MBA Masterclass Behavioral Economics Applications

Cat Goodin: Hello, everyone. Thank you for joining us. We're gonna wait just a few minutes as everybody starts filtering into the Zoom webinar. We will get started at 6:02. For those of you who are just now joining us. Thank you for joining. We're gonna wait to start the session at about 6:02 as everybody starts to filter into the Zoom webinar. OK, I think we can go ahead and get started. Hello, everyone. And welcome to the Chicago Booth MBA Masterclass Series. My name is Cat Goodin and on behalf of the full-time evening, weekend and executive MBA programs. It is my pleasure to welcome you and to moderate today's session. Before we get started, I want to make sure we have time at the end to answer some of your questions. Please send me your questions during the talk, using the Q and A box in your Zoom menu. I will compile a few to answer at the end of tonight's session. In this masterclass series, we feature world-renowned Chicago Booth professors to give you a glimpse inside the classroom and our approach to business education: what we call the Chicago approach. Rooted in data, fundamental disciplines and rigor, where you'll learn to define problems, ask better questions, and develop better solutions. Tonight you will hear from our featured faculty member, Professor Devin Pope, who will discuss with you behavioral economics applications. Professor Pope is a professor of Behavioral Science and Economics. He studies how psychological biases play out in field settings in economic markets, including left-digit bias and projection bias; in car markets; and time and consistency in housing markets. In today's talk, Professor Pope will provide an introduction to behavioral economics and share recent examples of how this discipline helps us understand how markets work and function. It is my pleasure to now turn it over to Professor Devin Pope. Thank you.

