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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in

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Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

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Speaker 2: This week on the podcast, another extra special guest, Judd Kessler,

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Speaker 2: Wharton professor, author of Lucky by Design, tells us about

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Speaker 2: market design, how we allocate scarce resources for everything from

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Speaker 2: Taylor Swift tickets to kidneys. I thought the book was

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Speaker 2: really interesting, kind of wonky economic analysis, but fascinating, and

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Speaker 2: the conversation absolutely fascinating. Also with no further ado, my

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Speaker 2: interview of Professor Judd Kesler. So I found the book

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Speaker 2: to be really fascinating look at market design, which is

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Speaker 2: an area that we don't usually think about. We'll talk

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Speaker 2: about the book in a few moments. I want to

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Speaker 2: start with your best bachelor's, masters and PhD from Harvard,

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Speaker 2: But that wasn't enough. You get a master's in philosophy

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Speaker 2: from Cambridge. What was the career plan?

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Speaker 3: So the career plan was to go to college and

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Speaker 3: then find a career not in academia. That was not

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Speaker 3: something that I even thought of as an option. I

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Speaker 3: was an econ major. I thought I'd be a consultant.

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Speaker 3: I accepted a Bay New York offer to take after graduation.

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Speaker 3: But senior year of college I wrote a thesis under

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Speaker 3: a supervisor named Alvin Roth, and I loved it. It

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Speaker 3: was my first experience doing research, and I thought, this

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Speaker 3: is really fun. I'm in the cutting edge of this

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Speaker 3: topic that I chose that interests me, and so that

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Speaker 3: made me think, all right, maybe I should give it

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Speaker 3: a shot. So that was the trip to Cambridge, England,

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Speaker 3: was me doing an mfil in economics to see, you know,

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Speaker 3: is this something that I might want to pursue as

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Speaker 3: a career. I didn't love the pedagogy in England, so

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Speaker 3: the courses I wasn't enjoying. But every day I'd come

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Speaker 3: home and think, oh, there's some research questions that that

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Speaker 3: lecture prompted. And I realized, oh, the research is keeping

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Speaker 3: me engaged in this program. Even though I don't love

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Speaker 3: the coursework, I probably should be doing this full time.

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Speaker 2: What was the research topic in your senior year that

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Speaker 2: led you to heading to Inland?

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Speaker 3: So it was a experiment. So I'm an experimental economist.

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Speaker 3: Most of my not all of it, but most of

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Speaker 3: my research is experimental, meaning undergrads are coming into the

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Speaker 3: lab playing a game I've designed or we're doing some

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Speaker 3: experimentation in the field where people are going about their lives.

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Speaker 3: They don't realize that half of them are getting one

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Speaker 3: version of an experience and half forgetting the other, and

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Speaker 3: we're seeing what the effect is. So my undergrad thesis

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Speaker 3: was trying to understand how pairs of people could contribute

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Speaker 3: to public go to things that benefited both of them.

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Speaker 3: And I learned about, you know, everything that I could

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Speaker 3: learn about public good provision, and I varied both the

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Speaker 3: structure of the game and how the benefits of the

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Speaker 3: public good were split across people. And this was something

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Speaker 3: that had never been done before. And my advisor, Al

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Speaker 3: was very encouraging, enthusiastic funded the research study. And I

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Speaker 3: had this experience of looking at the data and thinking,

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Speaker 3: this is the.

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Speaker 4: First I'm the first person. I'm on the frontier.

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Speaker 3: I'm never going to be an astronaut, but I'm on

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Speaker 3: the frontier here exploring the answer to this question that

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Speaker 3: interests me.

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Speaker 4: I'm looking at the data and discovering the answer.

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Speaker 2: So this sounds like it's a cross section of game

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Speaker 2: theory and behavioral economics.

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Speaker 4: Fair description, exactly right. And it's a paper.

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Speaker 3: You know, I've since become an academic, and I've been

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Speaker 3: writing research papers for a long time. This one was

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Speaker 3: never published, and the reason was that in my twenties

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Speaker 3: and even into my thirties, I didn't really know how

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Speaker 3: to motivate it.

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Speaker 4: I didn't know what it was about.

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Speaker 3: You know, at a deep level, I knew what I

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Speaker 3: had done, and I knew that it was new and different.

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Speaker 3: But I finally cracked the code in my late thirties

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Speaker 3: because what I had studied, unbeknownst to me was how

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Speaker 3: couples allocate effort to construct public goods in their household.

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Speaker 2: Does that mean who cooks, who cleans, who gets the kids,

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Speaker 2: who basically pays the bills? Is it just that simple?

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Speaker 4: Exactly?

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Speaker 3: Well, it's the game was we each put in some effort.

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Speaker 3: Sometimes the production is split evenly between the two, and

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Speaker 3: sometimes it's split unevenly, so one person gets more of

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Speaker 3: the output and the other person gets less.

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Speaker 4: And what I.

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Speaker 3: Found was that in some structures where we each have

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Speaker 3: to match each other's contribution to general rate and output,

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Speaker 3: then the inequality didn't matter. Pairs of people, these are

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Speaker 3: random strangers. They're able to contribute at high levels. It's

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Speaker 3: when we're contributing to the public good and one of

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Speaker 3: us can cut back and kind of free ride off.

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Speaker 4: Of the other one.

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Speaker 3: When we split it equally, we're able to sustain with

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Speaker 3: the pair, you know, high levels of contribution. But when

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Speaker 3: we're split unequally and one of us can free ride

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Speaker 3: off each other, the contribution collapses.

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Speaker 2: I was going to say that sounds like a recipe

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Speaker 2: for a divorce.

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Speaker 3: Well, you know, the reason I was able to later

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Speaker 3: understand what I had studied in my twenties was those

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Speaker 3: are the situations where, you know, my wife and I

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Speaker 3: have conflicts when we both need to contribute for the

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Speaker 3: public good to be provided, Like we both have to

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Speaker 3: be diligent with bedtimes with our kids because of one

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Speaker 3: of us slips.

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Speaker 4: Then the kids schedule in them up.

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Speaker 3: Then you know, it doesn't matter if I care more

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Speaker 3: about that or my wife we kind of both realize

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Speaker 3: we have to at the same level, or or we

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Speaker 3: both lose.

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Speaker 2: Where does it sort of where does that gap between effort? Yeah,

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Speaker 2: how does that manifest?

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Speaker 3: So if one of us cares more about say the

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Speaker 3: kids eating vegetables for.

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Speaker 2: How does it house neat? Yeah?

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Speaker 3: Yeah, for us, it's you know, the health helpful eating.

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Speaker 3: One of us might care more than the other. And

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Speaker 3: in that case, you know, if my wife cares more

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Speaker 3: and I free write off of her, she fed them

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Speaker 3: a healthy lunch, say yesterday, uh, and then I can

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Speaker 3: feed them a less healthy dinner when it's my turn.

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Speaker 4: That's where the recipe for a disaster.

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Speaker 2: Those are O missions? What about co missions? What about

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Speaker 2: doing things positively where one of you might slip? How

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Speaker 2: does that manifest?

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Speaker 5: Oh?

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Speaker 3: I mean most of the things I'm thinking of are like,

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Speaker 3: are we giving them too much screen time?

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Speaker 2: Oh?

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Speaker 4: I guess we could.

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Speaker 3: It could be uh reading them books at night, you know.

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Speaker 3: I mean, so we both care a lot about this,

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Speaker 3: so well we'll do it. But if you know, if

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Speaker 3: one of us cared more, that would be a recipe

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Speaker 3: for a disaster, because that person would read to the

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Speaker 3: kids and the other person would say, you know what,

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Speaker 3: like you got read to last night, I won't do

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Speaker 3: it right, And that's when the trouble.

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Speaker 4: Yeah, that's when trouble started.

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Speaker 3: So that was my first academic research was exploring those

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Speaker 3: kinds of dynamics in two person games.

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Speaker 2: How do you go from that to studying market design?

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Speaker 3: So this was Alvin Roth the mentor that I mentioned.

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Speaker 3: He was my undergrad thesis advisor. And when I was

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Speaker 3: getting my PhD, I committed a kind of sin, of

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Speaker 3: academic sin, which is you're not supposed to go back

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Speaker 3: to the institution you did your undergrad degree to get

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Speaker 3: your PhD. But I wanted to work with Al, so

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Speaker 3: I kind of I cheated a little bit. I went

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Speaker 3: back to Harvard, but it was technically a Harvard Business

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Speaker 3: School PhD joint with the Econ Department, so I pretended, oh,

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Speaker 3: I'm getting a different angle on this. I ended up

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Speaker 3: being helpful because being at the Business school helped me

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Speaker 3: transition to mind current job as a Wharton professor. But

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Speaker 3: I went back to work with Al, and he was

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Speaker 3: doing both He was doing experimental work and he was

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Speaker 3: doing market design work. And I had gotten exposure to

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Speaker 3: both of them in his course courses as an undergrad

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Speaker 3: and an early PhD student. And the research that kind

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Speaker 3: of transitioned me was on organ donation and organ allocation.

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Speaker 2: The book has some fascinating data points on that. We'll

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Speaker 2: talk about that in a bit. I want to stay

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Speaker 2: with your background. You end up winning the Vernon Smith

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Speaker 2: Scholar Prize in twenty twenty one. What work was that for?

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Speaker 3: So that was for a line of work starting with

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Speaker 3: this organ allocation work, because that was both a public

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Speaker 3: good study like I described in the earlier work and

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Speaker 3: policy relevant. And so the Vernon Smith Prize is for

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Speaker 3: somebody who's contributed with experimental research in a bunch of

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Speaker 3: areas and I had you know, I'd done that, I'd

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Speaker 3: done that in organ allocation, done that, and of course allocation.

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Speaker 3: I had done work on summer youth employment, but kind

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Speaker 3: of always with this experimental lens to try to understand

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Speaker 3: what the effects were.

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Speaker 2: And what's kind of fascinating is you're clicking off a

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Speaker 2: lot of chapters in the book, which are how do

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Speaker 2: we allocate scarce resources when there are a variety of

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Speaker 2: different ways to do it. Sometimes it's lottery, sometimes it's effort.

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Speaker 2: Sometimes it's people paying more to get it, which really

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Speaker 2: is I never thought of those things as market design,

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Speaker 2: and yet most people look at those things as just hey,

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Speaker 2: you got lucky. You got the summer job or the

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Speaker 2: course you wanted, or the kidney you needed because you

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Speaker 2: signed up. A big theme of the book is, hey,

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Speaker 2: this is in luck. This is recognizing all of these

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Speaker 2: market design structures and figuring out the rules and playing

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Speaker 2: them as well as you can.

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Speaker 3: Exactly right in the book. I call these hidden markets

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Speaker 3: because they're not the markets that we always think of.

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Speaker 3: When we think of markets, we think of the farmer's

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Speaker 3: market where you're paying a price for produce. We think

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Speaker 3: of the stock market where you're paying a price for

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Speaker 3: equity and publicly traded companies. But there are all of

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Speaker 3: these markets where you're trying to allocate a scarce resource.

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Speaker 3: You might have a price that gets paid, but it's

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Speaker 3: not doing all of the work. There's something else that

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Speaker 3: is deciding who gets access to the scarce resource. And

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Speaker 3: then there are markets where there is no price. We've

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Speaker 3: decided that we want to do the allocation without having

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Speaker 3: folks pay. We want to distribute it in some other way.

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Speaker 3: And these are areas that market design thinks about. But

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Speaker 3: that a standard ECON class like the ones I teach

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Speaker 3: to my undergrads or MBA students or executives would not

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Speaker 3: necessarily cover.

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Speaker 2: So other than the students in your warton class, how

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Speaker 2: can individuals become more aware of all of these hidden

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Speaker 2: market mechanisms and use them to their best advantage?

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Speaker 3: Yeah, so the first step is recognizing that these things

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Speaker 3: are markets. You want access to something, it's scarce, there

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Speaker 3: is a limited amount that can be allocated, and you're

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Speaker 3: competing with lots of other people who want them. Examples

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Speaker 3: might make it more concrete because we're, you know, thinking

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Speaker 3: about things that people participate in every day.

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Speaker 4: These markets.

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Speaker 3: So you want to get a reservation at a hot restaurant,

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Speaker 3: you want to get a ticket to a live event.

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Speaker 3: You want to get a product that is hard to

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Speaker 3: come by, either you know, a clothing special clothing drop

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Speaker 3: or this summer it was La Boo boos, the ugly

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Speaker 3: cute stuffy dolls, but you could think of beanie babies

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Speaker 3: or cabbage patch dolls if you're as.

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Speaker 4: Old as me.

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Speaker 3: And in these cases there is a price that you pay,

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Speaker 3: but there's also some other ordeal that you may have

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Speaker 3: to go through to get access to those scarce resources.

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Speaker 3: Same thing with benefits or services provided by the government.

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Speaker 3: You want to get your kid into public elementary school,

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Speaker 3: you want to get a library book, you want to

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Speaker 3: get a life saving organ transplant. These are environments where

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Speaker 3: you have to understand, okay, what is the rule that

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Speaker 3: is doing the allocation, and then how can you use

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Speaker 3: your knowledge of that rule to figure out the right strategy.

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Speaker 2: Your daughter trying to get into her favorite after school

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Speaker 2: program really resonated because it was such a little niche thing.

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Speaker 2: It's just one of those everyday life frictions. You don't

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Speaker 2: really think of those as markets that have been designed,

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Speaker 2: but you do a really nice job explaining any allocation

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Speaker 2: of scarce resources is a market decision.

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Speaker 3: Exactly, and that one is one of the banes of

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Speaker 3: my existence. I have to it's a first come, first

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Speaker 3: serve race, which is what I call the experience that

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Speaker 3: folks have on a regular basis of there is a

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Speaker 3: scarce resource, it is being made available at a point

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Speaker 3: in time, and whoever clicks first gets it, whoever calls

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Speaker 3: in first in different market structures, is the one who

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Speaker 3: can claim that scarce resource. After school programs at my

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Speaker 3: daughter's elementary school, this is what the kids are doing

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Speaker 3: between two thirty when school lets out, and five thirty

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Speaker 3: when working parents can pick them up. There are some

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Speaker 3: very popular classes for the kids to do during those hours,

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Speaker 3: but there are a lot of kids who want to

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Speaker 3: participate in them, and so every semester they will say okay.

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Speaker 3: On June twelfth at ten am. We're going to release

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Speaker 3: all of the fall semester courses, and whoever clicks to

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Speaker 3: claim the spots in those classes first will get them,

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Speaker 3: and everybody else will be disappointed.

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Speaker 4: And it is a.

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Speaker 3: First come for Serve race. And I like the race

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Speaker 3: analogy because I don't do a ton of cardio and

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Speaker 3: this is where my heart race is faster that you know,

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Speaker 3: this is fight or flight, because I know I'm competing

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Speaker 3: with all these other parents who want to get their

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Speaker 3: kids into these classes. And if my daughter gets what

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Speaker 3: she wants, mornings are wonderful, and if she doesn't, it's

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Speaker 3: you know, I don't want to go to school today.

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Speaker 2: There's some you seem to have an advantage because there

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Speaker 2: are some fascinating strategies here. Hey, maybe you don't start

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Speaker 2: with the Monday classes. Maybe you do Tuesday, Wednesday, Thursday,

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Speaker 2: because they're going to fill up while everybody's racing to

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Speaker 2: get Monday. And if you're disappointed in a Monday, but

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Speaker 2: you've locked in the three ones for the rest of

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Speaker 2: the week, there's an advantage. Thinking about how the rest

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Speaker 2: of the participants are playing the game.

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Speaker 4: This is exactly right.

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Speaker 2: So the ball game theory.

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Speaker 3: It's all game theory, and I wrote a book about

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Speaker 3: game theory. I didn't use the term game theory because

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Speaker 3: I didn't want to scare people off. But it's thinking

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Speaker 3: about what it is that you want and then what

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Speaker 3: it is that other folks might be going for and

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Speaker 3: developing the right strategy for that. Now, first come for

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Speaker 3: serve races. There's a bunch of strategy that is kind of,

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Speaker 3: you know, maybe obvious when you think about it. Of Okay,

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Speaker 3: you have to know that it's a race. You have

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Speaker 3: to know that this is a situation where you need

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Speaker 3: to be available to click.

