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Speaker 1: Bloomberg Audio Studios. Podcasts. Radio.

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Speaker 2: News.

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Speaker 3: Hello and welcome to another episode of the All Thoughts Podcast.

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Speaker 3: I'm Tracy Allaway.

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Speaker 1: And I'm Joe Weisenthal.

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Speaker 3: Joe, I have a very exciting episode for you today.

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Speaker 1: Go on.

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Speaker 3: We are going to be talking about pass-throughs, S-cores, and

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Speaker 3: the U.S. tax code.

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Speaker 1: This is so great. I'm so excited about this because

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Speaker 1: I remember during some of those tax cut negotiations and

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Speaker 1: you'd have, who is that? I think it was a

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Speaker 1: senator from Wisconsin, Ron Johnson. And he was like, or

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Speaker 1: I don't know which one it was, but he was like, oh,

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Speaker 1: we really need to preserve some pass-through. And I didn't

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Speaker 1: learn about it. And then I was like, well, you know, whatever.

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Speaker 1: And then I missed it. And I was like, great.

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Speaker 1: So finally here in 2026, I can learn about something

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Speaker 1: that I really should have learned earlier. in December of 2017.

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Speaker 3: Well, actually, I'm glad you said that because this is

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Speaker 3: sort of the thrust of what this entire conversation is

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Speaker 3: going to be, which is when we talk about tax

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Speaker 3: breaks for the wealthy, we normally talk about or we

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Speaker 3: think about Jeff Bezos paying like zero percent income tax

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Speaker 3: or something like that. But actually, a lot of the

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Speaker 3: tax cuts that we've seen, I guess, since the 1980s

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Speaker 3: and now most recently under the Trump administration are have

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Speaker 3: been for S-cores, for these pass-through entities, which generally get

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Speaker 3: framed as small businesses.

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Speaker 2: That's right.

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Speaker 3: But as it turns out, a lot of these businesses

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Speaker 3: are actually phenomenally large and very, very wealthy.

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Speaker 1: Yeah, there's a few things that I think are really

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Speaker 1: interesting here. So, obviously, people just, like, love the sound

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Speaker 1: of small business, right? Like, small business makes people feel good,

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Speaker 1: whatever it is. People, like, it's sort of something aesthetically interesting.

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Speaker 3: Something very American as well. You think about Tocqueville and

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Speaker 3: his big impression of America was, oh, it's this nation

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Speaker 3: of entrepreneurs.

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Speaker 1: Yeah. So there's something about small business that feels good, etc.

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Speaker 1: The other thing that I'm really interested in, and I

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Speaker 1: brought this up a couple of times on the podcast,

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Speaker 1: you know, I think when a lot of people think

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Speaker 1: about the United States or the American economy and the

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Speaker 1: sort of distribution of wealth and income and so forth,

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Speaker 1: there is an incredible, you know, people talk about inequality

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Speaker 1: and then they think, okay, there's like a 1% or

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Speaker 1: a 0.1% that's insanely wealthy. You mentioned Jeff Bezos and

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Speaker 1: a handful of others like that. And then people are

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Speaker 1: aware of the fact that for a country as rich

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Speaker 1: as ours, there's probably unacceptably high levels of like poverty

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Speaker 1: and people perceive there correctly to be a lot of

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Speaker 1: people who are financially struggling, et cetera. But I always

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Speaker 1: think that like the missing thing is like that fat

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Speaker 1: layer of rich people. And the thing that I'm always

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Speaker 1: reminded by it, you know, we're recording this September 9th.

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Speaker 1: I think by the time people listen to this, we'll

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Speaker 1: have already been there. But whenever we go to Southern California,

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Speaker 1: Orange County, Huntington Beach.

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Speaker 4: And you just see like all these people with boats

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Speaker 4: and you see all these people, you go to like

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Speaker 4: a steakhouse or something and they have their own private

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Speaker 4: like wine locker at the steakhouse.

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Speaker 5: And it's like.

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Speaker 1: There's a lot of rich people.

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Speaker 6: Yeah.

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Speaker 1: And they're not all like the tech billionaires.

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Speaker 3: They're not all on the Forbes 400 or whatever.

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Speaker 1: We have a big, fat layer of like just really

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Speaker 1: rich people. And they're not people who made some great invention,

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Speaker 1: et cetera, necessarily, or some Great, huge breakthrough. But like

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Speaker 1: whether it's like real estate or cars or just some

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Speaker 1: sort of simple business, a lot of really rich people

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Speaker 1: in this country.

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Speaker 2: Yeah.

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Speaker 3: And we should talk about them, try to figure out

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Speaker 3: who they are, how many of them there actually are.

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Speaker 3: And the one other thing I would also add that

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Speaker 3: makes this sort of a very oddlotsy episode is we

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Speaker 3: talk a lot about.

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Speaker 1: The private markets, right?

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Speaker 3: Private credit, private markets, private equity.

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Speaker 1: Yeah.

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Speaker 3: And often when we are talking about those specific businesses,

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Speaker 3: we are talking about these types of businesses. So, you know,

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Speaker 3: a private equity firm, you think of it as this

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Speaker 3: Wall Street entity, but it owns a portfolio of, I

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Speaker 3: don't know, HVAC companies in Missouri or something.

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Speaker 1: And the last thing I'll say, and I don't know

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Speaker 1: how much we'll get into this part per se. But

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Speaker 1: if you think about, I don't know, the dark matter

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Speaker 1: of American politics, et cetera, privately owned companies are often,

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Speaker 1: or the owners of privately owned companies some regional beverage

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Speaker 1: distributor or something like that, are often very important figures

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Speaker 1: in regional politics. And senators, if you want to get

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Speaker 1: your handle on that, should sort of know who these

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Speaker 1: people are and how they got there.

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Speaker 3: We'll get into that, definitely. So I'm happy to say

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Speaker 3: we do, in fact, have the perfect guest. We're going

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Speaker 3: to be speaking with the authors of the new book,

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Speaker 3: The Everywhere Millionaire, who is really rich in America and

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Speaker 3: how they got there. And the authors are Owen Zadar,

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Speaker 3: professor of economics at Princeton, and Eric Zwick, Professor of

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Speaker 3: Economics and Finance at Chicago Booth. So thank you both

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Speaker 3: so much for being here.

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Speaker 2: Thanks for having us. It's great to be here.

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Speaker 3: It's a great book. Legitimately, I really enjoyed it. When

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Speaker 3: I was reading it, I was thinking it's sort of

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Speaker 3: like an upscale Studs Terkel. You have all these stories

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Speaker 3: of millionaires and how they actually got there. But the

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Speaker 3: origin story of this book is something I wanted to

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Speaker 3: ask you about because it also sits squarely within Oblott's

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Speaker 3: territory when we talk about I guess, what a mess

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Speaker 3: government data and technology tends to be. Because this book

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Speaker 3: started with a project, a commission by the Treasury Department

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Speaker 3: to try to sift through IRS data to figure out

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Speaker 3: who these people actually were and how many of them

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Speaker 3: there are. Talk to us about what you found when

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Speaker 3: you started that project.

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Speaker 5: Yes. So we started more than a decade ago, commissioned

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Speaker 5: by the Treasury to go into the bowels of the

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Speaker 5: Treasury and work with tax data to try and link

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Speaker 5: all the businesses to their owners and workers because they

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Speaker 5: didn't have the ability to do that and they needed

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Speaker 5: it to do tax policy to sort of think about

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Speaker 5: what if we change the tax code, how much tax

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Speaker 5: these pass-through businesses pay. They didn't have the capacity to

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Speaker 5: do that because the data weren't connected. And so that

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Speaker 5: was the original kind of sin of our project. That

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Speaker 5: was the foundation of our project was to sort of

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Speaker 5: build this data that as we continued to expand it,

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Speaker 5: add years, add different types of layers on it by

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Speaker 5: looking at just the entrepreneurs, for instance, we could start

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Speaker 5: to see, oh, there's actually a picture of income, wealth, entrepreneurship,

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Speaker 5: prosperity in the American economy that is very different than

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Speaker 5: the picture that we were getting from the media, the

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Speaker 5: picture we were getting from the capital book and so on.

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Speaker 5: And so that's sort of the beginning was that kind

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Speaker 5: of nerdy tax data project, the computers, the databases, the

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Speaker 5: XML and so on, which we can talk more about,

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Speaker 5: but that's where we started.

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Speaker 3: Well, I wanted to ask about the databases, actually, because

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Speaker 3: it just sounded like a nightmare the way you described it.

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Speaker 3: And then there's this funny moment in the book where

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Speaker 3: you basically like randomly find the guy who kind of

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Speaker 3: built these systems and understood them and was able to

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Speaker 3: help you wrap your head around them. How difficult was this?

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Speaker 6: I mean, so the underlying data are basically everything on

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Speaker 6: a tax form. So, you know, you can have information

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Speaker 6: about the business. You can have investors in the business

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Speaker 6: who get money going to them. They're not made to

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Speaker 6: be linked, and many of them aren't electronically filed.

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Speaker 1: When you say they're not made to be linked, what's

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Speaker 1: not made?

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Speaker 3: They're separate databases.

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Speaker 6: So they're just different forms. So when you say Joe

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Speaker 6: and Tracy had an S Corp and you own it 50-50,

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Speaker 6: you can file the business's form, and then you have

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Speaker 6: to file an investor form that says, all right, half

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Speaker 6: the profits are going to Joe, half the profits are

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Speaker 6: going to Tracy. And the way they set up the

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Speaker 6: identifiers was, aren't made where you're supposed to be able

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Speaker 6: to add up all the things from investors and that

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Speaker 6: should add up everything from the business. So the story

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Speaker 6: about how we figured out how to do this, we're

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Speaker 6: going through old code books, like things were called different

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Speaker 6: variable names. And Eric, I don't know if you want

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Speaker 6: to tell the story about going to a part of Utah. Yeah.

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Speaker 5: So I'm like giving a talk at Utah state. And

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Speaker 5: I knew that one of the guys who designed the

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Speaker 5: XML system that when people electronically file their tax returns,

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Speaker 5: it's sort of like the formula for converting those tax

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Speaker 5: returns into rows and columns that then get processed, lived

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Speaker 5: on the way from the airport to the lecture I

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Speaker 5: was giving. So I was like, can I come and

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Speaker 5: just ask you some questions? And so I met him

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Speaker 5: for lunch.

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Speaker 1: We had burgers.

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Speaker 3: What were you thinking?

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Speaker 5: I was imagining like in the Matrix, you know, like

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Speaker 5: you go see the Oracle and it's like this old lady,

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Speaker 5: you know, who's like the spoon is bending, you know.

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Speaker 1: I was imagining like some guy who like like kind

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Speaker 1: of a hermit. It's like someone knocking on his door,

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Speaker 1: it's like, why are you here? What do you want?

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Speaker 1: And he's like, that's not me. And then finally after, no,

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Speaker 1: but keep going.

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Speaker 5: He was a super reasonable, he was kind of wearing

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Speaker 5: like a flannel shirt as everybody in Utah seems to do.

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Speaker 5: He wanted to have a beer at lunch and I

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Speaker 5: was happy to join him. And I had my like

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Speaker 5: list of 25 questions that are like very intricate questions

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Speaker 5: about the structure of this database. We had to rearrange

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Speaker 5: columns in order to create the links to to match

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Speaker 5: the business forms to the individuals. And we didn't know

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Speaker 5: what any of these columns were necessarily.

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Speaker 1: What year is this, by the way? This is like.

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Speaker 6: 2014, 15.

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Speaker 1: Okay, great.

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Speaker 5: So yeah, so you can't ask Claude to read the

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Speaker 5: manual and read the code. There's no way to do that.

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Speaker 5: There's no documentation. There's just this knowledge in people's brains

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Speaker 5: that you have to find to try and do this link.

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Speaker 5: And there were other people who kind of knew some

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Speaker 5: of these details, but we couldn't really get to the

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Speaker 5: bottom of it until... having a burger and beer with

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Speaker 5: the architect.

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Speaker 1: It's sort of a diversion, but I feel like I've

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Speaker 1: read so many NBER working papers, which include the line,

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Speaker 1: we exploit a novel data set. How much of economics

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Speaker 1: is finding a novel data set? We think of it

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Speaker 1: as like, oh, maybe we're going to apply some econometrics

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Speaker 1: or theory or whatever. How much are just, as professional economists,

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Speaker 1: is it about finding that data?

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Speaker 6: I think it's an important part, but it's also, you

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Speaker 6: need to ask an important question. And if you are

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Speaker 6: thinking about how do people get rich, and you only

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Speaker 6: see a small sliver of how it actually works, and

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Speaker 6: then suddenly you can say, You know, there's 1,000 private

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Speaker 6: business owners that have $ 25 million for every large public

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Speaker 6: company CEO. Like, we've missed all of these people. And now,

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Speaker 6: for the first time, you can trace back, like, where

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Speaker 6: did these people come from?

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Speaker 2: Like, what are they doing? How did they do it?

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Speaker 6: That, I think, opens it up and opens up a

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Speaker 6: lot of work, where I think you're right. Like, there's

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Speaker 6: one of Eric's advisors, Andre Schleifer, teaches a class at Harvard,

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Speaker 6: and he basically– gives the college students at the assignment, like,

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Speaker 6: find me a cool data set. And like, that's how

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Speaker 6: you write the paper. It's like, show me something I

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Speaker 6: haven't seen before. So there's certainly something to find a

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Speaker 6: novel thing to get a hook on something we care about. Yeah.

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Speaker 5: But figuring out the question to ask with that data

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Speaker 5: is kind of the trick. Like the data is sitting

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Speaker 5: there like, oh, we can use this to understand inequality.

