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Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have a special guest.

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Speaker 1: Her name is Constance Hunter. She is the chief economist

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Speaker 1: at accounting and consulting giant KPMG. She's also a fishing buddy.

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Speaker 1: I've spent some time with her up in Maine at

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Speaker 1: Camp Ko Talk discussing all manner of things. She has

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Speaker 1: a really interesting and fascinating career path not the typical economists.

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Speaker 1: She started on the bye side as a trader and

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Speaker 1: fund manager. Eventually UH did some work managing alternative investments

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Speaker 1: before she ended up as chief economist at KPMG. This

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Speaker 1: is a really interesting conversation I expect you will find

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Speaker 1: in intriguing with no further ado, my conversation with Constance Hunter.

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Speaker 1: My guest today is Constance Hunter. She has been chief

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Speaker 1: economist at KPMG since previously, she was the Deputy Chief

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Speaker 1: Investment Officer for fixed income at ACTA Investment Managers, where

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Speaker 1: she joined after fifteen years on the by side, investing

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Speaker 1: globally in fixed income equities and foreign exchange. She is

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Speaker 1: a member of the New York Association of Business Economics

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Speaker 1: and one hundred Women in Finance. She also participates in

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Speaker 1: the National Committee for US China Relations, working on track

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Speaker 1: to economic dialogues. She earned her master's degree in International

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Speaker 1: Affairs at Columbia. Constance Hunter, Welcome to Bloomberg. Berry is

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Speaker 1: so good to be here. So Constance, let's talk a

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Speaker 1: little bit about your background. How did you find your

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Speaker 1: way into finance? So a couple of ways. First, my father,

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Speaker 1: uh worked for an investment bank in Philadelphia, and so

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Speaker 1: there was investing going on in my family. That's where

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Speaker 1: you grew up. I grew up in Philadelphia. And and

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Speaker 1: then how did you manage to make your way to Manhattan? Well,

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Speaker 1: I went to ny U. But the other influence that

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Speaker 1: got me into finance my very good friend father, Dan Bongovanni,

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Speaker 1: was an avid reader, and so he was reading a

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Speaker 1: book called The Coming Economic War with Japan, and I was,

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Speaker 1: I know, this was the early eighties. I was in

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Speaker 1: high school. And let me just let me just set

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Speaker 1: the scene a little bit. The Japanese are dominating exports,

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Speaker 1: They're buying Rockefell Or Center. They're gonna take over America,

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Speaker 1: take over America a little bit like China today. But

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Speaker 1: we'll get back to that Uh. So, I remember he's

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Speaker 1: reading this book the Coming Economic War with Japan, and

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Speaker 1: the whole concept of an economic war seemed fascinating to me.

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Speaker 1: And I was fourteen or fifteen, and so he ended

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Speaker 1: up telling me about the book and I borrowed it.

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Speaker 1: I read it. I don't you know how much of

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Speaker 1: it went over my head, but it I was hooked.

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Speaker 1: I was hooked that I not only on finance, but

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Speaker 1: on international finance. You end up at n y U.

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Speaker 1: Was there a business or international relations in the undergraduate?

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Speaker 1: So I did um Liberal Arts, So I have a

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Speaker 1: b a. In economics. Uh, And I felt that was important.

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Speaker 1: I wanted to have that broad philosophical grounding that a

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Speaker 1: good liberal arts degree gives you, um in being able

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Speaker 1: to think and baby being able to understand, uh, sort

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Speaker 1: of history of Western thought, reading all of the great books.

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Speaker 1: That's I think a good grounding for someone who's going

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Speaker 1: to spend their career thinking and so. Um. So that

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Speaker 1: was my My undergraduate degree was in economics. And then

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Speaker 1: you go to Columbia for a master's in International Relationship

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Speaker 1: Master so it's a school of International and Public Affairs.

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Speaker 1: So my degree is a Master of International affairs, but

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Speaker 1: I focused on economics and international economics, and so I

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Speaker 1: had professors like jug desbug Waddy and Armenio Fraga, who

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Speaker 1: was a adjunct professor when he was at Soros and

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Speaker 1: then later went on to become the head of the

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Speaker 1: Central Bank of Brazil, for example. So world famous UM faculty. Yes,

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Speaker 1: just to say the very least. So you you come

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Speaker 1: out with this international relations graduate degree, how does that

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Speaker 1: translate to a career on Wall Street? So interestingly enough,

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Speaker 1: I was interviewing at Chase Manhattan Bank, so pre Chemical

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Speaker 1: Bank merger. I mean, so you're talking n early late

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Speaker 1: ninety three, early Ninet'm interviewing at Chase, and they were

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Speaker 1: recruiting for the private bank, and I wasn't terribly interested

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Speaker 1: in that. UM. I was also interviewing at a bankers

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Speaker 1: Trust if anybody go back in time and remember bankers

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Speaker 1: Trust for auto analyst position, so very heavily on economics.

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Speaker 1: International affairs is important. There's obviously trade component to that.

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Speaker 1: So I'm they're interviewing at the private bank, and I

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Speaker 1: see this research piece and it's a daily foreign exchange

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Speaker 1: update on what happened overnight and what's going on with

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Speaker 1: the currencies, and like it was one sentence, maybe two

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Speaker 1: sentences about every single economy and a font that I

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Speaker 1: couldn't read today to save my life. And and I

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Speaker 1: saw this, and I thought, this is what I want

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Speaker 1: to do. I want to do this, And so I

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Speaker 1: networked my way to the to the Foreign Exchange Department

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Speaker 1: and the person who was responsible for this, and it

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Speaker 1: turns out they were hiring and and so that person's

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Speaker 1: name is chriss Igo, who was also I worked with

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Speaker 1: him at ACCA later in my career, and um, he

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Speaker 1: had interviewed PhD economists who would say, well, there's a

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Speaker 1: perfect information and there's no foreign exchange markets, or you

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Speaker 1: know that you can't make any extra money, why forecast them?

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Speaker 1: It's all the information is already in the price. And

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Speaker 1: here I was coming in looking at the economics and

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Speaker 1: the politics and and how they all interplayed with one

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Speaker 1: another packed the foreign exchange market. So it was really

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Speaker 1: the perfect first job out of graduate school for me.

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Speaker 1: And it's funny that two of us are both sitting

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Speaker 1: here holding reading glasses in our hands, and you're talking

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Speaker 1: about funds. Let me mix this up a little bit

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Speaker 1: with you. McKenzie just came out with a study that

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Speaker 1: said by the year almost a million jobs will have

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Speaker 1: been replaced by and with technology. What does that tell

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Speaker 1: you about what's going on in the labor market these days? Okay,

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Speaker 1: so let's put this in a big context. The US

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Speaker 1: labor market is how many people, hundred and fifty million

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Speaker 1: people and fifty exactly, So this is percentagewise, a small

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Speaker 1: percentage of total jobs. It sounds big, right, it's a

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Speaker 1: little Austin powers. One million jobs are going to disappear, right,

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Speaker 1: But but the reality is, first of all, let's so

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Speaker 1: contextualize it in terms of the whole labor market. There's

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Speaker 1: two other things that need to be highlighted when thinking

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Speaker 1: about this issue, and we're actually working on some search

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Speaker 1: on this, which I can tell you about in more detail.

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Speaker 1: But the first thing is they don't talk about what

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Speaker 1: new jobs are going to be created, and that's a

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Speaker 1: really important thing to think about. They also don't talk

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Speaker 1: about the areas where labor shortages and pending labor shortages

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Speaker 1: intersect with the technology. So let me give you one example.

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Speaker 1: As the trucking industry and long haul trucking, it's widely

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Speaker 1: anticipated we're going to have self driving trucks. And while

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Speaker 1: they aren't necessarily we're not necessarily going to have the

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Speaker 1: robotics to do the loading of the truck, the unloading

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Speaker 1: of the truck, the bringing of the goods into the

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Speaker 1: home or the store. Eventually, who knows. We're making progress

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Speaker 1: in robotics, but just the driving part is certainly something

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Speaker 1: the and the long haul driving part is something that

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Speaker 1: could be automated. Young people don't want to do this job.

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Speaker 1: It's a very old industry and and you know, you

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Speaker 1: can't be on your little dopamine giving device all day

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Speaker 1: when that is your job when you're driving a truck.

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Speaker 1: And so if we had a self driving truck, that

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Speaker 1: could really help an industry that has had to continue

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Speaker 1: to raise wages to get people to even consider working

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Speaker 1: in that field. It's not something people want to do anymore.

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Speaker 1: And that we're talking about right there. It's a it's

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Speaker 1: a profession that has two and a half million people.

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Speaker 1: So there's there's a match that that's happening in some

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Speaker 1: cases where the disappearing of these jobs is a good thing.

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Speaker 1: The second thing that they don't talk about, um is

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Speaker 1: the number of new jobs that could appear. So someone's

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Speaker 1: got a design and build all those robotics. Well, and

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Speaker 1: interestingly enough, right now there are more people being employed

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Speaker 1: in in artificial intelligence and um robotics than than people

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Speaker 1: that are being than jobs that are being destroyed. Now,

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Speaker 1: I'm not saying that's going to go on forever, but

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Speaker 1: if we look back historically too, when we had guilt,

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Speaker 1: when artisans produced things where it took one guy a

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Speaker 1: month to produce let's say an ax, right, and he

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Speaker 1: produced all parts of that acts. Then we moved away

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Speaker 1: from artisans and we had factories and you had one

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Speaker 1: producing the handle and one guy producing the top part

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Speaker 1: of the metal part of the acts. And then you

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Speaker 1: had then you had multiple people working in this factory

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Speaker 1: and you could produce. You could have fifteen people producing

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Speaker 1: a hundred axes in a month. Right. That's a representative example.

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Speaker 1: Those aren't the exact numbers, but the point being that

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Speaker 1: you create actually created more jobs because the thing that

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Speaker 1: you're producing became less expensive. But you created new types

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Speaker 1: of jobs. So the idea of a clerical worker that

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Speaker 1: didn't exist, right, You that that role of clerical worker.

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Speaker 1: People to do the accounting, people to do hr management,

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Speaker 1: people to do building management, all that kind of stuff

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Speaker 1: evolved after we had corporate structure, which we couldn't have

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Speaker 1: until we had specialized jobs. So my suspicion is that

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Speaker 1: we will have new jobs that evolve as a result

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Speaker 1: of the technology that we're building. Now, let's jump right

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Speaker 1: into where we are in the modern economy. On the

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Speaker 1: perspective of a big accounting and consulting firm, they split

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Speaker 1: the two pieces into different groups. Is that right? So

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Speaker 1: we have some regulatory requirements that create Chinese walls, and

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Speaker 1: we there are a long list of advisory services that

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Speaker 1: we cannot offer to our audit clients. Um. But this

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Speaker 1: is a great business model because it diversifies our business.

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Speaker 1: We have audit and tax um, and then we have

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Speaker 1: our entire advisory business, which is very diverse. So we

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Speaker 1: do things like data and analytics, we do things. We

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Speaker 1: have an entire business called people and change, which when

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Speaker 1: you think about what is happening in the labor force

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Speaker 1: and the way technology is impacting firms and how they're

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Speaker 1: managing for that, how they're retraining their workers. Um, that's

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Speaker 1: a that's a huge practice for us. For example, so

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Speaker 1: you of course risk compliance, all that other stuff that

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Speaker 1: traditionally expect someone like KPMG to do so. You originally

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Speaker 1: were on the buy side, You were in an investor

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Speaker 1: in fixed income and alternative investments. How has that colored

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Speaker 1: how you see, uh, the world of economics, whether it's

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Speaker 1: for an audit slash consulting firm or anyone else. What

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Speaker 1: does coming from the buy side due to your economic perspective? Well,

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Speaker 1: for me, I think it's critical to my edge and

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Speaker 1: and that is that I've spent most of my career

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Speaker 1: using economics to make investment decisions and put money on

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Speaker 1: the line. So that sort of classic UH saying I'd

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Speaker 1: love to find a one handed economist. Right on the

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Speaker 1: other hand, um and and certainly that that discipline I

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Speaker 1: think is very important. And I think the other thing

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Speaker 1: that it really um and that's sort of a chicken

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Speaker 1: and egg thing. I noticed this about myself when I

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Speaker 1: was at Chase. I noticed that I had an ability

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Speaker 1: to do pattern recognition and see when there might be

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Speaker 1: opportunities in the market as a result of either shifting

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Speaker 1: market data or shifting economic data and matching those two

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Speaker 1: together and doing analysis on them. So because I had

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Speaker 1: this ability to see inflection points, it pushed me from

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Speaker 1: the cell side at chase into the by side, and

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Speaker 1: while I was at on the Bye side, that's really

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Speaker 1: where I had my edge. And coming back into doing

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Speaker 1: applied economics or business economics, I think when I go

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Speaker 1: speak with either the leaders of our firm, our clients,

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Speaker 1: when I write research, it's all oriented towards what is

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Speaker 1: the inflection point we need to be paying attention to

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Speaker 1: and how do you make money off of this inflection point.

