The Tim Ferriss Show - #566: John List — A Master Economist on Strategic Quitting, How to Practice Theory of Mind, Learnings from Uber, Optimizations to Boost Donations, the Primitives of Decision-Making, and How Field Experiments Reveal Hidden Realities

Starting point is 00:00:00 This episode is brought to you by Four Sigmatic, which is part of my morning routine, also part of my afternoon routine. Routine saves me. So there are a number of ways that I use Four Sigmatic. In the mornings, I regularly start with their mushroom coffee instead of regular coffee, and it doesn't taste like mushroom. Let me explain this. First of all, zero sugar, zero calories, half the caffeine of regular coffee. It's easy on my stomach, tastes amazing, and all you have to do is add hot water. I use travel packets. I've been to probably a dozen countries with various products from Four Sigmatic, and their mushroom coffee is top of the list. That's number one. I travel with it. I recommend it. I give it to my employees.

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Starting point is 00:02:27 mushroom coffee. Full discount is applied at checkout. This episode is brought to you by Allform. If you've been listening to this podcast for a while, you've probably heard me talk about Helix Sleep and their mattresses, which I've been using since 2017. I have two of them upstairs from where I'm sitting at this moment. And now Helix has gone beyond the bedroom and started making sofas. They just launched a new company called Allform, A-L-L-F-O-R-M, and they're making premium customizable sofas and chairs shipped right to your door at a fraction of the cost of traditional stores. So I'm sitting in my living room right now, and it's entirely all-form furniture. I've got two chairs, I've got an ottoman, and I have an L-sectional couch,

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Starting point is 00:05:11 The Tim Ferriss Show. Hello boys and girls, ladies and germs. This is Tim Ferriss. Welcome to another episode of The Tim Ferriss Show. This episode has something for everybody. It is my job every episode to deconstruct world-class performers of all different types to tease out their learnings, their lessons, their tools that can be applied to your lives. And what makes today's guest so special is not only is he a world-class performer, but he

Starting point is 00:05:47 spends time with a wide spectrum of other world-class performers, studying world-class performing companies as well as executives and doing that through the lens of data. All right, so here we go. John A. List is the Kenneth C. Griffin Distinguished Service Professor in Economics at the University of Chicago. His research has led to collaborative work with several different firms, many companies, including Lyft, Uber, United Airlines, Virgin Airlines, Humana, Sears, Kmart, Facebook, Google, General Motors, Tinder, Citadel, Walmart, and several nonprofits. And the list goes on. For decades, his field experimental research has focused on issues related to the inner workings of markets, the effects of various

Starting point is 00:06:29 incentive schemes on market equilibria and allocations. Incentives are a big part of our conversation, how to use them in the family, how to use them in companies, how to use them in manufacturing, fascinating stuff. How behavioral economics can augment the standard economic model, early childhood education and interventions. He's created schools, so we'll talk also about that. And most recently on the gender earnings gap in the gig economy using evidence from rideshare drivers. His research includes more than 200 peer-reviewed journal articles and several published books, including the bestseller he co-authored with Uri Ghanesi, The Y-Axis, Hidden Motives, and The Undiscovered Economics of Everyday Life, and his new book, The Voltage Effect, How to Make Good Ideas Great and Great Ideas Scale.

Starting point is 00:07:15 You can find him on Twitter at econ underscore four, the number four, underscore everyone. So at econ for everyone. We will put that in the show notes, tim.blog slash podcast. So you can click on that. And everything related to the new book, which we explore at some length and the principles within it, can be found at thevoltageeffect.com. So without further ado, please enjoy a very wide ranging and for me, enjoyable conversation with none other than John A. List. John, welcome to the show. It's nice to see you, sir. Thanks so much for having me, Tim. Now, I find your story endlessly fascinating, which means, of course, I have an embarrassment of riches in terms of materials and questions.

Starting point is 00:07:58 We're never going to get through all of it. But I was struggling with finding a place to begin. There's so many options, but I thought I would just mention for folks, you have eight kids. Is that right? That's correct. Eight kids. Eight kids. Your grandpa, dad, and brother, all truckers, if I'm not mistaken. That's correct. Proud truckers. Proud truckers. I'm just adding a little bit of color here and there because certainly we're going to

Starting point is 00:08:26 get into many different nooks and crannies. I thought I would just start with asking you to describe what a clawback incentive is and how you have used clawback incentives with your kids and incentives in general, what other incentives you have used. And then we'll use that as a jumping off point. The clawback incentive is an incentive scheme that actually reverses the way we think about traditional incentives. So in traditional incentives, I'm the chief economist at Lyft.

Starting point is 00:09:01 The way I incent my workers there is I have them work really hard all year. And then at the end of the year, they receive a bonus if they do a good job. That's great. Now, what the clawback incentive is, is at the beginning of the year, I give them the incentive, but then I tell them if they don't perform, I will take away the incentive. And the idea behind the clawback is I want to, first of all, show them the money. And then secondly, I want to invoke loss aversion. Loss aversion is something that psychologists have taught us goes as follows. If you own something, you really, really, really are hurt if you have to give it up. So the idea here is give somebody some money that will induce them to work harder.

Starting point is 00:10:00 And when they do work harder, everyone's better off because they get to keep the money. And my firm is better off because they work harder. Now, where I've used that, I've used that in Chinese manufacturing plants. I started a pre-K school about 12 years ago. I used that with the teachers. So at the beginning of the year in September, beginning of the school year, some of them get the bonus. And then I tell them, if your kids don't perform at the end of the school year, I will take some of it back. And lo and behold, what happens? They work harder and the kids learn more. Now for my own kids, I've also done the same thing. Any good scientist will use his or her own kids as subjects. I'm no different than that. As you mentioned, I have eight kids. Two

Starting point is 00:10:55 of those kids happen to be twins. So I have the perfect treatment and control group. Now, some things like my oldest daughter had a very hard time going potty on the toilet so what do i do i give her an incentive let's say a doll or something else and i tell her if you go potty on the toilet you get to keep it if you don't the doll is going to go and sit in a different room and you're not going to be able to play with the doll. That's the clawback incentive. And what were the results? The results are just remarkable. Whether it's school teachers, whether it's workers in a manufacturing plant, whether it's students, whether it's my own kids, just across the board, what happens is if you use the clawback, people work harder and everyone's better off in the end because

Starting point is 00:11:55 they're more likely to get the reward and you're happier because they've worked harder. So you're happy to give them the incentive. Hearing of your kids and the twins in particular, I have to imagine an unfair share of the experimental load lands on the twins, but you can disabuse me of that notion if I'm missing. But could you describe other ways that you have used incentives with your kids? Any stories or examples that come to mind? So first of all, you're right that whenever I need a really good control, that will fall on the twins. But to be fair, I have enough experiments.

Starting point is 00:12:37 I think about using experimentation to incent inputs. For example, when the kids are in third or fourth and fifth grade, I tend not to incentivize outputs because outputs are something that a person has a hard time controlling. But what they can control is the set of inputs. What I mean by inputs is how many books you read or how many hours you spend studying. Those are things that the child or the worker or whomever can control. Things like how well you do on the exam. A lot of exams, for example, are graded on a curve. So you can't control how well other students do in the classroom. So it's not really fair to give you an incentive based on that kind of output.

Starting point is 00:13:37 So my incentives roll from in the classroom to on the baseball field to on the soccer field. But it's all the time it's using input incentives with my kids. Now, with my workers, whether it's at Lyft or I used to be the department chair here at the University of Chicago's econ department, there I can't really observe inputs, so I have to reward outputs. And in the end, it's the organization is affected by the outputs, not the inputs. Could you give an example? And don't worry, folks listening, I'm going to expand beyond the family system. But I like to start with something that many listeners will identify with, and then we'll move in all sorts of directions. Are there any specific examples that you could

Starting point is 00:14:21 give of incentivizing and changing behavior or molding behavior with your kids? So let's think about studying. When I think about a clawback at the beginning of the week, I will tell them, if you work intensively for an hour each night, Monday night, Tuesday night, Wednesday night, Thursday night, Friday night, you can keep this incentive that I will give you on Monday morning. Now that incentive can be a pack of baseball cards for my boys. It could be a pass to the movies for my daughters. But the key is give them the incentive early in the week, and then they will perform all week. For example, an hour of studying math every night is something that happens all the time in my household. Then at the end of the week, they end up keeping the incentive because nearly every time they meet the goal. So I have a question about the clawback incentive and harnessing the loss aversion. And part of why I was looking forward to chatting with you

Starting point is 00:15:32 is that I think it's minimizing it to say sort of along the lines of say Freakonomics. And I know you've had a lot of involvement with Professor Levitt and have been featured regularly on the Freakonomics blog and elsewhere, there are many assumptions that we make or beliefs that we hold which turn out not to be terribly supported by data when you actually run experiments. And as an example, when I was doing

Starting point is 00:15:58 prep for this conversation, I read about, I might get the specifics slightly off, but your examination of matching donations with charitable organizations and asking them if matching works. The answer is, of course, yes. Do you have data to support it? No, not really. But looking at, say, one-for-one matching, you donate $1, we match $1, one to two, one to three. And it turns out matching does work, but the ratio is less important than just the matching mechanism itself, right? It would seem. And we can certainly explore that more. And if I screw it up, please let me know. On the harnessing of loss aversion, as an employer myself, or as someone who's hoping to have kids soon, I'm

Starting point is 00:16:46 probably not going to catch up with you unless I adopt a lot. But I imagine in my mind that workers will respond well when they are given a bonus at the end of their performance review period, whatever that is. But that over time, maybe in the long term and not in the short term, if people are given something that is taken away, that that might breed resentment or some type of learned helplessness where they'll just not attempt to put forth their best effort. Is that grounded in any of the data in any way? Or are there any side effects? When you think about using behavioral nudges or you think about using behavioral incentives, one very important issue that you always need to recognize and keep in mind is will it keep working over and over and over again? And then secondly, does it have any side effects? So in the case of the clawback, you could imagine that you run it

Starting point is 00:17:58 one time and it works. And then when you try it a second and a third time, it depreciates. It really doesn't work that well. Or you could imagine that you hire a bunch of school teachers like I did down in Chicago Heights and give them the clawback and within three months they all quit because they hate it. It's just too stressful and it's too burdensome on them. Now, those are two very important considerations for any incentive that we talk about. What we found so far is in terms of the depreciation, it does depreciate a little bit like most behavioral nudges, but not a ton. So on that side, you don't have to keep creating a new mousetrap. The real mousetrap in the first one, the clawback, works reasonably well and it has over time for a long time.

