Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas - 318 | Edward Miguel on the Developing Practice of Development Economics

Episode Date: June 16, 2025

Economics is seeing an upsurge in the importance of controlled, reproducible empirical studies. One area where this has had a great impact is on development economics, which studies the economies of low- and middle-income societies. Edward Miguel has been at the forefront of both the revolution in empirical methods, and in applying those techniques to alleviating poverty in sub-Saharan Africa and elsewhere. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/06/16/318-edward-miguel-on-the-developing-practice-of-development-economics/ Support Mindscape on Patreon. Edward Miguel received his Ph.D. in economics from Harvard university. He is currently Distinguished Professor of Economics and Oxfam Professor in Environmental and Resource Economics at the University of California, Berkeley. He is also Faculty co-Director of the Center for Effective Global Action and a Faculty Research Associate of the National Bureau of Economic Research. Among his awards are the Frisch Medal of the Econometric Society, the Kenneth Arrow Prize of the International Health Economics Association, and multiple teaching awards. Web site Berkeley web page Google Scholar publications Wikipedia

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Hello, everyone. Welcome to the Mindscape Podcast. I'm your host, Sean Carroll. I don't know if any of you out there are fans of the web comic Girl Genius. I'm a huge fan of Girl Genius. It's a comic by Phil and Katya Folio, the artists. And it tells a story, it's been going on for years now. So if you want to like take a weekend off and catch up, if you haven't read it, there's plenty of material there. And a very complicated story with a lot of moving parts. But it's kind of a steampunk, almost much. medieval setting. So there are dragons and there are swords, but there's also science of a sort, but it's kind of fantasy science. So rather than just ordinary scientists, they have mad scientists called Sparks in the comic. And there's a lot of jokes. It's a mostly humor-filled web comic. One of the characters, a minor character in Girl Genius, is presented as a mad social scientist, rather than most of the mad scientists who are, you know, building death. death rays and flying machines, he's experimenting on people. And even within the comic, they say, like, you don't meet a lot of mad social scientists. But at one point, there's a place where the

mad social scientist is explaining what a soft-hearted guy he is. And as evidence, he says, you know, on Christmas time, I sometimes like to take the children that I'm experimenting on out of their container tanks and give them presents for Christmas. And the other, you know, the scientists are portrayed as pretty devilish people most of the time anyway. The other characters look at him in horror at the idea of children in container tanks and he says, oh, don't worry, not the control group. So the joke, of course, is that if you're a mad social scientist, you have to do experiments on human beings, but then there's other people who you don't experiment on. That's the control group and you just leave them in their container tanks,

even for Christmas. And the reason I'm telling this extended, not especially human, anecdote is because it's not completely divorced from the reality of being a social scientist. The methodologies that are accessible to you are just different than those that are accessible to physicists or biologists or chemists, right, where you can literally run trials over and over again changing conditions in whatever ways you want because social scientists have to deal with human beings and human beings actually have some rights. That's certainly not an insuperable barrier. You can do things that people volunteer for, psychologists do it all the time, but also you can just study what happens in the world, right? And again, many kinds of science need to do that. If you're

an archaeologist or you're a cosmologist for that matter, you're not running experiments over and over again. You don't redo the universe. You just look at the universe and try to figure out what is happening. In a social science like economics, that's the traditional way of doing things, not running an experiment, or rather just seeing what happens in the world and trying to tease out the patterns. But that methodology is changing a little bit. Economists are increasingly trying to do controlled, randomized experiments, where they sort of do their best not to induce unwanted correlations between people in one condition and people in the other condition, of course, in ways that treat the dignity of the individual people as well as they can.

can. So today's guest, Ted Miguel, is an economist at the University of California, Berkeley, and his specialty is development economics. So he thinks about the economies of relatively poorer countries, right, developing countries, specializing, in fact, in sub-Saharan Africa, which is some of the poorest countries on the globe. And he does his best, as we'll talk about in the podcast, to do sort of methodologically careful analyses, running range. randomized controlled trials, making methods and data collection strategies as transparent, as open as possible, ideally making things reproducible. There is not as Ted, he goes by Ted, as he says in the discussion, economics does not

have as bad of a reproducibility crisis as feels like psychology have been struggling with, but there is still some there. So you want to make it possible for other people to run your experiments as well. And what's fascinating about this conversation is he makes a very convincing case that we've actually learned an enormous amount about how to help the most poor countries. It's not something where you just have to bash your head against the wall. There are clear strategies where you can intervene, sometimes as simple as just cash transfers, giving people money, but other times slightly more complicated strategies like curing diseases or building roads or whatever. And ironically, of course, we live in a world now, which is somewhat pulling back from the idea of foreign aid and helping other countries, just as we are getting better at actually knowing what kinds of interventions can help. As we say in the podcast, hopefully the pendulum will swing back once again and will put our knowledge to better use.

You know, we live in a world where there are wild disparities of wealth and health and education and conditions of life. all different sorts. I think that we in the wealthier parts of the country, parts of the world, rather, should do some part to help those in lesser off parts of the world. And we know how to do it. There's no excuse for us really. Hopefully we can convince some people to get on that bandwagon. So let's go. Edward Miguel, welcome to the Mindscape podcast. Thanks for having me. I wanted to start just by sort of getting a mind-set check about being, I guess, a developmental economist. Is that a good thing to call you? Yeah, we call it development economist, but...

Developmental would be... It sounds like childhood development or something like that, I guess. So, you know, economics obviously is central to many, many things, but I think a lot of economists associate with studying like, you know, rich people stuff, finance, you know, shipping and things like that. And here you are choosing of your own free will to think about places in the world that are less well off economically. So sort of what drives you to that and what's it like to be that kind of economist? There's no doubt about what you say. And, you know, certainly if family gatherings, you know, relatives always want stock tips, right? Because they No, I'm an economist. And it's like, well, you don't want my stock tip. But yeah, early on for me,

I came to economics through international development rather than the other way around. So I was always interested in global inequality. Even when I was a teenager, it fascinated me. You know, why do we live in a world with such just intense disparities between rich and poor countries? I think it was kind of heightened for me. Both my parents are immigrants. And so when I got to travel to see my cousins in different parts of the world, they were, you know, a lot poorer than we were growing up. I was growing up in the suburbs of New York City. So that just lit something in me. And I was trying to figure out how I could help make progress on those issues. How could I study them? I was a kid who really liked math and science. I was actually an undergrad at MIT. I studied math

at MIT, but then really discovered economics at MIT. So MIT, of course, is a science and engineering school, math school, but has an amazing economics department. It has for like almost 100 years. And I decided to, you know, apply my analytical abilities to try to understand global poverty and inequality. So that's how I came to it. I do love economics. And I think for folks who come at things with, you know, strong interest in math and stats and modeling, it's a great field because we are trying to bring structure to the world. We're trying to come up with general insights, and we're doing that formally and quantitatively

in a way that I think is appealing. You're a physicist, so you get it, and a lot of economists come from a math or physics background. But the challenges are complex, and human beings are complicated. So, you know, it's not an easy task. The spherical cows are hard to come by in economics. It's hilarious the extent to which, you know, here on the podcast, I try to have people from a wide variety of fields, and I do, but some huge

fraction of them always say, but as an undergraduate, I studied math or physics. Yeah, I mean, it was interesting, you know, being at MIT, because I felt like there were so many different fields we were studying, but like we were all fluent in math. You know what I mean? Like, that was kind of the common frame for us. And it gives you, you know, a way of thinking about the world and some analytical approaches that are very useful. And, you know, I mean, I think for us in economics, we've looked around other fields, whether it's physics for our kind of formal models or whether it's clinical trials for when we do statistical work. And we've tried to bring in those best tools from other fields into economics. So economics ends up being a pretty eclectic, pretty broad field and a very, like, practical field.

