Episode 31: Your Brain on Uber

[0] This is Hidden Brain.

[1] I'm Shankar Vedantham.

[2] My guest today is Keith Chen.

[3] He's a behavioral economist at UCLA.

[4] He's also the head of economic research at Uber, the right -sharing company.

[5] Keith's going to talk about some of the behavioral anomalies that Uber has observed, and we're then going to talk about some of Keith's earlier work, which explored the evolutionary and cultural origins of certain biases and heuristics.

[6] Keith Chen, welcome to Hidden Brain.

[7] Thank you so much for having me. I'm a big fan of the podcast.

[8] I want to start by talking about surge pricing.

[9] Uber charges more when demand is high based on the idea that this is going to draw more drivers into the pool and increase a supply of rides.

[10] Now, this makes perfect sense from the point of view of a traditional economist, but you're a behavioral economist, and you must know there's something about charging different prices for the same products that rubs customers the wrong way.

[11] They say, hey, five minutes ago, this ride was $10.

[12] Now it's 20.

[13] So as you can imagine, I hear over and over again, you know, both at my dinner table and at family gatherings that surge pricing can feel very unfair to customers.

[14] But it's been a really integral part of Uber success precisely because, you know, the whole kind of goal of the company was to replace a really frustrating experience with taxi, with a service that's just ultimately reliable, right?

[15] And the only way to do that, the only way to be able to get basically everyone who lives in a dense part of a city, a car within five minutes was to do that through dynamic pricing, through giving drivers a very, very strong incentive to want to get to the places where they're needed the most.

[16] And also to get riders who could afford to wait a little longer, say they're at a bar, to say, well, you know, if it's more expensive to take a ride right now, go ahead and relax and sit back.

[17] If you can wait 15 minutes, the drink's on the person who's got to go now.

[18] Right.

[19] The interesting thing, Keith, is that when I think about my own behavior, when Uber tells me that the price is now 1 .8 times the regular price, I notice that and I factor that in, and there's a part of me that feels it's a little unfair.

[20] When I'm waiting for just a taxi cab, and now the taxi cab doesn't show up, I don't actually think of someone whom I can blame or someone whom I hold responsible, even though it actually has a bigger effect on me that now I actually have to wait two hours or I can't get a cab at all.

[21] Yeah, that's absolutely right.

[22] And, you know, this basic question of how psychologically painful, kind of the experience of paying a price is, is something that I worry about every day, especially because I actually think that it's one of the reasons that we've grown so fast.

[23] It's one of the reasons that we've been able to displace taxi so quickly is because in a taxi, you sit in the car and when you're trapped in traffic, you literally watch your money ticking away, like in front of you.

[24] You're just kind of forced to watch it.

[25] There's nothing else.

[26] to see except this.

[27] It's hypnotic, absolutely.

[28] It's hypnotic, and it's the worst possible psychological experience.

[29] You know, taxis and gas pumps, right, are the two places where you just watch your money take away.

[30] And the typical Uber experience, you know, you just hit a button, get in the car, and if it's not surging, you don't even need to know what you paid until tomorrow morning if you want to open the email, and if you kind of trust that Uber's the kind of cheapest possible option, you don't even need to look.

[31] So you found that psychology plays a role in how surge pricing works, because there are places where it works in the way that a traditional economists would predict it would work, and there are places where it breaks down.

[32] Definitely.

[33] So just like traditional economics would predict, as you raise the price, you know, surge pricing starts to damp in demand.

[34] You know, when you go from kind of a surge of 1x, meaning no surge, to 1 .2, you actually see a very, very large drop in demand.

[35] Okay.

[36] And that initial drop in demand, actually early on when we first started search pricing at Uber, going from 1x to 1 .2x, you would see a 27 percentage points drop in people who would request.

[37] After some time, though, both after surge has been in the city for a while and after people have gotten a little used to it, that drops to 7%.

[38] So people start getting used to it.

[39] It's not such an alien experience anymore.

