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

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Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

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Speaker 2: This week on the podcast, I have an extra special guest,

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Speaker 2: really fascinating conversation with Philip Carlson Sleesact. He's got a

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Speaker 2: really interesting background. Chief economists at Sanford, Bernstein, worked at

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Speaker 2: the OECD, began at McKenzie, ended up as global chief

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Speaker 2: economists for the Boston Consultant Group, and really approaches economic

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Speaker 2: analysis from a very different perspective, critical of the industry's

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Speaker 2: over reliance on models, which have proven themselves to be

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Speaker 2: not great predictors of what happens next, especially when the

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Speaker 2: future in any way differs from the past, and so

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Speaker 2: when we have things like the dot com implosion, or

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Speaker 2: especially internal to the market, the financial crisis of OEEDO

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Speaker 2: nine and even COVID models just don't give you a

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Speaker 2: good assessment. And he describes how he reached this conclusion

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Speaker 2: in his book Shocks, Crises and False Alarms, How to

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Speaker 2: Assess True Macroeconomic Risk. He calls out a lot of

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Speaker 2: people who get things wrong, especially the doomsayers who not

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Speaker 2: only have been forecasting recessions incorrectly for I don't know

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Speaker 2: the better part of fifteen years, most especially since COVID,

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Speaker 2: but their models just simply don't allow them to understanding

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Speaker 2: a dynamic, changing, global, interconnected economy. I thought the book

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Speaker 2: was fascinating, and I thought our conversation was fascinating, and

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Speaker 2: I know you will also with no further ado, my

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Speaker 2: discussion with the Boston Consulting Groups. Philip Colson Slezak.

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Speaker 3: Thank you for having me.

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Speaker 2: So let's start with a little bit. I want to

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Speaker 2: talk about the book, but before we get to that,

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Speaker 2: let's talk a little bit about your background, which is

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Speaker 2: kind of fascinating for an American. You get a bachelor's

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Speaker 2: at Oxford, a PhD at the London School of Economics.

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Speaker 2: Was becoming an economist always the career plan.

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Speaker 3: Well, let me correct you right there.

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Speaker 2: I'm not American, You're not Where are you originally from?

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Speaker 4: I was born in Switzerland. I grew up there, but

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Speaker 4: in a number of other countries as well.

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Speaker 2: So you have sort of an American accent. How long

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Speaker 2: have you been here?

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Speaker 4: Early on as well in my youth and so, growing

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Speaker 4: up in different places, I always compared and contrasted what

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Speaker 4: I saw, So I developed an interest in economics. So

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Speaker 4: when it came to going to college, studying economics was

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Speaker 4: a very natural choice.

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Speaker 2: Where did you grow up in Switzerland?

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Speaker 3: Was born there? Okay?

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Speaker 2: I recently visited both Geneva and Lake Geneva up and

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Speaker 2: it's just spectacular. What a beautiful part of the world

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Speaker 2: it is. It really really impressive. So first job out

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Speaker 2: of school McKinsey, Is that right?

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Speaker 3: That's right?

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Speaker 2: And what was that experience like?

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Speaker 4: Well, so I studied economics at LSC actually not at Oxford,

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Speaker 4: at my PhD at Oxford, so the other way around,

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Speaker 4: and that was at the turn of the century.

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Speaker 3: Let me take a step back.

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Speaker 4: It was a turn of the century, and I emphasized

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Speaker 4: that because that was peak economics. So, you know, the

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Speaker 4: the ubras and the arrogance of the economics profession.

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Speaker 3: Was at its peak.

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Speaker 4: And you know, you're still seven eight years out from

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Speaker 4: the global financial crisis, which was a big humbling moment

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Speaker 4: for the profession. So everything was very model driven theory,

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Speaker 4: econometrics and all that.

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Speaker 2: Uh huh.

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Speaker 3: So you know, I didn't.

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Speaker 4: Feel comfortable even then as an undergraduate. Then as a

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Speaker 4: graduate student, I branched out. I started reading a lot more,

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Speaker 4: you know, going to political theory, finance history, much broader,

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Speaker 4: building a mosaic of knowledge and also methods and approaches,

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Speaker 4: frameworks and so at the end of my graduate studies

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Speaker 4: with a PhD, that's when I landed and consulting at McKinsey,

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Speaker 4: and the work was very different, so very nitty gritty, right,

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Speaker 4: you go deep into corporations other organizations.

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Speaker 3: You do very very granular work.

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Speaker 4: So coming with this big picture view of the world

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Speaker 4: and analyzing and going into this supernano micro part of

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Speaker 4: business was a big change.

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Speaker 2: Let's stay with the concept of peak economist. I think

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Speaker 2: it was Paul Krugman who did the saltwater versus freshwater comparison,

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Speaker 2: which was essentially the economists along the coasts seem to

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Speaker 2: have a very different model and very different approach to

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Speaker 2: doing macro versus people more inland, at least in the US.

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Speaker 2: Does that sort of di economy resonate with you, How

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Speaker 2: do you think about that?

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Speaker 4: Well, I generally view all of mainstream economics as as

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Speaker 4: too model based, master model mentality.

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Speaker 3: In the book sort of this belief.

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Speaker 4: That economics is a bit like a natural science and

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Speaker 4: we can pass it off as a natural science. That

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Speaker 4: belief is still still very much alive, and so physics envy,

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Speaker 4: which has long been identified as a problem of the discipline,

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Speaker 4: still reigns supreme in my view, and the book is

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Speaker 4: really partly a repudiation of that. So my co author

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Speaker 4: and I we take master model mentality to task in

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Speaker 4: the book, and we think economics deserves a much more

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Speaker 4: collectic approach, drawing many more disciplines than just sort of standards.

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Speaker 2: What are your thoughts on the impact of behavioral economics

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Speaker 2: that really took apart the homo economists that was front

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Speaker 2: and center of classical economics and showed, hey, people aren't

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Speaker 2: rational profit maximizing, they're emotional and flawed and human.

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Speaker 3: Right.

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Speaker 4: I think that is very very interesting. It's very valuable

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Speaker 4: that we have that strand of research and economics, but

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Speaker 4: it's more in the microside. It's not really macro predominantly,

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Speaker 4: and so I firmly live in the global macro space

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Speaker 4: where I think we still have very commoditized economics. You know,

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Speaker 4: it's all about a set of forecasts. People are still

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Speaker 4: wet it to their models. It's very much point forecast driven,

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Speaker 4: and I think what we need is much more narrative

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Speaker 4: based judgment based, more eclectic approaches to reading the landscape.

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Speaker 4: And that's what the book is really really about.

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Speaker 2: So we're going to talk more about how poorly economists

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Speaker 2: have done as forecasters over the past few decades. And

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Speaker 2: you have numerous, numerous examples, but let's stay with your

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Speaker 2: early career. You're going deep at McKenzie into the granularity

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Speaker 2: of corporate behavior. Then you very much a seat Angeliance

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Speaker 2: Bernstein or Sanford Bernstein, you become chief economist. How different

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Speaker 2: is it applying those wares on Wall Street in an

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Speaker 2: investment environment versus the corporate world in a more execution basis.

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Speaker 4: You know, the switch to the cell side was really

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Speaker 4: good for me. There was something I'd been missing in

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Speaker 4: my skill set. I'd done a lot of deep thinking, writing, researching,

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Speaker 4: I'd done the more microeconomics. I learned more about the

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Speaker 4: corporate world, but I hadn't been exposed to the finance

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Speaker 4: angle of it as much. I hadn't talked to the

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Speaker 4: byside at all really before. And being a Sanford Bernstein,

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Speaker 4: a firm with a storied history and in equity of

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Speaker 4: research really and swimming in this pool of really great

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Speaker 4: equity analysts just taught me a lot of things, not

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Speaker 4: least how to frame research angles, how to be quick

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Speaker 4: with research notes, how to get the thoughts out, and

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Speaker 4: then the concept exposure to investors on the buy side

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Speaker 4: really really helped me sharpen my research skills. So that

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Speaker 4: was almost like a missing piece in my recipe. I

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Speaker 4: really unlocked something for me and I learned a lot

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Speaker 4: there and I had a really good time doing that work,

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Speaker 4: publishing you many many research reports over those years and

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Speaker 4: often going very very deep, often going very historical in

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Speaker 4: the approach. So Bernstein is a firm that that very

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Speaker 4: much appreciates lateral thinking, differentiated approaches out there kind of ideas,

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Speaker 4: And so I ran wild for a while, just doing

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Speaker 4: work that I don't think I would have done anywhere else.

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Speaker 2: So you started a consultant, You briefly had a NGO

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Speaker 2: at the Organization of Economic Cooperation and OECD Development. As

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Speaker 2: I guess the last day, you're on the cell side.

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Speaker 2: So you see the universe of career options as an economist.

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Speaker 2: What brought you back to the Boston consulting room.

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Speaker 4: So I had a history with BCG already, and I

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Speaker 4: was well connected there, and at some point I was

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Speaker 4: approached if I'd like to come back and do the

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Speaker 4: same kind of work I was doing on the cell side.

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Speaker 4: But at BCG. BCG is a really great platform because

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Speaker 4: not only is it deeply ingrained in the corporate world,

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Speaker 4: so you know, the access to boardrooms is very wide.

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Speaker 4: You get to meet a lot of interesting executives and

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Speaker 4: the problems they're grappling with, but you also still have

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Speaker 4: access into the institutional investor world who are also clients,

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Speaker 4: so you really get both sides of the landscape, and

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Speaker 4: they were really different. Right on the by side, it's

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Speaker 4: mostly a look at firms outside in they're outside of

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Speaker 4: what's happening in the boardrooms. They're trying to decode it

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Speaker 4: from the outside. Being a consultants working and talking with them,

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Speaker 4: you're much closer to what's actually happening in their deliberations,

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Speaker 4: the problems they're facing, the questions.

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Speaker 3: They're trying to answer.

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Speaker 4: So to me, that platform is very attractive because it's

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Speaker 4: it's very versatile, it's it's it never gets boring, and

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Speaker 4: I've I've had a good run the last five years

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Speaker 4: doing my work on that BCG platform.

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Speaker 2: So I have no expertise in the consulting world, but

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Speaker 2: I kind of hear people lump all the consultants together, Mackenzy, BCG,

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Speaker 2: all these different firms. I get the sense from speaking

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Speaker 2: to various people that that's kind of inaccurate, that BCG

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Speaker 2: is not McKenzie. The very different organizations.

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Speaker 4: What's your experience been, Yeah, I mean they have different

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Speaker 4: cultures for sure. They certainly vie for the same business,

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Speaker 4: the three that you mentioned, so so you you constantly

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Speaker 4: bump into those other two competitors if you're at any

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Speaker 4: one of those three firms, a third being Bane, I think, yeah, mckenzy, BCG, Baane,

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Speaker 4: those three are there others, but those are the core

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Speaker 4: strategy consultants, if you will, and you know, I would

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Speaker 4: think the type of work that has done is obviously

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Speaker 4: very similar. They're vying for the same business, but culturally

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Speaker 4: it is different and uh uh, you know they're they're

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Speaker 4: slightly different sizes, these three firms. BCG today is about

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Speaker 4: twelve billion and revenues annually. We have about I think

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Speaker 4: sixty seventy offices and no, sorry, well well over one

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Speaker 4: hundred offices in sixty countries. I think it's the right

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Speaker 4: metric here, right, and you know it's it's it's a

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Speaker 4: space that is is very, very competitive, but that that

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Speaker 4: keeps everyone on their toes.

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Speaker 2: I would imagine. So let's let's talk about advising companies

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Speaker 2: and advising executives. You talk about explaining economic uncertainty and

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Speaker 2: as we'll get into in the book, why there is

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Speaker 2: this risk aversion and these fears of crises that never

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Speaker 2: seem to come around. How do you approaching executives on

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Speaker 2: navigating all this. It seems like there's always this fear

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Speaker 2: of a disaster, and lately it hasn't really showed up.

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Speaker 4: Yeah, So a lot of what I do in conversations

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Speaker 4: with executives is to unskew, if you will, some of

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Speaker 4: the perceptions they pick up in the press, in public discourse,

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Speaker 4: which is reliably dialed down to the sort of domongering

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Speaker 4: side of things.

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Speaker 3: Right, that's really true.

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Speaker 4: It's not just lately, since you mentioned it, sort of

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Speaker 4: the inevitable recession that never that never came. We're really

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Speaker 4: at the end of a string of such false alarms.

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Speaker 4: You know, when COVID hit, it was very common to

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Speaker 4: predict a depression, not just the recession, but a depression

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Speaker 4: was very conventional wisdom in twenty twenty that this would

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Speaker 4: take many years to recover. Then when interest rates rose,

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Speaker 4: it was it was fashionable to predict an emerging market

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Speaker 4: a cascade of defaults. Then, of course, when inflation spiked,

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Speaker 4: it was cast as a hyperinflation, hyper inflation, structural inflation,

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Speaker 4: regime break, the nineteen seventies, all that stuff that clearly,

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Speaker 4: even then I think was very clearly not not what

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Speaker 4: was playing out. And then the inevitable recession is really

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Speaker 4: just the most recent in a string of false alarm.

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Speaker 4: So often what I do is to meet people where

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Speaker 4: they are. They pick up doomsday narratives because they're very

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Speaker 4: prevalent in public discourse, and we often go back to

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Speaker 4: basics and ask, well, how does the system work and importantly,

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Speaker 4: what would it take for these big bad outcomes to happen.

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Speaker 4: It's not that they can't happen. They're part of a

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Speaker 4: risk distribution, but very often we take these risks in

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Speaker 4: public discourse that are the edges of the risk distribution

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Speaker 4: tail risks, tail risks, and we pretend that they're in

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Speaker 4: the middle of the distribution. Right, if you go through

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Speaker 4: financial news, if you go to financial TV kind of conversations,

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Speaker 4: you often get the impression that these rips which are

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Speaker 4: genin rest, they're real, they're part of the distribution, but

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Speaker 4: you get the impression that they're really the center of

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Speaker 4: everything we should be watching.