Starting point is 00:18:54 Now, the side effect one is also sort of interesting because what you find is that many workers actually like the clawback because they view it as a commitment device. And what I mean by that is, you know, most things in life are, you have to exert effort today and you receive benefits in the future. So think about climate change. Think about your own health. You don't really want to go to the doctor. You don't really want to go to the dentist because it's a cost now and the benefits are in the future. Think about why people drop out of school. As a 16-year-old, there's a lot of costs now and the benefits aren't for a long time. As humans, we do a really bad job at those types of issues. And that's why we have problems like

Starting point is 00:19:47 climate change and you don't take care of your health and too many people drop out. What's nice about the clawback is it takes that problem and turns it around because it takes the benefits from the end and brings them to the beginning. So you get the good stuff right away. And that serves as a commitment device for many people. So some people in our experiments will actually pay real money to have the clawback, to get to use the clawback. In the end, there are some, of course, that hate it, but there are enough that love it that on that dimension, it actually turns out to be a doubly good incentive because you not only are better as the owner, but the workers like it more too. Question about the Chinese manufacturing, speaking to the durability of the incentive.

Starting point is 00:20:40 Again, I'm getting probably the specifics off and you can correct me, but it seemed that it worked on the order of something like six months, not on the order of weeks, at least in the example that I read. And maybe that was just the sampling interval, so maybe it lasted for years for all I know. But my question is, what was the reward period? Does that make sense? No, it makes perfect sense. So what we did in the Chinese manufacturing plant is we had weekly reward periods. So every Monday morning, they were given the bonus and then that cleared on Friday afternoon. So our experiment lasted six months where we would move people in and out of clawback incentives.

Starting point is 00:21:27 We'd put some people in the standard incentive. The standard incentive, of course, is work all week. And at the end of the week, you get your bonus. And we were always comparing whether the clawback does better than the standard. And could you speak to inputs versus outputs and what your metrics were? So in that example, all of our metrics were outputs. So the output in China was the number of non-defective items that came off the manufacturing belt. And each team had a goal. And that goal was if it was met, you got to keep your bonus. If the goal wasn't met, you had to give back your bonus. So in the manufacturing plant, as in many of our experiments that involve workers, we are looking at outputs. Now, when I

Starting point is 00:22:24 talk about my kids, I want to incentivize an input, but it's really hard to do that in the workplace. Yeah, that makes sense. That makes a lot of sense. I mentioned donations earlier, and I'd love to segue to tipping. And I know you've spent more time than most looking at tipping. Do you want me to tell you the vignette about donations and then how I can pivot that into tipping? That's perfect. I would love that. Perfect. Okay. So my research agenda in charitable giving actually started back in the late 90s. And a lot of people ask me,

Starting point is 00:23:08 why did you get involved in working on the science of charitable giving? And a lot of times they assume it's because of my altruism. And I tell them that's not the case. What happened was I was an assistant professor at the University of Central Florida in 1998. An assistant professor means you're the lowest my own business, and I hear a knock on the door. I open my office door, and there's the dean of the College of Business standing in my doorway. So I say, Dean, his name was Thomas Keon. Dean Tom, how can I help you? He says, John, I want to start a center for environmental policy analysis. And I want you to help me start that center. And your first job is going to be to raise money. So I'm thinking, well, what? I've never raised any money before. I'm a truck driver's son. What's going on here? So he said, think about it and come back to me.

Starting point is 00:24:35 So I thought about it for a few days and I went to the dean's office and I told him, this is a great opportunity and I will do it if you satisfy two conditions. One condition is I want to run the fundraiser as a field experiment because I want to learn about why people give, what keeps them committed to the cause. And two, I need some upfront money. And that's what I kind of learned in those two days is I went out and talked to a bunch of fundraising specialists and they told me the key is to have upfront money. So the Dean gave me $5,000, big whoop, right? But importantly, he gave me a big list of potential donors who I could ask to give money to the Center world as my lab. And if you're ever trying to accomplish something, do it scientifically so you can not only figure out what works and why, but then you can get it right the next time. So I did this fundraiser. And what I first learned was I talked to dozens of fundraising gurus, practitioners,

Starting point is 00:26:12 strategists, et cetera, et cetera, et cetera. And they all had rules of thumb. They all said things like use a three-to-one match rather than a one-to-one match because a three-to-one match will raise more money. When I say three-to-one, I mean for every $100 somebody gives, the charity matches it with $300. That's a three-to-one match versus a one-to-one. All these great anecdotes. Each time I would ask them, can you give me the data behind these gut feelings? No, I can't. And I said, well, how do you know they're true? And every person said, well, that's just how we always do it.

Starting point is 00:26:59 That's how my boss did it. Now I'm thinking, holy cow, this is two to 3% of the GDP of our nation is charitable giving, right? Two or 3% of wallet is giving of money. Forget about the time, you know, add volunteerism to that and you might get 7% of GDP. So it's a very important sector. But when I learned that it's mostly built on anecdotes, I said, man, we're in business because now I can use this as a research agenda to explore generosity. And I can help charities use science to raise more money. So I've been at that for now close to 25 years, partnering with charities, helping them raise more money, helping them leverage science.

Starting point is 00:27:51 And you end up learning some really interesting facts about people. For example, when you look at what drives a man to give versus a woman. A man is driven a lot by the tax advantages that charitable giving gives you. That's called the price effect in giving. Whereas women are driven more by altruism and social pressure. So now these become two sort of important features that you might say, well, that affects charitable giving, and that's about generosity. But what about other parts of society like tipping? Can we take what we learn about the generosity to the Sierra Club or the United Way or what have you, and apply those elements to what happens in the world of tipping somebody, whether it's an Uber driver or tipping a waiter or what have you.

Starting point is 00:28:59 And it's sort of interesting because sometimes you can and sometimes you can't. One example is when you look at the charitable giving data. So I have the IRS data. So I have everyone's tax form from like 2008 to 2017. So I can explore all kinds of different cuts of those data. And I can tell you that as it turns out, at each income percentile, so if you'd say the average income earner in the United States is like $58,000. If you look at all people who make that amount of money, women give a lot more than men in that income bucket. So you look across all of the income buckets, and a fact is that women in every income bucket give more than men to charitable causes.

Starting point is 00:30:02 So now you'd say, okay, that must be also true in tipping. It's not. In fact, it's just the opposite in tipping. So you can say, well, where's John getting his tipping data from? I was a chief economist at Uber back in 2017, and TK, Travis Kalanick came to me and said, John, you need to help us keep the drivers in Uber because there's this crazy delete Uber campaign, hashtag delete Uber campaign, that's killing us on both the driver and the rider side. And my solution back then was let's roll out tipping on the app. So we rolled out tipping on the app. My team at Uber was called Ubernomics. So team Ubernomics helped roll out tipping. And what we find there is across the board, men tip a lot more than women.

Starting point is 00:31:01 So there's sort of this interesting dynamic that in charitable giving, where you do it face-to-face or you do it to be recognized, women are giving a lot more than men. But remember, on the Uber app, what happens is you do it anonymously and you don't do it face-to-face. And in that case, men actually give more than women in tips. What are some of the learnings around increasing frequency or amount of tipping from your many experiments? There are sort of some interesting facts about what happens in that Uber car. A first fact is that if your listeners want to join the 1%, a lot of times people say, I want to be in the 1%. Now, in this case, only 1% of riders tip every trip.

Starting point is 00:32:07 That kind of blew my mind. That's wild. I never would have guessed that. Yeah, because in the charitable giving game, nine out of 10 people give to a charitable cause at least once every year. But when you think about the Uber experience, only 1% of people tip every time.

Starting point is 00:32:28 And interestingly, roughly 60% never tip. Now, in the middle, of course, are the sometimes tippers. So we did this big data grab from our experiments. So now we're talking about 23 million observations, a big data set. And we can look at a lot of averages. And what you find are things like men tip female drivers more than they tip male drivers. That's a fact, especially when the female drivers are younger. So what happens is the older female drivers, say 60 years old and beyond, those female drivers are tipped the same

Starting point is 00:33:17 as the male drivers in that age group. But the big difference is with the 21 to 25-year-old drivers, where females earn about 6% more in tips than the males do. And it's all driven by male riders, which is sort of an interesting fact. Now, I'm going to let your imagination, Tim, go with that about why that happens. I don't want to go into that here. I'll let your imagination go. I don't want to go into that here. I'll let your imagination go. I don't have to imagine very long. Continue, please. But there are all these kinds of facts like this. The average trip, only about 15% of trips are tipped. Conditional on tipping, the average tip is about 10%, 12% of the fare. You do have things like a bigger bill is a bigger tip. That's what we have in restaurants too,

Starting point is 00:34:14 where people tip 20%. The big thing about tipping is people tip a lot more if it's face-to-face versus when it's anonymous. So the way we did tipping at Uber was you don't get asked to tip until after the ride is done and you're removed in space and time from the driver. And we did that on purpose because TK said, John, I don't want tipping to be a tax on riders. I want it to be something that is truly done for exceptional service. We want to separate it in space and time. And then that's what we got is roughly 15% of trips are tipped. Now, if I think to my restaurant experience, so I grew up working in restaurants and the checks have changed over time. If we're talking about computer generated bills slash checks. And one of the changes, I don't know when this started,

Starting point is 00:35:24 and I don't know if it's supported by data. I would hope so. It certainly has affected how I tip, but it has been checkboxes where it'll have, rather than making you do the math, it will say 15%, 20%, 25%. There's a checkbox and there's the amount and then there's the total. And that raises the question for me of what types of user interface or conditions for possibly engaging with Amazon being the ability to publish your findings. And just to give a snapshot of that, you didn't take the job because there were going to be trade secrets. They wouldn't be publishable. However, you helped introduce someone who was hired and I think still works there. And he sends you a, what is it? A Christmas card every year with his net worth at the bottom, something like that, which is outstanding. Just really fantastic. I hope he sends you some chocolates too.

Starting point is 00:36:38 He better send me a bag of cash the way Amazon stock has been doing. They gave me that offer. The stock was $7 a share, Jim. Oh, so TK was open, as I understand it, to you publishing. Could you share some of the maybe design experiments or changes or tweaks that turned out to be effective? It doesn't have to be uber specific, but it certainly could be. Early on, Amazon, Jeff Bezos came to me and asked me to be their chief economist. And boy, oh boy, oh boy, whenever I go back and look at that contract, it was a contract that was heavily laden with options. And Tim, Tim, Tim. So if I had to go back, I still think I can honestly say this. I would do the same thing today. I did give up millions of dollars, no doubt about that.