[40] They may not love you at the company because of it, but they're not quite as kind of put off by it as normal.

[41] And then as you take up the price further and further, you see further and further drops in demand.

[42] So 1 .2, 1 .3, 1 .4, you know, people like surge less and less because they understand that they're paying more.

[43] The surprising thing is there is a very, very strong round number effect, which we detect.

[44] So when you go from 1 .9 to 2 .0, you see six times larger of a drop.

[45] in demand than you saw from going from 1 .8 to 1 .9.

[46] So the amount more that you're paying for the trip is the same between those two steps, but 2 .0 just feels viscerally larger to people, right?

[47] It just seems a lot.

[48] It's very easy to understand.

[49] Everyone understands I'm paying twice as much for this trip as I would have.

[50] I saw a paper that came out on National Bureau of Economic Research a couple months ago.

[51] This is by Matthew Backus, Tom Blake, and Stephen Ted Ellis.

[52] where they looked at pricing on eBay, and they found something that sounds similar, which is they found that when you price things with round numbers, those things tend to sell faster, partly because people believe that sellers who price things at round numbers aren't really wedded to those prices, that they basically put those prices on because they want to move something quickly, and therefore they're actually willing to negotiate with you because they actually don't care so much about what the actual price is.

[53] Whereas when you pick a very specific price point, people say this couch costs $74 .26.

[54] Some thought must have gone into this, and I don't have much room to negotiate.

[55] And there's no back and forth where if somebody prices their couch at 100, you think, well, why don't I just counter with 50?

[56] And then the conversation gets starting from there, right?

[57] Yeah.

[58] Actually, you know, we see exactly the same thing at Uber.

[59] And I think that's the main explanation for something really, really puzzling.

[60] And that is more people will take a ride at a surge.

[61] multiplier of 2 .1, then would take a ride at 2.

[62] So I described to you how between 1 .9 and 2, a lot of people stop taking rides.

[63] If anything, people take more rides at 2 .1 than they did it too.

[64] When it's more expensive.

[65] It's as if they're telling you, I would rather pay you 2 .1 times the normal price than I would too.

[66] And I think just like your intuition on negotiations on eBay, that's exactly the same intuition I think that drives the behavior here.

[67] You know, People, when you tell someone, your trip is going to be two times more than it normally costs, they think, wow, that's capricious and unfair.

[68] Someone just made that up.

[69] Somebody just made that up.

[70] Like, you know, they must have seen it was raining and just decided to mess with me, right?

[71] Whereas if you say, oh, you know, your trip is going to be 2 .1 times more than it normally does, wow, you know, there must be some smart algorithm in the background here that's at work.

[72] It doesn't seem quite as unfair.

[73] We had the behavioral economist Richard Thaler on the podcast some time ago, and one of the things he was talking about is why it's often hard to find a cab on a rainy evening.

[74] And his theory is that this had something to do with what he calls mental accounting, which is the cab driver has a number in his or her head about how much money he wants to make on a given day.

[75] So the cab driver says, you know, my expenses are going to be $100 a day.

[76] I want to make $100 over that.

[77] And so when I hit $200, I'd go home.

[78] And on a rainy day, demand, is higher, so you hit 200 faster, and so the cab driver goes home.

[79] And so you end up with fewer cabs with more demand.

[80] Exactly.

[81] Now, you've done some work looking at dynamic pricing and search pricing, and you're finding that actually that isn't the case with Uber driver.

[82] Uber drivers do not necessarily go home when it's actually a smart time to be driving.

[83] Yeah, exactly right.

[84] I find very different results than Dick does with New York City cab drivers.

[85] And I think I understand a little bit of the psychology as to why we're finding different things.

[86] So exactly, as you said, if you're New York City cab driver, you're going to get the same amount for every trip, no matter what, it's a regulated fare.

[87] So when it's raining, the only thing that's really changing is you're just picking up more people, right?

[88] So in that world, it does feel very salient.