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Speaker 2: And so this leads to an obvious question. Whenever I

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Speaker 2: have an author in I often asked what inspired them

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Speaker 2: to write their book? It's pretty clear what inspired you?

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Speaker 2: It seems like it got to the point where, hey,

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Speaker 2: everybody is freaking out about things that are either not

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Speaker 2: happening or just so low probability events that they're not

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Speaker 2: contextualizing it. Well, what actually was the aha moment that

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Speaker 2: said I got to put all this down in a

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Speaker 2: book and instead of repeating myself over and over here,

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Speaker 2: read this and it'll it'll explain why you're fearing all

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Speaker 2: the wrong things.

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Speaker 4: Yeah, it was the It was the accumulation of situations

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Speaker 4: where my co author Paul Schwartz, and I felt we

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Speaker 4: had a pretty good access to this topic. We kind

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Speaker 4: of got that one right, not because we were using

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Speaker 4: models and sophisticated analysis, but we looked at it from

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Speaker 4: a narrative driven perspective. We asked the right questions about

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Speaker 4: what does it take to get to that really bad

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Speaker 4: structural situation, and so we wanted to wrap that into

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Speaker 4: a coherent story of how we think about economics, not

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Speaker 4: because we can get it right every single time. Even

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Speaker 4: if you use a more eclectic approach to economics, you

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Speaker 4: will get things wrong, but I think your hit rate

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Speaker 4: can improve. And that was the motivation to write that

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Speaker 4: all down in the book. And yeah, that's how this

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Speaker 4: came about.

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Speaker 2: So first, let's just start out generally. You described the

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Speaker 2: book as calling out pervasive doom saying in public discourse

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Speaker 2: about the economy, and demonstrating how to navigate real financial

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Speaker 2: and global risks more productively. Explain.

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Speaker 4: So, over the last few years, call it since the

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Speaker 4: COVID pandemic, we've had a string of false alarms, as

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Speaker 4: I would call them, right out the gate. In twenty twenty,

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Speaker 4: we were told this will be a greater depression, maybe

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Speaker 4: as bad as the nineteen thirty is, worse than two

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Speaker 4: thousand and eight. That wasn't the case at all. Then

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Speaker 4: we had an inflation spike that was spun into an

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Speaker 4: inflation regime break forever inflation, hyperinflation that didn't pan out.

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Speaker 4: Then we had rising interest rates, and that was spun

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Speaker 4: into a doomsday story of emerging markets, cascade of defaults,

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Speaker 4: and then we had the story of an inevitable recession

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Speaker 4: that we're still waiting for.

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Speaker 3: Right, So we have across the.

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Speaker 4: Board a lot of negativity. Across the board, we have

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Speaker 4: a lot of doom saying public discourse is pervasive in

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Speaker 4: that regard, the story is always skewed to the downside.

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Speaker 4: And what the book does it provides a framework to

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Speaker 4: think about this differently and more productively. And it does

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Speaker 4: so across real economy risks, I think recession, but also

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Speaker 4: sort of longer term growth. It does so in the

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Speaker 4: financial economy, think about stimulus and the effectiveness of stimulus,

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Speaker 4: interest rates, inflation bubbles, that type of stuff. And it

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Speaker 4: does so across the global space, the institutions that governed trade, etc.

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Speaker 2: So you combine data analysis with both narrative storytelling and

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Speaker 2: judgment over traditional macroeconomic models. Explain what led you to

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Speaker 2: this way to contextualize what's going on in the real

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Speaker 2: world economy.

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Speaker 4: So my path through economics was fairly eclectic. I started

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Speaker 4: out studying economics in a traditional theoretical macroeconomic econometric sense,

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Speaker 4: and then I went into studying much broader adjacent fields

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Speaker 4: that are relevant to economics, finance, history, political theory, political economy, etc.

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Speaker 4: Then I had different experiences in my career, just just

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Speaker 4: putting together different views of how to approach these problems.

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Speaker 4: And over time and working on the cell side, we

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Speaker 4: discussed they put all these together. And so it is

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Speaker 4: just the insight that the models will not deliver. You

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Speaker 4: cannot accurately forecast the economy. Economists shouldn't feel so ashamed

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Speaker 4: about that. It's not like natural scientists are always doing better.

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Speaker 4: Think about epidemiologists. They also struggle to accurately forecast COVID deaths,

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Speaker 4: for example. So you know, the whole physics envy and

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Speaker 4: the whole inferiority complex that often besids the economics profession

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Speaker 4: is misplaced. In my view, we should embrace the uncertainty

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Speaker 4: that prevents us from making precise point forecasts, and we

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Speaker 4: should deliver that uncertainty. Embrace the eclectic nature of what

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Speaker 4: we're trying to solve. It isn't just about economics and policy,

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Speaker 4: it's about myriad other things that play into this. And

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Speaker 4: when we do that and do it rationally, I think

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Speaker 4: often we're going to land and better predictions.

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Speaker 2: You know, it's funny about the physics envy. Richard Fiman

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Speaker 2: once said, imagine how much harder physics would be if

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Speaker 2: electrons had feelings? Right, So it's it's not a pure

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Speaker 2: natural world. You have human behavior getting in the way.

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Speaker 2: And you know, one of the quotes from the book

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Speaker 2: Doom Cells, hasn't that always been the case that it

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Speaker 2: appeals not only to our fear of existential threats from

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Speaker 2: an evolution perspective, but just generally speaking, good news is

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Speaker 2: sort of sneaks by and bad news gets our attention.

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Speaker 4: Yeah, it's the clicks and the eyeballs that that we're

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Speaker 4: trying to attract in the in the news business model,

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Speaker 4: and that that gives you the slant to the downside.

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Speaker 4: I think it's it's particularly pronounced these.

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Speaker 2: Days social media and the rest.

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Speaker 3: That's part of it.

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Speaker 4: But it's also the case that when you think about

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Speaker 4: the last forty years or so, there was a window

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Speaker 4: that we call good macro in the book. So a

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Speaker 4: lot of ma economic variables, a lot of macroeconomic context

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Speaker 4: was benign and was a tailwind, you know for executives,

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Speaker 4: but certainly for investors. So in the real economy, cycles

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Speaker 4: grew longer, volatility came down like recessions were less frequent.

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Speaker 4: The financial economy, inflation structurally declined, pulling down interest rates

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Speaker 4: with it. And in the global realm you had you know,

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Speaker 4: institutional growth and where we're aligning value chains and all

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Speaker 4: that really was a tailwind to executives and investors, and

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Speaker 4: more recently, not just COVID you you can go back

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Speaker 4: to two thousand and eight. It's sort of a growing

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Speaker 4: crescendo of new noise and new disturbances. I think that

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Speaker 4: good macro window is challenged. Right, we had a lot

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Speaker 4: of generations, We had a lot of shocks, all of

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Speaker 4: whiplash there, and so for executives, when it used to

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Speaker 4: be possible to ignore the macro world, to take it

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Speaker 4: for granted, it's now moved into the boardroom. Now you

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Speaker 4: need to have a view on what these things mean

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Speaker 4: for your bisness and you kind of need to do

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Speaker 4: that almost ongoingly. So that has changed, I mean, because

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Speaker 4: there's more gyrations, there's more whiplash. I think that has

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Speaker 4: dialed up all the angst, and it has dialed up

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Speaker 4: the doom saying and the string of false alarms that

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Speaker 4: I went through earlier, in my mind is pretty dense

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Speaker 4: it's you know, every year we had a new doomsday narrative,

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Speaker 4: and every single year it just didn't pen out that way.

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Speaker 2: You know, there was a I'm trying to remember which

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Speaker 2: economist wrote this up. At one point in history, your

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Speaker 2: whole world was your local region, and what happened globally

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Speaker 2: or what happens across the ocean was not relevant. Now

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Speaker 2: it doesn't matter what corner of the earth you're hiding in,

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Speaker 2: the global macro world is knocking in your door regardless.

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Speaker 2: How significant is that to both to both coming up

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Speaker 2: with a better macroeconomic framework, and all of these false

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Speaker 2: crises and fears seemed to be never ending.

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Speaker 4: Yeah, I think the greater interconnectedness and the real time

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Speaker 4: aspect of economics and the pass through of influences and

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Speaker 4: in often just hours transmitted often through financial markets, that

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Speaker 4: just adds to that. It's it never it never stops,

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Speaker 4: It never takes a break. You know, you go to

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Speaker 4: sleep with with sort of the latest data, you wake

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Speaker 4: up with the latest data, right. I mean, it's seat

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Speaker 4: of constant in that regard, and I think that certainly

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Speaker 4: feeds into that sense of heightened risk and crisis.

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Speaker 2: So let's talk about some shocks Over the past quarter century.

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Speaker 2: We had and this is really just less global than

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Speaker 2: US focused, but obviously international ramifications. We had the dot

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Speaker 2: Com implosion in two thousand, We had the September eleventh

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Speaker 2: attacks in O one. Not long after that, we had

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Speaker 2: the Great Financial Crisis. We had COVID. In between, we

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Speaker 2: had a couple couple of market events, the flash crash

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Speaker 2: of them again, and I don't know if you really

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Speaker 2: consider those true economic shocks, but certainly dot Com, nine

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Speaker 2: to eleven, GFC and COVID were huge. Is this have

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Speaker 2: we been through more than the usual number of shocks

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Speaker 2: or does it just seem that way recently.

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Speaker 4: Well, we've always had shocks. I think two thousand and

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Speaker 4: eight stands out among the ones you mentioned, because that's

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Speaker 4: where the US economy actually came close to the precipice

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Speaker 4: of this could be a structural depression. Without the intervention,

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Speaker 4: without the stimulus that was deployed at the time, this

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Speaker 4: could have gone a lot worse. COVID, in some sense,

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Speaker 4: was a replay of that risk, but action was more

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Speaker 4: swift and more decisive. So it seems like we'll learned

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Speaker 4: something there, and.

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Speaker 2: Much more fiscal as opposed to the financial crisis, which

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Speaker 2: was primarily a monetary response, and we ended up with

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Speaker 2: two very different years that followed addressed that.

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Speaker 4: If you would, Yeah, So I think in two thousand

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Speaker 4: and eight, you'll remember tarp Tart was what now looks

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Speaker 4: like a paltry sum of seven hundred billion, and it

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Speaker 4: got voted down in Congress, right right, So.

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Speaker 2: I remember that week in October the market ceased so

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Speaker 2: aggressively in the stock market sold off that was voted

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Speaker 2: down on a Monday. By Friday it passed overwhelmingly, exactly.

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Speaker 4: And I think this is one of the big themes

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Speaker 4: that we emphasize on the book. Stimulus comes down to

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Speaker 4: the willingness of politicians to act and the ability to act.

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Speaker 4: Ability is more about financial markets. Will bond markets finance

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Speaker 4: this kind of action, which they do in times of crisis,

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Speaker 4: but the willingness has to be there to act, and

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Speaker 4: in times of crisis, the willingness to act usually rises.

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Speaker 4: Partisanship is put aside. Politicians come together, they act to

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Speaker 4: you know, when the house is on fire, you will

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Speaker 4: step up and do something about it. And I think

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Speaker 4: in twenty twenty that was in display and there was

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Speaker 4: a learning curve from the more timid approach in two

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Speaker 4: thousand and eight and then and then perhaps it was

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Speaker 4: overdone twenty twenty and the following years, but certainly the

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Speaker 4: risk was perceived. Perhaps we're doing too little, so let's

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Speaker 4: rather go large and backstop the system.

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Speaker 2: My favorite story from the twenty twenty Cares Act was

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Speaker 2: a week before the country was shut down, Congress couldn't

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Speaker 2: agree on renaming a library in DC because it was

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Speaker 2: just along partisan lines. Everything got tabled. Then the world

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Speaker 2: shut down, and the largest fiscal stimulus since World War Two,

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Speaker 2: at least as a percentage of GDP, flew through the

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Speaker 2: House and Senate and was signed by Kars. Acting none

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Speaker 2: was President Trump Cares Act True two was President Trump Cares.

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Speaker 2: Act Three was President Biden. Did we learn something from

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Speaker 2: the financial crisis about the lack of fiscal stimulus and

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Speaker 2: maybe the pendulum swung too far the other way? What's

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Speaker 2: your takeaway from that?

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Speaker 3: No, for sure.

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Speaker 4: Look, I think two crises were very different. You had

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Speaker 4: in two thousand and eight damage balance sheets, not just

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Speaker 4: in the banking system, but households. Their balance sheets had

436
00:26:08,320 --> 00:26:10,600
Speaker 4: to be repaired. Households had to dig themselves out of

437
00:26:10,640 --> 00:26:13,479
Speaker 4: that hole, had to rebuild their wealth, and that that

438
00:26:13,520 --> 00:26:16,480
Speaker 4: would have called for more intervention than what we got

439
00:26:16,520 --> 00:26:21,240
Speaker 4: in two thousand and eight. In twenty twenty, I think policymakers, politicians,

440
00:26:21,880 --> 00:26:25,840
Speaker 4: they had internalized that learning, so they went extra large

441
00:26:25,920 --> 00:26:30,520
Speaker 4: on the fiscal side. And that hole that COVID created

442
00:26:30,760 --> 00:26:35,440
Speaker 4: was basically filled with fiscal stimulus. As you know, it's

443
00:26:35,480 --> 00:26:41,440
Speaker 4: widely believed and accepted that this was extremely big, too

444
00:26:41,520 --> 00:26:45,240
Speaker 4: much perhaps, and so we had an overshoot in certain

445
00:26:45,359 --> 00:26:48,479
Speaker 4: consumption areas, particularly in the good space. There was an

446
00:26:48,480 --> 00:26:53,199
Speaker 4: overshoot in consumption. It pushed up demand. It together with

447
00:26:53,280 --> 00:26:56,680
Speaker 4: supply crunches, it pushed up inflation in an idiosyncratic and

448
00:26:56,760 --> 00:27:01,119
Speaker 4: more tactical cyclical way, not structural, but but tactical. And

449
00:27:01,160 --> 00:27:04,879
Speaker 4: so I think, yes, policymakers did learn something and they

450
00:27:04,920 --> 00:27:07,000
Speaker 4: were risk averse, so they went extra large.