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Speaker 1: So let's address that precise topic. Where are we in

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Speaker 1: the economic cycle today and where do you see the

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Speaker 1: next inflection point coming, both in terms of sectors or chronology. Yeah,

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Speaker 1: so we're in the happy part of the cycle, We're

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Speaker 1: in the we're in the end of the cycle probably,

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Speaker 1: Although with that said, I firmly believe UH expansions don't

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Speaker 1: die of old age, right, they need to bump into something.

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Speaker 1: And so we have had consecutive months and this consecutive

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Speaker 1: is really important, consecutive months of jobs growth. If you

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Speaker 1: look back over previous recoveries, that is a streak that

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Speaker 1: is seventy longer than the previous record. So even though

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Speaker 1: people aren't necessarily happy about the jobs mix that we

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Speaker 1: have in the economy right now, that is still an

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Speaker 1: unprecedented statistic, and it is allowed consumers to really feel

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Speaker 1: very confident. We see that in the consumer confidence data.

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Speaker 1: The consumer has been the backbone of the recovery. We

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Speaker 1: haven't had as much investment, for example, as in previous recoveries,

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Speaker 1: and so it's it's helping us sort of continue that

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Speaker 1: consumption boom. But it's well founded. Household balance sheets are

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Speaker 1: fairly healthy, right, we haven't um In fact, because of

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Speaker 1: the crisis, we actually had a bit of a line

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Speaker 1: in in debt for households, mostly in the in the

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Speaker 1: housing sector. But it's allowed us to have sort of

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Speaker 1: a balanced recovery for the most part. And so in

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Speaker 1: terms of inflection points, at some point, I know people

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Speaker 1: have been watching and waiting for this. I expect we're

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Speaker 1: going to see some wage increases and we're seeing significant

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Speaker 1: shortages in a very important sector and that is the

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Speaker 1: construction sector. So the Associated General Contractors does a survey

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Speaker 1: and there are a number of categories where over seventy

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Speaker 1: of their respondents say they cannot find qualified workers and

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Speaker 1: that they have to go ahead and raise wages. How

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Speaker 1: much of that is related to the fact that during

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Speaker 1: the financial crisis and during the housing collapse, a huge

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Speaker 1: number of immigrants who were actually contractors, builders, electricians, plumbers, etcetera.

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Speaker 1: All went home, went back to Mexico or wherever because

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Speaker 1: the jobs weren't here. And so you created this void.

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Speaker 1: The economy created this void in that segment of the

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Speaker 1: labor force. So we lost about a million and a

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Speaker 1: half construction jobs, and some of them were filled by immigrants,

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Speaker 1: some of them were filled by native born individuals, and

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Speaker 1: a lot of people started to do new things. They

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Speaker 1: had to pivot to new careers, and they haven't been

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Speaker 1: drawn back into the construction sector. Um. Another thing that

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Speaker 1: we had during the housing boom is we had people

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Speaker 1: forego higher education and just leave high school and go

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Speaker 1: start doing construction because it was a career where you

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Speaker 1: could make a fair amount of money towards the end

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Speaker 1: of the boom. And and so those people, a lot

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Speaker 1: of them went back to university after the crisis and

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Speaker 1: then retooled themselves. If we get frothy enough again, if

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Speaker 1: the if the price of labor goes up enough, you

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Speaker 1: will draw people into that sector. The issue, of course,

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Speaker 1: is that they won't have the skills, right, So some

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Speaker 1: of these some of these jobs are our medium skilled

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Speaker 1: jobs that you need to learn how to do a

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Speaker 1: specialty where whether we're talking about electricians, plumbers, um carpenters,

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Speaker 1: so you you can't just walk into that job. Now

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Speaker 1: that we're ten years past the financial crisis, are things

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Speaker 1: beginning to normalize in the world of economics, Yes, in

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Speaker 1: a way, there's still a big mystery. So even though

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Speaker 1: I expect some of the labor shortage data that we're seeing,

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Speaker 1: whether it's National Federation of Independent Business or this associated

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Speaker 1: General Contractors, some of this labor shortage I expect to

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Speaker 1: eventually translate into wage increases. But I'll tell you, the

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Speaker 1: FETE has been waiting for this for a long time.

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Speaker 1: We have all been waiting for this for a long time,

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Speaker 1: and there is some slack in the labor market still

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Speaker 1: that that is um confusing. And I'll tell you why.

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Speaker 1: Most people that have been out of the labor force

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Speaker 1: for a period of time, they're not setting wages right,

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Speaker 1: they're not the most desirable people to hire. They've had

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Speaker 1: skills atrophy. They come back in generally if they come

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Speaker 1: back in at a lower rage, and it's not We've

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Speaker 1: had a pickup in the participation rate, but not to

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Speaker 1: the point where you would really expect this portion of

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Speaker 1: the labor force to be influencing wages, and so it

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Speaker 1: is perplexing to me, it is perplexing to the Fed.

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Speaker 1: Um Janet Yellen has called the lack of inflation and mystery.

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Speaker 1: So we have normalized in some ways, but in this

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Speaker 1: aspect of prices and whether um and and specifically related

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Speaker 1: to wages we have we don't seem to have normalized.

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Speaker 1: Although maybe we're all just misunderstanding the slack that's out there,

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Speaker 1: or perhaps want a tipping point of higher wages, which

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Speaker 1: I think in certain sectors is an absolute given. You've

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Speaker 1: had a number of pretty high profile jobs in the

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Speaker 1: world of finance. Does this tell us anything about women

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Speaker 1: in finance? We know women have been let me man

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Speaker 1: explain to you women in finance. We have very interested

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Speaker 1: in what you have to say. Well, we know that

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Speaker 1: women have not been promoted. There aren't as many women

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Speaker 1: on the board level, at the sea level, uh, managing assets, etcetera.

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Speaker 1: It's it's uh disproportionately a boys club, although there are

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Speaker 1: signs that that's starting to change. Tell us a little

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Speaker 1: bit about your career path and what it was like

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Speaker 1: being a woman in a world where these were, for

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Speaker 1: a long time traditionally male jobs. Well, I would say

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Speaker 1: that I'm still in a very male dominated industry in general.

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Speaker 1: Now at KPMG, we have a goal of having of

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Speaker 1: our partners be women. And we're at twenty five now,

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Speaker 1: so compared to Wall Street, that is definitely a better environment.

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Speaker 1: And our CEO is a woman. We oh, Lynn Duddy

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Speaker 1: is a woman. She's fabulous. She happens to be a woman.

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Speaker 1: She's fabulous CEO and um. And so we've made a

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Speaker 1: concerted effort. And the reason that's important, and the reason

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Speaker 1: I think that it's taken so long in places like

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Speaker 1: Wall Street that are male dominated, is that there's an

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Speaker 1: natural in group bias, right, so we tend to hire

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Speaker 1: people who look like us and who who act like

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Speaker 1: us and I share the same interests and and so

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Speaker 1: it's it's understandable, and it pervades everything. It pervades the

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Speaker 1: recruiting practices. It also pervades what when you look at

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Speaker 1: young women and they look out, well, what do I

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Speaker 1: want to be when I grew up? Now, I was

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Speaker 1: lucky I had My father was in finance, so he

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Speaker 1: never had a bias that I shouldn't be in finance.

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Speaker 1: In fact, he probably had a bias that I should

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Speaker 1: be in finance, right, So so that I think gave

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Speaker 1: me um an edge and uh and a unique perspective

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Speaker 1: and approach that I never thought it was d that

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Speaker 1: I would be in finance or that I would be

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Speaker 1: very successful in finance. Michelle Myers, who's one of the

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Speaker 1: senior economists at Merrill Lynch, specifically said at in the

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Speaker 1: current environment, there are now women who are role models,

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Speaker 1: whether it's real Seabird or Lizzie and Sanders at Schwab

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Speaker 1: or there's a whole run of different absolutely women you

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Speaker 1: can name, but go back at generation there really weren't

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Speaker 1: a whole lot of role models for women who wanted

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Speaker 1: to go to Wall Street. Well, let's focus on this

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Speaker 1: because this issue of role models is really important and

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Speaker 1: it's one of the things that's going to propel more women,

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Speaker 1: and it's one of the reasons where you need a

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Speaker 1: certain critical mass because if you were just the token woman,

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Speaker 1: you don't get really from a firm perspective, you don't

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Speaker 1: get necessarily all of the diversifying effects and all of

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Speaker 1: the beneficial effects of having a more diverse perspective in

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Speaker 1: your workplace. But once you hit a critical mass, let's

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Speaker 1: say it's you start to have a very different um

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Speaker 1: type of conversation around certain things. Your marketing appeals to

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Speaker 1: a broader segment of the purchasing population. Uh, you just um,

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Speaker 1: you tend to have less fraud. They are all sorts

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Speaker 1: of definite advantages to having a greater mix of men

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Speaker 1: and women in an organization. So since you've joined the

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Speaker 1: world of finance, what improvements have been made towards achieving

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Speaker 1: a better balance of males to females um in the workplace. So,

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Speaker 1: certainly in the field of business economics now is different

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Speaker 1: in academic economics. I was very fortunate to join the

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Speaker 1: National Association for Business Economics as a young economist, and

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Speaker 1: NAVE as it's called for short, has always had women

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Speaker 1: leaders within the organization. So we had women presidents of NAVE,

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Speaker 1: people like Maureen Haver who founded Haver Analytics, Diane Swank

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Speaker 1: when she was the chief economist at Mazzaro. Uh, Ellen

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Speaker 1: Hughes Cromwick who used to be the chief economy missed

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Speaker 1: it Ford. Uh you know I could, I could list

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Speaker 1: more right, and so that I saw these women who

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Speaker 1: were chief economists as role models and that, and I

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Speaker 1: saw women attending our meetings. Um, now when I have

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Speaker 1: I have a young woman on my team who you

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Speaker 1: who I brought into I said, you have to start

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Speaker 1: at name. Young, You're going to learn so much it

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Speaker 1: will be great. And to me, coming from Wall Street,

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Speaker 1: I thought, well, Name is great because we probably have

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Speaker 1: in the membership about women and in leadership probably about women.

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Speaker 1: So people on the board of Name, people who are

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Speaker 1: presidents of Name. And she's like, no, it's so she

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Speaker 1: sees all men, whereas I see, Oh, my gosh, that's

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Speaker 1: so much improvement because my frame of reference. So this

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Speaker 1: makes me excited for a couple of reasons. One, I

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Speaker 1: got to see progress too. It's obviously not yet and

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Speaker 1: the next generation is going to make that happen. The

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Speaker 1: fact that it's women, they look at its oh, this

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Speaker 1: isn't remotely what it Maybe it's thirty five, but still

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Speaker 1: compared with twenty years ago, it's miles ahead. And the

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Speaker 1: fact that it's looked at as oh, they're not getting

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Speaker 1: this done is pretty that's pretty amazing. So we now

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Speaker 1: have men who are feminists and and mainstream men people

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Speaker 1: like Justin Trudeau, who's the Prime Minister of Canada. Right,

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Speaker 1: and now I've heard I've heard more and more men

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Speaker 1: say I'm a feminist. And and the thing is is

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Speaker 1: that it takes both men and women to make this happen.

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Speaker 1: It takes us working together. This isn't just a woman's cause.

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Speaker 1: And it's not even just a men of men who

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Speaker 1: are fathers of daughters cause. Right. It benefits everybody. And

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Speaker 1: so the more men that can get out there and say, yeah,

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Speaker 1: I'm also a feminist, it benefits them. It benefits them financially,

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Speaker 1: it benefits them socially, and we're starting to see that

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Speaker 1: more and more. Let me ask you a question that

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Speaker 1: will give the compliance people at KPMG a heart attack,

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Speaker 1: since you you brought up Trudeau in Canada. I am

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Speaker 1: absolutely fascinated by the concept that Canada will become the

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Speaker 1: first G seven country that's going to completely legalize marijuana.