[89] You know, many of them are collecting cash.

[90] You can just see, you know, once there's a pile of $200 in the front seat, I'm just going to call it a day, kind of thing, right?

[91] Whereas on Uber, you know, the, The main way that we incentivize drivers to move to the places that riders need them and to stay out a little longer if they can afford to is through surge pricing.

[92] And so I think that's very, very sallyant.

[93] So if you're an Uber driver and say you were planning to do about a two -hour shift this afternoon, you're driving around, but all of a sudden, unexpectedly, it starts to surge 2 .1 times.

[94] All right, like every trip you're going to make, you know, I can't believe my mind.

[95] I just went to 2 .1.

[96] But, you know, you're an Uber driver and it's surging.

[97] You're going to get twice as much for every additional trip you do.

[98] What I actually see in our Uber data is that even compared to yourself a week ago in exactly this situation, Uber drivers will double, triple the length of the shift that they were planning to do.

[99] If it's surging 2x, if it's surging 3X, they're just going to stay out because, well, they can make a lot of money.

[100] right now, you know, they can take the whole weekend off if they put in another few hours today.

[101] When you yourself use Uber as a writer, what is it you pay attention to?

[102] I mean, do you use your own trips as sort of research into how the company is working?

[103] You must at some level.

[104] Oh my God, I do.

[105] All of the time.

[106] And it frustrates me because I kind of see so many kind of psychological biases in my own life.

[107] Like what?

[108] So, for example, partially because, I think it's just one of our most exciting products.

[109] I take Uber Pool a lot.

[110] So Uber Pool is the service by which now up to three passengers can share the same Uber car.

[111] And, you know, when you're in the car, you know, you can just somebody just kind of two blocks ahead.

[112] It just happens to be going, you know, along the exact same route that you are.

[113] You know, we'll just suddenly tell your Uber driver, pull over and, you know, take in this new person.

[114] And then because that we can get people to share rides and get more people into seats, we can, like, reduce the price.

[115] And on average, like, UberPool can save up to 50 % for most people's rides.

[116] Not to mention saving on gas and emissions and all the environmental issues.

[117] Emissions and increases driver pay because, you know, literally now drivers can be constantly utilized.

[118] They can be constantly making money and they don't have any of that downtime of driving to someone or sitting idle somewhere.

[119] but a big part of making Uber pool work is minimizing the amount of psychological frustration that people have with that experience.

[120] And so, for example, I find for myself that initially when we were writing those kind of Uber pool algorithms, we took a very rational view.

[121] You know, we want to pass people the most amount of savings that we can and inconvenience them as little as possible with inconvenience usually being kind of just really, really focused on how much time is it going to get you from your point A to your point B. One of the things that we've started to discover is that, and that I feel very viscerally when I'm in an Uber pool, is that there's something really, really psychologically painful about going backwards, right?

[122] So even if this is an amazing match for you, right, forcing your car to kind of, you know, make three right turns and circle around the block and for a short period drive backwards, right?

[123] drive away from your destination is just like three times more painful than you would have expected just from the added time.

[124] So, you know, so we try and avoid that as much as possible.

[125] You know, I feel there have been times that I miss an exit on the freeway, and you know that you have to go to the next exit and then turn around and come back.

[126] And every inch of the way, as you're going to the next exit, you're reminding yourself, I'm going to have to retrace this inch, I'm going to have to retrace this inch, I made a mistake.

[127] And you really, it's true, you really beat up on yourself when you feel like you're doing something that's taking you in the wrong direction.

[128] Yeah.

[129] I'll do crazy things.

[130] Like I realized the other day that, you know, there's two airports that I can fly out of.

[131] I have to fly south and there's two airports I can fly out of.

[132] One of them is north of me and one of them is south of me. I can't bring myself to book a flight out of the airport that's north of me because I feel like I'll spend the whole drive like going in the wrong direction.

[133] One of the things I wanted to talk to you about is that Uber is collecting massive amounts of information on what people are doing.