451
00:27:08,480 --> 00:27:12,280
Speaker 2: So you said, the financial crisis clearly a shock. The

452
00:27:12,480 --> 00:27:15,600
Speaker 2: other things not as much as a shock. And we've

453
00:27:15,600 --> 00:27:18,879
Speaker 2: had plenty of false alarms. How do you define what

454
00:27:19,040 --> 00:27:23,560
Speaker 2: a true shock or crisis is and what do you

455
00:27:23,600 --> 00:27:26,800
Speaker 2: put in the category of false alarms or things that

456
00:27:26,880 --> 00:27:31,280
Speaker 2: are genuine but just don't rise to the level as described.

457
00:27:31,359 --> 00:27:33,720
Speaker 4: Yeah, there are two things to consider. One, instead of

458
00:27:33,760 --> 00:27:38,840
Speaker 4: the new cycle level, we have a constant doom saying

459
00:27:39,480 --> 00:27:42,919
Speaker 4: about supposed things that could lead to recession or otherwise

460
00:27:42,960 --> 00:27:44,200
Speaker 4: downgrade the economy.

461
00:27:45,000 --> 00:27:45,159
Speaker 3: You know.

462
00:27:45,240 --> 00:27:47,679
Speaker 4: Just the last few years, we went numerous you know,

463
00:27:47,760 --> 00:27:49,919
Speaker 4: so for example, consumers were supposed to run out of

464
00:27:49,960 --> 00:27:53,160
Speaker 4: cash and consumers were not going to keep up their spending.

465
00:27:54,040 --> 00:27:56,720
Speaker 4: We had lots of false alarms about the labor market,

466
00:27:56,760 --> 00:27:59,040
Speaker 4: even last summer, right we had last summer in August,

467
00:27:59,119 --> 00:28:01,639
Speaker 4: there was a somewhat of a panic because supposedly the

468
00:28:01,680 --> 00:28:05,399
Speaker 4: labor market was going to be very soft and very weak.

469
00:28:05,680 --> 00:28:08,879
Speaker 4: So we have these new cycle false alarms stories that

470
00:28:09,359 --> 00:28:12,040
Speaker 4: often are rooted in a data point that is noteworthy,

471
00:28:12,080 --> 00:28:16,760
Speaker 4: that is interesting, that does signify risk, but we extrapolating

472
00:28:16,800 --> 00:28:19,520
Speaker 4: from the data point to conclusions that don't hold up.

473
00:28:19,560 --> 00:28:22,119
Speaker 4: That is one category of false alarms. The other categories

474
00:28:22,160 --> 00:28:26,080
Speaker 4: where you have real crises. But the question is are

475
00:28:26,119 --> 00:28:28,080
Speaker 4: they going to have structural impact? Are they going to

476
00:28:28,080 --> 00:28:30,560
Speaker 4: have a long term impact on the economy? Are they

477
00:28:30,600 --> 00:28:34,240
Speaker 4: going to downgrade the economy's capacity? So two thousand and

478
00:28:34,280 --> 00:28:37,119
Speaker 4: eight does qualify. Two thousand and eight left an indelible

479
00:28:37,160 --> 00:28:41,520
Speaker 4: mark on the US economy, but twenty twenty didn't in

480
00:28:41,600 --> 00:28:45,320
Speaker 4: terms of performance and output. We've regained the output to

481
00:28:45,440 --> 00:28:47,480
Speaker 4: trend output that we were wrong the path that we're

482
00:28:47,480 --> 00:28:50,640
Speaker 4: traveling on pre COVID. We've come back to that trend

483
00:28:50,640 --> 00:28:53,520
Speaker 4: output path. It has not left the kind of permanent

484
00:28:53,600 --> 00:28:57,240
Speaker 4: mark on economic performance that you saw after two thousand

485
00:28:57,240 --> 00:28:59,800
Speaker 4: and eight. So in that sense, we need to differentiate

486
00:29:00,000 --> 00:29:04,600
Speaker 4: between what is a likely shock that will pass and

487
00:29:04,640 --> 00:29:07,840
Speaker 4: that we can fix, versus what is something that changes

488
00:29:07,880 --> 00:29:11,920
Speaker 4: the structural composition structural setup of the economy durably. Those

489
00:29:11,920 --> 00:29:14,440
Speaker 4: are two very different types of situations.

490
00:29:14,440 --> 00:29:19,960
Speaker 2: That sounds like a usable framework for distinguishing between real crises.

491
00:29:20,560 --> 00:29:25,360
Speaker 2: And do I call it media alarmism or you know,

492
00:29:25,400 --> 00:29:28,960
Speaker 2: everybody is blaming the media these days, especially with this administration,

493
00:29:29,560 --> 00:29:34,560
Speaker 2: but there has been a fairly relentless negativity, especially in

494
00:29:34,640 --> 00:29:39,680
Speaker 2: social media. What's the best framework for you know, separating

495
00:29:39,720 --> 00:29:40,680
Speaker 2: the wheed from the chaff.

496
00:29:41,920 --> 00:29:46,240
Speaker 4: Well, typically when we see knee jerk reactions and doom

497
00:29:46,320 --> 00:29:51,680
Speaker 4: say stories, they're taking a data point and then they're extrapolating,

498
00:29:51,880 --> 00:29:54,600
Speaker 4: usually on the basis of a model. So I mean,

499
00:29:54,640 --> 00:29:58,360
Speaker 4: think about the inevitable recession. Even Larry summers people like

500
00:29:58,400 --> 00:30:00,480
Speaker 4: that that came out and said, look to bring down

501
00:30:00,520 --> 00:30:03,520
Speaker 4: wage growth, to bring down inflation, you need I don't know,

502
00:30:03,600 --> 00:30:05,880
Speaker 4: five years of unemployment at this and that level.

503
00:30:05,920 --> 00:30:08,920
Speaker 2: Why because he threw out ten percent, well, ten.

504
00:30:08,800 --> 00:30:11,240
Speaker 3: Percent for one year or five percent for five years.

505
00:30:11,280 --> 00:30:13,960
Speaker 4: So he had different configurations, but they're all based on

506
00:30:14,000 --> 00:30:16,480
Speaker 4: basically the Phillips curve. This was all a Phillips curve

507
00:30:16,960 --> 00:30:18,760
Speaker 4: take on the economy, which is.

508
00:30:18,720 --> 00:30:20,960
Speaker 2: Which was a great model fifty years ago, wasn't it.

509
00:30:21,320 --> 00:30:25,560
Speaker 4: Yeah, it described the UK and certain other countries empirically

510
00:30:25,640 --> 00:30:28,440
Speaker 4: quite well. It wasn't ever really a model and a theory.

511
00:30:28,480 --> 00:30:31,560
Speaker 4: It was more of a description of empirical facts. But

512
00:30:31,640 --> 00:30:34,200
Speaker 4: certainly it was useful for a window. It's still useful

513
00:30:34,240 --> 00:30:38,360
Speaker 4: as as an instrument to think about dynamics, right, But

514
00:30:38,480 --> 00:30:43,719
Speaker 4: it was basically used as as the truth. You know,

515
00:30:43,760 --> 00:30:45,960
Speaker 4: there's an input and there's an output, and my model

516
00:30:46,320 --> 00:30:49,240
Speaker 4: gives me the truth if I give it certain inputs,

517
00:30:49,720 --> 00:30:54,520
Speaker 4: and then well what happens. We're extrapolating data points, often

518
00:30:54,640 --> 00:30:58,320
Speaker 4: outside the range of empirical facts. The models are only

519
00:30:58,360 --> 00:31:01,160
Speaker 4: trained on historical facts. You know, you can't make up

520
00:31:01,240 --> 00:31:04,240
Speaker 4: data points to train your model. So when a crisis hits,

521
00:31:04,400 --> 00:31:07,040
Speaker 4: likely you get data points that were not empirically known

522
00:31:07,080 --> 00:31:08,760
Speaker 4: in the past. So what does the model do. It

523
00:31:08,800 --> 00:31:13,000
Speaker 4: extrapolates outside its historical empirical range, and then you get

524
00:31:13,000 --> 00:31:16,800
Speaker 4: these kind of point forecasts. It just don't work. I mean,

525
00:31:16,840 --> 00:31:20,360
Speaker 4: case in point, in two thousand and eight, unemployment goes

526
00:31:20,440 --> 00:31:23,280
Speaker 4: up to around ten percent, right, and it takes almost

527
00:31:23,320 --> 00:31:25,840
Speaker 4: the whole twenty tens a full decade almost to bring

528
00:31:25,880 --> 00:31:29,360
Speaker 4: down this very high unemployment rate. So in COVID, when

529
00:31:29,440 --> 00:31:32,560
Speaker 4: unemployment shoots up to fourteen percent, what does the model do.

530
00:31:32,640 --> 00:31:35,800
Speaker 4: It says, Well, if it takes a decade to bring

531
00:31:35,840 --> 00:31:38,040
Speaker 4: down ten percent unemployment, it will take even longer to

532
00:31:38,040 --> 00:31:40,959
Speaker 4: bring that fourteen percent of unemployment, right, And that is

533
00:31:41,040 --> 00:31:46,480
Speaker 4: exactly this kind of limitation of the model based approach. Empirically,

534
00:31:46,520 --> 00:31:49,320
Speaker 4: you never had fourteen percent unemployment, So if the model

535
00:31:49,320 --> 00:31:52,720
Speaker 4: extrapolates from past data points, it's going to go off

536
00:31:52,720 --> 00:31:54,880
Speaker 4: the tracks. And that's exactly what happened in that instance.

537
00:31:55,040 --> 00:31:59,440
Speaker 2: So the underlying flaw built into most models is that

538
00:31:59,480 --> 00:32:03,080
Speaker 2: the future will look like the past, and as we've learned,

539
00:32:03,600 --> 00:32:05,080
Speaker 2: that often is not the case.

540
00:32:05,280 --> 00:32:08,640
Speaker 4: It's always idiosyncratic look the US economy since the Second

541
00:32:08,680 --> 00:32:12,920
Speaker 4: World War has only seen a dozen recessions. Now, each

542
00:32:12,960 --> 00:32:16,719
Speaker 4: of those recessions is totally idiosyncratic, and even if they

543
00:32:16,800 --> 00:32:19,880
Speaker 4: had a lot of commonalities, twelve is not a sample

544
00:32:19,920 --> 00:32:23,040
Speaker 4: size that a natural scientist would consider large enough to

545
00:32:23,520 --> 00:32:26,440
Speaker 4: build sort of an empirical model around. Right, each of

546
00:32:26,480 --> 00:32:30,920
Speaker 4: these crises, or each of these recessions was idiosyncratic, and

547
00:32:30,960 --> 00:32:33,960
Speaker 4: the idiosyncrasy demands much more than a simple model or

548
00:32:33,960 --> 00:32:38,640
Speaker 4: even a sophisticated model. It demands the eclectic view across many,

549
00:32:38,680 --> 00:32:41,960
Speaker 4: many drivers. And that comes down to judgment. There isn't

550
00:32:42,000 --> 00:32:44,760
Speaker 4: There isn't an output in an Excel sheet or a

551
00:32:44,800 --> 00:32:47,640
Speaker 4: Python model or anything. In the end, it comes down

552
00:32:47,680 --> 00:32:50,280
Speaker 4: to human judgment, and I think that that is something

553
00:32:50,280 --> 00:32:51,800
Speaker 4: we lose sight of way too often.

554
00:32:51,920 --> 00:32:54,320
Speaker 2: You very much strike me as a fan of Professor

555
00:32:54,360 --> 00:32:58,200
Speaker 2: George Box. All models are wrong, but some are useful.

556
00:32:58,840 --> 00:33:01,720
Speaker 2: Tell us a little bit of about how models can

557
00:33:01,800 --> 00:33:02,320
Speaker 2: be useful.

558
00:33:02,360 --> 00:33:02,440
Speaker 3: Well.

559
00:33:02,440 --> 00:33:05,280
Speaker 4: They are always a good starting point. Even the Phillips

560
00:33:05,280 --> 00:33:09,040
Speaker 4: curve has a lot of validity to think about what

561
00:33:09,160 --> 00:33:12,600
Speaker 4: might be happening. There are always this sketch of reality,

562
00:33:13,280 --> 00:33:17,680
Speaker 4: but the moment we're translating that from you know, a

563
00:33:17,760 --> 00:33:21,000
Speaker 4: sketch and a map into something that is hardwired in

564
00:33:21,040 --> 00:33:24,800
Speaker 4: a quantity quantified model and the moment we then expect

565
00:33:24,800 --> 00:33:28,160
Speaker 4: that the output will resemble anything like the truth we're

566
00:33:28,240 --> 00:33:31,280
Speaker 4: we're sort of denying the reality of this. It just

567
00:33:31,320 --> 00:33:34,440
Speaker 4: doesn't work that way. Look, I'm not the first person

568
00:33:34,520 --> 00:33:38,240
Speaker 4: to make that point. In fact, you know, Hayek canes

569
00:33:38,440 --> 00:33:43,920
Speaker 4: fund mesas they've long basically trashed economics for saying like,

570
00:33:44,000 --> 00:33:48,120
Speaker 4: you're too gullible and you're too naive about the constant

571
00:33:48,200 --> 00:33:51,160
Speaker 4: nature of these variables. They they've long pointed out that

572
00:33:51,240 --> 00:33:55,680
Speaker 4: you don't have this this uh, what the natural sciences provide,

573
00:33:55,720 --> 00:33:58,400
Speaker 4: which is stability in all these relations of variables.