Starting point is 00:37:34 But in the end, I'm a scientist. And I've always thought that my calling was that to use the world as your lab is a great opportunity to make the world a better place. And I really felt I could help Amazon. I really did. But in the end, they just couldn't get over the hurdle of I needed to publish in academic journals as a scientist because I not only wanted to solve amazon.com's problems, I want to solve the world's problems. And I certainly get it. Their basic premise was, look, we want you to come in and help us make a ton of money. And at the time he was talking about something called the cloud, which I didn't even know what that was. In fact, he goes, John, the two next big things are going to be the cloud and the economics team. And you're

Starting point is 00:38:31 going to be the guy who hires all the economists. And he says, let me tell you about this cloud thing. He goes, you're going to help me price the cloud. Like, I don't even know what that is. And I just pretended I did, but like I honestly didn't know anything about it. Okay, so TK calls, Uber calls, and Uber says, we understand what happened at Amazon.com because of Jeff Holden. Jeff Holden was at Amazon.com back then, and then he ended up leaving for Groupon. He was at Groupon for a while, And then he ended up leaving for Groupon. He was at Groupon for a while. And then he went to Uber. And when TK wanted a chief economist, he ended up talking to his exec team, Jeff Holden's one of them. And Jeff said, you know, there's this guy

Starting point is 00:39:17 who we really liked at Amazon. And he wouldn't come because of this restriction or constraint that he said he's an academic and he wants to publish some of the work. And TK said, well, if he's good, let's bring him in and he can publish some of his work. And to his credit, TK stayed true to that promise. So not only around tipping, we have two academic papers about the economics of tipping, questions like, how should you do the presets? At the bottom of your app, you say you have zero, $1 or $2, or should it be one, two, and four? Or should it be 0%, 5%, 20%? We have all those configurations. And kind of the bottom line is you want that. You want there to be a choice. You don't want to have people fill in the number. You want there to be a box that you can check, but make sure the

Starting point is 00:40:21 boxes aren't too high because if they're too high, you're going to get a zero. Too high in dollar amount? Too high of like $50, $100, $150 or 25%, 45%, 70%. A lot of times people will say, those are out of my range, so I'm going to do zero. So you don't want to do too high. You don't want to do too low because then you might be leaving money on the table for the driver. So it becomes an art and it's an art that you can only figure out with field experiments. And a lot of those, you know, 5%, 10%, 20%, those are individual specific. So that doesn't fit with just everyone. Sometimes the low,

Starting point is 00:41:10 it means high for somebody else, or a really high set of presets is low for somebody else. So just like the price and the various other pieces of the algorithm, an optimal tipping scheme will take account of the consumer's traits and how they've tipped in the past. Okay. So another line of research that we worked on at Uber was what I call the economics of apologies. And as you know, it was pretty rocky at Uber when I was there. In fact, I think there's a new show coming out called Super Pumped or something. But I actually also am friends with the – I don't know if he'd be considered the creator, but he's certainly producing and writing Brian Koppelman and David Levine, super pumped, or adapting it for screen. I'm not involved with that at all, but he's a good guy. So, yes, based on the book.

Starting point is 00:42:20 So, there's never a dull moment in ride sharing. Yeah. Can you tell them that when the thing comes out that I want Matt Damon to play the chief economist? Is that too much to ask him? You know what? They have worked with Matt Damon before, so I will put in the request. All right. All right. Just a quick thanks to one of our sponsors and we'll be right back to the show this episode is brought to you by athletic greens i get asked all the time what i would take if i could only take one supplement the answer is invariably ag1 by athletic greens if you're traveling if you're just busy if you're not sure if your meals are where they should be it covers your bases with

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Starting point is 00:43:41 adding a vitamin D supplement to your daily routine is a great option for additional immune support. Support your immunity, gut health, and energy by visiting athleticgreens.com slash Tim. You'll receive up to a year's supply of vitamin D and five free travel packs with your subscription. Again, that's athleticgreens.com slash Tim. So let me tell you this story quickly about apologies. Is that okay, Tim? Do you want to hear that? Yes, please. Okay. So this is kind of a perfect example of, I think, the leadership of Uber and how they were open to science. So I'm scheduled to give a keynote talk at our big annual meetings they're called the american economic association meetings so these are annual meetings that take place every january

Starting point is 00:44:37 okay great so i'm on the schedule to give a keynote talk alongside a guy who ends up winning the Nobel Prize in Development Economics named Angus Deaton. He's there to criticize field experiments, and I'm there to be the supporter of field experiments. So I get picked up at my house here in Hyde Park, Chicago, because the meetings are downtown Chicago. Just happened to get lucky that year. The meetings rotate to a different big city every year. Okay. So the Uber car picks me up. And as usual, I'm still working on my slides. I look at my watch and it's noon, roughly noon,

Starting point is 00:45:20 and I'm supposed to be on stage at 1230. So the driver starts taking me down there and we get about three quarters of the way down. I look up, look at my watch and I'm like, this is perfect. I have to just finish this last slide. I'm going to walk in. These slides are going to be great. Next time I look up, I'm in the front of my house back in Hyde Park. Oh, no. Oh, no. And I'm like, no. What the blank is going on here? And the driver said, well, the app blinked. And professor, you were so busy working, I didn't want to disturb you. I thought that you changed the destination because

Starting point is 00:46:06 you forgot something at home. I said, no, take me back down to the conference. So, okay. So I get there, I run in, everything turns out fine. But that evening when I got home, I ended up walking out to our garage that's separated from my home. And my wife always knows when I go out to the garage, it means somebody's going to have hell to be paid. I call TK and I say, hey, TK, let me tell you about what I think of your blankety blank app. And guess what, TK? I will never take an Uber again.

Starting point is 00:46:49 I am just going to simply take a Lyft. I said, TK, you can call your chief economist a Lyft loyalist. Oh, wow. He must love that. Yeah. So I said, you know what, TK? The worst part about the story is that I never received an apology. And he says, well, John, we haven't gotten to that yet. And I said, we have now. So what my team did is we explored in big, big data sets, how much business does Uber lose from bad trips? Now, here you don't want to do an experiment because I don't want to just give people bad trips on purpose.

Starting point is 00:47:38 What I have to do is I need to find, for example, Tim, and then I need to find Tim's statistical twin in the data, and I need to find when Tim gets a bad trip, but at the exact same time and over the same route when his statistical twin gets a good trip. And then I can look at in the future, say for the next 90 days, does Tim who got a bad trip spend less money than Tim's statistical twin? What happens is you do. In fact, millions and millions and millions of lost revenue happened because of bad trips. Okay. So now I showed that bad trips cost Uber hundreds of millions of dollars. Now, what are you going to do about it?

Starting point is 00:48:35 That's where the science comes in. So we ended up developing a scheme where we did various apologies within an hour of when a person had a bad trip. It's like one apology could be simply a basic apology in an email. Tim, we're sorry about your bad trip. We know we should be doing better. Please accept our apology. That's one treatment. But another treatment is the exact same apology with a $5 coupon that please take this $5 coupon for your next trip. What we find is we can undo a lot of

Starting point is 00:49:15 the bad stuff of the bad trips, but nearly every time you need that cash coupon. Words just don't matter, but a little bit of cash with the apology ends up undoing about a third of the bad stuff. So now we're talking tens of millions of dollars. And true to his word, TK could have said, look, that's a great trade secret. We don't want anyone else, including Lyft, to know about that. But TK let us write that up as a scientific paper, and now it's in an academic journal and we can all learn from it about the economics of apologies. You know, I have to give TK credit because you think about the, and you obviously, but as the fierce competitor he is, it's all the more impressive to me that he would allow that. And you think of the potential ripple effects of that, not just within ride sharing,

Starting point is 00:50:13 but outside of ride sharing. I mean, it hopefully translates to a better customer experience in different sectors, right? And that's part of the potential consequence of being able to publish. No, you're right. And I think that TK really got, and Logan Green does too, and John Zimmer now at Lyft, we have great papers coming out of there too, but they totally get that we're not only here to change the face of transportation, but we also have another gem called big data and a lot of information that we can use and we can leverage to help some of societal problems be attenuated. And that's the great thing, I think, about firms and corporations and organizations today when they're willing to do that, even though it's not in their financial best interest to do so.

Starting point is 00:51:07 It's an incredible opportunity. I remember way back in the day, they ended up, I think, cutting this short, but there existed the God view and the ability to look at all this data, and you could really learn a lot about human nature. And they would publish on the Uber blog and elsewhere all sorts of correlations that were endlessly fascinating. But it's really when you have a big enough data set and you're dealing with individuals and you're able to identify not only their behaviors, but how their behaviors change with different incentives and UIs and so on, you're really just looking at this gigantic petri dish called humanity and human nature, right? At the end of the day.

Starting point is 00:51:51 You're spot on. Let me ask you if I can find this quickly enough, looking through my many notes here, about what you have learned about, if that's the right way to phrase it, human nature. And I want to bring up car dealerships. So I believe, and please correct me if I'm getting this wrong, but the behavioralist meets the market measuring social preferences and reputation effects in actual transactions. This is in the Journal of Political Economy, 2006. But the quote that I really liked, and this tells you I'm kind of a Hobbesian at heart, I guess. Quote, very few people will not screw each other, List says.

Starting point is 00:52:30 There are very few nice people out there. Could you just elaborate on that and kind of flesh out how that came about? So the paper you bring up is actually near and dear to my heart because, you trade things like Hank Aaron, Barry Bonds, or Ken Griffey Jr., whatever, baseball cards. And what I noticed was everything that was happening in that convention was not always in accord with economic theory. And I thought that what I was learning in the classroom could actually be tested at these baseball card conventions. So I started to do it in the early 90s. That's one of the very early work in economics that uses the world as the lab, and it's exploring things like, why do people trade? Why don't people trade? Why do people negotiate the way they do? Why do dealers give women higher price

Starting point is 00:53:55 offers than men, et cetera, et cetera, et cetera, discrimination, gender pay gap, et cetera, et cetera. So along comes the early 2000s. And what I notice in economics is that there is an explosion of research on something called social preferences. And what that means is we want to measure how much people care for other people. Okay. So clearly they do. Clearly giving to charitable causes is two and a half percent of GDP in America. So clearly people are giving to others. But the types of estimates that these studies were showing is that, you know, you'll give up half of your wealth to some anonymous stranger. And I started to wonder how far can you push these lab results if you go to a market where people are buying, selling, and trading for their livelihood? So what I did was I set up an experiment where everyone in the experiment knew that they were taking part in an experiment.

Starting point is 00:55:11 Guess what happened? They all were really nice. And they all were exactly like the literature in economics said they should be. They're all homo be nice to everyone rather than homo economicus, right? So then what I did is I observed those same people in the marketplace when they didn't know they were being observed doing the exact same thing that I had them do in the experiment. Guess what they do now? Totally screw the other guy over. And they're about maximizing their own incomes. So what happened to all this generosity and all of this social preference? I'm being nice to other people, the market and the competition and the anonymity that I'm doing

Starting point is 00:56:06 this. And if nobody can detect me, I'm going to actually do it. So then I added a twist to that paper. What I did is I had them act in the marketplace and they did not know that they're part of an experiment, but now what they sell can be graded by a professional certifier. In there, you find that they start to be nice again. So what's happening is that they care about their reputations. And whenever you can check them on their reputation, they're nice. Why? Because they care about the long run and they want repeat business. But when you can't check them on their reputation or you can't check the quality of the goods they

Starting point is 00:56:50 sell, they outright screw people over. This makes me think about one of my sensitive spots with respect to Uber and ride-sh sharing in general, which was how, and I'm not sure of the right kind of bias label to apply to this, but you would see in the news, Uber driver this, Uber driver that, all assorted bad behavior, which statistically was a very, very small percentage. But the implication was that somehow, in the early days especially, that ride sharing was more dangerous or lower quality than taxi rides. But the fact of the matter was there was no data capture with taxi rides. There was no accountability, no rating, no nothing. And the costs and the events, the adverse events were largely invisible. So it's just a very unfair comparison. Let's hop from car dealerships going to a place that is much more meta because I admire your ability and anyone's ability to test assumptions, even if they lead you to some very unexpected, surprising, or even uncomfortable conclusions. And I have, as you can see, and people who are on video, I've just

Starting point is 00:58:18 piles and piles of paper in front of me. You are a scholar. Well, I pretended I play one on the internet. And let me read a line from you, and I would love for you to explain what it means. Here we go. Just a few sentences. A first step to rise up my critical thinking hierarchy is to practice theory of mind. People have a really hard time doing this, but once you do, you avoid all kinds of silly mistakes at home, in the workplace, and on the streets. So there are two terms that I would love for you to define, and then you can expand it in any way that makes sense. Critical thinking hierarchy, and then theory of mind, practicing theory of mind. Critical thinking is a bit like creativity.