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Speaker 3: Okay. Okay.

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Speaker 1: Maybe this is like jumping forward, but just finally relieve me.

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Speaker 1: What is pass-through made? Like, I mean, finally, like, I'm

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Speaker 1: just going to ask it. When we talk about like, oh,

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Speaker 1: this center really wants to preserve or expand a pass-through induction.

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Speaker 1: What are we talking about here?

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Speaker 6: So pass-through businesses are. different than traditional corporations. So if

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Speaker 6: you look at like a typical public company like GM,

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Speaker 6: they're a C corporation. They face the corporate tax. So

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Speaker 6: when they make profits, they have to pay the corporate tax.

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Speaker 6: And then if they want to send money to individual investors,

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Speaker 6: they have to pay dividends. Pass-throughs are a different part

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Speaker 6: of the tax code where you don't have to face

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Speaker 6: the corporate tax, just the profits and losses go directly

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Speaker 6: to the owners. And they weren't that big of a

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Speaker 6: deal in the 80s. The lion's share of profits and

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Speaker 6: activity were in traditional corporations. And then after Reagan cut

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Speaker 6: taxes in 1986 on individuals lower than corporations, that suddenly

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Speaker 6: became the most attractive way to organize yourself. And so

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Speaker 6: we've seen this huge boom in pass-through entities where that's

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Speaker 6: the vast majority of businesses in America and like half

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Speaker 6: of profits.

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Speaker 3: So, OK, once you figured out the IRS code and

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Speaker 3: got the, I guess, IRS servers that seem to be

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Speaker 3: run by hamsters spinning on wheels to actually work, what

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Speaker 3: were your findings about wealth in America?

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Speaker 5: So this income is among the most concentrated. So pass-through

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Speaker 5: business income, 70% of every dollar pass-through business income goes

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Speaker 5: to the top 1%. And if you look at the

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Speaker 5: rise of income inequality, so like the famous graph, top 1%

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Speaker 5: share of income from Piketty and Saez going back to

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Speaker 5: the 1980s, more than half of the growth from that

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Speaker 5: period of time to the 2021 is coming in the

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Speaker 5: form of pass-through business income. So you want to understand

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Speaker 5: what's driving income inequality and wealth inequality, because it's sort

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Speaker 5: of a parallel story there. You really have to understand

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Speaker 5: this pass-through business sector. What is the nature of this activity?

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Speaker 5: We knew it was pass-through business income. We didn't really

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Speaker 5: know if this is like Monopoly Man, big business, that

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Speaker 5: you're just passively collecting the income, or what we turned

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Speaker 5: out to find, it was sort of these mid-market regional

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Speaker 5: businesses of which there were just millions of them across

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Speaker 5: the country. And that was kind of like the second

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Speaker 5: huge finding about you know, who these pass-through business owners

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Speaker 5: were and what they're doing. Owen, you want to tell them, like,

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Speaker 5: our mnemonic from reading our kids, like, little books?

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Speaker 2: Yeah, so I have three young kids. Eric has one

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Speaker 2: young kid.

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Speaker 6: And we're half-jokingly thinking about writing a kid's book of

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Speaker 6: the ABCs of getting rich.

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Speaker 2: And we just go through and have a fun fact

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Speaker 2: on each of them.

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Speaker 6: And so A is for auto dealer, B is for

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Speaker 6: beverage distribution business owner, and then C is for contractor owner.

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Speaker 2: D is dentist. You know, we could go through. We

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Speaker 2: actually did it. We haven't made the book.

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Speaker 1: You got to do it. Can I just say, my

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Speaker 1: son is obsessed with money. And finally, no, he's like raised.

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Speaker 2: Shocking. Yeah, where does he get it from?

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Speaker 1: I don't know where. No, it's like, he's always scheming.

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Speaker 1: So he would love this book. So please write it.

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Speaker 2: Okay. P is for podcaster.

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Speaker 3: Oh, yeah.

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Speaker 6: All right.

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Speaker 3: So give us an example that like, let's dive into

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Speaker 3: a specific one. And I'm just going to choose car

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Speaker 3: dealerships because this is an interesting one. I think everyone

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Speaker 3: knows how a car dealership theoretically is supposed to work.

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Speaker 3: And I don't think you normally think of used cars

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Speaker 3: or I shouldn't say used cars, but car dealers as

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Speaker 3: super wealthy. And yet you have a lot of examples

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Speaker 3: in the book of people who have accumulated millions, if

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Speaker 3: not sometimes billions from this business.

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Speaker 6: So.

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Speaker 5: The book opens with the wedding of the century, which

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Speaker 5: is a wedding, Bob and Paul of Brockway, Coral Gables, Florida.

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Speaker 5: They sent their daughter to first to the Amangiri for

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Speaker 5: the bachelorette party. It was a four-day party where they

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Speaker 5: dressed up like Marie Antoinette. But then they flew all

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Speaker 5: the guests to Paris for a five-day wedding.

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Speaker 2: On private jets. On jets. They rented.

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Speaker 5: Versailles, not for the wedding, but just for like a dinner.

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Speaker 5: What year are we talking about here?

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Speaker 6: 23.

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Speaker 2: 23, 23, yeah.

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Speaker 5: Okay, 24, yeah. This was 2023, 2024, a few years ago,

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Speaker 5: and it was all over social media.

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Speaker 1: I remember this.

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Speaker 5: The wedding of the century. Adam Levine sang the first dance.

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Speaker 5: You could see the Eiffel Tower illuminated in the distance.

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Speaker 5: And they're car dealers, third-generation car dealers from Florida, the

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Speaker 5: Mercedes dealer. And they had just sold the dealership. Uh,

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Speaker 5: for several hundred million dollars to actually a consortium that

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Speaker 5: Nick Saban was a co-owner of, um, but they dropped

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Speaker 5: $ 60 million on this wedding.

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Speaker 6: Wow.

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Speaker 5: And, um, you know, they're not even close to the

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Speaker 5: top of the list in terms of the most prodigious

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Speaker 5: car dealers in the country. So when we like zoom

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Speaker 5: out and look at the industries that generate the most

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Speaker 5: pass through business income and the top 0.1%, the auto

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Speaker 5: dealers were the number one bucket, which we were like, okay,

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Speaker 5: this is not the. Bickety story of like billionaire tech

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Speaker 5: or finance. This is something different. What are these car

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Speaker 5: dealers doing as an example industry? And then we found

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Speaker 5: them even more when we started looking in like yacht

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Speaker 5: and jet registration data. They just own all these yachts

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Speaker 5: and jets. And that's how we found them by name

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Speaker 5: because the tax data that we were talking about before,

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Speaker 5: we couldn't identify people that way. So when we set

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Speaker 5: out to write the book and tell a bunch of stories, okay,

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Speaker 5: let's find other data sources. We know the rough signatures,

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Speaker 5: we know where to look, but let's find the people

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Speaker 5: that by name so we can tell a little bit

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Speaker 5: more of their background, their story.

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Speaker 1: I love that.

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Speaker 3: Looking at yacht registries to find rich people. It makes sense,

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Speaker 3: but I'm guessing not many people have done it before. Yeah.

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Speaker 1: I'm curious, like from a pure like economics perspective, when

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Speaker 1: we think about, let's say someone's salary, it's like, okay,

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Speaker 1: here's a really talented person. Maybe they're really great with computers.

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Speaker 1: Maybe they have a lovely voice that sounds great on podcasts.

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Speaker 1: And they're like, okay, this is maybe a rare skill,

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Speaker 1: et cetera. So they're going to get a lot of money.

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Speaker 1: Or maybe we think about like someone who makes a

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Speaker 1: great invention or whatever, and everyone wants to buy it

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Speaker 1: and they get a slice of it. And so they

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Speaker 1: make a lot of money. Let's just stick with car dealers,

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Speaker 1: for example. How would you characterize what these very successful

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Speaker 1: car dealers have brought to the table such that the

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Speaker 1: economy has rewarded them with such riches?

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Speaker 2: So that's a great question.

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Speaker 6: I think, let me tell two stories of car dealers

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Speaker 6: that kind of give a flavor of the complexity, even

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Speaker 6: within car dealers. So one of my favorite ones is

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Speaker 6: a story we tell in the book is about Larry Miller.

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Speaker 6: And Larry Miller is from Utah. He basically worked in

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Speaker 6: the parts department at a Toyota and he learned every single,

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Speaker 6: almost like your Aldi guy. He like knew all the

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Speaker 6: parts and he turned it from the 961st best car

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Speaker 6: dealer of parts, of Toyota parts, to the first.

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Speaker 2: And he's killing it. He really is good.

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Speaker 6: He started managing a couple different dealerships for the owner.

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Speaker 6: And then he sits down at a Denny's, and the

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Speaker 6: owner is like, hey, you know, I know the plan

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Speaker 6: was for you to run things, but it turns out

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Speaker 6: that my sons need to run it.

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Speaker 3: Sorry.

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Speaker 5: And.

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Speaker 6: he like, you know, on his heels, he eventually finds

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Speaker 6: somebody else to buy one dealership and that becomes two

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Speaker 6: dealerships and eventually 40 something dealerships and he buys the

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Speaker 6: Utah Jazz. And so that is like, you know, the

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Speaker 6: good story. He developed some skill.

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Speaker 1: He really was- Wait, actually on that last point, can

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Speaker 1: you clarify? Okay, so he really gets to know the

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Speaker 1: parts better than anyone else. Can you actually just operationalize

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Speaker 1: that a little bit more? What he contributed to the market,

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Speaker 1: so to speak, such that either at his first place

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Speaker 1: of employment or at his own entrepreneurship, he goes, they

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Speaker 1: go from 961 to one or whatever it is.

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Speaker 2: Yeah.

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Speaker 6: So I think it's, you know, when you're dealing with

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Speaker 6: customers being really responsive, understanding what they need and just

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Speaker 6: You know, being hardworking and diligent and delivering and having

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Speaker 6: a reputation for, okay, if I have some need, I'm

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Speaker 6: going to go to this guy.

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Speaker 2: And it grew over time.

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Speaker 6: That's one, you know, broader lesson of the book is

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Speaker 6: that it takes a long time, you know, in contrast

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Speaker 6: to the Silicon Valley, move fast and break things. It's

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Speaker 6: often move slow and make things. It takes decades often.

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Speaker 6: So that's kind of, in my view, Larry Miller's the

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Speaker 6: good case.

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Speaker 2: But the more.

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Speaker 6: Troublesome case where you have more regulatory capture are other

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Speaker 6: auto dealers who are quite protected. And we can go

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Speaker 6: into the history of auto dealers if you want, but

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Speaker 6: the short version is they have a bunch of legal

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Speaker 6: protections that were meant to protect the little guy from

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Speaker 6: big manufacturers of auto companies. Ford or GM might put

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Speaker 6: a bunch of cars on your lot, and that was

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Speaker 6: hurting the small guy.

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Speaker 2: So over.

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Speaker 6: series of decades, there are a bunch of franchise protections.

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Speaker 6: And then if you go forward to say 2008, when

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Speaker 6: we had the financial crisis, the auto team had to

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Speaker 6: deal with the situation where Chrysler, for example, had three

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Speaker 6: times as many dealerships as Toyota, even though they sold

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Speaker 6: the same number of cars.

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Speaker 2: And so they had all these extra dealers. And why

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Speaker 2: do they have them?

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Speaker 6: It's because they have a bunch of protections where you

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Speaker 6: have to pay them off. You have to pay three

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Speaker 6: years of rent and a bunch of things that really

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Speaker 6: don't make sense. And so there's a lot of protections

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Speaker 6: from local competition. There are pretty sweet deals that were

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Speaker 6: in the name of protecting the little guy that don't

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Speaker 6: make a lot of economic sense. So I think some

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Speaker 6: of it is rents. And both of those things are true.

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Speaker 6: And that's partly why things are complex, is that you

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Speaker 6: can have stories like Larry Miller, and you can have

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Speaker 6: regional auto dealers that are getting more money than they

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Speaker 6: probably would because they're getting paid a lot for warranties,

440
00:20:53,680 --> 00:20:57,000
Speaker 6: for example. that are a little more than what the

441
00:20:57,040 --> 00:20:58,740
Speaker 6: market would pay them otherwise.

442
00:20:58,760 --> 00:21:03,649
Speaker 5: They have a local monopoly. You can't sell the Toyota

443
00:21:03,710 --> 00:21:08,330
Speaker 5: within a geographic boundary of the existing dealership. The manufacturer

444
00:21:08,350 --> 00:21:11,990
Speaker 5: has very limited power to affect that competition in any

445
00:21:12,030 --> 00:21:14,970
Speaker 5: way to introduce. There's really barriers to entry. That's a

446
00:21:14,990 --> 00:21:17,450
Speaker 5: big part of it. Then they sell these ancillary services

447
00:21:18,130 --> 00:21:20,129
Speaker 5: in the dealership where the markups are much higher than

448
00:21:20,190 --> 00:21:24,280
Speaker 5: on just the immediate sale of the car. The warranty

449
00:21:24,320 --> 00:21:27,550
Speaker 5: repair the financing is a big source of profits, I

450
00:21:27,570 --> 00:21:30,109
Speaker 5: think for the dealers. And it's like, well, you know,

451
00:21:30,170 --> 00:21:31,909
Speaker 5: are you going to buy the, you can get the

452
00:21:31,950 --> 00:21:33,510
Speaker 5: loan at the dealer. You're going to go to a

453
00:21:33,550 --> 00:21:35,430
Speaker 5: bank separately when you're buying the car there on the lot.