And that's one of the things that actually appeals to me about it. Well, that's great because the next question I wanted to ask about being a development, an economist was, unlike being theoretical physicist, there's some balancing you have to do between purely understanding the world and trying to make it a better place. And you mentioned that trying to make it a better place was part of your motivation. Is this an ongoing thing you have to keep balancing? It definitely isn't. And within, say, my department or any department, there are folks that really are kind of lined up along that continuum from more of the basic science to more of the applied work. And even in my own work, I kind of move back. And, you know, even in my own work,

I kind of move back and forth, you know, along that line because some of the work I've done is more conceptual or methodological. But the bulk of what I'm doing really is applied because I'm driven by these practical questions of how we can learn about how to improve lives for poor people around the world. And the idea is to bring the best tools and the best methods and then apply them to really important problems. So, you know, I've been working since early in my career on poverty in sub-Saharan Africa. the world's poorest region, the region of the world with the lowest life expectancy, the most armed conflict, the highest rates of HIV. We can kind of go down the list of challenges. So there's no shortage of big topics for me to study. And early in my career, I focused a lot on health impacts of dealing with tropical disease.

More recently, I've been focusing on impacts of cash transfers and how targeting cash transfers to poor people may or may not. improve their lives. And that's a really big question. There's been a big increase in those kinds of programs by government. So part of the reason we focus on it is it's just such a big public policy question right now. I think that it might be a little unfair, but there's at least a stereotype that in math and physics, there is a sort of prestige hierarchy where the more abstract thing you work on, the more prestigious it is. Is there anything like that in economics? There is. And Traditionally, it lined up exactly the way you said that the folks who were doing the really hard abstract theory and, you know, complicated modeling of how markets work in general equilibrium were really the high folks on the totem pole and the folks doing applied work in, say, development economics like myself, were farther down. And in fact, when I was an undergrad at MIT and I was applying, I went straight through to grad school.

I applied my senior year to grad school. and, you know, I'd gotten good grades, and I was, you know, my professors thought I could go to a good grad program. And when I told them, my main field was going to be development economics, it said, no, you can't write that on your application. That doesn't, that's not going to track with your math and stats skills. But no, I wrote it down. I was like, that's what I'm going to do. And I ended up going to get my Ph.D. at Harvard, and at that time at Harvard, there wasn't even a development economic sequence at Harvard. a small field. I had to go back to MIT where I'd done my undergrad to take development economics

graduate classes because it was such a small field. And the amazing thing is during my career, and I've been in the field, you know, I've been here at Berkeley 25 years. Things have changed, actually. So that traditional prestige structure has changed. And it's even reflected in things like Nobel Prizes, where over the last decade now, several groups of applied researchers have gotten the Nobel Prize for contributions that really are a combination of some new methods. So there's always some methodological development in there for a Nobel Prize,

but uses of new types of data, tackling new problems, that's being recognized in the field. So I think it's a little more even now in terms of the prestige between the theorists and the applied people. Well, okay, I did want to get that. That was very, very helpful as sort of a backgrounder.

The other big background question, which is almost hilariously overbroad, So how's the developing world doing these days? Is there some sense in which development is really going on, or are there places where we just seem to be stuck? Yeah, so I think the popular perception, if one were to be on social media or just kind of get the kind of vibes in the world today, it would be pretty negative.

People would think, well, we're stagnating, the world isn't doing well. And sure, there's a million challenges in the world. But if we look at data systematically over the last 30 years, 35 years, 40 years, and look at low-income regions of the world, they've really been transformed. Now, everybody knows the China case. Yeah, China 40 years ago was really poor. And now they're a middle-income country, you know, still way poorer than the U.S. You know, per capita income in China is like one-fifth of the U.S., right? So huge gap still, but, you know, it used to be 120th or something.

But even other parts of the world in South Asia and sub-Saharan Africa have experienced a lot of economic. growth, improve living standards, big reductions in infant mortality, big improvements in health indicators, reductions in malnutrition, et cetera. So, you know, objectively, the last 30-something years have been really great for international development in a way that I don't think the popular, you know, the man on the street or the person on the street really appreciates. And, you know, part of what we do in our field is measure these things. So it's like the most basic thing we do as development economists is measure hard things. If you're trying to measure living standards for a subsistence farmer in rural Mali, it's kind of hard. But we've developed tools to do that to measure

living standards. And those things, you know, tell the same story as the official government statistics. Things have gotten better. Hey, everyone. It's Cal Penn. I'm the host of Earsay, the Audible and I Heart audiobook Club. This week on the podcast, I am sitting down with Ray Porter, the narrator of Andy Weir's audiobook Project Hail Mary, massive sci-fi adventure about survival and science, and what happens when you wake up alone very far from Earth? I really had to make a decision because I caught myself getting that frog in my throat

and starting to get teary as I'm narrating some of these sections. And it's like, okay, yo, yeah, yo, is this indulgent? And I really thought about it. I was like, no, at this point it would kind of be betraying the trust the author and the listener have in telling this. story if I don't go through it. But there's places in this book that deeply emotionally affected me,

and I left it on the mic. That's great. Because it served the story. People will say like, oh my God, I cried at the end. It's like, yeah, dude, me too. Listen to Earsay, the Audible and IHeart Audio Book Club on the IHeart Radio app or wherever you get your podcasts. Premier Protein.

It's for getting after life, not just fitness, with 30 grams of protein, 160 calories, and no sugar. added, helping people fuel their joyful lives. With Premier Protein, you can say yes to more, whether it's crushing a big presentation at work, building an epic fort with the kids, or hitting the hiking trail with friends. Premier Protein offers delicious flavors like cafe latte, chocolate, caramel, vanilla, strawberry, and cake batter, to name a few. Find your favorite flavor at premierprotein.com. I mean, maybe talking about those tools would be useful. I know that there's a, to me as a scientist, a weird discussion in economics about the role of the empirical side of things.

You know, in physics, like, of course, the empirical side of things is the point, whereas like in economics, there's at least a little debate about it, or maybe the debate is just more about how to do it correctly. Yeah, I think it's both. I think, again, traditionally the folks doing theory, yeah, kind of were the, the, have the most prestige and the most influence and did look down on folks that got their hands dirty with data. But that's less of a majority view now. There really is a debate about how to do empirical work, how to do the science, how to test things, how to test theory. The big debate in

our field is how close should those tests be to theory? And should the tests be really motivated by economic theory? Or should they be a little more theory agnostic? That's really really. the debate. And I've been in my branch of development economics and in my own career, I've been part of the group that's been a little more skeptical of a lot of the accepted theory because in development economics, a lot of the assumptions of those theories about how markets work and how, you know, how well markets work and how good information is in the world about how markets work, all those things, just don't hold as well. So we've been a little skeptical about just like applying those theories off the shelf. There's also been the whole what's called behavioral

economics revolution, which is like the injection of psychology into economic theory, which has proven really valuable in sort of knocking down some traditional theory. So I've been on this more theory agnostic side, and what we've done is brought the tools of medical trials and randomized control trials into development economics. I was very lucky as an undergrad at MIT, my advisor, the faculty member I work closely with Michael Kramer, was one of the first economists who started doing field experiments and RCTs in Africa. And I was his undergrad R.A. coding up the data entry software and working on the statistics.

And then I went to grad school. I continued working with Kramer there as well. And Michael won the Nobel Prize with colleagues, I guess, six years ago for that work. So kind of right at the start, I was part of that view that said, hey, theory is really useful. But let's not get so caught up in the theory that we don't see the world as it is. Let's run experiments. Let's establish some empirical facts that then can drive the next generation of theory. That was really the mentality.

And it's been an exciting thing to be part of. Neither physicists nor philosophers really do randomized control trials. So why don't you tell us about what exactly that means in the economics context? I'm actually more familiar with it probably in the medical context. Yeah, the way we do trials in economics builds on what's been done. medicine. And in fact, a lot of the early trials, like the first study that I worked on with Michael Kramer, and this was one of the studies that was cited heavily in the Nobel citation in 2019, was a project to improve child health in rural Kenyan schools. So there's a lot of health problems

faced by East African kids, but one of them that's really widespread is intestinal worm infections, intestinal parasites like hookworm. And in our data, you know, 90-something percent of kids had these infections, but they were super cheap to treat. And I think this is what attracted us to this intervention as economists, basically for less than a dollar a year, you can really clear these worm infections out of kids. And worm infections can lead to anemia and growth stunting and weakness, fatigue, etc. So this seemed like potentially very cost-effective.