Starting point is 00:59:08 You know it when you see it. It's very hard to define what critical thinking means. Now, in my work, I've written a recent academic paper on critical thinking. I view it sort of in two camps. One camp is how you use data and how you gather data to make logical decisions. A lot of times people make fundamental errors when generating data. For example, if I really think Uber is a great idea early on, every data point that I get back that says it's a bad idea, I put on the sidelines. And every data point that comes to me that it's a great idea, I say that data is right. That's called confirmation bias. And it's one of the most important impediments to figuring out if your idea is good or not. Okay, so we have biases in how we generate, accumulate, and interpret data.

Starting point is 01:00:14 That's one kind of critical thinking. The other kind of critical thinking is more abstract. And this is where theory of mind comes in. So theory of mind means how well do you put yourself in the shoes of another person? So there's a theory in economics called game theory. And game theory was started by a really old mathematician who's now unfortunately dead named John Nash. John Nash, yeah. John Nash. It's like the bar scene in A Beautiful Mind.

Starting point is 01:00:53 Exactly. One of my favorite movies, Beautiful Mind. Even though the bar scene got it wrong. Oh, really? Yeah, they messed up the Nash equilibrium. The reason why is because at least one of them should have gone for the blonde. So an equilibrium is given everyone else's choices, would I change my choice? And if you wouldn't change your choice, that's an equilibrium.

Starting point is 01:01:18 If you would change your choice, then that's not an equilibrium. Remember what happened in the bar scene? They all went for the brunettes, even though they all wanted the blonde. So that was like a definition of not an equilibrium. But anyway, we'll cut Ron Howard a break. Yeah, yeah, yeah. Don't let facts get in the way of a good story now. Exactly. How could you write that bar scene at the Palmer House? Okay. So now theory of mind ends up being super important for the abstract part of critical thinking because your next move needs to be in part determined by how you think that person will react. And if you can put yourself in the shoes, whether it's, do I lower my price on Uber,

Starting point is 01:02:07 and how would Lyft respond? Or should I spend more money in ads? If I'm United Airlines, how will Southwest respond? Et cetera, et cetera, et cetera. All of these kinds of examples. Now, theory of mind is, how well do you put yourself in the shoes of another person and figure out then what your optimal action should be. Now you see, Tim, when you start having kids, you're going to have to use theory of mind to say, okay, how am I going to get my kid to pay attention on the soccer field? Or how am I going to get my kid to pay attention in second grade math if they're really not interested at all because they're doing math at a seventh grade level? And that's, you need to put yourself in the shoes of whether it's your student, your worker,

Starting point is 01:02:57 your collaborator, your adversary, whomever, and you need to then backward induct and act in an optimal way. That's theory of mind is a valuable skill with tangible benefits, how do you develop it? And I suppose if we wanted to speak broadly, a lot of folks would say, well, it sounds like you're practicing empathy. But then on perhaps the other side of the spectrum, on the more concrete side of things, I would say, well, if I just gave you a thousand bucks and made you play online poker and set a max amount that you could lose per game to ensure a certain number of repetitions, you would probably get better at predicting how others, or at least get better at attempting to predict how others will respond, giving certain gambits and strategies and so on. How would you suggest people develop this skill? Or are there different approaches that you might suggest people take, or books or resources, anything, for people who want to get better at

Starting point is 01:04:17 this? What I would tell the listener is to start with what an economist calls the primitives of decision-making. And what I mean by that is try to figure out what are the preferences of that other person? What are the beliefs of the other person? And what are the constraints of the other person? Because all choice will be governed by these three primitives. So for example, if I know that somebody adheres to the social norm of never ever tipping, then if I'm trying to do something to get them to tip, if I put myself in their shoes, regardless of what I do, they're never going to tip. So I should not spend a lot of money trying to convince them or induce them to tip because what is governing their tipping

Starting point is 01:05:12 behavior is the strong social norm that they will never tip. So that's called a constraint in a person's world. So I think the first step is to try to figure out what are the primitives of the person's decision-making. And after you figure that out, you then say, well, what's my optimal action given that the person operates in this way? A lot of times it would be how much money do they have? Do they have the mental capacity to actually solve the problem that I'm giving them? When I play poker, I think poker is a hard game because it's really hard to randomize. So if you're playing against really good players, you need to first of all decide what is the fraction of time that you're going to randomize. And then you need to do it.

Starting point is 01:06:03 When I say randomize, I mean, what fraction of hands are you going to randomize. And then you need to do it. When I say randomize, I mean, what fraction of hands are you going to bluff? And then if you say it's, let's say, 10% of the time, you then need to figure out which 10% of the time. Now, humans are terrible at that. Humans are terrible at doing something in a random way. So what I do when I play poker, I use a second hand in a clock to say, well, if I'm going to bluff 10% of the time,

Starting point is 01:06:34 then I look at the second hand and say, if it's at a certain point in the clock, I'll bluff. And if it's not, I won't. And then that helps me because it's an exogenous machine that is not susceptible to my own biases. Because when you ask somebody to, for example, create a random sequence of coin flips, you can ask a really smart person to say, create 10 coin flips in a row. And what they'll do is they'll say, heads, heads, tails, tails, heads. They'll have nearly exactly five heads and five tails over the 10, and they won't have enough runs. Because sometimes you have tail, tail, tail, tail. But when a human mind creates it, they don't do enough runs and they do too close to 50-50. That's just

Starting point is 01:07:27 how we're built. So you need an exogenous force to help you with things like that in poker or anything else. Do you play poker often? Have you played for a long time? During the rage I played and- Wait, did you say the rage? Was that your rage? Yeah. You had this rage of the explosion. Yeah. The World Series of Poker on television and all that. Yeah. World Series was starting. Phil Helmuth was on, Ivy, these guys. I ended up flying to Australia to give a keynote in a place called Alice Springs. It's way out in the boondocks of Australia. I don't know if you've ever been there, but- I've never been to Alice Springs. I've been to Australia. Don't recognize Alice Springs.

Starting point is 01:08:16 Yeah. So I'm out there and there's a casino there. And it so happens that the conference is at the casino. So as the keynote speaker on, I think it was a Friday night, they were going to take me out to dinner, wine and dine me. That's typically what you do with the keynote speaker. So around one o'clock that afternoon, I said, well, the dinner doesn't start till eight. I'm going to join this poker tournament. And thinking I'm going to be out, I won't make the next table, et cetera. It turns out that I get on this magnificent run and I make the final table. And by this time it's eight o'clock. So the conference organizers are standing around saying, come on, John, we have to go. I ended up making the final two. It was an Aussie woman in

Starting point is 01:09:06 me. It was kind of an out-of-body experience, but I won the bracelet. And it wasn't that I was very good, but I did a little theory of mind. I'm sure I got lucky a few times on the flop. Maybe on the river, I got lucky once or twice, but I ended up kind of retiring there. I'm a little bit like Michael Jordan. Let's retire on top. So I got my Australian bracelet and I haven't really played that much since. I just don't have time to play, but it's an interesting game that I think people undervalue skill. I think poker is a lot more skill. Obviously there's a ton of luck. I get that. But there's a lot more skill than people realize. It's really a great game, especially if you have a long

Starting point is 01:09:51 time series of tournaments. The best end up winning. And I certainly wasn't the best. So I kind of retired after that bracelet. I am fascinated by poker. And I've actually interviewed Phil on the podcast. He's hilarious. Very, very smart, very aggressive guy. And I recall also really enjoying, I recognize this as blackjack. And specifically, I think it was single deck or maybe two deck blackjack in Bringing Down the House, which was later made into 21. Let's hop to scaling. So the new book is The Voltage Effect, How to Make Good Ideas Great and Great Ideas Scale. Why write this book? You have many options with how to spend your time. You have many different projects. I don't know how

Starting point is 01:10:46 on earth you honestly have your hands in so many things. We haven't covered 5% of it. Why write this book? We should always, always, always evaluate how we want to spend our time because opportunity cost of time is very expensive. On the one hand, when I go and give lectures, a lot of the audience, especially the younger audience, will ask me a question along the lines of, you know, we've been fighting poverty for decades. We've been doing study after study after study to fight poverty. How come we're not making a dent in it? You can substitute poverty out for discrimination, public education, what have you, and we haven't really made a deep impact, even though scientists have been working on this

Starting point is 01:11:46 for a long time. Now, that coupled with an experience that I had in Chicago Heights. In Chicago Heights, I started a preschool from scratch with my friends Roland Fryer and Steve Levitt. Steve Levitt's Freakonomics author, Roland Fryer and Steve Levitt. Steve Levitt's Freakonomics author, Roland Fryer's famous economist at Harvard. We start a preschool for three, four and five-year-olds and it took a lot of shoe leather. And we end up running this for four years and I'm on the ground day to day to day running it and I get great results.

Starting point is 01:12:32 And then I go to scale it and I talk to policymakers and they tell me, John, that's a great little study, but it's not going to scale. And I ask them why. And they say, it doesn't have the silver bullet. And I say, what do you mean silver bullet? What does that exactly mean? I've been doing social science research for 25 years, and I've never heard this argument that my idea won't scale because it doesn't have the silver bullet. And then they say, well, we just don't know about this. But every time an expert tells us they have a great idea, it always fails to scale.

