehensive sex education and strong parental communication sort of demonstrate in research a greater ability to critically evaluate pornographic content. That said, I think one of the interesting things about this policy space is that there's actually pretty broad acceptance that, like, trying to block access of kids watching porn is a good thing that everyone wants to move towards. Even Pornhub and the Free Speech Coalition, which is the sort of professional lobbying group of the adult-entertainment industry, are all sort of directionally on board with 'kids shouldn't have access to adult content.' As I'm sure we'll discuss on this show, though, the challenge becomes: How do you move from there to, like, a set of policy interventions that actually work? Demsas: Set the stage for us a bit here about porn usage among kids. And to the best of our knowledge, how much usage do we see? How much do we see it differ by gender? What's the age of first exposure, as best as we can tell? Sanderson: Yeah. So in terms of what we know, a lot of it, like we discussed, starts from survey-based studies because we're not going to be treating kids with certain types of adult content. And so surveys suggest that first exposure typically occurs between ages of 11 and 13. And so I think one of the interesting pieces is that we have always attempted to restrict kids' access to porn. And before the internet, that largely was done via needing to show an ID at a convenience store or an adult shop in order to get access to the products that they were selling. The internet really complicated things, and policy makers in the '90s largely tried to solve this and failed. The main laws that were passed were struck down on First Amendment grounds. And so as a result, what we've sort of gotten in this shift from primarily offline access to primarily online access has largely been self-regulation. So if you access an adult website, they have a pop-up that asks you to verify if you're over 18. In sort of more nascent examples, there was an adult video game called Leisure Suit Larry in the '80s that would ask trivia questions as an age-verification system. Demsas: Wait. Like what? Sanderson: Like 'Who is Spiro Agnew?' Nixon's former VP. Demsas: (Laughs.) That maybe also shuts out a bunch of adults too, right? Sanderson: Totally. Yeah, it would be a weird cross section of, like, very precocious kids and adults who have access to it. Another funny one is that one of the questions was, 'O. J. Simpson is ______,' and it was a multiple choice, and it was 1987, and one of the wrong answers was 'under indictment.' And so there would also be a time feature to who had access to this. Demsas: That's so funny. I don't know if this is an apocryphal story or a real one, but there's the park ranger trying to design a trash can. And he's, like, the overlap between the smartest bear and the dumbest human is quite large, so designing trash cans in national parks is difficult. Sanderson: Exactly. Demsas: Cool. Okay. I think that sets a stage for us a bit here because we've seen in recent years kind of more attention towards how regulators can really engage in this space. The internet's kind of like the Wild West, and it's a place where you don't see a ton of regulation, not because I think there's not a desire to do so, but people kind of feel like it's futile, which is maybe a theme of this podcast today. But my colleague Marc Novicoff wrote a great article in The Atlantic that goes over some kind of personal history here in Louisiana. So Louisiana passes a law to force pornography websites or websites containing, quote, 'substantial adult content' to verify their users' ages. And Marc writes that it happened, in part, because the Louisiana Republican state representative Laurie Schlegel decided to act. Schlegel is a sex-and-porn-addiction counselor and had heard Billie Eilish describe how porn had affected her as a child. 'I started watching porn when I was, like, 11,' Eilish said on The Howard Stern Show. 'I think it really destroyed my brain, and I feel incredibly devastated that I was exposed to so much porn.' Obviously, Billie Eilish is not solely responsible for this trend, but I think that those kinds of accounts have become more common as the internet generation has grown up and are now adults and reflecting back on their own experiences, and you have some people kind of having the same experiences as Billie. So can you walk us through Louisiana's law? What did Act 440 do? Sanderson: Yeah, so essentially what Act 440 did: It was implemented on January 1, 2023. And there were a few key features of what it was doing. So the first is that it sets specific technical requirements for verification providers. So these are the providers that essentially sit between a website that hosts adult content and a user, in order to verify the user's age. The second is that it clearly defined covered content and websites. And it also introduced substantial penalties for noncompliance. Demsas: And what were those penalties? Sanderson: So the penalties were not to exceed $5,000 for each day of violation and not to exceed $10,000 for failure