Episode Transcript
Steven LEVITT: In this week’s episode, I continue my conversation with my University of Chicago colleague Sendhil Mullainathan, an economist, data scientist, and MacArthur “Genius” Award-winner who has remarkable talent both for generating ideas and explaining those ideas in a way that anyone can understand.
Welcome to People I (Mostly) Admire, with Steve Levitt.
LEVITT: Sendhil has so many interests that in the first half of our conversation — that’s last week’s podcast episode — I didn’t even manage to get to the topics he’s thought about most. So, if you missed last week’s episode, no worries. The order of the two episodes doesn’t matter. But in this week’s episode, I pick up in the middle of our conversation, with me telling Sendhil about my first reaction to hearing that he had written a book entitled Scarcity.
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LEVITT: One of the things I found so unbelievable about that book is that I saw the title and it’s called Scarcity. And I thought to myself, “This is so arrogant because economics is the study of scarcity. And for the last 200 years, we’ve all been studying scarcity. So, who does Sendhil think he is that he’s going to have something new to say?” And I read the book and I’m like, “Wow. Sendhil had something new to say.” So, first tell us what you had new to say. And then I think bring it home with the experiments.
Sendhil MULLAINATHAN: So, economists study the physical fact of scarcity, you know, everything is scarce. You buy something, you’re not buying something else. There’s no constraint. What we are studying here is the psychology of scarcity, the feeling of having too little. And the hypothesis is that when you have too little of something that tends to capture your attention, your mind automatically goes towards it. When you’re very busy, your mind goes towards the things taking up your time, and the deadlines, the things that are due. For the poor who are scarce in money. Their mind automatically goes towards, “Oh my God, will I be able to make rent?” And, for me, the most satisfying thing about the book, it’s the sheer amount of feedback I’ve gotten from people who have experienced poverty or who are experiencing poverty saying, “This captures my experience of being poor.” And I’ve had periods of poverty and that’s what you feel. Your mind just keeps going to this thing. So I’ll give you the example — this is a study, not from the book. This is with Anuj Shah. And what we did was we reran the same word study that we just did around the word “sleep,” but we ran it with a new set of words.
Sendhil’s referencing a study we talked about in part one of this interview, if you tell people a series of words like…
MULLAINATHAN: Bed, rest, awake, nap, dream, wake, doze, snore, slumber, blanket, tired.
Things that revolve around sleep, but you don’t actually say the word “sleep” and you ask people to recall the words, 50 percent of people will think you said the word “sleep”. You can induce a false memory. Sendhil’s now talking about rerunning that same study, but instead of using words that relate to sleep…
MULLAINATHAN: They’re words like “cash, pay, loan, dollar, gas, grocery.” For rich people, if you give them that list, there’s no word that they remember having heard — that wasn’t there. They remember some of these words, but they don’t make up a word. But for poor people, when you give them the same list, a huge fraction of them remember having heard the word “money” because for the poor, you hear “grocery,” they think money, you hear “gas,” they think money. And it’s like many roads lead back to money. The consequence of this is that if your mind was a processor, like a computing processor, a fraction of it beyond your control is constantly churning on these concerns about money, which obviously makes it much harder to think about anything else.
LEVITT: I love these studies. Can you tell the one about when you bring people into the lab and you tell them not to eat beforehand?
MULLAINATHAN: Oh, yeah, yeah. This is great. People were brought into the lab — everyone’s hungry when they show up, but some of them are given some food and now they’re less hungry. And they’re all given the same word search tasks. Imagine a grid of letters and you have to look for a word and we say, “Okay, first word ‘cookie’.” And so you search for “cookie.” Second word “tent.” And what you’re really testing is after you’ve seen the word “cookie,” how long does it take for you to find the word “tent”? If you’re not hungry, whatever, it’s just another word. Just go right along. If you’re hungry, it takes much longer to find the word “tent” because your mind is still on that cookie. Not even that real cookie, but those letters C-O-O-K-I-E. All it takes is a little bit of a prime for the hungry people and suddenly their mind is off thinking about food. And I think that’s an experience I’m sure everybody’s had — that it’s very hard to think about something else when you’ve just been reminded of a cookie.