454
00:21:35,450 --> 00:21:37,969
Speaker 5: So this like first mover advantage to sell all these

455
00:21:38,010 --> 00:21:41,130
Speaker 5: ancillary services. And it's a huge part of like the

456
00:21:41,210 --> 00:21:43,950
Speaker 5: margins and the profitability of these dealerships. And as the

457
00:21:43,990 --> 00:21:47,940
Speaker 5: economy grows, you still have this like fixed entrant, you know,

458
00:21:48,140 --> 00:21:51,340
Speaker 5: and like, you can't get a car through any other

459
00:21:51,420 --> 00:21:53,480
Speaker 5: pipe other than through that pipe. And so they just

460
00:21:53,520 --> 00:21:55,900
Speaker 5: grab a chunk. as it's passing through.

461
00:21:56,140 --> 00:21:58,060
Speaker 3: Yeah, this was really interesting to me in the book

462
00:21:58,080 --> 00:22:01,709
Speaker 3: because we're used to thinking about antitrust or competition issues

463
00:22:01,790 --> 00:22:05,810
Speaker 3: as these like big national scale companies, but actually you

464
00:22:05,850 --> 00:22:10,230
Speaker 3: talk a lot about sort of regionalized competition problems, which

465
00:22:10,270 --> 00:22:13,070
Speaker 3: was very interesting. I have a bunch of other questions,

466
00:22:13,109 --> 00:22:14,570
Speaker 3: but I want to ask you one more just on

467
00:22:14,710 --> 00:22:17,930
Speaker 3: sort of local regulatory protections and loopholes. Can you talk

468
00:22:17,970 --> 00:22:21,030
Speaker 3: about beer distribution? Because that was a really interesting thing,

469
00:22:21,410 --> 00:22:21,740
Speaker 3: one to me.

470
00:22:23,030 --> 00:22:25,389
Speaker 5: We're very amused by the beer distributor. So one of

471
00:22:25,450 --> 00:22:27,810
Speaker 5: the things for folks that are listening, if you're like walking,

472
00:22:27,970 --> 00:22:31,460
Speaker 5: like I walked from my home to the office and

473
00:22:32,240 --> 00:22:33,940
Speaker 5: passed by a truck that says Budweiser on the side.

474
00:22:34,560 --> 00:22:36,700
Speaker 5: You know, Budweiser doesn't own that truck. A distributor does.

475
00:22:36,740 --> 00:22:38,500
Speaker 5: You look on the door and there's the name of

476
00:22:38,520 --> 00:22:41,200
Speaker 5: the distributor. You might see Reyes or somebody like that.

477
00:22:41,850 --> 00:22:45,510
Speaker 5: These are incredibly wealthy businesses that sit between manufacturers and

478
00:22:45,590 --> 00:22:49,189
Speaker 5: end users, like bars, restaurants, groceries, and so on. And

479
00:22:49,250 --> 00:22:51,510
Speaker 5: depending on the state, similar to the auto dealers or

480
00:22:51,530 --> 00:22:54,080
Speaker 5: these franchise protections, So you have to pass through the

481
00:22:54,119 --> 00:22:58,080
Speaker 5: distributors and these way these contracts get protected. So then,

482
00:22:58,560 --> 00:23:01,239
Speaker 5: you know, as demand for beer grows, the distributors just

483
00:23:01,260 --> 00:23:03,920
Speaker 5: sort of like in the middle, this middle tier was again,

484
00:23:03,970 --> 00:23:07,169
Speaker 5: sort of protecting, you know, the little guy from the

485
00:23:07,210 --> 00:23:10,490
Speaker 5: macro brewers. But over time, these little guys have become

486
00:23:10,710 --> 00:23:12,129
Speaker 5: really big guys.

487
00:23:13,130 --> 00:23:15,250
Speaker 3: Um, and you know, the idea was you didn't want

488
00:23:15,270 --> 00:23:18,869
Speaker 3: the big brewers to dominate distribution as well as the

489
00:23:18,930 --> 00:23:20,090
Speaker 3: actual selling of the product.

490
00:23:20,170 --> 00:23:22,689
Speaker 5: That's right. And it's sort of emerged post prohibition as

491
00:23:22,730 --> 00:23:25,669
Speaker 5: this like sort of system let's avoid the system, the

492
00:23:25,730 --> 00:23:29,830
Speaker 5: situation where, um, the manufacturers have so much power over

493
00:23:30,210 --> 00:23:32,580
Speaker 5: the rest of the system in terms of what is available.

494
00:23:33,400 --> 00:23:35,760
Speaker 5: But then the distributors end up having just a ton

495
00:23:35,780 --> 00:23:37,760
Speaker 5: of power and they get super rich. So they show

496
00:23:37,859 --> 00:23:40,100
Speaker 5: up within the jet data a lot. And there was

497
00:23:40,119 --> 00:23:43,389
Speaker 5: this like conference in San Antonio. And the mayor called

498
00:23:43,450 --> 00:23:46,310
Speaker 5: the lobbyists who organized the conference, was like, you know,

499
00:23:46,330 --> 00:23:48,770
Speaker 5: this is amazing. I've never seen so many jets at

500
00:23:48,810 --> 00:23:52,140
Speaker 5: the San Antonio airport as when the beer wholesalers were there.

501
00:23:53,300 --> 00:23:58,500
Speaker 1: You mentioned a disagreement or a different interpretation than capital

502
00:23:58,580 --> 00:24:01,919
Speaker 1: picketing and size, et cetera. What is the essence of

503
00:24:01,940 --> 00:24:05,209
Speaker 1: the conclusions or the things that you've discovered today? that

504
00:24:05,270 --> 00:24:08,510
Speaker 1: you feel like someone coming at the wealth inequality question

505
00:24:08,850 --> 00:24:11,990
Speaker 1: from their work, you would have a different interpretation of it.

506
00:24:12,060 --> 00:24:13,940
Speaker 1: But we've never actually had either one of them on

507
00:24:13,960 --> 00:24:16,600
Speaker 1: the show, so maybe we'll get them on soon because

508
00:24:16,760 --> 00:24:19,840
Speaker 1: we should. But what is the sort of the crux

509
00:24:19,940 --> 00:24:21,180
Speaker 1: of how you see things differently?

510
00:24:21,820 --> 00:24:23,880
Speaker 5: So we joke that our book is kind of like

511
00:24:23,960 --> 00:24:26,899
Speaker 5: pickety with people. So if you happen to read Capital,

512
00:24:27,380 --> 00:24:29,650
Speaker 5: which is a difficult read, I think we actually also

513
00:24:29,810 --> 00:24:31,859
Speaker 5: joke that our book we wrote so people could read it,

514
00:24:31,869 --> 00:24:32,730
Speaker 5: not just collect it.

515
00:24:33,160 --> 00:24:34,940
Speaker 1: Because that book, it's a really good read.

516
00:24:34,980 --> 00:24:36,320
Speaker 3: I'm just going to reiterate that.

517
00:24:36,460 --> 00:24:38,420
Speaker 5: So it's like, we're going to have, you know, that

518
00:24:38,460 --> 00:24:40,260
Speaker 5: book is like the top 1% is this kind of

519
00:24:40,420 --> 00:24:46,130
Speaker 5: nameless faceless entity. Um, and they're just generating capital income.

520
00:24:46,170 --> 00:24:48,250
Speaker 5: And so you kind of are meant to think about

521
00:24:48,510 --> 00:24:52,570
Speaker 5: almost gilded age monopolists who have massive companies and are

522
00:24:52,590 --> 00:24:54,690
Speaker 5: just collecting the rents or the capital income or the

523
00:24:54,730 --> 00:24:58,129
Speaker 5: interest or whatever. And, um, it's about this accumulation of

524
00:24:58,190 --> 00:25:00,830
Speaker 5: massive troves of wealth that are generating this almost passive return.

525
00:25:01,720 --> 00:25:03,659
Speaker 5: And we're saying, well, actually, when you look at the data,

526
00:25:03,720 --> 00:25:06,660
Speaker 5: it's actually more this like these millions of private business

527
00:25:06,740 --> 00:25:09,200
Speaker 5: owners who are actively owning and operating businesses in a

528
00:25:09,220 --> 00:25:14,109
Speaker 5: large case. Their concentrated portfolio is really the business is

529
00:25:14,170 --> 00:25:18,270
Speaker 5: kind of the main asset. And it's not really just

530
00:25:18,750 --> 00:25:21,260
Speaker 5: financial capital. It's kind of like human capital is a

531
00:25:21,280 --> 00:25:23,859
Speaker 5: pretty important part of it. Broadly defined, it's sort of

532
00:25:23,900 --> 00:25:27,879
Speaker 5: like the networks, the experience. Maybe it's like having access

533
00:25:27,920 --> 00:25:30,880
Speaker 5: to the contract or the special like market position, that's

534
00:25:30,900 --> 00:25:33,020
Speaker 5: an important asset. But that's like sort of a different

535
00:25:33,060 --> 00:25:35,909
Speaker 5: story about who's really rich. That's sort of this more

536
00:25:36,030 --> 00:25:39,389
Speaker 5: active kind of human. And yeah, there's good, bad and

537
00:25:39,430 --> 00:25:41,730
Speaker 5: ugly of the humans there. But that's kind of the

538
00:25:41,790 --> 00:25:43,290
Speaker 5: distinction I think that we draw.

539
00:25:44,090 --> 00:25:45,850
Speaker 3: Since you talk to a lot of these people, I

540
00:25:45,869 --> 00:25:49,030
Speaker 3: have a sort of anthropological question, but do they see

541
00:25:49,070 --> 00:25:52,300
Speaker 3: themselves as wealthy? Because we touched on this in the beginning,

542
00:25:52,359 --> 00:25:57,160
Speaker 3: but everything is sort of like shaded as small business ownership, entrepreneurs.

543
00:25:57,740 --> 00:26:00,130
Speaker 3: You talk to a lot of people who like Did

544
00:26:00,230 --> 00:26:03,430
Speaker 3: start from humble beginnings, but now have millions and millions

545
00:26:03,450 --> 00:26:06,270
Speaker 3: of dollars. Do they do they see themselves as the rich,

546
00:26:06,369 --> 00:26:07,210
Speaker 3: as the one percent?

547
00:26:08,010 --> 00:26:11,010
Speaker 6: Usually they they have a pretty good idea. Often it's

548
00:26:11,090 --> 00:26:14,010
Speaker 6: because it takes so long to get wealthy there. You know,

549
00:26:14,030 --> 00:26:16,550
Speaker 6: they've gone through the life cycle. So one of my

550
00:26:16,650 --> 00:26:18,990
Speaker 6: favorite characters that we talk to in the book is

551
00:26:19,040 --> 00:26:19,760
Speaker 6: Dick Portillo.

552
00:26:19,800 --> 00:26:21,460
Speaker 2: And he started he grew up in the projects.

553
00:26:22,190 --> 00:26:25,159
Speaker 6: Of Chicago, the Cabrini greens, and he didn't know how

554
00:26:25,180 --> 00:26:27,640
Speaker 6: to make a hot dog and then started a hot

555
00:26:27,660 --> 00:26:30,220
Speaker 6: dog company and was washing the dishes.

556
00:26:30,000 --> 00:26:31,480
Speaker 2: By hand because there wasn't running water.

557
00:26:32,500 --> 00:26:36,340
Speaker 6: And he ended up over decades selling a lot of

558
00:26:36,359 --> 00:26:38,780
Speaker 6: hot dogs to become a billionaire. And I think it's

559
00:26:38,800 --> 00:26:42,179
Speaker 6: a really useful example because it shows like, well, do

560
00:26:42,200 --> 00:26:45,600
Speaker 6: you have to have market power to be a billionaire? No,

561
00:26:45,640 --> 00:26:48,380
Speaker 6: you can actually sell hot dogs to super competitive industry.

562
00:26:48,930 --> 00:26:51,480
Speaker 2: And then is he rich? Well, he has a really

563
00:26:51,520 --> 00:26:52,300
Speaker 2: big penthouse.

564
00:26:52,400 --> 00:26:55,000
Speaker 6: He's got a yacht called Top Dog, which is just

565
00:26:55,040 --> 00:26:57,900
Speaker 6: a great name for the yacht. And so it depends

566
00:26:57,940 --> 00:27:00,240
Speaker 6: on when you're talking to him. I think when you

567
00:27:00,580 --> 00:27:03,040
Speaker 6: find him in his 30s washing stuff by hand, he

568
00:27:03,080 --> 00:27:05,750
Speaker 6: does not feel rich. But when he's got a yacht

569
00:27:05,950 --> 00:27:09,930
Speaker 6: and a huge IPO that they couldn't send him a

570
00:27:09,970 --> 00:27:13,230
Speaker 6: check because he's his bank account wasn't sophisticated enough to

571
00:27:13,250 --> 00:27:14,830
Speaker 6: take the check when he was just like, I'm getting

572
00:27:14,890 --> 00:27:17,330
Speaker 6: on my yacht and leaving. I think he knew. The

573
00:27:17,350 --> 00:27:19,830
Speaker 6: short answer is it depends on when in their lifetimes,

574
00:27:19,850 --> 00:27:22,430
Speaker 6: because it does really take a long time. There's another

575
00:27:22,470 --> 00:27:27,060
Speaker 6: woman who talked to, she spent two decades making frozen quiche.

576
00:27:27,080 --> 00:27:31,199
Speaker 6: Oh yeah, the quiche lady. Yeah. And it was pretty

577
00:27:31,380 --> 00:27:33,619
Speaker 6: tenuous for a long time before she got some big

578
00:27:33,660 --> 00:27:36,379
Speaker 6: contracts to go through. And so I don't think she felt...

579
00:27:36,790 --> 00:27:39,230
Speaker 6: that wealthy from her business. It seemed like it was

580
00:27:39,270 --> 00:27:41,430
Speaker 6: losing money until it really wasn't.