Starting point is 01:13:15 Okay. So then the third part of this trifecta was my wife and I are sitting down the street from the Uber headquarters on Market Street. We're sitting in a cafe and I'm telling my wife about the Chicago Heights project and how it's so frustrating. I'm telling her about all these people who always ask me, why aren't we making a dent in poverty or discrimination? And then I'm telling her about scaling at Uber. And she says, you know what, for the next part in your career, you should take on scaling. And I said, what the heck, let's do it. I said, let's do it. And because all of these threads in my life, the White House,

Starting point is 01:14:02 the creating the early childhood program, working at Uber and now Lyft, all of these had features of scaling. And then when you come to the realization that you only make big change at scale, you start to say, why don't we have science around scaling? Why is it the case that people write a book that say something like move fast and break things or throw spaghetti at the wall? And if it sticks, good, or fake it till you make it. You know, you say, where's the science behind that? And there isn't any. So my chore back then when I first started saying, I'm going to take scaling seriously, is basically I want to do the science of using science. And I've created science in the past, but we haven't created it in a way

Starting point is 01:15:01 that is meant to scale. What we do is we say, I want to do a great idea in the Petri dish. And then after I'm done with it, I move on to the next idea. I don't take seriously from the beginning that if I want to change the world, somebody is going to have to scale this. And I need to understand the features that are important when scaling in the Petri dish itself. So all of that came together. And I said, look, we're going to change the world by understanding the science of using science. I want to first just give voice to an observation. And that is thinking back to your story of the onstage debate, the pro-field studies camp represented by you, and then the, I suppose, criticisms of field

Starting point is 01:15:55 studies on the opposite side. It strikes me that one of the enormous benefits of field studies, and again, I don't know what I'm talking about, so please correct me if I'm wrong, is that you can, in some respects, get around the replicability crisis that is found in basically every nook and cranny of science because you don't have to write grants or raise funding for a tiny pilot study that will never be replicated. If you're working with an Uber, you have incentives aligned, you have incredible longitudinal data, and you have millions of data points. I don't know. I just wanted to lob that out there. Tim, you're right. When you think about the credibility crisis or the replication crisis in science, it really goes along the lines of you had a camp of people do lab experiments. And a lab experiment is you bring a group of

Starting point is 01:16:55 sophomores into a classroom and you have them do a task that they typically don't do. And that's the experimental approach in psychology. And it was the experimental approach before I came into economics. It was largely do things in the lab. And what was difficult there is that if you wanted to generalize those results to the extra lab world. A lot of the features of the extra lab world that might matter in decision-making are set at different points of the dial in the lab. So the first obvious thing is the world is not filled by rich white kids who are in the lab doing the experiments at the fancy universities, right? The world is filled with truck drivers and all kinds of other people. That's where it starts,

Starting point is 01:17:52 but it doesn't end there. Because remember, anonymity, or whether somebody's watching you, or whether you have experience in doing something or whether people opt in or out of a market, people in a market tend to be the winners. The ones who you don't observe may have tried and they're gone. They're long gone. So there's selection into markets too. So in the field, you have not only the right people, but you also have the right set of circumstances. And what you're getting to is right. That when you can partner with a government, I work a lot with governments, I work a lot with firms, but when you can get big data quickly at scale, you can in a very fast way, find out, did that result in Seattle on green cars? A bunch of people

Starting point is 01:18:48 in Seattle love green cars if you're a Lyft consumer, but they really don't like it that much in Akron. So you can quickly find these things out and then you scale where you should scale and you try something new where you shouldn't be scaling. And I think that organizations, whether they're for-profit or nonprofit or governmental, these are unique opportunities that we're just scratching the surface. And one of the external benefits, what economists call a positive externality, is that it's going to be a lot easier to replicate. And the firm has a really strong incentive to replicate because they don't want to waste a bunch of money. Yeah.

Starting point is 01:19:34 Yeah, absolutely. Let's come back to this mention of the silver bullet. So I've read that you have said scaling is not a silver bullet problem, rather, it is an Anna Karenina problem. Could you explain what you mean by that? And then at some point, we don't have to do it right now, but I would love for you to kind of map out a success case and then a failure case or a semi-failure case and sort of do an autopsy on both, if that makes any sense. But let's begin with not a silver bullet problem. Scaling is rather an Anna Karenina problem. What on earth does that mean? The typical policymaker argues two things. One, that the result in the Petri dish won't scale.

Starting point is 01:20:26 And what they really mean by that is that there will be a voltage effect. So what I mean by voltage effect is you have a great result in the Petri dish, but when you scale it up, you turn that mountain into a molehill. Okay, so great result in the original research. Scale it up, you get nothing. That's the voltage effect. Okay, now policymakers will say that truly scalable ideas need to have the silver bullet. Now, they're exactly wrong there. What I found in my research and what I talk about in the book is that the silver bullet is really like if you have this one great characteristic, you can scale. It's like a best shot technology. Scaling is actually a weakest link problem and what i mean by weakest link is so go back to

Starting point is 01:21:29 tolstoy tolstoy starts anna karenina one of the best first lines ever in a novel happy families are all alike each unhappy family is unhappy in its own way. Scaling is identical to that. The way I think about scaling is scalable ideas are all alike. Each unscalable idea is unscalable in its own way. So now you can say, well, what do you mean? And what I found is that there are five major reasons why ideas don't scale. And that's a weakest link. When I say weakest link, think about airport security. The airplane is only as secure as the weakest link. Think about your automobile. The automobile will work, but it breaks if you have a flat tire or if a piston isn't firing. That's a weakest link problem because it only works as long as each of the features is at a certain level. That's how I want you to think about the Anna K problem.

Starting point is 01:22:52 Okay. So now once you determine, does your idea have these five vital signs? Let's go through an example to help bring these vital signs to life. Yes, please. Let's talk about one of my favorite scientists, Jonas Salk. So Salk is responsible for the polio vaccination. What Salk does is he first does like a lot of great scientists do. He tries it out on his own kids. He finds it works. Skin in the game. Yeah. You got a lot of skin in the game. And then he finds that he can replicate that result. So those original results were not a false positive. That's vital sign number one. Does your idea have voltage? So Salk found that it does.

Starting point is 01:23:48 It's not a false positive, his initial result. Now what Jonas Salk does is he tries it out on a lot of different kids. What he finds is it works for every kid. So now he knows that the audience, what I call know your audience or know your slice of the pie, he finds, wow, it works for all kids. That's vital sign number two. Understand how big of a piece of the pie you have or know your audience and be true to yourself about measuring how big can this market be. Okay. Now here is where Salk enters sort of a thin ice and that's understand the situation in which your idea can work. So think about COVID. COVID vaccinations have worked, but it's really hard to get the shot in people's arms. So like a lot of medications, medication adherence matters. And getting people to take the vaccination matters a lot.

Starting point is 01:24:58 The medicine's good, according to science. Now, how do you get it in people's arms? That's where this COVID vaccination scheme that we're up to right now has failed. It hasn't been able to deliver. This is where Salk is brilliant because what Salk does is he leverages the healthcare system to deliver the vaccination. Okay. Now, how does he do that? Here's what happens. Tim, you don't have kids yet, but when you have kids, you're going to be in the delivery room. Kid's going to come out. Everyone's going to be happy. You're going to make sure

Starting point is 01:25:36 your wife is healthy, the baby's healthy. And then the doctor is going to give you a playbook. And the playbook is going to say, we're going to give these vaccinations today. You're going to bring your baby back in six months. It's going to get a checkup and some new vaccinations. You're going to bring your baby back in 12 months, same thing, 18 months, same thing. The polio vaccination occurs during that time period. Any parent is always going to do well by their kid and make sure that they bring their kid to the doctor checkups. So it's not an extra cost and you just naturally get it. So now you've overcome a really hard problem about the situation here. Now in Chicago

Starting point is 01:26:21 Heights, the situation that I have is I hired 30 really good teachers. If I want to scale that thing up, I need to hire 30,000 good teachers. And if I have to do that all in the same input market, say I have to do that all around Chicago Heights, I'm screwed because I can't replicate the quality of the original 30 good teachers. That's what I mean by properties of the situation. The fourth vital sign is understand spillovers or understand unintended consequences. In the Jonas Salk case, he's again got that in spades because the polio vaccination has these great externalities.

Starting point is 01:27:04 Once you get it, you can't give polio vaccination has these great externalities. Once you get it, you can't give polio to somebody else. The fifth vital sign is understand the supply side. And what that means is how much is it going to cost you to provide it? With medications, the first feature of a medication is most of our expenditures are up front. And then after you expend a bunch of money on R&D, you have what's called economies of scale. To actually make the vaccination or make the pill, it's really, really cheap. But you also need to get into the people's arms. And that's where we're failing now with the COVID vaccinations is it costs a lot of money. Whereas in the Salk case,

Starting point is 01:27:49 we've leveraged the healthcare system. So it's free because they're going to come in anyway and your baby just gets that vaccination. So that's kind of a running example that is really useful to know because it checks all the boxes. And as you know, it's scaled with great brilliance. Could you expand on the unintended side effects, the spillage, maybe give some examples of where things have gone well or gone sideways? One thing that is invisible to the entrepreneur or invisible to the firm is

Starting point is 01:28:32 an unintended consequence or what happens after I push my idea out there. Now, one example of an unintended consequence is what happened to us at Uber. Remember, summer of 2017, we have this idea that we are going to add tipping to the Uber app. In doing so, we beta test it. What we find is we go to a market and we allow 5% of the drivers to be able to receive tips. Okay, so what happens? They receive tips, they work more, and they make more money per hour. So it looks great when 5% of the drivers have it, their wages go up, and their labor supply goes up. Win, win, win. Now, when you roll tipping out to every driver in that market, guess what happens? They see tipping, they work more, But the market dynamics, because everyone has tipping, they undo

Starting point is 01:29:48 the tipping effect. In fact, more drivers are working, but they're driving around with an empty car more often because so many drivers are driving that the extra tip effect is exactly offset. So that's an unintended consequence. Now, when we scaled it up, drivers don't make any more per hour with tipping. They exactly offset it. And you didn't see that when only 5% of the drivers had tipping. That's an example of an unintended consequence

Starting point is 01:30:21 that caused all the good stuff of tipping to go away. Now, on the other side, sometimes you have good stuff. Think about Facebook. So Facebook, if only five people in the world have Facebook, it's pretty useless, right? It's not very valuable to me. But if every one of my friends and every one of their friends and every one of my family members is on Facebook, there's something called network externalities that as more and more people get it, Facebook becomes even more valuable. So you need to pinpoint, does your idea have these really cool network externalities in this case?

Starting point is 01:31:04 Or at scale, the good stuff becomes even better. These are things that I'm asking people to measure and take account of. Makes me think of Andy Grove at Intel. And I think he used to refer to it as paired incentives. But for any incentive, I'm sure I'm getting the details wrong here, but he would ask, what is the unintended side effect? Like what is the sort of perverse behavior that you are? And I mean, perverse as in undesirable behavior that you are also incentivizing that is not explicit.

Starting point is 01:31:41 And you should measure both. I have, let me make this just purely self-indulgent for a second. Let me lay out a challenge and you're the perfect person to ask. So I have seen this and heard of this across ride-sharing apps. So I don't think it's unique to anyone. I have certainly run into this. If drivers are penalized for canceling trips, but when they accept a trip, they realize, oh wait, that's a really short ride. I want an airport ride or something like that. They will sometimes turn in the opposite direction and just drive away to force the rider to cancel. And then the rider gets penalized. I'm wondering how, presented with that issue, how you'd think about or how you have thought about

Starting point is 01:32:31 mitigating it or solving it. When we did a deep dive into Uber wages across men and women, what we found is something really surprising. We found that men earned about 7% more per hour than women as an Uber driver. And that really surprised us because wages aren't negotiable. All men and women have the same rate card. And what a rate card means is you're paid the same time and distance. And women receive more in tips than men receive. So we were really surprised. And one reason why is because men strategically reject trips, and women accept all trips. So a bad trip on Lyft or Uber, you're right. One type of bad trip is I have to drive a half hour to pick the person up, and then I only have a five-minute trip. One way to get around that problem is to pay the driver for time that they spend from when they get the dispatch to when they pick the customer up. We do that now at Lyft.