Devin Pope: Fantastic! Thank you very much, Cat. And thank you all for joining. I'm glad to have you all here, I'm looking forward to spending the next little bit with you all. As Cat said, I like to do behavioral economics and that's what I teach here at Booth as well, in addition to my own research. And I wanted to give you just a glimpse of that today. So here is my plan, is I'm gonna spend about five minutes giving kind of a very, very broad kind of overview about what behavioral economics is. And then I want to hit four examples of behavioral economics in real-world markets, and in markets that economists care about. And then, we'll end there. So it's gonna be a brief introduction plus some examples. And so, I'll drone on for probably about 45 minutes here. And then at the end, we'll have 10 or 15 minutes left over for Q and A. All right. So let me start with this very brief introduction. So many of you know that economics goes all the way back to Adam Smith in the late 18th Century. And this was what's referred to as classical economics. So the classical economic period began arguably with Adam Smith in say, 1776, when he published his premier work. And this was kind of an interesting time for economics. It was just starting to talk about demand and supply, and how markets kind of regulated themselves. There was this invisible hand that took over. But for the point of talking about the history of behavioral economics, what is particularly interesting about this period of time in economics is there was a lot of psychology. So Adam Smith talked a lot about how humans had passions and emotions, and how we were always struggling with trying to tap down our emotions and our passions, and have kind of the impartial spectator in our head make the right decision, right? He talked about overconfidence and overworking, and self-control problems. And lots of things that today we're talking about in behavioral economics were actually being talked about during this classical economic period. All right. In about 1870, a new, a paradigm shift occurred in economics and what we now refer to as neoclassical economics took over. And basically, what happened with neoclassical economics is that there were some very good mathematicians that came in, and said, "Hey, this economics stuff is pretty cool. But man, we could do some really cool stuff, if we made some assumptions. And man, we could start to prove some really interesting theories -- and we could really start to model mathematically how people make decisions." And so what happens is, is this took over economics, this neoclassical paradigm. And that was great for economics, right? There was a lot of really good things that came out of this. But one byproduct of this neoclassical econ takeover was that the psychology or the passions that Adam Smith had talked so much about kind of disappeared. And partly, 'cause those were just harder things to mathematically model. It was easier to model the impartial spectator and much harder to model the passions that people happen to have. All right. So this marginal revolution this neoclassical economic period was kind of cemented in around 1948, when Paul Samuelson, the first winner of the Nobel Prize in economics, wrote a textbook for graduate students that laid out the assumptions in neoclassical economics, and neoclassical economics was there to change. And it was really built on kind of rational choice assumptions that were being made. All right. This is a very, very brief summary. But I've gotta hit on a little bit of a local thing. There was what's referred to as the Chicago School of Economics, right? This happened in kind of the mid-1900s, mid-to-late 1900s. And it featured a bunch of really smart economists that were trained in this neoclassical paradigm. And they started to take this, you know, very rational economic framework and apply it all over the place. So for example, Gary Becker applied it to sociology; Eugene Fama applied it to finance; Ronald Coase to law; Bob Fogel to economic history; Milton Friedman and Robert Lucas to macro. And it was awesome. It was kind of the heyday for neoclassical economics and so much really incredible material came out of this period of time. And this is partly why I fell in love with economics, is because of some of these interesting applications that people here at Chicago and in other places were doing with applying economics. All right. There was one problem, though, with all of this economics and that is this: It was really built on some assumptions that in some cases were a little bit hard to swallow, right? So there started to be some rumblings from various people like Herbert Simon and Daniel Ellsberg, and Maurice Allais, about, man, there's some strong assumptions that we're making in our models. And it really came to a head in around the 1970s, when these three individuals -- Richard Thaler, who's a professor here at Booth, Danny Kahneman and Amos Tversky -- when they, who are kind of known as the fathers of behavioral economics, really kind of cemented the idea that there was important psychology that economics was missing. And one of the key things that they pointed out is that there can be systematic biases that people make: biases that we can predict that people will make. We can predict ahead of time what biases they'll make. And these individuals started to lay out, what are those biases that people are making? And that has what has led to this behavioral economics revolution, where economics is starting to be infused with more psychologically relevant and intuitive aspects. And this includes things like, you know, self-control and fairness, and that people have biased beliefs, that people have a hard time paying attention to everything. Probably many of you have heard about stuff like nudges and choice architecture, and loss aversion. All of these things are really interesting insights that were brought in the '70s and '80s and now have continued to happen over the last couple of decades. And it's been kind of, in my opinion, a really beautiful movement within economics to make things a little bit more relevant to the real world, where we know that people make mistakes and have biases and have passions. All right. That's your five-minute history of behavioral economics. There's obviously much, much more that one could read and talk about here. And there's a lot of interest in fights and various things, and Chicago has been a really interesting place where a lot of the action has taken place in this field. And anyway, so one could talk a lot more about this. But what I wanna do is talk about an even more recent movement within behavioral economics. And that is, is to start applying these psychological concepts and ideas to more real-world situations. So let me read a couple of quotes from kind of famous economists. These are Chicago economists. So Gary Becker, the Nobel Prize-winning economist who applied neoclassical economics to sociology and other things -- he said, "One can get excellent suggestions from experiments, but economic theory is not about how people act in experiments, but how they act in markets. And those are very different things. That may be useful to get suggestions, but it is not a test of the theory. The theory is not about how people answer questions. It is a theory about how people actually choose in market situations." All right: Does everyone see what Gary here is trying to push behavioral economics to do? He's saying, "Hey, yeah, we can kind of run lab experiments and do different things but let's figure out what happens in the real world when we apply a lot of this stuff to economic markets." And, you know, Steve Levitt and John List have kind of a similar comment here where they push behavioral economics to start showing anomalies and biases that are taking place in real markets. All right. That is what I happen to love to do. That has been my research agenda since I graduated with a PhD in Economics from Berkeley in studying behavioral economics. And what I've wanted to do is try to explore how psychology matters in real world context. And so what I wanna do today is share four examples of behavioral economics in the real world, right? Or behavioral economics, as it plays out in markets that economists care about. I'm gonna start by sharing a little bit of information about wholesale car auctions. Then we're gonna talk about how people process odometer values on used cars. I'm gonna talk about lift pricing, and then I'm gonna conclude with paying for blood donations. All right? So these first three are projects that I'm a co-author and have researched. And the fourth one is some friends of mine that have done this work that I'll share. Ah, OK. So let's start with the wholesale car auctions. So here in the United States, at least, wholesale car auctions are kind of an interesting thing. Almost nobody knows about them. But they're really, really cool. So this is how these markets work. There are a bunch of car dealers. So for example, imagine you own a new Ford dealership. What you do is you often receive trade-ins. So someone will bring, come, and buy a new Ford from you, but they want to get rid of their old Honda or whatever it might be. And so what do they do? They send them off to these car auctions, so that you'll take the trade-in of the Honda; you don't wanna sell that on your new Ford lot. So you send the Honda to these auctions and the Honda is auctioned off. And the people buying at these auctions are other used car dealers who buy the cars and then sell them on their final auction, on their final car lot. All right. These auctions are really incredible places. If you ever have a chance, you should go to one of these wholesale car auctions and see what it's like. But, because many of you won't ever be able to have that chance, I wanted to show you a short video clip to tell you, to show you what it's like. All right. So let me pull this up here. All right. So in this auction that you're gonna see here, there's this car. It's a black truck and it's coming through the auction lane. This guy's standing here in the middle with his finger up. He is the auctioneer and he's gonna auction off this car. The owner of the car is this guy in kind of this yellow, this ugly yellow shirt. And his job is just to basically sit there and at the end of the auction, he's gonna agree to the final price that the auctioneer was able to get, or say, "Ah, nah, I'm gonna pass. I'm not gonna take that price." All right. So let me just start and I'll stop a couple times as we go through this, but I wanna show you what this auction is like. So here we go.