581
00:27:41,730 --> 00:27:44,750
Speaker 1: This is maybe more of a sociological question than an

582
00:27:45,050 --> 00:27:47,830
Speaker 1: economics question, but I think it relates to who feels

583
00:27:47,910 --> 00:27:51,020
Speaker 1: rich and who doesn't. When I think about coastal rich,

584
00:27:51,080 --> 00:27:54,660
Speaker 1: and by that I mean San Francisco or New York

585
00:27:54,740 --> 00:27:59,720
Speaker 1: City rich, I often think about heads of really gigantic...

586
00:28:00,240 --> 00:28:07,629
Speaker 1: heavily bureaucratic, layered hierarchical organizations. I imagine someone like Jamie

587
00:28:07,650 --> 00:28:11,890
Speaker 1: Dimon or the CEO, there are numerous layers. A, they're

588
00:28:11,930 --> 00:28:15,310
Speaker 1: often very educated, and there are also going to be

589
00:28:15,350 --> 00:28:20,379
Speaker 1: just numerous layers of the organization, and there will be

590
00:28:20,420 --> 00:28:23,000
Speaker 1: people who work at J.P. Morgan or whatever who never

591
00:28:23,480 --> 00:28:27,680
Speaker 1: interact ever with the CEO, etc., Whereas in my mind,

592
00:28:27,740 --> 00:28:30,980
Speaker 1: the sort of everyday millionaires, the head of the beverage

593
00:28:31,040 --> 00:28:35,120
Speaker 1: distributor and the car dealership, in my mind, I suspect

594
00:28:35,160 --> 00:28:38,980
Speaker 1: these organizations are like that CEO is probably much more

595
00:28:39,110 --> 00:28:43,270
Speaker 1: in touch on some level on a day-to-day basis with

596
00:28:43,330 --> 00:28:48,110
Speaker 1: the mass of like the workers at that organization, maybe

597
00:28:48,170 --> 00:28:51,570
Speaker 1: on the floor with them, many cases like at a dealership, etc.,

598
00:28:52,330 --> 00:28:56,210
Speaker 1: And I'm curious if that resonates and whether that's sort

599
00:28:56,270 --> 00:28:58,050
Speaker 1: of like, you know, when we think about sort of,

600
00:28:58,050 --> 00:29:02,030
Speaker 1: you know, someone who has a yacht is obviously mega rich,

601
00:29:02,310 --> 00:29:06,460
Speaker 1: but on the, that's unquestionable, but also that just on

602
00:29:06,520 --> 00:29:09,580
Speaker 1: a day-to-day basis, I suspect they have a lot more

603
00:29:09,640 --> 00:29:14,320
Speaker 1: connection and like interaction with capital W workers than many

604
00:29:14,360 --> 00:29:15,920
Speaker 1: is sort of like the coastal rich do.

605
00:29:16,220 --> 00:29:16,400
Speaker 6: Yeah.

606
00:29:16,420 --> 00:29:19,290
Speaker 5: So like the Portillo, when he was growing the business,

607
00:29:20,470 --> 00:29:22,760
Speaker 5: They didn't have offices for the managers because he wanted

608
00:29:22,780 --> 00:29:24,380
Speaker 5: the people to be on the floor all the time.

609
00:29:25,000 --> 00:29:26,780
Speaker 5: There was a bit of a marine ethic that he

610
00:29:27,320 --> 00:29:30,880
Speaker 5: brought to running this business. He was there for opening.

611
00:29:31,460 --> 00:29:33,430
Speaker 5: He was very detail-oriented.

612
00:29:33,490 --> 00:29:34,410
Speaker 2: He had no investors.

613
00:29:34,870 --> 00:29:36,870
Speaker 5: He owned all the real estate. He tried to take

614
00:29:36,930 --> 00:29:41,020
Speaker 5: no debt, reinvesting every dollar. He had kids, but it

615
00:29:41,060 --> 00:29:43,190
Speaker 5: was pretty clear when he was moving into his 80s,

616
00:29:43,660 --> 00:29:46,100
Speaker 5: this is not a family business. This is Dick's business.

617
00:29:46,880 --> 00:29:50,000
Speaker 5: And there's kind of a relentlessness to that character that

618
00:29:50,020 --> 00:29:52,670
Speaker 5: I think we see in many of the stories. The

619
00:29:52,710 --> 00:29:54,550
Speaker 5: first generation founders look like that.

620
00:29:54,980 --> 00:29:55,180
Speaker 6: Yeah.

621
00:29:55,290 --> 00:29:57,490
Speaker 3: One thing that stood out to me is all of

622
00:29:57,550 --> 00:29:59,110
Speaker 3: these people don't seem to have hobbies.

623
00:29:59,490 --> 00:30:00,850
Speaker 5: They hate hobbies. Yeah.

624
00:30:00,990 --> 00:30:01,550
Speaker 2: They hate hobbies.

625
00:30:01,570 --> 00:30:02,910
Speaker 5: John Osher is so funny.

626
00:30:03,310 --> 00:30:03,630
Speaker 2: Dr.

627
00:30:03,670 --> 00:30:06,090
Speaker 5: John, he's not a doctor, who started the $ 5 electric

628
00:30:06,110 --> 00:30:10,590
Speaker 5: toothbrush and sold that business after having sold other businesses

629
00:30:10,630 --> 00:30:13,370
Speaker 5: before and probably had enough money before starting that business.

630
00:30:13,850 --> 00:30:16,710
Speaker 5: But he tried to take up golf from Florida. He's like,

631
00:30:17,160 --> 00:30:19,960
Speaker 5: So bored by it. Maybe not a great swing.

632
00:30:20,010 --> 00:30:20,380
Speaker 6: I don't know.

633
00:30:20,790 --> 00:30:24,070
Speaker 5: But went back and started this $ 5 toothbrush business in

634
00:30:24,090 --> 00:30:27,070
Speaker 5: the dot-com era, which is exactly the kind of company

635
00:30:27,090 --> 00:30:28,710
Speaker 5: you would think would make you hundreds of millions of

636
00:30:28,730 --> 00:30:32,080
Speaker 5: dollars during the dot-com era. It's only $ 5 electric toothbrushes.

637
00:30:33,140 --> 00:30:36,100
Speaker 5: But that's what he did because he just couldn't sit still.

638
00:30:36,440 --> 00:30:40,040
Speaker 5: I mean, he had a personality, clearly, because even as

639
00:30:40,080 --> 00:30:44,150
Speaker 5: a kid, his first business was was um don't tell

640
00:30:44,190 --> 00:30:49,690
Speaker 5: your son the story his first business so his parents

641
00:30:49,930 --> 00:30:52,810
Speaker 5: just took like one of those painting classes where you

642
00:30:52,850 --> 00:30:54,830
Speaker 5: like have models come in and do the like the

643
00:30:54,890 --> 00:30:58,750
Speaker 5: nude paintings and they hid the paintings from like five

644
00:30:58,830 --> 00:31:00,710
Speaker 5: seven year old i don't know like uh in the

645
00:31:01,030 --> 00:31:03,950
Speaker 5: attic and he would charge a nickel to his friends.

646
00:31:03,580 --> 00:31:06,220
Speaker 1: To look at the paintings i would love this.

647
00:31:06,020 --> 00:31:10,720
Speaker 5: Yes that's the best margins of any business he ever started.

648
00:31:10,340 --> 00:31:15,270
Speaker 1: He can't uh He can't listen to this episode.

649
00:31:15,530 --> 00:31:16,470
Speaker 2: I'll stick to the children's book.

650
00:31:16,690 --> 00:31:17,250
Speaker 6: Yeah, yeah.

651
00:31:17,330 --> 00:31:18,350
Speaker 1: Maybe you just.

652
00:31:18,250 --> 00:31:18,930
Speaker 5: Paint still lifes.

653
00:31:20,990 --> 00:31:22,970
Speaker 1: Can we talk a little bit about, you mentioned dentists,

654
00:31:23,310 --> 00:31:26,830
Speaker 1: and I'm sort of curious whether dentists or doctors, what

655
00:31:26,910 --> 00:31:29,860
Speaker 1: is their role in the story of the medical profession

656
00:31:30,280 --> 00:31:31,520
Speaker 1: and how rich can they get?

657
00:31:31,670 --> 00:31:34,280
Speaker 6: Yeah, no, that's a great question because there's kind of

658
00:31:34,300 --> 00:31:38,800
Speaker 6: the Dick Portillo's more tactile, blue-collar rich, and then there

659
00:31:39,080 --> 00:31:41,280
Speaker 6: are the more white-collar ones. But if you own eight

660
00:31:41,300 --> 00:31:44,610
Speaker 6: dental practices— you can be really rich. I mean, I

661
00:31:44,630 --> 00:31:47,010
Speaker 6: think if you're just a normal dentist, you have a

662
00:31:47,030 --> 00:31:49,530
Speaker 6: pretty good life. But if you own a few offices,

663
00:31:49,550 --> 00:31:52,550
Speaker 6: you can have a great life. Dentists have some amazing stats.

664
00:31:52,570 --> 00:31:55,170
Speaker 6: I think they're number 21 on our list of top

665
00:31:55,710 --> 00:32:02,060
Speaker 6: pass-through four-digit industries by profit. And if you add up

666
00:32:02,160 --> 00:32:06,330
Speaker 6: all the revenue of dentists in America, it exceeds the

667
00:32:06,370 --> 00:32:11,760
Speaker 6: revenue generated by the NFL, the NBA, MLB, basically all

668
00:32:11,840 --> 00:32:15,400
Speaker 6: professional sports, and dentists are earning more. The joke we

669
00:32:15,420 --> 00:32:18,660
Speaker 6: like to tell is that everybody's got teeth. And so

670
00:32:18,700 --> 00:32:20,680
Speaker 6: if you own several of these, this is great. And

671
00:32:20,700 --> 00:32:24,760
Speaker 6: then they also are in every congressional district. And so

672
00:32:25,060 --> 00:32:27,340
Speaker 6: when you think about things like, oh, let's include dental

673
00:32:27,380 --> 00:32:30,660
Speaker 6: services and Medicare, for example, they lobby against that to

674
00:32:30,680 --> 00:32:32,680
Speaker 6: be like, well, we don't want price pressure from the government.

675
00:32:32,700 --> 00:32:36,020
Speaker 6: We want to make sure So there is a bit

676
00:32:36,420 --> 00:32:38,960
Speaker 6: of regulatory might, and that's really the case more for doctors, too.

677
00:32:39,480 --> 00:32:42,200
Speaker 5: Yeah, the doctors showed up in one of our early

678
00:32:42,480 --> 00:32:44,760
Speaker 5: statistical breakdowns. We were trying to figure out, okay, who

679
00:32:44,820 --> 00:32:47,890
Speaker 5: are these people entering all this pass-through income? We said,

680
00:32:47,950 --> 00:32:50,970
Speaker 5: what are the top four-digit industries and the top 1%? Doctors'

681
00:32:51,010 --> 00:32:54,150
Speaker 5: offices was number one. So this is like their industry

682
00:32:54,210 --> 00:32:58,550
Speaker 5: codes by the BEA. And so four-digit, there are about

683
00:32:58,550 --> 00:33:01,750
Speaker 5: 300 industries in the four-digit system. So that's like a pretty...

684
00:33:02,020 --> 00:33:04,500
Speaker 5: We're fine. Like doctor's office is different from dentist's office

685
00:33:04,540 --> 00:33:09,220
Speaker 5: is different from like a physical therapy type industry. As

686
00:33:09,240 --> 00:33:12,940
Speaker 5: an example, within services, doctors were number one in terms

687
00:33:12,960 --> 00:33:15,320
Speaker 5: of the number of amount of profits. And they had

688
00:33:15,340 --> 00:33:16,420
Speaker 5: just a ton of them.

689
00:33:16,700 --> 00:33:20,200
Speaker 1: Is it true that like dentists, like they're all really bored.

690
00:33:20,240 --> 00:33:23,060
Speaker 1: So they get really like tempted to finance films. Isn't

691
00:33:23,100 --> 00:33:25,620
Speaker 1: that like a big thing where it's like filmmakers, like

692
00:33:25,660 --> 00:33:27,760
Speaker 1: go to dentists, like this would be more exciting than

693
00:33:27,800 --> 00:33:30,160
Speaker 1: teeth put a hundred thousand dollars into this film.

694
00:33:30,880 --> 00:33:34,520
Speaker 5: They are, I think, sitting on pools of cash and

695
00:33:34,760 --> 00:33:36,560
Speaker 5: maybe they don't have so many ideas of what to

696
00:33:36,600 --> 00:33:39,980
Speaker 5: do with it. And so they make for maybe easy targets.

697
00:33:40,060 --> 00:33:43,830
Speaker 5: And because it's a big pool of cash, you know,

698
00:33:43,870 --> 00:33:45,990
Speaker 5: there's a lot of fishing to be done in that pool.

699
00:34:01,740 --> 00:34:05,370
Speaker 3: So speaking of dentists and doctors, this reminds me, you

700
00:34:05,410 --> 00:34:09,250
Speaker 3: have a working example in the book showing how the

701
00:34:09,469 --> 00:34:13,219
Speaker 3: S-Corp pass-through tax benefits actually works for, I think it

702
00:34:13,260 --> 00:34:18,600
Speaker 3: was for an anesthesiologist. And the one who is in

703
00:34:18,660 --> 00:34:22,569
Speaker 3: private practice who has their own S-Corp pays less tax

704
00:34:22,620 --> 00:34:27,060
Speaker 3: than the one who's employed and earning income. What exactly

705
00:34:27,120 --> 00:34:30,940
Speaker 3: are we incentivizing or supposed to be incentivizing through the

706
00:34:31,000 --> 00:34:33,219
Speaker 3: tax code here? Because again, these are two people who

707
00:34:33,260 --> 00:34:35,930
Speaker 3: are ostensibly doing the same jobs. One is paying a

708
00:34:35,950 --> 00:34:38,730
Speaker 3: lot less tax than the other one by virtue of

709
00:34:38,790 --> 00:34:39,810
Speaker 3: the pass-through arrangement.