Starting point is 01:33:55 And that's an important reason that you can lower rejection rates. Now, there are still cases of cancellations. What we do is there's a fine for drivers. In fact, there's a fine for riders who do this too. There's a cancellation fee of $5. And I think using that mixture of actually paying drivers to go pick somebody up and having the relevant fines, right? Taking money away, clawing it back is kind of two instruments that we find can be successful in lowering exactly what you're saying because it's irritating to sit out outside in the cold. I'm in Chicago here and it's freezing. I sit out in the corner waiting for a driver and I see them going the wrong way because they don't want to pick me up. I'll feel a lot better if I know they're being penalized versus

Starting point is 01:34:51 if they just go to a better trip, right? And you're right. The best trips are, I'm going to deadhead to the airport. And you also can't use your app while they're attempting to force you to cancel, right? So I remember staying with a friend in Malibu once and there's PCH and it's a mess, right? And if the drivers are not paid for their transit time to the pickup, then more often than not, I mean, you would confirm the ride and then go back to doing what you were doing, knowing that it would be there in 12 minutes and then check 10 minutes later and notice it's canceled. So you can solve that by paying them, the driver, for the transit time. What if it's a situation where it's like downtown Chicago or downtown Austin or wherever it is,

Starting point is 01:35:34 and they're just looking for longer trips, so they start going in the opposite direction? How would you address that? So they're not supposed to know that until you get in the car i wonder why yeah i wonder why i mean it does happen to a fair number of people i just wonder what drivers usually do is they call you and say by the way where are you going and and then that's how a lot of times they find out yeah one way that we tackle that is if we have under supply for short trips, we end up giving incentive bonuses for drivers on number of trips. For example, you say you get the ride bonus of $500 if you take 50 trips this week. The only way you can take 50 trips is if you do a bunch of short trips.

Starting point is 01:36:20 So there are ways to incentivize outside of the rate card. There are ways to incentivize drivers for shorter, long trips. So there are ways to incentivize outside of the rate card. There are ways to incentivize drivers for shorter, long trips. And those are all at the disposal. And Uber and Lyft are constantly doing these things. Yeah. So I'm going to provide some foreshadowing for a question to come just so it can slow bake. And so I want to talk about the four secrets to high voltage after launch, scalable incentives, marginal thinking, optimal quitting, and building culture. So I'm going to set that on keep warm for now. I have to ask you, though, because you, in a sense, alluded to this, but we didn't get into the details. So you received your PhD. You went then to sort of sell yourself on the academic job

Starting point is 01:37:07 market. This is 96. Applied to 150 jobs. Usually the hit rate is around 20. So one would expect around 30 interviews. You got one. So there's that. And then you were able to alchemize your career trajectory, and here you are, obviously, in a very different position. And my question is, what did people miss about you, or what did you miss, or what mistake did you make, or what did you lack? Why did that happen? I've been doing the post-op for over 25 years now. And I think it's sort of two parts. One part is academia is very hierarchical. And the way the funnel works is that the top schools, Harvard, University of Chicago, Princeton, MIT, etc., we put out about 20 to 30 students per year on this market, PhDs. So we put out 25 students this year with a PhD from the University of Chicago. But we only hire back one or two. So the unwritten rule in academia is where you get your PhD, the ranking of that school will be much, much higher

Starting point is 01:38:38 than where you get your first job. And then where you get tenure will be much, much lower ranked than where you get your first job. Tough gig. That's just sort of how the funnel works. So when you start at the University of Wyoming, which is, I'm not sure what the ranking is, but it's certainly not in the top five schools. If you follow that rule, those top schools, they're not even going to open my envelope. I didn't know this, but when I applied to the University of Chicago, I'm quite confident that my application envelope was never opened because they saw the return address was Laramie, Wyoming. Okay. So I don't think it was anything that I did wrong per se. Although it is true that people were very, very skeptical of my field experiments in the early nineties. Everyone said, you're an idiot for doing it. You know, we think you seem smart.

Starting point is 01:39:42 Why are you throwing your career away? If you want to do experiments, you do them in the lab. What is this field experiment stuff? So you're right. I applied to 150 schools because I wanted to be an academic. I don't want to be a truck driver. Nothing wrong with truck driver, but that was my outside option. Let's be honest. If I don't get an academic job, I'm back in Madison, Wisconsin, driving truck. So I applied to 150 schools and I still wear that today. I got 149 people who told me, you're not even worthwhile talking to in an initial interview. And I saved every one of those envelopes that said, thanks, but no thanks. If I ever need motivation, I can look at that box of envelopes that says, go fly a kite list. Yeah.

Starting point is 01:40:32 When you get your yacht, you can laminate them and put them in the floorboards. Yeah. You know, University of Central Florida worked in it. It ends up working well, I think, too, because what I see is at the University of Central Florida, I could take chances and I can continue to take high variance plays. And what I mean by that is take risks in the research where if I started at the University of Chicago, it might have been harder because I might have wanted to march to the same beat of every other drummer around me. And I think University of Wyoming as a grad student, University of Central Florida, these sort of relieved the social constraints of having to be like everyone else. And I could just be me. And I think in the end, it really helped a lot.

Starting point is 01:41:22 And that was kind of the silver lining of my approach to academia, I think. I have to ask you, and this may be a dead end and we can cut it if it is, this is going to show you just how much I don't know, which is not going to come as a surprise to anyone listening. But when I think about the cold reception that you received based on the field studies and the directionality of real world experimentation. People are like, you seem smart. This is stupid. Why are you being stupid kind of reception? It reminds me of a documentary that I recently watched. It's a Nova documentary on fractals and Benoit Mandelbrot. And I'm wondering, because part of that documentary discussed the wide spectrum of applications for fractals, lots of applications to physiology, cardiology,

Starting point is 01:42:15 I mean, extremely wide ranging. Is there any application for fractal geometry or fractal mathematics to economics? Is there any intersection there? I think there absolutely is. And I think as we move to higher order computing, and as we move to a higher order understanding that humans have of how to apply mathematical principles to real world questions, I think some of the big breakthroughs will be in that area. And I think part of it is going to be, how do you solve these problems and get a what's called a closed form or an analytical solution rather than have something be, well, anything can happen? Because when you deal with humans, you know, the sciences are great because we have laws. And we can talk about these laws because they're physical constants. They apply

Starting point is 01:43:29 everywhere that we at least understand they can exist. These laws apply. In economics, when you talk about predicting human behavior, we don't have quantitative laws. And what we have are things that on the one hand are kind of obvious. One of our big laws is the law of demand. And basically what that says is when price goes up, quantity demanded goes down. Well, of course, if price goes up, you buy less. And then we don't even have a physical or quantitative estimate because that's called an elasticity. But the elasticity depends on the good and the circumstance, et cetera, et cetera. So we always have to measure it. I think when you start to add fractal and other aspects, it gives us a shot at least to try to be more scientific. And I think going down those

Starting point is 01:44:28 dimensions can be useful. I can't imagine an application right now, but that's the fun part about the question, right? Is that there are possibilities. Because back when I started doing field experiments, everyone said, in part, they're dumb because I'm going to a baseball card convention to gather data. And they said baseball card collectors are weird and the results won't generalize. And that might be fine. They might generalize or they might not. But what I argued was the tool will generalize. And when we get to work

Starting point is 01:45:06 with firms or governments or public education, we can do large-scale field experiments there, just like hard scientists. And then we can say something causal about the relationships that we observe. Yeah. And also if you're working, obviously the reference here is within the halls of academia, but if you're working in private industry also, that weird group could actually generalize to a much larger subset of the population that is equally weird as a standard deviation from whatever the hell normal is. So you can have a great business focusing on weirdos. Exactly. As long as there are enough weirdos, you sure can. Yeah.

Starting point is 01:45:46 So let's come back to the four secrets to high voltage after launch. And I would love if we could explore these through an example or an illustration, whether real or hypothetical. Scalable incentives, marginal thinking, optimal quitting, and building culture. Would you mind expanding on those? The back nine of the book is really meant for the manager or the decision maker who has scaled an idea. So at Houston, we have launch. And after we've launched, what we have to think about is how can we make sure or at least give our idea its best chance for high voltage? So the back half of the book really draws upon my experiences in government, in firms,

Starting point is 01:46:49 to draw out what are the constant threads that people make mistakes around. And the first chapter is how to think about incentives that scale. And I think one of the big features that we've learned in behavioral economics is that using framing or loss aversion, like the clawback incentive, which is essentially give somebody an incentive up front and tell them you will take it away unless they performed. That's called loss aversion, or it is built on the concept of loss aversion and also non-financial incentives. I think a lot of times we undervalue both behavioral incentives and incentives that don't involve

Starting point is 01:47:47 cash. So things like norms, things like peers, how much does my coworker earn? That's interesting information or what are their benefits or how many days off do they get? Or if I perform in a certain way, am I adhering to the norms of my company? Should I tip or not? In many cases, if we can leverage the insights from behavioral economics and leverage what we've learned about non-financial incentives. In many ways, these types of incentives have the highest ROI because it's really cheap to give non-financial incentives by definition. Could you give an example of a non-financial incentive that you've seen work well? I think one example that you can think about with non-financial incentives is what we've been doing with governments around the world. So think about the UK behavioral insights team or in the Dominican

Starting point is 01:48:57 Republic, I've helped the tax authorities raise more money through getting people to pay their taxes. In each of those cases, we've used messaging such as 70% of people have paid their taxes on time. Why haven't you? That's a type of message that works. It gets people to pay their taxes. In the Dominican Republic, we have a paper titled the $100 million nudge. The Dominican Republic has a real problem with getting both individuals and their firms to pay their taxes. So what we did is we went to them and said, here's a suite of behavioral interventions that are all non-financial, things like just a reminder that if you don't pay your taxes, you might have to serve jail time. Things like just to remind you, if you don't pay your

Starting point is 01:49:53 taxes, we can make your name public and let the world know that you haven't paid your taxes. Messaging like that, we raised $100 million more in tax receipts, which is like 0.2% of GDP in the Dominican Republic. Boring. Maybe, I don't think it's boring, but a tactical or logistics question here, how was that messaging delivered? Was it billboards, newsletters, direct mail? How was that actually conveyed? No, not boring at all. These are direct letters that the Dominican Republic, the IRS, sent.