And we worked with a nonprofit and the government of Kenya to roll out a randomized control trial. Now, the way it worked was the nonprofit we worked with had a limited budget. They were going to start in a small number of schools and gradually roll out this school health program. And what we did is we convinced them in one of these earliest experiments in development economics, we said, hey, why don't you randomize the order of that rollout? So it's very much like a medical trial where some schools were getting these drugs earlier,

some later. That was what the rollout was going to look like. But now because it was randomized, there's no reason to think that the, early phase in schools should be any different statistically, on average, compared to those that got it, you know, a few years later as the rollout happened. So that was the way it, you know, it worked for us. Very much like a medical trial, you have something like a treatment group and a control group, and you can compare outcomes across those groups and get unbiased estimates of the effect of the program. And, you know, what we found in that case, again, that was a program that was launched my,

the summer after my first year of grad school, I went to Kenya to set it up in the field. And there was very little research infrastructure in place. It was a lesson for me in persistence because things didn't really work. And, you know, it was hard to get, you know, an internet. There were no internet cafes even in 1997, you know, in Kenya at that time was very hard to connect. So we got it done. We set it up. We work with great partners and found really interesting stuff.

So, you know, first of all, the drugs were very effective. We had, you know, collaborations with health authorities to measure the worm infections. They went down a lot. And kids started going to school a lot more. So it really looked like tropical disease was holding kids back from going to school. So there was a big increase in school attendance. And that was sort of a finding of that first paper. The other thing we found, and this really links to the medical literature and the broader epidemiological literature.

We were collecting these peristological surveys on worm infections in the local area. and even people that hadn't gotten the drugs who lived within a few miles of these schools showed big reductions in worm infections because we had broken the cycle of transmission. If you mass treat a school, then they're not infecting other people. So we documented the kind of like first order direct effects

and then the spillover effects. And that approach, we had to develop some new econometric approaches to estimate those spillover effects. It went beyond the simple medical, trial treatment versus control group comparisons. We had to put a little more statistical structure on the problem. And it was super exciting. I mean, for me, as a young scholar, you can imagine, I was working on a project that ended up, you know, kind of being a Nobel Prize winning

project, you know, with my advisor, Michael Kramer. So very exciting stuff. We built on medical trials and tackle this issue and were able to really document a highly cost-effective intervention. So the economists and the policymakers and the finance ministers cared about it too. But it's a great story, and I love it. But I did read about it a little bit before we are doing an interview here. And apparently not everyone agrees with all the conclusions. And there is literally something called the worm wars, which I was unfamiliar with. I don't know how that escaped my attention.

Yeah, the worm war. So that was about 10 years ago that that kind of broke out. It's a very interesting thing to be part of when academic debates on statistical models and what the right statistical approach, kind of, you know, when that goes, you know, into the mainstream media, it's not our, you know, comfort zone as scientists. Like we're used to be debating, you know, certain models. And so in some ways it was very, it was very strange, you know, there. I think that, you know, the broader debate about worms is for folks in the global health community, You know, worms aren't the most important thing, very understandably, right? Folks are dying of HIV. Folks are dying of malaria and TB. And there's just like, you know, in sub-Saharan Africa, huge health challenges. And if you were looking at just mortality risk, you know, these worm infections aren't,

aren't a first-order issue. And I, you know, I get that. And I think as a result, some of the work on worms just automatically gets a lot of pushback where people like, wait, you're focusing all this attention here and advocating for treating, you know, worms. We should be putting whatever scarce resources we have into. something else. And I think our perspective, as economists, a little bit different in saying, hey, if you can really treat a kid for 30 cents a year, then for like very little money,

you could be mass treating a whole county. And, you know, one of the things that we were able to document, not just in that first study, but we've continued following up this population of thousands of kids in rural Kenya from the early experiment over time. We're still following them up. So now it's 27 years later to look at long run life outcomes is these kids that got additional years of health treatments when they were like 10 years old. If you look at them in their 30s, they're earning more in the labor market. Like there's not just like a short run education return. There's like a real economic labor market return. On average, they're earning like 10% more. So for 30 cents a year, you know, with that investment, people could be earning, you know,

$50 more a year as adults. It's just like a huge rate. rate of return. So we're not just looking at sort of absolute effects, but just also rate of return. Like the rate of return on this is super, super high. So there were debates about the statistical models used in that early study. In the first paper, I, because I was the one doing the statistical coding, made one coding error as we estimated those spillover effects. So the coding was fine for the direct effects, for the effects within three kilometers. But then when I was coding up the spillover effects between three to six kilometers, I made a coding error. I basically capped the number of schools I would consider just in the code. And so actually, when you go out to six kilometers, the kind of

corrected code has standard errors on the estimate, sort of like the degree of uncertainty on the estimate that gets really large. So the worm wars fundamentally was about folks. And I shared that code with them. I found the error, corrected it, shared it with other scholars. And they said, well, if you go out to six kilometers, now you have like really imprecise estimates. So we don't know if we believe anything. And of course, our response was, wait, we have direct effects. We have effects to three kilometers. Like those are really precisely estimated. Those are meaningful. But yes, we can't really say anything with confidence from three to six kilometers. Those estimates are noisy. So again, there was kind of a methodological point that I thought was, you know, pretty straightforward. And folks who

evaluated the statistical side of the worm wars, I think more or less, you know, sided with our views, although you know, you can read about it. But I think because worms were controversial, it just got all this attention. Like, wait a minute. These economists are saying we should be focusing on worms and they're, you know, distracting attention from malaria or something else. So anyway, it was an interesting experience as a researcher who's not a, you know, media person. But I think at the end of the day, there was a scientific debate. And I think, you know, we kept it, you know, both sides kept it largely professionals. Just some folks in the media, I think, gave it a lot more attention than it maybe should have gotten.

Look, there were string wars back in the 90s about super string theory. You can have wars about anything. Like, you're actually spending money and saving lives. It's a higher stakes game. But is part of the media attention because this kind of thing is at least adjacent to effective altruism and other things that get labeled as movements in the popular imagination? For sure. And I think the whole our early study on deworming and the follow-up study showing the rate of return. Like, we were doing these kinds of effective altruism style calculations and creating those estimates before the term had even been coined. And I think our work was picked up on by that movement. Tons of funding from effective altruist organizations has gone literally into

deworming programs around the world. So first of all in Kenya, and again, it's been a great part of my career just to see how ideas from research can get translated into action. But because we did this work in Kenya and we did the follow-up work, we were very active in disseminating those findings to policymakers and media and whatnot in Kenya to the World Bank, to the WHO as well. And the government of Kenya in 2009 put in place a national deworming program with a design of the school-based deworming that was like based on what we had done in our earlier study. So that scaled it up to millions. But then really powered, I think, by effective altruist organizations. It was scaled up to other countries. India has had mass campaigns with 100 million kids a year getting treated.

You know, Vietnam has had campaigns, Nigeria, you know, Ethiopia, et cetera. So hundreds of millions of kids per year have been getting these treatments at very low cost, in part because of our work and the work of other scholars. So I do think that it became like the flagship for the movement, and that did increase media attention for sure. And again, this study, the kind of original study and the follow-ups were, you know, very heavily cited even in the Nobel announcement. you know, in 2019 for Kramer and co-authors.

So, yeah, we've been in the middle of that media attention. And, you know, one of the things I tell my students and the folks in my group here, because there has been so much attention on it is like, let's make sure we don't get caught up in all that. Like, whatever we find in the data is what we find. Some effects are going to be significant. Some are going to be large. Some are going to be small. And, yes, our results are going to be used by policymakers, but we just have to stand by them.

we have to believe in them and just keep the scientific ethos front and center and not just become policy advocates. Like if I just, if I do new research and the new research shows deworming has harms or deworming doesn't do anything good, I'm going to try to put it out there just as strongly as my first results. And if that changes the policy calculation, great, actually. That's a good thing. So, you know, we're trying to keep the science front and center here.