710
00:34:39,910 --> 00:34:44,530
Speaker 5: Yeah, so we are taxing salaries at a different rate

711
00:34:44,650 --> 00:34:49,319
Speaker 5: than taxing business income. And so that anesthesiologist that runs

712
00:34:49,900 --> 00:34:53,719
Speaker 5: most of the business through the S Corp, is not

713
00:34:53,760 --> 00:34:58,420
Speaker 5: paying payroll tax, not paying like ACA sort of surcharge,

714
00:34:58,560 --> 00:35:02,560
Speaker 5: which is uncapped Medicare tax on the profits that come through,

715
00:35:03,300 --> 00:35:08,009
Speaker 5: which is maybe particularly galling because they're billing Medicare for

716
00:35:08,050 --> 00:35:11,310
Speaker 5: so much services and they themselves on their mostly labor

717
00:35:11,350 --> 00:35:14,890
Speaker 5: income aren't paying the Medicare tax on it. So, you know,

718
00:35:14,910 --> 00:35:17,470
Speaker 5: there is a minimum level of compensation, which the IRS

719
00:35:17,510 --> 00:35:19,730
Speaker 5: rules say you're supposed to pay yourself in wages. So

720
00:35:19,750 --> 00:35:22,930
Speaker 5: you pay payroll tax on that. everything above that reasonable

721
00:35:22,969 --> 00:35:26,570
Speaker 5: compensation limit is kind of profit and just falls in

722
00:35:26,610 --> 00:35:30,819
Speaker 5: this other bucket. And then in the 2017 reform, they

723
00:35:30,840 --> 00:35:33,480
Speaker 5: brought down the pass-through rate even further from, say, the

724
00:35:33,500 --> 00:35:38,360
Speaker 5: top rate of 37% to below 30%. They put in

725
00:35:38,380 --> 00:35:41,819
Speaker 5: these guardrails for doctors and skilled service professions so they

726
00:35:41,840 --> 00:35:45,380
Speaker 5: couldn't get that rate. But there are strategies that certain

727
00:35:45,520 --> 00:35:47,640
Speaker 5: ones take to try and move a bunch of the

728
00:35:48,170 --> 00:35:51,450
Speaker 5: ancillary services again, or the surgery piece, or the Maybe

729
00:35:51,469 --> 00:35:53,529
Speaker 5: they rent some real estate back. And so they can

730
00:35:53,590 --> 00:35:55,590
Speaker 5: shift more and more of that income, I think, into

731
00:35:55,650 --> 00:35:58,830
Speaker 5: forms that then get that below 30% rate. Whereas the

732
00:35:58,910 --> 00:36:01,270
Speaker 5: surgeon that's working in the hospital system as a salaried

733
00:36:01,330 --> 00:36:05,180
Speaker 5: worker is paying the top individual tax rate, 37%. The

734
00:36:05,200 --> 00:36:07,660
Speaker 5: payroll tax, another 3%. You have all the state and

735
00:36:07,680 --> 00:36:10,839
Speaker 5: local taxes. The cap on state and local tax deductions

736
00:36:11,480 --> 00:36:13,859
Speaker 5: applies to that salaried worker. But if you run all

737
00:36:13,880 --> 00:36:17,080
Speaker 5: your income through the business, then the states have allowed

738
00:36:17,100 --> 00:36:19,580
Speaker 5: them to basically take the deductions through the business instead

739
00:36:19,620 --> 00:36:20,520
Speaker 5: and get that uncapped.

740
00:36:20,880 --> 00:36:23,460
Speaker 3: What's the thinking behind this? We just want lots of

741
00:36:23,540 --> 00:36:25,920
Speaker 3: small businesses to be created. And so here are some

742
00:36:25,960 --> 00:36:26,720
Speaker 3: tax benefits.

743
00:36:27,719 --> 00:36:31,649
Speaker 5: I think it's kind of an unintended consequence of trying

744
00:36:31,670 --> 00:36:33,850
Speaker 5: to simplify the tax code for the mom and pops

745
00:36:33,890 --> 00:36:34,730
Speaker 5: in the small business.

746
00:36:34,850 --> 00:36:35,089
Speaker 3: Yeah.

747
00:36:35,489 --> 00:36:38,549
Speaker 5: And then people sort of look at the rules and say, well,

748
00:36:38,570 --> 00:36:40,630
Speaker 5: how do I minimize my tax given the rules are

749
00:36:40,650 --> 00:36:43,840
Speaker 5: what they are? And then how do we protect those

750
00:36:43,980 --> 00:36:46,830
Speaker 5: loopholes that sort of pop up over time? I like

751
00:36:46,850 --> 00:36:48,430
Speaker 5: to think of the tax code like a house that

752
00:36:48,450 --> 00:36:50,969
Speaker 5: sort of depreciates over time and you kind of need

753
00:36:50,989 --> 00:36:53,969
Speaker 5: to like invest in it, like repair windows, replace the floor,

754
00:36:54,010 --> 00:36:57,080
Speaker 5: this kind of thing. These loopholes are like the depreciation

755
00:36:57,100 --> 00:37:00,379
Speaker 5: that's applied to this house over time. The reason they've

756
00:37:00,400 --> 00:37:03,279
Speaker 5: been so persistent, we haven't fixed them, is because it

757
00:37:03,320 --> 00:37:07,620
Speaker 5: turns out that like a quarter of federal elected officials

758
00:37:07,660 --> 00:37:09,040
Speaker 5: are also pass-through business owners.

759
00:37:11,400 --> 00:37:14,160
Speaker 1: Yeah, this is always like my impression is like, you know,

760
00:37:15,110 --> 00:37:18,890
Speaker 1: pick a state, Iowa or Ohio. And it's like, you know,

761
00:37:18,930 --> 00:37:21,880
Speaker 1: I get some politician who's worth a hundred million dollars,

762
00:37:22,520 --> 00:37:25,000
Speaker 1: but I, and they often tend to do very well.

763
00:37:25,140 --> 00:37:29,070
Speaker 1: And they like position themselves as like, you know, popular

764
00:37:29,110 --> 00:37:32,190
Speaker 1: among the working class voters and, And they probably do

765
00:37:32,430 --> 00:37:35,109
Speaker 1: connect well with them in part because they like, this

766
00:37:35,170 --> 00:37:36,529
Speaker 1: is what I was going back to. They're on the

767
00:37:36,550 --> 00:37:39,790
Speaker 1: shop floor. They probably do connect with those voters better

768
00:37:40,370 --> 00:37:43,649
Speaker 1: than like, you know, CEO of a big bank who's

769
00:37:43,690 --> 00:37:48,549
Speaker 1: like completely disconnected from them. But on the tax question specifically,

770
00:37:48,930 --> 00:37:51,260
Speaker 1: you know, when it comes to the prospect of raising

771
00:37:51,340 --> 00:37:54,920
Speaker 1: revenue for the government or raising taxes, you get a

772
00:37:54,960 --> 00:37:58,580
Speaker 1: litany of issues. So, A, no one likes paying higher taxes, period.

773
00:37:59,280 --> 00:38:01,120
Speaker 1: Then there's you get to say, well, you know, we

774
00:38:01,160 --> 00:38:06,440
Speaker 1: don't want to disincentivize entrepreneurship or success or whatever. And

775
00:38:06,480 --> 00:38:09,080
Speaker 1: taxes are bad for that reason. And then there's the

776
00:38:09,140 --> 00:38:11,910
Speaker 1: third layer. It's like, well, even if we want, even

777
00:38:11,969 --> 00:38:14,150
Speaker 1: if we want to have higher taxes and even if

778
00:38:14,190 --> 00:38:16,790
Speaker 1: we weren't worried about that, it would be impossible. And

779
00:38:16,830 --> 00:38:19,790
Speaker 1: the rich always find a way. And so it'd be self-defeating.

780
00:38:19,810 --> 00:38:22,210
Speaker 1: There's no way to like, actually, you can't do it.

781
00:38:22,210 --> 00:38:26,830
Speaker 1: They'll find some loophole or whatever. And that third part

782
00:38:26,890 --> 00:38:31,710
Speaker 1: I'm really curious about, is that real? Or in your research,

783
00:38:31,910 --> 00:38:35,890
Speaker 1: if there were the political will to raise taxes, are

784
00:38:35,930 --> 00:38:37,460
Speaker 1: there straightforward ways to do it?

785
00:38:37,730 --> 00:38:40,010
Speaker 6: Yeah, so it's certainly real. I mean, we could start

786
00:38:40,030 --> 00:38:42,360
Speaker 6: with the most recent. So first of all, you asked

787
00:38:42,400 --> 00:38:46,380
Speaker 6: about Iowa and Ohio. So Ohio, one of the senators

788
00:38:46,540 --> 00:38:49,400
Speaker 6: is Bernie Moreno, who's an auto dealer. So that's a

789
00:38:49,980 --> 00:38:51,880
Speaker 6: great one. And he had a proposal with Elizabeth Warren

790
00:38:52,380 --> 00:38:53,840
Speaker 6: to shore up Social Security.

791
00:38:54,410 --> 00:38:55,180
Speaker 2: How do you want to do it?

792
00:38:55,930 --> 00:38:59,530
Speaker 6: You put it on the payroll of all these salaried workers.

793
00:38:59,969 --> 00:39:03,440
Speaker 6: There's no mention of these very wealthy business owners, like

794
00:39:03,460 --> 00:39:06,480
Speaker 6: it's all salaries. So the anesthesiologist that you were talking

795
00:39:06,500 --> 00:39:09,570
Speaker 6: about before, one of them is getting off, you know,

796
00:39:09,590 --> 00:39:12,310
Speaker 6: scot-free and the other one is paying a lot more.

797
00:39:12,330 --> 00:39:15,900
Speaker 6: So I think That's one very recent example.

798
00:39:16,140 --> 00:39:17,180
Speaker 2: Now, it's interesting.

799
00:39:17,200 --> 00:39:20,180
Speaker 6: We've been talking to folks on the Hill about, you know,

800
00:39:20,400 --> 00:39:22,960
Speaker 6: what the prospect of raising taxes on the rich is.

801
00:39:23,610 --> 00:39:25,810
Speaker 6: One natural place to start is this trillion dollar tax

802
00:39:25,830 --> 00:39:27,710
Speaker 6: cut that was part of the One Big Beautiful Business.

803
00:39:30,040 --> 00:39:32,940
Speaker 6: bill that no one likes to talk about. And even

804
00:39:33,000 --> 00:39:36,400
Speaker 6: among Democratic staffers, there's a lot of reticence of like, oh,

805
00:39:36,420 --> 00:39:38,560
Speaker 6: I don't know, small business. And you're like, this is

806
00:39:38,600 --> 00:39:40,739
Speaker 6: not small. You know, if you take all pass-through income,

807
00:39:40,840 --> 00:39:44,070
Speaker 6: only 20% of it is actually small business, according to

808
00:39:44,110 --> 00:39:47,590
Speaker 6: an old Treasury study. The vast majority are big businesses

809
00:39:48,110 --> 00:39:51,210
Speaker 6: that happen to be private. And so I think the

810
00:39:51,250 --> 00:39:53,750
Speaker 6: most realistic thing, if you can't get rid of some

811
00:39:53,790 --> 00:39:56,050
Speaker 6: of these loopholes, is to limit them by income.

812
00:39:56,910 --> 00:39:58,860
Speaker 2: And that, to me, seems like one path.

813
00:39:58,960 --> 00:40:01,380
Speaker 1: When you say limit them by income, explain that further.

814
00:40:01,400 --> 00:40:04,380
Speaker 6: So, you know, say you cannot get a deduction that

815
00:40:04,420 --> 00:40:05,739
Speaker 6: takes your rate from 37 to 30 if you have

816
00:40:05,760 --> 00:40:11,180
Speaker 6: income in excess of. a million dollars or 400K. You

817
00:40:11,200 --> 00:40:13,600
Speaker 6: pick the number that, I don't like these numbers, but

818
00:40:14,080 --> 00:40:17,359
Speaker 6: that would be one mechanism for saying, okay, somebody has

819
00:40:17,420 --> 00:40:19,569
Speaker 6: more than a million dollars in business income. They are

820
00:40:19,820 --> 00:40:22,310
Speaker 6: doing very well. You can't plausibly call them small.

821
00:40:22,550 --> 00:40:24,770
Speaker 3: This reminds me, actually, can we talk about the role

822
00:40:24,950 --> 00:40:28,290
Speaker 3: of private equity in wealth creation? Because again, when we

823
00:40:28,330 --> 00:40:31,950
Speaker 3: talk about a lot of these businesses, whether it's HVAC or,

824
00:40:31,969 --> 00:40:36,580
Speaker 3: I don't know, veterinarian services or dentist offices, A lot

825
00:40:36,620 --> 00:40:39,940
Speaker 3: of the exits come in the form of a PE buyout.