Starting point is 01:50:32 So they send a letter every year to people who haven't paid their taxes. All we did is told them, we think we can do better than your letter. So we had them send the letter to a group of people, and then we augmented their letter with a sentence or two. And they always remind people to pay their taxes, but we used a little bit of non-financial incentive, and we made bank for them. By the way, the DR should have a statue soon of me and a park named after me, I hope, but if there are any people from the DR listening. Yeah, arm in arm with Matt Damon. Exactly, exactly. So that's the first chapter in the second half. The second chapter is about thinking about making decisions on the margin. So a lot of your listeners who have taken Economics 101

Starting point is 01:51:29 always hear this, make decisions on the margin. And what we do terribly in the classroom as economics professors is actually explain what that means and give an example of what it means. What does it actually mean to think on the margin? So one example that I use in the book is to think about what we do at Lyft. So I'm sitting in a conference room at Lyft and the driver acquisition team comes in. The driver acquisition team is a team that is responsible for recruiting new drivers. We need to keep putting new drivers in the funnel because we want to grow our supply. What they present to me is a slideshow that has, here's how much money we spent on Google for ads to get new drivers. Here's how

Starting point is 01:52:28 much we spent on Facebook for ads to get new drivers. And then they say, on average, we had to pay $500 per driver on Facebook. And on average, we had to pay $600 for drivers on Google. And then they say, because of that average, what we're going to do is we're going to spend the next tranche on Facebook ads. And I say, whoa, whoa, whoa, whoa, whoa. I don't care about the average in the last half year. What I care about is how much did we have to spend to get the last handful of drivers, the last 10 or 20 or 50 on Google and Facebook? They say, well, let us check.

Starting point is 01:53:23 They go and then they come back and say, well, on Facebook, it was 700 per driver. And on Google, it was 350. Then I'm like, well, on Facebook, it was 700 per driver. And on Google, it was 350. Then I'm like, well, wait a second. Don't you think we should be moving money then from Facebook to Google? And they're like, yeah, we didn't think about the marginal driver. What was the last driver and what would be the cost for the next driver? That's marginal thinking. And I had this in the White House with Superfund cleanup. And we talked about how we should be spending money across hazardous and

Starting point is 01:53:51 non-hazardous waste sites to clean them up. Averages in some cases are very misleading when you really should be thinking about the last one and the next one rather than a big average. So that's marginal thinking. Now, the third one is quitting. And here, this gets a little bit personal because, let's face it, really the only reason why I went to college, I'm a first-gen kid. I went to college because I wanted to be a golf bro. And I was offered a partial golf scholarship to go to UW-Stevens Point,

Starting point is 01:54:32 University of Wisconsin at Stevens Point. And I went up there. This would have been the fall of 1987. And I started playing on the golf team. About midway through that fall season, we had a weekend off and I went back to Madison, Wisconsin, and I happened upon a bunch of players. One guy was named Steve Stricker. He's been on the PGA Tour for years. One is Jerry Kelly. These are guys I played against in high school. They were a few years in front of me. Steve went to University of Illinois. Jerry Kelly went to Hartford to play hockey and golf. And then they both made a ton of money later. But it was really a hard lesson because I watched them on the driving range. And I said, wow, these guys are really a lot better

Starting point is 01:55:21 than me. I didn't realize they were that much better than me. But practicing confirmation bias, I said, but I'm better than them around the greens. So when the scores come out, I'm going to show them. They went out in the morning, I went out in the afternoon and saw the scores and they were just a lot better than me. And I ended up doing a bunch of data crunching. And I realized that everything I was told as a kid, because remember, I grew up in Wisconsin, and that's Vince Lombardi country. Vince Lombardi is a venerable coach of the Green Bay Packers. When people win the Super Bowl, the trophy is called the Lombardi Trophy. So I was raised in the shadows of Vince Lombardi, who famously said, winners never quit and quitters never win. So and my parents taught me that time and time again.

Starting point is 01:56:16 Johnny, you have grit. You stick with it. So it was really hard for me to say, that dream is done. I'm going to have to quit that dream. I'm going to fulfill the golf scholarship. And I went and played and I had a very successful college golf career. But from that day on, my dream was to become an economist. I was still going to invest in golf, but it wasn't going to be all in on golf. It's going to be all in on golf and all in on econ and learning about economics and how I can use it as a trade when I, quote,

Starting point is 01:56:47 grow up. But for me, what I see in the business world and in government is that people don't quit enough. They don't quit enough because they've been taught not to quit. They also don't quit enough because they don't understand or appreciate the opportunity cost of time. You know, when you think about the person who moves to a different job or they move to a different apartment. Nearly every time they're doing it because something bad happened in the workplace or something bad happened in the apartment. That's all well and fine. But you should also be moving when you get better opportunities. And you should always be looking at your opportunity set.

Starting point is 01:57:47 We don't do that. We don't look at our opportunities until our own lot in life is soiled. And that's too late most of the time because a lot of opportunities have come and gone. So that's called opportunity cost because we don't realize that if I'm working on this idea with all of my might, that there's an idea out there that I can't work on. And we tend to neglect that opportunity cost of time. So we did this large scale experiment. I helped Steve Levitt design it and run it that had people flip a coin. And if it comes up heads, you know, they had a tough decision in life, whether it was a job, a relationship, an apartment, whatever. If it came up heads,

Starting point is 01:58:31 they need to change. If it comes up tails, they don't in our Freakonomics experiment. And what you find is when you track them over time is that most of the people are much happier that they made the change. So they didn't realize that there were greener pastures out there until they were sort of forced to do it. So that's the optimal quitting chapter. And the reason why, again, people don't like to say the Q word. So what I say is call an audible. We always call an audible in football.

Starting point is 01:59:03 We always pivot. When people pivot or call an audible in football. We always pivot. When people pivot or call an audible in football, they're glorified. But when you say I'm quitting, you're chastised. So I think part of this is just reframing when you quit and we should understand that it's pivoting or calling an audible in your life. That's the optimal quitting chapter. How would you give a pep talk to someone, could be a CEO, could be a student, could be anyone really, who's dealing with, let's just call it a data set or just a life experience that is not black and white. And not to imply that your experience is black and white, but if you

Starting point is 01:59:45 go into the gym for basketball practice and you're standing next to Michael Jordan, you're like, okay, there's not much debate here at some point. That is just a superior player. And maybe I should choose a game where I can be number one or number two or something like that. There are many life circumstances or business circumstances where it's kind of, or it appears to be like a 51%, 49% type of situation. And they're like, well, maybe if we continue to split test and iterate and, you know, I've read of all these success stories could be survivorship bias, but nonetheless, you know, there are all these studies of like pivot, pivot, pivot, and then, oh my God, now you have Twitter.

Starting point is 02:00:27 What do you say to those people? Or can you think of a real-world example where it was tougher to determine whether to quit or not? You're right. In many cases, we have these tweeners where the idea shows some signs and just enough to keep me going. The job shows some promise, just enough to kind of keep me going. I always say that you don't want to quit unless you know you have a good option to go to. And that means that you should periodically, say once a month, look at your options and make sure that the option is real and also make sure that it is along the lines of your

Starting point is 02:01:17 comparative advantage. So there not only needs to be something that you can go to, that something exists, but also a comparative advantage is what are you good at and what are you passionate about doing? The long run is filled with success only if you can wake up every morning and be passionate about something that you're good at. That's your comparative advantage. So I would say never move or rotate unless you know that what you're rotating to, you're good at, and you're going to love, and also that it's a real option. Otherwise, there's no reason just quitting to quit. I think that there are a lot of tweener cases where it could still work out. The problem is, is that every time the Olympics comes, when this is aired, the Olympics will be on.

Starting point is 02:02:11 And there will be a story every night about the person who persevered, the person who had to go and work at the grocery store. And then they drove in the forklift in the basement of a cheese factory. And lo and behold, that person has become a great bobsledder. And then the mom is on there saying, I love Johnny. And the dad is on there saying how we knew Johnny would never quit. And these are great feel-good stories. And that causes us to want to persevere. But where is the story for the billions of people who went down the hole and they kept going down the same hole every day to a mine that had no gold in it? Where is that story written? I've never read one. So we don't realize that there are billions of people who have tried and tried and tried, but they keep hitting their head against

Starting point is 02:03:13 the same wall and they never succeeded. That's lost opportunity right there. It's a harder story to use on the cover to sell magazines. Nobody wants to buy that story because that's a story of many people's lives. Nobody wants to read about their own life. Building culture, let's touch on that. The culture chapter was really fun to write because it allowed me to sort of reflect on what happened at Uber and also reflect on the research that I've been engaged in for over 20 years, which is along the questions of why do women get paid less than men in doing the same job? Why do people discriminate against one

Starting point is 02:04:07 another in markets? What are the underpinnings for that discrimination? And how can we think about diversity and inclusiveness in a way that a firm really wants to do it? And they're doing it for the bottom line, not just to get moral cookies. Because in the end, we want both. We want both morality to tug at it, but we also want to make sure the firm is doing right by their shareholders, because that's what a lot of times people are going to make decisions around. So the chapter in a way begins in Brazil. We did some work amongst Brazilian fishermen in two very distinct communities. One community had fishermen who had to fish in teams. And in those teams, they had to go out in the boat.

Starting point is 02:04:58 The ocean's very rocky. They all go out together. They bring back their catch, and they all share it. And those villages are also built on community when it's outside of the workplace too. So just like this brilliant utopia of everyone cooperating and getting along. Now inland, there's a similar village, except those fishermen go to a lake and they all work in a soloist. And what happens there is when they get to the community, they also act in a soloist form. So what's kind of cool is that you observe there that the workplace itself bleeds over to society. And in the one society, you have a lot more of the great public goods being provided. In the

Starting point is 02:05:53 other society, it looks more selfish. So we did a bunch of experiments in these two societies. And lo and behold, we find that what happens in the workplace really spills over to the community. So bringing all that together, what I say in the culture chapter is, as a firm or as an organization, we should want to build our own kabuchu. That's the society that is built on community spirit. And we should do that from the very beginning. And very subtle things like letting people know that wages are negotiable in our job advertisements. You know, that seems kind of innocuous. If you say wages are negotiable, well, what does that do?

Starting point is 02:06:40 What it does is something interesting. It gives women sort of the license to negotiate because what we find in our data is when you say in your job ad that wages are negotiable, women will negotiate as much as men and they will end up with similar wages. But if you leave that sentence out, women will not negotiate at all, really. And then they start with lower wages. So right away, just your job advertisement, it leads to a pool of applicants that can differ, but also the wages might differ. And if you want to scale a culture of equality and you're paid your marginal product, then we're not going to let subtle things happen that lead to very different wage profiles. We can do that from our very beginning of our firm. So the idea is there are a lot of little nuggets in that chapter about how you can build your kaboochoo. And those are the tips in the

Starting point is 02:07:44 chapter on build your own culture. I don't know if this is going to tie in culture. It certainly doesn't have to. But I am looking at some of your favorite things, and I see StubHub. Could you please explain why StubHub is on your favorites list? One of my favorite apps is indeed StubHub. StubHub is great because it's the ultimate market for seats to events. I can wait till the last minute.

Starting point is 02:08:11 I can do a model about what prices do. I've bought Super Bowl tickets on StubHub. The other night I went to the Bulls game. I sat courtside at the Bulls game. Wait till the end. There are a few courtside seats for $3.50 each. My wife gets to hug Benny the Bull. I don't have to make plans till the very end.