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Speaker 1: I'm fascinating. I don't smoke weed. I mean it's not

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Speaker 1: I'm you know, not a college kid anymore. But I'm

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Speaker 1: fascinated from an economic perspective what that's going to do

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Speaker 1: to a country in terms of taxing, it taking money

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Speaker 1: away from the black market and saving countless amounts of

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Speaker 1: money that's currently spent incarcerating people for you know, simple

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Speaker 1: marijuana expenses. What is the potential economic impact of of

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Speaker 1: something like that? There? And then the harder question is,

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Speaker 1: and what does that due to the United States? So

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Speaker 1: Canada produces actually a lot of marijuana over one of theirs,

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Speaker 1: one of their bigger crops. I did not know that,

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Speaker 1: and uh, and so it'll be it'll be very interesting.

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Speaker 1: Now there is a lot of data which suggests that

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Speaker 1: if you smoke or I guess now you can eat

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Speaker 1: marijuana and under the age of certainly, it really is

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Speaker 1: very very bad for you, sod for brain development, brain

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Speaker 1: for a different precisely. So unless they can manage that

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Speaker 1: part of it, which I think will be challenging. They

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Speaker 1: do a pretty good job with that with alcohol and

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Speaker 1: with cigarettes, certainly with twenty one. You know, you're not

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Speaker 1: keeping weed away from kids today regardless. So and they

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Speaker 1: have to be smarter about labeling gummy bears so it

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Speaker 1: doesn't look like candy and that sort of stuff. I mean, see,

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Speaker 1: when we look at Colorado and Seattle and California, they've

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Speaker 1: all done a pretty mediocre job. It's kind of Hey,

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Speaker 1: it's a case of first impression. They eventually figured out

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00:25:49,200 --> 00:25:52,600
Speaker 1: if you make the stuff look like candy and leaving around,

431
00:25:53,080 --> 00:25:56,159
Speaker 1: guess what kids are gonna eat it? But you know,

432
00:25:56,240 --> 00:25:59,800
Speaker 1: they've kind of a learned that lesson. Assuming Canada addresses

433
00:25:59,840 --> 00:26:03,800
Speaker 1: that part of it and handles it well, um, then

434
00:26:04,280 --> 00:26:07,439
Speaker 1: it's it's like you said that the benefit is twofold, right,

435
00:26:07,440 --> 00:26:09,119
Speaker 1: And I don't know the size of the market in

436
00:26:09,160 --> 00:26:12,320
Speaker 1: Canada or their incarceration rate for this sort of thing,

437
00:26:12,720 --> 00:26:15,359
Speaker 1: but you get rid of the cost of incarcerating, so

438
00:26:15,400 --> 00:26:18,560
Speaker 1: then we have more people in your labor force, right,

439
00:26:18,640 --> 00:26:20,480
Speaker 1: So that's always a good thing when we have these

440
00:26:20,480 --> 00:26:26,240
Speaker 1: population declines. And then you also have the economic benefit

441
00:26:26,240 --> 00:26:27,840
Speaker 1: of this industry. And if we based it on the

442
00:26:27,840 --> 00:26:31,000
Speaker 1: rest of the sin industry, right, it's pretty large. Let's

443
00:26:31,080 --> 00:26:35,080
Speaker 1: talk a little bit about the state of the economy today,

444
00:26:35,160 --> 00:26:37,280
Speaker 1: and there are so many different things we could go over.

445
00:26:37,359 --> 00:26:40,560
Speaker 1: I have to start with the issue of the Federal

446
00:26:40,640 --> 00:26:45,199
Speaker 1: reserve tightening normalizing first, what should we call what the

447
00:26:45,240 --> 00:26:48,639
Speaker 1: Federal Reserve is doing? Well, they're definitely raising rates, so

448
00:26:48,680 --> 00:26:52,280
Speaker 1: they're tightening. Okay, they're selling assets off of their balance sheet.

449
00:26:52,440 --> 00:26:54,760
Speaker 1: Are they selling them or they letting them just roll

450
00:26:54,840 --> 00:26:58,239
Speaker 1: off when there My understanding is they're doing both. That is,

451
00:26:58,320 --> 00:27:02,480
Speaker 1: in effect a tightening. Now there's a debate about this, right,

452
00:27:02,880 --> 00:27:05,879
Speaker 1: is it the stock of the Fed's balance sheets that

453
00:27:05,960 --> 00:27:08,640
Speaker 1: is the size of the balance sheet or is it

454
00:27:08,840 --> 00:27:16,520
Speaker 1: the flow that is the change? And generally speaking, the

455
00:27:16,520 --> 00:27:20,320
Speaker 1: Fed thinks it's the stock. Wall Street thinks it's the flow.

456
00:27:21,040 --> 00:27:23,280
Speaker 1: The Fed might end up being right, or I think

457
00:27:23,280 --> 00:27:26,760
Speaker 1: the Fed is being proven correct because we look at

458
00:27:26,840 --> 00:27:30,560
Speaker 1: ten yere yields and they're much lower than they were,

459
00:27:30,680 --> 00:27:34,520
Speaker 1: for example, but that's due to a confluence. Are you

460
00:27:34,560 --> 00:27:37,480
Speaker 1: surprised that Wall Street looks at the flow as opposed

461
00:27:37,520 --> 00:27:40,879
Speaker 1: to the balance sheet. That's how the street gets paid,

462
00:27:40,960 --> 00:27:42,800
Speaker 1: so of course that's what matters to them. There is

463
00:27:42,840 --> 00:27:46,520
Speaker 1: a little classic confirmation bias. So what about the idea

464
00:27:46,680 --> 00:27:50,600
Speaker 1: of the Fed isn't so much tightening as we're still

465
00:27:50,640 --> 00:27:54,280
Speaker 1: at very accommodative rates and they're just getting off their

466
00:27:54,320 --> 00:27:59,120
Speaker 1: emergency footing. This is really normalization. So I went back

467
00:27:59,119 --> 00:28:02,760
Speaker 1: and looked at real tenure yields. Right over the last

468
00:28:02,920 --> 00:28:08,879
Speaker 1: four decades, average is about two. Absolutly, there's wide variations,

469
00:28:08,920 --> 00:28:13,600
Speaker 1: but the average is about two. Real real adjusted for inflation.

470
00:28:13,840 --> 00:28:18,639
Speaker 1: Right ten year yields we are now almost zero, so

471
00:28:18,760 --> 00:28:22,000
Speaker 1: still very kind, We are very it's by that metric

472
00:28:22,119 --> 00:28:25,480
Speaker 1: we are still pretty accommodated emergency funding. There'n't really no

473
00:28:25,560 --> 00:28:28,120
Speaker 1: other way to describe that. By the way, I find

474
00:28:28,119 --> 00:28:32,760
Speaker 1: myself having to always define real because some people anytime

475
00:28:32,800 --> 00:28:35,040
Speaker 1: I mentioned real, I always want the audience to hear

476
00:28:35,600 --> 00:28:41,840
Speaker 1: after inflation, after inflation adjusted. Let's talk about the yield curve. Historically,

477
00:28:41,920 --> 00:28:44,840
Speaker 1: an inverted yield curve was a sign of an impending

478
00:28:44,840 --> 00:28:48,400
Speaker 1: and recession, not immediately, but a year or two later.

479
00:28:49,000 --> 00:28:51,760
Speaker 1: And a lot of people are all up in arms

480
00:28:51,800 --> 00:28:55,360
Speaker 1: over the flattening yield curve as the FED is tightening.

481
00:28:55,440 --> 00:28:58,520
Speaker 1: What what do you think about what's taking place in

482
00:28:59,040 --> 00:29:03,720
Speaker 1: that part of the curve. So a few things. One is,

483
00:29:03,960 --> 00:29:06,120
Speaker 1: we may very well be a year and a half

484
00:29:06,280 --> 00:29:10,240
Speaker 1: away from the next recession. Uh that is, that is

485
00:29:10,400 --> 00:29:13,560
Speaker 1: very possible. But you also have to look at what's

486
00:29:13,560 --> 00:29:17,160
Speaker 1: going on globally in terms of central bank balance sheets.

487
00:29:17,240 --> 00:29:22,560
Speaker 1: So if we look globally right now, we have fourteen

488
00:29:22,720 --> 00:29:26,160
Speaker 1: trillion dollars of central bank balance sheets. Now the feed

489
00:29:26,280 --> 00:29:30,320
Speaker 1: is about four and a half um four point three

490
00:29:30,360 --> 00:29:35,360
Speaker 1: I think to be exact and trillion. And so we

491
00:29:35,480 --> 00:29:38,320
Speaker 1: have the Bank of Japan and the e c B,

492
00:29:38,320 --> 00:29:41,320
Speaker 1: both with significant balance sheets. So just to put this

493
00:29:41,440 --> 00:29:45,360
Speaker 1: in context, the FEEDS balance sheet is of the U

494
00:29:45,480 --> 00:29:50,320
Speaker 1: S economy. The ECB balance sheet is thirty eight percent

495
00:29:50,680 --> 00:29:54,560
Speaker 1: of the of the European economy, the EU economy or

496
00:29:54,640 --> 00:29:59,959
Speaker 1: I'm sorry that the Monetary Union, right, And Japan's by

497
00:30:00,000 --> 00:30:03,160
Speaker 1: balance sheet is ninety one per cent of GDP at

498
00:30:03,160 --> 00:30:06,400
Speaker 1: the Bank of Japan's balance sheet. So these are very

499
00:30:06,640 --> 00:30:10,959
Speaker 1: very significant amounts of capital, and they're influencing bond markets

500
00:30:10,960 --> 00:30:14,760
Speaker 1: all over the world, but especially the tenure yield. So

501
00:30:14,880 --> 00:30:17,800
Speaker 1: let's let's talk about that. You have the United States,

502
00:30:18,360 --> 00:30:21,240
Speaker 1: which really seemed to get hit by the financial crisis

503
00:30:21,360 --> 00:30:25,320
Speaker 1: first and then it spread globally um, and you also

504
00:30:25,360 --> 00:30:28,080
Speaker 1: have the United States is the first to really aggressively

505
00:30:28,200 --> 00:30:31,800
Speaker 1: respond to the financial crisis, and then Japan and then Europe.

506
00:30:32,480 --> 00:30:35,960
Speaker 1: Is how unusual is it to have the three major

507
00:30:36,040 --> 00:30:41,400
Speaker 1: economic centers um of the world plus China all sort

508
00:30:41,440 --> 00:30:44,720
Speaker 1: of out of phase with each other in terms of economic,

509
00:30:44,800 --> 00:30:50,400
Speaker 1: fiscal and especially monetary policy. Well, yes, it's both the

510
00:30:50,440 --> 00:30:53,320
Speaker 1: monetary and the fiscal I think that's really important because

511
00:30:53,720 --> 00:30:58,760
Speaker 1: Europe did fiscal tightening after the Great Recession Sterity right

512
00:30:58,800 --> 00:31:01,840
Speaker 1: into the teeth of of collapse turns out not an

513
00:31:01,920 --> 00:31:06,040
Speaker 1: especially great UH policy. Well, that and then the ECB

514
00:31:06,200 --> 00:31:10,480
Speaker 1: raised rates prematurely, if you recall, and and that coincided

515
00:31:10,520 --> 00:31:14,560
Speaker 1: with the Greek crisis and sort of spilled over into

516
00:31:14,640 --> 00:31:20,160
Speaker 1: widening bonnields in Italy and Spain, Portugal, Ireland. UH. Now,

517
00:31:20,240 --> 00:31:22,160
Speaker 1: a lot of these economies have come out the other

518
00:31:22,200 --> 00:31:25,960
Speaker 1: side right or they're they're on the upswing. So really,

519
00:31:26,000 --> 00:31:30,680
Speaker 1: since Draggy did his whatever it takes speech, and since

520
00:31:30,720 --> 00:31:34,480
Speaker 1: they really started growing the balance sheet, you've seen consecutive

521
00:31:34,520 --> 00:31:38,440
Speaker 1: months of jobs growth, You've seen consumption, you've seen bank

522
00:31:38,560 --> 00:31:40,840
Speaker 1: lending expand. I mean to get to negative rates to

523
00:31:40,920 --> 00:31:43,280
Speaker 1: do it, but you still saw the bank lending expand.