574
00:33:58,400 --> 00:33:59,640
Speaker 3: You don't have that in economics.

575
00:33:59,640 --> 00:34:02,280
Speaker 4: And there's a there's an anecdote that we pick up

576
00:34:02,280 --> 00:34:05,400
Speaker 4: in the book when Hayek receives the Nobel Prize in

577
00:34:05,440 --> 00:34:09,360
Speaker 4: nineteen seventy four, he actually uses his acceptance speech, or

578
00:34:09,400 --> 00:34:11,160
Speaker 4: I think it was a dinner speech he gave right

579
00:34:11,200 --> 00:34:15,359
Speaker 4: after being awarded the prize. He uses that speech to say, look,

580
00:34:15,360 --> 00:34:17,600
Speaker 4: you shouldn't do this prize in economics. You should you

581
00:34:17,640 --> 00:34:19,600
Speaker 4: should have you should have never done the Nobel Prize.

582
00:34:19,640 --> 00:34:23,200
Speaker 4: In economics. But if you must have this prize, at

583
00:34:23,320 --> 00:34:26,239
Speaker 4: least ask the recipients to swear an oath of humility,

584
00:34:27,040 --> 00:34:31,640
Speaker 4: because unlike physicists and in chemistry and other natural sciences,

585
00:34:32,680 --> 00:34:36,160
Speaker 4: economists have a big microphone, right. Policymakers listen to them,

586
00:34:36,440 --> 00:34:39,520
Speaker 4: politicians listen, public listens to them. But they don't have

587
00:34:39,600 --> 00:34:42,560
Speaker 4: that certainty of analysis. They don't have that stability in

588
00:34:42,600 --> 00:34:44,360
Speaker 4: their model. So they're going to go off the tracks

589
00:34:44,360 --> 00:34:46,960
Speaker 4: all the time. So at least ask them to be

590
00:34:47,280 --> 00:34:50,000
Speaker 4: humble about what they're doing. And I think that that

591
00:34:50,080 --> 00:34:53,359
Speaker 4: is a good reminder of the long history of recognizing

592
00:34:53,360 --> 00:34:56,600
Speaker 4: the limits of model based approaches through the eyes of

593
00:34:56,640 --> 00:34:59,200
Speaker 4: some of the leading leading thinkers in the space.

594
00:35:00,000 --> 00:35:03,400
Speaker 2: So let's talk a little bit about a lot of

595
00:35:03,400 --> 00:35:09,000
Speaker 2: the false alarms and faux crises. So many economists got

596
00:35:09,160 --> 00:35:12,919
Speaker 2: twenty twenty two, Room, twenty twenty three, twenty twenty four.

597
00:35:13,400 --> 00:35:18,000
Speaker 2: They were expecting a recession. It never showed up. Why

598
00:35:18,120 --> 00:35:18,439
Speaker 2: is that?

599
00:35:19,320 --> 00:35:22,359
Speaker 4: It starts with the master model mentality that we call

600
00:35:22,440 --> 00:35:25,240
Speaker 4: out in the book, where we placed too much trust

601
00:35:25,480 --> 00:35:30,279
Speaker 4: in models. So the Phillips curve was essentially used by

602
00:35:30,320 --> 00:35:31,719
Speaker 4: many forecasters.

603
00:35:31,400 --> 00:35:33,600
Speaker 2: Call it define the Phillips curve for the lay reader

604
00:35:33,600 --> 00:35:34,319
Speaker 2: who may not be free.

605
00:35:34,400 --> 00:35:37,680
Speaker 4: The Phillips curve is as an old theory going back

606
00:35:38,480 --> 00:35:42,600
Speaker 4: middle of the last century, describing the relationship between wage

607
00:35:42,600 --> 00:35:45,480
Speaker 4: growth and unemployment. So the idea is that you trade

608
00:35:45,480 --> 00:35:49,120
Speaker 4: off the two variables, and that led commentators like Larry

609
00:35:49,120 --> 00:35:52,480
Speaker 4: Summers to say to bring inflation out of control, you

610
00:35:52,480 --> 00:35:56,680
Speaker 4: wouldn't need either many years of high unemployment or a

611
00:35:56,719 --> 00:35:59,600
Speaker 4: sharp recession ten percent uneployment for a year to reset

612
00:35:59,640 --> 00:36:02,520
Speaker 4: the picture. In other words, in layperson's terms, a soft

613
00:36:02,600 --> 00:36:04,960
Speaker 4: landing is impossible, right, and this is what fed into

614
00:36:05,000 --> 00:36:08,440
Speaker 4: the inevitable recession that was the dominant received wisdom in

615
00:36:08,440 --> 00:36:09,520
Speaker 4: the last few years.

616
00:36:10,280 --> 00:36:11,840
Speaker 3: Now, you know, these.

617
00:36:11,680 --> 00:36:14,960
Speaker 4: Things are good starting points, they have validity historically and

618
00:36:15,000 --> 00:36:17,919
Speaker 4: a lot of empirical data. But in the end it's idiosyncratic.

619
00:36:18,160 --> 00:36:21,160
Speaker 4: It's very idiosyncratic constellation of drivers and risks, and so

620
00:36:21,280 --> 00:36:23,960
Speaker 4: it was in the last few years. So let's look

621
00:36:24,000 --> 00:36:27,000
Speaker 4: at that for a moment. One of these master models

622
00:36:27,080 --> 00:36:28,719
Speaker 4: was also interst rate sensitivity.

623
00:36:28,960 --> 00:36:29,080
Speaker 3: Right.

624
00:36:29,160 --> 00:36:31,879
Speaker 4: We think interest rates go up and that eats into

625
00:36:31,920 --> 00:36:36,279
Speaker 4: disposable incomes for households, right, But in reality, mortgages in

626
00:36:36,320 --> 00:36:39,600
Speaker 4: the US unlike in Canada, mortgages or long term didn't

627
00:36:39,600 --> 00:36:43,480
Speaker 4: actually take a big bite at a disposable exactly, very

628
00:36:43,480 --> 00:36:45,200
Speaker 4: long term, fixed, very low and most of them were

629
00:36:45,200 --> 00:36:47,120
Speaker 4: done at low rates because we had low rates for

630
00:36:47,160 --> 00:36:51,080
Speaker 4: a long time. Contrasts that with the flexible contracts and

631
00:36:51,160 --> 00:36:54,560
Speaker 4: mortgages in Canada where they lost a lot of disposable income.

632
00:36:54,760 --> 00:36:58,040
Speaker 4: That wasn't the case here. Same thing about interest rate

633
00:36:58,120 --> 00:37:00,880
Speaker 4: sensitivity in the corporate sector. So the textbook tells you

634
00:37:00,920 --> 00:37:03,640
Speaker 4: interest rates go up and investment will fall, but does

635
00:37:03,680 --> 00:37:06,480
Speaker 4: it You know, when you do the empirical analysis for

636
00:37:06,960 --> 00:37:11,040
Speaker 4: whatever window, you'll see a very flimsy correlation between interest

637
00:37:11,080 --> 00:37:14,920
Speaker 4: rates and capex. Firms invest when they have a narrative

638
00:37:15,200 --> 00:37:17,880
Speaker 4: to do so, when they see a return on the investment,

639
00:37:18,320 --> 00:37:21,160
Speaker 4: and if they believe the investment is beneficial to them,

640
00:37:21,160 --> 00:37:22,960
Speaker 4: they'll do it whether the interest rate is two, three

641
00:37:23,000 --> 00:37:24,680
Speaker 4: or four percent. And just look at what happened in

642
00:37:24,680 --> 00:37:27,880
Speaker 4: the last few years. You had a lot of narrative

643
00:37:28,040 --> 00:37:34,319
Speaker 4: and belief in worthwhile investments data centers software. So with

644
00:37:34,520 --> 00:37:37,359
Speaker 4: or without higher interest rates, firms are going to do that.

645
00:37:37,840 --> 00:37:41,000
Speaker 4: Particularly also because a lot of our investment has shifted

646
00:37:41,080 --> 00:37:46,200
Speaker 4: away from you know, fixed structures physical investment to intellectual

647
00:37:46,200 --> 00:37:51,279
Speaker 4: property software type of investment which has a much higher

648
00:37:51,360 --> 00:37:54,200
Speaker 4: rate of depreciation. So a bridge or road will be

649
00:37:54,239 --> 00:37:56,960
Speaker 4: good for thirty forty years, but software is maybe three

650
00:37:57,000 --> 00:37:59,680
Speaker 4: or four years. So you constantly have to invest just

651
00:37:59,680 --> 00:38:01,520
Speaker 4: to stay and still just to keep the stock of

652
00:38:01,560 --> 00:38:04,080
Speaker 4: investment in the space to keep it steady. You constantly

653
00:38:04,160 --> 00:38:07,319
Speaker 4: have to run faster just to maintain that. And so

654
00:38:07,800 --> 00:38:12,680
Speaker 4: there is a lot of idiosyncratic drivers that that led

655
00:38:13,320 --> 00:38:15,880
Speaker 4: two very different outcomes from what was predicted from a

656
00:38:15,920 --> 00:38:20,160
Speaker 4: model based Philip's Carve type approach to reading that context.

657
00:38:20,440 --> 00:38:24,120
Speaker 2: So a lot of highly regarded economists like Larry Summers

658
00:38:24,680 --> 00:38:28,759
Speaker 2: kind of reminded me of the Paul Graham quote. All

659
00:38:28,960 --> 00:38:32,279
Speaker 2: experts are experts in the way the world used to be,

660
00:38:32,880 --> 00:38:35,799
Speaker 2: and we're seeing a lot of that in that. So,

661
00:38:35,960 --> 00:38:39,719
Speaker 2: not only did people get the recession calls wrong for

662
00:38:39,760 --> 00:38:42,399
Speaker 2: the past couple of years, what have we had two

663
00:38:42,480 --> 00:38:45,960
Speaker 2: months of recessions in the past fifteen years? Are we

664
00:38:46,080 --> 00:38:48,480
Speaker 2: in a post recession economy? Now?

665
00:38:49,760 --> 00:38:52,880
Speaker 4: You can still get recessions, but I think we've become

666
00:38:52,960 --> 00:38:56,279
Speaker 4: better at fighting them. So this is the topic of stimulus.

667
00:38:56,320 --> 00:38:59,160
Speaker 4: There are three different types of There are two different

668
00:38:59,160 --> 00:39:01,840
Speaker 4: types of stimulus that we describe in the book across

669
00:39:02,000 --> 00:39:07,200
Speaker 4: three chapters, and we differentiate between what we call tactical stimulus,

670
00:39:07,280 --> 00:39:11,480
Speaker 4: which is just too smooth the cycle, accelerate growth in

671
00:39:11,520 --> 00:39:16,880
Speaker 4: between recessions, maybe dearist the cycle when necessary, versus existential stimulus,

672
00:39:16,880 --> 00:39:20,520
Speaker 4: which is when policymakers politicians step in when the economy

673
00:39:20,560 --> 00:39:23,640
Speaker 4: is truly at risk of a structural break. Those two

674
00:39:23,680 --> 00:39:27,160
Speaker 4: types of stimulus, they're evolving differently. I think the tactical

675
00:39:27,280 --> 00:39:29,880
Speaker 4: kind is more challenge going forward. It was very easy

676
00:39:29,920 --> 00:39:33,200
Speaker 4: when inflation was below target. It was very easy when

677
00:39:33,239 --> 00:39:36,040
Speaker 4: interest rates were very very low. There was little cost

678
00:39:36,080 --> 00:39:38,279
Speaker 4: to the Fed put you could do that. There wasn't

679
00:39:38,320 --> 00:39:42,080
Speaker 4: sort of an inflation risk associated with it. That's different now,

680
00:39:42,120 --> 00:39:44,439
Speaker 4: and I think they will remain different now that we're

681
00:39:44,480 --> 00:39:46,879
Speaker 4: skewed to the upside in terms of inflation, where interest

682
00:39:46,960 --> 00:39:50,200
Speaker 4: rates are likely to be higher for much longer. But

683
00:39:50,320 --> 00:39:53,239
Speaker 4: the existential type of stimulus, the ability to step up

684
00:39:53,360 --> 00:39:55,680
Speaker 4: when it's needed, I think that is still very strong.

685
00:39:56,000 --> 00:39:59,080
Speaker 4: And if you have another shock or a crisis or

686
00:39:59,120 --> 00:40:02,520
Speaker 4: a recession, I think will be able to deploy stimulus effectively.

687
00:40:02,560 --> 00:40:06,720
Speaker 2: Still so we said earlier, all recessions are not homogeneous.

688
00:40:06,760 --> 00:40:11,200
Speaker 2: They're all idiosyncratic and unique. But one of the things

689
00:40:11,200 --> 00:40:12,960
Speaker 2: you mentioned in the book that kind of intrigued me,

690
00:40:13,480 --> 00:40:19,120
Speaker 2: we shouldn't conflate recession intensity and recovery. Explain what that means.

691
00:40:19,480 --> 00:40:25,239
Speaker 4: Yeah, when COVID hit, we had extreme data prints. Unemployment

692
00:40:25,320 --> 00:40:29,520
Speaker 4: is sort of exhibit A of this story. Unemployment went

693
00:40:29,640 --> 00:40:31,560
Speaker 4: to ten percent in two thousand and eight, but it

694
00:40:31,640 --> 00:40:34,840
Speaker 4: went to fourteen percent in twenty twenty. Right, So the

695
00:40:34,880 --> 00:40:38,640
Speaker 4: intensity that the sudden collapse of activity was much more

696
00:40:38,640 --> 00:40:41,600
Speaker 4: pronounced in COVID than it was in two thousand and eight.