[134] And I think a lot of people don't quite realize how powerful this information is.

[135] I remember doing a story some months ago looking at some social science research analyzing how people were using cell phones in a poor country and how the way people use their cell phones and whether they kept their cell phones topped off.

[136] the country where you sort of prepay your cell phone.

[137] Whether you keep your cell phone prepaid and whether you have a lot of incoming calls or you have a lot of outgoing calls, how wide your network is, all these things can predict with a remarkable degree of accuracy your creditworthiness.

[138] And so if a bank sort of just looks at your cell phone usage data, it can make remarkably accurate predictions about whether you're likely to repay a loan or not repay a loan.

[139] And I mean, who would think that just the way that you're using your cell phone could take.

[140] tell a bank that, you know, they should give you a loan to start a new business.

[141] And I feel like Uber has sort of similar access to vast amounts of data.

[142] I mean, there have been reports, for example, of, you know, so Uber broadly knows where I live and where I work because I'm often taking cabs back and forth between those two locations.

[143] But let's say one day I leave work.

[144] And instead of going home, I go to another location, which is known to be the address of a bar.

[145] And then a few hours later, I go to another location, which is not my home.

[146] address.

[147] Sure.

[148] And then I come to work the next morning, you know, you can draw the conclusion of what I was doing that, that evening.

[149] It feels like you actually know more about people's lives than perhaps they realize they're letting you know.

[150] We do have, we do, we do have access to a tremendous amount of data.

[151] And because of that, we have kind of a privacy officer, you know, within the firm.

[152] Because of that, kind of even as an employee of the firm, I have to be very, very careful about what kinds of queries and what I look at in people's data.

[153] Yeah, precisely because this is people's lives.

[154] And you're right that we have to take very seriously this responsibility that we're becoming a big part of how people move around the world.

[155] And we just want to be very careful with that.

[156] Have you ever been concerned about the way you're using these services that might reveal things about you?

[157] I mean, that's someone who sees things from the point of view of the institution of the company and knows how powerful this information is.

[158] Has it changed your own behavior and how you interact with any number of these sites?

[159] Well, now that you're talking about it, I'm getting even more worried.

[160] But, you know, and this might be naive, but I've often thought to myself that, you know, so my experience inside of large companies that have access to these huge kind of treasure troves of data is that, you know, you almost always just look at these like broad, broad aggregates.

[161] And I guess I've always just taken comfort in the fact that I'm boring enough of a person that no one would ever.

[162] I mean, it's almost like, you know, so for example, like some people I know, like my sister kind of shreds all of her credit card statements and kind of does everything before she tosses paper in the trash.

[163] And I always just throw everything in the trash because I figure like, I just don't feel important enough for like someone to rummage through my trash.

[164] We do, though, you know, in the Uber data, see a lot of really, really interesting patterns.

[165] So, for example, a data scientist named Peter at Uber discovered.

[166] somewhat accidentally, there's really, really kind of interesting fact.

[167] And that is one of the strongest predictors of whether or not you're going to be sensitive to surge.

[168] In other words, whether or not you're going to kind of say, oh, 2 .2, 2 .2, 2 .3, I'll give it a 10, 10 to 15 minutes to see if, to see if surge goes away, is how much battery you have left on your cell phone.

[169] Oh, that's fascinating, of course.

[170] When your cell phone is, like, down to, like, below 5 % battery and that little icon on the iPhone turns red, you know, then people start saying, well, I better get home, like, because I don't quite know how I'm going to get home otherwise.

[171] Now, we absolutely don't use that to kind of push you a higher surge price, but it's an interesting kind of psychological fact of human behavior.

[172] I'm talking with Keith Chen.

[173] He's a behavioral economist at UCLA, and he heads up economic research at Uber.

[174] When we come back, I'm going to ask Keith about some of his earlier work, which explores the origin of some very interesting biases.

[175] Stay with us.