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Speaker 4: As fast as you can at that time.

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Speaker 3: Right when I get the email and it says June

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Speaker 3: twelfth at ten am is when registration opens. Even if

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Speaker 3: I've never participated in the registration for these after school classes,

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Speaker 3: the ten am tips me off that something is going

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Speaker 3: on at my son's school where the classes are not

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Speaker 3: as demanded. There is no ten ams. I mean, there

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Speaker 3: might be a time when the registration begins, but nobody

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Speaker 3: really cares about it because you can go whenever you

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Speaker 3: want that day or later in the week, and there's

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Speaker 3: plenty of options available. But this market, the ten am

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Speaker 3: tells you all right, by ten oh one or ten

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Speaker 3: o two, the good stuff might be taken.

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Speaker 2: Did your wife ever get to the French laundry in Napa?

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Speaker 3: This was a first time, first of race that I

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Speaker 3: talk about in the book. It's for milestone birthdays only

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Speaker 3: because it's so expensive. So in the book I talk

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Speaker 3: about how we did not get it for her fortieth birthday,

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Speaker 3: so she will try again when she's fifty.

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Speaker 2: I was reading that and using your strategy imediately thought, oh,

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Speaker 2: you're flying out just for a weekend. Four o'clock is

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Speaker 2: essentially seven o'clock in New York. Why not do four

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Speaker 2: or four to thirty and bang, they're good to go.

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Speaker 3: This is exactly so the strategy to play there is

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Speaker 3: to think about what I call settling for silver versus

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Speaker 3: going for gold. So the settling for silver strategy is,

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Speaker 3: you know that seven o'clock or seven thirty is the

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Speaker 3: most desirable time to eat, at least you know for

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Speaker 3: regular people not in retirement communities. That's the ideal time.

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Speaker 3: If you're going to go on a Saturday, that is

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Speaker 3: what most people are going to be aiming for. When

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Speaker 3: I went for her fortieth birthday to try to register.

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Speaker 3: I knew it was a race. I knew when the

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Speaker 3: starting gun was going off, I was there, My heart

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Speaker 3: was beating fast, and I went first for the seven

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Speaker 3: thirty reservation page reloads.

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Speaker 4: I don't get it, and I see that.

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Speaker 3: Four to thirty still available, so I click for four thirty,

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Speaker 3: thinking it's better than nothing, and the page reloads and

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Speaker 3: that is also gone.

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Speaker 4: So struck out there.

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Speaker 3: That's why we're waiting a decade to your point, if

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Speaker 3: I had I was doing it as a surprise for

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Speaker 3: my wife, if I had planned and talked to her

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Speaker 3: about it in advance, we might have recognized four four

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Speaker 3: thirty is just is.

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Speaker 2: Nearly as good youer an East Coast, it's.

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Speaker 3: Nearly as good as seven thirty. And that's the kind

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Speaker 3: of situation where you want to settle for silver, where

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Speaker 3: you want to go for something where there's less competition. Again,

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Speaker 3: the game theory coming in, fewer people are going to

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Speaker 3: be going for four four thirty. That is something that

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Speaker 3: you actually have a much better shot at if you

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Speaker 3: go for it first. You have to act as if

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Speaker 3: that is what you wanted all along, right, because when

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Speaker 3: the page reloads the first time, four thirty is still available,

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Speaker 3: Meaning if instead of going for seven thirty, I gone

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Speaker 3: for four to thirty initially as if it was my

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Speaker 3: first choice, she would have been able to cross French laundry,

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Speaker 3: the bucket list.

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Speaker 5: There you go.

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Speaker 2: Aside from the east coast West coast difference post pandemic,

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Speaker 2: My wife likes to point out that you use apps

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Speaker 2: like Open Table RESI, and she says six thirty is

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Speaker 2: the new seven thirty because everybody wants to get home

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Speaker 2: and stream whatever they're streaming. It's so different than it

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Speaker 2: was in the twenty tens. And I'm like, am I

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Speaker 2: just getting old? Are we going to start going to

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Speaker 2: the early bird specials? She's like, no, no one wants

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Speaker 2: to have dinner at ten, nine, ten o'clock at night.

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Speaker 4: I believe that.

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Speaker 3: And at the French laundry the meals take forever. So

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Speaker 3: getting out of there, you know, early.

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Speaker 2: For thirty gets you out at nine, ten and five, right,

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Speaker 2: Absolutely fascinating. Coming up, we continue our conversation with Judd Kessler,

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Speaker 2: Howard Mark's professor at the Wharton School at University of Pennsylvania,

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Speaker 2: discussing his new book, Lucky by Design, The Hidden Economics.

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Speaker 2: You need to get more of what you want. I'm

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Speaker 2: Barry Ridhelts. You're listening to Masters in Business.

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Speaker 5: On Bloomberg Radio.

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Speaker 2: I'm Barry Dults. You're listening to Masters in Business on

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Speaker 2: Bloomberg Radio. My extra special guest this week is judde Kessler.

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Speaker 2: He is the Howard Marx Professor at the Wharton School

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Speaker 2: at the University of Pennsylvania. So I want to talk

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Speaker 2: about Lucky by Design. The book is really interesting. I'm

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Speaker 2: intrigued by how much of what we think of as

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Speaker 2: luck is really just the way allocation mechanisms have been

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Speaker 2: either well designed or poorly designed. Lotteries, waitlists, queues, scoring rules, algorithms,

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Speaker 2: norms explain this concept that luck by design may really

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Speaker 2: be based on some hidden economic rules.

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Speaker 3: Yeah, so the way the markets operate, there's a set

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Speaker 3: of rules that decides who gets what. So we talked

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Speaker 3: about first come for serve races, but as you point out,

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Speaker 3: there's first come for serve waiting lists. There's lines, there

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Speaker 3: are lottery systems where you're putting your name in the

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Speaker 3: hat and we're pulling people out. And then they're centralized

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Speaker 3: clearing houses where you might rank your preferences over things

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Speaker 3: you want. And then there's some priorities or rules in

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Speaker 3: the background. And I have this sense that people look

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Speaker 3: at these systems and they don't have a framework for

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Speaker 3: thinking about them, and so when they participate in these markets,

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Speaker 3: they don't really realize they're doing so, and then the

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00:20:33,560 --> 00:20:39,520
Speaker 3: outcomes seem like they're based on chance, or they try

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Speaker 3: to understand them and they struggle and they feel overwhelmed

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Speaker 3: and stressed out, and then play a strategy that might

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Speaker 3: not be right for them, and then they look around

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Speaker 3: and they think, oh, man, that person got what they

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Speaker 3: wanted I didn't.

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Speaker 4: They must have been lucky.

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Speaker 3: Because if you don't understand the system, then it all

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Speaker 3: seems like it's happening by chance. But by understanding the

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Speaker 3: rules that are applying in each market, you then can recognize, Okay,

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Speaker 3: this is a situation where it's a first come, first

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Speaker 3: serve waiting list. So I have to put my name

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Speaker 3: down early. Then I have to think about the strategy

399
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Speaker 3: I'm going to play when it's my turn. Do I

400
00:21:16,680 --> 00:21:19,199
Speaker 3: take what's offered to me? Do I keep waiting? And

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00:21:19,280 --> 00:21:23,439
Speaker 3: you have a develop a framework for that. Even in

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Speaker 3: lottery allocations, which we often think of as being the

403
00:21:26,720 --> 00:21:30,480
Speaker 3: ones that are based entirely on chance. If you understand

404
00:21:30,560 --> 00:21:33,840
Speaker 3: the rules, you can develop strategies that help you do better.

405
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Speaker 3: So you want to go see a theater production, and

406
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Speaker 3: there's going to be a ticket lottery. You can go.

407
00:21:41,640 --> 00:21:44,600
Speaker 3: You can enter your name for two tickets. But maybe

408
00:21:44,720 --> 00:21:46,200
Speaker 3: you can bring your friend that you're going to go

409
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Speaker 3: see the show with or your partner, and you both enter.

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Speaker 3: Now you have twice as many chances. Maybe you get

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Speaker 3: a bunch of friends who work nearby to enter, maybe

412
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Speaker 3: it's online. They enter for you. If they lose, they lose,

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Speaker 3: but if they win, they come down to the theater,

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Speaker 3: pick up the tickets, give them to you. All of

415
00:22:01,080 --> 00:22:04,119
Speaker 3: a sudden, Now you've dramatically increased your chances of winning.

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Speaker 3: These are technically allowed. Often by the rules. Sometimes you

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Speaker 3: can enter a lottery in years you intend to lose,

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Speaker 3: because the system rewards you in subsequent years for prior losses.

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Speaker 3: It's trying to be fair over time, and so the

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Speaker 3: rules are if you have lost nine years in a row,

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Speaker 3: then in your tenth year, you'll get ten entries or

422
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Speaker 3: one hundred entries or a thousand entries relative to someone

423
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Speaker 3: who's entering for the first time. Or maybe you win

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Speaker 3: the lottery in a year you don't want to win,

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Speaker 3: and you defer what you get for a year, and

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Speaker 3: now you basically get to enter this year in the

427
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Speaker 3: hopes of deferring for next year, and if you lose

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Speaker 3: this year, you get to enter next year for the

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Speaker 3: chance of getting whatever it is next year.

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Speaker 2: So you've gone to school at Harvard and Cambridge, you

431
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Speaker 2: teach at University of Pennsylvania. When we look at college admissions,

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Speaker 2: that seems to be like a mess of everything, some

433
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Speaker 2: some skill, some checking the boxes, a little bit of lottery,

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Speaker 2: a little bit of early admission, first come, first serve.

435
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Speaker 2: What do you think of that entire college admission process?

436
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Speaker 2: What's driving that design?

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Speaker 3: Yeah, so that is what I call a choose me market.

438
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Speaker 3: It's a two sided market where it's as you point out,

439
00:23:20,080 --> 00:23:22,400
Speaker 3: it's not as simple as any one rule. It's not

440
00:23:22,520 --> 00:23:24,879
Speaker 3: like whoever applies first to the school gets it or

441
00:23:24,880 --> 00:23:27,600
Speaker 3: they're going to totally pick people by lottery.

442
00:23:28,640 --> 00:23:31,880
Speaker 4: They have a strategic decision as an institution.

443
00:23:32,160 --> 00:23:34,360
Speaker 3: Maybe I should say we as an employee of one

444
00:23:34,359 --> 00:23:39,280
Speaker 3: of these institutions. But the features of a choose me market,

445
00:23:39,320 --> 00:23:42,119
Speaker 3: of a two sided market are that there are market

446
00:23:42,119 --> 00:23:46,040
Speaker 3: participants on both sides. So for college admissions, there's applicants

447
00:23:46,040 --> 00:23:48,239
Speaker 3: who are trying to get into the colleges, and then

448
00:23:48,240 --> 00:23:50,760
Speaker 3: there are colleges that are deciding who am I going

449
00:23:50,800 --> 00:23:55,960
Speaker 3: to admit. We're trying to make a class of smart, motivated,

450
00:23:57,280 --> 00:24:00,640
Speaker 3: well rounded or very pointy people who are gonna make

451
00:24:00,680 --> 00:24:05,879
Speaker 3: the class, you know, a rich, fun environment, and the

452
00:24:06,000 --> 00:24:09,159
Speaker 3: dynamics are about you know, are we both succeeding and

453
00:24:09,200 --> 00:24:12,720
Speaker 3: getting what we want? So I think the thing that

454
00:24:12,760 --> 00:24:15,520
Speaker 3: people think of is okay, is the candidates strong enough?

455
00:24:15,560 --> 00:24:18,240
Speaker 3: Do they have good enough grades, good enough SAT scores,

456
00:24:18,320 --> 00:24:22,520
Speaker 3: good enough extracurriculars. But something that we think less about

457
00:24:22,800 --> 00:24:25,480
Speaker 3: as an applicants say, because it requires thinking about the

458
00:24:25,480 --> 00:24:28,680
Speaker 3: other side of the market, is you know, how do

459
00:24:28,800 --> 00:24:32,119
Speaker 3: the universities or colleges feel about the applicants. Well, one

460
00:24:32,160 --> 00:24:37,720
Speaker 3: of the things that they value is high yield. We

461
00:24:37,800 --> 00:24:42,480
Speaker 3: want the people who we admit to enroll in our school.

462
00:24:42,520 --> 00:24:45,880
Speaker 3: We want them to matriculate. When I was applying to college,

463
00:24:46,400 --> 00:24:49,200
Speaker 3: that yield, the fraction of folks you admit who come

464
00:24:49,760 --> 00:24:52,000
Speaker 3: was in the US News and World Report rankings of

465
00:24:52,040 --> 00:24:56,200
Speaker 3: best colleges and universities. It's not anymore, but it's still

466
00:24:56,240 --> 00:24:58,480
Speaker 3: a matter of pride and reputation, because it's hard to

467
00:24:58,520 --> 00:24:59,960
Speaker 3: say you're one of the best schools in the CUNT

468
00:25:00,400 --> 00:25:02,400
Speaker 3: if you know half or two thirds of the people

469
00:25:02,400 --> 00:25:05,800
Speaker 3: you admit choose to go somewhere else. And so when

470
00:25:05,880 --> 00:25:10,040
Speaker 3: you mentioned early admissions, early decision or early action, that

471
00:25:10,240 --> 00:25:13,680
Speaker 3: is where this kind of yield question comes into play.

472
00:25:13,720 --> 00:25:16,720
Speaker 3: So the way that it works with early decision is

473
00:25:16,760 --> 00:25:20,320
Speaker 3: when you apply, say to pen where my employer early decision.

474
00:25:21,560 --> 00:25:23,760
Speaker 3: If you do that, you are committing to come if

475
00:25:23,760 --> 00:25:27,880
Speaker 3: we admit you. So that's great for our yield because

476
00:25:28,080 --> 00:25:32,800
Speaker 3: now you know you're guaranteed to come. And lots of

477
00:25:32,840 --> 00:25:37,400
Speaker 3: schools do this they have an early admission deadline. There's

478
00:25:37,440 --> 00:25:39,359
Speaker 3: also early action where you're not committing, but you can

479
00:25:39,440 --> 00:25:41,600
Speaker 3: only apply to one school early action, and so it

480
00:25:41,640 --> 00:25:43,719
Speaker 3: has similar properties where you're kind of giving up applying

481
00:25:43,760 --> 00:25:49,480
Speaker 3: elsewhere early. And the kind of deal is that emission

482
00:25:49,520 --> 00:25:53,200
Speaker 3: standards appear to be a little lighter, and so researchers

483
00:25:53,200 --> 00:25:56,439
Speaker 3: have estimated it's kind of worth about one hundred SAT

484
00:25:56,600 --> 00:25:58,919
Speaker 3: points if you apply early. It's kind of we're going

485
00:25:58,960 --> 00:26:00,880
Speaker 3: to where we like idea that you want to come.

486
00:26:00,920 --> 00:26:02,960
Speaker 3: We like that you're going to help us with our yield,

487
00:26:03,200 --> 00:26:06,159
Speaker 3: and so you know we're going to kind of be

488
00:26:06,359 --> 00:26:09,879
Speaker 3: more open to having you enter. Now I just say,

489
00:26:09,920 --> 00:26:11,480
Speaker 3: I don't work in the ad missions committee, so this

490
00:26:11,560 --> 00:26:13,240
Speaker 3: is right. This is as an outsider doing, you know,

491
00:26:13,280 --> 00:26:16,879
Speaker 3: looking at the research about it. But all of a sudden,

492
00:26:16,920 --> 00:26:19,680
Speaker 3: then it becomes a strategic decision as you as an applicant,

493
00:26:20,160 --> 00:26:25,080
Speaker 3: So you have one shot in the early decision game

494
00:26:25,600 --> 00:26:28,880
Speaker 3: where do you want to apply? We talked a little

495
00:26:28,880 --> 00:26:31,280
Speaker 3: bit about going for gold or settling for silver. Do

496
00:26:31,320 --> 00:26:32,720
Speaker 3: you go for the thing you really want, the seven

497
00:26:32,720 --> 00:26:35,399
Speaker 3: to thirty reservation at the hot restaurant, or do you

498
00:26:35,480 --> 00:26:37,960
Speaker 3: go for something there where there's less competition, like a

499
00:26:37,960 --> 00:26:42,800
Speaker 3: four to thirty and the early decision game, that strategy

500
00:26:42,920 --> 00:26:45,359
Speaker 3: that's settling for silver strategy might be a smart play

501
00:26:45,520 --> 00:26:49,720
Speaker 3: because if the place you really want to enroll, that

502
00:26:49,840 --> 00:26:52,600
Speaker 3: gold medal option for you is too far out of

503
00:26:52,600 --> 00:26:56,199
Speaker 3: reach that even if you apply early, you're you're not

504
00:26:56,240 --> 00:26:59,359
Speaker 3: going to hit that admission cutoff. Then you're essentially wasting

505
00:26:59,400 --> 00:27:03,040
Speaker 3: that application in that school, and you should be applying

506
00:27:03,040 --> 00:27:06,119
Speaker 3: instead to a place where if you applied early, you

507
00:27:06,119 --> 00:27:08,359
Speaker 3: would actually get in. But if you didn't apply early,

508
00:27:08,520 --> 00:27:10,119
Speaker 3: you wouldn't someplace where you're kind of.