524
00:31:43,680 --> 00:31:47,920
Speaker 1: And that's helped give the European economies legs. But like

525
00:31:47,960 --> 00:31:50,920
Speaker 1: you said, they're not synchronized with and the fiscal and

526
00:31:50,960 --> 00:31:53,600
Speaker 1: the fiscal austerity has waned right there, so they've they've

527
00:31:53,640 --> 00:31:57,200
Speaker 1: moved through that. So I would say there maybe two

528
00:31:57,360 --> 00:31:59,800
Speaker 1: or three years behind us in terms of the business

529
00:32:00,080 --> 00:32:03,360
Speaker 1: coal um if you look at Japan, how but in

530
00:32:03,480 --> 00:32:05,680
Speaker 1: terms of the monetary cycle, how far are they behind

531
00:32:05,720 --> 00:32:09,760
Speaker 1: the US that is sort of coming off, I would

532
00:32:09,760 --> 00:32:12,920
Speaker 1: say I would say about two years behind, maybe eighteen

533
00:32:13,160 --> 00:32:15,280
Speaker 1: eighteen months to two years. Well, Japan is its own

534
00:32:15,440 --> 00:32:18,920
Speaker 1: special case, and it's its own special case because it's

535
00:32:18,960 --> 00:32:24,120
Speaker 1: demographics are so much worse. Right, so they they're working

536
00:32:24,120 --> 00:32:28,240
Speaker 1: age population began to decline in which was right about

537
00:32:28,240 --> 00:32:31,520
Speaker 1: the time their debt bubble burst. What was the peak

538
00:32:31,560 --> 00:32:34,320
Speaker 1: in the of the nine I think it was nine,

539
00:32:35,080 --> 00:32:36,440
Speaker 1: and so it took a little while for the debt

540
00:32:36,440 --> 00:32:41,640
Speaker 1: bubble to fully burst. And then they of course have longevity,

541
00:32:41,720 --> 00:32:45,120
Speaker 1: so people living in retirement for longer. So that's a

542
00:32:45,240 --> 00:32:49,800
Speaker 1: drag fiscally, uh, and it's also disinflationary or deflationary. That

543
00:32:49,960 --> 00:32:54,040
Speaker 1: damn healthy lifestyle and low red meat diet that's right,

544
00:32:55,000 --> 00:32:59,920
Speaker 1: is causing that that issue and almost zero immigration where

545
00:33:00,080 --> 00:33:04,680
Speaker 1: very immigration, fairly uniform society and culture. But even with

546
00:33:04,720 --> 00:33:09,200
Speaker 1: that said, is the year the European the Europe's working

547
00:33:09,240 --> 00:33:12,640
Speaker 1: age population started to decline, and our working age population

548
00:33:12,880 --> 00:33:16,240
Speaker 1: is merely growing at a slower rate. So we're growing

549
00:33:16,280 --> 00:33:22,320
Speaker 1: at zero point three percent a year. And it's problem yes, absolutely,

550
00:33:22,320 --> 00:33:24,719
Speaker 1: but it's problematic but it's definitely better than if it

551
00:33:24,760 --> 00:33:27,200
Speaker 1: was outright declining like you have in Japan. So that

552
00:33:27,560 --> 00:33:29,640
Speaker 1: in Japan is now declining at a rate of zero

553
00:33:29,640 --> 00:33:33,080
Speaker 1: point three percent per year, and that puts Japan in

554
00:33:33,120 --> 00:33:36,760
Speaker 1: a very very special case. And something that and and

555
00:33:36,760 --> 00:33:40,960
Speaker 1: and their debt to GDP ratio is a hud. Nobody

556
00:33:41,000 --> 00:33:43,000
Speaker 1: else is even close. Nobody else is even close. But

557
00:33:43,040 --> 00:33:44,719
Speaker 1: I'll tell you something that they're doing. So if you

558
00:33:44,760 --> 00:33:47,720
Speaker 1: look at the debt held by the public versus the

559
00:33:47,760 --> 00:33:50,200
Speaker 1: debt held by the central bank, because they have been

560
00:33:50,200 --> 00:33:53,719
Speaker 1: purchasing so much of the debt, the debt held by

561
00:33:53,760 --> 00:33:59,600
Speaker 1: the public has gone down to of debt to GDP. Yes,

562
00:33:59,760 --> 00:34:01,920
Speaker 1: And and I'm glad we introduced the concept of real

563
00:34:02,120 --> 00:34:05,640
Speaker 1: versus nominal before because if you look at their nominal

564
00:34:05,680 --> 00:34:09,799
Speaker 1: growth rate, it is about two basis points above their

565
00:34:09,840 --> 00:34:14,440
Speaker 1: ten uere yield, and that is allowing them to obtain

566
00:34:14,560 --> 00:34:18,120
Speaker 1: greater and greater fiscal health. So they're using this negative

567
00:34:18,200 --> 00:34:22,040
Speaker 1: rate policy and this extreme balance sheet example again balance

568
00:34:22,040 --> 00:34:27,480
Speaker 1: sheet expansion again to their GDP. Right, they're using this

569
00:34:27,840 --> 00:34:32,520
Speaker 1: to uh refinance their government debt effectively at a much

570
00:34:32,520 --> 00:34:36,239
Speaker 1: lower rate well negative rates negative So imagine your corporation

571
00:34:36,760 --> 00:34:41,600
Speaker 1: and you're overindebted and you can't default, but over time

572
00:34:42,080 --> 00:34:45,640
Speaker 1: you can refinance your debt with negative rates, meaning you

573
00:34:45,680 --> 00:34:49,760
Speaker 1: get paid for your debt basically, which is which is shocking.

574
00:34:49,800 --> 00:34:53,160
Speaker 1: You know, you hinted at something I want to expand

575
00:34:53,160 --> 00:34:56,680
Speaker 1: on because it's so fascinating when when we talk about

576
00:34:56,719 --> 00:35:00,880
Speaker 1: the declining labor for US, we really have to discuss

577
00:35:01,680 --> 00:35:06,719
Speaker 1: the falling um fertility rates and the falling um I

578
00:35:06,800 --> 00:35:12,160
Speaker 1: forgot the replacement percentage what it's called, just the declining

579
00:35:12,360 --> 00:35:17,800
Speaker 1: birth rates effectively in Japan, in Europe, and up until recently,

580
00:35:17,840 --> 00:35:20,440
Speaker 1: the United States was marginally positive. I think we just

581
00:35:20,480 --> 00:35:24,640
Speaker 1: slipped negative also in terms of our birth replacement rate

582
00:35:24,719 --> 00:35:29,719
Speaker 1: replacement rate of new birth versus deaths. The US population

583
00:35:30,000 --> 00:35:34,760
Speaker 1: is becoming fairly stable, and it's outright shrinking in Europe

584
00:35:34,760 --> 00:35:38,080
Speaker 1: and Japan. What does this mean for the economy ten

585
00:35:38,920 --> 00:35:42,920
Speaker 1: years since? Yeah, so it's very challenging. And let's not

586
00:35:42,960 --> 00:35:46,080
Speaker 1: forget China's working age populations started to decline this year

587
00:35:46,440 --> 00:35:48,640
Speaker 1: right now, how much of that has to do with

588
00:35:48,680 --> 00:35:52,520
Speaker 1: the one child at the child right so, they haven't

589
00:35:52,560 --> 00:35:55,319
Speaker 1: been at replacement rate for decades now. And even though

590
00:35:55,360 --> 00:35:58,680
Speaker 1: that rule has been overturned. People are not rushing to

591
00:35:58,719 --> 00:36:01,920
Speaker 1: have more than one child. It's not it's not like

592
00:36:02,120 --> 00:36:04,960
Speaker 1: the minute that was overturned. In the next year you

593
00:36:05,000 --> 00:36:07,120
Speaker 1: saw a whole bunch of two and three child families.

594
00:36:08,760 --> 00:36:11,479
Speaker 1: Right now. That may change, and they may may need

595
00:36:11,520 --> 00:36:14,759
Speaker 1: to do things and create incentives for that, but that

596
00:36:15,000 --> 00:36:18,759
Speaker 1: it's it's not going to change overnight. Um and so.

597
00:36:20,160 --> 00:36:22,480
Speaker 1: And then we have South Korea. They're working age population

598
00:36:22,520 --> 00:36:24,480
Speaker 1: started to decline. So this is a phenomenon in a

599
00:36:24,480 --> 00:36:27,040
Speaker 1: lot of a lot of different places. So it's starting

600
00:36:27,080 --> 00:36:30,760
Speaker 1: to impact emerging markets more in Asia, more in Asia

601
00:36:30,960 --> 00:36:34,240
Speaker 1: x X India than in Latin America or Africa. Because

602
00:36:34,320 --> 00:36:36,880
Speaker 1: I've thought of this as a developed world issue, not

603
00:36:36,960 --> 00:36:39,360
Speaker 1: an emerging markets issue. Are you saying that this is

604
00:36:39,440 --> 00:36:41,880
Speaker 1: even spilling fast forward fifteen years and it's going to

605
00:36:41,920 --> 00:36:44,120
Speaker 1: be Look at Mexico. Their birth rates are declining. It's

606
00:36:44,160 --> 00:36:46,160
Speaker 1: one of the reasons we have. There are two reasons

607
00:36:46,160 --> 00:36:48,120
Speaker 1: we have fewer Mexican immigrants into the U. S M.

608
00:36:48,160 --> 00:36:50,759
Speaker 1: Why the flow is actually been the other direction first

609
00:36:50,800 --> 00:36:53,200
Speaker 1: of the wall, and then what's the what's the other reason.

610
00:36:53,400 --> 00:36:57,279
Speaker 1: So as their birth rates have declined, they've needed more

611
00:36:57,360 --> 00:36:59,800
Speaker 1: labor in their own economy. Right, So there's just a

612
00:36:59,800 --> 00:37:02,960
Speaker 1: few were people that come here are going up their

613
00:37:03,200 --> 00:37:04,799
Speaker 1: and their wages have been going up a little bit

614
00:37:04,800 --> 00:37:09,919
Speaker 1: more so. So not the wall, and we actually have flow.

615
00:37:10,040 --> 00:37:12,400
Speaker 1: The flow has actually gone the other direction for several years.

616
00:37:12,600 --> 00:37:15,960
Speaker 1: But that's an aside since the financial crisis, since the

617
00:37:16,320 --> 00:37:18,799
Speaker 1: and which which loops back into when we were talking

618
00:37:18,800 --> 00:37:24,560
Speaker 1: about housing earlier. Right. So anyway, we are digressing massively here.

619
00:37:24,600 --> 00:37:26,720
Speaker 1: So let me get us back on track. Something about

620
00:37:26,800 --> 00:37:29,920
Speaker 1: the yield curve. I think we started with, yes, and

621
00:37:29,920 --> 00:37:35,120
Speaker 1: and so this these disinflationary or deflationary pressures are also

622
00:37:35,200 --> 00:37:37,560
Speaker 1: distorting the shape of the yeld curve. So you have

623
00:37:37,920 --> 00:37:41,239
Speaker 1: you have a completely new demographic phenomenon that aside from

624
00:37:41,280 --> 00:37:46,359
Speaker 1: a brief period uh during the Great Depression when we

625
00:37:46,440 --> 00:37:48,840
Speaker 1: had a declining working age population for a little blip

626
00:37:48,880 --> 00:37:52,040
Speaker 1: of time about seven years UM, we have never had

627
00:37:52,120 --> 00:37:54,799
Speaker 1: before and we've never and and while we have that,

628
00:37:54,840 --> 00:37:57,600
Speaker 1: we did not have the longevity that we're having now. Right.