[176] We'd like to say a quick thank you and share a message from one of our sponsors, History.

[177] This Memorial Day, the groundbreaking series Roots returns with a new vision, a story of survival, hope, and courage.

[178] Starring Forrest Whitaker, Lawrence Fishburn, Jonathan Reese Myers, Anika Noni Rose, Anna Pacquin, Tip T .I. Harris, and introducing Regéjejean Page and Malachi Kirby as Kunta Kinte.

[179] The journey of an American family begins with a name.

[180] Roots premieres Memorial Day, only on History.

[181] Support for Hidden Brain also comes from Wells Fargo.

[182] Wells Fargo understands that you work hard to get the most out of life.

[183] From starting a business to saving for retirement, there are a lot of things you want to accomplish.

[184] Working together, Wells Fargo will take the time to understand what you're trying to achieve to help you reach your financial goals.

[185] That way you can care for the people and things that matter most.

[186] Learn more at Wells Fargo .com slash together.

[187] Wells Fargo.

[188] Together we'll go far.

[189] Keith, in your life before Uber, you conducted different experiments into the origins of various human biases, including some issues that often come up in behavioral economics.

[190] Some of your most fascinating early work was with monkeys.

[191] And I understand to conduct these experiments, you first train the monkeys to become economic actors.

[192] How did you go about doing that?

[193] Oh, my gosh.

[194] This feels like a lifetime ago.

[195] But with a bunch of colleagues at Yale University, we set out to answer this kind of somewhat ill -defined question, but one that we were kind of obsessed with, which was if monkeys were taught to use money, one, could monkeys be taught to use money?

[196] And two, if monkeys could be taught to use money, would they behave in the same way that humans do in all of the kind of psychologically rich ways that we interact with kind of prices and think about wealth and think about, think about how we spend our own money?

[197] So basically what we did was, you know, a small colony of capuchin monkeys that were living with us at Yale.

[198] I also did some work with a colony of tamarid monkeys back at Harvard.

[199] And my Yale students were thrilled to hear that the Yale monkeys were much smarter than the Harvard monkeys.

[200] I didn't tell them it was because it was a different species.

[201] They attributed it to the school.

[202] But basically what you do to teach a monkey to use money is you, you, you, you, you, you, hire a bunch of Yale undergrads to basically just live with the monkeys for a long time.

[203] So, you know, these Yale undergraduates were typically psychology majors.

[204] They were studying the monkeys for various other things.

[205] You know, the monkeys live in a big, spacious, comfortable habitat.

[206] You know, they typically ignore the kind of humans that are moving around, but you'd have a Yale undergraduate every now and then just drop a coin on the floor.

[207] Okay?

[208] And we had chose these, like, kind of large metal washers to kind of stand in for a coin.

[209] Now, a monkey thinks that's fascinating, runs over grass.

[210] grabs the coin, kind of chews on it, like, you know, bangs it on the floor, every now and then would throw it around kind of almost a little dangerously, but would eventually kind of lose interest in this metal disc.

[211] Then you'd have the Yale undergraduate stand there and stare at the monkey with an open hand outstretched, okay, and just stare at that monkey uncomfortably, almost just kind of setting this uncomfortable situation.

[212] And every now and then, the monkey would actually pick up the coin and just put it in the undergraduate's hand, like give it back to the student.

[213] And then what we did was we trained the students to say, say, why thank you, in really exaggerated tones, and then, like, hand the monkey a piece of food.

[214] Okay, so like a little apple slice.

[215] One undergraduate came to be known to the monkeys as, like, the apple undergraduate.

[216] If you gave that person a coin, they would always, like, hand you an apple slice.

[217] Another person was the pineapple undergraduate, and another person was the orange slice undergraduate.

[218] Now, what's amazing is you do this for about six months, all right, nonstop for about six months.

[219] And eventually, you start to realize that the monkeys understand.

[220] that this is fiat money, right?

[221] Now what does that mean?