to perform reasonable age verification. One of the challenges in actually implementing this, though, is that people in Louisiana, like everywhere in the world, have access to websites all over the world. And so if there is a website that sits—you know, the servers are in another country, and let's say it's owned by a company in another country, and they have no sort of U.S. legal presence. Being able to actually levy those penalties against companies is pretty much impossible. And thus companies don't have to comply. Demsas: In the paper, your goal is to see how this law and other laws in other states—21 states have passed similar laws. Are they all kind of in the same form as Act 440, or is there a lot of variation? Sanderson: So 21 states have passed age verification. In 18 states, the laws are in effect, and in three, they're going into effect this summer. Interestingly, 17 other states and D.C. are also considering age-verification bills. So the question then is: How similar are they? In short, they're relatively similar. They're all based off of sort of a similar model for the policy. Where they really differ, though, is the technical requirements or the mechanism for age verification. And as a result, you actually see Louisiana be a little bit of an outlier relative to the other 17 states where the laws are in effect, because Louisiana has a digital-ID program called LA Wallet, and part of the sort of age-verification mechanism in Louisiana specifically is able to leverage LA Wallet in order to give users access to adult content in a privacy-preserving way. Whereas in other states, they had different age-verification mechanisms, including uploading a copy of a government-issued ID, like a driver's license, relying on a third-party vendor to verify a user's age using various data. And all of these were relatively privacy invasive. And so as a result of these other laws, Pornhub, which is the most popular adult website in the U.S., pulled out of all of those states. The only state where it's still active in which an age-verification law has passed is Louisiana. Demsas: Wow. And the reason for that is because it was concerned about privacy? Sanderson: It's concerned about a bunch of different things, all of which are extremely valid. So one is privacy. Does the user have to turn over any personally identifiable information to a service, and in particular to the website that's doing the verifying, like Pornhub, that at some point could be used to reidentify that user? That's one of the main concerns. And the reidentification could obviously happen in certain ways. It could be everything from a hack, and so a ton of users' history of actually watching particular adult content is made visible. But also, there are other legal mechanisms by which somebody could access it, like potentially a subpoena. And so there's this big question, which was how was age verification being done? Who was doing it? And whether users' privacy was protected. And at least Pornhub's perspective was that Louisiana was the only state where they felt comfortable complying with the law versus just pulling out entirely. Demsas: Part of the reason why I wanted to talk to you is because you had a preanalysis plan and preregistration of your study. For folks who don't know what that is, can you explain why that's important? Sanderson: Essentially, what a preanalysis plan is, is it specifies the way that we are going to analyze data before we see those data. And that's really important because it gets around some of the issues that I know you've been interested in—and your colleague Derek Thompson has been interested in—around the ability to do really good, open, transparent science that we can trust. And this is one way of doing it. It's sort of calling your shot, almost Babe Ruth–style, you know, pointing over the fence. And it doesn't allow us to do some things afterwards where, you know, researchers have been shown, at times, to essentially use statistical methods in order to find an effect that often doesn't replicate in the future, because it really wasn't as robust or rigorous as we wanted. And preregistration is one of the tools that we have to do that. Demsas: And so what did you expect the impact would be then? Because you also preregistered, sort of locking in, your hypothesis ahead of time. Sanderson: Yeah, we largely expected what, in fact, has happened, which was that there were, aligning with the theme of this show, all sorts of unintended consequences that maybe took this policy that was good on paper and, at best, complicated it and, at worst, you know, has suggested that it's ineffective or potentially even harmful. Demsas: So walk us through those. What were the main findings of your paper? Sanderson: Yeah. So we had three primary questions. The first is: Did compliant websites see lower search volume as a result of the laws? The second was: Did noncompliant websites see higher search volume as a result of the laws? And the final was: Did people search for VPNs, which would help them circumvent these laws? And I should mention that we use Google Trends data for a few different reasons. The