LEVITT: And in these studies, the effects are huge. I mean, you talk about effects that, when you prime people to think about what their problem is, we’re talking about like a difference of 10 IQ points between otherwise identical people, some of whom are primed to be thinking about the problem that fills their mind?
MULLAINATHAN: Yeah, exactly. The hypothesis is because it’s taking up our bandwidth, as we call it, the effect on your effective IQ can be quite large. There’s a study by Claire Duquennois where there are these sort of math exams that have word questions. And some of the word questions involve monetary things. Bob has $10. Fred has $5. And she basically shows that only for poor students, after you get a monetary-themed question, you do much worse on the question that comes after. So much so that just having one in 10 more monetary questions in an exam reduces the performance gap by 6 percent.
LEVITT: Wow. That’s crazy.
MULLAINATHAN: I should tell you one other study that we’re just finishing cause what’s been fun for me in the last 10 years is seeing people take these ideas and do new different studies. So this is a study by Suanna Oh, and Supreet Kaur, and Frank Schilbach. So what they do is these are workers in India — they get paid every two weeks. Now this is lean times. So, you know what it’s like, you haven’t gotten paid yet, so you’re pretty far away from your pay day. So you’re like, sh*t, you’re pretty tight on money. And what you find is if you just give them some of their paycheck, like four days early, their actual productivity goes up. They’re actually just more productive because they can focus more at work. And the way that they’re focusing more at work is pretty cool because what these people are doing is they’re making leaf plates, they’re taking lots of little leaves and stitching them together to make a plate. And when you look at the workers who have just gotten a little cash infusion and don’t feel as strained, you can look at the plates they make and they just have far fewer errors. Whereas the ones who did not get the financial infusion, you can see places where they made a mistake, had to take the stitches out and do it again and again. It’s striking you can see it in the way people work. And I started to notice how much of my own work comes from — is my mind fully there? That’s just a big part. If I have anxiety or something that my mind keeps going to, that’s the biggest detriment to all my work.
LEVITT: This makes total sense, but it doesn’t really give a lot of clues of getting out of poverty other than giving people a bunch of money. Are there other implications?
MULLAINATHAN: I think there’s definitely other implications. Economists are very sensitive to taxes. Like we try not to tax when we can avoid it, but we’re very insensitive to cognitive taxes. If I said to you, “Okay, you want to get financial aid? Go ahead, fill out this 50-page form.” Yeah, that’s a time tax, it’s whatever, it’s two hours. In fact, our theories say, that’s a good thing because hey, if you really need it, you can fill it out. But you forget, just like those kids in the math exam, asking a poor person to fill out a 50-page form all about their finances is incredibly cognitively taxing. It’s like asking you to think about the thing that’s stressing you out the most. And so we impose these cognitive burdens on the poor without really realizing that we’re imposing these cognitive burdens. And when you start looking at that, you realize how many of our programs are cognitively silly. So like TANF, this is welfare, this is a welfare program. The way you find out that you’re about to hit your five-year max is you get a letter in the mail with two, three months left. How does this make any sense that we’re asking you to keep track? It’s absurd, especially once you realize these types of results that it’s cognitively very challenging to think about these things. So, I think that angle of rethinking how we design all the programs that poor people encounter through this lens, I think of as a promising angle.
LEVITT: So, you embraced modern data science tools earlier and more fully than almost any other economist I know. And from other prior conversations we had, I know you see huge breakthroughs on the horizon at the intersection of economics and big data and artificial intelligence. So what’s your vision in this area?