629
00:37:57,920 --> 00:38:03,680
Speaker 1: So the combination of these two things disinflationary um disinflationary

630
00:38:03,760 --> 00:38:06,799
Speaker 1: meaning you're not seeing inflation go up and the rate

631
00:38:06,840 --> 00:38:10,960
Speaker 1: of inflation is going down correct, not not outright deflationary,

632
00:38:11,080 --> 00:38:15,399
Speaker 1: just five four two inflation or one and a half

633
00:38:15,480 --> 00:38:18,759
Speaker 1: percent these days, right, correct? So what does that tell

634
00:38:18,880 --> 00:38:22,359
Speaker 1: us that the future economy looks like? If if you

635
00:38:22,800 --> 00:38:25,480
Speaker 1: if you had a draw a conclusion from the disinflation

636
00:38:25,880 --> 00:38:29,120
Speaker 1: in the system, is that informing us of anything? So

637
00:38:29,680 --> 00:38:31,880
Speaker 1: a few things to say about that. One is that

638
00:38:31,880 --> 00:38:34,960
Speaker 1: I'm going to introduce the concept of potential GDP. So

639
00:38:35,000 --> 00:38:38,239
Speaker 1: in its simplest form, potential GDP is the sum of

640
00:38:38,280 --> 00:38:41,480
Speaker 1: the growth rate of the working age population plus the

641
00:38:41,560 --> 00:38:46,920
Speaker 1: growth rate of productivity. And historically, if we look back

642
00:38:47,280 --> 00:38:51,239
Speaker 1: fifty years, we had average working age population growth of

643
00:38:51,280 --> 00:38:56,080
Speaker 1: one point six percent, average productivity growth of one point eight. Quickly,

644
00:38:56,160 --> 00:38:59,320
Speaker 1: that gets us to a three point six percent potential GDP.

645
00:39:00,080 --> 00:39:03,239
Speaker 1: We now have a working age population growth rate of

646
00:39:03,400 --> 00:39:08,560
Speaker 1: zero point three and until recently, productivity during the recovery

647
00:39:08,719 --> 00:39:11,600
Speaker 1: was averaging zero point eight, so that's a one point

648
00:39:11,680 --> 00:39:14,640
Speaker 1: one potential GDP. Now productivity is picked up to one

649
00:39:14,680 --> 00:39:17,239
Speaker 1: point five, so that gets us up to about a

650
00:39:17,239 --> 00:39:20,399
Speaker 1: one point eight percent potential GDP, which is the growth

651
00:39:20,520 --> 00:39:23,880
Speaker 1: rate that you can grow without inducing inflation in the economy.

652
00:39:23,880 --> 00:39:26,880
Speaker 1: It's that mythical beast equilibrium. Let me ask you a

653
00:39:26,960 --> 00:39:31,800
Speaker 1: question about productivity, because this is an ongoing UM question

654
00:39:31,880 --> 00:39:33,800
Speaker 1: that I keep wrestling with, and every time I have

655
00:39:33,840 --> 00:39:36,640
Speaker 1: an economist in front of me, I feel obligated to

656
00:39:36,680 --> 00:39:40,799
Speaker 1: ask the question. In the real world, we are all

657
00:39:40,840 --> 00:39:48,600
Speaker 1: experiencing a massive amount of personal productivity gains software the

658
00:39:48,640 --> 00:39:52,520
Speaker 1: ability to walk around with with a powerful computer in

659
00:39:52,600 --> 00:39:57,640
Speaker 1: my pocket. That's ten x what the what the astronauts

660
00:39:57,640 --> 00:40:02,520
Speaker 1: took to the moon. UM is in a amazing productivity enhancer.

661
00:40:02,560 --> 00:40:05,000
Speaker 1: And I'm not being on Facebook, so I don't get

662
00:40:05,040 --> 00:40:08,520
Speaker 1: the productivity killing aspect of it, although I guess Twitter

663
00:40:09,080 --> 00:40:12,960
Speaker 1: substitutes for that. But in the office, what we're capable

664
00:40:12,960 --> 00:40:16,160
Speaker 1: of doing in terms of communicating with clients and doing

665
00:40:16,280 --> 00:40:20,280
Speaker 1: screen shares to say, show them different charts and documents

666
00:40:20,280 --> 00:40:23,560
Speaker 1: and this and that, it feels like we're doing so

667
00:40:23,680 --> 00:40:27,960
Speaker 1: much more with a handful of people versus a hundred

668
00:40:28,000 --> 00:40:32,000
Speaker 1: people that was required a few years ago. It makes

669
00:40:32,000 --> 00:40:36,200
Speaker 1: me ask the question, do we have a productivity problem

670
00:40:36,320 --> 00:40:39,880
Speaker 1: or do we have a problem in measuring productivity? It

671
00:40:39,920 --> 00:40:42,600
Speaker 1: depends on the industry. So in finance, you're in an

672
00:40:42,600 --> 00:40:47,080
Speaker 1: industry whereas there's been huge measured productivity gains, all right,

673
00:40:47,120 --> 00:40:50,360
Speaker 1: So what you're saying makes sense. It's what's shown in

674
00:40:50,360 --> 00:40:54,200
Speaker 1: the data, and it is true for other financial services firms.

675
00:40:55,160 --> 00:40:57,479
Speaker 1: This is something that is puzzling everybody because we can't

676
00:40:57,520 --> 00:40:59,319
Speaker 1: change the birth rate, so we're only going to get

677
00:40:59,360 --> 00:41:03,160
Speaker 1: saved by increasing productivity. Although some Nordic countries are actually

678
00:41:03,280 --> 00:41:06,120
Speaker 1: running p s a s to encourage people to go

679
00:41:06,200 --> 00:41:09,040
Speaker 1: on vacation in the South of France and leave their

680
00:41:09,800 --> 00:41:13,279
Speaker 1: birth control at home. I mean they're literally television commercials

681
00:41:13,280 --> 00:41:16,360
Speaker 1: for that. I don't see that happening here. That said,

682
00:41:16,880 --> 00:41:18,879
Speaker 1: but that's still a long term prospect, right, You're still

683
00:41:18,920 --> 00:41:21,080
Speaker 1: it's twenty one years until you see benefit from that,

684
00:41:21,160 --> 00:41:24,120
Speaker 1: maybe twenty six given the current education rates. So it's

685
00:41:24,160 --> 00:41:26,840
Speaker 1: not an immediate solution, right. We need to do something

686
00:41:26,840 --> 00:41:30,040
Speaker 1: in the intervening years. And the only savior then is productivity.

687
00:41:30,080 --> 00:41:32,120
Speaker 1: Because so many people are focusing on this. One of

688
00:41:32,160 --> 00:41:35,080
Speaker 1: the things that the o e c D did was

689
00:41:35,120 --> 00:41:40,440
Speaker 1: they did a study on the diffusion of technology throughout firms.

690
00:41:40,920 --> 00:41:44,400
Speaker 1: So it may be Barry that you guys are really

691
00:41:44,800 --> 00:41:48,520
Speaker 1: advanced and early adopters of technology, no doubt about that.

692
00:41:48,719 --> 00:41:52,960
Speaker 1: So so, but the diffusion is important. So they looked

693
00:41:52,960 --> 00:41:55,480
Speaker 1: at the productivity at the firm level, so not at

694
00:41:55,480 --> 00:41:58,680
Speaker 1: the economy wide level, at the firm level of the

695
00:41:58,719 --> 00:42:03,240
Speaker 1: frontier firm, so that the top five percent versus everybody else.

696
00:42:03,560 --> 00:42:06,360
Speaker 1: And if you look at these graphs, it's really alarming.

697
00:42:06,520 --> 00:42:09,920
Speaker 1: The gap is really really wide, and it's wider in

698
00:42:10,040 --> 00:42:14,680
Speaker 1: services than it is in manufacturing. Yes, and and and

699
00:42:14,719 --> 00:42:17,520
Speaker 1: the question is will we get to a point where

700
00:42:17,520 --> 00:42:20,040
Speaker 1: this gap closes, like what is the cause of this

701
00:42:20,480 --> 00:42:24,000
Speaker 1: lack of diffusion? And and is there a tipping point

702
00:42:24,280 --> 00:42:28,040
Speaker 1: where we can start to see wider diffusion, meaning that

703
00:42:28,840 --> 00:42:32,600
Speaker 1: a handful of firms are especially productive and the rest

704
00:42:32,719 --> 00:42:38,640
Speaker 1: of the economic force um or firms and businesses are

705
00:42:38,920 --> 00:42:42,000
Speaker 1: lagging in products are really not reaping the rewards of

706
00:42:42,040 --> 00:42:45,239
Speaker 1: the technological advances we've had over the last fifteen years.

707
00:42:45,320 --> 00:42:49,839
Speaker 1: There is a have and have not way of structuring.

708
00:42:50,320 --> 00:42:54,360
Speaker 1: We're looking at just about every aspect of our economy.

709
00:42:54,400 --> 00:42:58,560
Speaker 1: It is so bifurcated, and that duality exists in so

710
00:42:58,640 --> 00:43:02,600
Speaker 1: many places. It's fascinating. I had never heard of the

711
00:43:03,120 --> 00:43:07,960
Speaker 1: this diffusion between productivity, but I guess it makes some

712
00:43:08,239 --> 00:43:12,560
Speaker 1: sense because you would think the early adapters of technology

713
00:43:12,640 --> 00:43:16,640
Speaker 1: and other productivity hanswers would see not only a gain,

714
00:43:17,080 --> 00:43:21,680
Speaker 1: but a giant differential against their competitors who aren't adopting

715
00:43:21,680 --> 00:43:24,920
Speaker 1: the newest technology. We're actually studying this at KPMG, so

716
00:43:24,960 --> 00:43:27,759
Speaker 1: we're taking the O A C D work and we're

717
00:43:27,800 --> 00:43:32,560
Speaker 1: expanding it and and building a knowledge database of a

718
00:43:32,640 --> 00:43:36,560
Speaker 1: multitude of different firms across different countries, across different sectors

719
00:43:36,680 --> 00:43:38,839
Speaker 1: and um hopefully we'll have some research out on that

720
00:43:38,880 --> 00:43:41,719
Speaker 1: in the next six to nine months. We have been

721
00:43:41,760 --> 00:43:45,879
Speaker 1: speaking with Constance Hunter. She is the chief economist at KPMG.

722
00:43:46,800 --> 00:43:49,520
Speaker 1: If you enjoy this conversation, be sure and check out

723
00:43:49,560 --> 00:43:52,480
Speaker 1: our podcast extras, where we keep the tape rolling and

724
00:43:52,520 --> 00:43:56,000
Speaker 1: continue to discuss all things economic. You can find that

725
00:43:56,080 --> 00:44:02,280
Speaker 1: at iTunes, overcast, SoundCloud, Bloomberg, or wherever final podcasts are sold.

726
00:44:02,760 --> 00:44:07,040
Speaker 1: We love your comments, feedback and suggestions right to us

727
00:44:07,200 --> 00:44:10,839
Speaker 1: at m IB podcast at Bloomberg dot net. Be sure

728
00:44:10,880 --> 00:44:13,680
Speaker 1: and check out my daily column. It's on Bloomberg View

729
00:44:13,719 --> 00:44:16,840
Speaker 1: dot com. You could follow me on Twitter at rid Halts.

730
00:44:17,480 --> 00:44:20,800
Speaker 1: I'm Barry rid Halts. You're listening to Masters in Business

731
00:44:21,040 --> 00:44:38,000
Speaker 1: on Bloomberg Radio. Welcome to the podcast, Constance. Thank you

732
00:44:38,040 --> 00:44:40,239
Speaker 1: so much for doing this. I have been looking forward

733
00:44:40,280 --> 00:44:43,120
Speaker 1: to this for a while. I give economists a lot

734
00:44:43,120 --> 00:44:47,920
Speaker 1: of grief for for both making terrible predictions or or

735
00:44:47,960 --> 00:44:50,680
Speaker 1: predictions that turn out not to be accurate, and for

736
00:44:50,719 --> 00:44:53,480
Speaker 1: the most part, missing the financial crisis, which we talked

737
00:44:53,520 --> 00:44:57,200
Speaker 1: about earlier. You're not big into making a lot of predictions.

738
00:44:57,320 --> 00:45:01,000
Speaker 1: I don't really. I see you more as analyzing the

739
00:45:01,080 --> 00:45:04,560
Speaker 1: situation than doing the usual Wall Street forecast. So I

740
00:45:04,600 --> 00:45:07,840
Speaker 1: have to ask why our economists so bad at making

741
00:45:07,880 --> 00:45:12,080
Speaker 1: forecasts and and how did they miss the financial crisis? Well,

742
00:45:12,160 --> 00:45:13,920
Speaker 1: and you probably also know that I didn't miss the

743
00:45:13,920 --> 00:45:18,000
Speaker 1: financial crisis, so I was short a lot of things,

744
00:45:18,760 --> 00:45:22,560
Speaker 1: uh that went down, which is always fun, which was

745
00:45:22,719 --> 00:45:25,560
Speaker 1: it's it's actually nerve wracking to be honest with you,

746
00:45:25,640 --> 00:45:27,880
Speaker 1: and people give you so much grief for it. Totally.