first is that it's granular, and it's free, and it's accessible. And so what that allowed us to do was actually drill down with some temporal granularity to see the way that search volume around these topics—in our case, Pornhub, which was the compliant firm, XVideos, which is the most popular noncompliant website in the U.S., and then searches for VPNs—we were interested to see how those shifted over time. Obviously, Google search results are imperfect. We would prefer to have access to the actual sort of data of who was visiting these websites. However, that's not data that's freely available. It actually costs hundreds of thousands of dollars. And so instead, we use Google search results. But what we do is we look at the correlation between Google Trends data and similar web data, which actually looks directly at traffic at the national level, and we show very high levels of correlation. And so we expect that what we're seeing in our results would actually sort of directionally align with real, actual visits to those sites. Demsas: Give us a sense of the magnitudes here. How much did you see search traffic decline towards the compliant websites? Sanderson: Yeah, so for the compliant website—again, we focus specifically on Pornhub because it's the most popular adult-content website in the country—we see over the three months after implementation, search volume drops 51 percent. Demsas: That's a lot. Sanderson: Yeah. It's a lot. And I think one of the important things to emphasize about Google Trends data is that it's all relative. So we actually don't know exactly how many searches someone did. Instead, it's normalized on this sort of zero to 100 scale, where 100 is the peak search interest in the given region in the given time. So in this case, it would be the states that we were focusing on in the time period of the study. So we also think about this in a slightly different way that might be more meaningful, which is that Pornhub lost about 4.4 weeks of peak search traffic over those three months. Similarly, or rather conversely, we saw XVideos, which didn't comply with the laws, see a dramatic increase in search volume. So over the three months after state implementation, we saw searches increase 48.1 percent—which, you know, similar to the previous statistic, would sort of account for roughly a 3.62-week gain of their peak traffic during that period. Demsas: So it's, like, almost offsetting the decline in Pornhub traffic. Sanderson: Yeah. So, I mean, because Pornhub started at a higher level, it doesn't fully offset it. But it does certainly offset some of it. Demsas: I hear what you're saying. Yeah. Sanderson: One of the other interesting things, though, is: You can think about this law as attempting to do many things, right? The main thing that it's attempting to do is protect kids from having access to adult content. But there's also this economic effect, which is that these are really large websites that make a lot of money. And what you've effectively done via these laws is you have benefited a firm that was noncompliant, because it was noncompliant—which creates these really perverse incentives in this sort of regulatory environment where noncompliance allows you to gain market share from your main competitor that complies with the laws. Demsas: But I guess on the first question, it seems like you probably did see less people watching porn online, given the information that you had. I would expect there's a decline, even if it is offset by increases in traffic to noncompliant sites. Sanderson: Yeah, so I largely agree that what I would assume is that there probably was some drop in overall porn consumption in these states. Again, it's tough for us to tell, because we're using Google search data. And one person, when we were presenting this paper, asked, Who searches for porn? Like, Why is this actually good data to use? Demsas: That's so funny. Any question you ask is a confession in this space. (Laughs.) Sanderson: Totally. But one of the reasons that we think these data end up being relevant to our question and why we would see this behavior is because Chrome is the most popular browser in the U.S., and if you go into the [search] bar and you type in a word—let's say you type in 'New York Times'—and you don't put '.com' on it, it does a Google search. And it would do the same thing for pornography. And so what we expect is happening here is: People, essentially, are just typing in a word, and that's why we're picking it up in the overall search volume. Demsas: I guess the way I would think about it if this were to matter a lot is, I guess, the more sophisticated porn users, whether they have pages saved or whatever, those folks are less likely to come up on your Google search as part of that traffic. And then so you're getting—I don't know what it means to be a less-sophisticated porn watcher, but using that terminology, like, those folks are the ones you're largely capturing, because— Sanderson: But I think there's another important dynamic here, bringing us back to kids—which, again, is the focus of the law—which is if we describe this dynamic: The major