MULLAINATHAN: I think what’s most exciting here is we just have a fundamentally new way of doing science. Let me use the example of like an EKG. I don’t know if you’ve ever had an EKG — it’s basically, they put 12 leads, these are like electrical leads, on your chest. It reads the electrical activity of your heart and you get these little waveforms. Now we’ve been doing this for a hundred-and-something years, and we’ve got an expert at looking at those EKGs and — “Oh, this little glimmer is a problem.” But if you ask a simple question — “Where do you put the leads?” It’s, “Oh, these are the places. That’s the way we’ve always been doing it.” In fact, if you trace it back, it goes back to the first person who put the leads on the EKG. There isn’t even many leads on the back and the heart exists on the back as much as on the front. I mean, there’s no constraint on how many leads you can put on. So the whole thing is absurd. The second thing that’s weird is you look at these waveforms and everyone is taught to read these waveforms. It’s like the Rosetta Stone — it’s like its own little language. But why are we looking at these waveforms? Now you can take the waveforms you’re getting and actually just simulate out the electrical activity of the heart itself. In fact, it’s totally plausible that within 20 years we will say, “Hey, between the EKG and maybe some CT scans of your heart, we’ve just built a digital version of your heart. And now we can do sh*t on the digital version of the heart.” I think that’s what — the combination of algorithms plus data science — rather than thinking within the data frame that you have, think past the data frame and wow you could totally reconstruct reality in a very different way.
LEVITT: And what I find so interesting about that is before I talked to you, I was limited in my vision of what more data and better models could do to what everybody talks about, which is we have these wiggles from the EKG and a machine could be 17-percent better than the human at seeing what the wiggles meant. Which is fine, and is great. And it’s wonderful. Like I’m all in favor of that. But it was so interesting to hear you transform it into, “Hey, let’s build a three-dimensional model of the human heart,” and then once you’ve got that in your mind, you say, “Well, I definitely would want to get a model of my heart when it’s healthy.”
MULLAINATHAN: Yeah.
LEVITT: And I want to have that on file because then when I start to get sick, it’s probably super informative to the doctors about, “Oh, wait, now I can see how the heart has changed. That tells me exactly where maybe I want to operate.” And then again, it just feeds back — it ripples through the whole way you think about health and about medicine.
MULLAINATHAN: I’ll give you another example. You and I have run these big experiments. You run these experiments and you collect some variables. “Oh, what happened to income? What happened to” — whatever. And it’s somewhat a little unsatisfying I always find. Because yeah, you saw things on income, but you’re like, I don’t know, maybe these people’s lives changed in other ways. You spend a lot of energy running a big experiment and now you only get to see a few things. So, here’s a different way you could run an experiment — you collect the things that you meant to collect, et cetera. But you also just have people talking into a microphone about what’s happening in their lives. Now we don’t think of people just talking as data, but now these algorithms, given that they can process language, that is every bit as data as someone’s income or their test score. And so, I think there’s almost another possibility there from language to radically change how we even think of data collection. And I think that, to me, if you look in all research, quantitative research is in one place and qualitative research is considered something altogether different, but I think algorithms are going to put these much closer in conjunction than we think. I think in 10, 15 years, simply getting people to talk about what’s happening to them might end up being on par if not higher than simply these quantitative metrics that we have.
LEVITT: That’s fascinating. Are you at all worried by what a machine-learning and artificial-intelligence, algorithm-driven future might hold? I haven’t really paid much attention to this debate about the end of work and living machines that are going to make humans redundant. Do you have thoughts about any of that?