Starting point is 02:08:32 It's the ultimate market that's based on supply and demand. And then there's a little bit of behavioral economics in there because I've sold on StubHub too. And it's kind of neat because I have to try to figure out how should I price? And if my tickets aren't selling, how should I depreciate my prices if I really don't want to go to this event? It's an economist's playground here, StubHub is. But plus, it's great in many cases for the customer, but also for the seller. I don't like StubHub's fees that much, but nevertheless, they're taking their bite from the apple as well. And I'm still getting some surplus. So I love StubHub.

Starting point is 02:09:10 You know, I was just thinking, having had a number of podcasts now interviewing people about Web3, it's a somewhat controversial term, but let's just make it more specific. NFT marketplaces where all of the transactions are recorded and can be reviewed on the blockchain, I would imagine that that would provide an incredible playing field for behavioral economists who have the ability to crunch data like data scientists. I would imagine you could probably pull all sorts of fascinating behaviors, also see bad behaviors. I'm sure people are doing wash trading

Starting point is 02:09:50 and things like that. But because it is all preserved and there's this self-similarity in the blockchain in the sense you have all these nodes that have, in effect, copies of these ledgers, you could really do some fascinating digging, I would have to imagine. But that's just me.

Starting point is 02:10:10 No, you're right 100%. And just thinking about fintech in general, we've thought hard about how we can be players there. And when I say we, I mean my team. And we have a few irons in the fire. Now, question for you. Is this your university team, or is this like a private kind of SEAL Team 6 for-profit team that you've assembled?

Starting point is 02:10:32 Combo. Combo. Oh, hybrid. So it's a little combo platter. Because what we have is we have great PhD students here who want to work on cutting-edge data science. And why wouldn't I look there? And not only because there's a wealth of data, but also because there are a lot of big juicy apples that are hanging pretty low. And it's going to be really important, not only for consumers, but also for regulators. Regulators are thinking very hard now about what they should be doing. And in many cases, they're flying blind. And they're flying

Starting point is 02:11:12 blind because people have not taken seriously a ton of what are the learnings from the data and what are the social costs or potential. Back in the day, in the 2008 financial crisis, we learned a lot from that and a lot about the system and some of the weak points. Remember, a lot of this comes back to the weakest link problem. A lot of these examples are you're as weak as your weakest point. And if that implodes, you're done. And it's really trying to understand questions around that. So we're dabbling a bit, Tim and FinTech, but nothing to talk about just yet. Well, I'll take that as a teaser. I look forward to when you can discuss more publicly. The next book.

Starting point is 02:12:04 The next book. The next book. The next book. So speaking of books, I do want to ask you about a specific book. And I will admit right up front that I'm embarrassed I haven't read this book. And that is Wealth of Nations by Adam Smith, which I know you're a fan of. Part of the reason I haven't read this book is the same reason that I had some misgivings about this Nova documentary I watched about Mandelbrot and fractals, because the documentary was made more than 10 years ago. And I know a lot has happened in the intervening 10 years, and I don't know what has been disproven or what has been updated, what has been amended. So my question is, if one were to read

Starting point is 02:12:46 Wealth of Nations by Adam Smith, what caveats or preface would you provide so that they are not misled? I don't want to say misled, so that they read it in as informed and contemporary a way as possible. I would probably say don't read the original, right? So my advice is don't read it. And the reason why is because, A, it's painstakingly written. So Smith had this feature that a lot of writers back then had to make a point that can be made in a paragraph ends up being 25 pages of words that you have to look up what a lot of them mean. But for an economist, the true beauty is I sort of know how the profession has evolved and how economic thinking has evolved. And for me, the wealth of nations is so beautiful because it's set off. He's a father of economics and it's set off.

Starting point is 02:14:01 His level of thinking must have been the deepest of any philosopher I've ever read. I can appreciate the beauty because I know a lot about economics and where it's gone since then. He talks about specialization as an example, specialization in the pin factory. And he goes through this long and tortuous and arduous explanation of the pin factory. I'm like, Adam, just get through it. This is crazy. So I can jump and see the beauty about what was known. But to me, the beauty is a lot of that was not known and he had these gems. So I think it would be incredibly difficult for a person who is not well versed in economics to read Adam Smith because it would be a ton of head scratching. I think he's trying to get at this issue, but I don't know. It's kind of that one.

Starting point is 02:15:02 And maybe I should just read the theory of moral sentiments. So I think for the person who is not an astute and expert reader, I would say go to the moral sentiments because there you got kind of the behavioralist at work. Adam Smith is one of the early behavioral economists and he talks about the moral Hector on one shoulder, et cetera, et cetera. And you see a lot of the underpinnings of behavioral economics. A lot of the underpinnings of behavioral economics are common sense. And then you can kind of see, wow, he had that idea back then. It took him a long time to say it, but nevertheless, right? He's verbose in the theory as well. But nevertheless, I would probably say the reader would direct there and then after you take several

Starting point is 02:15:52 econ classes, then go to the Wealth of Nations. Right. So you're not reading an ancient medical textbook and coming up with phrenology as the cutting edge treatment or diagnostic tool. You know, another approach that I might take is to read, as a starting point, the Wikipedia entry on Adam Smith and the Wikipedia entry on Wealth of Nations, because there will be footnotes and references and also mentions of different controversies or updates or factual inaccuracies or whatever it might be. So I might start with that and then jump to moral sentiments. Well, John, this has been so much fun. People can certainly learn more about the latest book at thevoltageeffect.com. That is effect with an E, E-F-F-E-C-T, Voltage voltage effect, subtitle, How to Make Good Ideas Great and Great Ideas

Starting point is 02:16:47 Scale. You're an excellent teacher. I have really enjoyed this conversation and you're very, very good at weaving together examples and illustrations with principles, which I just think is so critical if you want anything to stick, which makes me all the more interested in studying what you did with this preschool. So another conversation for another time. We didn't even get to dig into the soccer team that you have at home, these eight kids. So we'll go further with that maybe in another conversation. But is there anything else that you would like to say, any closing comments, requests of the audience, anything at all that you'd like to say, any closing comments, requests of the audience, anything at all that you'd like to add before we close up for this first conversation? I just want to say thanks, Tim, for having me. The book does cover my work with the Chicago White Sox. I built a draft model

Starting point is 02:17:37 for them. I call it Moneyball Infinity because it takes a Moneyball idea and the data science idea to a new level. I have data all the way back to kids who are seven and eight years old. So I know Bryce Harper's stats when he was eight and I scraped a bunch of data and built a model for the White Sox. And I bring that up because economics is life and life is economics. And with a little bit of data and just an economics 101 understanding, you can really go a long way to change the world and make it a better place. From the White Sox to the White House to Uber and Lyft to the academy, the type of thinking, not only around scaling and how to scale ideas, but also just very simple economic principles will absolutely make your life and your family's life a lot better. Well, I am very excited to not just dig into the new book, but dig into just about

Starting point is 02:18:46 everything that you've done. It's been so fun to prepare for this conversation. And I really appreciate you taking so much time. And again, probably at some point, we'll see if it happens, but do a round two just to talk about how in the hell you get all of this done. I mean, I'm looking at the list of your projects. It goes on for pages and pages and pages. We didn't even get to talk about Vernon Smith and Gary Becker another time. Prospect theory, although I think we touched on a good portion of that. There is so much here.

Starting point is 02:19:19 So I look forward to the book and learning more from you, virtually, maybe directly. And really appreciate you sharing your knowledge and learnings and successes and failures with us in this conversation. So thank you very, very much. And for everybody listening, we will have links to everything we've discussed in the show notes as per usual at tim.blog slash podcast. And until next time, thank you so much for tuning in.

Starting point is 02:19:53 Hey, guys, this is Tim again. Just one more thing before you take off. And that is Five Bullet Friday. Would you enjoy getting a short email from me every Friday that provides a little fun before the weekend. Between one and a half and two million people subscribe to my free newsletter, my super short newsletter called Buy Bold Friday. Easy to sign up, easy to cancel. It is basically a half page that I send out every Friday to share the coolest things I've found or discovered or have started exploring over that week.

Starting point is 02:20:22 It's kind of like my diary of cool things. It often includes articles I'm reading, books I'm reading, albums, perhaps, gadgets, gizmos, all sorts of tech tricks and so on that get sent to me by my friends, including a lot of podcast guests. And these strange esoteric things end up in my field, and then I test them, and then I share them with you. So if that sounds fun, again, it's very short, a little tiny bite of goodness before you head off for the weekend, something to think about. If you'd like to try it out, just go to tim.blog slash Friday,

Starting point is 02:20:54 type that into your browser, tim.blog slash Friday, drop in your email and you'll get the very next one. Thanks for listening. This episode is brought to you by Four Sigmatic, which is part of my morning routine, also part of my afternoon routine. Routine saves me. So there are a number of ways that I use Four Sigmatic. In the mornings, I regularly start with their mushroom coffee instead of regular coffee. And it doesn't taste like mushroom. Let me explain this. First of all, zero sugar, zero calories,

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Starting point is 02:23:42 2017. I have two of them upstairs from where I'm sitting at this moment. And now Helix has gone beyond the bedroom and started making sofas. They just launched a new company called Allform, A-L-L-F-O-R-M, and they're making premium, customizable sofas and chairs shipped right to your door at a fraction of the cost of traditional stores. So I'm sitting in my living room right now and it's entirely Allform furniture. I've got two chairs, I've got an ottoman, and I have an L-sectional couch. I'll come back to that. You can pick your fabric. They're all spill, stain, and scratch resistant. The sofa color, the color of the legs, the sofa size, the shape to make sure it's perfect for you in your home. Also, Allform arrives in just three to seven days and you can assemble it all yourself in a few minutes. No tools needed. I was quite astonished by how modular and easy these things fit together, kind of like Lego pieces. They've got armchairs, love seats, all

Starting point is 02:24:33 the way up to an eight seat sectional. So there's something for everyone. You can also start small and kind of build on top of it if you wanted to get a smaller couch and then build out on it, which is actually in a way what I did because I can turn my L-sectional couch into a normal straight couch and then with a separate ottoman in a matter of about 60 seconds. It's pretty rad. So I mentioned I have all these different things in this room. I use the natural leg finish, which is their lightest color, and I dig it. I mean, I've been using these things hours and hours and hours every single day. So I am using what I am sharing with you guys. And if getting a sofa without trying it in-store sounds risky, you don't need to worry. All Form sofas are delivered directly to your home with fast, free shipping.

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The Tim Ferriss Show - #566: John List — A Master Economist on Strategic Quitting, How to Practice Theory of Mind, Learnings from Uber, Optimizations to Boost Donations, the Primitives of Decision-Making, and How Field Experiments Reveal Hidden Realities

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The Tim Ferriss Show - #566: John List — A Master Economist on Strategic Quitting, How to Practice Theory of Mind, Learnings from Uber, Optimizations to Boost Donations, the Primitives of Decision-Making, and How Field Experiments Reveal Hidden Realities

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