I did have Joshua Green from Harvard on the podcast a while back. and he's involved with Give directly, and I'm pretty sure that I've given money to deworming projects because of that involvement. So I've done my little bit of part there. But there's another thing you mentioned, which is the long-term follow-up. You know, speaking about this theme

of how to be a good empiricist as an economist, I get the feeling that there is some difficulty associated with doing long-term studies. Like there's a feeling that if you do, if you try an intervention and it doesn't pan out, then you give up. And you need to like really think very hard about spending resources on doing something longer term. Yeah, it is very resource intensive to follow people up, especially in settings, you know, low resource settings like Kenya. We have been following up the original deworming sample now, a representative subset of them.

So there's around 6,000 folks we've been following up. For 20,000, seven years, we've gotten funding for many different sources to do it because we need to like have, we need to maintain a standing team of enumerators, of surveyors who are going and finding respondents wherever they move. We don't only want to look at the people that just stay in the original study area. It turns out once you get 10 or 15 years after the original intervention, about half of them have moved to cities. So we need to like go and find them in Nairobi and find them in Mombasa. Some of them move to Uganda. Some of them have moved even to, you know, the Gulf states. And we do phone surveys with them. So, you know, there's there's a whole enterprise there to keep in touch with them,

but we've managed over 27 years to have an effective survey response rate of 83, 84%. So we're very happy with that. It's balanced between the treatment and control group. So we're actually able to speak about the central tendency in the data, you know, in a pretty representative way following the vast majority of respondents. And as I mentioned, you know, when you invest in child health, we find short-run effects on health and education, and we find these longer run effects on, for instance, likelihood to move to Nairobi, likelihood of moving to a city. It goes up by about 10%. Earnings go up around 10% in that sample, and people move into different types of occupations. They move out of agriculture into various non-agricultural occupations, like manufacturing jobs

that are better paid, et cetera. So by tracing the data over a long period of time, we can see how a health investment can transform people's lives in multiple different ways. And it's something that I'm really proud of. It's taken, you know, again, decades of work to build. Yeah, and it's hard to do. So not that many randomized control trials or any other studies sort of have had that, you know, ability to follow people over time. They either haven't been able to raise the resources to do it. Or like you said, maybe they were discouraged by initial results, which is a bit unfortunate, but understandable too, because they have limited time and they're going to kind of focus on big questions where they can maybe find answers that are interesting. The idea of the control group

obviously is crucial to everything going on here, but much like in medicine, is there some feeling, I don't know how to describe the feeling, that if you really think the intervention is going to help, do you feel bad about not giving it to some random fraction of the people you're studying? Yeah, definitely. And I think, you know, for us, you know, the ethics of it. It's not exactly the same with our interventions in economics as it is in health, but a lot of the same considerations apply. Certainly with a deworming intervention, which is a health intervention, you know, we had to go through all the same kind of ethical approvals and whatnot as any medical trial, you know, would have. In our case, I think the ethics were

interesting in the following sense. Again, we were working with a nonprofit that was sort of like expanding a health program. The government wasn't doing mass deworming. It wasn't kind of a standard program at that time in the late in the late 90s and this nonprofit was just scaling up gradually so like we no one was being denied treatment it was really just a question like hey they had identified a region with really high infection rates and actually you could make the case that randomizing was a pretty fair way to do it rather than just going to politically connected areas or something first so um you know in that case with resource scarcity you know not everybody was going to get treated and that's true for a lot of the development economics interventions that we study that like when you're dealing with you know

governments or programs that have limited resources they're not going to be able to treat everybody and you know i've worked as i mentioned briefly before i've been working on cash transfer interventions with you know give directly and others and um they don't have enough cash to like give cash transfers to all the poor people in kenya or Tanzania or other countries like it's just limited they just they get donations from people like you and others they have a budget and um and so So again, you might think that a lottery is a fair way to allocate resources. Once you've identified poor folks, it's not clear which of them is more or less deserving. But, yeah, the ethical issues are very important.

We debate them and talk about them a lot. And, you know, the resource scarcity in some ways, you know, makes it inevitable that not everybody's going to, that someone's going to be in a control group of some kind. Yeah. Yeah. Premier protein. It's for getting after life, not just fitness, with 30. grams of protein, 160 calories, and no sugar added.

Helping people fuel their joyful lives. With Premier Protein, you can say yes to more, whether it's crushing a big presentation at work, building an epic fort with the kids, or hitting the hiking trail with friends. Premier Protein offers delicious flavors like Cafe Latte, Chocolate, caramel, vanilla, strawberry and cake batter, to name a few. Find your favorite flavor at PremierProtene.com. Is there something in economics that is analogous to the replication crisis? that we've seen in psychology and other social sciences?

There is, and that's something that we're working on here. In fact, we're having a conference here today at Berkeley from the Berkeley Initiative for Transparency and the Social Sciences Bits, which is an effort to kind of bring open science tools into the social sciences. We have had a replicability crisis. It hasn't been as severe in economics as in psychology.

I think in social psychology in particular, it's been so high-profile. there have been, you know, real kind of bruising battles in that field. So many instances of fraud and other things. There haven't been as many in economics. There's been more consensus since early on in the field around issues of data sharing and the importance of, you know, computational reproduction of our work. But we've had our own crisis.

So I don't know how much you followed the news. But last week, there was a kind of scandal coming out of the MIT economics department where a grad student has been accused of fabricating a whole data set, like a massive data set, which I think purportedly was from a biotech company and, you know, claiming that the use of AI tools was accelerating discovery in this company. And then, you know, pretty quickly, you know, based on the description of the company, I think people in that field were like, wait, that could only be three or four companies, which one is it? And then it became clear that it was, apparently, it's allegedly fabricated.

So we have our own crises and, you know, there are tools to deal with these things, things like data sharing, just allowing other people to scrutinize your work is important. And this is an area where actually economics is a bit ahead of medical research in that data sharing and sharing of underlying datasets and survey instruments and whatnot has been a lot slower in the health field. I think there's been a lot of concern about sharing personally identifiable information, PII and the health field understandably. But, you know, sometimes it may be a little bit of a pretext in that field for people who have collected data not to share their data with others so they can write papers. I think that's at least, you know, a little bit of a concern. In econ journals have been much more aggressive in forcing authors to post their data, to make replication data public. And I'm really proud of that in our field because I think we were ahead of the curve on that. And that's just central to science.

We can't do science if no one else can scrutinize your work and verify your work. It's just like a basic principle. The irony of the MIT paper was it was supposed to show that AI improved productivity, but it actually showed that AI improved the ability to be fraudulent, to make up data and things like that. Because I remember the old days in physics, people would reuse a graph of one thing for an entirely different claimed result, and you would be able to eventually figure out that must be faked. But with AI, you can fabricate data really well. Yeah, apparently not well enough, I guess.

for this person. Thankfully, hopefully the AIs don't get so good in a few years. We can't even detect it. Yeah, no, I mean, I think it's just touching on this other open question of the role that AI will play in our work going forward. And, you know, for the kind of work that we do where we're collecting original data in the field, like there's just going to be a lot of machinery and data generation in our field that will not be automated that AI can't do.

We have to actually talk to people. It's complicated to measure living. standards. There's like ways that we do that kind of measurement. But other parts of the research process will change. And we're already seeing it. I mean, even in this the conference, this Bits, you know, transparency conference that I'm at today, you know, there's a lot more use of AI in extracting estimates from existing studies for use in meta-analysis. And actually, that's a pretty good use of AI tools. Like if we get the right training data, you know, because our papers have a certain format and results are being presented in a certain way. But if we can,

you know, get the right kind of, you know, high quality training data, then it could be that the LLMs can go pretty far in certain forms of data extraction and speed up meta research. So I'm optimistic about certain applications, but it's not going to take over all of our research. There's still realms of research where we need to generate original data that at least for us in development economics, the AIs can't do. Let's go back to the issue of the cash transfers. in Kenya because I know that that's been a big project. And I guess is there a distinction between cash transfers and basic income or is one just a subset of the other? They're very closely related. I think the conception of basic income is it's a kind of ongoing income support over,

you know, say multiple years. And, you know, basic income is a form of cash transfer, but there are also one-time cash transfers where cash transfers given at different frequency or, you know, with different sizes. So that's right. I think cash transfers are kind of the catch-all frame. For us, the work we started working on about 11 years ago in Kenya with give directly was a very large RCT of a one-time cash transfer, but a really big cash transfer. So in U.S. nominal terms in 2014, it was $1,000 per household. And given the difference in prices between the U.S. and Kenya, where prices tend to be lower there, that would be the equivalent. in real U.S. dollar terms of the time of closer to $2,000,

which for Kenyan living standards at that time would be something like 75% of average household income. So a really, I mean, a huge transfer, right? That'd be the equivalent of giving tens of thousands of dollars to a poor household in the U.S. So we studied that with Give Directly. It was kind of relatively early days in the cash transfer movement. And the way we designed it, and this is actually, you know, going back to your earlier point about theory,

This was actually an intervention that we very much designed with economic theory in mind. So it was an RCT, it was a randomized control trial, but we were very interested in not just understanding the effect on the recipients, but a little bit like the deworming study, understanding those second order effects, the spillover effects on the local economy. And there are models in macroeconomics of what are called multipliers. So, you know, if you inject money into the economy and people spend it in certain ways, if they have a very high, what we call, marginal propensity to consume. Like for every dollar they get, they spend most of it.