697
00:40:41,680 --> 00:40:45,279
Speaker 2: GDP also much worse during the first few months of

698
00:40:45,320 --> 00:40:46,960
Speaker 2: COVID than all variables.

699
00:40:46,960 --> 00:40:48,759
Speaker 4: So we have a chart early in the book that

700
00:40:48,840 --> 00:40:55,000
Speaker 4: shows the fifth to ninetieth percentile of historical experience of

701
00:40:55,040 --> 00:40:58,520
Speaker 4: these variables, and COVID is like far outside that historical range.

702
00:40:58,560 --> 00:41:01,040
Speaker 4: So you get data prints that you're not used to,

703
00:41:01,160 --> 00:41:03,480
Speaker 4: that the models don't know. The models were trained on

704
00:41:04,400 --> 00:41:07,440
Speaker 4: data points that were simply not experienced until they happened

705
00:41:07,440 --> 00:41:11,080
Speaker 4: in COVID. Now all of that fed into extreme intensity

706
00:41:11,320 --> 00:41:13,640
Speaker 4: was equated with This will be a very long and

707
00:41:13,680 --> 00:41:17,480
Speaker 4: difficult recovery. Why the ten percent unemployment rate led to

708
00:41:17,680 --> 00:41:20,839
Speaker 4: many years of recovering the twenty tens. Right, So now

709
00:41:20,840 --> 00:41:22,600
Speaker 4: if the unemployment rate is even higher, it's going to

710
00:41:22,600 --> 00:41:24,680
Speaker 4: take even longer to work it down to a level

711
00:41:24,719 --> 00:41:27,440
Speaker 4: that is, you know, a good economy again. But that

712
00:41:27,480 --> 00:41:29,880
Speaker 4: wasn't that wasn't the case. Twenty twenty wasn't about a

713
00:41:29,920 --> 00:41:33,959
Speaker 4: balance sheet recession. It wasn't about banks repairing their balance sheets.

714
00:41:33,960 --> 00:41:37,040
Speaker 4: It wasn't about households repairing the balance sheet. We took

715
00:41:37,040 --> 00:41:40,560
Speaker 4: care of that with stimulus, and therefore the ability to

716
00:41:41,200 --> 00:41:43,560
Speaker 4: recover was much faster and much stronger. There were other

717
00:41:43,560 --> 00:41:49,120
Speaker 4: idiosyncratic factors. Essentially, what was underestimated was the ability to

718
00:41:49,200 --> 00:41:53,279
Speaker 4: adapt of society. You know, societies found ways to work

719
00:41:53,320 --> 00:41:56,560
Speaker 4: around the virus. The pathway to a vaccine was faster.

720
00:41:56,920 --> 00:41:59,360
Speaker 4: So there were a lot of things that were underestimated.

721
00:41:59,520 --> 00:41:59,719
Speaker 3: You know.

722
00:42:00,040 --> 00:42:02,600
Speaker 2: Kind of reminds me of the why two K fear

723
00:42:03,320 --> 00:42:07,120
Speaker 2: that when there's a little bit of a fear of panic,

724
00:42:08,320 --> 00:42:12,920
Speaker 2: the expected crisis may not show up because we're taking

725
00:42:12,960 --> 00:42:16,480
Speaker 2: steps to avoid it. We don't know what was Y

726
00:42:16,520 --> 00:42:20,560
Speaker 2: two K a false alarm or did the fear lead

727
00:42:20,640 --> 00:42:24,440
Speaker 2: us to make sufficient changes to avoid problems. I honestly

728
00:42:24,560 --> 00:42:28,200
Speaker 2: can't answer that question. I'm wondering how you look at

729
00:42:28,280 --> 00:42:32,880
Speaker 2: crises in terms of do some of the fear mongering

730
00:42:32,960 --> 00:42:38,560
Speaker 2: and some of the you know, media absolute extremism lead

731
00:42:38,680 --> 00:42:42,600
Speaker 2: to government action that prevents the worst case scenario from happening.

732
00:42:43,120 --> 00:42:47,600
Speaker 4: It's possible that has shapes the perception of policy makers

733
00:42:47,600 --> 00:42:51,840
Speaker 4: and politicians, but I think the reality is on the ground.

734
00:42:51,920 --> 00:42:56,520
Speaker 4: You know, the variables that are visible and measurable, unemployment rates, GDP, growth,

735
00:42:57,200 --> 00:43:00,440
Speaker 4: you know, imports, exports, all of that was under pressure.

736
00:43:01,080 --> 00:43:05,000
Speaker 4: I think that is more telling for those who take

737
00:43:05,080 --> 00:43:09,720
Speaker 4: decisions than what public discourse does. Is public discorse, particularly fearful,

738
00:43:09,719 --> 00:43:12,839
Speaker 4: and a lot of angst pervades how we think about

739
00:43:12,840 --> 00:43:13,360
Speaker 4: the economy.

740
00:43:13,360 --> 00:43:16,080
Speaker 3: Does that spur action? Maybe that's part of it.

741
00:43:16,320 --> 00:43:20,040
Speaker 4: So we don't know, as you rightly say, what would

742
00:43:20,040 --> 00:43:24,160
Speaker 4: have been in a counterfactual world. But essentially, when the

743
00:43:24,200 --> 00:43:27,480
Speaker 4: economy is genuinely in trouble, I think the willingness to

744
00:43:27,560 --> 00:43:29,280
Speaker 4: act on the stimulus side is very strong.

745
00:43:29,360 --> 00:43:32,160
Speaker 2: So let's talk about some of those metrics. You have

746
00:43:32,239 --> 00:43:36,960
Speaker 2: an image in the book Scanning the Recession Barcode, So

747
00:43:37,080 --> 00:43:40,959
Speaker 2: tell us about that and the history of US recessions,

748
00:43:41,560 --> 00:43:45,520
Speaker 2: which seem to have been more frequent and more intense.

749
00:43:46,160 --> 00:43:49,440
Speaker 2: You go back a century, they were depressions, not even recessions.

750
00:43:50,000 --> 00:43:52,800
Speaker 2: Tell us about how this has changed over the past,

751
00:43:53,160 --> 00:43:54,800
Speaker 2: I don't know, a couple of hundred years.

752
00:43:55,040 --> 00:43:58,640
Speaker 4: Yeah, So if you do a very long run chart

753
00:43:59,719 --> 00:44:02,200
Speaker 4: for sessions in the US economy and you shade each

754
00:44:02,239 --> 00:44:06,759
Speaker 4: recession as a bar what you get as a barcode

755
00:44:06,760 --> 00:44:08,840
Speaker 4: of image that looks a bit like a barcode, but

756
00:44:08,960 --> 00:44:10,040
Speaker 4: it thins out.

757
00:44:09,960 --> 00:44:10,960
Speaker 3: As you move to the right.

758
00:44:11,280 --> 00:44:14,759
Speaker 4: So you had recessions very frequently one hundred years ago

759
00:44:14,840 --> 00:44:18,960
Speaker 4: and further back, the economy was constantly in recession.

760
00:44:19,040 --> 00:44:20,279
Speaker 3: Essentially half the time it was in.

761
00:44:20,280 --> 00:44:22,160
Speaker 2: Recession, banking panics all the time.

762
00:44:22,239 --> 00:44:24,600
Speaker 4: Yeah, but also the real economy, you know, the economy

763
00:44:24,760 --> 00:44:28,920
Speaker 4: was very agrarian. A bad harvest could drag down performance

764
00:44:28,960 --> 00:44:30,840
Speaker 4: of the economy, so there were a lot of shocks.

765
00:44:30,880 --> 00:44:33,080
Speaker 4: But yes, they're also banking crises and things like that.

766
00:44:33,160 --> 00:44:35,920
Speaker 4: And what we identify in the book is a recession

767
00:44:36,040 --> 00:44:39,200
Speaker 4: risk framework. We say, look, all recessions come in one

768
00:44:39,239 --> 00:44:42,120
Speaker 4: of three flavors. They are either real economy or recessions,

769
00:44:42,160 --> 00:44:46,120
Speaker 4: which is when investment in consumption drop abruptly and pull

770
00:44:46,280 --> 00:44:49,960
Speaker 4: GDP growth down. So that's the real economy time of recession.

771
00:44:49,960 --> 00:44:53,600
Speaker 4: The second is a policy era when policy makers get

772
00:44:53,640 --> 00:44:56,480
Speaker 4: it wrong they raise interest rates too fast or too high,

773
00:44:56,800 --> 00:44:59,040
Speaker 4: which only you ever know expost whether it was the

774
00:44:59,120 --> 00:45:01,200
Speaker 4: right thing to do. It's a very tricky thing to do.

775
00:45:01,600 --> 00:45:04,279
Speaker 4: And the third type of recession is the most pernicious kind.

776
00:45:04,400 --> 00:45:07,000
Speaker 4: It's a financial recession, when something blows up in the

777
00:45:07,000 --> 00:45:10,600
Speaker 4: financial system like two thousand and eight, and what we're

778
00:45:10,680 --> 00:45:14,600
Speaker 4: showing in this chapter of the book, over the long run,

779
00:45:14,920 --> 00:45:18,840
Speaker 4: the composition of these two drivers has changed over the

780
00:45:18,880 --> 00:45:22,239
Speaker 4: last forty years. The real economy recessions, they really took

781
00:45:22,280 --> 00:45:26,680
Speaker 4: a back seat because the economy calmed down, the volatility

782
00:45:26,680 --> 00:45:29,520
Speaker 4: came down. Services play a bigger role in the economy today,

783
00:45:29,600 --> 00:45:34,880
Speaker 4: so the less volatile than physical production. But also policy

784
00:45:34,920 --> 00:45:38,560
Speaker 4: makers just got better at managing the cycle, so you know,

785
00:45:38,719 --> 00:45:42,160
Speaker 4: policy errors kind of also lost a lot of share,

786
00:45:42,200 --> 00:45:46,239
Speaker 4: if you will, in the overall prevalence of recessions. But

787
00:45:46,320 --> 00:45:48,680
Speaker 4: when you think about what has given us the biggest headaches,

788
00:45:48,719 --> 00:45:51,160
Speaker 4: it was two thousand and eight a financial recession, and

789
00:45:51,239 --> 00:45:54,520
Speaker 4: dot com in away is also a financial type of recession.

790
00:45:54,600 --> 00:45:57,399
Speaker 4: So the share and the risk from financial blow ups

791
00:45:57,640 --> 00:45:59,800
Speaker 4: is significant if you look at it in recent history.

792
00:46:00,160 --> 00:46:02,040
Speaker 4: And that doesn't mean that the next recession will be

793
00:46:02,120 --> 00:46:05,920
Speaker 4: that type, but its share of the risk spectrum is

794
00:46:06,360 --> 00:46:07,240
Speaker 4: relatively high.

795
00:46:07,760 --> 00:46:10,400
Speaker 2: So what should we be listening to when we hear

796
00:46:11,880 --> 00:46:17,640
Speaker 2: economists discussing various risks? What are the red flags that hey,

797
00:46:17,680 --> 00:46:20,240
Speaker 2: maybe this is a little too doom and gloomy for

798
00:46:21,080 --> 00:46:23,319
Speaker 2: our own portfolio's best interests.

799
00:46:23,680 --> 00:46:26,719
Speaker 4: Yeah, I think the the litmus test for me is

800
00:46:26,760 --> 00:46:30,160
Speaker 4: often what would it take for a certain outcome of

801
00:46:30,400 --> 00:46:32,239
Speaker 4: a certain doomsday outcome.

802
00:46:32,040 --> 00:46:33,240
Speaker 3: To actually come to pass?

803
00:46:33,400 --> 00:46:36,719
Speaker 4: Not just will it happen and what would be the damage,

804
00:46:36,880 --> 00:46:39,560
Speaker 4: but walk me through the conditions that actually lead us

805
00:46:39,560 --> 00:46:41,960
Speaker 4: to the precipice and then make us fall off that

806
00:46:42,040 --> 00:46:46,280
Speaker 4: microeconomic cliff. Right, we need to talk about drivers, causes,

807
00:46:46,800 --> 00:46:50,400
Speaker 4: We need to talk about their probabilities and their constellations. So,

808
00:46:50,760 --> 00:46:54,279
Speaker 4: you know, it's it's not good enough to say, you know,

809
00:46:54,520 --> 00:46:57,359
Speaker 4: the model says the recession will happen. Walk us through

810
00:46:57,400 --> 00:47:01,040
Speaker 4: exactly what is the confluence of headwinds that together to

811
00:47:01,080 --> 00:47:06,040
Speaker 4: make that credible? Right, It's more than the point forecast.

812
00:47:06,719 --> 00:47:11,040
Speaker 2: Really kind of intriguing. I also noticed that I'm not

813
00:47:11,080 --> 00:47:15,280
Speaker 2: an economist, but when I listened to economists talk about

814
00:47:15,760 --> 00:47:18,799
Speaker 2: the possibility of a black swan or the possibility of

815
00:47:18,840 --> 00:47:22,959
Speaker 2: this event, it's almost as if there won't be any

816
00:47:23,120 --> 00:47:28,319
Speaker 2: intervening actions, either by the market or the policymakers. Tell

817
00:47:28,360 --> 00:47:34,680
Speaker 2: us a little bit about that. What was George Soros's phrase, reflexivity,

818
00:47:35,320 --> 00:47:39,080
Speaker 2: that when certain events happen, there are going to be

819
00:47:39,160 --> 00:47:44,080
Speaker 2: natural reactions that just prevent this extrapolation to infinity or

820
00:47:44,200 --> 00:47:46,000
Speaker 2: to zero, as the case may be.