MULLAINATHAN: Yeah. I think this stuff is so overstated. There’s two groups of people who are complicit, even though they hate each other. And one group wants to say, “These algorithms are gonna ruin our lives and it’s going to get rid of all jobs.” Other people are gonna say, “Oh, these algorithms are amazing. They’re going to do great things.” They both agree the algorithms are super powerful. Just not whether they’re good or bad. The reality is this is just a tool. It’s not some magical, super powerful thing. And one reason it’s not going to get rid of jobs, I think, is sure, it’ll automate some jobs, but it creates so many more amazing opportunities. So, I’ll give you one concrete example. Right now, a big share of healthcare costs involve people who are very sick, but who need some kind of monitoring, a little bit, just enough that we have to keep them in the hospital. And so that’s very expensive. They’re taking up hospital beds for that reason. Between the digital technologies we have and these algorithms, you can send these people home to be cared for by people they love, or by nurses that come to their home, who don’t need to be nearly as skilled because when something goes really wrong, the hospital system can be alerted and so on. So, what you’ve now done is you’ve decomposed one part of the job, the rare event, into something that the algorithm handles and you’ve created a new job to handle the everyday event. Caretaking is something a lot of people can do. The sort of highly skilled doctor, nurse who needs to be at the hospital is something only a few people can do. So, you’ve actually created a much better package of jobs now. And if anything, this is bad for the very highly paid, but you know, they’ll always find something else to do. So, I think that it’s a bit of sort of drama.
LEVITT: Here’s the one thing I find unnerving. I read a book and I can’t remember anything about it 15-minutes later. So let’s just say that machines get the ability to read books in a few seconds and to keep all of that material stored. That machine becomes potentially better at just about anything I could do than I am. And then what happens? I just wonder whether you get in the situation where when machines are just so good, then the ownership of the machines becomes everything and everybody else is marginalized.
MULLAINATHAN: Yeah, so that’s a great way to put it, Steve. I think all of those things rely on a misleading model of artificial intelligence. And I think it starts with the name artificial intelligence. There’s a scale of intelligence — ants are somewhere here. Baboons are somewhere here. We’re somewhere above that. Right now algorithms are between us and baboons or maybe slightly above us. But as computing gets more powerful, they’ll get to stratospheres above us that we can’t imagine. So, let’s call this the unidimensional scale of intelligence. So this is what leads people to say, “Wow, if an algorithm can play chess so well, imagine all the other things that it can do.” So, that’s like the unidimensional scale and the unidimensional scale is just such a bad way of thinking of machine intelligence, because it is astonishingly good at certain activities and astonishingly bad at other activities. So, one of the things that I do — if enough people do this, Apple will fix this by hand — but I do this illustration in this A.I. class I teach. I take out my phone and I say, “Siri, don’t tell me the score to the Sixers game.” And Siri will dutifully tell me the score to the Sixers game. Like it’s astonishing. It was like, “Oh, Sixers game? I know what you want.” I’m like, “I just used the word ‘not.’” And you just have to remember, this is like the world’s best engineers are working on this problem. This is not some like goofy product built in a summer intern’s lab. These algorithms are very dumb in some respects and very smart on other respects. And so I think one of the things that we’re all trying to do is get a better understanding of what machine intelligence really looks like. What tasks will they excel at and what tasks will they do badly at? They are definitely not a substitute for human intelligence.
You’re listening to People I (Mostly) Admire with Steve Levitt, and his conversation with economist Sendhil Mullainathan. After this break, they’ll return to talk about Sendhil’s knack for great ideas.
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LEVITT: Hi, Morgan.
Morgan LEVEY: Hi, Steve.
LEVITT: All right. So, last week I invited you on the show and I thought it went pretty well. You helped me with the Q and A session, so let’s do it again.
LEVEY: Yeah, I think it went well, too. So, this week, you’re going to answer a question from a listener named Brett, he’s from Philadelphia. He wrote in to ask about the number of listener emails we get. Specifically, if we noticed an uptick after you admitted that you read every email, which you started doing a few weeks ago with our episode with economist Dambisa Moyo.
LEVITT: So, I’m glad you picked that question, Morgan, because, you know I love to crunch data. So there are three different regimes we’ve had within People I (Mostly) Admire. First, we had an email address, but we only mentioned it at the end of the episode.
LEVEY: Right.
LEVITT: Then I started mentioning our email address along the way, asking people to write. And then we went to the third phase where I told people that I read every email. Well, in that first phase, not surprisingly, we weren’t getting very many emails. We were getting about half an email a day.