747
00:45:28,040 --> 00:45:33,160
Speaker 1: I remember when I first started shorting ubs and oh,

748
00:45:33,200 --> 00:45:35,360
Speaker 1: they have this activist investor in there. You don't know

749
00:45:35,400 --> 00:45:38,120
Speaker 1: what you're doing. It's gonna go up, bah blah blah blah.

750
00:45:38,120 --> 00:45:41,200
Speaker 1: And you know you're constantly second guessing yourself, more so

751
00:45:41,280 --> 00:45:44,160
Speaker 1: on a short than along right, because the nature of

752
00:45:44,200 --> 00:45:46,879
Speaker 1: things is to go up for the most part. Over

753
00:45:46,960 --> 00:45:49,920
Speaker 1: the long term, and everybody that works at a company,

754
00:45:50,040 --> 00:45:53,000
Speaker 1: their entire goal is to make everything go up there

755
00:45:53,000 --> 00:45:56,319
Speaker 1: there there. They make new plan every year, they're they're

756
00:45:56,320 --> 00:45:59,560
Speaker 1: incentivizing their employees, they're cutting costs, like every single company

757
00:45:59,600 --> 00:46:02,120
Speaker 1: is focus done that as well, and so that is

758
00:46:02,160 --> 00:46:04,279
Speaker 1: the nature of things. So it's very nerve racking to

759
00:46:04,280 --> 00:46:08,400
Speaker 1: be short. But nevertheless, that and the theoretical infinite losses

760
00:46:08,600 --> 00:46:10,239
Speaker 1: kind of hangs over your head a little bit that

761
00:46:10,320 --> 00:46:13,160
Speaker 1: in the theoretical infinite losses or the actual infinite losses

762
00:46:13,160 --> 00:46:16,239
Speaker 1: that can occur, people of you know, what's that? What's

763
00:46:16,280 --> 00:46:19,600
Speaker 1: that famous Keynes expression? The market can remain irrational a

764
00:46:19,640 --> 00:46:22,600
Speaker 1: lot longer than you can remain solvent. So so in

765
00:46:22,680 --> 00:46:30,000
Speaker 1: answering your question, I think it is because of several things. First, models,

766
00:46:30,600 --> 00:46:33,080
Speaker 1: I've been working on shocking my model to give it

767
00:46:33,160 --> 00:46:36,120
Speaker 1: a recession because I think that we're going to see

768
00:46:36,120 --> 00:46:38,320
Speaker 1: some labor shortages, that those labor shortages are going to

769
00:46:38,400 --> 00:46:41,440
Speaker 1: cause wage increases, that those wage increases are going to

770
00:46:41,520 --> 00:46:45,320
Speaker 1: push up inflation, that that increase in inflation, increase in

771
00:46:45,360 --> 00:46:49,360
Speaker 1: prices will reduce demand. That reduction in demand will help

772
00:46:49,600 --> 00:46:53,080
Speaker 1: spur us into the next recession. Plus as wages go up,

773
00:46:53,120 --> 00:46:54,759
Speaker 1: as there are shortages, we're going to get to a

774
00:46:54,800 --> 00:46:57,480
Speaker 1: point I would imagine where you can't find someone at

775
00:46:57,480 --> 00:47:00,120
Speaker 1: any price, and so you want to expand your business,

776
00:47:00,160 --> 00:47:03,160
Speaker 1: but you just can't at any price. Well, there's some

777
00:47:03,280 --> 00:47:06,920
Speaker 1: professions where we just for example, let's all this technology,

778
00:47:08,040 --> 00:47:11,000
Speaker 1: you can't find data scientists at any place. There's a

779
00:47:11,000 --> 00:47:14,759
Speaker 1: handful of them, there's an handful of programmers there. They're

780
00:47:14,880 --> 00:47:17,960
Speaker 1: really top notch. And so everybody is competing in this

781
00:47:18,080 --> 00:47:20,319
Speaker 1: space for the same talent. And it's a very new

782
00:47:20,360 --> 00:47:23,279
Speaker 1: set of skills. So isn't that just temporary? Aren't we

783
00:47:23,320 --> 00:47:25,680
Speaker 1: going to see more people come in from Indian China

784
00:47:26,120 --> 00:47:30,880
Speaker 1: and more graduate degrees and people moving into the space

785
00:47:31,239 --> 00:47:34,839
Speaker 1: just because it's so lucrative. Sure, but that doesn't mean

786
00:47:35,000 --> 00:47:38,719
Speaker 1: that we're going to see shortages of that labor over

787
00:47:38,719 --> 00:47:41,080
Speaker 1: the next couple of years. Right. It takes time for

788
00:47:41,120 --> 00:47:43,000
Speaker 1: those people to get trained and for people to move

789
00:47:43,000 --> 00:47:48,239
Speaker 1: into a professional Yeah, six years, a decade something like that.

790
00:47:48,320 --> 00:47:50,520
Speaker 1: So the first thing is that what I had to

791
00:47:50,560 --> 00:47:52,680
Speaker 1: do to shock this model because it just kind of

792
00:47:52,800 --> 00:47:56,120
Speaker 1: bump it ump, goes along on trend and and it

793
00:47:56,120 --> 00:47:59,920
Speaker 1: would never forecast a recession unless you made it forecast recession, right,

794
00:48:00,360 --> 00:48:03,239
Speaker 1: So you have to look for tipping points. So there

795
00:48:03,239 --> 00:48:05,879
Speaker 1: were there were three things that allowed me to call

796
00:48:05,960 --> 00:48:08,799
Speaker 1: the financial crisis. And I didn't call it as big

797
00:48:08,840 --> 00:48:11,120
Speaker 1: as it ended up being, right because that was just large.

798
00:48:11,400 --> 00:48:13,400
Speaker 1: But I've done this my whole career, the same with

799
00:48:13,520 --> 00:48:16,920
Speaker 1: Russia crisis, right. So so the thing that allowed me

800
00:48:16,960 --> 00:48:20,480
Speaker 1: to call the financial crisis was I was looking at

801
00:48:20,480 --> 00:48:25,120
Speaker 1: mortgage equity withdrawal and g Well, not only was it giant,

802
00:48:25,200 --> 00:48:27,440
Speaker 1: but if you look historically, it used to be a

803
00:48:27,520 --> 00:48:31,680
Speaker 1: random walk around a hundred and fifty billion a quarter,

804
00:48:31,880 --> 00:48:34,560
Speaker 1: and we go up, we'd go down. Basically a random walk.

805
00:48:34,560 --> 00:48:36,400
Speaker 1: You couldn't there's no way to predict it. It It was

806
00:48:36,440 --> 00:48:40,560
Speaker 1: a modest number that practically in the US fifteen trillion

807
00:48:40,560 --> 00:48:44,160
Speaker 1: dollar economy is a rounding era. But also its pattern

808
00:48:44,640 --> 00:48:47,880
Speaker 1: was was some some quarters were up, somewhere down. It

809
00:48:48,000 --> 00:48:49,520
Speaker 1: was a random walk. It wasn't like it was a

810
00:48:49,560 --> 00:48:52,319
Speaker 1: hundred and fifty billion every quarter got withdrawn. It was

811
00:48:52,400 --> 00:48:55,000
Speaker 1: up and down. Wasn't on trend the way it became right,

812
00:48:55,080 --> 00:48:58,560
Speaker 1: So we started withdrawing more and withdrawing more and withdrawing

813
00:48:58,600 --> 00:49:01,360
Speaker 1: more to the point where we had withdrawn three and

814
00:49:01,360 --> 00:49:04,799
Speaker 1: a half trillion dollars and you could just see that

815
00:49:04,920 --> 00:49:07,759
Speaker 1: this there's no way it could keep going. There's just

816
00:49:07,840 --> 00:49:10,479
Speaker 1: no It made no sense. It made absolutely no sense.

817
00:49:10,480 --> 00:49:12,959
Speaker 1: And then if you look at the demographics, that made

818
00:49:12,960 --> 00:49:15,320
Speaker 1: no sense either, because you could see that the working

819
00:49:15,320 --> 00:49:18,759
Speaker 1: age population started to decline in two thousand, so you

820
00:49:18,800 --> 00:49:22,800
Speaker 1: could see it's shifting demographic demand if you were looking

821
00:49:22,840 --> 00:49:25,560
Speaker 1: for it, right. But you have to be open and

822
00:49:25,640 --> 00:49:28,840
Speaker 1: searching for the things that are going to be inflection points,

823
00:49:28,920 --> 00:49:31,560
Speaker 1: and then you have to have the conviction to shock

824
00:49:31,600 --> 00:49:35,200
Speaker 1: your model, in other words, come up with some set

825
00:49:35,360 --> 00:49:41,320
Speaker 1: of exogenius or or maybe normal economic shifts that tip

826
00:49:41,800 --> 00:49:44,719
Speaker 1: an economy into a recession or or or into a

827
00:49:44,760 --> 00:49:48,480
Speaker 1: big growth period as well. Um So, so I think

828
00:49:48,520 --> 00:49:50,560
Speaker 1: you have to be willing to look for those things

829
00:49:51,080 --> 00:49:55,359
Speaker 1: and and and understand inflection points. But that is not

830
00:49:56,239 --> 00:50:00,600
Speaker 1: the way most people study economics necessarily. And I'm fortunate

831
00:50:00,680 --> 00:50:05,320
Speaker 1: that I have this pattern recognition skill combined with a

832
00:50:06,320 --> 00:50:11,239
Speaker 1: lifetime of investing that has honed that skill. So the

833
00:50:11,239 --> 00:50:13,440
Speaker 1: other question I didn't get to that I have to

834
00:50:13,480 --> 00:50:17,800
Speaker 1: ask you about we really haven't touched upon China And

835
00:50:18,040 --> 00:50:20,200
Speaker 1: let's talk a little bit about China for a moment.

836
00:50:20,840 --> 00:50:22,920
Speaker 1: What do you see going on in China? Is it

837
00:50:23,000 --> 00:50:26,240
Speaker 1: a growth area? Is it a risk? Does China become

838
00:50:26,480 --> 00:50:30,440
Speaker 1: the biggest economy by far? Just extrapolating trends out for forever.

839
00:50:31,200 --> 00:50:35,040
Speaker 1: What is the role of China in the modern economy?

840
00:50:35,600 --> 00:50:40,040
Speaker 1: So China is a big economy. It's the same size

841
00:50:40,040 --> 00:50:42,759
Speaker 1: as the US. Obviously, their population is much larger, so

842
00:50:42,800 --> 00:50:46,799
Speaker 1: their per capita GDP is smaller. So they're big, but

843
00:50:46,880 --> 00:50:51,160
Speaker 1: they're not rich, right, They're not first world economy. Obviously

844
00:50:51,200 --> 00:50:53,920
Speaker 1: the coast are rich. You've multiple because it's so large,

845
00:50:53,920 --> 00:50:57,040
Speaker 1: you have many different China's. But if we just think

846
00:50:57,080 --> 00:51:01,200
Speaker 1: about that for a second, their sheer size um makes

847
00:51:01,239 --> 00:51:03,520
Speaker 1: them an amazing market. And if we think about it

848
00:51:03,560 --> 00:51:07,680
Speaker 1: in the context of what's happening with artificial intelligence and

849
00:51:07,800 --> 00:51:12,560
Speaker 1: data and analytics, you have huge amounts of data there um,

850
00:51:12,600 --> 00:51:17,800
Speaker 1: which is an interesting, uh situation given our modern focus

851
00:51:18,000 --> 00:51:23,520
Speaker 1: on platform companies and and um the network effect. Right,

852
00:51:23,560 --> 00:51:25,640
Speaker 1: So you have that sort of on steroids in China,

853
00:51:25,680 --> 00:51:30,799
Speaker 1: and it's interesting to watch. However, like any economy, imbalances

854
00:51:30,840 --> 00:51:34,839
Speaker 1: developed and China is still a command economy. It is not.