821
00:47:46,440 --> 00:47:48,960
Speaker 4: Yeah, I mean this is back to the topic of stimulus.

822
00:47:49,120 --> 00:47:53,719
Speaker 4: For first and foremost, two thousand and eight came as

823
00:47:53,719 --> 00:47:57,080
Speaker 4: a big surprise because the models in the early part

824
00:47:57,160 --> 00:47:59,799
Speaker 4: of the two thousands, they didn't even really look at

825
00:47:59,800 --> 00:48:02,319
Speaker 4: the financial sector as a risk driver. They kind of

826
00:48:02,360 --> 00:48:05,960
Speaker 4: assume the financial system away. And then when the problem

827
00:48:06,080 --> 00:48:08,680
Speaker 4: brewed and the financial system itself, the models were kind

828
00:48:08,680 --> 00:48:12,680
Speaker 4: of blind to that, and the reaction couldn't couldn't be

829
00:48:12,719 --> 00:48:14,920
Speaker 4: gauged if you didn't have view of that, And the

830
00:48:15,000 --> 00:48:19,160
Speaker 4: reaction really depended on stimulus, and stimulus is about politics.

831
00:48:19,200 --> 00:48:21,960
Speaker 4: It is about policy, it's not about economics. First and foremost,

832
00:48:22,040 --> 00:48:25,400
Speaker 4: it's about political economy, it's about people coming together and

833
00:48:25,960 --> 00:48:27,160
Speaker 4: fighting crises.

834
00:48:27,200 --> 00:48:27,520
Speaker 3: And so.

835
00:48:29,000 --> 00:48:32,839
Speaker 4: I think that remains the case that the idiosyncrasy happens

836
00:48:33,520 --> 00:48:36,520
Speaker 4: before the crisis. The drivers are idiosyncratic, but the moment

837
00:48:36,520 --> 00:48:39,400
Speaker 4: a crisis starts, as shock hits, what happens as a

838
00:48:39,440 --> 00:48:44,280
Speaker 4: reaction is also idiosyncratic. It is political, it is about society,

839
00:48:44,360 --> 00:48:47,720
Speaker 4: it's about choices. It's not stuff that you can model

840
00:48:47,880 --> 00:48:51,080
Speaker 4: in a rigid natural science way.

841
00:48:50,880 --> 00:48:55,120
Speaker 2: So let's talk about something that clearly wasn't in the models.

842
00:48:55,600 --> 00:48:58,279
Speaker 2: Forget twenty years ago, they weren't in the models five

843
00:48:58,320 --> 00:49:01,040
Speaker 2: years ago or even three years ago. And that's the

844
00:49:01,080 --> 00:49:06,200
Speaker 2: impact of artificial intelligence on our economy, on the labor pool,

845
00:49:06,480 --> 00:49:10,680
Speaker 2: and on productivity. How do you look at a giant

846
00:49:10,800 --> 00:49:14,399
Speaker 2: structural change like AI. How do you put this into

847
00:49:14,440 --> 00:49:17,600
Speaker 2: context as to what it might mean across all these

848
00:49:17,600 --> 00:49:24,000
Speaker 2: different areas within both traditional economic modeling and the real world.

849
00:49:25,160 --> 00:49:29,840
Speaker 4: You know, we've had productivity growth the last few decades,

850
00:49:29,920 --> 00:49:32,520
Speaker 4: even though often the narrative as productivity growth is really

851
00:49:32,640 --> 00:49:36,920
Speaker 4: really low. We've had productivity growth, just not as services,

852
00:49:36,960 --> 00:49:39,840
Speaker 4: but in the physical economy, there's been pretty decent productivity

853
00:49:39,840 --> 00:49:42,680
Speaker 4: growth even the last twenty years where we didn't have

854
00:49:42,719 --> 00:49:45,680
Speaker 4: prouctivity growth with services because it didn't have the technology

855
00:49:46,239 --> 00:49:50,560
Speaker 4: to move that part of the economy along. Now, why

856
00:49:50,680 --> 00:49:56,120
Speaker 4: is that Essentially productivity growth goes up when technology displaces labor.

857
00:49:56,560 --> 00:49:59,600
Speaker 4: That is really the definition of productivity growth. You need

858
00:49:59,640 --> 00:50:02,560
Speaker 4: to produce the same with less labor inputs or more

859
00:50:02,600 --> 00:50:05,520
Speaker 4: with the same labor inputs. But either way, technology, whether

860
00:50:05,520 --> 00:50:07,520
Speaker 4: we like it or not, is about the displacement of labor,

861
00:50:07,560 --> 00:50:09,560
Speaker 4: and we weren't able to do that in the service economy.

862
00:50:10,200 --> 00:50:13,360
Speaker 4: Now with AI, I think you have a better chance

863
00:50:13,400 --> 00:50:16,040
Speaker 4: of doing this. At least the promise is very strong

864
00:50:16,880 --> 00:50:20,920
Speaker 4: that this will work. But I think we're getting ahead

865
00:50:20,920 --> 00:50:23,200
Speaker 4: of ourselves. And I'm not saying that now. We've published

866
00:50:23,239 --> 00:50:25,920
Speaker 4: on this over the last few years, even as COVID

867
00:50:25,960 --> 00:50:28,360
Speaker 4: head and even before AI, when the zoom economy it

868
00:50:28,400 --> 00:50:31,520
Speaker 4: was sort of this dominant narrative. It's a hard slog

869
00:50:31,719 --> 00:50:34,160
Speaker 4: to do this. It happens over years, and it's little

870
00:50:34,160 --> 00:50:36,640
Speaker 4: by little. It's not a flip of the switch. It

871
00:50:36,680 --> 00:50:42,600
Speaker 4: happens very incrementally, and I don't think AI will turbocharge

872
00:50:42,640 --> 00:50:46,040
Speaker 4: GDP growth. It is a lift to growth over the

873
00:50:46,080 --> 00:50:48,839
Speaker 4: medium term. But there are many little obstacles. There are

874
00:50:48,840 --> 00:50:50,840
Speaker 4: many little things that need to fall into place for

875
00:50:50,880 --> 00:50:53,560
Speaker 4: people to really adopt the technology and for this to,

876
00:50:53,640 --> 00:50:56,000
Speaker 4: little by little give us a tailwind. So it's not

877
00:50:56,080 --> 00:50:59,919
Speaker 4: an abrupt step change. It's something that is credible, something

878
00:51:00,040 --> 00:51:02,160
Speaker 4: we need to work through and then it will it

879
00:51:02,200 --> 00:51:05,080
Speaker 4: will show impact over a ten year frame, fifteen year frame.

880
00:51:05,640 --> 00:51:07,719
Speaker 2: So let me push back a little bit on one

881
00:51:07,719 --> 00:51:11,080
Speaker 2: thing you said. And I seem to have this ongoing

882
00:51:11,120 --> 00:51:16,000
Speaker 2: debate with economists who work in a larger corporate framework.

883
00:51:16,640 --> 00:51:21,279
Speaker 2: We're here in Bloomberg giant company Big Operation. My day

884
00:51:21,400 --> 00:51:25,080
Speaker 2: job is a much smaller company, under one hundred employees.

885
00:51:25,840 --> 00:51:28,799
Speaker 2: And I have noticed just over the course of the

886
00:51:28,840 --> 00:51:34,480
Speaker 2: past decade how our productivity has skyrocketed. And it's a

887
00:51:34,520 --> 00:51:38,080
Speaker 2: services busy, finance as a services business, and it just

888
00:51:38,239 --> 00:51:42,040
Speaker 2: feels like the things that used to take so long

889
00:51:42,120 --> 00:51:47,480
Speaker 2: to do fifteen and twenty years ago are now automated.

890
00:51:47,520 --> 00:51:49,640
Speaker 2: And it's not that we're hiring fewer people, and it's

891
00:51:49,640 --> 00:51:54,160
Speaker 2: not that we're working shorter hours, but the same sized

892
00:51:54,200 --> 00:51:57,839
Speaker 2: team can just accomplish so much more than they were

893
00:51:57,880 --> 00:52:01,399
Speaker 2: capable of. Per Like I RecA all the days of

894
00:52:01,719 --> 00:52:08,560
Speaker 2: quarterly reporting and having to literally run a model, create

895
00:52:08,600 --> 00:52:12,360
Speaker 2: a print out for every client, print it out, stick

896
00:52:12,360 --> 00:52:15,319
Speaker 2: it into the right and like it was like a

897
00:52:15,400 --> 00:52:20,520
Speaker 2: week long process that all hands on deck every quarter

898
00:52:21,239 --> 00:52:25,640
Speaker 2: and now it's updated twenty four to seven, tick by tickets, automated.

899
00:52:26,200 --> 00:52:29,759
Speaker 2: No one cares about quarterly reports because you could get it.

900
00:52:30,960 --> 00:52:33,200
Speaker 2: And the joke is, you have twenty four to seven

901
00:52:33,280 --> 00:52:39,000
Speaker 2: access to your daily, weekly, monthly, year to date, five year,

902
00:52:39,080 --> 00:52:42,520
Speaker 2: ten year performance reports. Just try not to check it

903
00:52:42,800 --> 00:52:47,040
Speaker 2: second by second, but the way, and that's just one example.

904
00:52:47,680 --> 00:52:51,600
Speaker 2: Being able to communicate with clients to record and embed

905
00:52:52,280 --> 00:52:56,960
Speaker 2: an interactive video with charts and everything else. That was

906
00:52:57,239 --> 00:53:03,200
Speaker 2: like a massive undertaking and now it's like child's play.

907
00:53:03,239 --> 00:53:06,760
Speaker 2: Even though you're doing the same thing, you're just doing

908
00:53:06,760 --> 00:53:12,640
Speaker 2: it faster, better, cheaper, easier. Are we somehow underestimating the

909
00:53:12,640 --> 00:53:17,640
Speaker 2: productivity gains or are these just specific to you know that?

910
00:53:18,280 --> 00:53:18,520
Speaker 3: Yeah?

911
00:53:18,560 --> 00:53:19,120
Speaker 2: One area?

912
00:53:19,320 --> 00:53:22,080
Speaker 4: Yeah, so I have some pushback on that. I think

913
00:53:22,120 --> 00:53:25,880
Speaker 4: the bar for productivity growth is a little higher and

914
00:53:25,920 --> 00:53:30,279
Speaker 4: it's very specific. It's less inputs per output. So do

915
00:53:30,360 --> 00:53:32,880
Speaker 4: things get more comfortable? Are they moving faster? Are they

916
00:53:32,960 --> 00:53:34,640
Speaker 4: qualitatively perhaps better?

917
00:53:34,800 --> 00:53:35,040
Speaker 3: Yes?

918
00:53:35,160 --> 00:53:37,960
Speaker 4: But are we using less inputs to generate the same value,

919
00:53:38,160 --> 00:53:40,000
Speaker 4: or are we using the same level of inputs to

920
00:53:40,040 --> 00:53:42,760
Speaker 4: generate more value. That is what we need to achieve

921
00:53:42,840 --> 00:53:44,759
Speaker 4: to speak of productivity growth. And let me give you an

922
00:53:44,719 --> 00:53:48,200
Speaker 4: example that we use in the book. You know, I

923
00:53:48,239 --> 00:53:50,759
Speaker 4: took an Uber from my apartment to come here into

924
00:53:50,800 --> 00:53:54,600
Speaker 4: the studio today. And Uber is often upheld as the

925
00:53:54,640 --> 00:53:57,879
Speaker 4: epitome of progress and tech and it is fascinating. It's

926
00:53:57,880 --> 00:53:59,919
Speaker 4: a great app. I love to use it. It's nice.

927
00:54:00,560 --> 00:54:03,040
Speaker 4: But look, if you want to improve the productivity growth

928
00:54:03,320 --> 00:54:07,919
Speaker 4: in taxi transportation, we have to talk about inputs and outputs, right,

929
00:54:07,960 --> 00:54:10,240
Speaker 4: And the inputs are on the capitol side of car

930
00:54:10,600 --> 00:54:12,960
Speaker 4: and you're not getting rid of that car. And on

931
00:54:13,000 --> 00:54:15,920
Speaker 4: the labor side it's the driver and the Uber car

932
00:54:16,120 --> 00:54:17,120
Speaker 4: still has that driver.

933
00:54:17,560 --> 00:54:21,000
Speaker 2: Not weaim in parts of the West Coast.

934
00:54:21,080 --> 00:54:23,799
Speaker 4: Yes, and this is why I said it takes time. Incrementally,

935
00:54:23,840 --> 00:54:26,239
Speaker 4: that will happen and that will unfold. But do you

936
00:54:26,280 --> 00:54:28,480
Speaker 4: think you're gonna have driverless taxis in New York in

937
00:54:28,520 --> 00:54:30,040
Speaker 4: twenty twenty eight or twenty thirty.

938
00:54:30,120 --> 00:54:30,479
Speaker 3: I don't.

939
00:54:30,760 --> 00:54:34,240
Speaker 2: Well, we'll have it in twenty fifty, probably in twenty forty.

940
00:54:34,680 --> 00:54:37,440
Speaker 2: I can't tell you what exact year it'll happen, but

941
00:54:38,239 --> 00:54:38,880
Speaker 2: it's coming.

942
00:54:39,080 --> 00:54:39,719
Speaker 3: I agree with you.

943
00:54:39,760 --> 00:54:44,759
Speaker 2: And the sooner we embed those RFID devices in vehicles

944
00:54:44,800 --> 00:54:48,319
Speaker 2: and on street corners, like doing it visually in light

945
00:54:48,480 --> 00:54:51,440
Speaker 2: r is very twentieth century.

946
00:54:51,200 --> 00:54:53,600
Speaker 4: Right, Yeah, And that's why I said it takes time.

947
00:54:53,760 --> 00:54:58,920
Speaker 4: Over time, this will be substantial lyft to economic output.