adult website in the country that complies with laws, in one state, they actually have age verification. In all the other states that pass these laws, they pull out. And it requires either substitution or circumvention, right? You either need to substitute with a different website, or you need to circumvent via some technology like a VPN. If I just said, Who's probably better positioned to navigate this new sort of legal environment if they are motivated to access adult content: digitally native kids or adults? I think, you know, as people living in the world, even though we don't have direct access to this data, our priors would probably be that kids are much better equipped to substitute and circumvent. And so in some ways, even if we saw overall porn consumption drop, which, again, is something that we hope to test in the future, I personally wouldn't really expect kids to be part of that drop. I mean, they're quite ingenious at getting around technical barriers. Demsas: You think they wouldn't have dropped at all? Sanderson: I'm not sure. Demsas: I think I would expect that there's some drop. Like, there are some people who are just marginally like, Okay, I'm just not going to search this now. Sanderson: Anytime you add friction to anything, it's very rare to see an increase as a result of the friction. So again, our prior should be that you would see some drop. But the challenge, of course, is: How much of a drop was there? And for the kids, or for everybody but especially the kids who are still consuming content that the policy makers and the public are quite concerned about, has that content changed? And I think that's a really important question for the policy community to ask here, because these two firms are not the same, right? Pornhub and XVideos are qualitatively different, if only to start because we know that one complies with the law and the other doesn't. Demsas: And so I want to talk about this, though, this noncompliance, because I think that obviously you probably would see a much larger decrease if there were no major noncompliant websites at all. Yes, some people would figure out VPNs, but a lot of people have trouble figuring that out and don't even know what that is or don't know how to set it up—and, like, it's not crazy complicated, but it does take some effort to set that up for yourself. And it feels a little bit more illegal than, like, just, Oh, I'll just go to the next site on Google. That's a very different sort of friction you've created for people, to use your language there. But, you know, the reason XVideos is noncompliant is, in part, because the government wasn't willing to go nuclear and say, ISPs, you have to stop hosting websites that are noncompliant, right? Sanderson: Yeah. And, I mean, obviously, it's possible to do that sort of with the scalpel, right? To say, okay, you know, ISPs don't route any data from XVideos to the states that have passed these laws. I've made the mistake exactly once in a public context of speculating on legal questions, so I'll try not to do that again here. But my guess is that would need to come from the federal government, given its various effects on other states. There's also been a lot of development in much more sophisticated age-verification protocols that many of the states just decided not to take up here. And to a certain extent, that wouldn't solve this problem, which is that any age-verification protocol will be accompanied by some level of friction. And so, you know, if any level of friction is a deterrent to using a compliant site, then maybe you would still see people move over to noncompliant sites. But there were much better ways to age verify with fewer privacy considerations where potentially we wouldn't have seen such stark effects. That said, we saw stark effects in Louisiana, where Pornhub stayed active in the state, where they had this sort of digital wallet. And in our numbers, like I mentioned, we show a 40 to 50 percent drop, depending on the state. But Pornhub itself actually reports an 80 percent drop in volume from Louisiana after the law, so even larger than ours. [] Demsas: After the break: Is there really a right to access porn privately? [] Demsas: I want to ask you about this privacy question, because I think it's at the core of a lot of the pushback to this. As you said, there are a lot of people who would be amenable to stopping or blocking kids from accessing this sort of content. But when it runs up against their own ability, as adults, to access adult content or any kind of content on the internet without the government having to verify their ID or their age, I think that's when it becomes kind of tricky for a lot of people. And, you know, I started thinking about this because, I mean, I'm a digital native. I grew up on the internet. I was on Tumblr with my pseudonymous account, and I enjoyed being anonymous on the internet. That was, like, a fun thing, and I think that can be valuable. And, you know, there are free-speech concerns and, of course, you know, political-activism concerns with the government intervening too much in this space and with corporations