855
00:51:35,280 --> 00:51:38,879
Speaker 1: Let's not mistake it for a market economy. It has

856
00:51:38,920 --> 00:51:41,760
Speaker 1: some features of a market economy, but it has many

857
00:51:41,840 --> 00:51:45,920
Speaker 1: more features of a command economy. They're essentially planned communist

858
00:51:45,960 --> 00:51:50,239
Speaker 1: regime that seems to dabble in capitalism precisely. Okay, So

859
00:51:50,400 --> 00:51:52,120
Speaker 1: I just had to touch upon that because I know

860
00:51:52,200 --> 00:51:55,640
Speaker 1: it's it's such a fascinating area. So I think China

861
00:51:55,840 --> 00:51:58,640
Speaker 1: is gonna see some bumps in the road that are

862
00:51:58,680 --> 00:52:01,440
Speaker 1: going to surprise people over the next two to three years.

863
00:52:01,840 --> 00:52:03,880
Speaker 1: Do those bumps in the road have the potential to

864
00:52:04,040 --> 00:52:07,200
Speaker 1: tip any major economies into a recession? Yeah, I think

865
00:52:07,239 --> 00:52:10,920
Speaker 1: they might. And and so if you look back right,

866
00:52:10,960 --> 00:52:13,759
Speaker 1: we were at three percent almost on the tenure if

867
00:52:13,800 --> 00:52:18,480
Speaker 1: you use the p m I index, uh, seventy nine

868
00:52:18,520 --> 00:52:21,680
Speaker 1: percent of the world was growing. And if you look

869
00:52:21,719 --> 00:52:25,880
Speaker 1: at what happened in China, China's demand shifted downward. And

870
00:52:25,920 --> 00:52:27,719
Speaker 1: so what I look at I look at a hole

871
00:52:27,719 --> 00:52:30,600
Speaker 1: host and we saw that across commodities especially well especially

872
00:52:30,640 --> 00:52:32,560
Speaker 1: and so if you look at China's imports and you

873
00:52:32,680 --> 00:52:34,440
Speaker 1: use the O E C D data, and this is

874
00:52:34,480 --> 00:52:37,520
Speaker 1: important because they get the real the real number, not

875
00:52:37,560 --> 00:52:40,040
Speaker 1: the nominal number. Because obviously for commodities there's a huge

876
00:52:40,120 --> 00:52:45,160
Speaker 1: fluctuation in nominal price changes. And they compile the data

877
00:52:45,239 --> 00:52:47,640
Speaker 1: using all of China's trading partners, so they don't rely

878
00:52:48,280 --> 00:52:51,560
Speaker 1: on the Chinese data collection. And this is important because

879
00:52:51,560 --> 00:52:53,560
Speaker 1: we all know that there are some question marks around

880
00:52:53,640 --> 00:52:56,799
Speaker 1: China's data collection. They make Bernie made off look like

881
00:52:57,000 --> 00:53:00,239
Speaker 1: he was actually using real numbers. That that's what you

882
00:53:00,280 --> 00:53:02,799
Speaker 1: can't say, but I can set you. I cannot say that.

883
00:53:03,280 --> 00:53:07,040
Speaker 1: So in any case, um, what we saw was a

884
00:53:07,080 --> 00:53:11,480
Speaker 1: big year, your year over year decline in imports and

885
00:53:11,560 --> 00:53:17,640
Speaker 1: this impacted all of the commodity producers Australia, Russia, South Africa, Brazil,

886
00:53:18,200 --> 00:53:21,520
Speaker 1: you name it. And we felt only six of the

887
00:53:21,560 --> 00:53:25,319
Speaker 1: pm I reporting countries experiencing growth. So let's in the

888
00:53:25,400 --> 00:53:27,480
Speaker 1: last and so we had a massive impact, I guess.

889
00:53:27,520 --> 00:53:30,160
Speaker 1: So when to answer your question, it could have a

890
00:53:30,280 --> 00:53:33,880
Speaker 1: very significant impact on global liquidity and global demand. All right,

891
00:53:33,960 --> 00:53:36,719
Speaker 1: So now in our remaining minutes, let's jump to our

892
00:53:36,760 --> 00:53:41,120
Speaker 1: favorite questions. Tell us about the most important thing people

893
00:53:41,360 --> 00:53:45,120
Speaker 1: don't know about your background. So this is a tough

894
00:53:45,360 --> 00:53:47,600
Speaker 1: question to answer to, Mary, I know, I like, I mean,

895
00:53:47,640 --> 00:53:50,399
Speaker 1: one question is you know genos or Pats, I can

896
00:53:50,440 --> 00:53:53,120
Speaker 1: ask you that, but give us okay, So that could

897
00:53:53,160 --> 00:53:57,759
Speaker 1: be and with not without okay. So that's if you

898
00:53:57,760 --> 00:54:01,200
Speaker 1: know about Philadelphia, about your people email what that was

899
00:54:01,239 --> 00:54:03,799
Speaker 1: she talking? You can google there's an NPR story about this,

900
00:54:03,920 --> 00:54:07,759
Speaker 1: with or without, and Pats got it. So I think

901
00:54:07,800 --> 00:54:11,000
Speaker 1: people don't know because it's been some time has passed

902
00:54:11,000 --> 00:54:14,120
Speaker 1: that I used to invest in the former Soviet Union.

903
00:54:14,320 --> 00:54:17,560
Speaker 1: I have traveled all over the former Soviet Union. I've

904
00:54:17,600 --> 00:54:21,319
Speaker 1: been a mile underground in the nickel mine. Really, yeah,

905
00:54:22,000 --> 00:54:23,920
Speaker 1: you know, I look at the Soviet Union as an

906
00:54:24,040 --> 00:54:27,960
Speaker 1: organized crime family with a standing army attached? Is that

907
00:54:28,160 --> 00:54:31,839
Speaker 1: is that an exaggeration? Or well, back when I was there,

908
00:54:31,840 --> 00:54:36,880
Speaker 1: it was in the hopeful days of glassnost Prestoika, right, um,

909
00:54:37,040 --> 00:54:41,920
Speaker 1: Yeltson was was president. He was he was trying to reform.

910
00:54:41,960 --> 00:54:45,800
Speaker 1: Now was there some corruption, Yes, there were? They're oligarchs

911
00:54:45,920 --> 00:54:49,799
Speaker 1: most definitely. Are you surprised that the way Russia has

912
00:54:49,920 --> 00:54:58,359
Speaker 1: changed to Underputin? Not really? You know when he first came, so,

913
00:54:58,640 --> 00:55:02,480
Speaker 1: yelts In hand picked him, which is interesting, that's yeah.

914
00:55:02,560 --> 00:55:05,840
Speaker 1: And and and Putin came into power and let Yelson

915
00:55:05,920 --> 00:55:11,359
Speaker 1: live and just as well, you know, pre Gorbachev, this

916
00:55:11,440 --> 00:55:14,960
Speaker 1: was not the way of Soviet power transition or Russian

917
00:55:14,960 --> 00:55:18,399
Speaker 1: power transition. So he was a drunk old man at

918
00:55:18,400 --> 00:55:21,719
Speaker 1: that point though, wasn't It was pretty harmless, um, but

919
00:55:23,000 --> 00:55:25,960
Speaker 1: you could tell that it was a very different approach,

920
00:55:26,719 --> 00:55:29,879
Speaker 1: a very very different approach. And remember they had they

921
00:55:29,880 --> 00:55:34,760
Speaker 1: were just coming off the default and devaluation, right oil

922
00:55:34,840 --> 00:55:37,960
Speaker 1: was it about twenty barrel, So we had to do

923
00:55:38,120 --> 00:55:41,359
Speaker 1: something to get the economy going and to do things

924
00:55:41,560 --> 00:55:45,200
Speaker 1: and and coming off of that crisis, and what happened

925
00:55:45,239 --> 00:55:49,720
Speaker 1: in his first decade in power really improved people's lives.

926
00:55:49,760 --> 00:55:53,440
Speaker 1: So the loyalty that he has among Russians who remember

927
00:55:53,480 --> 00:55:56,960
Speaker 1: that time period is very very high. Some people have

928
00:55:57,040 --> 00:55:59,640
Speaker 1: said that he is actually the wealthiest man in the

929
00:55:59,680 --> 00:56:02,480
Speaker 1: world than he's worth three. I have heard that from

930
00:56:02,520 --> 00:56:06,879
Speaker 1: a lot of reliable sources. Really, so it's not number two.

931
00:56:07,160 --> 00:56:10,680
Speaker 1: That's fascinating. Early mentors. Tell us about some of your

932
00:56:10,719 --> 00:56:14,960
Speaker 1: early mentors. So I think my earliest mentor was probably

933
00:56:15,000 --> 00:56:22,480
Speaker 1: my father, who uh had me calculate saber metrics and baseball. Yeah,

934
00:56:22,600 --> 00:56:26,080
Speaker 1: learned That's how I learned statistics. And he he would

935
00:56:26,200 --> 00:56:28,520
Speaker 1: he would This is back when people would read the

936
00:56:28,520 --> 00:56:31,440
Speaker 1: Wall Street Journal and look at all the stock metrics

937
00:56:31,480 --> 00:56:33,520
Speaker 1: on the back of the back pages of the journal.

938
00:56:33,800 --> 00:56:37,160
Speaker 1: So he taught me what a pe was and dividends

939
00:56:37,600 --> 00:56:40,840
Speaker 1: pre money ball, pre money ball. This is the seventies,

940
00:56:40,880 --> 00:56:43,480
Speaker 1: so you were literally doing this with baseball stats or

941
00:56:43,520 --> 00:56:48,040
Speaker 1: with baseball stats, with baseball stats, NI six Philadelphia Phillies

942
00:56:48,080 --> 00:56:54,040
Speaker 1: World Series, rally fingers. That's right that I'm old enough

943
00:56:54,080 --> 00:56:56,960
Speaker 1: to remember. That's that is impressive. You're gonna have more

944
00:56:57,000 --> 00:57:00,640
Speaker 1: googling and more more questions that mustash. Who could forget that?

945
00:57:00,719 --> 00:57:05,560
Speaker 1: Must um? And then I had really amazing professors like Columbia.

946
00:57:05,680 --> 00:57:09,400
Speaker 1: So we had a class that was taught by two

947
00:57:09,960 --> 00:57:12,520
Speaker 1: gentlemen who were at George Soros running money for George

948
00:57:12,600 --> 00:57:15,960
Speaker 1: at the time. And so it was Armenio Fraga and

949
00:57:16,080 --> 00:57:19,280
Speaker 1: Robert Johnston. Robert Johnson is now with i NET Institute

950
00:57:19,280 --> 00:57:21,960
Speaker 1: for New Economic Thinking, and Armenia Fraga went on to

951
00:57:22,040 --> 00:57:25,280
Speaker 1: be the head of Brazil's Central Bank and then has

952
00:57:25,640 --> 00:57:28,840
Speaker 1: started a fund called Cavilla. I think, um it's been

953
00:57:28,880 --> 00:57:31,720
Speaker 1: around for for a decade or more. So these were

954
00:57:31,720 --> 00:57:35,600
Speaker 1: two people who really it was a little confirmation bias

955
00:57:35,680 --> 00:57:39,000
Speaker 1: on my part, but they really solidified this idea that

956
00:57:39,040 --> 00:57:42,440
Speaker 1: you need to read widely on a lot of different subjects.

957
00:57:42,480 --> 00:57:44,120
Speaker 1: You need to be a student of history, you need

958
00:57:44,160 --> 00:57:47,920
Speaker 1: to be a student of people. You need to understand

959
00:57:48,040 --> 00:57:51,680
Speaker 1: as many different financial crises as you possibly can. So

960
00:57:51,760 --> 00:57:55,880
Speaker 1: this was right after the UH Nordic banking crisis, right,

961
00:57:55,920 --> 00:57:58,280
Speaker 1: so we were delving into what was the cause of

962
00:57:58,280 --> 00:58:02,000
Speaker 1: that Nordic banking crisis? Um, so it was it was

963
00:58:02,480 --> 00:58:06,400
Speaker 1: those were two really just um big, big thinkers who

964
00:58:06,480 --> 00:58:09,160
Speaker 1: had a very strong influence on the way I practice

965
00:58:09,200 --> 00:58:13,280
Speaker 1: economics and the way I invest. So I've interviewed almost

966
00:58:13,360 --> 00:58:17,040
Speaker 1: a hundred seventy people here. I can't tell you how

967
00:58:17,160 --> 00:58:21,720
Speaker 1: often the issue of being well rounded, being well read

968
00:58:22,520 --> 00:58:25,160
Speaker 1: comes up over and over and over again from people.