509
00:27:10,119 --> 00:27:10,800
Speaker 4: More on the market.

510
00:27:10,840 --> 00:27:13,040
Speaker 2: That's a very narrow little slice. You have to figure

511
00:27:13,040 --> 00:27:15,439
Speaker 2: out exactly what your odds are.

512
00:27:15,359 --> 00:27:17,720
Speaker 3: Yeah, which you can do with research, with talking to

513
00:27:17,880 --> 00:27:20,120
Speaker 3: other people, you know, seeing you know, how does your

514
00:27:20,160 --> 00:27:22,600
Speaker 3: SAT score compare to the ones that are published on

515
00:27:22,640 --> 00:27:24,960
Speaker 3: the website. You know, the people who went to your

516
00:27:25,040 --> 00:27:27,400
Speaker 3: high school in prior years who succeeded in getting in.

517
00:27:28,640 --> 00:27:31,280
Speaker 3: What were their grades like, what were their extracurriculars like?

518
00:27:31,359 --> 00:27:33,480
Speaker 3: So you know when these when it matters for you,

519
00:27:33,520 --> 00:27:36,960
Speaker 3: the research that you do to figure out how to

520
00:27:36,960 --> 00:27:41,840
Speaker 3: succeed in these markets will inform what strategy you should play.

521
00:27:41,960 --> 00:27:45,159
Speaker 2: Huh, really interesting. Let's talk about the three e's. You

522
00:27:45,280 --> 00:27:50,120
Speaker 2: discuss what's equitable, efficient, and easy when people are designing

523
00:27:50,800 --> 00:27:54,320
Speaker 2: various types of market mechanisms. Give us a little overview

524
00:27:54,359 --> 00:27:56,359
Speaker 2: of that core framing device.

525
00:27:56,520 --> 00:27:56,800
Speaker 4: Yeah.

526
00:27:56,800 --> 00:28:01,719
Speaker 3: So the three e's are about how well a market operates,

527
00:28:01,800 --> 00:28:06,399
Speaker 3: So you mentioned them, efficiency, ease, and equity. Equity is

528
00:28:06,440 --> 00:28:10,480
Speaker 3: about fairness. It's about are we treating the market participants

529
00:28:11,200 --> 00:28:13,840
Speaker 3: equally if that's our goal, right, if we want everybody

530
00:28:13,880 --> 00:28:16,360
Speaker 3: to have an equal chance at getting the scarce resource

531
00:28:16,560 --> 00:28:21,040
Speaker 3: is our allocation mechanism, are our market rules allowing us

532
00:28:21,080 --> 00:28:24,640
Speaker 3: to do that. Efficiency is about making sure that we're

533
00:28:24,720 --> 00:28:29,200
Speaker 3: not wasting any scarce resources and whether the scarce resources

534
00:28:29,240 --> 00:28:31,280
Speaker 3: that we're giving out are being put to their best

535
00:28:31,280 --> 00:28:33,679
Speaker 3: possible use. So if there's someone who really wants something,

536
00:28:34,080 --> 00:28:37,840
Speaker 3: are we recognizing that and saying, oh, actually, as a society,

537
00:28:37,840 --> 00:28:40,280
Speaker 3: we're better off if we give that scarce resource to

538
00:28:40,040 --> 00:28:44,520
Speaker 3: that person. And then ease is the one that think

539
00:28:44,560 --> 00:28:48,160
Speaker 3: standard econ doesn't think that much about. And the reason

540
00:28:48,280 --> 00:28:53,000
Speaker 3: is that prices are easy to work with. You might

541
00:28:53,000 --> 00:28:54,400
Speaker 3: not love that you have to pay a lot of

542
00:28:54,400 --> 00:28:57,720
Speaker 3: money to buy something, but the actual process of buying

543
00:28:57,760 --> 00:29:00,880
Speaker 3: something in a market where price doing all the work

544
00:29:01,040 --> 00:29:03,800
Speaker 3: is trivial. You go to the website, you click a

545
00:29:03,840 --> 00:29:05,600
Speaker 3: button and you know the thing is shipped to you,

546
00:29:05,720 --> 00:29:07,640
Speaker 3: or you walk into a store you pay a price,

547
00:29:07,800 --> 00:29:11,200
Speaker 3: or you know, go online and execute some trade or

548
00:29:11,240 --> 00:29:12,640
Speaker 3: call your broker and do it.

549
00:29:12,640 --> 00:29:14,800
Speaker 4: It's very straightforward to work with prices.

550
00:29:15,280 --> 00:29:17,840
Speaker 2: So let's let's talk a little bit about one of

551
00:29:17,840 --> 00:29:21,960
Speaker 2: the most fascinating market mechanisms that's out there, which is

552
00:29:22,160 --> 00:29:26,320
Speaker 2: live performance tickets. You use the example of Taylor Swift,

553
00:29:26,320 --> 00:29:29,160
Speaker 2: who could have charged a whole lot more for her tour,

554
00:29:29,240 --> 00:29:32,800
Speaker 2: which still made billions of dollars, But lots of other

555
00:29:32,960 --> 00:29:38,320
Speaker 2: artists charge less than the market bears. Why do these

556
00:29:39,240 --> 00:29:43,160
Speaker 2: artists not go for you know, revenue maximizing. What's the

557
00:29:43,200 --> 00:29:43,880
Speaker 2: downside of that?

558
00:29:44,120 --> 00:29:48,160
Speaker 3: Yeah, So I when I think about sellers designing, should

559
00:29:48,160 --> 00:29:51,240
Speaker 3: I set a price below the market clearing price? The

560
00:29:51,240 --> 00:29:53,400
Speaker 3: price that I would teach in my econ class is

561
00:29:54,160 --> 00:29:55,120
Speaker 3: going to be the best for them.

562
00:29:55,320 --> 00:29:58,280
Speaker 2: Here's the price, here's the demand. Where that intersects is

563
00:29:58,880 --> 00:30:01,680
Speaker 2: the profit maximize? But they don't do that.

564
00:30:01,800 --> 00:30:03,880
Speaker 3: So before we get to Taylor Swift, let's think about

565
00:30:03,880 --> 00:30:06,400
Speaker 3: the restaurant that is letting there be a line around

566
00:30:06,400 --> 00:30:10,480
Speaker 3: the block, or the fad product that is making it

567
00:30:10,520 --> 00:30:13,560
Speaker 3: hard to get access to their you know, to what

568
00:30:13,600 --> 00:30:16,520
Speaker 3: they're selling. In those cases, I think one of the

569
00:30:16,560 --> 00:30:20,160
Speaker 3: reasons is to bolster future demand. I see a line

570
00:30:20,200 --> 00:30:22,520
Speaker 3: around the block for a restaurant I walk by. I

571
00:30:22,600 --> 00:30:24,440
Speaker 3: might have never heard of the restaurant before, but I

572
00:30:24,480 --> 00:30:26,720
Speaker 3: look and I think, oh man, that restaurant must be

573
00:30:26,720 --> 00:30:27,160
Speaker 3: really good.

574
00:30:27,240 --> 00:30:28,080
Speaker 4: Look at that line.

575
00:30:29,160 --> 00:30:33,960
Speaker 3: That might mean that I get turned into a future customer,

576
00:30:34,080 --> 00:30:36,040
Speaker 3: or at least somebody who's interested in going when the

577
00:30:36,080 --> 00:30:37,200
Speaker 3: line might be a little shorter.

578
00:30:37,440 --> 00:30:41,880
Speaker 2: Lots of buzz, lots of pr just by the virtue

579
00:30:41,960 --> 00:30:45,400
Speaker 2: that it looks like more people want a limited scarce restart.

580
00:30:45,240 --> 00:30:48,280
Speaker 3: Correct, and that we see that, you know, throughout with

581
00:30:48,360 --> 00:30:51,600
Speaker 3: lots of fad products or with you know, scarcity driving

582
00:30:51,680 --> 00:30:54,440
Speaker 3: interest in demand. Now, I don't think Taylor Swift has

583
00:30:54,480 --> 00:30:56,160
Speaker 3: to do that. I think we all know who she is.

584
00:30:56,560 --> 00:31:01,160
Speaker 3: Her fans are famously loyal, and she, you know, doesn't

585
00:31:01,160 --> 00:31:04,920
Speaker 3: have to worry so much about getting more people to

586
00:31:04,960 --> 00:31:07,280
Speaker 3: be interested in her as an artist, so she might

587
00:31:07,360 --> 00:31:11,440
Speaker 3: have other considerations. She might think more about the equity

588
00:31:11,800 --> 00:31:15,000
Speaker 3: and the efficiency of the allocation of her scarce resource.

589
00:31:15,400 --> 00:31:19,040
Speaker 3: And you know, one reason she might not charge very

590
00:31:19,120 --> 00:31:22,320
Speaker 3: high prices, which might be you know, hundreds of dollars

591
00:31:22,320 --> 00:31:25,040
Speaker 3: for the cheapest ticket and thousands for the kind of

592
00:31:25,040 --> 00:31:28,960
Speaker 3: closer to the stage seats, is that she's a billionaire.

593
00:31:29,520 --> 00:31:30,360
Speaker 4: Her swifties.

594
00:31:31,040 --> 00:31:33,360
Speaker 3: I'm sure she has billionaire fans because she's such a

595
00:31:33,400 --> 00:31:36,680
Speaker 3: great artist, But most of her fans, a few thousand

596
00:31:36,720 --> 00:31:38,600
Speaker 3: dollars is going to be a big chunk of their

597
00:31:39,440 --> 00:31:43,400
Speaker 3: income for that month or more, and so it might

598
00:31:43,400 --> 00:31:45,720
Speaker 3: not be a great look if she's charging what would

599
00:31:45,760 --> 00:31:48,520
Speaker 3: be market clearing prices. So for the Eras tour, she

600
00:31:48,640 --> 00:31:51,720
Speaker 3: charges forty nine dollars for the cheapest seats. The average

601
00:31:51,800 --> 00:31:55,720
Speaker 3: ticket price was just above two hundred and so at

602
00:31:55,720 --> 00:31:59,160
Speaker 3: those prices, you know there's going to be massive access demand.

603
00:32:00,120 --> 00:32:02,800
Speaker 3: She might think that that's more fair that that might be,

604
00:32:02,920 --> 00:32:04,720
Speaker 3: you know, not just because it will make her look bad,

605
00:32:04,720 --> 00:32:07,520
Speaker 3: but also she might want her fans to be able

606
00:32:07,560 --> 00:32:11,040
Speaker 3: to come and only have to pay forty nine or

607
00:32:11,120 --> 00:32:13,920
Speaker 3: ninety nine or one hundred and ninety nine dollars. It

608
00:32:14,000 --> 00:32:17,520
Speaker 3: might also be that those folks really really want to go.

609
00:32:17,560 --> 00:32:20,640
Speaker 3: So you think about efficiency, there's no guarantee that the

610
00:32:20,640 --> 00:32:24,440
Speaker 3: person who can pay the most actually values going to

611
00:32:24,520 --> 00:32:28,800
Speaker 3: the concert the most. If her biggest fans have less

612
00:32:28,800 --> 00:32:32,960
Speaker 3: income than you know, the fans who can pay more,

613
00:32:33,880 --> 00:32:38,080
Speaker 3: they're going to get pushed out. Whenever the market is

614
00:32:38,080 --> 00:32:39,920
Speaker 3: relying exclusively on price, you.

615
00:32:39,920 --> 00:32:43,800
Speaker 2: End up with a series of rentiers and middlemen that

616
00:32:44,400 --> 00:32:49,520
Speaker 2: arguably contribute nothing positive to society and just exact a cost.

617
00:32:49,560 --> 00:32:51,600
Speaker 2: In the book, I don't remember was it the UK

618
00:32:52,120 --> 00:32:56,560
Speaker 2: or Europe EU that they banned places like stub hub

619
00:32:56,600 --> 00:32:58,920
Speaker 2: and those sort of ticket middlemen.

620
00:32:59,120 --> 00:32:59,320
Speaker 4: Yeah.

621
00:32:59,360 --> 00:33:03,200
Speaker 3: So this is one of the super interesting things about

622
00:33:03,480 --> 00:33:08,080
Speaker 3: these hidden markets is that whenever you are giving folks

623
00:33:08,160 --> 00:33:11,520
Speaker 3: access to a scarce resource at a below market price

624
00:33:11,640 --> 00:33:15,880
Speaker 3: and there is the opportunity to resell it, you will

625
00:33:15,920 --> 00:33:20,200
Speaker 3: get middleman. You will get speculators or brokers who come

626
00:33:20,240 --> 00:33:25,400
Speaker 3: in exclusively to try to extract surplus from the fact

627
00:33:25,400 --> 00:33:29,360
Speaker 3: that the market is letting the price kind of be low.

628
00:33:29,560 --> 00:33:30,040
Speaker 4: Initially.

629
00:33:31,480 --> 00:33:33,440
Speaker 3: Now there are economists who say, oh, that's how it's

630
00:33:33,440 --> 00:33:35,640
Speaker 3: supposed to work, We're supposed to get to market clearing prices.

631
00:33:35,680 --> 00:33:38,080
Speaker 4: But that's not what I.

632
00:33:38,120 --> 00:33:42,000
Speaker 3: Argue, because the seller has decided that she, in this case,

633
00:33:42,120 --> 00:33:46,360
Speaker 3: wants to keep the price low. She wants people, regular

634
00:33:46,400 --> 00:33:48,320
Speaker 3: people to be able to buy for forty nine ninety

635
00:33:48,360 --> 00:33:53,080
Speaker 3: nine hundred nine ninety dollars. So the middleman becomes a

636
00:33:53,160 --> 00:33:56,520
Speaker 3: problem that the market has to address. One way to

637
00:33:56,560 --> 00:34:00,000
Speaker 3: do that is ban resale. But then you get situations

638
00:34:00,160 --> 00:34:05,719
Speaker 3: where this was the London Olympics, where there were seats

639
00:34:05,760 --> 00:34:08,600
Speaker 3: that were empty to see some of the events, when

640
00:34:08,640 --> 00:34:11,879
Speaker 3: there are people standing outside the stadium who would desperately

641
00:34:12,200 --> 00:34:14,600
Speaker 3: want to get in, but because there's no way for

642
00:34:14,680 --> 00:34:17,640
Speaker 3: the tickets to be redistributed. Know, you end up with

643
00:34:17,680 --> 00:34:19,759
Speaker 3: empty seats, which is clearly inefficient.

644
00:34:19,840 --> 00:34:22,840
Speaker 2: Well, you could redistribute them at a ten percent markup

645
00:34:22,960 --> 00:34:25,960
Speaker 2: or something like that. So there's only once you can't

646
00:34:26,000 --> 00:34:26,799
Speaker 2: just go ten ten ten.