It's just going to generate lots of multipliers in the economy because, hey, local businesses will get more revenue. And then they'll be able to hire more workers. And then those workers will spend more, et cetera, et cetera. So you get this kind of multiplier effect, which is well modeled in macroeconomic theory. So we were very interested in those models. And we work with Give Directly to design a large enough experiment so we could understand multipliers and estimate multipliers.

And that was novel at the time because most of the macroeconomic estimates of multipliers are not experimental. And they're not based on RCTs. They're just like, well, the government had a stimulus program and let's kind of do a time series analysis or a pre-post analysis effectively

or in some cases more sophisticated designs. But this was an experimental design. So it has some more validity. And we put in place this, you know, the data collection to, to estimate effects on the local economy and found really big multipliers, much bigger multipliers than you would see in the U.S. And there's good reasons why.

The marginal propensity to consume in rural Kenya is really high. If you target poor households, we find that in the first year after they get the cash, they spend like 90% of it, like almost none of it's being saved. It's just being spent. So local business revenue goes up. They hire more people.

Like all those hypothesized things in the models come through in our data. And that means the dollars, you give to poor folks in, say, a rural African setting, you know, generate more than that dollar in local GDP. So it makes cash transfers targeted to the poor look like a very attractive policy. It is a more expensive experiment to run than the deworming, it sounds like. Much more because, you know, like I said, if it's 30 cents per kid, you know, for deworming, this was $1,000 per household. It's a very expensive experiment for sure. but really big economic impacts too.

So, you know, I think that was the flip side. You know, in many ways, the cash transfer program I've worked on in Kenya follows the model of deworming study because from the start, we were really interested in doing longer-term tracking of this population. We really wanted to understand not just spillovers in the short run, but these kind of like broader long-run effects. So we've continued data collection. Now it's been 11 years.

We're still in the field, collecting lots of data. and we've been able to track economic effects over time as well as health effects. So kind of things go full circle here in terms of understanding, you know, causal relationships between income and health and health and income in my own work. What we found with cash transfers in Kenya were really big health effects, in particular for children. So we were able to collect data and get a full birth census of this population in Kenya we were working in. And again, the scale of the cash transfer program in Kenya was. was very large. We're working in an area with 300,000 people. We collected full birth histories

for women around the time the cash was going out. And there's just tens of thousands of births in the data set. And we find that in the year the cash transfer, this big $1,000 cash transfer was received by households. In that year, infant mortality in recipient areas fell by 40%. Wow. It's just a huge infant mortality reduction. Just like a massive number. And, we're writing that up now. We're about to finish that analysis. So again, it's a gratifying thing about this work. And it goes back, you know, early in my career, I could only have dream that I'd be able to work on big programs where we were like studying how economic interventions like save lives

and affected lives in this profound way. And this seems to be an example. Cash transfers don't just benefit the local economy. They're like literally saving lives. I can see how it would be extraordinarily gratifying. I wonder if it's also, and there's an element of frustration that we live in a world with so much inequality that a relatively tiny amount of money is life-saving to so many people. Yeah, there's no doubt about it. And again, it goes back to sort of, you know, like if you talk to the person on the street here in the U.S., it's not clear they understand just exactly how much could be done with a few dollars. And it's also not clear that they understand. understand how little we've been spending on foreign aid and these forms of redistribution to poor people.

So, you know, there are some surveys out there where they ask Americans, you know, how much, how much do you think America is spending on foreign aid as a share of the government budget? You know, and people say, oh, I think it's, you know, 20%. I think it's 15%. Of course, in truth, and this is even before what just happened with USAID, the dismantling of USAID, it was like, you know, 0.3 of 1% of the budget or 0.2 of 1% of the budget was going to foreign aid. So like minuscule amounts. But for those really small amounts, we were having big impacts.

We were saving lives with HIV treatments and malaria treatments and cash transfers. And now even that like minuscule amount is being slashed by the current administration. It's really unclear what the future holds. So there's this kind of misunderstanding about. about what we're doing, and there's a misunderstanding about what we can do. And I think it's frustrating. And maybe it's a, I guess ultimately,

it's a failure of ours as researchers and communicating what we're doing. I mean, we're trying. But somehow we're not getting the traction we need to tell people, hey, if you like don't drink that cup of coffee, that's $6 now or $5, you could give deworming drugs to 20 kids. Yeah.

Like that's pretty powerful, but people don't know it. Well, you're on the podcast. doing your part. I mean, that's hopefully we'll reach some people. I hope we're not only, hopefully we're not only reaching the converted already. But in terms of understanding why the effect is so big, I had this vague feeling that I read people talking about the cost of being poor, right? Like if you're poor, not only do you not have a lot of money, but you just hit with all of these fines and fees and things that make it impossible to pull yourself out. And, and I'm guessing, hypothesizing, that a big one-time pile of money will just help release you into

the world to do your work. Exactly. And that's really what we're finding. Even when we look at these folks who receive cash transfers five or seven years later, they're still doing better. Partially because of this, they're more likely to have invested in certain types of durable assets like livestock that could pay off over time. You know, even very basic things.

And you alluded to it, like in shops in rural Kenya, if you're only able to buy small amounts of sugar or small amounts of flour or small amounts of other stuff, the per unit cost is higher. Like, they're really just our fixed cost to doing things. And so having more liquidity pays off. And, you know, it just, it goes on from there. There's also really, you know, great work in the field of development economics that's bringing in insights from psychology. And this is this really exciting intersection of, you know, behavioral economics.

and psychology, which shows that even beyond that, there's just psychological implications of poverty that make it harder to break out of poverty. So, you know, people who are poor are really worried about their money and they're really worried about their kids going hungry and it can affect decision making. And so people who are just super stressed out about money systematically make certain types of errors. They're less productive. So, you know, poverty breeds more poverty. And again, these large one-time cash transfers or basic income, anything that can just temporarily lift you out of that state can allow you to make investments that improve your life. And I think that's one of the powerful things about, you know, the cash transfer model.

It's so simple, right? It's like this very pure form of redistribution from folks like you or me who have an extra $100 or $1,000 here or there that we may not even think about or notice missing. And that's just transformative for somebody in a rural African setting. Hey, everyone, it's Cal Penn. I'm the host of Earsay, the Audible and I Heart Audiobook Club. This week on the podcast, I am sitting down with Ray Porter, the narrator of Andy Weir's audiobook Project Hail Mary. Massive sci-fi adventure about survival and science. And what happens when you wake up alone very far from Earth? I really had to make a decision because I caught myself getting that frog in my throat and starting to get teary. as I'm narrating some of these sections, and it's like, okay, yo, yeah, yo, is this indulgent? And I really thought about it. I was like, no, at this point, it would kind of be betraying the trust the author and the listener have in telling this story if I don't go through it. But there's places in this book that deeply emotionally affected me, and I left it on the mic.

That's great. Because it served the story. People will say like, oh my God, I cried at the end. It's like, yeah, dude, me too. Listen to EIRSA, the Audible and IHeart Audio Book Club on the IHart Radio app or wherever you get your podcasts. Premier Protein. It's for getting after life, not just fitness. With 30 grams of protein, 160 calories, and no sugar added. Helping people fuel their joyful lives. With Premier protein, you can say yes to more, whether it's crushing a big presentation at work, building an epic fort with the kids, or hitting the hiking trail with friends. Premier Protein offers delicious flavors like cafe latte, chocolate, caramel, vanilla, strawberry, and cake batter, to name a few.