969
00:58:26,000 --> 00:58:28,680
Speaker 1: Uh and if you read what Charlie Munger and Warren

970
00:58:28,680 --> 00:58:33,040
Speaker 1: Buffett and Howard Marks has said this, and Cliff Astnes

971
00:58:33,120 --> 00:58:36,120
Speaker 1: has said this, and Bill McNab a vanguard, its time

972
00:58:36,120 --> 00:58:38,880
Speaker 1: and time and time again people say, if you were

973
00:58:38,960 --> 00:58:42,440
Speaker 1: not well rounded, well read, if you're just focused in

974
00:58:42,480 --> 00:58:45,760
Speaker 1: a tiny niche within finance, you won't be able to

975
00:58:45,800 --> 00:58:48,080
Speaker 1: do that job well because you need to be outside

976
00:58:48,120 --> 00:58:52,160
Speaker 1: of that specialty in order to develop skills and that specialty,

977
00:58:52,200 --> 00:58:57,160
Speaker 1: which lead me to everybody's favorite question, tell us about

978
00:58:57,320 --> 00:59:01,160
Speaker 1: some of your favorite books and what you're reading now. Okay,

979
00:59:01,280 --> 00:59:05,000
Speaker 1: So I am actually rereading a book that was my

980
00:59:05,080 --> 00:59:10,120
Speaker 1: mother's book called The Worldly Philosophers, which is yes, it's

981
00:59:10,160 --> 00:59:14,400
Speaker 1: a great book about all of the sort of foremost

982
00:59:14,520 --> 00:59:19,400
Speaker 1: economists that are the basis of economic theory. Right. So

983
00:59:19,920 --> 00:59:25,160
Speaker 1: Adam Smith keenes Um, I think that he has a

984
00:59:25,480 --> 00:59:31,920
Speaker 1: chapter on Veblen. Uh. Yeah, yeah, it's a it's a

985
00:59:31,960 --> 00:59:36,880
Speaker 1: great it's a great book. And then uh, I love

986
00:59:36,960 --> 00:59:42,400
Speaker 1: reading biographies, and I love reading autobiographies of the few

987
00:59:42,480 --> 00:59:44,800
Speaker 1: great ones around. Oh, there are many great ones. So

988
00:59:45,000 --> 00:59:47,920
Speaker 1: one of one of my favorite biographies that I read

989
00:59:47,920 --> 00:59:49,920
Speaker 1: a long time ago was called West with the Night

990
00:59:50,160 --> 00:59:53,200
Speaker 1: by Beryl West. West with the Night. It's by Beryl

991
00:59:53,240 --> 00:59:59,880
Speaker 1: Markham and she was a female pilot in East Africa

992
01:00:00,080 --> 01:00:04,040
Speaker 1: in the thirties and she made money taking people on

993
01:00:04,120 --> 01:00:08,840
Speaker 1: hunting trips. Yeah, it's a great it's a great book.

994
01:00:09,400 --> 01:00:12,160
Speaker 1: And um. One of the things that she talked about

995
01:00:12,240 --> 01:00:17,520
Speaker 1: in the book was how the elephants would know that

996
01:00:17,680 --> 01:00:19,400
Speaker 1: the planes were coming and then it meant that they

997
01:00:19,400 --> 01:00:22,160
Speaker 1: were going to be shot and they would literally take.

998
01:00:22,360 --> 01:00:26,640
Speaker 1: She talks about how the the largest female elephant would

999
01:00:26,640 --> 01:00:29,560
Speaker 1: often hide with her head in a tree, so you

1000
01:00:29,600 --> 01:00:31,280
Speaker 1: couldn't see if it was a male or female. You

1001
01:00:31,280 --> 01:00:34,479
Speaker 1: couldn't see the tusks, while the hole rest of the herd,

1002
01:00:34,600 --> 01:00:37,720
Speaker 1: including the bull, would go off away from where the

1003
01:00:37,760 --> 01:00:41,560
Speaker 1: plane was. And then when they were far enough away,

1004
01:00:42,000 --> 01:00:44,240
Speaker 1: the female elephant would bring her head out, and they

1005
01:00:44,240 --> 01:00:47,160
Speaker 1: would be circling above, kind of waiting and waiting, and

1006
01:00:47,240 --> 01:00:49,200
Speaker 1: it turns out it was the female and not the bull.

1007
01:00:49,360 --> 01:00:51,800
Speaker 1: And then they would they want the bull with the tust,

1008
01:00:51,840 --> 01:00:54,840
Speaker 1: and now they had lost the herd. And so this

1009
01:00:54,840 --> 01:00:57,800
Speaker 1: this intelligence that these animals possessed, and just the way

1010
01:00:57,880 --> 01:01:02,280
Speaker 1: she tells the story, and and so I just think again,

1011
01:01:02,320 --> 01:01:03,880
Speaker 1: to go back to your point of being well rounded

1012
01:01:03,920 --> 01:01:06,760
Speaker 1: and the different ways that people think creatively in the

1013
01:01:06,800 --> 01:01:09,800
Speaker 1: different life experiences and things you can learn, it's just endless.

1014
01:01:10,240 --> 01:01:14,240
Speaker 1: Any other modern any modern biographies you're looking at or read.

1015
01:01:14,960 --> 01:01:17,400
Speaker 1: I have a giant stack of things I'm dying my

1016
01:01:17,560 --> 01:01:20,600
Speaker 1: reading list I can I purchase at a much greater

1017
01:01:20,680 --> 01:01:23,960
Speaker 1: rate than I could possibly ever read. I call that

1018
01:01:24,200 --> 01:01:31,720
Speaker 1: the bought to read ratio. I actually one one. I

1019
01:01:31,840 --> 01:01:35,360
Speaker 1: actually went back and made a list of everything I

1020
01:01:35,440 --> 01:01:38,960
Speaker 1: purchased in the previous year and then how much I read.

1021
01:01:39,480 --> 01:01:41,880
Speaker 1: And you end up with like a three to one ratio.

1022
01:01:41,960 --> 01:01:44,960
Speaker 1: For every three books you bought this year, you've probably

1023
01:01:45,000 --> 01:01:47,560
Speaker 1: read one. Well, I'm definitely a ten to one. So

1024
01:01:47,680 --> 01:01:50,120
Speaker 1: I'm definitely because every year, so twice a year I

1025
01:01:50,160 --> 01:01:52,480
Speaker 1: do here's my favorite books for the winter. Here in

1026
01:01:52,480 --> 01:01:55,160
Speaker 1: my summer books and in the fall. I said, you

1027
01:01:55,200 --> 01:01:57,840
Speaker 1: know what, let me just go to my bookshelf and

1028
01:01:57,960 --> 01:02:01,160
Speaker 1: pick ten books that I want read that I haven't

1029
01:02:01,200 --> 01:02:04,560
Speaker 1: read that are sitting there and it's embarrassing. It's like,

1030
01:02:04,680 --> 01:02:07,000
Speaker 1: how have I not read this? So I have It's

1031
01:02:07,000 --> 01:02:09,560
Speaker 1: a first all problem, though, Barry, it's a good first

1032
01:02:09,560 --> 01:02:11,840
Speaker 1: of all problem. It's a very good first I have

1033
01:02:11,920 --> 01:02:14,840
Speaker 1: a so I'll give you. I'm going to a reference.

1034
01:02:15,320 --> 01:02:18,440
Speaker 1: So I have, I have Grant, I have Springsteen, and

1035
01:02:18,480 --> 01:02:21,200
Speaker 1: I have Galileo on my bookshelf. Oh, I want to

1036
01:02:21,200 --> 01:02:25,760
Speaker 1: read Springsteen's biography. Let me get to my last two questions,

1037
01:02:25,800 --> 01:02:28,160
Speaker 1: my favorite two questions that I have to ask you

1038
01:02:28,200 --> 01:02:31,960
Speaker 1: because you and I keep digressing. Um, So a millennial

1039
01:02:32,040 --> 01:02:33,680
Speaker 1: comes to you when they say they want they're thinking

1040
01:02:33,680 --> 01:02:36,720
Speaker 1: of a career in finance or a recent college graduate.

1041
01:02:36,960 --> 01:02:39,760
Speaker 1: What sort of advice would you give them? Okay, so

1042
01:02:39,840 --> 01:02:45,280
Speaker 1: you need to have rapacious curiosity, rapacious curiosity, and you

1043
01:02:45,320 --> 01:02:49,040
Speaker 1: need to indulge your curiosity. You need to read, you

1044
01:02:49,040 --> 01:02:52,400
Speaker 1: need to learn, You need to really enjoy finding out

1045
01:02:52,440 --> 01:02:56,280
Speaker 1: about the world. It helps if you are doing something

1046
01:02:56,320 --> 01:03:00,200
Speaker 1: you are infinitely curious about. Right. If you're not is

1047
01:03:00,200 --> 01:03:02,000
Speaker 1: about it, you're not going to be engaged. You're not

1048
01:03:02,000 --> 01:03:05,800
Speaker 1: gonna love it. And and you can't only do what

1049
01:03:05,840 --> 01:03:08,360
Speaker 1: you love. Every job has has parts that are that

1050
01:03:08,400 --> 01:03:11,640
Speaker 1: are annoying or tedious, so you need to do those two.

1051
01:03:12,320 --> 01:03:15,360
Speaker 1: You need to do what I call play the scales. Right,

1052
01:03:15,440 --> 01:03:17,600
Speaker 1: so it doesn't matter to play the scale, to learn

1053
01:03:17,640 --> 01:03:20,320
Speaker 1: the basics and practice the basics over and over and

1054
01:03:20,360 --> 01:03:23,600
Speaker 1: over again. So UM, I still go and make my

1055
01:03:23,640 --> 01:03:27,880
Speaker 1: own graphs sometimes well not by hand, no, in havior analytics.

1056
01:03:27,960 --> 01:03:30,360
Speaker 1: But I still go in and and immerse myself in

1057
01:03:30,360 --> 01:03:33,800
Speaker 1: the data, do my own uh EViews calculations, just because

1058
01:03:33,840 --> 01:03:36,760
Speaker 1: it keeps your you keep your your fingers in the dirt,

1059
01:03:36,840 --> 01:03:39,240
Speaker 1: so to speak. Um, And then I would say, get

1060
01:03:39,280 --> 01:03:42,160
Speaker 1: in before the boss and leave after the boss, all right,

1061
01:03:42,240 --> 01:03:45,560
Speaker 1: And the last question that our favorite question. What is

1062
01:03:45,600 --> 01:03:48,800
Speaker 1: it that you know about economics and investing today that

1063
01:03:48,840 --> 01:03:51,280
Speaker 1: you wish you knew twenty years ago when you started?

1064
01:03:51,680 --> 01:03:55,720
Speaker 1: Everything is the knowing, just the amount of knowledge you have,

1065
01:03:55,840 --> 01:04:00,040
Speaker 1: and how that becomes cumulative. It's it's so valuable, and

1066
01:04:00,040 --> 01:04:04,280
Speaker 1: and knowing the history, just having it your fingertips, having experienced,

1067
01:04:04,440 --> 01:04:07,240
Speaker 1: you know, having experienced crisis. So what I experienced the

1068
01:04:07,280 --> 01:04:10,840
Speaker 1: Russia crisis and as we were approaching the dot com crisis,

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Speaker 1: and that was raising reins. I said, you know what,

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Speaker 1: I think I've seen this movie before. I think I

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Speaker 1: know how this is probably going to end right. And

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Speaker 1: so every every experience becomes cumulative and it helps you

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Speaker 1: if you let it. We have been speaking with Constance Hunter.

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Speaker 1: She is the chief economist at KPMG. If you enjoy

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Speaker 1: this conversation, look up an Inch or Down an Inch

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Speaker 1: on Apple iTunes and you could see any of the

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Speaker 1: other hundred and seventy five such podcasts we have recorded previously.

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Speaker 1: We love your comments, feedback and suggestions right to us

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Speaker 1: at m IB podcast at Bloomberg dot net. I would

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Speaker 1: be remiss if I did not thank my crack staff

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01:04:51,440 --> 01:04:54,840
Speaker 1: that helps to put this together. Taylor Riggs is our booker,

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Speaker 1: Medina Parwana is our recording producer. Michael Batnick is our

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Speaker 1: head of research, and Attica Valbron is our business director.

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Speaker 1: I'm Barry Ritolts. You've been listening to Masters in Business

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Speaker 1: on Bloomberg Radio.