[222] Well, what that means is, you know, the monkeys had been very familiar with like basic ideas.

[223] Like there's a lever on the wall.

[224] If I pull this lever, an apple slice falls from the ceiling.

[225] But money is something fundamentally different.

[226] When I find this coin, right, it's not just an apple lever.

[227] It's actually a choice between an apple lever and orange lever and a pineapple lever because I can take this coin.

[228] I can carry it around with me. And I can wait till the, if I feel like having an apple, I've got to, to run over to the Apple person and I can spend it there.

[229] Money is kind of fungible across different kinds of foods that I can purchase.

[230] So we started to see that.

[231] We saw that monkeys started to use these with each other and to save them, to kind of hide them from other monkeys, and then to make very, very rational decisions, rational looking decisions.

[232] When the price of Apple doubled, you know, when that Apple undergraduate started only giving one piece of Apple, not two, when you hand them a coin, you know, demand for that for apples went down and demand for oranges and for pineapples went up.

[233] How did the monkeys protect their money?

[234] A fascinating component of the monkeys starting to understand money was that they displayed signs that they realized not only that they understood the value of this disc, but that they understood other monkeys recognize the value of this disk.

[235] So, for example, you know, early on when the money that we were using was these physical discs, it's a little bit hard to shield from other monkeys, right?

[236] And you don't want to be kind of carrying all of these things around.

[237] So you would see monkeys like hide the discs, like, you know, over in the corner, under a pile of like wood shavings, right?

[238] They'd kind of hide the discs.

[239] Later, actually, we taught these monkeys to use touchpads.

[240] And so like then kind of in some sense, like monkeys learned, you know, to use currency as if it's just kind of in an ATM.

[241] And is if it just gets like Ven mode or transferred to other, to other players, to the Apple guy.

[242] So this is like an Apple wallet?

[243] Yeah, basically.

[244] They're ahead of us on this dimension.

[245] You know, they're a completely cashless economy at this point.

[246] You eventually got to the point where you would also introduce to the monkeys the idea that sometimes when they would give a certain amount of money to an experimenter, the experimenter might give the monkey three things, and sometimes the experimenter might give the monkey one thing.

[247] So in other words, it was unpredictable what the reward was going to be.

[248] You taught the monkeys to gamble.

[249] Yeah, that's basically exactly what we did.

[250] So we introduced them to two new undergraduates.

[251] So one undergraduate would always approach a monkey with three pieces of apple in their outstretched hand.

[252] Let's call this undergraduate Adam, okay, would show three and either give over all three or would take two back and would only give one.

[253] Then we introduced him to another undergraduate, Ben, right, who always showed one, okay?

[254] But if you gave Ben a coin, Ben would half the time hand over just that one, half the time would add two apple pieces to his hand and hand over three, right?

[255] So now the fascinating thing is both Adam and Ben are presenting you exactly the same deal.

[256] There are 50 -50 gamble between three apple pieces and one apple piece, right?

[257] And you can give the monkeys a lot of experience, trade.

[258] only with Adam and a lot of experience trading only with Ben, so they kind of get that this is a 50 -50 gamble.

[259] The interesting thing is what the monkey showed us through their preferences between Adam and Ben is that they experience something just like humans do, and that's this very powerful psychological force called loss aversion.

[260] And that's the idea that it's more than twice as painful to experience a loss than it is to experience a similar -sized gain, right?

[261] Now, what does that have to do with Adam and Ben?

[262] Well, Adam half the time gives you, in some sense, what he's shown you, right, the three apple pieces, but half the time he delivers you a loss.

[263] He takes away two, and then hands over only one.

[264] All right?

[265] So half the time he delivers you a loss of two.

[266] Ben, half the time just hands you the one that he's showing you, and half the time delivers you a gain.

[267] He gives, he puts an extra two pieces of apple there and hands you an extra two apple pieces.