647
00:34:27,280 --> 00:34:29,319
Speaker 3: So this is the question is you know, how do

648
00:34:29,400 --> 00:34:31,319
Speaker 3: we innovate in this market? So in the book, I

649
00:34:31,320 --> 00:34:32,919
Speaker 3: have some ideas about how to do it. I think

650
00:34:33,000 --> 00:34:36,240
Speaker 3: one key problem with how a lot of live event

651
00:34:36,280 --> 00:34:39,919
Speaker 3: tickets are being allocated is that they're relying on first come,

652
00:34:39,920 --> 00:34:44,319
Speaker 3: first serve. First come, first serve races is the way

653
00:34:44,360 --> 00:34:46,959
Speaker 3: that we do it on the internet now, and that

654
00:34:47,640 --> 00:34:52,200
Speaker 3: allows the ticket brokers to program bots that will race

655
00:34:52,400 --> 00:34:57,359
Speaker 3: faster than any human can, and that is going to

656
00:34:57,400 --> 00:35:00,440
Speaker 3: mean that the folks who are building the bots with

657
00:35:00,480 --> 00:35:03,280
Speaker 3: the intention of getting a bunch of tickets and reselling

658
00:35:03,360 --> 00:35:06,560
Speaker 3: them are going to be at an advantage and be

659
00:35:06,600 --> 00:35:11,680
Speaker 3: able to extract surplus. The FTC sued Ticketmaster a few

660
00:35:11,680 --> 00:35:16,800
Speaker 3: months ago about this issue, basically letting their b bots

661
00:35:16,840 --> 00:35:18,399
Speaker 3: on the platform that extract.

662
00:35:18,040 --> 00:35:20,480
Speaker 2: Loading their own bots that then resell at higher price.

663
00:35:20,719 --> 00:35:22,400
Speaker 3: Well, this is part of the problem, which is the

664
00:35:23,719 --> 00:35:27,480
Speaker 3: secondary market platforms, the ones that are facilitating the trades

665
00:35:27,520 --> 00:35:31,239
Speaker 3: between the brokers or regular people who buy tickets, but

666
00:35:31,280 --> 00:35:34,240
Speaker 3: then you can't go and have to resell them. Those

667
00:35:34,640 --> 00:35:39,160
Speaker 3: platforms are benefiting from the sales. They get hefty fees.

668
00:35:39,200 --> 00:35:42,960
Speaker 3: I bought tickets recently, and my calculation was that Ticketmaster,

669
00:35:43,040 --> 00:35:45,759
Speaker 3: where I was reselling them, I was buying them and

670
00:35:45,800 --> 00:35:48,359
Speaker 3: then I had friends who canceled how to resell them,

671
00:35:48,719 --> 00:35:51,200
Speaker 3: was getting thirty percent of the transaction.

672
00:35:51,239 --> 00:35:54,520
Speaker 2: Probably you would think the artists would be the one

673
00:35:54,560 --> 00:35:59,759
Speaker 2: that should garner those gains. I've heard I love the

674
00:36:00,080 --> 00:36:04,080
Speaker 2: Russian crypto is a solution in search of a problem.

675
00:36:04,440 --> 00:36:06,880
Speaker 2: One would imagine that if the tickets and I'm not

676
00:36:07,120 --> 00:36:11,040
Speaker 2: a you know, bitcoin bro, but if you could sell

677
00:36:11,040 --> 00:36:13,920
Speaker 2: tickets on the blockchain, and there's a smart contract built

678
00:36:13,920 --> 00:36:17,040
Speaker 2: into that that the artist gets half of the resale price,

679
00:36:17,760 --> 00:36:19,640
Speaker 2: it changes dynamics there a lot.

680
00:36:19,760 --> 00:36:20,360
Speaker 4: So you could do that.

681
00:36:20,400 --> 00:36:23,120
Speaker 3: But of course, if the artists wanted more, they could

682
00:36:23,160 --> 00:36:25,839
Speaker 3: just raise the prices. Right, they don't need they don't

683
00:36:25,840 --> 00:36:29,600
Speaker 3: need the resale market to extract that surplus. Right the

684
00:36:29,640 --> 00:36:31,399
Speaker 3: what I describe in the book, it doesn't You don't

685
00:36:31,440 --> 00:36:33,719
Speaker 3: need to go all the way to the blockchain for it.

686
00:36:34,080 --> 00:36:38,879
Speaker 3: You do need names on tickets, meaning really yeah, because.

687
00:36:38,719 --> 00:36:40,799
Speaker 2: Right, so it's ticket and ID not just as.

688
00:36:41,360 --> 00:36:43,560
Speaker 3: But yeah, and it seems complicated because then it's like, oh,

689
00:36:43,560 --> 00:36:44,920
Speaker 3: I'm going to the it's like going to the airport.

690
00:36:44,960 --> 00:36:46,080
Speaker 3: I don't want to go to a concert to be

691
00:36:46,120 --> 00:36:48,520
Speaker 3: like going to an airport. Although last concert I went

692
00:36:48,560 --> 00:36:51,200
Speaker 3: to had to go through security anyway detector.

693
00:36:50,880 --> 00:36:52,920
Speaker 2: Of Madison Square Garden for a Knix game.

694
00:36:52,920 --> 00:36:55,759
Speaker 3: So exactly. But but no, your phone can identify who

695
00:36:55,760 --> 00:36:58,120
Speaker 3: you are. If I tap my tickets through my phone, right,

696
00:36:58,200 --> 00:37:00,760
Speaker 3: it can validate who I am. Facial recognition is getting

697
00:37:00,840 --> 00:37:05,440
Speaker 3: very good, and so you know, there's clear the service

698
00:37:05,480 --> 00:37:08,120
Speaker 3: you use at the airport, if that's something you've subscribed to,

699
00:37:08,239 --> 00:37:11,800
Speaker 3: they have similar style products that get.

700
00:37:11,719 --> 00:37:13,080
Speaker 4: Used at venues.

701
00:37:13,600 --> 00:37:15,960
Speaker 3: Major League Baseball has had a version of this that

702
00:37:16,000 --> 00:37:20,239
Speaker 3: they rolled out at some point. And so validating that

703
00:37:20,280 --> 00:37:23,120
Speaker 3: you are named on the ticket is easy to do

704
00:37:23,880 --> 00:37:24,720
Speaker 3: or getting easier.

705
00:37:25,400 --> 00:37:27,040
Speaker 4: If you don't have.

706
00:37:26,960 --> 00:37:31,239
Speaker 3: That, then you could put some cap on resale, but

707
00:37:31,360 --> 00:37:34,600
Speaker 3: then you know, it doesn't. Nothing stops anybody if they

708
00:37:34,600 --> 00:37:38,160
Speaker 3: have a physical ticket from doing what used to happen,

709
00:37:38,160 --> 00:37:40,480
Speaker 3: which is standing you know, outside the venue and selling

710
00:37:40,520 --> 00:37:43,240
Speaker 3: them or or selling them on some third party platform

711
00:37:43,239 --> 00:37:47,359
Speaker 3: that's not tracking how much more the ticket is you know,

712
00:37:47,400 --> 00:37:50,000
Speaker 3: being paid for right if it's in cash, there's no

713
00:37:50,040 --> 00:37:51,640
Speaker 3: way to validate that it's only ten percent.

714
00:37:52,360 --> 00:37:53,719
Speaker 4: And then the other thing you have to do, and

715
00:37:53,719 --> 00:37:54,520
Speaker 4: this is trickier.

716
00:37:55,120 --> 00:37:59,120
Speaker 3: It is a different type of change is get away

717
00:37:59,120 --> 00:38:02,560
Speaker 3: from first comfort because even if you have names on tickets,

718
00:38:02,960 --> 00:38:05,960
Speaker 3: but you're doing a first comfort serve race, the folks

719
00:38:06,000 --> 00:38:08,520
Speaker 3: who program the fastest bots are still going to be

720
00:38:08,560 --> 00:38:10,200
Speaker 3: able to extract surplus.

721
00:38:10,280 --> 00:38:15,120
Speaker 2: So could someone like Taylor Swift with an army of Swifties. Hey,

722
00:38:15,239 --> 00:38:18,520
Speaker 2: sign up here and it's two tickets per name, and

723
00:38:18,600 --> 00:38:21,520
Speaker 2: you have to be in their system for that long.

724
00:38:21,640 --> 00:38:24,560
Speaker 2: And so at least what is Madison Square Garden twenty

725
00:38:24,600 --> 00:38:27,840
Speaker 2: five thousand people? At least the first ten thousand tickets

726
00:38:28,080 --> 00:38:30,319
Speaker 2: are going to go to local people who are her

727
00:38:30,400 --> 00:38:30,919
Speaker 2: fan base.

728
00:38:31,080 --> 00:38:34,879
Speaker 3: So for the aristour that we started talking about, they

729
00:38:34,920 --> 00:38:40,840
Speaker 3: did something similar where they did a verified fan process.

730
00:38:40,880 --> 00:38:42,680
Speaker 3: We had a validate who you were, and then folks

731
00:38:42,760 --> 00:38:47,040
Speaker 3: came to the website if they were lucky enough to

732
00:38:47,360 --> 00:38:49,480
Speaker 3: after being verified, to win a lot. So they're still

733
00:38:49,520 --> 00:38:53,480
Speaker 3: a lottery, still a lottery. But then then fair lottery

734
00:38:53,520 --> 00:38:57,480
Speaker 3: among people that they thought were real folks rather than brokers,

735
00:38:58,160 --> 00:39:01,080
Speaker 3: and then you had to wait in a virtual queue

736
00:39:01,120 --> 00:39:04,440
Speaker 3: and wait for some people hours. So what ended up

737
00:39:04,440 --> 00:39:06,879
Speaker 3: happening was they claimed there were a lot of bot

738
00:39:06,920 --> 00:39:11,160
Speaker 3: attacks trying people that didn't have the verified fan code

739
00:39:11,160 --> 00:39:13,400
Speaker 3: that they needed to buy. The tickets were coming and

740
00:39:13,480 --> 00:39:15,960
Speaker 3: the systems ground to a halt and they crash and

741
00:39:15,960 --> 00:39:17,680
Speaker 3: people were waiting for hours, and so all of a

742
00:39:17,719 --> 00:39:19,840
Speaker 3: sudden you had to first come for a serve line

743
00:39:19,960 --> 00:39:22,560
Speaker 3: built into the system that was supposed to be a lottery,

744
00:39:23,000 --> 00:39:25,600
Speaker 3: where now you're going through an ordeal. We talked about,

745
00:39:25,640 --> 00:39:27,839
Speaker 3: you know, is the market easy. It's not easy. If

746
00:39:27,880 --> 00:39:30,240
Speaker 3: you have to wait online in front of your computer

747
00:39:30,320 --> 00:39:33,719
Speaker 3: for hours, it's almost as painful as having to wait

748
00:39:33,760 --> 00:39:36,080
Speaker 3: outside the box office, which we used to do for hours.

749
00:39:36,560 --> 00:39:39,279
Speaker 3: And so, you know, I think the solution is to

750
00:39:39,320 --> 00:39:43,080
Speaker 3: actually lean more on the lottery and basically say, look,

751
00:39:43,760 --> 00:39:46,000
Speaker 3: at some point, we're just going to have you put

752
00:39:46,000 --> 00:39:48,560
Speaker 3: your name in, say what your preferences are, what shows

753
00:39:48,600 --> 00:39:51,080
Speaker 3: you want to see her perform at, which sections you'd

754
00:39:51,080 --> 00:39:53,560
Speaker 3: be willing to buy tickets for, and you're going to

755
00:39:53,640 --> 00:39:55,759
Speaker 3: enter yourself in and we will tell you whether you won,

756
00:39:56,480 --> 00:39:57,759
Speaker 3: but you don't have to be there and pick the

757
00:39:57,800 --> 00:39:58,840
Speaker 3: specific seats.

758
00:39:59,200 --> 00:39:59,359
Speaker 4: Right.

759
00:39:59,360 --> 00:40:00,799
Speaker 3: You can say I want, I prefer to be in

760
00:40:00,800 --> 00:40:02,480
Speaker 3: the center, and I'm willing to pay more whatever. But

761
00:40:03,360 --> 00:40:06,800
Speaker 3: that would make the participation in the market way easier.

762
00:40:07,200 --> 00:40:09,840
Speaker 3: You could run, you know, whole tours or sections of

763
00:40:09,880 --> 00:40:13,440
Speaker 3: tours at once. You could reward folks who are more flexible.

764
00:40:13,480 --> 00:40:16,080
Speaker 3: If I'm willing to see Taylor Swift perform at any

765
00:40:16,200 --> 00:40:19,319
Speaker 3: venue you know on the East Coast, and I'll go

766
00:40:19,360 --> 00:40:21,839
Speaker 3: to any show and sit in any section, i am

767
00:40:21,960 --> 00:40:25,440
Speaker 3: revealing myself to be a very big swiftye that.

768
00:40:25,400 --> 00:40:27,600
Speaker 2: There is more a broker that wants to flip the ticket.

769
00:40:27,719 --> 00:40:29,560
Speaker 3: Well, but if the names are on it, then I'm

770
00:40:29,600 --> 00:40:31,640
Speaker 3: stuck if I have to return it. If I can't go,

771
00:40:31,719 --> 00:40:33,560
Speaker 3: I have to return it to the you know, to

772
00:40:33,600 --> 00:40:35,680
Speaker 3: Swift and she can give it to somebody on the waitlist,

773
00:40:36,280 --> 00:40:38,720
Speaker 3: but on the randomized waitlist, right like, you could design

774
00:40:38,800 --> 00:40:42,760
Speaker 3: the system to basically cut out the brokers altogether.

775
00:40:42,840 --> 00:40:45,880
Speaker 2: So the craziest thing about the brokers in the US,

776
00:40:46,280 --> 00:40:50,359
Speaker 2: I heard stories from several people who said, rather than

777
00:40:50,480 --> 00:40:53,719
Speaker 2: pay the markup in the US, it was cheaper to

778
00:40:53,880 --> 00:40:58,600
Speaker 2: secure tickets in Paris or London, fly over there, stay

779
00:40:58,600 --> 00:41:00,799
Speaker 2: in a hotel for a few days, go to the

780
00:41:00,840 --> 00:41:03,720
Speaker 2: show and go home. That was less expensive than paying

781
00:41:03,760 --> 00:41:07,120
Speaker 2: full boat to any of the seat geeks. Stub hub

782
00:41:07,520 --> 00:41:10,319
Speaker 2: middlemen that you know they charge with the market, will back.

783
00:41:10,360 --> 00:41:12,040
Speaker 3: They charge with the market, will bear. They don't add

784
00:41:12,040 --> 00:41:14,719
Speaker 3: anything to the production. And yeah it could be you know,

785
00:41:14,760 --> 00:41:16,480
Speaker 3: and you get a vacation out of it. You go

786
00:41:16,520 --> 00:41:17,600
Speaker 3: to Paris to see the show.

787
00:41:17,920 --> 00:41:21,480
Speaker 2: Coming up, we continue our conversation with Judd Kessler, professor

788
00:41:21,520 --> 00:41:25,240
Speaker 2: at the Wharton School, discussing Lucky by Design The Hidden

789
00:41:25,280 --> 00:41:27,920
Speaker 2: Economics You Need to Get More of what you Want.

790
00:41:28,320 --> 00:41:31,759
Speaker 2: I'm Barry Ridults. You're listening to Masters in Business on

791
00:41:31,880 --> 00:41:48,719
Speaker 2: Bloomberg Radio. I'm Burry Redults. You're listening to Masters in

792
00:41:48,760 --> 00:41:51,759
Speaker 2: Business on Bloomberg Radio. My extra special guest this week

793
00:41:51,800 --> 00:41:56,040
Speaker 2: is Professor Judd Kessler. He teaches behavioral economics and market

794
00:41:56,080 --> 00:42:00,239
Speaker 2: design at the Wharton School at University of Pennsylvania. His

795
00:42:00,280 --> 00:42:03,800
Speaker 2: new book, Lucky by Design, The Hidden Economics You Need

796
00:42:03,960 --> 00:42:06,240
Speaker 2: to Get More of what you Want is out now.

797
00:42:06,960 --> 00:42:09,440
Speaker 2: So let's talk a little bit about some things that

798
00:42:09,480 --> 00:42:15,400
Speaker 2: are lucky by Design. One of the things that slightly

799
00:42:15,440 --> 00:42:20,400
Speaker 2: before my time but certainly resonated was what took place

800
00:42:21,040 --> 00:42:25,600
Speaker 2: during Vietnam with the draft lottery. Some of the data

801
00:42:25,840 --> 00:42:29,480
Speaker 2: was pretty shocking. At the time African Americans made up

802
00:42:29,520 --> 00:42:33,120
Speaker 2: eleven percent of the population, they were twenty two percent

803
00:42:33,160 --> 00:42:37,880
Speaker 2: of the people that were drafted and twenty two percent

804
00:42:37,880 --> 00:42:43,040
Speaker 2: of the casualties, two x the representative population. Why did

805
00:42:43,080 --> 00:42:44,479
Speaker 2: that go down that way?