LEVEY: Okay.
LEVITT: And the second phase, we were getting about two emails a day.
LEVEY: Right.
LEVITT: Now, after I said we were reading every email, that number went up to a still highly manageable five emails per day. So 10 times as many as we had before, but still not exactly a huge sacrifice, on my part to read all the emails.
LEVEY: Not a huge sacrifice, but I’ve definitely felt the uptick. However, if working at Freakonomics has taught me anything, correlation does not mean causation. So, how do we know that you saying you read every email caused this increase?
LEVITT: Oh, A+ answer. Morgan, you’re finally thinking like an economist. That’s great. Because in order to assess causality, you have to think that the only thing that changed was me saying that I read all the emails, but of course, lots of things changed. I’ve been asking readers for much more interesting feedback along the way. So actually, I don’t think me saying I answer the emails had a very big impact at all. And still, let’s be realistic. The number of people who write is not very big. I did another calculation and it turns out that if you were to be a regular listener, you would be expected to write me about once every 10 years, which seemed about right. That seems about how often I write to people who I listen to. I just don’t have that much to say.
LEVEY: Yeah, that sounds right. That’s a little discouraging, but the one prompt that you gave to our audience that got people really excited was at the end of our episode with David Epstein. And that episode focuses on generalists versus specialists. And you wanted to encourage people to come up with ideas in areas that are outside of their specialization. So, you ask people to write in and we got a lot of responses. What’d you think of those ideas?
LEVITT: I thought they were fantastic. I was really surprised at how many good ideas we got, I mean, let me just call up by name some of the ones I thought were interesting. Robert Schreib sent us a bunch of ideas, a guy named Mike Benning, Chance Lacina, Tim Blausey — these are all good ideas that I’ve got my team working on because they were awesome.
LEVEY: Really?
LEVITT: Yeah, these were ideas that stopped me in my tracks. And one of the things I promised in that episode is that I would try to come up with a way that would facilitate generalists being able to get their ideas out and I’m making good on that progress. So, we have a web page for my University of Chicago center, it’s called centerforrisc.org, where risk is spelled R-I-S-C. So centerforrisc.org. And if you go to that website, we now have an ideas tab. And if you click there, you can leave us your good ideas. And you can also look at everyone else’s ideas and upvote the ones that you really like. So, I’m hoping we will have a flurry of ideas that are even half as good as the ones we got right off the bat.
LEVEY: That’s great, Steve, but we don’t want listeners to stop writing into us. So, please send us your questions. We are pima@freakonomics.com. Thanks, Steve.
LEVITT: Thank you, Morgan.
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LEVITT: Sticking with the topic of ideas, I’m eager to find out whether Sendhil has some secrets for generating so many amazing ideas, because I would love to find a way to increase my own output of good ideas. I’m also curious to hear whether he feels like he’s had some great ideas that the world has just completely ignored.
LEVITT: So, for most people, great ideas are one of the rarest things, but you are literally overflowing with great ideas. Maybe more so than anyone else I know. Why do you think that is? And is there anything you consciously do or is it just the way you are?
MULLAINATHAN: Oh no. I invest a lot in this activity. There are three things I do to keep generating ideas. The first is, you know, how you can develop a taste for things? You’re like, “Oh, I’m going to develop a taste for coffee or wine.” I have been trying to develop a taste for ideas in that, at this point, I get so much pleasure from hearing a good idea. I know you do too, Steve. The way you get good at generating ideas is first and foremost, enjoying hearing a good idea, just truly enjoying it and passing it on and saying, “Oh my God, I heard this cool idea from Steve the other day.” And just that enthusiasm for hearing good ideas, not your own, just hearing good ideas is to me, the foundation of generating anything.