- 24, 24, 24, 24? 19, two and half? 17?

Devin: All right. Let me pause here for one second. I hope that you guys are understanding, maybe a little bit but it's pretty hard to understand, right? These auctions, the whole point of these auctions, is they get people excited and fired up. If you weren't able to hear or understand, what the auctioneer did is he actually started the auction at $26,000. So he threw out a price of $26,000 and they have a name for this. This is called the "fish price" of the auction. All right. So he says, "Hey, 26,000!" And no one ever bids on the fish price. That's the whole point of the fish price, is you put out a price that's too high that people won't bid on. And we've asked these auctioneers, we asked them, "Why is it that you're putting out this price? Why not just start low?" And they say, "Ah, you know what? I don't know. We just want people thinking pretty high prices." And if you're familiar with some psychology, you might think of that as an anchoring effect where they start with a high price. Anyway, he called out $26,000 as a fish price. No one bid. He then called out 24, 23, 22,000. And then he went down to $19,000. And then someone bid at 19,000, and now he's on his way back up. All right. So listen, he's -- I think he was at 19,200, was his last call. So listen for that and I'll keep playing it here...

Devin: All right. I hope you guys are hearing it. I think he's at 20,900, was the last call I heard. And so, he's just slowly moving up but I wanna explain one other thing. Do you guys all see this guy in the bottom left corner? That's kind of, he's going, "Eh-yup," and he's raising his hand. This guy's called the "ring man" of the auction. And so, his job is just to help the auctioneer; his official job title or his job description, is to help the auctioneer identify interested bidders. But what he's really there for is, he just gets in people's faces and he gets them to try to bid. He's kind of encouraging people to bid. He's just getting people riled up, all right? So watch for him as we keep going. Let me play this to the end now...

Auctioneer: $24,700, sold! Sold!