Find your favorite flavor at premierprotein.com. There is maybe like a moral aspect on some side of the things that the simplicity of it gets in the way because people are like, well, how do I know we're giving money to deserving people? Like, shouldn't we test that we're giving it to the right people? And my feeling is maybe in the ideal world you could target it precisely, but in fact, it's just simpler or more effective just to give everyone money. Yeah, and that's really been the model of give directly and several other programs. They do have targeting criteria that are pretty rough. So they're looking at, you know, in the early days, like in our project, looking at the building materials in someone's home. And it turns out in our data, the people with like rough building materials who have not as nice home,

homes really are poorer than the people with fancy homes, right? That's true everywhere. So they can use something that's really cheap and very visible, very easy to observe for targeting. And that cuts out a lot of overhead costs because if you really want to go and carefully measure people's living standards, it's pretty expensive. We have tools to do it. But you may need to go through a two-hour survey to carefully measure their agricultural production and how many assets they own exactly. And you still end up with something very highly correlated with like, you know, how rough is the roofing on your house. So I'm a fan of that approach because it just means more of every dollar actually gets to people. And, you know, there's another aspect of the deserving

poor is this question, which we often get asked of like, well, are people wasting the money? Are they like drinking away the money they get or spending it on gambling? And it's impossible to know for sure exactly how everybody spends every dollar. But there have been follow-up surveys, like in our study and in just dozens of other cash transfer studies at this point that do, in careful ways try to get it, how much is being spent on what you might call sin goods or temptation goods, like alcohol, cigarettes. And, you know, across these studies, you know, very small shares are being spent on those things a little bit. Like people do buy some alcohol with the money, but in our date, it's less than 1%. So, you know, it's something that we can live with.

And, you know, the caricature of poor folks, you know, wasting this windfall on, you know, booze and cigarettes and drugs or something. I'm sure there's cases, where that's true, but it's really not the vast majority of households. You said a provocative thing about not only is poverty expensive with all these extra costs, per unit costs, et cetera, but it also drives people towards making worse decisions about things. That's very interesting, and I'm glad I learned it, but then you've also worked on other things that hurt decision making like the temperature outside. Like climate change is making us dumber as well as all sorts of

other things to slightly oversimplify. Yeah, I mean, that's another branch of my research that's really not as grounded in a lot of these field experiments, but I have another line of research that's been really exciting to work on on climate change and what it's going to do to developing countries, especially in Africa. And again, it's part of my overall research focus on African development and poverty. We cannot ignore climate change if we're going to understand the evolution of living standards in poor countries over the next 20, 30, 40 years.

One of the things we were able to do is some lab experiments, and we did lab experiments, both in Berkeley and in Nairobi, Kenya, seeing how people react to higher temperatures. Again, trying to simulate, like, okay, if they're in a warmer environment, we manipulated actually the lab to make some rooms hot and some rooms cold, and we randomized that. And then we measured how do people's economic decisions change. And it was a fun thing to work on, and actually, it took like months to figure out how we could regulate the temperature. We actually brought some engineers in to like, we had to hide the heaters because we didn't necessarily want everybody to know we were doing this experiment in that way

and that like had temperature, you know, sensors all over the room. It was an interesting applied engineering problem. Sorry, this was at Berkeley at the university? At Berkeley in the experimental lab here. And in Nairobi, Kenya in a lab called the Bussara lab, which, you know, does experimental and sort of behavioral economics research there. And, you know, some of the work, you know, on both ends is supported, you know, by my center here, Center for Effective Global Action, which is kind of an overall development economic center. So there were a bunch of different partners in this. But yeah, we set up these labs. Some were like normal temperature, you know, 20 degrees Celsius at like 68 Fahrenheit. And some were hot, 30 degrees Celsius, like 86, 87

degrees Fahrenheit's like kind of uncomfortable. And then we saw what happened to people's decision making. It was pretty interesting. In Berkeley, whether you were in the hot room or the core room, like nothing really changed people's answers, how they solved puzzles, math problems, how impatient they were in the lab. Like, folks in Berkeley were just kind of chill regardless of the settings. It kind of fit the stereotype. But in the Nairobi lab, we found something pretty interesting. There's a bunch of different games that experimental economists have developed to try to measure how altruistic people are to others, to measure what we call social preferences.

Like, how nice are you to other people? And one of the games they've developed is a game called the Joy of Destruction Game, which is really, the game is exactly what you would think. When you're in a lab with a bunch of other people, you can costlessly to yourself just destroy some of their payoff. You don't even get anything out of it. It isn't like, oh, I'm stealing it from them. There's other games like that.

This is just called The Joy of Destructions, purely measuring whether I will choose to, like, destroy other people's payoffs. And in general, you know, both in Berkeley and in the Nairobi Lab, most people don't do this. I think actually the Berkeley rate, back to the stereotypes of our, you know, West Coast folks, like I think of the dozens of studies out there, the rate in Berkeley of people destroying other people's payoffs was like the lowest ever observed. It was like, only 2% of people did it. Everybody was just chilling. Like, hey, you keep your money, I'll keep mine.

They would like to destroy their own if they had the chance. That's right. Exactly. That would be the caricature. But in the Nairobi lab, it was like 15%, which is pretty similar to what you see in some European studies, some U.S. studies. But what was very interesting in the Kenyan setting was in the hot room, it went up a lot. So in the hot room was like another 50% higher, you know, on top of that.

So there was something about being in this uncomfortable environment where people were kind of agitated. Like when we asked them how they felt, they didn't feel that good in the hot room, et cetera, that made them kind of antisocial. And it's something this is a paper that was recently published. I think it's important to do this kind of work, try to understand the underlying psychology of heat, how it can affect our behavior, how it affects how we treat others. Because, you know, one of the things that's been seen in the overall climate economics literature and climate literature is a lot of social processes break down at very high temperatures. The economy doesn't work as well. If you look at an African country in an anomalously hot year versus a normal year, in that hot year, there's more political instability. It's more likely to have an outbreak of civil conflict.

So there's a link between extreme temperature and social breakdown. And what we were able to do in the lab is start pinpointing that, like, you know, even if I'm just in a hot room for a couple hours, that process kind of gets started in a troubling way. Is there a psychological or even neuroscientific theoretical understanding of what's going on here? There are claims about it, and we read through that literature. It's a little bit above our pay grade to fully understand it, but I don't think it's fully understood. There are, you know, some changes in neurotransmitters at high temperatures. You know, we know there are physiological changes, right? We all experience them at high temperatures.

But I don't think it's very well understood. At least that was our take on the literature. So I think there's more work to be done. I'd be fascinated about a version of the study done in like Phoenix or El Paso, where you're in the United States, but in a very hot environment or Atlanta, like the South where it's humid also. Is it like people are more willing to put up with it in Berkeley because they almost never experience it? So it's not as frustrating for them.

Yeah, that question of adaptation, I think, is really important. And yeah, I think it would be a great, you know, extension of the study. Maybe over time, people just get used to it and they deal with it and they have different ways of dealing with it. Of course, you know, one of the ways people adapt to high temperatures is they avoid it. They like crank up the AC and, you know, and deal with it that way. But I think that, you know, that's one of the big open questions in the climate literature in general. Like, you know, adaptation writ large is something we need to understand to understand how an increase of two or three degrees Celsius is going to affect us.

you know, how quickly can we adapt, either physiologically or in terms of our institutions. That's a big open question, I think, for social scientists studying climate. I guess this might be the result of some stereotype buried in my mind, but if I'm honest, I would have guessed that in Nairobi they would have bore the heat better. Like, aren't they used to it? Yeah, you know, it's interesting. like Nairobi was chosen as the capital of Kenya because it's in the highlands and it's actually pretty cool.