806
00:42:44,960 --> 00:42:48,359
Speaker 3: Yeah, So the situation that arises when you have these

807
00:42:48,400 --> 00:42:51,479
Speaker 3: hidden markets is whoever's in charge of the market gets

808
00:42:51,520 --> 00:42:56,040
Speaker 3: to pick the rules, and they might not be equitable

809
00:42:56,120 --> 00:43:02,440
Speaker 3: or efficient or easy. And the Vietnam draft, before the lottery,

810
00:43:02,480 --> 00:43:06,160
Speaker 3: which was introduced to kind of correct some of these issues,

811
00:43:07,320 --> 00:43:09,680
Speaker 3: had a bunch of loopholes in them. In the system,

812
00:43:09,800 --> 00:43:12,880
Speaker 3: so you could get a deferment for various reasons. You

813
00:43:12,920 --> 00:43:16,080
Speaker 3: could have some of your friends or family members, lobby

814
00:43:16,080 --> 00:43:19,040
Speaker 3: folks on the local draft boards that were going to

815
00:43:19,120 --> 00:43:22,600
Speaker 3: decide which men of that age were going to be

816
00:43:22,640 --> 00:43:23,040
Speaker 3: sent to be.

817
00:43:23,080 --> 00:43:25,440
Speaker 2: So if you were politically connected, you had a better shot.

818
00:43:25,520 --> 00:43:31,000
Speaker 3: Politically connected, wealthier, wider. Those were the things that let folks.

819
00:43:30,719 --> 00:43:34,160
Speaker 2: So you could have a medical disability, you could be

820
00:43:34,200 --> 00:43:36,200
Speaker 2: in college or grad school you.

821
00:43:36,120 --> 00:43:38,000
Speaker 4: Could go to the National Guard, so you would be

822
00:43:38,160 --> 00:43:38,880
Speaker 4: kind of stay.

823
00:43:38,640 --> 00:43:42,360
Speaker 2: Staty, and not everybody gets gets admitted to the National Guard.

824
00:43:42,200 --> 00:43:46,560
Speaker 3: Correct, So having a kind of way to circumvent what

825
00:43:46,600 --> 00:43:51,719
Speaker 3: would have been the task of going overseas. The loopholes

826
00:43:51,760 --> 00:43:54,399
Speaker 3: were kind of played by a few, and a bunch

827
00:43:54,400 --> 00:43:57,480
Speaker 3: of folks did not either have the means or have

828
00:43:57,560 --> 00:44:00,319
Speaker 3: the connections or kind of know that this was a

829
00:44:00,320 --> 00:44:01,680
Speaker 3: way out.

830
00:44:01,719 --> 00:44:05,840
Speaker 2: Did the lottery, And I learned a lot more than

831
00:44:05,880 --> 00:44:08,360
Speaker 2: I knew about it in the book. So they just

832
00:44:08,520 --> 00:44:11,760
Speaker 2: randomly picked birth dates, your month and day of birth,

833
00:44:12,239 --> 00:44:14,480
Speaker 2: and that was the order in which people were drafted.

834
00:44:14,880 --> 00:44:17,839
Speaker 2: How did that impact how things? Yeah, so they see

835
00:44:18,040 --> 00:44:19,640
Speaker 2: they did have the desired effects.

836
00:44:19,680 --> 00:44:21,400
Speaker 3: Well, I mean it had the desired effect. There were

837
00:44:21,440 --> 00:44:23,880
Speaker 3: still loopholes that you could use to get out of service,

838
00:44:23,920 --> 00:44:26,279
Speaker 3: but it had the desired effect of three hundred and

839
00:44:26,320 --> 00:44:30,000
Speaker 3: sixty six possible birth dates, including February twenty ninth, because

840
00:44:30,239 --> 00:44:32,680
Speaker 3: you didn't want to leave those folks out. You know,

841
00:44:32,719 --> 00:44:35,560
Speaker 3: they got pulled in order, they randomly when at a time,

842
00:44:35,719 --> 00:44:38,560
Speaker 3: and then the way the lottery worked was they were

843
00:44:38,680 --> 00:44:40,400
Speaker 3: just called down the number. So folks who were old

844
00:44:40,480 --> 00:44:43,000
Speaker 3: enough to remember this. At my father's generation, this was

845
00:44:43,040 --> 00:44:46,000
Speaker 3: a big deal and you know you wanted to have

846
00:44:46,080 --> 00:44:49,400
Speaker 3: a later you have your birth date picked later, so

847
00:44:49,440 --> 00:44:53,200
Speaker 3: you were less likely to be called now again, still loopholes.

848
00:44:53,320 --> 00:44:56,880
Speaker 3: But folks looking back history, you know, at history, say

849
00:44:57,239 --> 00:45:00,920
Speaker 3: this helped facilitate the anti war movement and that eventually

850
00:45:00,920 --> 00:45:04,120
Speaker 3: got the US out of Southeast Asia. Because when you

851
00:45:04,239 --> 00:45:07,880
Speaker 3: have well educated, wealthier kind of folks who have a

852
00:45:07,880 --> 00:45:12,920
Speaker 3: little bit more socioeconomic status, a little more power in society,

853
00:45:13,280 --> 00:45:17,200
Speaker 3: when their sons started getting called up and you know

854
00:45:17,239 --> 00:45:19,080
Speaker 3: this was a fair system and so it was harder

855
00:45:19,120 --> 00:45:23,000
Speaker 3: to get out. Then you saw a bunch more kind

856
00:45:23,040 --> 00:45:26,239
Speaker 3: of influential folks saying, no, no, this is not a war

857
00:45:26,280 --> 00:45:27,879
Speaker 3: we want to be in for the long term.

858
00:45:27,960 --> 00:45:28,160
Speaker 4: Huh.

859
00:45:28,280 --> 00:45:31,719
Speaker 2: Really really interesting. Let's talk about the area that you

860
00:45:31,760 --> 00:45:36,520
Speaker 2: spent so much of your career on, which is organ donation,

861
00:45:36,800 --> 00:45:41,600
Speaker 2: and what those rules look like. To start, I have

862
00:45:41,719 --> 00:45:45,480
Speaker 2: to ask about the numbers. There are one hundred thousand

863
00:45:45,480 --> 00:45:50,640
Speaker 2: Americans on an organ waiting list, organ transplant waiting list.

864
00:45:51,080 --> 00:45:54,000
Speaker 2: I was shocked to read of that one hundred thousand,

865
00:45:54,320 --> 00:45:56,080
Speaker 2: ninety percent or waiting for a kidney.

866
00:45:56,120 --> 00:45:56,960
Speaker 5: That's amazing.

867
00:45:57,320 --> 00:46:00,799
Speaker 3: Yeah, I mean, so the reason for that is that

868
00:46:01,360 --> 00:46:03,319
Speaker 3: there is dialysis.

869
00:46:02,560 --> 00:46:04,759
Speaker 5: For kidney fails, which can keep you alive.

870
00:46:04,520 --> 00:46:08,000
Speaker 3: For five or more years. I mean, you want to

871
00:46:08,000 --> 00:46:11,480
Speaker 3: get a kidney as soon as possible. Dialysis is time intensive,

872
00:46:11,520 --> 00:46:15,279
Speaker 3: it's painful, it's expensive, although the cost is borne by

873
00:46:15,320 --> 00:46:19,800
Speaker 3: Medicare primarily after a certain number of months that your insurance,

874
00:46:20,080 --> 00:46:22,720
Speaker 3: your private insurance covers it and then it gets handed

875
00:46:22,719 --> 00:46:25,319
Speaker 3: off to Medicare. So there's a lot of costs that

876
00:46:25,360 --> 00:46:27,080
Speaker 3: go into it. But of course, if you don't have

877
00:46:27,160 --> 00:46:31,400
Speaker 3: a kidney that you can get as a recipient, if

878
00:46:31,400 --> 00:46:34,000
Speaker 3: there isn't a donor kidney available, this is your only option.

879
00:46:34,440 --> 00:46:37,400
Speaker 3: But this is why the waiting list for kidneys is

880
00:46:37,440 --> 00:46:40,040
Speaker 3: so long, because folks can kind of wait for an

881
00:46:40,040 --> 00:46:43,359
Speaker 3: extended period of time. Think about another organ, like a liver,

882
00:46:43,640 --> 00:46:45,920
Speaker 3: there is no dialysis, so at some point, if your

883
00:46:45,960 --> 00:46:49,279
Speaker 3: liver function gets bad enough, you either need a transplant

884
00:46:49,480 --> 00:46:54,120
Speaker 3: or that's it. So this is why the kidney list,

885
00:46:54,800 --> 00:46:56,720
Speaker 3: you know, goes on for so long. But it's also

886
00:46:57,160 --> 00:47:02,000
Speaker 3: a major financial burden in addition to all the emotional

887
00:47:02,040 --> 00:47:05,160
Speaker 3: and physical burden that the folks, the patients in their

888
00:47:05,160 --> 00:47:10,040
Speaker 3: families face That estimate suggests that it's basically one percent

889
00:47:10,840 --> 00:47:15,640
Speaker 3: of the federal budget is spent on end stage renal disease,

890
00:47:15,760 --> 00:47:19,239
Speaker 3: on on folks who have kidney failure because Medicare is

891
00:47:19,280 --> 00:47:23,080
Speaker 3: covering it. You know, medicare big chunk of the federal budget,

892
00:47:23,120 --> 00:47:26,520
Speaker 3: and this in particular, this line item is very expensive.

893
00:47:26,640 --> 00:47:30,160
Speaker 2: And the crazy thing about this another data point that

894
00:47:30,239 --> 00:47:35,080
Speaker 2: shot me is sometimes an imperfect match shows up and

895
00:47:35,120 --> 00:47:37,719
Speaker 2: you have the option of you know, rolling it over

896
00:47:37,760 --> 00:47:40,400
Speaker 2: and saying I'll wait for the next one. Twenty percent

897
00:47:40,440 --> 00:47:43,200
Speaker 2: of the donated kidneys are just thrown away.

898
00:47:43,680 --> 00:47:47,239
Speaker 3: This is the nature of these first come, first serve

899
00:47:47,320 --> 00:47:50,680
Speaker 3: waiting lists. So you know, there's when an organ becomes available,

900
00:47:50,880 --> 00:47:54,080
Speaker 3: there's a list that's generated. It's based on how close

901
00:47:54,120 --> 00:47:56,960
Speaker 3: you are to the transplant center, on the medical match

902
00:47:57,080 --> 00:48:00,319
Speaker 3: between the organ donor and the recipient. So there's a

903
00:48:00,360 --> 00:48:02,799
Speaker 3: bunch of considerations that come in, including how long you've

904
00:48:02,840 --> 00:48:06,000
Speaker 3: been waiting. So if you've been waiting many years on dialysis,

905
00:48:06,000 --> 00:48:08,399
Speaker 3: you're going to be closer to the top of the list.

906
00:48:08,800 --> 00:48:11,680
Speaker 3: So an organ becomes available and you, as the patient,

907
00:48:12,200 --> 00:48:15,000
Speaker 3: with the help of your doctor, have to decide is

908
00:48:15,040 --> 00:48:16,799
Speaker 3: this going to be an organ I take or not.

909
00:48:18,000 --> 00:48:20,880
Speaker 3: Some of the information about whether it's a good match

910
00:48:20,880 --> 00:48:24,440
Speaker 3: for you we only can learn after the organ has

911
00:48:24,480 --> 00:48:27,840
Speaker 3: been removed from the donor who's you know, passed away.

912
00:48:28,520 --> 00:48:32,279
Speaker 3: And so now we have testing that's getting done on

913
00:48:32,360 --> 00:48:33,560
Speaker 3: the organ and there's.

914
00:48:33,400 --> 00:48:35,080
Speaker 4: Only so much time.

915
00:48:35,280 --> 00:48:37,880
Speaker 3: Yeah you so you know, we have this testing getting

916
00:48:37,920 --> 00:48:40,600
Speaker 3: done and folks are getting are learning all right, this

917
00:48:40,840 --> 00:48:42,640
Speaker 3: it could be this organ that you take, or we

918
00:48:42,640 --> 00:48:44,239
Speaker 3: can keep waiting and if you're at the top of

919
00:48:44,280 --> 00:48:47,080
Speaker 3: the list, maybe you get offered a bunch of organs.

920
00:48:47,120 --> 00:48:50,920
Speaker 3: And so you know, it is hard to get through

921
00:48:51,000 --> 00:48:54,120
Speaker 3: the whole list of you know, you know, ninety thousand

922
00:48:54,160 --> 00:48:56,359
Speaker 3: folks waiting for the organ, and so you know, if

923
00:48:56,360 --> 00:48:59,200
Speaker 3: the organ's not great and we can't identify somebody who

924
00:48:59,360 --> 00:49:01,840
Speaker 3: might take it, a way to suscort it.

925
00:49:01,920 --> 00:49:05,200
Speaker 2: As kind of fascinated by the example you describe with

926
00:49:05,320 --> 00:49:09,759
Speaker 2: what they do in Israel that if you check I'm

927
00:49:09,800 --> 00:49:14,160
Speaker 2: willing to be an organ donor three years prior, you're

928
00:49:14,239 --> 00:49:18,239
Speaker 2: higher on the recipient list. The theory being, hey, if

929
00:49:18,280 --> 00:49:22,680
Speaker 2: everybody understands this is that many more organs available for transplant.

930
00:49:23,200 --> 00:49:26,680
Speaker 2: How has that tested out in real life? And are

931
00:49:26,800 --> 00:49:30,560
Speaker 2: any states here putting them into work.

932
00:49:30,640 --> 00:49:34,840
Speaker 3: This was the research that got me into market design.