intervening too much in this space. But at the same time, the expectation that your access to pornography is private is pretty new. I feel like I was watching a Gilmore Girls episode when I realized how normal this was, and there's a back room of the video store where they're all going to get porn, and I was just like, What? I can't even believe this. But it's like, that's genuinely the main way that people were accessing porn, or they were going to get it shipped to their house. But there was already verification with these steps. Like, you had to have some sort of verification happening. It was difficult to get it. Obviously, kids were still able to, like, you know, get someone else's magazine, have someone buy it for them. But in the same way that we ban alcohol, even though some kids can get around it, we see that as possible. So walk me through how you think about this privacy question, because it is one where my knee-jerk reaction around the internet is that I care about privacy. But it also is quite new to demand a right to privacy around getting porn. Like, that's quite novel. Sanderson: Yeah. So I feel like in this episode, I probably have already pissed off some psychologists, some First Amendment lawyers, and now I'll add the privacy community to the mix. But so I think that there are sort of two things here, right? So the first is that I sort of broadly agree with you. This is sort of, like, a novel privacy right to affirm that we can have access to porn in a fully private setting where we don't need to affirm our identity in any way. However, on the other hand, in order to build a sort of identity architecture into age verification across various, you know, websites and apps, we really need to fundamentally rethink the way the internet works. And I don't want to pretend like trying to solve the problem around age verification on adult content would get rid of, like, anonymity everywhere. That's certainly not the case. But I do want to emphasize that really thinking about identity affirmation online is something that comes with all sorts of trade-offs and broadly is not the norm, right? Broadly, while we might need to identify ourselves as a user, right—we have a username and a password—in many contexts, we don't actually need to turn over any personally identifying information about ourselves. And so one of the interesting things here is that this is where a lot of work—and really exciting work—has been done, and there are various methods for thinking about how you might be able to do age verification in a way that actually does preserve privacy. I don't really want to go into—I think the technical details, in some ways, are less important than the overall logic here. And the logic is that you sort of have a service or a platform or a website on one side that needs to verify someone's age. And on the other side, you know, you have another service that knows, at minimum, an age range. And what you really want to do in order to effectively do age verification while preserving privacy is let the website know that a particular user is above or below a certain age, without letting that website know anything about that user and without letting the age-verification system know what website is asking the question. So recently, Apple came out with a white paper where they sort of proposed a particular mechanism by which parents set up child accounts, and they have an age range that is stored on the phone, and that age range can be made available to apps via the App Store. But again, these two things aren't really talking to each other. And obviously, Apple has long held privacy as a core of what they're doing. So yes, there are some companies doing this. I really hate saying this word at all, but, like, this is an application for a blockchain or some sort of— Demsas: I was waiting for it. I knew it was going to come up. (Laughs.) Sanderson: —or some sort of distributed technology. There's been a lot of technical advancements in something called zero-knowledge proofs—so, essentially, a protocol in which one party can convince another party that some given statement is true, without conveying any information to the verifier beyond the fact of that statement. So, like, that's the sort of logic of the computation that's going on. And so, you know, again, not a crypto person, though I think that, in general, thinking about genuinely useful applications of distributed technologies is interesting. And this might be one. Demsas: I wonder, from the perspective of trying to attack this from a different actor, like, right now, we've talked a lot about: How do you address this by finding the website, by making the websites compliant, by creating that sort of change? How do you think about this from a parent standpoint? Like, holding parents responsible in the same way that we hold them responsible for truancy, for instance, in some states? Responsible for installing porn blockers on kids' computers and, you know, responsible for ensuring that kids are not using this on their smartphones. Like, what do you think about that approach, and is there research that illuminates whether this is