Okay. So the kind of mean temperature in Nairobi and Berkeley isn't that different and people aren't really used to very high temperature. So I think it kind of still fits the story that, you know, they're not used to, you know, say 90 degree Fahrenheit days or, you know, 20 degrees Celsius days that often. It's a little more of a puzzle, you know, why it differs so much between Berkeley and Nairobi, and that's something we can only hypothesize about, yeah, people were just very chill in Berkeley. There's a kind of wrinkle of that finding in Nairobi that I'll just briefly mention, which is we also have data on the ethnicity of our participants.

And actually, we were carrying out this experiment in Kenya during a general election season. That was very heated politically. And some ethnic groups are very much aligned with the government, which won that election, others were aligned with the opposition, which felt very hard done by and kind of felt like the election wasn't being conducted fairly. This response to the high temperature was concentrated among people who belonged to ethnic groups aligned with the opposition, actually. So it was actually like kind of a marginalized group in this stressful high temperature condition that started destroying other people's payouts. Folks aligned with the kind of pro-government groups, the kind of dominant

group in Kenya didn't respond that much to temperature. So there could even be a sub-wrinkle to this that, like, if I have some, like, underlying latent, you know, anger or feeling of marginalization, then I get triggered by the environmental stressor. So I think that's an even more kind of complex story, but one that I think resonates with our intuition about how people respond in society. I mean, it makes perfect sense to me. And is yet another example of the not assuming that people are perfectly rational actors as economists sometimes like to do. In the pandemic, when people were locked down, there was certainly anecdotally a fraying of the social fabric. And I can imagine that being on the losing side of election, being in a country that has a lot of poverty and disease, all these

feed in and the causality can be hard to tease out sometimes. Yeah, no, for sure. And, you know, it's been a very big challenge for those of us studying the impacts of climate. on economic outcomes or political outcomes that, you know, we can't just compare hot countries to poor countries, right? Like, they just differ in so many different ways. People that may adapt in certain ways. And the way we've actually dealt with it and just getting into the statistics a little bit, because I know the audience is more sophisticated, a lot of the, even though we're interested

in long run effects of climate change, a lot of the statistical analysis among social scientists trying to understand the effect of climate has focused on shorter-term climatic shocks, like an anomalously hot year or month or maybe two years, rather than like a multi-decadal scale analysis because we just haven't had, you know, the data and the variation over, say, 40 years. Like, we care about the next 40 years, but, you know, we're forced statistically to look at shorter-term variation. And that's where there's kind of a gap between what we estimate from short-term variation

and the possibility that maybe over the long run we can adapt to longer-term variation. So that's, again, one of these big open questions in terms of the empirical analysis of climate among social scientists like economists or political scientists. I forget, were you part of the research on the parking tickets and corruptions

and the United Nations? I was. That was one of the funnest projects I've worked on. That was with Ray Fisman, my colleague who's now at BU. and, you know, that was something that, you know, it's just one of those things. You get an idea and it's a little bit wacky, but it turned out it had, you know, a lot of legs. And just to summarize it briefly for the audience, sorry, you know, we thought of ways to understand how country institutions and country characteristics, how persistent they are. And this was the case of corruption, you know, in particular.

And our insight was, you know, maybe we can get insight into the behavior of government officials by looking at them all in the same place. How do they behave in the same environment? And this was UN diplomats in New York. So, you know, how often do they abuse their diplomatic immunity to rack up parking tickets? And that's a pretty good, you know, you could definitely think of that as corruption. You're abusing your official position so you don't have to pay something. You kind of gain monetarily. You don't have to pay for parking.

And you can kind of get away with it. Basically, do diplomats from some countries abuse their position and others don't? And does that correlate with how much corruption there is in the home country? That was the question we wanted to ask. And it turns out home country corruption very strongly predicts which diplomats abuse their position in New York City by racking up unpaid parking tickets. So, you know, folks from countries with very, very. high corruption indices like Nigeria. They're diplomats in New York.

We're getting parking tickets all the time. And, you know, diplomats from Japan or Norway, which have very low corruption indicators, basically almost never rack up an unpaid parking ticket. So that was a study that got a lot of attention in the popular media because it's a fun thing to kind of like, you know, imagine diplomats choosing to double park on some narrow street in New York City. But it does seem to feed into this. wider thing that culture matters,

like everything feeds back on everything else. I mean, it's a fun thing, but corruption in a society, if that's what you expect to come across, might be kind of friction in the economic gear wheels on a much larger scale. Yeah, for sure. And political scientists and economists have written about how persistent corruption is,

partially because it's a culture, it's self-reinforcing. if I expect you to behave a certain way and you expect me to behave a certain way and we all feel that way in society, it's very hard to break out of that equilibrium unless something really radical changes. And again, even if you fly diplomats all the way around the world to New York City, they kind of keep acting the same way as they did in the home country. We're often asked in that study, you know, how did the U.S. perform? How did U.S. diplomats do in terms of racking up unpaid parking tickets in New York City. And unfortunately, we can't measure it because U.S.

diplomats in New York City don't have diplomatic immunity. They're in their home country. So we can't observe it. And people always wanted, oh, how do the U.S. diplomats do? How corrupt are we? But yeah, with that research, we can't shed light on that issue. Probably, yeah. Okay. I don't want to guess. It's just going to get me in trouble about how corrupt the United States is. And the other thing that it actually, is fed into by this stuff on climate change, etc. that you've worked on that is fascinating is conflicts, you know, big, large-scale conflict, civil wars and things like that in a country or external wars and how they affect what's going on economically or, for that matter, how what's

going on economically leads to a conflict. And especially if the whole world is getting warmer and that slows down our economic engines, is that going to start increases in violence in a lot of places. Yeah, some of my early, really my earliest work on climate was related to exactly that question. Focusing on African societies, is it the case that when you have a bad rainfallfall year or you have an especially warm year, we do get, you know, more risk of civil conflict? And we find very strong relationships there. You know, these anomalously warm years, anomalously dry years tend to be associated with more conflict risk. And in a lot of that work, we do interpret it through an economic lens. So we do know in, say, a poor African society that the agricultural sector is a huge part

of the economy. And when there is a drought, incomes fall for a lot of citizens. It's just a major economic shock. And so those climate shocks turn into economic shocks that are destabilizing. Because when people are poor, they have much less to lose. They may be more willing to fight, you know, fight the government, join an insurgency. And that comes through very clearly in data over, you know, many decades and it's been replicated in a lot of other countries and a lot of other settings. It turns out that in years with climate shocks of various sorts, high temperatures, bad rainfall, there's more crime. There's more Hindu Muslim riots in India. There's more land invasions in Brazil. There's just a whole literature that's developed around those ideas, which is very

troubling because, you know, at this point, the world is warming. It's going to continue warming. I think even the most optimistic assessments are we're going to warm by a couple degrees Celsius in the coming decades. And so if we take the existing estimates seriously, there could be far more civil conflict risk and crime in the coming decades. Our best guess from some meta-analysis work that we've done is with the warming we'd expect around the world in the next 30 or 40 years, the risk of armed conflict could go up by something like 40% around the world, unless we can adapt in some effective way. way. And we already have a lot of violence in this world. So a 40% increase on top of what we have is scary. Is there, I mean, since you've been there and been thinking about it, like, what are the how much of the challenge of helping the poor nations of sub-dehabber and Africa is a cultural shift kind of thing? I guess, let's put it in terms of a coordination problem. Like if everyone

work together, it could obviously be much better, but when the system isn't either working or established, people are going to try to take advantage, and that does get in the way. Yeah, you know, I would say speaking scientifically, I don't think we have very high quality evidence about the best ways to carry out that kind of cultural shift or shift in social norms. It's a very hard thing to study. We do have some case studies that are interesting. There are, particular African societies where governments have carried out large-scale reforms that have had social impact. So one that I've worked on and that's gotten quite a bit of attention is the case of Tanzania. So, you know, not a country, maybe a lot of readers are super familiar with, but, you know,

Kenya's neighbor in East Africa. And right after independence in the 60s and really for the next, you know, 15, 20 years, it was really a concerted effort to build up a Tanzanian identity that was very different than most of its neighbors. It was a big emphasis. on building up the national language of Swahili rather than regional languages or English to kind of bind people together with an African language. There was a big reform of the school curriculum with a very kind of nationalistic focus and pan-African focus. That was unusual among African countries.