LEVITT: You know, what’s weird about that is I didn’t think anybody thought anyone else’s ideas were good at all. My experience in life is that everybody thinks their own ideas are brilliant and everybody else’s ideas are trash. People have often asked me, are you worried about other people stealing your ideas? And I would say, “No, not at all because everyone else thinks my ideas are terrible.” So, I don’t worry at all. Like I’m very open about my ideas because everyone only likes their own ideas. You’d be the exception, Sendhil.
MULLAINATHAN: It’s a great point, Steve. I hadn’t seen it so crisply before. It is noteworthy how few people enjoy hearing a good idea from someone else. Isn’t that weird? It’s like imagine being a musician, but not enjoying hearing divine music from someone else. Like Beethoven didn’t say, “I only like my own music.” It doesn’t make any sense. First learn to love other ideas. That’s the beginning of like really getting creative.
LEVITT: So, you said you did three things. So the first thing you did is you cultivated a true love of savoring other people’s ideas.
MULLAINATHAN: Savoring. That’s a good word. That’s right. The second thing I do is I always tell people ideas I’ve heard, whether they’re my own or something else. If you’re not sure sharing ideas, they’re not running through your system. Creating new ideas is largely reassembling stuff you’ve seen or putting things together. So part of what you wanna do is just enjoy having this wealth of stuff inside of you that you love and cherish sharing and do it. It’s like why I love talking to you. I’m amazed at how few people are good sharers in the world of ideas. I feel like I showed up to swap baseball cards, but I’m the only one giving out baseball cards. Why aren’t the baseball cards coming back? I feel like all three of these sounds so trite, but the third one is even more trite, which is you get good at something by repeatedly doing it. I have a lot of bad ideas, but I’m just like, “I just have to keep generating ideas, and I just have to keep going.” And I tried this with my Ph.D. students. I’m like, “You’re willing to meet every week and you’re just gonna tell me all your ideas this week.” And they’ll come in, like, “I had one idea.” I’m like, “You did not have one idea this week. It is a week. Look, I don’t care. If you had 18 sh*tty ideas, that’s fine. It’s just, this is a numbers game, let’s hear them.” I have to tell you an amazing experiment. This is an experiment on creativity. It’s by Brian Lucas and Loran Nordgren. So, what they do is they say, “Okay, we’re going to give you 20 minutes to come up with ideas.” It might be like titles of sitcoms, whatever, some little thing where you’re doing a creative activity. Say it’s 20-minutes long. At the 10 minute mark they stop and say, “Okay, we just want you to assess how productive you’ve been for the last 10, how productive you’ll be for the next 10, how good your ideas were, how good your ideas will be.” And they’re like, “Yeah, I’m pretty tapped out. I had my good ideas in the first 10. I’m not going to have any the next 10.” It’s like pretty universal. Then they go ahead and they do the next 10. And then they have either people themselves rate it or have others rate it. And what you find is the second 10 minutes are exactly as fertile as the first 10. The ideas are exactly as good as the first 10. We have this feeling of diminishing returns. So, they call it the “creativity cliff” — that somehow we believe after a moment, creativity falls off a cliff, like we’ve tapped out. But in fact, the mind doesn’t tap out. And I always remember this study because that is one thing I think people get very wrong about creativity. It works through just sheer volume and time. After a month at it, that’s when you might have your best idea or it might be after three months or six or nine, it’s very uniform flow. Even though every part of you makes it feel like you’re tapping out.
LEVITT: When you have so many ideas, how do you know in the end which ones are good and bad? Obviously, you can only pursue a trivial share of what you come up with.
MULLAINATHAN: That’s what I struggle with the most. If you have answers to that, Steve. I’m drawn to people who have such good judgment, their ability to just say, this is the right thing to work on. I’ve tried to really understand what makes these people good at what they do. Some of them are great C.E.O.s, for example. They’re not necessarily creative people. They’re just people who know how to look at a lot of different things and say — number 14, that’s what we’re going to do. I find that such an amazing trait. So amazing. I wish I had that skill.