actually effective? Sanderson: Yeah, so I'm really happy you asked this question, because it emphasizes, I think, this sort of broad dynamic in tech policy that you can't solve, sort of, 'insert societal challenge' at the level of tech policy. If what we're after is more developmentally appropriate content consumption broadly around kids, because we care about their development, tech policy like age verification is going to be one small piece of a much larger policy and nonpolicy agenda. And parents play a huge role in this. Demsas: When I was doing some research for this episode, I came across this interesting survey that was trying to ask people about their first exposure to pornography. It's not a huge sample, but it was a 2017 study that surveyed 330 undergrad men, 17 to 54 years old. I assume that is an outlier 54-year-old. But the participants were 85 percent white, primarily heterosexual. And when they were asked about their first exposure, the mean age was 13.37 years of age, so kind of in line with what you told us at the top of the episode. But what's interesting is that 43 percent of men indicated that their first exposure was accidental, which reminded me—again, who knows—maybe there's social-desirability bias here, where you don't want to say you were looking for porn at 12. I have no idea. But part of what struck me is: It is very, very normal, particularly now that X has changed its protocols significantly, to just be, like, on the internet and come across porn accidentally. Like, that will happen. Like, now you see this on Reels, on TikTok, where you see content that is very close to porn or, like, porn adjacent or even really explicit content on websites that are not normally predominantly serving that sort of content. And that's something that I think that these sorts of laws really don't do much about but I would imagine have a larger impact on, you know, adolescents that we're trying to prevent from having to see this in an unwanted way. So, you know, when I was, like, in elementary school, I remember I was at the school library, and these were big desktop computers. And I saw a group of kids huddled around a computer, and I walk over. And, like, I'm 6 or 7 years old at this point. And they're, like, kids looking at porn, and they're laughing and showing this around. And I remember being horrified at what had just occurred, and I kind of ran away and pretended it hadn't happened. But it stuck in my brain for a long time. And I imagine, like, that's the sort of thing—beyond just, like, normal healthy sexual interactions people are having—you're not trying to prevent kids from, in a way that feels uncomfortable or unwanted, having to experience sexual content like that. Are there laws that could even address something like that? Because that is not something that you can go to, like, a central provider like Pornhub or XVideos or whatever it is. That's just, like as you said, kind of littered throughout the whole internet. Sanderson: Yeah. So the short answer is yes. There are sort of policy mechanisms by which we could imagine getting there. And I say, 'imagine getting there,' because, you know, we don't pass a ton of tech policy at the federal level. A lot's being passed at the state level. But for various reasons, a lot of what's being passed at the state level, it's sort of simple approaches to quite complicated problems. And what I'm about to sort of try to describe is, like, a complicated problem to try to solve. But you could imagine, let's say, on something like X or Reddit or Instagram that there's some legal requirement where they're making some determination about the type of content that's on the platform, right? So on Instagram, you have two photos. They can have a bunch of automated classifiers running that are able to say, This photo is not adult content, and that photo is adult content. And baked into this general push to try to expand age verification across the, like, social internet—think about what sort of social media platforms kids have access to and how—one of the things that you could do, as part of that, is if you're age verifying kids to go on social media, you also have legally mandated content filters that strip out adult content from that feed. And it would obviously be imperfect, but it would probably solve for a fair amount of what you just described, which is, like, large-scale incidental exposure. Demsas: Yeah. It doesn't stop random 7-year-olds from, I guess, showing each other porn. But— Sanderson: Yeah, I think that's sort of an age-old problem. Demsas: Exactly. I know that you're not a lawyer, but I did want to ask you about the changing—it seems changing—legal environment around these questions. For a long time, as you mentioned, there's been kind of this distinction between getting porn in person, and you can check ID at, like, the video store or whatever it is, versus getting it online, where there's been sort of a free-speech argument that you can't really regulate that in the same way. That might be changing. Can you tell us what's going on? Sanderson: Yeah, essentially, there was a major law in the mid-'90s that was