And there were also attempts to diminish ethnic divides, for instance, by abolishing traditional chiefs in the country. So a bunch of these reforms were meant to build up a new identity that could kind of of bring people together, kind of along the lines that you're saying, Sean, of like, are there ways we can bridge these gaps and get everybody kind of pulling in the same direction as a country? And really all the kind of historical evidence and survey evidence since then points to the fact that it was pretty successful. Like Tanzanians feel very proud of being Tanzanian.

Ethnic identities have kind of fallen in importance relative to this national identity. Swahili is very widely spoken. People are very proud of it as their national language. So there are ways that large-scale reforms can transform social identities and social norms in African settings. But I think we need to do a lot more work to understand how and how these ideas can be applied in other countries. Thank goodness. Like halfway through, I wasn't sure which way that was going to go. I really was not familiar enough with the current state.

And I was really worried you were going to say, and it all failed dramatically. But apparently there is some of it. Well, we could talk about all the nuances of it. But in many ways, there have been positive outcomes. Another country I'll mention, and this is really a country that's seen as a model economic and political country in Africa is Botswana, which, again, some readers may be familiar with, but a country with a very visionary leader that promoted a kind of coherent national identity. And also, this was Sorette-Kama, the independence leader of Botswana, but also really promoted

democratic institutions. Yeah. And even today, Botswana has had just continuous democracy, since independence, alternation of power in the last election, free press, not that it's, you know, not without its problems. And they've also had very good economic performance. So we can point to particular country cases in sub-Saharan Africa where the right types of political and social reforms have really brought people together in a common identity and governments, you know, have promoted economic development.

So it's not hopeless. And that's something that I definitely want readers to take away. And it goes back to your first question. What does development look like? Yeah. There are success stories. And a lot of the aggregate trends are good. And that's something that I really hope, you know, the listeners here can take away from the podcast.

I do think that maybe I'm being overly optimistic or simplistic, but one message I'm getting is that sometimes the best thing to do is just to throw money at a problem. Like if you just flooded certain places with money, I guess that people would worry, be worried that, you know, the oligarchs or the monarchs would just take it all and it wouldn't help anything. but I don't know, I'm kind of getting the message that at least in some cases, it would help a lot, just throwing money at the problem. Yeah, I think so. And I think I would add the caveat that throwing money in ways where there's an evidence base to support how to do it. So, you know, I would say like just, you know, indiscriminately transferring foreign aid to governments that are very corrupt, that may not yield huge development impacts, but say the give directly approach of literally person to. person transfers, like literally putting money in the pockets of real people. The research literature on that is just overwhelmingly positive at this point.

It improves living standards. I mentioned how it, you know, it saves children's lives. It improves health. It has these spillover effects on local businesses on the private sector. Like, that's something that a large party of randomized control trials with good scientific evidence has shown is very effective. And that would be something that I would strongly advocate for. Yes, throwing money at the problem in that way could be very, you know,

very effective. And that's why, like, again, some of the cuts in foreign aid in the U.S., but not even just the U.S. There's big cuts in foreign aid in Europe are discouraging because finally after the last 20 or 30 years, we have a scientific evidence base in development economics. We really know certain things based on multiple experimental studies with good measurement have been shown to improve living standards, have been shown to improve health. We have that evidence base now. Like, now is not the time to be cutting funding. Now is the time where we need. know what to do. And it's been very frustrating to see governments in the U.S. and the UK and France. And China, too, China has pulled back a lot from foreign aid since the pandemic and even a little bit before. So we're at a

moment where we know what to do and the money isn't there. And that's something that's been discouraging. I'm hopeful the pendulum will swing back. I'm hopeful that communicating the scientific evidence will at least mean that private citizens in the U.S. and other rich countries will privately give to organizations that are doing good work. So that's my hope. It's a particular kind of discouragement when you know what it would, what the right thing to do would be. And you see people choosing not to do it.

Like there's a different kind of discouragement when you're at loss, right? But this must be frustrating. But, okay, we're reaching the end. It is frustrating. I wanted to give you a chance just to chime in on a couple of other things that I saw. somewhere on the internet that you were interested in, but weren't the things you've been doing a lot of already. One is tax policy, and the other is the economics of aging. What is it that is pulling you in these particular directions?

Yeah, let me focus more on the economics of aging because it's something that I'm working on very actively now. So traditionally in development economics, we didn't focus much on aging because countries' age structures are just very young. And so, you know, because of disease and basically bad health care combined with high fertility, like, you know, the population pyramid was just pointing towards folks being very young. But things are changing. And so in a lot of low and middle income countries now from Latin America to China, kind of famously, people know China's aging very quickly, India. And now increasingly in sub-Saharan Africa, they're just growing shares of old people in the population.

And in a lot of poor societies, the institutions, And policies really aren't set up to deal with aging populations. They don't have in most countries pensions for old people. So very few African countries have an old age pension that covers more than a few percent of the workers. You know, maybe government employees get a pension, but, you know, nobody else. Healthcare systems aren't set up to deal with the problems that old people have. There isn't sort of long-term care solutions set up for older people and on and on. And so a lot of poor countries are reaching this point where,

they're going to have to start making big policy choices. They're going to have to design systems, health systems and pension systems. And it's kind of an exciting time because they can design them from scratch in many cases. And they can learn from the research on these issues in the rest of the world. And so part of what we want to do is help translate those lessons so that African countries that are starting to move in this direction towards health insurance and elder care and pensions do so with the best evidence base. So I've mainly focused on this in African settings. We're starting to collect some new data on middle-aged populations and older populations, you know, systematic data on their cognition and on their health and what their needs are. So I see this

as a really exciting new area that, again, is a big problem. It's a big policy problem. But this is one where the scientific evidence base is really limited. So it's a little bit like when we were tackling cash transfers 10 or 15 years ago, we didn't know much about them. I hope in the next 10 or 15 years, we can make a lot of progress on these economics of aging issues so governments can make good choices in the coming years. It's impossible not to think, though, when we were talking about topics like corruption and the existence of poverty and an inability to take care of older citizens, that all of these are right here in our country very, very strongly and not obviously getting better, not to

pull it back to the discouraging side of things. But we'll have to take some lessons ourselves, I guess. I agree with that. And I think, you know, for those of us that study low and middle income countries, we've always looked to the U.S. and Canada and the U.K. and, you know, Japan and rich countries as models. And rightfully so, right? The U.S. is the richest, most prosperous society in the history of human civilization.

We shouldn't forget that. Like, we've done a lot of things really, really well. And so, you know, there's a lot other countries can learn. But my fear right now is it feels like we're kind of unconstitutional. learning some of our own lessons. Like, there's a lot of things that have powered our prosperity, big social investments in old folks are being threatened politically. Obviously, big investments in scientific research. I mean, and health care, NIH, and NSF. And the last few months have seen just, you know, slashing of budgets there. But we wouldn't know how to design policies,

and we wouldn't know how to, you know, carry out social programs or health programs or public health programs if we didn't have the evidence base. So, you know, again, I'm hoping the pendulum will swing back. I'm hoping that we can convince folks who are skeptical about scientific research and health spending. I hope we can find a way to convince them and show them the benefits of all these investments. They really are investments so that we don't become poorer and less healthy. I think it really comes down to that. Like, we wouldn't have the life expectancy we do. We wouldn't have the prosperity we do. We wouldn't have our iPhones. We wouldn't have airplanes, whatever, if we hadn't been investing in scientific research for generations now. And I'm so proud that the

U.S. since World War II has been the focal point for global science. And, you know, hopefully we don't lose that. I don't think that quite counts as an optimistic place to end, but we can at least aspire to the pendulum swinging back. I think that's a good thing to cross our fingers for. So Ted Miguel, thanks so much for being on the Mindscape podcast. Thanks, Sean. It's really fun to talk to you. Thanks. And I shop Ashtro. At Ashtro, I find statement dresses, powerful suits and Afrocentric styles made for my life, church, celebrations, date nights, moments that matter. From head-turning outfits to the finishing touches, wigs, hats, handbags, and shoes, Ashtro helps me show up confident. For me, it isn't just fashion. It's how I express who I am.

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