826
00:40:40,320 --> 00:40:44,030
Speaker 5: We love this line from your colleague, Matt Levine, who

827
00:40:44,570 --> 00:40:46,840
Speaker 5: tells this joke, what am I doing after business school? Oh,

828
00:40:46,850 --> 00:40:48,630
Speaker 5: I'm going to work for a prestigious private equity firm

829
00:40:48,650 --> 00:40:50,790
Speaker 5: in San Francisco so I can run their plumbing company

830
00:40:50,830 --> 00:40:56,220
Speaker 5: in Mississippi. And I teach private equity to MBA students

831
00:40:56,780 --> 00:40:59,879
Speaker 5: and I talk about these unglamorous deals. So we're talking

832
00:40:59,900 --> 00:41:02,400
Speaker 5: about founder-owned firms and more than half of the deal,

833
00:41:02,420 --> 00:41:04,759
Speaker 5: the typical deals in the pipeline from private equity are

834
00:41:04,780 --> 00:41:08,120
Speaker 5: going to be founder- generated firms that maybe there's no

835
00:41:08,160 --> 00:41:11,500
Speaker 5: natural successor. So private equity sort of comes in and said,

836
00:41:11,580 --> 00:41:13,600
Speaker 5: we can take the baton and maybe we'll be willing

837
00:41:13,620 --> 00:41:15,239
Speaker 5: to pay a pretty high price because we can see

838
00:41:15,280 --> 00:41:18,300
Speaker 5: the growth trajectory for it. And so they're looking for

839
00:41:18,320 --> 00:41:21,420
Speaker 5: these unglamorous businesses that are quite profitable. Maybe they have

840
00:41:21,440 --> 00:41:25,020
Speaker 5: some competitive advantage because of either a barrier to entry

841
00:41:25,060 --> 00:41:28,380
Speaker 5: or established brand and customer network and so on. And

842
00:41:28,400 --> 00:41:30,980
Speaker 5: they're going to go in and take it over for

843
00:41:31,020 --> 00:41:33,680
Speaker 5: the founder who doesn't really want to give it to

844
00:41:33,700 --> 00:41:35,180
Speaker 5: the kids or doesn't have a kid that wants to

845
00:41:35,219 --> 00:41:39,060
Speaker 5: take over the plumbing company So private equity portfolios, if

846
00:41:39,100 --> 00:41:42,739
Speaker 5: you go below the glamorous New York job where you

847
00:41:42,780 --> 00:41:44,460
Speaker 5: go like where they're actually on site, what are they

848
00:41:44,480 --> 00:41:48,120
Speaker 5: looking at? It's a mid-market manufacturer making cabinets or it's

849
00:41:48,140 --> 00:41:50,620
Speaker 5: sort of the shade store or something like that. These

850
00:41:50,700 --> 00:41:54,180
Speaker 5: regional businesses that they want to grow further, maybe consolidate

851
00:41:54,239 --> 00:41:57,120
Speaker 5: a few of them. Maybe they can list it or

852
00:41:57,160 --> 00:41:59,440
Speaker 5: break it up or whatever down the road. But it's

853
00:41:59,500 --> 00:42:02,609
Speaker 5: transformed finance in a way because a lot of capital

854
00:42:02,640 --> 00:42:03,240
Speaker 5: is flowing.

855
00:42:03,260 --> 00:42:03,760
Speaker 1: Yeah.

856
00:42:04,620 --> 00:42:05,459
Speaker 5: And there's the end point.

857
00:42:05,540 --> 00:42:08,430
Speaker 1: Are these search funds? Is that what they are called? Like,

858
00:42:08,950 --> 00:42:11,290
Speaker 1: I've heard that term. And I'm curious. I've been like

859
00:42:11,330 --> 00:42:14,910
Speaker 1: seeing the, you know, people on Twitter, but they like, oh,

860
00:42:15,390 --> 00:42:19,180
Speaker 1: all this stuff. I'm rolling up cabinet makers or whatever.

861
00:42:19,690 --> 00:42:24,930
Speaker 1: pool servicing providers or lawnmowers, or like, I imagine like

862
00:42:25,030 --> 00:42:27,910
Speaker 1: a pest repellent is a pretty big thing, but I've

863
00:42:27,930 --> 00:42:30,650
Speaker 1: been reading about them for like a long time now.

864
00:42:31,190 --> 00:42:33,730
Speaker 1: Is there still more juice for the squeeze? Is there

865
00:42:33,770 --> 00:42:37,010
Speaker 1: still a bunch of these boomer businesses where the kids

866
00:42:37,050 --> 00:42:39,160
Speaker 1: don't want it? And in theory, you could take over

867
00:42:39,200 --> 00:42:42,000
Speaker 1: the local lawn maintenance company and make more money or

868
00:42:42,040 --> 00:42:43,040
Speaker 1: how much is left there?

869
00:42:43,360 --> 00:42:45,399
Speaker 5: Well, we think about the great wealth transfer, right? This

870
00:42:45,880 --> 00:42:49,680
Speaker 5: tens of trillions of dollars of wealth in the sort

871
00:42:49,700 --> 00:42:53,060
Speaker 5: of silent generation or baby boomer generation, more than half

872
00:42:53,100 --> 00:42:55,280
Speaker 5: of that is like in the form of business wealth,

873
00:42:55,469 --> 00:42:57,610
Speaker 5: but there's a huge amount of business wealth that's coming.

874
00:42:58,350 --> 00:43:01,950
Speaker 5: And there aren't necessarily kids or great, you know, people

875
00:43:01,989 --> 00:43:05,190
Speaker 5: who want to run it from within those businesses. Some

876
00:43:05,210 --> 00:43:08,190
Speaker 5: of them are too small to attract private equity and trust.

877
00:43:08,690 --> 00:43:11,450
Speaker 5: And so, yeah, so I see students first, it was

878
00:43:11,469 --> 00:43:13,570
Speaker 5: like a few students that knew about the search fund thing.

879
00:43:15,330 --> 00:43:17,640
Speaker 5: I started in 2014 teaching and I was talking about

880
00:43:17,680 --> 00:43:21,480
Speaker 5: search funds. So this is like a student spends two years,

881
00:43:21,960 --> 00:43:25,360
Speaker 5: usually have a partner, looking for one of these businesses

882
00:43:25,400 --> 00:43:28,200
Speaker 5: that's kind of too small to attract private equity interest.

883
00:43:28,620 --> 00:43:30,739
Speaker 5: So they can do a mini LBO. They can do

884
00:43:30,800 --> 00:43:34,009
Speaker 5: a mini buyout. If they find a good candidate, the

885
00:43:34,050 --> 00:43:36,510
Speaker 5: investors that funded the search will do a second step.

886
00:43:37,070 --> 00:43:40,149
Speaker 5: So there's optionality for the investors that makes it such

887
00:43:40,170 --> 00:43:42,590
Speaker 5: that even the median return in search funds is negative.

888
00:43:43,200 --> 00:43:44,500
Speaker 5: Most of them lose a little bit of money because

889
00:43:44,520 --> 00:43:46,859
Speaker 5: the search doesn't work. But the average return is like

890
00:43:48,620 --> 00:43:51,450
Speaker 5: low 20s, I think. And the sort of data is

891
00:43:51,469 --> 00:43:53,029
Speaker 5: a little hard to come by. But I think the

892
00:43:53,050 --> 00:43:54,410
Speaker 5: people who are trying to do a good job of

893
00:43:54,450 --> 00:43:58,570
Speaker 5: comprehensively measuring it. And it's rolling up pest control businesses.

894
00:43:58,950 --> 00:44:00,410
Speaker 5: One of the most successful ones I talk about in

895
00:44:00,430 --> 00:44:03,810
Speaker 5: class is drug treatment facilities in California. There was like

896
00:44:03,850 --> 00:44:07,550
Speaker 5: a huge growth in demand for that, unfortunately. And these facilities,

897
00:44:07,610 --> 00:44:11,379
Speaker 5: running them, running them well, doing the billing. Doing the

898
00:44:11,430 --> 00:44:14,020
Speaker 5: kind of, you know, they're running those businesses is complicated.

899
00:44:14,040 --> 00:44:16,879
Speaker 5: A lot of regulatory things you have to manage as well.

900
00:44:17,040 --> 00:44:19,799
Speaker 5: But that person, that was like 100x investment for the

901
00:44:19,860 --> 00:44:22,980
Speaker 5: investors and for the founder. Oh, I could go to

902
00:44:23,040 --> 00:44:25,620
Speaker 5: banking and then maybe flip into PE and then maybe

903
00:44:25,660 --> 00:44:28,610
Speaker 5: get like junior partner and get up there in like

904
00:44:28,650 --> 00:44:31,969
Speaker 5: a decade, 15 years or something. Or I could like

905
00:44:32,150 --> 00:44:34,250
Speaker 5: start this business and run a business, get all this

906
00:44:34,290 --> 00:44:40,330
Speaker 5: weird experience doing deals, industry learning, raising money, operating something.

907
00:44:41,030 --> 00:44:43,370
Speaker 5: like kind of bootstrap my way into being a seasoned

908
00:44:43,410 --> 00:44:47,030
Speaker 5: founder and an executive and own 20% of the company

909
00:44:47,090 --> 00:44:48,230
Speaker 5: on the other end if it does well.

910
00:44:48,910 --> 00:44:51,799
Speaker 3: This also reminds me, in terms of just creating an

911
00:44:51,880 --> 00:44:57,339
Speaker 3: environment that encourages entrepreneurs, you actually found some really interesting

912
00:44:58,400 --> 00:45:00,859
Speaker 3: or did some really interesting research showing that if you

913
00:45:00,920 --> 00:45:03,500
Speaker 3: are in a place with a lot of entrepreneurs, you

914
00:45:04,140 --> 00:45:06,969
Speaker 3: are more likely to be a business owner yourself.

915
00:45:07,860 --> 00:45:10,319
Speaker 6: Yeah. So one of the cool data things, you know,

916
00:45:10,340 --> 00:45:13,180
Speaker 6: to go back to the very beginning, when we built

917
00:45:13,219 --> 00:45:15,700
Speaker 6: this data set that for the first time linked owners

918
00:45:17,219 --> 00:45:20,790
Speaker 6: to businesses and their workers, we could track every single

919
00:45:21,110 --> 00:45:24,290
Speaker 6: new firm in America since 2000. So we have the

920
00:45:24,330 --> 00:45:26,790
Speaker 6: best data on entrepreneurs that has ever existed. And we

921
00:45:26,810 --> 00:45:30,230
Speaker 6: can say, you know, how many people are going to

922
00:45:30,290 --> 00:45:32,590
Speaker 6: start businesses if they're poor, if they grow up in

923
00:45:32,610 --> 00:45:34,110
Speaker 6: Salt Lake City versus Mississippi.

924
00:45:34,130 --> 00:45:37,490
Speaker 2: So Salt Lake, the probability you start a star business.

925
00:45:37,550 --> 00:45:40,960
Speaker 6: Meaning the top 10% of revenue or employment is three

926
00:45:40,980 --> 00:45:44,719
Speaker 6: times higher than if you grew up in Mississippi. And anecdotally,

927
00:45:44,739 --> 00:45:48,620
Speaker 6: it's not entirely clear what's going on. There's the nickname,

928
00:45:48,660 --> 00:45:52,239
Speaker 6: the Silicon Slopes, Salt Lake, but some of it might

929
00:45:52,320 --> 00:45:56,300
Speaker 6: be that there's a large Mormon population and a common

930
00:45:56,340 --> 00:45:58,279
Speaker 6: thing to do is to go on missionary work and

931
00:45:58,550 --> 00:46:00,790
Speaker 6: bang on doors and have people tell you no, and

932
00:46:00,810 --> 00:46:02,969
Speaker 6: you keep going, you get some resilience and grit and

933
00:46:02,989 --> 00:46:06,120
Speaker 6: like there's no hard data and that's what's going on,

934
00:46:06,140 --> 00:46:07,879
Speaker 6: but that seems kind of plausible to me.

935
00:46:08,200 --> 00:46:09,109
Speaker 2: So that's, that's one thing.

936
00:46:09,120 --> 00:46:11,910
Speaker 6: And you see it also for the children of business owners,

937
00:46:11,950 --> 00:46:14,310
Speaker 6: they're way more likely to start a business themselves, even

938
00:46:14,390 --> 00:46:16,870
Speaker 6: if you take all of the family firms out of

939
00:46:16,890 --> 00:46:19,850
Speaker 6: the data. So it's like, you're not just inheriting, you know,

940
00:46:19,870 --> 00:46:23,239
Speaker 6: your son is not inheriting the podcast, but just here's

941
00:46:23,280 --> 00:46:24,520
Speaker 6: all about money all the time.

942
00:46:25,300 --> 00:46:28,460
Speaker 2: Yeah. Round two, fourth generation podcast.

943
00:46:28,500 --> 00:46:28,660
Speaker 5: Yeah.

944
00:46:28,680 --> 00:46:30,759
Speaker 2: You know, you have to start the escort first.

945
00:46:31,480 --> 00:46:33,239
Speaker 1: You know, I'm sort of curious, it's sort of the

946
00:46:33,280 --> 00:46:36,759
Speaker 1: search funds thing. You know, one of the things you

947
00:46:36,780 --> 00:46:39,319
Speaker 1: hear is like, okay, some guy has a pool company. Well,

948
00:46:40,420 --> 00:46:43,100
Speaker 1: maybe people like that guy. And maybe like some, then

949
00:46:43,140 --> 00:46:45,839
Speaker 1: some guy from Wharton, it's like, we don't know you,

950
00:46:45,860 --> 00:46:48,950
Speaker 1: we don't. And then, so I imagine that's tricky. And

951
00:46:48,989 --> 00:46:52,350
Speaker 1: then also I imagine, you know, the cost of labor

952
00:46:52,469 --> 00:46:56,730
Speaker 1: has gone up and low end wages have risen a lot.