passed called the Communications Decency Act, and it was the first really serious piece of federal legislation that attempted to regulate minors' access to online materials. And it did it in a few different ways, but ultimately, it was struck down. And it was struck down not because the government didn't have a legitimate state interest in regulating or limiting access for kids to adult content, but instead because the court believed that the way that it was happening would have infringed upon the First Amendment right of adults. And so in general, there is sort of this legal precedent that kids' access to adult content is not First Amendment–protected speech so long as the mechanism by which you do it doesn't limit adult First Amendment–protected speech and that there's a legitimate state interest in attempting to accomplish what I just described. And in the mid-'90s, they really didn't have a good way of doing it. They also, you know, didn't have a great way of defining what adult content was. I think that largely because age-verification mechanisms have gotten so much more sophisticated and granular, that we're moving towards—and I think we saw this in the court hearings—we're moving towards that because there is this precedent that this state can attempt to regulate kids' access to porn, so long as it doesn't infringe upon adults. Demsas: So we've kind of, throughout this conversation, really accepted the premise that this is a problem, that children accessing pornography is a problem. And one thing I want to do is just maybe stress test out a bit with you, because some people think this is just another moral panic. Whether it's about youth and internet porn, whether it's about smartphones, whether it's about, you know—it's just like comic books, like it's rock music, like it's video games. Public fear can often race ahead of what the evidence shows. And this is a difficult space where finding really high, qualitative, causal evidence is difficult, if not impossible to do. Are you afraid that this is kind of just a spun-up moral panic, and that's driven by these high-profile anecdotes from Billie Eilish or whatever, and we're having kind of, like, a social-conservative backlash and a bunch of vectors, but that this is really not the sort of thing that requires a bunch of government intervention, and that maybe the best thing to do is just hold off and see if private-sector technologies and culture can kind of correct for itself? Sanderson: I mean, what's interesting to me is that this is much more an ethical question than it is an empirical question. I think one of the fascinating things studying tech policy, in general, and then especially this area, is that the sort of evidentiary standard that we have to be able to definitively say, X causes Y, is something that in so many areas around technology policy trying to protect kids we just don't have. And so the question is: What do we do in a context where getting that sort of causal standard or, you know, the gold standard for causal evidence probably isn't possible? And so whether or not this is a real problem is, I know, a debate in the psychology literature. It's a debate amongst parents. And in many ways, what politics and policy making are is an infrastructure to sort of figure out or come to some consensus of that debate. However, I think the challenge becomes, we want policy to do something, to have some effect. And as part of that, what we also want then is this sort of evidence-based feedback loop, where we're not just passing policy, wiping our hands, and saying our job is done, but instead actually doing something similar to what we've done here. You could imagine policy makers partnering with academics, preregistering studies to understand the effect of these sorts of laws on the outcomes that we're really interested in. And so my fear is less that this is just a moral panic, because I think, in part, politics is there to figure out a distribution of moral preferences across a population. And instead, what I'm more concerned about is that there isn't this really rigorous, evidence-based feedback loop where we're able to just continue to iterate and make policy better. And I think this is one area where we've clearly seen it, where we show, Look—like, a compliant firm has dropped. A noncompliant firm that platforms content that we would be more concerned about has risen. And it's not clear to me that any laws are going to change as a result. And that's where I don't think we want to be in a policy environment. Demsas: It feels like a lot is about to change with AI in this space. Right? Like, I was on Instagram, and I don't know if you've seen these suggested AI chatbots that they have. And there have been stories of people kind of developing, you know, romantic relationships with them. There was a really sad one in the New York Times about a young boy who actually took his own life after having a relationship with a chatbot. I don't know if it's causal there, but the story indicated that he had really developed a romantic and personal relationship with this AI agent. And, you know, it's not going to be just