933
00:49:34,920 --> 00:49:37,680
Speaker 3: In particular, I was working with al we started thinking

934
00:49:37,680 --> 00:49:40,600
Speaker 3: about organs. It has this nice feature of being a

935
00:49:40,600 --> 00:49:43,880
Speaker 3: public good. If I agreed a register as an organ donor,

936
00:49:44,840 --> 00:49:47,880
Speaker 3: hopefully I won't be in this situation, but my organs

937
00:49:47,880 --> 00:49:50,520
Speaker 3: are available for transplant if I die in a way

938
00:49:50,280 --> 00:49:55,359
Speaker 3: that makes them available. When folks agreed a register, they

939
00:49:55,400 --> 00:50:00,719
Speaker 3: are making this scarce resource the organ They're making it

940
00:50:00,800 --> 00:50:03,839
Speaker 3: be less scarce. There are more organs available in expectation

941
00:50:04,160 --> 00:50:08,280
Speaker 3: if folks are registered. So a bunch of countries, including Israel,

942
00:50:08,360 --> 00:50:13,160
Speaker 3: but also Chile, China and Singapore have built into their

943
00:50:13,200 --> 00:50:18,240
Speaker 3: allocation rules an incentive to get people to provide this resource,

944
00:50:18,239 --> 00:50:20,640
Speaker 3: to make it less scarce. And as you said, they

945
00:50:21,480 --> 00:50:23,960
Speaker 3: in Israel it's three years. But you can think about

946
00:50:24,160 --> 00:50:27,239
Speaker 3: doing it different ways. Where if you're eighteen years old

947
00:50:27,320 --> 00:50:29,799
Speaker 3: and you're going to the DMV and it's the first

948
00:50:29,800 --> 00:50:31,400
Speaker 3: time you've ever been asked do you want to be

949
00:50:31,440 --> 00:50:36,319
Speaker 3: an organ donor? You are rewarded if you say yes,

950
00:50:37,800 --> 00:50:40,040
Speaker 3: insofar as you know fifty years later, if you end

951
00:50:40,120 --> 00:50:42,799
Speaker 3: up needing a kidney, you're going to get priority over

952
00:50:42,880 --> 00:50:45,080
Speaker 3: someone that's kind of in the same situation as you,

953
00:50:45,200 --> 00:50:46,960
Speaker 3: but when they were eighteen, said no, no, no, I don't

954
00:50:47,000 --> 00:50:50,000
Speaker 3: want to help out other people. And so we saw

955
00:50:50,080 --> 00:50:53,600
Speaker 3: when we did our research that this incentive of being

956
00:50:53,920 --> 00:50:56,680
Speaker 3: given the option to be a donor so that you

957
00:50:56,719 --> 00:51:00,000
Speaker 3: can have priority later on induced a lot more people

958
00:51:00,360 --> 00:51:03,359
Speaker 3: to say they wanted to donate in a game that

959
00:51:03,440 --> 00:51:06,000
Speaker 3: was modeled on that decision. We looked at Israel and

960
00:51:06,040 --> 00:51:10,080
Speaker 3: we saw when Israel implemented it, our estimates suggests about

961
00:51:10,080 --> 00:51:12,800
Speaker 3: one hundred thousand more people. And Israel's a small country,

962
00:51:12,920 --> 00:51:15,799
Speaker 3: so it's a lot. Yeah, it's a lot signed up

963
00:51:15,960 --> 00:51:19,480
Speaker 3: in the run up, you know, kind of before they

964
00:51:19,560 --> 00:51:21,640
Speaker 3: announced it, and they were letting people sign up, and

965
00:51:22,200 --> 00:51:24,480
Speaker 3: it was the date when if you signed up before,

966
00:51:24,480 --> 00:51:27,239
Speaker 3: you'd immediately have priority. Otherwise you'd have to wait the

967
00:51:27,280 --> 00:51:29,480
Speaker 3: three years to avoid I mean the three years, by

968
00:51:29,480 --> 00:51:32,319
Speaker 3: the way, is to avoid a loophole where I get

969
00:51:32,360 --> 00:51:33,880
Speaker 3: sick and need a kidney and I go sign a

970
00:51:33,880 --> 00:51:36,200
Speaker 3: donor card and then I have priority that would totally

971
00:51:36,239 --> 00:51:38,880
Speaker 3: underline purpose, which we have research showing that you know

972
00:51:39,000 --> 00:51:41,160
Speaker 3: that it would in fact undermine it. So yeah, so

973
00:51:41,200 --> 00:51:43,560
Speaker 3: then they they implemented it, and it seems to work.

974
00:51:44,120 --> 00:51:46,640
Speaker 2: Have any places in the US adopted this.

975
00:51:46,800 --> 00:51:48,640
Speaker 3: No, so it would have to be this is not

976
00:51:48,719 --> 00:51:52,440
Speaker 3: a state by state thing. That Oregon allocation systems are national,

977
00:51:52,560 --> 00:51:55,160
Speaker 3: and so you would need the country as a whole

978
00:51:55,320 --> 00:51:59,200
Speaker 3: the to have a change in the allocation rules. So

979
00:51:59,719 --> 00:52:02,239
Speaker 3: I have been advocating for that since we did that

980
00:52:02,280 --> 00:52:05,040
Speaker 3: research over a decade ago, but you know, we have

981
00:52:05,120 --> 00:52:08,640
Speaker 3: not yet had movement on that, although I remain perennially

982
00:52:08,680 --> 00:52:12,640
Speaker 3: optimistic because it's been you know, many years, and the

983
00:52:12,640 --> 00:52:15,200
Speaker 3: problem isn't getting better. So basically, anything we can do

984
00:52:15,920 --> 00:52:18,680
Speaker 3: to make this scarce resource less scarce is valuable.

985
00:52:18,960 --> 00:52:22,960
Speaker 2: I'm recalling Richard Thaylor's book Nudge. I think he co

986
00:52:23,000 --> 00:52:26,560
Speaker 2: wrote that with Cas Sunstein, And there was questions about

987
00:52:26,600 --> 00:52:30,160
Speaker 2: opt out and opt in. In other words, if everybody

988
00:52:30,200 --> 00:52:33,120
Speaker 2: by default is an organ donor, but you have to

989
00:52:33,160 --> 00:52:35,960
Speaker 2: opt out. The reasons people don't want to do that

990
00:52:36,920 --> 00:52:40,240
Speaker 2: religious reasons and other But is that a potential?

991
00:52:40,520 --> 00:52:41,600
Speaker 4: So that was.

992
00:52:43,040 --> 00:52:45,680
Speaker 3: That was like a great hope of behavioral economics, was

993
00:52:45,680 --> 00:52:49,799
Speaker 3: that these kinds of nudges in choice arrket. Yeah, so

994
00:52:49,880 --> 00:52:52,239
Speaker 3: this is a case. There are many places where it works. Well,

995
00:52:52,280 --> 00:52:54,600
Speaker 3: this is a case where it doesn't. And the reason

996
00:52:54,640 --> 00:52:57,839
Speaker 3: it doesn't is that, you know, if you are say

997
00:52:57,840 --> 00:53:00,080
Speaker 3: it's an opt out system, so I should pause and

998
00:53:00,080 --> 00:53:01,560
Speaker 3: say we don't. We can't do that in the US

999
00:53:01,600 --> 00:53:05,719
Speaker 3: without a major law change because oregons fall under the

1000
00:53:05,760 --> 00:53:08,560
Speaker 3: Gift Act, So you know, you have to actually make

1001
00:53:08,600 --> 00:53:11,160
Speaker 3: an affirmative statement that you want. You know, you could

1002
00:53:11,160 --> 00:53:13,319
Speaker 3: make it salvage law. So if you're not using it,

1003
00:53:13,400 --> 00:53:15,840
Speaker 3: we can take it, right, But that's people might not

1004
00:53:15,880 --> 00:53:17,920
Speaker 3: want to do that. But but in the in the

1005
00:53:17,920 --> 00:53:21,360
Speaker 3: countries that use these opt out systems, the next of

1006
00:53:21,440 --> 00:53:24,560
Speaker 3: kin are still consulted, right, so you know, what the

1007
00:53:25,360 --> 00:53:27,040
Speaker 3: next of kin are told is that the person did

1008
00:53:27,080 --> 00:53:30,200
Speaker 3: not opt out and the defaults basically to the next

1009
00:53:30,239 --> 00:53:33,400
Speaker 3: of kin to decide. And so what the research shows

1010
00:53:33,440 --> 00:53:36,400
Speaker 3: is that there just isn't a delta between the countries

1011
00:53:36,400 --> 00:53:38,920
Speaker 3: that use opt in and the countries that use opt out.

1012
00:53:38,760 --> 00:53:42,000
Speaker 2: Because it always goes to that affirmative decision.

1013
00:53:42,239 --> 00:53:44,560
Speaker 3: Yeah, and when when you know I've opted in, I

1014
00:53:44,600 --> 00:53:46,440
Speaker 3: mean I might be special because I talk about it

1015
00:53:46,520 --> 00:53:49,160
Speaker 3: a lot, right, But but if you've opted in, then

1016
00:53:49,200 --> 00:53:51,360
Speaker 3: the next of kin see that and they know that

1017
00:53:51,400 --> 00:53:53,319
Speaker 3: it was your wish right, you did, you said at

1018
00:53:53,320 --> 00:53:56,080
Speaker 3: some point, yes, I want to register. They know that

1019
00:53:56,080 --> 00:53:58,719
Speaker 3: that you want to do that, and so if you

1020
00:53:58,760 --> 00:54:01,439
Speaker 3: pass away, they know that they should donate your organs,

1021
00:54:01,440 --> 00:54:04,319
Speaker 3: so they're not going to stand in the way. It is,

1022
00:54:04,400 --> 00:54:06,920
Speaker 3: you know, a binding agreement to be an organ donor.

1023
00:54:06,920 --> 00:54:10,680
Speaker 3: But of course if the next of kin don't want it, right,

1024
00:54:11,000 --> 00:54:13,280
Speaker 3: they're the only ones who are left around to sue

1025
00:54:13,280 --> 00:54:16,440
Speaker 3: the doctors. Right so so so, but it ends up

1026
00:54:16,440 --> 00:54:19,080
Speaker 3: being the case if you register. You know, the vast

1027
00:54:19,080 --> 00:54:22,480
Speaker 3: majority of folks who register have their organs recovered.

1028
00:54:22,560 --> 00:54:25,480
Speaker 2: I found a lot of the book had really surprising

1029
00:54:26,160 --> 00:54:30,360
Speaker 2: themes and data doing your research. What's the thing that

1030
00:54:30,440 --> 00:54:34,160
Speaker 2: surprised you most about market design? What sort of things

1031
00:54:34,200 --> 00:54:37,040
Speaker 2: did you go, huh that that doesn't make any sense.

1032
00:54:38,000 --> 00:54:39,600
Speaker 3: So at the end of the book I talk about

1033
00:54:39,600 --> 00:54:41,960
Speaker 3: you know, how you are a market designer. We've talked

1034
00:54:42,000 --> 00:54:44,120
Speaker 3: about you as a market participant. We've talked about others

1035
00:54:44,160 --> 00:54:48,239
Speaker 3: as market designers. But hidden market is one where there's

1036
00:54:48,239 --> 00:54:52,840
Speaker 3: a scarce resource that needs to get allocated without prices

1037
00:54:52,880 --> 00:54:56,080
Speaker 3: being what determines who gets what. And if you think

1038
00:54:56,080 --> 00:54:58,239
Speaker 3: about it that way, you are a market designer for

1039
00:54:58,360 --> 00:55:01,000
Speaker 3: things like your time and attention, which lots of people want.

1040
00:55:01,040 --> 00:55:03,279
Speaker 3: They want to have you respond to their emails. They

1041
00:55:03,280 --> 00:55:04,920
Speaker 3: want to get on your calendar, and you have to

1042
00:55:04,960 --> 00:55:08,480
Speaker 3: decide who you serve and who you don't. That's the preface.

1043
00:55:08,640 --> 00:55:11,240
Speaker 3: The thing that I learned was a set of market

1044
00:55:11,280 --> 00:55:14,000
Speaker 3: rules that I thought made no sense, which was how

1045
00:55:14,040 --> 00:55:17,759
Speaker 3: we used to allocate water from the Colorado River. So

1046
00:55:18,080 --> 00:55:22,719
Speaker 3: for many years the rule was first in time, first

1047
00:55:22,800 --> 00:55:27,680
Speaker 3: and right, which meant that the first folks to tap

1048
00:55:27,719 --> 00:55:29,960
Speaker 3: the river to take water out to divert for their

1049
00:55:29,960 --> 00:55:33,880
Speaker 3: own purposes kind of always got their allotment. So that

1050
00:55:33,960 --> 00:55:36,520
Speaker 3: was California in nineteen oh one, where they diverted water

1051
00:55:36,560 --> 00:55:41,520
Speaker 3: from the river to turn a desert into farmland. And

1052
00:55:41,600 --> 00:55:46,080
Speaker 3: then decades later, when there was a drought and there

1053
00:55:46,120 --> 00:55:50,120
Speaker 3: was less water coming down the Colorado, the California got

1054
00:55:50,160 --> 00:55:53,439
Speaker 3: to keep the exact same allotment, and folks who tapped

1055
00:55:53,480 --> 00:55:56,160
Speaker 3: the river later, like the city of Phoenix, Arizona, which

1056
00:55:56,160 --> 00:55:59,080
Speaker 3: in nineteen oh one was ten or fifteen thousand people

1057
00:55:59,120 --> 00:56:01,680
Speaker 3: but it's now a million and a half, they had

1058
00:56:01,680 --> 00:56:03,560
Speaker 3: to cut back, even though for them it was drinking

1059
00:56:03,600 --> 00:56:05,799
Speaker 3: water and for California it was you know, to grow

1060
00:56:05,840 --> 00:56:09,200
Speaker 3: alfalfa to feed to livestock. So I looked at that

1061
00:56:09,200 --> 00:56:12,480
Speaker 3: and I thought, oh man, what a terrible It's not efficient,

1062
00:56:12,560 --> 00:56:15,879
Speaker 3: it's not equitable. The race that determined who got what

1063
00:56:16,040 --> 00:56:20,600
Speaker 3: was run, you know, a century ago. And so you know,

1064
00:56:20,640 --> 00:56:22,759
Speaker 3: I'm thinking of feeling like, oh man, isn't it great

1065
00:56:22,800 --> 00:56:25,160
Speaker 3: that we don't use these kinds of systems anymore? And

1066
00:56:25,200 --> 00:56:27,880
Speaker 3: then I looked at my calendar and I saw my

1067
00:56:28,000 --> 00:56:30,960
Speaker 3: recurring meetings on there, and I thought, that's first and

1068
00:56:31,000 --> 00:56:33,120
Speaker 3: time first, and right I'm doing what they were doing

1069
00:56:33,120 --> 00:56:35,239
Speaker 3: with the Colorado river a meeting that I put on

1070
00:56:35,239 --> 00:56:38,520
Speaker 3: my calendar, you know, two years ago that takes Thursday

1071
00:56:38,600 --> 00:56:42,000
Speaker 3: at eleven am. Every week Thursday at eleven am. Is

1072
00:56:42,040 --> 00:56:45,719
Speaker 3: being allocated to you know, this this project, even if

1073
00:56:45,760 --> 00:56:49,320
Speaker 3: it's not the most efficient or equitable use of my time.

1074
00:56:50,000 --> 00:56:52,319
Speaker 3: And I realized, like, oh man, you know, there's some

1075
00:56:52,320 --> 00:56:55,000
Speaker 3: stuff that's sacrosanct, like my teaching, but a lot of

1076
00:56:55,000 --> 00:56:59,480
Speaker 3: these recurring meetings, it's not adhering to the efficiency and

1077
00:56:59,480 --> 00:57:03,239
Speaker 3: equity standards that I would want for my allocations.

1078
00:57:03,360 --> 00:57:06,920
Speaker 2: Huh. Having read the book, I keep coming across things.

1079
00:57:07,600 --> 00:57:09,880
Speaker 2: As I was reading it. I was thinking about different things.

1080
00:57:10,320 --> 00:57:13,000
Speaker 2: And then either yesterday or this morning, I saw a

1081
00:57:13,000 --> 00:57:17,080
Speaker 2: Wall Street Journal piece the new mayor elect in New

1082
00:57:17,160 --> 00:57:21,040
Speaker 2: York once the freeze prices not just on apartments but

1083
00:57:21,160 --> 00:57:24,439
Speaker 2: at Yankee Stadium on hot dogs and beer. And there's

1084
00:57:24,600 --> 00:57:28,080
Speaker 2: a couple of interesting issues that, hey, are people going

1085
00:57:28,160 --> 00:57:31,520
Speaker 2: to get too drunk? But some other stadiums have done

1086
00:57:31,520 --> 00:57:35,320
Speaker 2: this and it's worked out really well. When we look

1087
00:57:35,360 --> 00:57:38,680
Speaker 2: at price controls, how do you think about rent control

1088
00:57:38,720 --> 00:57:43,880
Speaker 2: for apartments or capping the price of hot dogs? Costco

1089
00:57:44,120 --> 00:57:47,360
Speaker 2: very famously has the dollar fifty hot dog for them

1090
00:57:47,480 --> 00:57:50,640
Speaker 2: thirty six years. How do you think about those different

1091
00:57:50,720 --> 00:57:55,160
Speaker 2: price mechanisms really as a form of branding or marketing.

1092
00:57:55,480 --> 00:57:58,040
Speaker 3: Yeah, I mean I love the Costco hot dog, so

1093
00:57:58,040 --> 00:58:00,000
Speaker 3: I can see the branding of marketing, you know, benefit

1094
00:58:00,040 --> 00:58:03,320
Speaker 3: it's there. I have thought a bunch about this because

1095
00:58:04,560 --> 00:58:07,960
Speaker 3: that has the potential to create hidden markets or exacerbate

1096
00:58:08,000 --> 00:58:10,760
Speaker 3: hidden markets that are already there. So I recently wrote

1097
00:58:10,760 --> 00:58:14,280
Speaker 3: a piece about affordable housing lotteries. So this is in

1098
00:58:14,320 --> 00:58:17,960
Speaker 3: New York City and a bunch of major cities around

1099
00:58:18,000 --> 00:58:22,160
Speaker 3: the world. Do this where a new development will be

1100
00:58:22,200 --> 00:58:27,200
Speaker 3: built and you'll have thirty percent of the units that

1101
00:58:27,240 --> 00:58:30,919
Speaker 3: are built are going to be designated affordable, meaning folks

1102
00:58:30,960 --> 00:58:32,840
Speaker 3: are only expected to spend thirty percent.