LEVITT: The only advice I have on that is to have a cooling off period, that the moment an idea pops in my head, it is always the best idea I’ve ever had. And sometimes it takes only 30 seconds, but sometimes it can take a week or so. I do best if I have a plethora of ideas. If I have a big backlog of ideas —.
MULLAINATHAN: Oh, I love it.
LEVITT: So that any new idea goes to the bottom of the list.
MULLAINATHAN: I love it. It reminds me a little about what you said about re-reading Scarcity. Cause when you were saying that I was thinking, “I have this experience myself with things I’ve written.” Like I’m going back and reading it, I’m like, “What is this thing? Who wrote this? Oh, wait, that’s me.” And so this happens with ideas too. If I write them down And you come back to it like two weeks later. It’s awesome. Cause it feels alien. It’s no longer your idea. Did you ever see the Seinfeld episode where he wakes up in the middle of the night and he writes down this joke and he’s convinced it’s the most hilarious thing ever? And the entire episode involves him taking this piece of paper and asking people, what does this say? Cause he’s convinced he has the best joke ever written on this piece of paper. And the end of the episode involves him figuring it out and it is something super inane. And he’s like, sh*t. And I think this cooling off period, exactly captures that. A lot of this stuff you think is great, even three days later is not so exciting.
LEVITT: On the flip side of that. I was working on a paper with a professor at Yale, Ian Ayres, and I sent him a draft of the paper. He sends me back his version of the paper. It is so terribly written. I cannot believe it. He has taken an incredible paper and made it unreadable. So I’m like, “Oh God, I’m just going to go back to the version I wrote, that I had printed out. I’m going to re-type this whole thing in.” And I went back to it — he had changed four words in the entire thing. The terrible writing was all my own and it was shocking to me. I had this self-perception I knew how to write, which I’ve been much more humble about ever since.
MULLAINATHAN: That is great. That is terrific.
LEVITT: Is there any research you’ve done, ideas you’ve had that you think are really important, but just got ignored for some reason?
MULLAINATHAN: This is a good question. Let me flip this around a little bit and talk about a paper that I did about five years ago that I don’t think it was ignored, but these days, I just think a lot about this paper and I’ve been trying to understand it. So in some sense, I’m as guilty of ignoring this paper as anyone else. This is a paper about a youth violence-prevention program in Chicago. The program was called Becoming a Man. Kids would get together once a week. They would talk in this sort of group sessions, and kids were randomized into this program. What you found was even a year or two later, much lower arrest-rates and much higher education-performance, just from getting together and talking with this counselor. Which as you know, very few programs do anything. How did this thing work? And since then there’ve been other randomized control trials of similar programs. Even three to four years out, people were finding big effects. All of these programs go under the title, which I truly dislike — it’s called cognitive behavioral therapy. Everything about it is terrible, including the word therapy, as if these kids need some therapy. And so, one thing I’ve been thinking about is, “What on Earth is this program doing?” And as you dig into what they’re doing, it’s actually just teaching kids to be better at thinking. To just reason better. And even though lots of people claim to teach you to think, really, we don’t have many successes of teaching people to think better. And if that’s what this program is doing, it’s an astonishing success because it’s not only teaching kids to think better it’s teaching kids in the most dangerous South Side schools to think better and help them avoid getting arrested and committing crimes. It’s the equivalent of, I think, penicillin and finding it on a piece of bread. We found some way of helping people reason about the world and one of the experiments there is just so awesome. They basically bring the kids in, they line them up in pairs and they say, “Okay, half of you are given a ball.” Now, again, these are like, adolescent teenage boys from somewhat rough neighborhoods. Okay. So, they give half of them the ball, they say to the other one, “Your job is to get the ball from this kid. And the only rule is there are no rules go to it.” They blow the whistle. The kids are grabbing and like pulling. It’s like, crazy. After about three minutes of this business, they blow the whistle again and they say, “Okay, how many of you managed to get the ball?” It turns out almost nobody gets the ball because if somebody’s gripping a ball and they don’t want to give it, it’s very hard. So they say, “Okay, fine. You didn’t get the ball.” And they say, “How many of you asked for the ball?” Like stunned silence. Obviously none of them asked for the ball. And of course, sheepishly, someone will say, “I didn’t ask, but he wouldn’t have given it any way.” I said, “Okay, let’s turn to one of the kids with the ball. If you were asked for the ball, would you have given it?” He’s like, “Just a f*cking ball. Of course I would have given it. I’m not a maniac. What do I care?” And in that moment, the kids realize they just immediately dove in, but they didn’t step back and say, “What are my options in this situation? And which one kind of gets me to where I want?” And a lot of the teaching points in these classes are like this. It’s — we’re not telling you what’s right or wrong. We’re going to get you to slow down your thought process and become better at the process of thinking. And if I had only heard all of this, I’d be like, “Yeah, sounds good. But how is this ever really going to work?” But you see the outcome data and you’re like, “Holy cow, it actually is working.” So that study I remain, especially in the last few years. I’m like, “Oh, I have to really understand what’s happening here because if we can get it, distill it, and scale it, that seems like it could have really huge effects.”