948
00:54:59,400 --> 00:55:02,480
Speaker 4: But it doesn't have happen overnight. It's actually it takes time, right,

949
00:55:02,840 --> 00:55:07,640
Speaker 4: And there's an additional important point about productivity growth that

950
00:55:08,000 --> 00:55:12,160
Speaker 4: can also be shown in this taxi example. When technology

951
00:55:12,360 --> 00:55:15,200
Speaker 4: is truly productivity enhancing, you see that in falling prices,

952
00:55:15,400 --> 00:55:17,240
Speaker 4: technology is deflationary.

953
00:55:17,600 --> 00:55:17,880
Speaker 3: Right.

954
00:55:17,960 --> 00:55:22,839
Speaker 4: As technology does a way with input cost, firms will

955
00:55:22,920 --> 00:55:26,440
Speaker 4: compete with lower prices to gain market share. So across history,

956
00:55:26,480 --> 00:55:30,600
Speaker 4: wherever you look, as technology is becoming a credible force

957
00:55:30,680 --> 00:55:34,120
Speaker 4: in production, prices will fall. Now look at Uber. Uber

958
00:55:34,160 --> 00:55:35,960
Speaker 4: prices in New York tend to be higher than a

959
00:55:36,040 --> 00:55:40,040
Speaker 4: yellow cab. Why because despite this expensive technology, you're not

960
00:55:40,080 --> 00:55:42,560
Speaker 4: able to produce this ride more cheaply.

961
00:55:42,719 --> 00:55:43,040
Speaker 3: You're not.

962
00:55:43,200 --> 00:55:46,160
Speaker 4: In fact, you kind of have to monetize the technological expense.

963
00:55:46,200 --> 00:55:50,000
Speaker 4: The app is expensive, all is expensive, so generally you're

964
00:55:50,040 --> 00:55:53,960
Speaker 4: paying a premium for the smoothness of the app and

965
00:55:54,000 --> 00:55:57,440
Speaker 4: all that. Over time that may change, but watch prices.

966
00:55:57,480 --> 00:56:00,160
Speaker 4: You want to see productivity growth, whether it's happening or not,

967
00:56:00,440 --> 00:56:01,880
Speaker 4: you got to look at prices. And that's one of

968
00:56:01,920 --> 00:56:03,000
Speaker 4: the arguments we're making the book.

969
00:56:03,120 --> 00:56:07,440
Speaker 2: So let's hordonically adjust. We'll stay with Uber. Let's hordonically

970
00:56:07,520 --> 00:56:10,400
Speaker 2: adjust that. In New York City, if you want a

971
00:56:10,440 --> 00:56:14,279
Speaker 2: taxi during rush hour, hey, sorry, you're out of luck

972
00:56:14,800 --> 00:56:19,000
Speaker 2: because the monopoly that was imbued by the Taxing Limousine

973
00:56:19,000 --> 00:56:25,080
Speaker 2: Commission and a handful of big medallion chain owners decided

974
00:56:25,120 --> 00:56:28,520
Speaker 2: in their infinite wisdom that we don't need to move

975
00:56:28,520 --> 00:56:31,680
Speaker 2: people in them rush hour. We're going to change shifts then, which,

976
00:56:31,719 --> 00:56:35,239
Speaker 2: by the way, is my pet theory for how Uber penetrated.

977
00:56:35,680 --> 00:56:39,080
Speaker 2: And so a, you could get an Uber during rush

978
00:56:39,120 --> 00:56:43,359
Speaker 2: hour that you can't during cab rides. You could get

979
00:56:43,400 --> 00:56:46,399
Speaker 2: an Uber when it's raining. Good luck hailing a cab

980
00:56:46,480 --> 00:56:49,400
Speaker 2: in New York City rain, and you have the ability

981
00:56:49,440 --> 00:56:52,359
Speaker 2: to schedule an Uber. You have the ability to get

982
00:56:52,360 --> 00:56:54,880
Speaker 2: a higher quality car. You could get an electric car

983
00:56:54,920 --> 00:56:57,920
Speaker 2: if you choose a larger car. Like I'm not a

984
00:56:58,000 --> 00:57:02,400
Speaker 2: huge fan of traditional hit donic adjustment because it was

985
00:57:02,440 --> 00:57:04,520
Speaker 2: a way of kind of tamping down on the cost

986
00:57:04,600 --> 00:57:08,880
Speaker 2: of living. Adjustments always felt sort of disingenuous. But I

987
00:57:08,920 --> 00:57:13,000
Speaker 2: don't think you could get anybody to say that Uber

988
00:57:13,120 --> 00:57:16,640
Speaker 2: is not only better. And I'm not a big Uber fan,

989
00:57:16,760 --> 00:57:20,760
Speaker 2: but as a user, Uber is certainly better than a

990
00:57:20,800 --> 00:57:25,760
Speaker 2: cab and in many ways orders of magnitude better, more choices,

991
00:57:25,840 --> 00:57:32,360
Speaker 2: more options, and just a higher quality experience. Plus you know,

992
00:57:32,480 --> 00:57:35,240
Speaker 2: just the idea of having hey, is this a work

993
00:57:35,280 --> 00:57:37,800
Speaker 2: thing or I'm going to use that card on the app? Well, no,

994
00:57:37,840 --> 00:57:42,240
Speaker 2: this is personal, I'll use that card. So maybe taxis

995
00:57:42,680 --> 00:57:47,160
Speaker 2: aren't the best example. But when let's talk about economists,

996
00:57:48,240 --> 00:57:49,959
Speaker 2: I want again, I want to stay with this because

997
00:57:50,000 --> 00:57:55,120
Speaker 2: I love the topic. Think about the quantity of research

998
00:57:55,200 --> 00:57:59,160
Speaker 2: you push, you push out, the ability to integrate charts

999
00:57:59,200 --> 00:58:02,200
Speaker 2: and data, and like, I have been in this business

1000
00:58:02,240 --> 00:58:04,400
Speaker 2: long enough that I can remember, first of all, when

1001
00:58:04,480 --> 00:58:09,320
Speaker 2: I started, the guys in the technical group, they were

1002
00:58:09,320 --> 00:58:12,959
Speaker 2: doing charts with pencil and graph paper. I'm not exaggerating.

1003
00:58:13,280 --> 00:58:15,919
Speaker 2: Maybe that's just a function of my age, but think

1004
00:58:15,960 --> 00:58:20,720
Speaker 2: about how and the cheat was you get a different

1005
00:58:20,760 --> 00:58:23,120
Speaker 2: feel when you're doing it point by point than when

1006
00:58:23,120 --> 00:58:25,560
Speaker 2: you're just generating it. Whether that's true or not, at

1007
00:58:25,640 --> 00:58:29,000
Speaker 2: least that was the When computers came along, people continued

1008
00:58:29,040 --> 00:58:33,880
Speaker 2: to do that. But think about the access you have

1009
00:58:34,120 --> 00:58:37,240
Speaker 2: to the just endless array of data, the ability to

1010
00:58:38,240 --> 00:58:42,360
Speaker 2: do that. I haven't even mentioned your Fortune column. Think

1011
00:58:42,400 --> 00:58:45,000
Speaker 2: about how much time and effort goes into putting out

1012
00:58:45,560 --> 00:58:49,120
Speaker 2: a column and you go back twenty five years and

1013
00:58:49,200 --> 00:58:53,840
Speaker 2: it was just a horrific grind. Like at this point,

1014
00:58:53,920 --> 00:58:57,880
Speaker 2: everybody seems to use some version of Grammarly or some

1015
00:58:58,080 --> 00:59:02,120
Speaker 2: other editing saw where the ability to put out and

1016
00:59:02,160 --> 00:59:05,880
Speaker 2: I'm not talking about asking chat GPT to generate a

1017
00:59:06,240 --> 00:59:10,240
Speaker 2: garbage article for you. You writing something, cleaning it up and

1018
00:59:10,360 --> 00:59:15,640
Speaker 2: betting a lot of data and images. It just feels like,

1019
00:59:16,040 --> 00:59:19,240
Speaker 2: you know, to quote Hemingway, you know, gradually and then

1020
00:59:19,280 --> 00:59:22,240
Speaker 2: all at once. It just feels like it's so much

1021
00:59:22,280 --> 00:59:27,360
Speaker 2: easier to put out a much higher quality product with

1022
00:59:27,960 --> 00:59:30,680
Speaker 2: either the same or less effort than twenty five years ago.

1023
00:59:31,560 --> 00:59:34,480
Speaker 2: Maybe I'm just hyper focused on the junk I do,

1024
00:59:34,960 --> 00:59:36,480
Speaker 2: but what's your experience?

1025
00:59:37,160 --> 00:59:41,720
Speaker 4: Incrementally there's progress, But again, the bar we need to

1026
00:59:41,760 --> 00:59:45,200
Speaker 4: meet is value. Are we generating more value with the

1027
00:59:45,200 --> 00:59:48,720
Speaker 4: same inputs, or are we generating the same value with

1028
00:59:48,960 --> 00:59:52,920
Speaker 4: less inputs. That's the definition of productivity growth. So if

1029
00:59:52,960 --> 00:59:55,240
Speaker 4: you can make all these charts faster and you save

1030
00:59:55,440 --> 00:59:58,280
Speaker 4: one economist on the team, well that's productivity growth. Or

1031
00:59:58,320 --> 01:00:00,920
Speaker 4: you keep the economist and you double your number of

1032
01:00:00,920 --> 01:00:03,240
Speaker 4: reports and you also manage to monetize them and earn

1033
01:00:03,280 --> 01:00:06,680
Speaker 4: revenue for it, well that's productivity growth. If the charts

1034
01:00:06,720 --> 01:00:11,200
Speaker 4: get prettier, faster, fancier, with the same number of economists

1035
01:00:11,200 --> 01:00:14,080
Speaker 4: and the same number of revenues, well from an economic

1036
01:00:14,160 --> 01:00:18,640
Speaker 4: sense perspective, that's not productivity growth. So it's got to

1037
01:00:18,680 --> 01:00:21,560
Speaker 4: be a change in the relationship of inputs to outputs

1038
01:00:21,640 --> 01:00:25,360
Speaker 4: if we're comfortably talking about productivity growth. And back to

1039
01:00:25,400 --> 01:00:29,280
Speaker 4: the Uber example. You write you can get different cars

1040
01:00:29,320 --> 01:00:31,479
Speaker 4: to write in. You can get the car, the Uber

1041
01:00:31,560 --> 01:00:34,240
Speaker 4: car when it's raining, but you're paying for that, so

1042
01:00:34,280 --> 01:00:37,560
Speaker 4: it's not produced more productively. Right, You're paying a search arge,

1043
01:00:37,600 --> 01:00:40,520
Speaker 4: you're paying the search pricing. I think they call it

1044
01:00:40,560 --> 01:00:42,720
Speaker 4: an uber so you know, yeah, you can get it

1045
01:00:42,720 --> 01:00:45,080
Speaker 4: when it rains, but you'll pay twice as much.

1046
01:00:45,080 --> 01:00:47,760
Speaker 3: So it wasn't it wasn't done more productively, right, Huh?

1047
01:00:47,920 --> 01:00:53,280
Speaker 2: Really? Interesting, the gap between the increased quantity and quality

1048
01:00:53,320 --> 01:00:57,840
Speaker 2: of output. If we're not monetizing it or as a consumer,

1049
01:00:57,880 --> 01:01:01,560
Speaker 2: if you're not seeing price declines, then it doesn't really

1050
01:01:01,600 --> 01:01:02,560
Speaker 2: count as productivity.

1051
01:01:02,720 --> 01:01:04,480
Speaker 4: No, It's got to be a change in the ratio

1052
01:01:04,480 --> 01:01:07,200
Speaker 4: of inputs to outputs on either side. Either we keep

1053
01:01:07,200 --> 01:01:09,320
Speaker 4: all the staff and we earn more revenue with.

1054
01:01:09,240 --> 01:01:10,640
Speaker 3: It, that's productivity growth.

1055
01:01:10,760 --> 01:01:12,840
Speaker 4: Or we keep the revenue constant and we do it

1056
01:01:12,880 --> 01:01:18,040
Speaker 4: with less inputs. That's more productivity growth. But you know, again,

1057
01:01:18,080 --> 01:01:20,480
Speaker 4: I'm not saying there isn't productivity. There is, and there

1058
01:01:20,480 --> 01:01:23,400
Speaker 4: will be more and AI will have impact. It just

1059
01:01:23,600 --> 01:01:27,240
Speaker 4: needs to show up in value in that relationship between

1060
01:01:27,360 --> 01:01:28,280
Speaker 4: inputs and outputs.

1061
01:01:28,600 --> 01:01:31,880
Speaker 2: I see it qualitatively, but I completely get what you're

1062
01:01:31,920 --> 01:01:36,640
Speaker 2: saying quantitatively. Are you still doing the Fortune column on.

1063
01:01:36,600 --> 01:01:41,840
Speaker 4: A regular Yeah. We publish in Fortune relatively regularly. Whenever

1064
01:01:41,920 --> 01:01:45,280
Speaker 4: we see a cyclical or thematic topic that we feel

1065
01:01:45,400 --> 01:01:47,800
Speaker 4: is pressing, we publish with Fortune.

1066
01:01:47,920 --> 01:01:51,120
Speaker 2: Yeah. Really really interesting. All right, I only have you

1067
01:01:51,120 --> 01:01:52,960
Speaker 2: for a limited amount of time. I know you're catching

1068
01:01:53,000 --> 01:01:56,440
Speaker 2: a flight today. Let me jump to our favorite questions

1069
01:01:56,440 --> 01:02:00,920
Speaker 2: that we ask all of our guests, starting with what

1070
01:02:00,960 --> 01:02:03,760
Speaker 2: are you streaming these days. What's keeping you entertained? Either

1071
01:02:04,560 --> 01:02:06,440
Speaker 2: Netflix or podcasts or whatever.