953
00:46:56,790 --> 00:47:02,410
Speaker 1: And of course, immigration crackdowns, et cetera. Are people stepping on,

954
00:47:02,890 --> 00:47:05,170
Speaker 1: do you see a lot of people stepping on rakes

955
00:47:05,230 --> 00:47:08,770
Speaker 1: here and they're like, you know what? Maybe this business

956
00:47:08,810 --> 00:47:12,970
Speaker 1: idea to like, you know, power wash people's sidewalks was

957
00:47:13,050 --> 00:47:15,640
Speaker 1: not quite the path to riches that I expected it

958
00:47:15,680 --> 00:47:15,960
Speaker 1: to be.

959
00:47:16,200 --> 00:47:19,799
Speaker 5: So there is risk in doing it, right? But you

960
00:47:19,840 --> 00:47:21,620
Speaker 5: can take that experience as long as you don't do

961
00:47:21,660 --> 00:47:22,340
Speaker 5: anything illegal.

962
00:47:22,360 --> 00:47:22,940
Speaker 1: Okay.

963
00:47:23,320 --> 00:47:27,030
Speaker 5: You actually have on your resume this experience. You learned

964
00:47:27,050 --> 00:47:28,850
Speaker 5: a bunch of industries. You did a bunch of kind of,

965
00:47:29,390 --> 00:47:33,510
Speaker 5: deal M & A type work, basically analyzing industries, making

966
00:47:33,550 --> 00:47:36,800
Speaker 5: pitch decks, this kind of thing, like leading up the search.

967
00:47:37,420 --> 00:47:40,180
Speaker 5: So that actually is part of maybe the sales pitch

968
00:47:40,320 --> 00:47:42,399
Speaker 5: if you decide to return to the workforce or do

969
00:47:42,420 --> 00:47:43,020
Speaker 5: something else.

970
00:47:43,100 --> 00:47:48,200
Speaker 1: Do people get SBA loans? Is there publicly subsidized leverage

971
00:47:48,239 --> 00:47:48,960
Speaker 1: for these buyers?

972
00:47:49,480 --> 00:47:51,630
Speaker 5: It is possible. Most of the debt that's used in

973
00:47:51,650 --> 00:47:54,870
Speaker 5: those deals is coming from banks. I mean, they're established

974
00:47:54,890 --> 00:47:57,089
Speaker 5: businesses with a decent track record, most of the case.

975
00:47:57,130 --> 00:47:59,719
Speaker 5: So you can really You can leverage that. They're not

976
00:47:59,760 --> 00:48:05,070
Speaker 5: leveraging like 80% leverage. They're more like 40, 50. The

977
00:48:05,110 --> 00:48:08,130
Speaker 5: typical search fund deal is there's a lot of equity

978
00:48:08,150 --> 00:48:09,830
Speaker 5: there too. So I think there's more cushion than a

979
00:48:09,850 --> 00:48:11,150
Speaker 5: typical larger LBO.

980
00:48:12,050 --> 00:48:15,530
Speaker 3: Just going back to the pickety part of this conversation

981
00:48:15,710 --> 00:48:18,719
Speaker 3: and the idea of income inequality, what do we see

982
00:48:18,760 --> 00:48:21,060
Speaker 3: when we look at the S-Corp data in terms of

983
00:48:21,140 --> 00:48:24,920
Speaker 3: how much is actually accruing to owners versus workers?

984
00:48:25,620 --> 00:48:28,720
Speaker 5: Yeah, so- It is quite striking. There are different ways

985
00:48:28,780 --> 00:48:32,080
Speaker 5: to think about growth of this group and their income.

986
00:48:32,520 --> 00:48:35,800
Speaker 5: It's like, is the pie growing? Are they just capturing

987
00:48:35,840 --> 00:48:38,060
Speaker 5: a bigger and bigger slice of a pie that's not growing?

988
00:48:38,520 --> 00:48:41,219
Speaker 5: And we can sort of decompose the growth. So first

989
00:48:41,239 --> 00:48:42,380
Speaker 5: of all, if you look at the growth of the

990
00:48:42,400 --> 00:48:45,340
Speaker 5: business sector, more than half of it, like 60% of

991
00:48:45,380 --> 00:48:47,940
Speaker 5: the growth over the last 25 years is coming in

992
00:48:47,980 --> 00:48:52,140
Speaker 5: pass-through forms. There's a lot of economic activity coming in

993
00:48:52,160 --> 00:48:56,150
Speaker 5: pass-through form. The pie is growing. the share that the

994
00:48:56,210 --> 00:48:58,710
Speaker 5: owners are capturing of the pie has gone from like

995
00:48:58,750 --> 00:49:01,870
Speaker 5: a third or 40% to like 50%. They're capturing a

996
00:49:01,910 --> 00:49:03,029
Speaker 5: larger and larger slice.

997
00:49:03,860 --> 00:49:05,939
Speaker 3: Can I just ask, what's your explanation for that?

998
00:49:06,520 --> 00:49:08,960
Speaker 5: I think some of it is, it's easy to think

999
00:49:08,980 --> 00:49:12,720
Speaker 5: about in the skilled services. The demand for the skilled

1000
00:49:12,739 --> 00:49:16,910
Speaker 5: service at the top, that doctor's practice, is really driven

1001
00:49:17,010 --> 00:49:19,610
Speaker 5: by kind of the scarce factor being brought, which is

1002
00:49:19,630 --> 00:49:21,640
Speaker 5: the expertise. the reputation of those folks at the top

1003
00:49:21,680 --> 00:49:24,540
Speaker 5: of the business. So they can just raise the price.

1004
00:49:24,560 --> 00:49:29,160
Speaker 5: I mean, it is the case that we have 30%

1005
00:49:29,160 --> 00:49:33,450
Speaker 5: fewer doctors in the OECD average. So the AMA is

1006
00:49:33,489 --> 00:49:35,589
Speaker 5: helping restrict the supply of doctors in a way that's

1007
00:49:35,630 --> 00:49:40,080
Speaker 5: quite useful, the So as demand grows for that stuff,

1008
00:49:40,840 --> 00:49:42,950
Speaker 5: like it's, there's a lot of surplus, but you don't

1009
00:49:43,010 --> 00:49:45,670
Speaker 5: need to pay the nurses or the assistants in that

1010
00:49:45,710 --> 00:49:47,710
Speaker 5: medical practice. So there's a piece of it there. I

1011
00:49:48,610 --> 00:49:50,350
Speaker 5: don't know, like in some of the other industries that

1012
00:49:50,390 --> 00:49:55,200
Speaker 5: are more like competitive, maybe there's like a mixed, you know,

1013
00:49:55,260 --> 00:49:57,820
Speaker 5: productivity and invention and stuff, but I think that's an

1014
00:49:57,840 --> 00:49:58,580
Speaker 5: important part of it.

1015
00:49:58,820 --> 00:49:59,020
Speaker 2: Yeah.

1016
00:49:59,060 --> 00:50:01,339
Speaker 6: So, you know, we tell different examples, like we have

1017
00:50:01,460 --> 00:50:04,620
Speaker 6: a colleague, Kevin Murphy, who's one of my favorite economists.

1018
00:50:05,040 --> 00:50:07,780
Speaker 6: And he just, his first language is economics, not English.

1019
00:50:08,580 --> 00:50:10,080
Speaker 6: He can just go up to the board. Just as

1020
00:50:10,120 --> 00:50:13,090
Speaker 6: a quick aside, I used to, as a first year

1021
00:50:13,150 --> 00:50:15,430
Speaker 6: faculty member, I went and sat in his class. It

1022
00:50:15,450 --> 00:50:18,590
Speaker 6: was the most amazing class. You sit there and he's like,

1023
00:50:18,610 --> 00:50:20,390
Speaker 6: what are you guys interested in? It's like, why is

1024
00:50:20,410 --> 00:50:22,739
Speaker 6: the labor share falling? And he just goes up to

1025
00:50:22,780 --> 00:50:25,200
Speaker 6: the board and just derive like three or four equations

1026
00:50:25,219 --> 00:50:28,060
Speaker 6: and have the most It's coherent and beautiful, and.

1027
00:50:28,020 --> 00:50:28,550
Speaker 2: He just does it.

1028
00:50:28,560 --> 00:50:29,340
Speaker 1: That's cool.

1029
00:50:29,360 --> 00:50:32,299
Speaker 6: So anyway, so Kevin, he does some econ consulting. And

1030
00:50:32,800 --> 00:50:36,140
Speaker 6: if Kevin makes a merger more likely to happen, say

1031
00:50:36,320 --> 00:50:38,720
Speaker 6: it's 5% more likely to happen, this is a billion-dollar merger,

1032
00:50:39,350 --> 00:50:42,370
Speaker 6: that's a lot of value from the perspective of the

1033
00:50:42,610 --> 00:50:45,010
Speaker 6: people paying them to try to help the merger get through.

1034
00:50:45,610 --> 00:50:48,350
Speaker 6: And so, you know, mergers are increasingly valuable as the

1035
00:50:48,390 --> 00:50:50,890
Speaker 6: economy grows, and Kevin's just doing his thing. And so

1036
00:50:50,930 --> 00:50:52,350
Speaker 6: I think that's also part of the story. So some

1037
00:50:52,370 --> 00:50:55,529
Speaker 6: of it is real skill and expertise involved. Sometimes it's

1038
00:50:56,090 --> 00:50:59,450
Speaker 6: induced scarcity from policy. And all of those things are

1039
00:50:59,750 --> 00:51:01,790
Speaker 6: kind of a blend. It depends on the complexities of

1040
00:51:01,910 --> 00:51:05,190
Speaker 6: what specifically is going on in that industry. But a

1041
00:51:05,210 --> 00:51:07,450
Speaker 6: material part is a bigger slice of the pie. And

1042
00:51:07,469 --> 00:51:10,670
Speaker 6: then also another big chunk is growing the pie. And

1043
00:51:10,710 --> 00:51:13,379
Speaker 6: both of those things are true. And we have stories

1044
00:51:13,430 --> 00:51:17,739
Speaker 6: and examples where workers are brought up along the CEO.

1045
00:51:18,520 --> 00:51:21,200
Speaker 6: And then another example is where most of it is

1046
00:51:21,239 --> 00:51:24,700
Speaker 6: just going to the owner. And That's a big part

1047
00:51:24,739 --> 00:51:27,410
Speaker 6: of what's actually going on in the data for rising inequality.

1048
00:51:27,430 --> 00:51:29,690
Speaker 6: It's a bigger pie and a bigger slice. And it's

1049
00:51:29,710 --> 00:51:32,930
Speaker 6: like 50-50 for the quantitative breakdown between those two.

1050
00:51:33,230 --> 00:51:36,209
Speaker 5: Profit sharing isn't the default motive, I think, for these folks,

1051
00:51:36,230 --> 00:51:38,490
Speaker 5: especially the first gen. They're not like, oh, how do

1052
00:51:38,510 --> 00:51:41,330
Speaker 5: I share more of the profits with the workers? It's like, well,

1053
00:51:41,350 --> 00:51:43,469
Speaker 5: I pay people for what they do and I incentivize them.

1054
00:51:43,510 --> 00:51:46,110
Speaker 5: And then, you know, if we have more money at

1055
00:51:46,130 --> 00:51:47,380
Speaker 5: the end of the day, I'm going to reinvest in

1056
00:51:47,390 --> 00:51:49,279
Speaker 5: the business or I'm going to take it. And that's

1057
00:51:49,340 --> 00:51:53,569
Speaker 5: kind of like the mentality of this founder mindset. generation, especially.

1058
00:51:54,570 --> 00:51:57,969
Speaker 5: This chemical importer exporter called Chem Impacts, you know, really

1059
00:51:58,010 --> 00:51:59,780
Speaker 5: like sexy name.

1060
00:52:01,560 --> 00:52:03,640
Speaker 3: There are a lot of unsexy names in this book,

1061
00:52:03,680 --> 00:52:04,100
Speaker 3: I must say.

1062
00:52:04,120 --> 00:52:05,660
Speaker 1: This is right. Glamorous, right.

1063
00:52:05,680 --> 00:52:08,379
Speaker 5: We like to talk about like the NBA player who

1064
00:52:08,719 --> 00:52:11,620
Speaker 5: made way more money running Wendy's franchises than being an

1065
00:52:11,719 --> 00:52:15,700
Speaker 5: NBA player, Junior Bridgman. So yeah, the whole franchise thing

1066
00:52:15,739 --> 00:52:19,089
Speaker 5: is a whole nother list of unsexy businesses to get

1067
00:52:19,130 --> 00:52:22,670
Speaker 5: really rich. But the Chem Impacts, they're She talked about

1068
00:52:22,710 --> 00:52:25,620
Speaker 5: how they were able to offshore and have the middle

1069
00:52:25,660 --> 00:52:28,279
Speaker 5: of the business sort of Indian people helping with the

1070
00:52:28,320 --> 00:52:30,940
Speaker 5: supply that, you know, through their family, they're able to

1071
00:52:30,980 --> 00:52:33,700
Speaker 5: connect to and hire. And so they're using the same

1072
00:52:33,760 --> 00:52:37,730
Speaker 5: kind of ways to offset sort of labor scarcity or,

1073
00:52:37,810 --> 00:52:40,170
Speaker 5: you know, to reduce labor demand, I think. And that

1074
00:52:40,250 --> 00:52:43,589
Speaker 5: then means that they can be more productive. But the

1075
00:52:43,630 --> 00:52:47,010
Speaker 5: workers are not, you know. capturing those proceeds.

1076
00:52:47,030 --> 00:52:48,190
Speaker 2: But let me just give you some numbers because they

1077
00:52:48,210 --> 00:52:49,290
Speaker 2: really are striking.