LEVITT: So, I was in Cook County jail talking to some of the inmates. And one of the guys I was talking to was an armed robber. And he was in a program that had a lot of the features of Becoming a Man. There was some therapy, there was a bunch of sitting down and counseling and training. And I said to him, “What do you think has been most helpful?” This is an armed robber, 21-year-old guy. He says, “I really liked the therapy.” So, he had real therapy. Like he was going to a therapist. He said, “I haven’t cried in my entire life. I cry every week with my therapist.” And that was a weird thing for a guy in jail to say, and I’ll never forget it. And it was interesting that maybe something to throw into what you’re thinking about is, what is it about therapy that it’s okay for him to cry? It’s just — it was really striking to me. It’s really striking that he said that.
MULLAINATHAN: Yeah. If you think of the, just complex personal problems, a 16-year-old kid is going through on the South Side of Chicago without any assistance. You’re right. Being able to just have a place where they can just chew on those things is super valuable. I think that is a totally reasonable hypothesis. When I was saying that I don’t love the word therapy. Here’s what I think I don’t like about the word. It suggests there’s something wrong to begin with. Like you do physical therapy because your knee is bad. And this therapy frame almost makes it feel like, “Oh, there’s something wrong with this kid or this robber and now we need to like” — no. They’re having emotional troubles that every living human being has, like, if we all have to go to therapy, at some point we should stop calling it therapy. We just started calling it exercise. Just, it’s absurd. I think that’s the sense in which the word “therapy” can lead us all so astray because it makes you feel like, “Oh, there must be some problem we’re fixing.” No, we’re dealing with the problem of being alive. That’s the problem we’re dealing with.
LEVITT: If two episodes of Sendhil weren’t enough for you, check out his new app called Pique, P-I-Q-U-E or his book Scarcity: Why Having Too Little Means So Much. If you’d like to learn more about the Becoming a Man project that Sendhil mentioned, there’s a Freakonomics Radio episode about it, called “Preventing Crime for Pennies on the Dollar.” As always, send us your thoughts, questions, comments — the email address is pima@freakonomics.com. Thanks for listening.
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People I (Mostly) Admire is part of the Freakonomics Radio Network, which also includes Freakonomics Radio and No Stupid Questions. This show is produced by Freakonomics Radio and Stitcher. Morgan Levey is our producer and Jasmin Klinger is our engineer. Our staff also includes Alison Craiglow, Greg Rippin, Joel Meyer, Tricia Bobeda, Emma Tyrrell, Lyric Bowditch, and Jacob Clemente. All of the music you heard on this show was composed by Luis Guerra. To listen ad-free, subscribe to Stitcher Premium. Thanks for listening.
MULLAINATHAN: There was this recent study by Claire Duquennois. I’m not sure I’m saying her right name. But I’m pretty sure she won’t say my name right either, so I think we’re even.
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