Devin: All right . So ... So that was a wholesale car auction that you've now seen. So it takes on average, about 90 seconds to auction off one of these cars, so it goes really fast. And by the time they're done with that 90-second auction, the next car is already rolling in and they start auctioning the next car immediately. And at the really big auction houses, they'll have dozens of these lanes going at the same time with a different auctioneer in each lane. And they're just auctioning on off those cars like mad, just every 90 seconds. All right. This is a really incredible market. And the reason I give this as my first example is, this is not at all like what a standard neoclassical traditional economist would think a car auction market should function as. Right? This is not at all what it should go as. If you ask Paul Milgrom, an auction theorist, this is not how auctions are supposed to work, right? There's something really interesting about the psychology that is involved in this market; and how they set fish prices; and how they do the whole auction; and how they have this ring man who's encouraging people. So co-authors and I have worked with this auction company to try to understand what makes a good auctioneer. And I'll tell you, it's not about the economics. It's about the psychology. The good auctioneers are good at knowing how to build momentum and how to create a sense of urgency among bidders. And we've talked with this company as well about ideas of how to move from this more live scenario to, for example, an online platform to sell used cars. And it's tricky, 'cause when you go to an online platform, you'll lose a lot of the energy and a lot of the psychology that's playing along with these live auctions. So anyway, that's the first example I wanted to give you, is, if you're not thinking about the psychology or the behavioral economics of a market, it's hard to under, even understand what's going on in a something like one of these wholesale car auctions. All right. That's example number one. I wish we were in person and I could get feedback and see your faces a little bit better and everything. But hopefully, you're kind of following along and we'll have a chance for some question and answer at the very end. OK. Let me give a second example that shows behavioral economics in real markets and it's related to the same markets. So this is gonna build on the previous example I just gave. So we got data from this wholesale auction company and we wanted to know how much people were willing to pay for cars that had odometer values that were just-above versus just-below round numbers. So for example, imagine you had an odometer value so that the number of miles on a car was 69,000 versus a different car that had 70,000. Could you get different amounts of money for those two cars? Even though the actual odometer values were very close to each other -- but they kind of feel different. And there's something called left-digit bias, which is a bias that's been discussed in psychology, that shows that people pay more attention to left digits than digits further to the right. And this left-digit bias is what leads to, for example, supermarkets and other places pricing things at 99 cents, right? So a store will price something at, you know, the 99, a candy bar at a $1.99. And the idea is it just feels cheaper, right, than if they priced it at $2. All right? So we wanted to see if this same phenomenon would happen in a really well-functioning, big, important economic market like the car market. Would people pay a lot more for cars that had an odometer value that was just under a round number, like 69,000 miles, than if it had 70,000 miles on it? All right. You wanna just see the data? So we had data for millions and millions of car sales that had happened in this auction. And I'm gonna show you the following plot. I'm gonna show you, it's just the raw data. There's no tricks up my sleeve here at all. All I'm gonna show you is the average auctioned sales price. And I'm gonna show you that for different buckets of cars with different miles on them. So I'm gonna show you what was the average sales price for cars that had between 1000 and 1500 miles; and then 1500 and 2000 miles; and 2000 and 2500 miles; and 2500 and 3000. So I'm gonna show you 500-mile bins and what was the average auction price of those cars. And here it is. So this might be what you were predicting, it might not be. But what do we see is that well, the car values are decreasing as you get more miles on the car; that's not too surprising, right? Older cars that have been driven more aren't gonna get as much money at these auctions as younger, newer cars. But if you look at these black lines, these vertical lines, you can see that there tend to be discontinuities that are happening right at these 10,000-mile thresholds. So for example, if you look here, this dot represents cars that were between 69,500 miles and 70,000 miles -- and then these are cars that just barely passed over the 70,000 mark. They were between 70,000 and 70,500. And you can see that you get a drop in value by a few hundred dollars that occurs right at that discontinuous round number threshold. Pretty cool, right? So this fits the model or the proposed, the hypothesis that we had, that there would be left-digit bias in how people process these odometer values. Now you might see it's not perfect. For example, look here at the 30,000-mile mark. You don't really see anything going on there. There's kind of some weird little bumpy stuff in some of these other places. So part of that is driven by the fact that there are just different types of cars showing up in this market. For example, there are a lot of lease cars that show up right before the 30,000 mark. And so you're getting kind of a weird mix of cars, so we can control for that. So we can say, what if we control for the type of car, the make, the model, the body, the age, and all of those things? And then let's do this graph again. And this is what you get once you control for some of the messiness of the cars. And now you see it looks even cleaner, right? At 30,000-mile mark, you now see a really nice little discontinuity. You see a couple-of-hundred dollar drop at each of these 10,000-mile marks. All right. So, I think that's pretty cool. It's evidence that there is clear psychology that's going on. This goes against any kind of rational model that you might wanna write down of how cars should depreciate But this is how people actually process odometer values when they're seeing kind of a number like this. Now, by the way, let me show a couple of other kind of interesting things on this graph. You can see that the, if you look closely, these dots are kind of moving in pairs. Do you see how there's two dots? And then it drops a little bit, and there's two