1078
00:52:49,370 --> 00:52:54,560
Speaker 6: So I think if memory serves, in 2001, the value

1079
00:52:54,700 --> 00:52:56,540
Speaker 6: add per worker, so that's like add up profits and

1080
00:52:56,680 --> 00:52:58,320
Speaker 6: pay for people, was.

1081
00:52:58,719 --> 00:52:59,540
Speaker 2: 34K a worker.

1082
00:53:00,120 --> 00:53:06,350
Speaker 6: And then that went up 18K to 52K in 2021.

1083
00:53:06,350 --> 00:53:08,470
Speaker 2: So there's 18K per worker of growth.

1084
00:53:09,610 --> 00:53:12,629
Speaker 6: Owners got 15 of that and workers got the rest.

1085
00:53:12,730 --> 00:53:15,680
Speaker 6: So the numbers are really striking in terms of how

1086
00:53:15,780 --> 00:53:19,339
Speaker 6: big owner you know that's part of why um there's

1087
00:53:19,420 --> 00:53:22,239
Speaker 6: so much abundant wealth in america that's why when you

1088
00:53:22,260 --> 00:53:25,399
Speaker 6: go to california or any nice mountain town or any

1089
00:53:25,500 --> 00:53:28,360
Speaker 6: nice lake there's a lot of boats like i used

1090
00:53:28,380 --> 00:53:29,520
Speaker 6: to go to the lake of the ozarks when i

1091
00:53:29,540 --> 00:53:32,339
Speaker 6: was growing up and You know, there's so many people

1092
00:53:32,380 --> 00:53:34,480
Speaker 6: with huge boats and you're like, what are these people doing?

1093
00:53:34,719 --> 00:53:35,640
Speaker 2: And it's pest control.

1094
00:53:35,660 --> 00:53:38,230
Speaker 6: It's all of these, you know, they're making the windows

1095
00:53:38,270 --> 00:53:41,590
Speaker 6: for drive-thrus, for example. Like there's so many stories you hear.

1096
00:53:41,730 --> 00:53:43,390
Speaker 6: And one of our favorite things now is just to

1097
00:53:43,430 --> 00:53:45,090
Speaker 6: go ask people like, who is the richest person in

1098
00:53:45,130 --> 00:53:45,630
Speaker 6: your high school?

1099
00:53:45,730 --> 00:53:46,390
Speaker 2: Like, what did they do?

1100
00:53:47,510 --> 00:53:50,090
Speaker 6: And the stories, I mean, we have a list of

1101
00:53:50,790 --> 00:53:53,140
Speaker 6: hundreds and hundreds of millions. We didn't even put in

1102
00:53:53,160 --> 00:53:56,080
Speaker 6: the book because there's too many good ones. It's a

1103
00:53:56,100 --> 00:53:56,779
Speaker 6: pretty fun game.

1104
00:53:57,620 --> 00:54:01,500
Speaker 3: Invented post-its. Does anyone get that reference?

1105
00:54:01,820 --> 00:54:02,900
Speaker 1: Oh, what's that from?

1106
00:54:03,300 --> 00:54:05,359
Speaker 3: Romy and Michelle's high school reunion.

1107
00:54:05,400 --> 00:54:06,620
Speaker 2: Yes, yes. I knew this.

1108
00:54:06,950 --> 00:54:07,189
Speaker 4: Yeah.

1109
00:54:07,430 --> 00:54:07,790
Speaker 2: All right.

1110
00:54:08,290 --> 00:54:10,210
Speaker 3: Eric and Owen, we're going to have to leave it there.

1111
00:54:10,250 --> 00:54:12,230
Speaker 3: But thank you so much for coming on All Plots.

1112
00:54:12,310 --> 00:54:15,450
Speaker 3: The book is The Everywhere Millionaire, and it is very,

1113
00:54:15,510 --> 00:54:17,020
Speaker 3: very good. So thank you so much.

1114
00:54:17,500 --> 00:54:18,580
Speaker 1: Thanks so much for having us.

1115
00:54:19,320 --> 00:54:19,600
Speaker 3: Yeah, thanks.

1116
00:54:19,660 --> 00:54:20,259
Speaker 2: It was a lot of fun.

1117
00:54:20,440 --> 00:54:21,140
Speaker 1: Thank you so much.

1118
00:54:35,210 --> 00:54:37,570
Speaker 3: So, Joe, I know I've been kind of gushing about this,

1119
00:54:37,770 --> 00:54:40,510
Speaker 3: but I do genuinely think that this is a piece

1120
00:54:40,590 --> 00:54:44,330
Speaker 3: of research based on a novel data set, to your

1121
00:54:44,390 --> 00:54:46,850
Speaker 3: point earlier, that has been missing from a lot of

1122
00:54:46,870 --> 00:54:49,410
Speaker 3: the discussion of the U.S. economy and what it looks

1123
00:54:49,450 --> 00:54:51,980
Speaker 3: like now. When we talk about billionaires, when we talk

1124
00:54:52,000 --> 00:54:54,720
Speaker 3: about rich people, we are so used to thinking about

1125
00:54:55,230 --> 00:54:59,590
Speaker 3: public companies, CEOs, tech, venture capitalists, that sort of thing.

1126
00:55:00,030 --> 00:55:03,640
Speaker 3: And as Owen and Eric have pointed out, the field

1127
00:55:03,880 --> 00:55:07,459
Speaker 3: of millionaires across America is much more varied than that.

1128
00:55:07,920 --> 00:55:09,540
Speaker 1: No, I couldn't agree more. I think this is such

1129
00:55:09,560 --> 00:55:14,640
Speaker 1: an important conversation and it's so interesting. I do think

1130
00:55:14,940 --> 00:55:17,920
Speaker 1: there's one part that I think is fairly understood. People

1131
00:55:18,020 --> 00:55:24,489
Speaker 1: know that car owners... are significant within their congressional districts.

1132
00:55:24,890 --> 00:55:28,210
Speaker 1: I think people have seen a lot of car owner politicians.

1133
00:55:29,030 --> 00:55:32,290
Speaker 1: They mentioned Bernie Moreno. I think people have intuition that

1134
00:55:32,370 --> 00:55:36,489
Speaker 1: car owners are often influential, particularly in the Republican side

1135
00:55:36,510 --> 00:55:40,970
Speaker 1: of the aisle, et cetera. But I think like, okay, oh,

1136
00:55:40,980 --> 00:55:43,379
Speaker 1: that's car owners. And I think the idea that there

1137
00:55:43,460 --> 00:55:51,560
Speaker 1: is this big swath of similarly structured businesses And across

1138
00:55:51,660 --> 00:55:56,340
Speaker 1: so many different industries with political influence is not sufficiently

1139
00:55:56,500 --> 00:56:01,259
Speaker 1: appreciated when people talk about inequality and when people think

1140
00:56:01,300 --> 00:56:04,029
Speaker 1: about the tax code, because there is so much talk

1141
00:56:04,090 --> 00:56:08,070
Speaker 1: about like, you know, taxing wealth in California. And I

1142
00:56:08,130 --> 00:56:10,089
Speaker 1: do think that if we're going to have, you know,

1143
00:56:10,230 --> 00:56:13,390
Speaker 1: at any point, if we're going to meaningfully raise revenue

1144
00:56:13,510 --> 00:56:15,950
Speaker 1: for the federal government, if we're ever going to do

1145
00:56:15,969 --> 00:56:20,739
Speaker 1: something to ameliorate inequality, then like clearly, you know, tech wealth,

1146
00:56:20,800 --> 00:56:23,700
Speaker 1: et cetera, is part of it. But there is so

1147
00:56:23,780 --> 00:56:29,610
Speaker 1: much more than just public company equity wealth that constitutes

1148
00:56:29,690 --> 00:56:32,750
Speaker 1: wealth in America that not only does it not really

1149
00:56:32,770 --> 00:56:35,989
Speaker 1: get talked about, but because people have this sort of

1150
00:56:36,090 --> 00:56:39,370
Speaker 1: impulsive love for small business, it's almost never on the table.

1151
00:56:39,730 --> 00:56:39,950
Speaker 6: Right.

1152
00:56:40,010 --> 00:56:42,839
Speaker 3: And it is worth really asking what you're incentivizing through

1153
00:56:42,860 --> 00:56:45,620
Speaker 3: the S-Corp structure if the idea is like, oh, well,

1154
00:56:45,719 --> 00:56:48,160
Speaker 3: we want a bunch of small businesses, but in effect,

1155
00:56:48,180 --> 00:56:52,420
Speaker 3: you're protecting a bunch of companies that are generating millions

1156
00:56:52,460 --> 00:56:53,940
Speaker 3: of dollars in revenue. Totally.

1157
00:56:54,300 --> 00:56:57,600
Speaker 1: And I like the characterization of the tax code as

1158
00:56:57,660 --> 00:57:03,839
Speaker 1: a house. The idea that the economy evolves. I mean,

1159
00:57:03,860 --> 00:57:06,839
Speaker 1: the economy evolves for structural reasons. There are new technologies,

1160
00:57:06,880 --> 00:57:08,880
Speaker 1: there are new things, so there are new types of businesses.

1161
00:57:09,400 --> 00:57:12,870
Speaker 1: And then, of course, tax optimization strategies evolve and people

1162
00:57:12,910 --> 00:57:15,910
Speaker 1: slowly discover there are different ways to structure their own

1163
00:57:15,990 --> 00:57:18,110
Speaker 1: work and business so that they can take advantage of

1164
00:57:18,170 --> 00:57:21,030
Speaker 1: lower rates, etc., And so the idea that like we

1165
00:57:21,090 --> 00:57:24,290
Speaker 1: need to, I mean, it seems like mostly it's a

1166
00:57:24,410 --> 00:57:27,650
Speaker 1: one way ratchet. Taxes just keep going lower. But the

1167
00:57:27,750 --> 00:57:30,380
Speaker 1: idea that like, no, like we need to repair this.

1168
00:57:30,400 --> 00:57:33,200
Speaker 1: We need to fix this. We need to adjust it

1169
00:57:33,240 --> 00:57:37,500
Speaker 1: for the current conditions rather than just finding places to cut.

1170
00:57:37,900 --> 00:57:39,360
Speaker 1: I like the house analogy.

1171
00:57:39,500 --> 00:57:41,720
Speaker 3: I think of the tax code as an amorphous blob

1172
00:57:41,780 --> 00:57:43,660
Speaker 3: that people are always poking with a stick.

1173
00:57:43,850 --> 00:57:44,970
Speaker 1: Can I just say something?

1174
00:57:45,190 --> 00:57:46,750
Speaker 6: Yeah. No.

1175
00:57:46,770 --> 00:57:54,040
Speaker 1: No. People like us who are like wage earners at

1176
00:57:54,340 --> 00:57:58,240
Speaker 1: companies who just get like a normal W-2 income. We

1177
00:57:58,280 --> 00:58:00,640
Speaker 1: are truly the most oppressed class in America. No, I

1178
00:58:00,680 --> 00:58:02,560
Speaker 1: don't believe that. But, you know, like all like the

1179
00:58:02,760 --> 00:58:06,440
Speaker 1: only the only like the only sort of class of

1180
00:58:06,500 --> 00:58:10,120
Speaker 1: people that really does not have some like very obvious

1181
00:58:10,240 --> 00:58:16,210
Speaker 1: way to minimize taxes is basically just workers at companies.

1182
00:58:16,860 --> 00:58:17,470
Speaker 3: Poor us.

1183
00:58:17,610 --> 00:58:18,190
Speaker 1: Poor us.

1184
00:58:18,370 --> 00:58:18,650
Speaker 2: All right.

1185
00:58:18,690 --> 00:58:19,390
Speaker 3: Shall we leave it there?

1186
00:58:19,490 --> 00:58:20,090
Speaker 2: Let's leave it there.

1187
00:58:20,590 --> 00:58:22,870
Speaker 3: This has been another episode of the Odd Thoughts Podcast.

1188
00:58:23,010 --> 00:58:25,670
Speaker 3: I'm Tracy Allaway. You can follow me at Tracy Allaway.

1189
00:58:25,830 --> 00:58:28,630
Speaker 1: And I'm Joe Weisenthal. You can follow me at The Stalwart.

1190
00:58:28,970 --> 00:58:33,200
Speaker 1: Check out Erica Nguyen's book, The Everywhere Millionaire. Follow our producers,

1191
00:58:33,240 --> 00:58:36,600
Speaker 1: Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashbot, Kale

1192
00:58:36,620 --> 00:58:40,180
Speaker 1: Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano.

1193
00:58:40,500 --> 00:58:43,190
Speaker 1: And for more Odd Lots content, go to Bloomberg.com slash

1194
00:58:43,210 --> 00:58:45,310
Speaker 1: Odd Lots. We have a daily newsletter on all of

1195
00:58:45,390 --> 00:58:47,430
Speaker 1: our episodes. And you can chat about all of these

1196
00:58:47,470 --> 00:58:51,890
Speaker 1: topics 24-7 in our Discord, discord.gg slash oddlots.

1197
00:58:52,310 --> 00:58:54,460
Speaker 3: And if you enjoyed this conversation, if you want us

1198
00:58:54,520 --> 00:58:56,820
Speaker 3: to do more episodes on the U.S. tax code, then

1199
00:58:56,900 --> 00:59:00,080
Speaker 3: please leave us a positive review on your favorite podcast platform.

1200
00:59:00,480 --> 00:59:02,880
Speaker 3: And remember, if you are a Bloomberg subscriber, you can

1201
00:59:02,900 --> 00:59:06,110
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1202
00:59:06,150 --> 00:59:08,410
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1203
00:59:08,430 --> 00:59:11,640
Speaker 3: Podcasts and follow the instructions there. Thanks for listening.

1204
00:59:29,200 --> 00:59:29,880
Speaker 2: Thank you.