1103
00:58:32,560 --> 00:58:33,760
Speaker 4: Of their income on housing.

1104
00:58:35,040 --> 00:58:39,640
Speaker 3: But there are so many people who are finding rents

1105
00:58:39,680 --> 00:58:41,400
Speaker 3: hard to bear in New York City and these other

1106
00:58:41,440 --> 00:58:45,840
Speaker 3: big cities that the lotteries get flooded. So in a

1107
00:58:46,400 --> 00:58:51,200
Speaker 3: last full year, there were about six million applicants for

1108
00:58:51,240 --> 00:58:54,880
Speaker 3: about ten thousand units. So each lottery entry has a

1109
00:58:54,920 --> 00:58:59,320
Speaker 3: one and six hundred chance of winning, and so as

1110
00:58:59,320 --> 00:59:02,080
Speaker 3: a result, folks are kind of constantly applying to these

1111
00:59:02,120 --> 00:59:04,440
Speaker 3: lotteries because that's the only way that you're gonna have

1112
00:59:04,440 --> 00:59:06,000
Speaker 3: a chance of getting something, is if you're applying to

1113
00:59:06,000 --> 00:59:09,960
Speaker 3: every possible lottery. But then there's all these inefficiencies that

1114
00:59:10,000 --> 00:59:12,400
Speaker 3: can crop up. Maybe I win a lottery, I get

1115
00:59:12,400 --> 00:59:16,080
Speaker 3: really lucky, but it's it's not in my desirable, desired neighborhood.

1116
00:59:16,600 --> 00:59:18,160
Speaker 3: It was still a good lottery to enter. It is

1117
00:59:18,200 --> 00:59:21,080
Speaker 3: better to get affordable housing than not. Maybe you win

1118
00:59:21,560 --> 00:59:24,400
Speaker 3: in the neighborhood that I want, there's no way, but

1119
00:59:24,440 --> 00:59:26,160
Speaker 3: there's no way for us to switch, right, It's kind

1120
00:59:26,160 --> 00:59:28,800
Speaker 3: of like golden handcuffs. If you forgetting an affordable place,

1121
00:59:29,440 --> 00:59:32,120
Speaker 3: and you know the same thing with freezing rent, where

1122
00:59:32,520 --> 00:59:35,640
Speaker 3: you know, the folks who are in rent controlled or

1123
00:59:35,640 --> 00:59:38,640
Speaker 3: rent stabilized apartment that you know are going to be

1124
00:59:38,720 --> 00:59:42,000
Speaker 3: able to kind of keep paying that low rate. You know,

1125
00:59:42,040 --> 00:59:45,640
Speaker 3: that's great for them, but that doesn't solve the bigger problem.

1126
00:59:45,680 --> 00:59:45,800
Speaker 4: Right.

1127
00:59:45,840 --> 00:59:50,080
Speaker 3: It's it's affordability for the lucky few, but not for

1128
00:59:50,320 --> 00:59:51,880
Speaker 3: other folks who are moving to the city for the

1129
00:59:51,920 --> 00:59:54,880
Speaker 3: first time and want to make a life here. And

1130
00:59:54,960 --> 00:59:58,800
Speaker 3: so you know, I can see why folks are eager

1131
00:59:58,840 --> 01:00:02,280
Speaker 3: to do that, but it's hard to think about how

1132
01:00:02,280 --> 01:00:06,880
Speaker 3: to solve that problem without broader changes, right.

1133
01:00:06,920 --> 01:00:10,640
Speaker 2: It doesn't move the needle on the broader underlying problem.

1134
01:00:11,040 --> 01:00:14,240
Speaker 2: It just for that handful. It kind of raises the

1135
01:00:14,320 --> 01:00:19,160
Speaker 2: issue of nimbiism and just not building well. Since the

1136
01:00:19,840 --> 01:00:25,680
Speaker 2: Great Financial Crisis, we've wildly underbuilt single family homes. Affordable

1137
01:00:25,720 --> 01:00:28,360
Speaker 2: housing go down the list. The focus has been on

1138
01:00:28,440 --> 01:00:31,920
Speaker 2: luxury properties because hey, there's the most amount of economic

1139
01:00:31,960 --> 01:00:35,320
Speaker 2: benefit for the builders to put their time and energy.

1140
01:00:35,360 --> 01:00:38,040
Speaker 3: But this is definitely, you know, a supply and demand problem.

1141
01:00:38,200 --> 01:00:40,680
Speaker 3: If you want to bring costs down, you have to

1142
01:00:40,720 --> 01:00:43,280
Speaker 3: increase supply. And you know, as an economist, we are

1143
01:00:43,320 --> 01:00:47,080
Speaker 3: trained to kind of think one step further. So I'd

1144
01:00:47,120 --> 01:00:49,640
Speaker 3: have to read the specific policy about the you know,

1145
01:00:50,040 --> 01:00:54,120
Speaker 3: Yankee Stadium concessions, right, but my first instinct would be, Oh,

1146
01:00:54,160 --> 01:00:57,880
Speaker 3: if you cap the price of concessions, the logical next

1147
01:00:57,880 --> 01:00:59,720
Speaker 3: step is that the ticket price goes up a little bit.

1148
01:00:59,800 --> 01:01:00,000
Speaker 4: Right.

1149
01:01:00,120 --> 01:01:02,200
Speaker 3: It's like, now, all of a sudden, it's cheaper to

1150
01:01:02,240 --> 01:01:04,720
Speaker 3: go to you know, to have the full night out

1151
01:01:04,880 --> 01:01:07,240
Speaker 3: at the you know, city Field or at Yankee Stadium.

1152
01:01:07,880 --> 01:01:09,680
Speaker 3: And so, you know, is there also going to be

1153
01:01:09,720 --> 01:01:12,440
Speaker 3: a price control on the ticket or or not? And

1154
01:01:12,520 --> 01:01:15,360
Speaker 3: then are we actually getting the gains that we want

1155
01:01:15,600 --> 01:01:18,720
Speaker 3: or you know, are we are we getting nice sound bites.

1156
01:01:18,760 --> 01:01:23,480
Speaker 2: The thing about constructing your own market design is kind

1157
01:01:23,520 --> 01:01:26,600
Speaker 2: of interesting. I have a friend Dave, who comes to

1158
01:01:26,720 --> 01:01:30,320
Speaker 2: New York a couple of times a quarter, works in finance,

1159
01:01:30,360 --> 01:01:33,760
Speaker 2: comes down from the Berkshires, and since he's one person

1160
01:01:34,040 --> 01:01:39,680
Speaker 2: and doesn't care which Broadway show he goes to, his

1161
01:01:40,160 --> 01:01:43,600
Speaker 2: market design hack is he waits to depending on the day,

1162
01:01:43,640 --> 01:01:46,120
Speaker 2: five minutes to seven or five minutes to eight, the

1163
01:01:46,200 --> 01:01:50,840
Speaker 2: price's plummet. Yes, they're about to expire worthless. Oh, Hamilton

1164
01:01:50,960 --> 01:01:54,040
Speaker 2: for half price, let's go wicked half price and he's

1165
01:01:54,080 --> 01:01:58,960
Speaker 2: seen half the stuff on Broadway at shockingly reasonable prices.

1166
01:01:59,000 --> 01:02:01,760
Speaker 2: So as I was reading this, I thought about that,

1167
01:02:02,280 --> 01:02:05,640
Speaker 2: and then I just had an experience up in Newport

1168
01:02:05,720 --> 01:02:09,520
Speaker 2: my first time visiting. You wrote about this in the book,

1169
01:02:09,760 --> 01:02:12,920
Speaker 2: about the restaurant reservations in the waiting list. So we

1170
01:02:12,960 --> 01:02:15,680
Speaker 2: stayed at this hotel and there's a super hot restaurant

1171
01:02:15,760 --> 01:02:18,080
Speaker 2: there which I didn't even know about. We made other

1172
01:02:18,160 --> 01:02:23,280
Speaker 2: reservations for the weekend, and we stop in and there's

1173
01:02:23,360 --> 01:02:28,400
Speaker 2: a line of people at five o'clock waiting to sit

1174
01:02:28,480 --> 01:02:31,040
Speaker 2: at the bar. I said, we're on the waiting list.

1175
01:02:31,080 --> 01:02:33,600
Speaker 2: He's like, well, we have like one hundred seats and

1176
01:02:33,640 --> 01:02:36,680
Speaker 2: the waiting list is about four hundred long. Oh forget it.

1177
01:02:37,000 --> 01:02:40,600
Speaker 2: And I said something to the matre d and he said,

1178
01:02:40,760 --> 01:02:42,720
Speaker 2: why don't you come down later and see what the

1179
01:02:42,760 --> 01:02:45,160
Speaker 2: line looks like. It usually moves pretty and it was

1180
01:02:45,200 --> 01:02:46,840
Speaker 2: beautiful out you could see it at the bar. It

1181
01:02:46,880 --> 01:02:50,080
Speaker 2: was outdoors, so it was like we had a seven

1182
01:02:50,120 --> 01:02:55,160
Speaker 2: o'clock reservation. The goal the goal, and we walked and

1183
01:02:55,160 --> 01:02:56,440
Speaker 2: we were going to walk to the restaurant. So we

1184
01:02:56,560 --> 01:03:00,920
Speaker 2: come down like six thirty, no line the bar. So

1185
01:03:01,080 --> 01:03:03,920
Speaker 2: I said, hey, you're not seeing people at the bar.

1186
01:03:03,960 --> 01:03:06,520
Speaker 2: You no, there's no line, get over there. And it

1187
01:03:06,600 --> 01:03:08,720
Speaker 2: was just simply being nice in the matri d and

1188
01:03:08,760 --> 01:03:11,880
Speaker 2: asking a question was was that behavioral?

1189
01:03:11,920 --> 01:03:12,120
Speaker 4: Heck?

1190
01:03:12,600 --> 01:03:15,000
Speaker 3: This is, you know, learning about the market and the

1191
01:03:15,040 --> 01:03:19,520
Speaker 3: market rules and getting inside information about when when is

1192
01:03:19,560 --> 01:03:23,800
Speaker 3: there less demand? What is the optimal strategy in this environment?

1193
01:03:23,920 --> 01:03:27,840
Speaker 3: It requires often doing a little bit of research, but

1194
01:03:27,960 --> 01:03:31,760
Speaker 3: it's not unattainable. We all have the ability to think

1195
01:03:31,800 --> 01:03:33,920
Speaker 3: about the market rules, think about who will know the

1196
01:03:33,960 --> 01:03:37,520
Speaker 3: matre d. You know, if you're polite to him or her,

1197
01:03:38,560 --> 01:03:40,760
Speaker 3: they might want you to come and give you the

1198
01:03:40,800 --> 01:03:44,200
Speaker 3: inside tips. But you know, you have to kind of

1199
01:03:44,280 --> 01:03:47,400
Speaker 3: have to either do your own research or have folks

1200
01:03:47,800 --> 01:03:51,600
Speaker 3: advise you that you trust. And yeah, you can. You

1201
01:03:51,640 --> 01:03:54,560
Speaker 3: can often succeed in markets where other people fall.

1202
01:03:55,160 --> 01:03:57,320
Speaker 2: So I only have you for another ten minutes, So

1203
01:03:57,440 --> 01:04:01,000
Speaker 2: let me jump to my favorite questions school my guests,

1204
01:04:01,120 --> 01:04:04,840
Speaker 2: starting with tell us about your mentors who helped shape

1205
01:04:04,840 --> 01:04:05,400
Speaker 2: your career.

1206
01:04:05,720 --> 01:04:08,640
Speaker 3: Yeah, so the main one, I mean already mentioned him,

1207
01:04:08,720 --> 01:04:11,120
Speaker 3: so you can see how big an influence is. Alvin Roth.

1208
01:04:11,280 --> 01:04:14,440
Speaker 3: So he took me in as an undergrad mentee. I

1209
01:04:14,800 --> 01:04:16,600
Speaker 3: had this story in the book and then it got

1210
01:04:16,600 --> 01:04:18,720
Speaker 3: cut for space. So I put it in the acknowledgments

1211
01:04:18,800 --> 01:04:21,960
Speaker 3: because it was such a formative experience for me where

1212
01:04:22,560 --> 01:04:26,640
Speaker 3: I was taking his PhD class as a senior in

1213
01:04:26,760 --> 01:04:28,440
Speaker 3: undergrad and so I was kind of a little bit

1214
01:04:28,480 --> 01:04:32,160
Speaker 3: out of place already, and I wanted to write a

1215
01:04:32,200 --> 01:04:34,320
Speaker 3: senior thesis the thing that kicked me off to this

1216
01:04:34,400 --> 01:04:36,440
Speaker 3: career and you know, put me at this table talking

1217
01:04:36,440 --> 01:04:39,960
Speaker 3: to you. Now, I had procrastinated asking him to be

1218
01:04:40,160 --> 01:04:42,680
Speaker 3: my advisor because I was a little intimidated, I felt

1219
01:04:42,680 --> 01:04:45,080
Speaker 3: attle out of place, and you know, I came up

1220
01:04:45,120 --> 01:04:46,960
Speaker 3: to him on the day the form was due and

1221
01:04:47,040 --> 01:04:49,600
Speaker 3: I said, I want to write a senior thesis. I

1222
01:04:49,600 --> 01:04:52,480
Speaker 3: would really like for you to advise it. You know,

1223
01:04:52,520 --> 01:04:54,160
Speaker 3: the form is due today, but you know, and this

1224
01:04:54,200 --> 01:04:55,760
Speaker 3: is the commitment. He was like, all right, why don't

1225
01:04:55,760 --> 01:04:57,560
Speaker 3: I sign the form and we'll see how it goes.

1226
01:04:58,120 --> 01:05:00,760
Speaker 3: And he signed the form and you know, the his history,

1227
01:05:01,320 --> 01:05:04,760
Speaker 3: but it was both the idea that you know, I

1228
01:05:04,800 --> 01:05:07,920
Speaker 3: could be involved in learning new things that people didn't

1229
01:05:07,920 --> 01:05:11,000
Speaker 3: know before and do it with somebody who was willing

1230
01:05:11,000 --> 01:05:11,600
Speaker 3: to mentor me.

1231
01:05:12,120 --> 01:05:13,840
Speaker 4: That was a real, real big impact.

1232
01:05:14,000 --> 01:05:16,440
Speaker 2: Yeah, I can imagine what are some of your favorite books?

1233
01:05:16,440 --> 01:05:17,760
Speaker 2: What are you reading right now?

1234
01:05:18,040 --> 01:05:21,280
Speaker 3: So there, this is a pop Econ book, my book

1235
01:05:21,360 --> 01:05:25,680
Speaker 3: Lucky by Design. It's in the spirit of another pop

1236
01:05:25,720 --> 01:05:28,320
Speaker 3: Econ book about market design which Al wrote, which is

1237
01:05:28,320 --> 01:05:30,800
Speaker 3: called Who Gets What and Why? And so for a

1238
01:05:30,840 --> 01:05:32,760
Speaker 3: while I didn't want to write another, you know, pop

1239
01:05:33,240 --> 01:05:34,920
Speaker 3: market design bookcase I didn't want to step on his

1240
01:05:34,960 --> 01:05:35,960
Speaker 3: toes as a.

1241
01:05:35,880 --> 01:05:38,040
Speaker 2: Great title who Gets What and Why?

1242
01:05:38,120 --> 01:05:40,240
Speaker 3: Yeah, And there's a little in the text of my

1243
01:05:40,280 --> 01:05:42,480
Speaker 3: book and kind of I drop that every so often,

1244
01:05:42,560 --> 01:05:46,240
Speaker 3: the kind of question, that wording of that question. So

1245
01:05:46,320 --> 01:05:49,320
Speaker 3: that was an idea for me, This idea that you

1246
01:05:49,360 --> 01:05:52,440
Speaker 3: can communicate these market design concepts to regular folks. I

1247
01:05:52,480 --> 01:05:54,840
Speaker 3: recommend my book, but also that book for folks who

1248
01:05:54,840 --> 01:05:59,120
Speaker 3: are interested in this. My colleague, whose office is next

1249
01:05:59,160 --> 01:06:00,920
Speaker 3: to mine, had a book that came out a few

1250
01:06:00,920 --> 01:06:01,920
Speaker 3: weeks before mine.