more dots. And then those drop and there's two dots. And you kind of see that throughout, right? There's two dots there, and then two dots, and two dots, and two dots. So think to yourself, why is it that these dots are moving in pairs? So the answer is, each of these dots is a 500-mile bin. So two dots is a 1000-mile bin, right? And so, what you're seeing is that there are these large discontinuities at every 10,000 miles. But there are also smaller little discontinuities that are occurring at every 1000-mile mark, right? And that's represented by these dots that are dropping down every two because those are the thousand-mile discontinuities. Anyway, you -- and if you blow up on the data you can even go in and look to see if there are little discontinuities at the 100-mile marks, right? If some people are paying attention to the third digit, even. All right. I think this is a really cool finding and we show how it plays out not only in the wholesale market but it also translates into the retail market. And you can do lots of interesting things with this. But let me actually move on to example number three, which is related again. So we showed this left-digit bias stuff that's happening in the car market with odometer values -- and then we had another idea. We wanted to think about how this same pricing phenomenon, this 99-cent pricing idea, might matter for a big, large new tech company. So let's take Lyft, right? So Lyft, as you know, is a competitor with Uber. And they provide, it's a rideshare app where you can get on and request a ride. They offer you a price for that ride and then you could choose whether you want to take that ride or not. All right: So this research was in part motivated by us pulling up a Lyft ride, or it was actually an Uber ride, and seeing that they were offering us an Uber fare of $11 and two cents or something. And the thought that we had was, "That is crazy, right? Why should they offer us a ride at $11.02? They'd be way better off by reducing the price a little bit from $11 and two cents to say $10.99." Because our prediction was, is that they'd get a lot better conversion. People would be more likely to accept the ride, if it was at $10.99 versus $11.02. And it would be worth dropping that three cents of price in order to get all of the extra conversion that one would get from people being better, more willing to take the ride. All right. So we had to have data but fortunately, one of my co-authors on this paper is the chief economist at Lyft. And so we had all the data. And so, we went and looked at the data for a seven-month period in 2019. And let me just give you, start by giving you a sneak peek of the data. So there are 1.7 million rides that were offered during this seven-month period for a fare between $10 and 96 cents, and $11 and three cents. So we had a 1.7 million offers that were right around $11. And they were equally likely to be offering an $11 and one cent fare or a $10 and 99 cent fare. Lyft wasn't doing any sort of 99-cent pricing. But let me show you the conversion rate for these various prices. So the conversion rates for when the fare was offered at $10.96 to 97, 98, and 99, was right around just over 50 percent. Right? So again, we had about 200,000 observations for each of these ride offers. And about just over 50 percent of people were accepting the fare that was offered. But now let me show you the fares right above $11. So for the fares that were offered at $11, $11.01, 02 and 03, now we're in the high 48 percent range. So you can see that it looks like there's a discontinuous drop, just like we were seeing in the car market. But we're seeing that here again with Lyft pricing. And I can show you the entire dataset -- here, I'm just cherry picking and showing you right around the $11 mark -- but let me show you across a wider range. So I'll go all the way from the $10 mark to the $30 mark and this is the standard fare that Lyft was offering. And this is the probability of not getting a conversion for that fare. So what do we see here? Well, we see exactly what we predicted. As you cross over a dollar value, there's a discontinuous jump in your probability of not accepting the fare. So I was showing you right here, the example I was showing was the $11 mark, right? The $10 and 98 cent ones were being offered, were less likely to convert than the $11 fares. But you see that at the $12, and the $13, and the 14 -- you see it at every dollar mark, right? -- people are way more likely to accept a $21.99 ride than they are a $22 ride. All right. We showed this evidence to Lyft and they decided to run an experiment. So we ran an experiment with them where for half of all Lyft riders in the US, we randomly took some -- for a random half of the riders on Lyft -- we took their price if it was right over a dollar value and we lowered it, to ride under a dollar value. And what did we find? Well, we found exactly what this shows, right? They were way more likely to convert and Lyft was able to make a lot more money. And they've now of course, rolled this out company wide. And they're now using a 99-cent pricing strategy. And I actually can't disclose exactly how much money they're making each year because of this now but it's considerable. It's a big deal. And what I like about this example is, again, it shows the power of psychology and behavioral economics when thinking about how companies should be running their business. Now, clearly there's a lot of standard economics that goes into how Lyft is doing their pricing and how they're thinking about, right; they're estimating demand, elasticities and, you know, they're doing some very sophisticated stuff. But if they don't think about the psychology, they're missing out on some of the important stuff that could generate a lot of profits. I actually, I work part-time for Amazon as well doing behavioral economics within Amazon. And again, it's a very sophisticated company that's thinking very hard about the economics of how to, you know, run an online retail platform. But what are they also really interested in? Is thinking about the psychology of it. And there's a lot of interesting applications that one can do at Amazon when thinking about behavioral economics. All right, let me give one final example and then we'll open it up to a question-and-answer period. My final example is about a recent field experiment by Nicola Lacetera, and Mario Macis, and Bob Slonim. And they worked with the American Red Cross to advertise blood drives. And specifically, what they did is they, that the Red Cross sent out 100,000 flyers to potential donors that were gonna come and donate blood. And the experimenters here, is they randomized the 100,000 flyers. So half of the flyers was just the standard flyer. It just said, "Hey, come give blood. You know, we need blood. Come