[268] What we find is that when given the choice between Adam and Ben, monkeys vastly prefer Ben, the guy who initially only shows one apple piece, but half the time gives you a gain, then they prefer Adam, who initially shows you three apple pieces, and then half the time delivers you a loss.

[269] They, you know, six to one prefer trading with Ben to Adam.

[270] And that's fascinating because that cuts against their very, very core instinct, which is, well, if I have got a coin, why don't I go trade it with the guy who's showing me three instead of the guy who's showing me one, right?

[271] Like every kind of fiber in their body tells them that they should be going for more food, not less.

[272] And yet, because of loss aversion and because they actually feel this very viscerally in the same way that people do, they actually prefer the guy who promises less but delivers more.

[273] I have to say that I'm thinking about what I would do in that situation, and I have to say I'm clearly no smarter than a monkey because I would definitely prefer to trade with Ben because you're getting something that seems like a surprise.

[274] It's a gift.

[275] It's like it's wonderful.

[276] It's unexpected.

[277] It's happy.

[278] And at the worst, it's only going to be what he's promised you in the first place.

[279] But with Adam, he's taking away something that you thought was yours.

[280] Yeah, yeah, absolutely.

[281] And interestingly, we see in our Uber data this loss aversion at work as well.

[282] And you can you could even just feel it, psychological.

[283] Like when you're a rider and, you know, surge pricing hits, like, that's a loss, right?

[284] And that feels very, very bad.

[285] We've often been asked, well, why didn't you frame search pricing as discounts instead of as, instead of surcharges, right?

[286] Why isn't Uber's standard price twice as high as it is?

[287] And then most of the time you're getting a discount, right?

[288] Why not kind of frame it that way?

[289] Because behavioral economics would predict that you would actually make people much happier if you did it that way.

[290] Well, behavioral economics would predict that you would make riders happen.

[291] if you did that.

[292] I mean, the critical thing to notice is that we're a two -sided market.

[293] That move, which would make riders happier, would also make drivers feel less well.

[294] So, I mean, so we had thought for a while, why not just frame Uber's pricing as how much cheaper we are than taxi?

[295] Because in many major cities in the United States, we're up to 60 % cheaper than taxi fares when we're not surging.

[296] And, you know, what that means is that, you know, you can surge like 2 .1 and still becoming basically out even if you would instead just take in a taxi.

[297] But even though because Uber drivers are in such an efficient system, they constantly have a paying rider in the back of the car.

[298] They're actually making more than they would have if they had been working as a taxi driver.

[299] Framing this to them, oh, you're making 60 % less per mile than you would if you were driving a taxi, that would be a cost of this pricing system.

[300] Keith Chen is a behavioral economist at UCLA.

[301] He heads up economic research at Uber.

[302] Keith Chen, I want to thank you for joining me today on Hidden Brain.

[303] Shankar, it's been incredibly fun.

[304] Thank you for having me on.

[305] The Hidden Brain podcast is produced by Kara McGarack Allison, Maggie Penman, and Max Nestrack.

[306] You can follow us on Facebook, Twitter, and Instagram, and follow my stories on your local.

[307] public radio station.

[308] If you like this episode, consider giving us a review on iTunes.

[309] It will help other people find the podcast.

[310] I'm Shankar Vedantam, and this is NPR.

[311] Thanks for listening to Hidden Brain.

[312] You should try out the NPR One app for your phone for conversations you won't hear anywhere else.

[313] This week, Guy Raz interviews TED curator Chris Anderson.

[314] They discuss the secrets of giving a great talk.

[315] You can find that conversation by searching for TED Radio on the NPR One app, where you can also find stories from your local station and more great podcasts.

[316] NPR One is in the app store now.

[317] I also wanted to let you know that Invisibilia's season two returns on June 17th.

[318] This season, the show goes to a prison, an oil rig, a McDonald's in Russia, and a beach in New Jersey to explore the worlds of work, family, and government.

[319] Catch up on season one and listen to the season two preview on May 20th at npr .org slash podcasts, and on the NPR One app.