1251
01:06:02,120 --> 01:06:03,160
Speaker 4: It's called Having It All.

1252
01:06:03,800 --> 01:06:07,960
Speaker 3: So my colleague is Caren Low, and I'm really enjoying

1253
01:06:07,960 --> 01:06:09,800
Speaker 3: that book. I mean, I've heard about everything that was

1254
01:06:09,840 --> 01:06:13,120
Speaker 3: in it, you know, along along the way. But she

1255
01:06:13,320 --> 01:06:18,720
Speaker 3: writes about the time that women and their partners spend

1256
01:06:19,000 --> 01:06:24,200
Speaker 3: in doing household production and how society has progressed over

1257
01:06:24,440 --> 01:06:29,320
Speaker 3: decades where women have entered the workforce, but the norms

1258
01:06:29,560 --> 01:06:31,880
Speaker 3: at home about how time is.

1259
01:06:32,040 --> 01:06:33,680
Speaker 4: You know, household chores are split.

1260
01:06:34,560 --> 01:06:37,560
Speaker 3: It has not changed, so women end up really yeah,

1261
01:06:38,320 --> 01:06:41,520
Speaker 3: the data in that book is quite shocking. I mean,

1262
01:06:41,520 --> 01:06:44,320
Speaker 3: I'd been hearing about it as she was doing the research.

1263
01:06:44,360 --> 01:06:49,280
Speaker 3: But you know, women, even in households where they earn

1264
01:06:49,440 --> 01:06:53,120
Speaker 3: more than their male partners, will still be doing more

1265
01:06:54,320 --> 01:06:57,920
Speaker 3: labor all of that stuff at home, so that you know,

1266
01:06:58,120 --> 01:06:59,720
Speaker 3: in my book, I talk a little bit about how

1267
01:06:59,760 --> 01:07:05,200
Speaker 3: my wife and I manage our household responsibilities using the

1268
01:07:05,240 --> 01:07:08,400
Speaker 3: concepts of market design to kind of avoid some of

1269
01:07:08,440 --> 01:07:13,000
Speaker 3: these systematic problems that that on average couples display.

1270
01:07:13,320 --> 01:07:15,640
Speaker 2: Huh really, you know I love to cook, but I

1271
01:07:15,720 --> 01:07:18,800
Speaker 2: tend to make a giant mess. And my wife is

1272
01:07:18,880 --> 01:07:22,080
Speaker 2: convinced that it's a purposeful strategy, so she could and

1273
01:07:22,120 --> 01:07:25,400
Speaker 2: it's I'm like, honey, we're married thirty years, you know me?

1274
01:07:26,000 --> 01:07:27,800
Speaker 2: Is this how I rolled? You know?

1275
01:07:27,840 --> 01:07:31,160
Speaker 3: You gotta yeah, sometimes this is just my technique. But

1276
01:07:31,440 --> 01:07:33,360
Speaker 3: one of the things that I talk about in the

1277
01:07:33,360 --> 01:07:34,640
Speaker 3: book and must.

1278
01:07:34,440 --> 01:07:35,760
Speaker 2: You pour from so high?

1279
01:07:35,920 --> 01:07:37,560
Speaker 4: It's olive oil.

1280
01:07:37,880 --> 01:07:39,240
Speaker 2: It doesn't need that high.

1281
01:07:39,240 --> 01:07:42,680
Speaker 4: You got to get that aired and it's it's part

1282
01:07:42,680 --> 01:07:43,800
Speaker 4: of the fun. But no.

1283
01:07:44,320 --> 01:07:48,200
Speaker 3: One of the strategies is having one person be in

1284
01:07:48,320 --> 01:07:52,960
Speaker 3: charge of the whole task from conception to execution. So

1285
01:07:53,000 --> 01:07:56,560
Speaker 3: that means if you are cooking, you're also cleaning. Would

1286
01:07:56,560 --> 01:07:59,000
Speaker 3: be part of that, right, because then the incentives are aligned.

1287
01:07:59,040 --> 01:08:01,680
Speaker 3: When when you don't do that, the person who cooks

1288
01:08:02,120 --> 01:08:04,200
Speaker 3: can leave a giant mass the other person cleans. That

1289
01:08:04,520 --> 01:08:09,000
Speaker 3: seems fair, but the decisions, the resentment comes in and

1290
01:08:09,040 --> 01:08:10,760
Speaker 3: why are you making such a big mess? And so

1291
01:08:10,800 --> 01:08:12,640
Speaker 3: it turns out it, you know, might be more efficient

1292
01:08:12,680 --> 01:08:15,440
Speaker 3: and equitable if one person does that whole task and

1293
01:08:15,440 --> 01:08:17,760
Speaker 3: somebody else does a whole other time. Right, you cook

1294
01:08:17,800 --> 01:08:20,160
Speaker 3: and I'll do the laundry and clean the floors. And

1295
01:08:20,200 --> 01:08:22,840
Speaker 3: that's the you know, the split that might be fair.

1296
01:08:23,000 --> 01:08:25,040
Speaker 2: You know, I'm usually out the door early, or if

1297
01:08:25,040 --> 01:08:27,920
Speaker 2: I'm home, I'm at the desk writing, so she takes

1298
01:08:27,960 --> 01:08:31,719
Speaker 2: care of the dogs and during the weekend I try

1299
01:08:31,720 --> 01:08:34,000
Speaker 2: and give her a break and feed and walk the

1300
01:08:34,080 --> 01:08:38,080
Speaker 2: dogs early. We've never discussed it. It just kind of

1301
01:08:38,680 --> 01:08:39,679
Speaker 2: worked out that way.

1302
01:08:39,560 --> 01:08:41,120
Speaker 4: That sometimes that's how it happens.

1303
01:08:41,160 --> 01:08:43,640
Speaker 3: But you know, for those who are not finding it

1304
01:08:43,680 --> 01:08:46,680
Speaker 3: that easy, talking it out and kind of splitting the

1305
01:08:46,680 --> 01:08:49,040
Speaker 3: tasks is an effective strategy.

1306
01:08:49,200 --> 01:08:51,439
Speaker 2: Tell us what's keeping you entertained these days? What are

1307
01:08:51,439 --> 01:08:57,479
Speaker 2: you watching or listening to? Either podcasts or Amazon Prime, Netflix, whatever.

1308
01:08:57,680 --> 01:09:02,439
Speaker 3: So I am a major fan of The Simpsons. Yeah,

1309
01:09:02,560 --> 01:09:03,639
Speaker 3: I have always loved it.

1310
01:09:03,720 --> 01:09:05,719
Speaker 2: What are we up to season forty something?

1311
01:09:05,840 --> 01:09:09,040
Speaker 3: It's crazy almost Yeah, And but what has been great

1312
01:09:09,080 --> 01:09:12,760
Speaker 3: I found throughout my life, you know, rewatching old episodes

1313
01:09:12,800 --> 01:09:14,599
Speaker 3: that I kind of get jokes that I didn't get before,

1314
01:09:14,640 --> 01:09:16,280
Speaker 3: which makes sense right when I started watching.

1315
01:09:16,800 --> 01:09:18,639
Speaker 2: It's one of those things that works for just both

1316
01:09:18,720 --> 01:09:21,000
Speaker 2: kids and adults at the same time, exactly.

1317
01:09:21,040 --> 01:09:24,640
Speaker 3: And so what has been phenomenal for me is exposing

1318
01:09:24,640 --> 01:09:27,160
Speaker 3: my kids to it for the first time. And you know,

1319
01:09:27,240 --> 01:09:29,240
Speaker 3: I'll say, so, I'll make some reference to something, and

1320
01:09:29,240 --> 01:09:30,519
Speaker 3: my kids will be like, what are you talking about?

1321
01:09:30,520 --> 01:09:32,479
Speaker 3: And then I'll pull up the YouTube clip I'll show

1322
01:09:32,520 --> 01:09:33,920
Speaker 3: it to them, and then they'll be like, oh, can

1323
01:09:33,960 --> 01:09:36,120
Speaker 3: you know can we watch that episode? So that has

1324
01:09:36,120 --> 01:09:36,800
Speaker 3: been phenomenal.

1325
01:09:37,000 --> 01:09:39,800
Speaker 2: Our final two questions, What sort of advice would you

1326
01:09:39,800 --> 01:09:43,080
Speaker 2: give to a recent college grad interest in the career

1327
01:09:43,320 --> 01:09:48,600
Speaker 2: in either market design or economics or academia.

1328
01:09:49,080 --> 01:09:53,479
Speaker 3: Yeah, So, academia. There is a path forward for folks

1329
01:09:53,520 --> 01:09:57,080
Speaker 3: who have just graduated college and are thinking about this,

1330
01:09:57,760 --> 01:10:01,120
Speaker 3: and that is to get some experience doing research to

1331
01:10:01,160 --> 01:10:05,960
Speaker 3: see if you like it. Because the market for tenure

1332
01:10:06,000 --> 01:10:11,200
Speaker 3: track academic positions is getting tightened, we're feeling the political

1333
01:10:11,240 --> 01:10:12,679
Speaker 3: wins as well as other things.

1334
01:10:12,680 --> 01:10:17,680
Speaker 2: They've kind of demographically, there's slightly smaller admission classes and

1335
01:10:17,800 --> 01:10:20,920
Speaker 2: I just read seventeen percent drop in international students. That's

1336
01:10:20,960 --> 01:10:21,599
Speaker 2: a big number.

1337
01:10:21,920 --> 01:10:26,400
Speaker 3: For a lot of the universities that have big PhD programs.

1338
01:10:26,760 --> 01:10:32,880
Speaker 3: The budgets for things like the PhD program will depend

1339
01:10:33,000 --> 01:10:37,240
Speaker 3: on their revenue streams, and foreign students coming for undergrad

1340
01:10:37,320 --> 01:10:40,000
Speaker 3: or for master's degrees is a big revenue source for

1341
01:10:40,000 --> 01:10:42,439
Speaker 3: a lot of institutions. So all of that is to

1342
01:10:42,479 --> 01:10:45,800
Speaker 3: say that academia remains a great option for folks who

1343
01:10:45,840 --> 01:10:48,320
Speaker 3: are interested in it, but it's getting harder and harder.

1344
01:10:49,160 --> 01:10:51,360
Speaker 3: But if folks are interested getting and they have not

1345
01:10:51,439 --> 01:10:53,160
Speaker 3: yet done it, they may have done it in undergrad

1346
01:10:53,400 --> 01:10:57,840
Speaker 3: getting exposure to the research experience. There are predoctoral programs

1347
01:10:57,840 --> 01:11:03,240
Speaker 3: for folks who work with academic researchers on their projects

1348
01:11:03,280 --> 01:11:05,200
Speaker 3: and kind of get a sense of what is actually like,

1349
01:11:05,880 --> 01:11:08,400
Speaker 3: there are master's programs that folks can participate in to

1350
01:11:08,400 --> 01:11:11,160
Speaker 3: see what the coursework is like, So that is definitely

1351
01:11:11,479 --> 01:11:15,040
Speaker 3: one way to go. There are kids who started earlier,

1352
01:11:15,040 --> 01:11:17,840
Speaker 3: who started in college, but I was not one of them,

1353
01:11:17,920 --> 01:11:20,519
Speaker 3: right I told the story about senior year kind of realizing, oh,

1354
01:11:20,560 --> 01:11:22,800
Speaker 3: I want to do academia. So I hadn't done any

1355
01:11:22,840 --> 01:11:26,479
Speaker 3: research assistant work until after I graduated, but is that

1356
01:11:26,600 --> 01:11:28,240
Speaker 3: was the next step for me, was saying, Okay, I

1357
01:11:28,240 --> 01:11:30,320
Speaker 3: want to see what this is like. So it is

1358
01:11:30,439 --> 01:11:32,720
Speaker 3: a path to do, but you should only do it

1359
01:11:32,760 --> 01:11:36,880
Speaker 3: if you really want to have a job that only

1360
01:11:37,040 --> 01:11:39,800
Speaker 3: someone with a PhD can have, because there are a

1361
01:11:39,840 --> 01:11:41,760
Speaker 3: lot of great jobs out there for folks interested in

1362
01:11:41,800 --> 01:11:44,560
Speaker 3: these topics, but not in academia.

1363
01:11:44,640 --> 01:11:47,880
Speaker 2: So final question, what do you know about the world

1364
01:11:48,080 --> 01:11:53,000
Speaker 2: of market designs and economics or even academia that would

1365
01:11:53,000 --> 01:11:55,559
Speaker 2: have been useful twenty plus years ago when you were

1366
01:11:55,880 --> 01:11:56,879
Speaker 2: first getting started.

1367
01:11:57,080 --> 01:11:57,320
Speaker 4: Yeah.

1368
01:11:57,360 --> 01:12:02,320
Speaker 3: I think something I've recently developed in part researching for

1369
01:12:02,360 --> 01:12:06,479
Speaker 3: the book is just how diverse the hidden markets are

1370
01:12:06,960 --> 01:12:11,760
Speaker 3: that we participate in every day. As a young economist

1371
01:12:11,800 --> 01:12:17,320
Speaker 3: being trained in how markets worked, I thought basically exclusively

1372
01:12:17,360 --> 01:12:20,720
Speaker 3: about prices and the price mechanisms. That was how I

1373
01:12:20,840 --> 01:12:22,720
Speaker 3: was taught that those were the markets I looked at.

1374
01:12:23,320 --> 01:12:26,439
Speaker 3: And it's only recently that I've realized markets are much

1375
01:12:26,479 --> 01:12:30,920
Speaker 3: broader than that, and thinking through the rules of those markets,

1376
01:12:31,240 --> 01:12:34,160
Speaker 3: how we could design them better so that, you know,

1377
01:12:34,200 --> 01:12:36,960
Speaker 3: they're more equitable and efficient and easy for participants. I

1378
01:12:37,000 --> 01:12:39,639
Speaker 3: think there's a lot of gains for us to have

1379
01:12:39,680 --> 01:12:41,840
Speaker 3: as a society, So I'm excited to be working on

1380
01:12:41,880 --> 01:12:43,720
Speaker 3: it for the next twenty years. But if I if

1381
01:12:43,720 --> 01:12:45,800
Speaker 3: I could go back twenty years and say, hey, maybe

1382
01:12:45,800 --> 01:12:48,679
Speaker 3: focus on these markets a little bit more, because there's

1383
01:12:48,720 --> 01:12:51,280
Speaker 3: a lot of low hanging fruit where we could be

1384
01:12:51,320 --> 01:12:55,360
Speaker 3: making things better for everybody. You know, I wish I

1385
01:12:55,400 --> 01:12:57,880
Speaker 3: knew that, and I want others to kind of look

1386
01:12:57,920 --> 01:13:00,240
Speaker 3: at these markets and say, yeah, this could be better.

1387
01:13:00,640 --> 01:13:05,439
Speaker 2: Fascinating. Professor really enjoyed the conversation we have been speaking with.

1388
01:13:05,640 --> 01:13:11,200
Speaker 2: Judd Kessler, professor of behavioral economics and market design at

1389
01:13:11,240 --> 01:13:14,400
Speaker 2: Wharton at the University of Pennsylvania and author of the

1390
01:13:14,439 --> 01:13:18,320
Speaker 2: new book Lucky by Design, The Hidden Economics. You need

1391
01:13:18,640 --> 01:13:21,640
Speaker 2: to get more of what you want. I would be

1392
01:13:21,720 --> 01:13:24,680
Speaker 2: remiss if I failed to thank the crack team that

1393
01:13:24,840 --> 01:13:29,000
Speaker 2: helps me put these conversations together each and every week.

1394
01:13:29,400 --> 01:13:33,760
Speaker 2: Alexis Noriega is my video producer. Joan Russo is my

1395
01:13:33,920 --> 01:13:39,320
Speaker 2: research assistant. Anna Luke is my producer. I'm Barry Rutolts.

1396
01:13:39,720 --> 01:14:04,280
Speaker 2: You've been listening to Master's in Business on Bloomberg Radio.