and donate if you'd be willing." The other half of flyers told potential donors that they would be given a $10 gift card if they showed up to give blood. All right. So think about this: You're a potential blood donor. You get a flyer that says, "Hey, would you be willing to come and give blood? We really need blood." And then -- or you get a flyer that says, "Hey, can you come and give blood? We really need blood. And we'll give you a $10 gift card if you show up." Which one of those conditions is going to generate, and how differently are these conditions gonna generate people showing up to give blood? All right. So let me start by showing you the results from the non-financial reward condition. All right. So 0.63 percent of the people who got a flyer that didn't say anything about getting a financial reward -- not, they didn't see anything about the gift card -- but 0.63 percent of people showed up to give blood when they just got the normal standard flyer. And my question for you is, what percent of people are gonna show? And now you might say that's a really small number but these are just flyers that were sent out to people; you would expect a pretty small turnout based on just a flyer, right? But my question to you is, now what about the other condition where people were given a $10, were told they'd be given a $10 gift card if they gave blood. What percent of those people are gonna show up? All right. Now, I don't wanna destroy the Q -- Hey, let's destroy it. Everyone go ahead and type into the question and answer feature -- sorry, Cat and Kimberly if I'm making a mess of things -- but everyone type in what percent of the $10 gift card condition do you think is gonna show up to give blood? The non-financial reward 0.63 percent came in. How much of the $10 reward, gift card reward? Go ahead and type in your answer. All right! I like it! All right. A lot of good guesses here. All right. So let me go ahead and show you the result; let's talk about though, for one second first, before I show you the results. So I just skimmed through your answers: It'd be fun to collate them and actually produce a graph, and if we were in class, we could do that. But I'm seeing actually an interesting thing. It looks like a good fraction of you, made a guess that if you offered people a $10 gift card that they would be less likely to show up to give blood than if you didn't offer them the gift card. So many of you put like 0.2 percent or 0.3 percent. All right. That's kind of interesting, right? Now I wish we, again, we were in person and I could hear your thoughts about why you think that. But my guess is I kind of know why some of you are thinking that. You're probably thinking, "Ah, I'm already kind of intrinsically motivated to show up and give blood," right? And so, if you offer me a gift card maybe I just am like, ah, now I'm not gonna show up because now it's like, you're paying me to do it and $10 isn't very much. All right, if you paid me like a million dollars, yeah, I would show up. But $10? Man, now I just feel like it's not even worth showing up at all. Right? My guess is that's what a lot of you are thinking. And that's kind of an interesting psychological idea, right? That people might, their intrinsic motivation might be crowded out when you offer them an actual financial reward. All right. Now some of you might have been thrown off by my vertical axis here, too, thinking why did I go all the way up to 5 percent if this number's not gonna be up here high? But maybe, I knew you were gonna be thrown off by that. So I tricked you. I don't know. Anyway, there's lots of things that could be going on here, lots of psychology. Let me show you the actual results. So the results are that when you offer people the $10 gift card, they're more likely to show up -- and in fact, about twice as likely to show up. So 1.11 percent of them show up, whereas only 0.63 percent showed up without the gift card. Now some of you are thinking, right now, "Ah, he tricked us!" And you're right, I did trick you. This is a lecture about behavioral economics. You're expecting crazy behavioral stuff, right? And I just showed you that offering people money is motivating. It incentivizes them to do stuff. That's standard economics. And the reason why I give this example -- because you're right, it's not, this doesn't show kind of a psychological, you know, strange anomaly. It's showing that standard economics works: Giving people money leads people to, you know, do what you want 'em to do. And I give this example because I wanna make it clear that behavior economics is an important phenomenon. It shows that psychology really does matter in the real world. But standard, good old fashioned economics is also pretty darn important, right? I would feel really bad if people came to Chicago Booth and took my MBA class, and left thinking that standard economics was all stupid and garbage. And all you have to do is, like do a bunch of little tricks and that's how you actually run a business. That's not true at all. Right? Behavioral economics and psychology certainly have some interesting things to say about how to run a business. But a lot of good old standard economics does as well. And so, I don't want people to lose sight of the fact and take a behavioral economics class, and then think that, you know, just throw everything you've ever learned out the window. It's important to realize what works and what doesn't. And I try to make a concerted effort in my class to go through and try to show, well, what has been shown to be robust and plays out in economic markets the way we would think it should; and what psychology really matters; and when does psychology maybe not matter so much; and when should we just offer people a gift card, because that's what we want them to do and we know that gift cards motivate people. All right. So, let me just conclude by saying I think behavioral economics is a super interesting field. It's relatively new when you think about the overall life of economics in general. And one of the things that's been, even in just the last decade or two, is taking behavioral economics and applying it to real-world situations: places like car markets, and tech companies, and blood donations, and, you know, non-profits, and all of these places. Behavioral economics has a lot to say about these areas. And I'm excited about, to see what's the next 30 years has in store. But there's a lot that we've learned already and, you know, I hope to get to meet many of you or I hope if you're not in one of my classes someday that you'll have the chance to learn more about behavioral economics and think about how it might apply to the various business situations that you find yourself in. All right. With that, Cat, I will turn it over to you to moderate the question and answers period.