porn websites soon. It's going to be people having, like, personal interactions with AI girlfriends, boyfriends, whatever. And that sort of thing, I think, would require even greater privacy violations to prevent from happening, and would create bigger problems for companies trying to be compliant with regulation. It feels like any solution is going to be kludgey. So if you're going to try to stop kids from accessing porn online, you're going to stop them and adults from accessing a lot of things. And it's going to create a bunch of friction and annoyance. It's going to create some level of privacy violation, some level of First Amendment violation, and maybe not literally constitutionally, but it's going to create some feeling that your speech has been quelled. How do you think through this problem? Because, to me, if you're asking me, okay, you either have to accept a world where you know, kids are having really intimate relationships with AI chatbots, and it's degrading their ability or desire to interact with people who they're attracted to in real life, and that continues the degradation of, you know, the children's experiences in the real world—I guess 'real' in quotes. It's real to them, but, you know— Sanderson: The embodied world. Demsas: The embodied world. That's a better word. Then it's a much more difficult question. I think as policy wonks in D.C., we want there to be this really perfect solution—there's, like, some technological solution or some sort of policy solution that actually targets the specific thing you're worried about. But largely, a lot of effective policies are effective because they're expansive. I don't know how you think about that. Sanderson: Yeah. I mean, tech policy, like every policy area, is just a set of trade-offs that we figure out how to navigate. I think if we want to steelman the argument for age verification broadly, is that if we develop sort of low-friction ways of verifying age without any serious sort of privacy violations, we're able to essentially do that quite broadly, but we're never going to be able to be perfect. Perhaps it's that, you know, porn dropped somewhat overall, but the stuff that remains shifted to worse places. Like, those are the sort of trade-offs that we constantly need to make when we think about policy interventions here. The one interesting, unique challenge, though, about regulating the sort of digital-information space is that the companies that are making these tools or running these platforms have a monopoly on the data they collect. And that's really different from other policy spaces. Can you imagine if we needed to figure out sort of, like, interest-rate policy, but some company owned all of the employment-rate data? Like, that would just be this really challenging, I would argue impossible, environment in which to craft good policy. And that's essentially what we're doing here. And so I don't think that data access solves everything, but I think one of the things that I wish that—there was some momentum around this a few years ago in Congress, and I would love to see it come back up, is as we think about making policies and as we really try to rigorously quantify the trade-offs that will inevitably be there, we need to do so with as much really good data as possible. Otherwise, the fear is that there are all sorts of unintended consequences, the severity of which we're not able to measure. And so I think that needs to be part of any sort of broad solution that we bring to regulating online spaces and online access. Demsas: So I think that's a great place for our last and final question: What is something that you thought was a good idea but ended up being only good on paper? Sanderson: Yeah, so I was a basketball player growing up, and I was a pretty good basketball player, and I ultimately became a mediocre Division I point guard. Demsas: That's pretty impressive. This is turning into a humblebrag already. Sanderson: No. Not at all. It'll quickly not. And, you know, I dreamed my entire life of sort of playing in a Division I program. And I got there. I played at Brown. And when I was there, we were sort of the back of the Ivy League, which itself was one of the worst leagues in America. And, you know, I went from, like, a high school where lots of people would show up to games to a number of friends not even knowing we had a basketball team to, you know, practicing 40 hours a week while all of my other friends were having fun, and thinking, Is this something that I really want to do? What was it that I was dreaming of? Demsas: Well, Zeve, thank you so much for coming on the show. Sanderson: Yeah. Thank you so much, Jerusalem. [] Demsas: Good on Paper is produced by Rosie Hughes. It was edited by Dave Shaw, fact-checked by Ena Alvarado, and engineered by Erica Huang. Our theme music is composed by Rob Smierciak. Claudine Ebeid is the executive producer of Atlantic audio. Andrea Valdez is our managing editor. And hey, if you like what you're hearing, please leave us a rating and review on Apple Podcasts. I'm Jerusalem Demsas, and we'll see you next week. Article originally published at The Atlantic