1072
01:02:07,120 --> 01:02:10,320
Speaker 4: Yeah, I'm not very big on on shows or Hollywood.

1073
01:02:11,280 --> 01:02:12,760
Speaker 4: I mean to give an idea. I think I'm on

1074
01:02:12,800 --> 01:02:16,160
Speaker 4: the second season of Slow Horses. I think I think

1075
01:02:16,160 --> 01:02:17,600
Speaker 4: there are four seasons of it, and I'm kind of

1076
01:02:17,640 --> 01:02:19,360
Speaker 4: slowly making my way through the second one.

1077
01:02:19,480 --> 01:02:21,480
Speaker 3: It's very entertaining. I love Gary Old.

1078
01:02:23,000 --> 01:02:24,840
Speaker 4: Yeah, it was sort of the taking down the genre

1079
01:02:24,880 --> 01:02:27,320
Speaker 4: of spy movies in a very entertaining way.

1080
01:02:27,400 --> 01:02:28,280
Speaker 3: So I'm doing that.

1081
01:02:28,360 --> 01:02:30,480
Speaker 4: But also I tend to watch late in the day

1082
01:02:30,520 --> 01:02:32,840
Speaker 4: when I'm tired, so it's it's entirely possible I fall

1083
01:02:32,840 --> 01:02:34,880
Speaker 4: asleep and I take like two three evenings to get.

1084
01:02:34,720 --> 01:02:35,800
Speaker 3: Through one episode.

1085
01:02:36,640 --> 01:02:39,520
Speaker 4: Yeah, so I'm not I'm not all that big on that.

1086
01:02:39,640 --> 01:02:42,280
Speaker 2: On that front, tell us about your mentors who helped

1087
01:02:42,400 --> 01:02:43,640
Speaker 2: to shape your career.

1088
01:02:44,520 --> 01:02:46,920
Speaker 4: So many people, right, because a lot of it is teamwork,

1089
01:02:47,000 --> 01:02:49,600
Speaker 4: and you don't you don't progress with that mentors and

1090
01:02:49,920 --> 01:02:52,160
Speaker 4: role models. I would say in that in my current role,

1091
01:02:53,400 --> 01:02:56,480
Speaker 4: I would probably call out two people. Rich Lesser are

1092
01:02:56,480 --> 01:02:59,560
Speaker 4: a long time CEO and our chairman. He had the

1093
01:03:00,200 --> 01:03:03,640
Speaker 4: vision for a Macro product, as did Martin Reeves, who

1094
01:03:03,760 --> 01:03:06,920
Speaker 4: runs our research institute at Henderson Institute, And they are

1095
01:03:06,960 --> 01:03:08,840
Speaker 4: really the two people who brought me into this role

1096
01:03:08,880 --> 01:03:12,000
Speaker 4: and coached me, so they stand out outside of BCG.

1097
01:03:13,320 --> 01:03:17,040
Speaker 4: Kathleen Stephanson, she had many, many different roles on Wall

1098
01:03:17,080 --> 01:03:21,040
Speaker 4: Street and economist roles. She's been a great help navigating

1099
01:03:22,280 --> 01:03:27,720
Speaker 4: my career the last many years, and further back and academia,

1100
01:03:28,120 --> 01:03:31,800
Speaker 4: thesis advisors and many others. There's always teamwork in a way,

1101
01:03:31,880 --> 01:03:33,920
Speaker 4: so you have many, many role models and mentors.

1102
01:03:34,280 --> 01:03:36,560
Speaker 2: Let's talk about books. What are some of your favorites?

1103
01:03:36,600 --> 01:03:37,720
Speaker 2: What are you reading right now?

1104
01:03:39,000 --> 01:03:43,200
Speaker 4: All right now, I'm almost done with Making Sense of

1105
01:03:43,280 --> 01:03:46,280
Speaker 4: Chaos by Doin Farmer, came out last year.

1106
01:03:46,520 --> 01:03:48,200
Speaker 3: Doun Farmer is a very interesting character.

1107
01:03:48,440 --> 01:03:52,080
Speaker 4: He's a complexity scientist at the Santa Fe Institute and

1108
01:03:52,120 --> 01:03:55,240
Speaker 4: I think at Oxford University as well. And his book

1109
01:03:55,320 --> 01:03:57,000
Speaker 4: is interesting to me. I bumped into him at one

1110
01:03:57,040 --> 01:03:59,240
Speaker 4: or two conferences. But it's interesting to me, particularly because

1111
01:03:59,280 --> 01:04:01,480
Speaker 4: he kind of argues the opposite of what we are

1112
01:04:01,640 --> 01:04:06,320
Speaker 4: in our book. So he thinks he agrees that economics

1113
01:04:06,160 --> 01:04:10,120
Speaker 4: is poor if you just take standard models and theory,

1114
01:04:10,160 --> 01:04:12,280
Speaker 4: but he believes he can crack the complexity of it.

1115
01:04:12,360 --> 01:04:16,400
Speaker 4: So he thinks, with complexity signs and better data and

1116
01:04:16,440 --> 01:04:19,440
Speaker 4: better models, you'll essentially be able to make those forecasts.

1117
01:04:20,000 --> 01:04:22,280
Speaker 4: I read it because it's always important to see what

1118
01:04:22,320 --> 01:04:26,120
Speaker 4: others are arguing. I don't read stuff that reconfirms what

1119
01:04:26,240 --> 01:04:28,120
Speaker 4: I think. I want to see what other people are

1120
01:04:28,160 --> 01:04:30,440
Speaker 4: saying about the same topic from different angles of That

1121
01:04:30,440 --> 01:04:34,440
Speaker 4: book's been very useful and also well written. That's what

1122
01:04:34,480 --> 01:04:38,800
Speaker 4: I'm currently reading. I think of other books that have

1123
01:04:38,880 --> 01:04:40,680
Speaker 4: read over the years, I mean, there's so many, many

1124
01:04:40,880 --> 01:04:43,400
Speaker 4: great ones. Of course, I think one that early on

1125
01:04:43,520 --> 01:04:47,200
Speaker 4: made an impression on me was Seeing Like a State

1126
01:04:47,360 --> 01:04:50,600
Speaker 4: by James Scott, a't at least twenty five years old.

1127
01:04:50,680 --> 01:04:51,920
Speaker 3: I read it as a grand student.

1128
01:04:52,680 --> 01:04:56,520
Speaker 4: And what he does he looks at the ability of

1129
01:04:56,560 --> 01:04:59,960
Speaker 4: governments to do top down policy to improve the lie

1130
01:05:00,480 --> 01:05:03,560
Speaker 4: of large amounts of people, and he shows all the

1131
01:05:03,600 --> 01:05:07,000
Speaker 4: pitfalls in a sort of Hyaekian way. It's tough to

1132
01:05:07,040 --> 01:05:09,120
Speaker 4: have the local knowledge, it's tough to do the top

1133
01:05:09,160 --> 01:05:10,040
Speaker 4: down improvements.

1134
01:05:10,120 --> 01:05:10,840
Speaker 3: Things have to.

1135
01:05:10,720 --> 01:05:14,040
Speaker 4: Grow bottom up. And that book kind of stood out

1136
01:05:14,040 --> 01:05:17,560
Speaker 4: for being very, very eclectic, very multidisciplinary, and still I

1137
01:05:17,600 --> 01:05:21,920
Speaker 4: think an excellent book to how to think laterally and

1138
01:05:21,920 --> 01:05:23,800
Speaker 4: not in a sort of strict model based way.

1139
01:05:24,000 --> 01:05:28,160
Speaker 2: Huh. Really interesting. Our final two questions what sort of

1140
01:05:28,200 --> 01:05:32,280
Speaker 2: advice would you give a recent college grad interested in

1141
01:05:32,320 --> 01:05:36,160
Speaker 2: the career and economics, investment, finance, anything along those lines.

1142
01:05:36,400 --> 01:05:40,280
Speaker 4: Yeah, you know, I think a career as an economist

1143
01:05:40,400 --> 01:05:44,320
Speaker 4: is challenging in some ways. There's so many economists out there.

1144
01:05:44,400 --> 01:05:48,640
Speaker 4: Often when I hire, you see the flood of cvs,

1145
01:05:48,680 --> 01:05:51,680
Speaker 4: and often very good cvs, and there's I think there's

1146
01:05:51,680 --> 01:05:56,720
Speaker 4: been an overproduction of economists. So I think doing something

1147
01:05:56,880 --> 01:06:01,000
Speaker 4: adjacent to economics, you know, working finance, work on the

1148
01:06:01,040 --> 01:06:04,000
Speaker 4: buy side, work on the cell side. Unless unless your

1149
01:06:04,000 --> 01:06:08,000
Speaker 4: heart truly beats for economics, I think, you know, you

1150
01:06:08,000 --> 01:06:11,720
Speaker 4: can use economic skills and many adjacent disciplines and careers

1151
01:06:11,760 --> 01:06:18,800
Speaker 4: I think are plentiful in those adjacent disciplines. If economics

1152
01:06:18,800 --> 01:06:22,120
Speaker 4: graduates really feel strongly about economics, it's fascinating, but your

1153
01:06:22,160 --> 01:06:25,040
Speaker 4: heart has to be in it, and there aren't all

1154
01:06:25,040 --> 01:06:28,960
Speaker 4: that many seats as economists, so one has to build

1155
01:06:28,960 --> 01:06:30,040
Speaker 4: that over the long term.

1156
01:06:30,440 --> 01:06:32,720
Speaker 2: And our final question, what do you know about the

1157
01:06:32,760 --> 01:06:36,120
Speaker 2: world of economics today? You wish you knew twenty five

1158
01:06:36,200 --> 01:06:38,440
Speaker 2: thirty years ago when you were first getting started.

1159
01:06:39,240 --> 01:06:41,200
Speaker 4: Yeah, well, I mean that's really what I wrote down

1160
01:06:41,200 --> 01:06:41,720
Speaker 4: on the book.

1161
01:06:42,040 --> 01:06:42,280
Speaker 3: You know.

1162
01:06:42,360 --> 01:06:45,680
Speaker 4: The book is the twenty twenty five year journey through

1163
01:06:46,200 --> 01:06:51,120
Speaker 4: the maze of the economics profession and discipline. The themes

1164
01:06:51,120 --> 01:06:54,360
Speaker 4: we touched on the master model mentality, the pitfalls of

1165
01:06:54,600 --> 01:06:58,080
Speaker 4: treating economics like a like a physical science, the dow

1166
01:06:58,200 --> 01:07:01,680
Speaker 4: mongering which we have to simply ignore most of the time.

1167
01:07:02,840 --> 01:07:05,920
Speaker 4: And then the eclectic approach to economics. I call it

1168
01:07:05,960 --> 01:07:10,960
Speaker 4: economic eclecticism, drawing on a broader range of disciplines. Those

1169
01:07:11,000 --> 01:07:14,160
Speaker 4: are the things that I that I learned through that

1170
01:07:14,360 --> 01:07:16,080
Speaker 4: path the last twenty years.

1171
01:07:16,080 --> 01:07:17,240
Speaker 3: I wrote them up on the book.

1172
01:07:17,800 --> 01:07:20,000
Speaker 4: You know, it would have been would have been interesting

1173
01:07:20,000 --> 01:07:21,720
Speaker 4: for me to read that twenty years ago, but I

1174
01:07:21,760 --> 01:07:23,560
Speaker 4: wrote it now, and so I'm happy with that.

1175
01:07:24,080 --> 01:07:27,520
Speaker 2: Really really intriguing. Philip, Thank you for being so generous

1176
01:07:27,560 --> 01:07:31,160
Speaker 2: with your time. We have been speaking with Philip Carlson Slezak.

1177
01:07:31,560 --> 01:07:35,360
Speaker 2: He's global chief economist for the Boston Consulting Group. His

1178
01:07:35,520 --> 01:07:39,520
Speaker 2: new book, Shocks, Crises and False Alarms, How to Assess

1179
01:07:39,680 --> 01:07:44,040
Speaker 2: True Macroeconomic Risk, co authored with Paul Schwartz, is an

1180
01:07:44,040 --> 01:07:48,760
Speaker 2: absolutely fascinating read. If you enjoyed this conversation, well, check

1181
01:07:48,800 --> 01:07:51,560
Speaker 2: out any of the past five hundred we've done over

1182
01:07:51,600 --> 01:07:56,360
Speaker 2: the previous ten years. You can find those that iTunes, Spotify, YouTube,

1183
01:07:56,760 --> 01:08:00,440
Speaker 2: wherever you find your favorite podcasts, and be sure to

1184
01:08:00,520 --> 01:08:04,120
Speaker 2: check out my new book, How Not to Invest The

1185
01:08:04,160 --> 01:08:08,960
Speaker 2: Bad Ideas, Numbers and Behavior that Destroys Wealth, coming out

1186
01:08:09,120 --> 01:08:12,760
Speaker 2: March eighteenth, twenty twenty five. I would be remiss if

1187
01:08:12,760 --> 01:08:14,560
Speaker 2: I did not thank the Crack team that helps us

1188
01:08:14,560 --> 01:08:19,120
Speaker 2: put these conversations together each week. My audio engineer is

1189
01:08:19,200 --> 01:08:23,760
Speaker 2: Andrew Gavin, My producer is Anna Luke. Sage Bauman is

1190
01:08:23,760 --> 01:08:28,840
Speaker 2: ahead of podcasts at Bloomberg. Sean Russo is my researcher.

1191
01:08:29,080 --> 01:08:32,519
Speaker 2: I'm Barry Ridults. You've been listening to Master's in Business

1192
01:08:33,280 --> 01:08:50,760
Speaker 2: on Bloomberg Radio.