Cat: Thank you, Professor Pope for that informative talk. I have quite a bit of questions I pulled from the Q and A. So hopefully, we can get to several of these before our time has run out. But the first one I have here is, "What are some behavioral biases consumers have to be aware of today, and do you feel like there will be likely a shift in consumer behavior with the continued increase in technological change?"

Devin: Yeah, great question. So ... so yeah, I mean, there's lots and lots of biases that I think are important to think about. I mean, certainly some are more important than others. Let me maybe just mention one and that is inattention. I think inattention is such an important part of the way that companies currently run. So think about, like, think about an airline that makes a lot of their profits from baggage fees, right? Or other companies that you sign up and then you have an automatic withdrawal from your bank account, because it's a subscription service or something. And now you forget to cancel your account, right? I think this inattention is a big deal and the reason why I bring this one up is related to your question. I think it's becoming a bigger and bigger deal with the advent of technology where our attention is being drawn to so many places. If we can't even pay attention to the second digit of a number, how are we supposed to possibly pay attention to, you know, detailed service and terms agreements, and various other things that companies are doing today, that in some ways are kind of tricking customers who aren't paying attention. So that's one exam of a bias that I think is of growing importance.

Cat: So I have one on here, an attendee stated that he works for a firm that dug into wholesale car auction software market. And he asked, "What would you say that shopping apps and websites such as Wish, that prioritize quickly checking out to get the best prices, are attempting to capitalize on a similar psychological reaction as these wholesale auctions?

Devin: Yeah. Great question. Man, I would love -- whoever made this comment -- I'd love to sit down for lunch. It sounds like you've got kind of an interesting background and we could have a fun discussion. Yeah. I think one of the main things that come that came out of this online wholesale auction stuff, or some of the wholesale car auction stuff, is that people really like to compete. There's an irrational exuberance that happens when people are competing with each other. And so if you imagine, like, an online platform -- like ebay or anything where people are kind of bidding on an item -- it suggests that the more you can kind of pit each other against each other, the more people are gonna get kind of fired up. So maybe, you know, having people's faces next to their names, it gives you now someone to hate on or just so, you know, someone that you're competing against that makes it more real. Or there are other things that you could do. The speed at which the auction takes place, right? Does an ebay auction go over several days or should you limit it? And there's a lot of actioning happening all at once, right? If it's happening very quickly, it might create more buzz and energy, and things. Anyways, so there's lots of these ideas that could potentially kind of expand to an online-type setting. But it's very specific to the exact setting and one would have to think carefully about it. But, yeah. Great question.

Cat: I have another one that's related to the auctioning model. Someone asked, "Is this a unique auctioning model that performs well for cars only? Or are there other product categories in which this high-energy and fast-paced type of auctioning also impacts consumer behaviors?"

Devin: Yeah. Great question. Yeah, I mean, I think there are lots of settings where the high-energy stuff matters. I mean, one could go to, for example, sports as a setting. I've done actually a lot of research, probably a little too much research, in sports where you look at kind of the psychology of momentum and the psychology of how teams set reference points, and how they care about hitting different points. So for example, some work I've done with marathon runners, you know, show that marathon runners have targets that they're trying to hit. But man, it's -- imagine, trying to think about the goals that you've set for yourself while your body is slowly falling apart. And so, your mind is competing with your body, and it's all of this going on. And so, all of these are kind of interesting spaces where there's a lot of kind of high-energy action that's taking place. So sports, I think, is a fun example where you see a lot of this psychology that's played out. There are also, I mean, clearly, kind of auction markets and other things like that. You know, there are also maybe some other places where you have to make quick decisions. I think this would be, you know, not uncommon for things like buying tickets and like airline tickets, but also stadium tickets and other things. Oftentimes, there's a bit of a frenzy or a new product that just comes out. There can be kind of a frenzy at that moment. And so, you want to think hard if you are the business owner of a new product that's coming out, of how you want to set things up and how it's gonna go those first couple of days when things are a bit frenetic, right? We have things like Cyber Monday, which is today's? Today, anyway, whatever. All right? Those things are built to create this type of energy. Ah, so yeah: I think there's lots of interesting examples of this.

Cat: And so one other one I have on here, "Are there outliers to when the phenomenon of left-digit bias doesn't work and how does this factor in a way with typical demand?"

Devin: Yeah. So I'll be honest, man. I've looked at left-digit bias in several different places, and it shows up very consistently. This is one of the most consistent and robust biases that you'll see. Maybe, I'll give one more example. So this was actually an idea from an MBA student; an MBA student gave me this idea. They suggested the diamond market. And they said, "What about diamonds? Does like a 0.99-karat diamond sell for significantly less than a one-karat diamond?" And I thought, man, that's such a great idea! So, I went home actually that night and scraped all of the sale, all of the online diamonds that were being sold online and looked at the data and everything. And it ends up that they were exactly right. Although, there was something kind of interesting in the diamond market; it ends up that I couldn't find any 0.99-karat diamonds. There were tens of thousands of one-karat and 1.01-karat diamonds that were being sold online -- but no 0.99-karat diamonds or 0.98-karat diamonds, right? And the reason is, is because diamond cutters actually cut the diamonds to sure that they stay above these round numbers. So you can't even find 0.99-karat diamonds on the market. Anyway, so I think there are some interesting facets of how left-digit bias plays out in different markets. And some markets, it affects how you even produce the product. And in other markets it just affects prices or other things. But I would say it's a very robust thing. If there's a number that people are looking at when making a decision, they typically have left-digit bias when looking at that number. It seems to be a pretty hard and fast rule.

Cat: And we also, I know you received a lot of questions regarding your example with Lyft. And so, one in particular says, "If Uber and other major competitors to Lyft also adopt the same strategy, would you project this impact of the strategy to diminish?"

Devin: Yeah. Good question. So I think we're gonna see that play out. I would be stunned. So Lyft is currently following the strategy that we helped work with them on. Uber currently does not still, at least last time I checked is still not using any 99-cent pricing strategy. I would be stunned if they don't follow suit sometime in the next little bit -- especially if I keep giving talks like this. And it'll be interesting to see what happens once they start competing on those same psychological effects. And, I mean, it's gonna benefit both companies to some degree because there are a lot of people that are very loyal to one of the apps and not the others. So some people you only use Lyft, other people only use Uber. The interesting question that I think, the questioner is asking is, "Well, what happens to those people that use both applications?" And they're kind of price checking, and now they're, man, now both prices are maybe at $10.99 where otherwise they would've been at, you know, $11.07 and $11.04 or something. So it will start to create some interesting duopoly situations with pricing, and we have some ideas of how that might play out. But I think there's a lot of uncertainty exactly how that'll work. I think it'll benefit both companies and it could benefit riders as well, though we'd have to talk more about that. But yeah, some of the interactions or the synergies between the two companies is gonna be interesting to see.

Cat: The last question I have written down here is from an attendee. Jake asked, "How has the incorporation of big-data analytics impacted the ability to study and identify trends in behavioral economics as we have seen demonstrated in this webinar?" And he also is interested in, you know, any knowledge that you have that may be accessible to this kind of data in becoming ... interested in any knowledge of how accessible this kind of data is becoming for clients like car dealerships, Lyft, or Amazon?

Devin: Good question. Yeah. So, kind of big data and having, you know, more broadly accessible data sets is huge for behavioral economics. A lot of psychological effects are certainly there but they're small. And so they're hard to measure unless you have a lot of data. So using, you know, hundreds of millions of Lyft ride offers makes it easy to see these discontinuities. If we only had a few thousand observations? Man, it would be almost impossible to see those discontinuities that I showed, right? Other companies like Amazon, for example, they have the ability to do stuff at scale with large data sets to pick up on small effects. And so, I think having these large data sets is super important, once we start looking at psychological effects that tend to be small. But it also makes it hard, right? Like using these big data sets require skill. And so one worry maybe almost that I have is that, is that, with the advent of big data there's gonna be more inequality across the different types of companies, right? Clearly, Amazon can go out and hire people that can do the data analysis and things; smaller companies might struggle with this. And even, for example, this car market -- this wholesale car company that I discussed -- they came to us and kind of begged us to use their data, to try to look at it, in fact. Yeah. So they were very excited about us using their data because they didn't really have anyone in house that was doing some of the stuff that we were interested in trying to do with it. So yeah, I think it's a big deal. I think it'll become more and more common, especially for the larger companies to be doing this, and people will develop better tools for analyzing big data as well. And so, yeah: The future is very bright when it comes to thinking about the intersection of behavioral economics and big data.

Cat: I know we had a number of questions submitted today, but with that last one, I know we're running up on time here. But first, I just wanted to say thank you again, Professor Pope, for sharing your insights with everybody today and speaking with us, and giving a glimpse inside the Chicago Booth classroom. I know all the attendees really appreciate hearing your examples and giving us, providing us time to answer questions in the audience as well. For everybody attending today, if you're interested in learning more about an MBA from Chicago Booth, I encourage you to reach out to our admissions team. I can drop a link in the chat box for you to learn a little bit more. So you should be able to see it there in chat box. So, thank you all for attending today. Hope you enjoy the start of your week and stay healthy and be well.

Devin: Thanks everyone. See you later.