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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is Masters in

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Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

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Speaker 2: This week on the podcast what Can I Say? Tour

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Speaker 2: de forced conversation about all things economic with Ellen Zettner.

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Speaker 2: She's been at Morgan Stanley for just about a decade

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Speaker 2: now better part of a decade.

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Speaker 3: She was chief Economist.

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Speaker 2: She has morphed into the Chief Economic Strategist and global

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Speaker 2: head of thematic and macro investing for Morgan Stanley Wealth Management.

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Speaker 2: The firm runs something crazy number like seven trillion dollars.

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Speaker 2: She's also a member of the firm's Global Investment Committee.

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Speaker 2: She's won every accolade and economic award you can as

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Speaker 2: a Wall Street economist, and her interest just rangers far

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Speaker 2: and wide. We talk about everything from tariffs to fit

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Speaker 2: independence to data integrity at the BLS. She's just a

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Speaker 2: very thoughtful, insightful economist who spends a lot of time

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Speaker 2: thinking about how can I fashion this information in a

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Speaker 2: way that will be useful for my clients, many of

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Speaker 2: whom are investors, And now in her new role at

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Speaker 2: Morgan Stanley Wealth Management, she becomes the client she's helping

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Speaker 2: to run that big pile of money. I thought this

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Speaker 2: conversation was absolutely fascinating, and I think you will also

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Speaker 2: with no further ado my discussion with Morgan Stanley's Allen Zettner.

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Speaker 4: Hi, Barry, thanks for having me. I'm really glad that

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Speaker 4: you got my title correct and without losing your breath

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Speaker 4: because it's a long one.

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Speaker 2: Well, you know, AI helped me assemble that, and I

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Speaker 2: know that's a theme of yours, so well, we'll get

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Speaker 2: that to that a little later. It's been it's been

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Speaker 2: a while since we had you on the last time

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Speaker 2: you hear it was the first Trump administration. We're going

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Speaker 2: to talk about a lot of policy issues, but before

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Speaker 2: we get there, I just want to talk a little

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Speaker 2: bit about your background because it's so interesting and not

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Speaker 2: what we think of as the typical path to Wall Street.

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Speaker 2: You get a bachelor's and an MBA from the University

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Speaker 2: of Colorado. What was the original career planning.

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Speaker 4: Yeah, bachelor's and masters from Denver, University of Colorado at Denver,

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Speaker 4: which I think surprises people even more. Yeah, so I

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Speaker 4: had gotten a late start, as I would put it,

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Speaker 4: with the university. After high school, I was partying, having

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Speaker 4: a great time gap year it was well, it turned

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Speaker 4: out to be an unplanned gap year.

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Speaker 5: And you know, in.

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Speaker 4: The state of Texas, there's a lot of room. You

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Speaker 4: don't need to live at home, and at least back

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Speaker 4: then you didn't need to live at home in order

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Speaker 4: to afford you know, you could afford to live.

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Speaker 5: On your own.

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Speaker 4: So I remember turning eighteen and my mother looked at

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Speaker 4: her watch and basically said, why are you still here?

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Speaker 4: And so I moved out with my friends. Was just

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Speaker 4: having a great time. And so by the time I

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Speaker 4: decided to get serious and said, hey, you know I

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Speaker 4: want to I want to go somewhere else for university.

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Speaker 4: I was starting university when my friends were graduating, and

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Speaker 4: so I wanted a commuter campus, and University of Colorado

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Speaker 4: Denver was just a phenomenal place to be with an

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Speaker 4: amazing economics department.

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Speaker 2: So Texas girl up in Denver. How to be a

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Speaker 2: climate shock to you?

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Speaker 3: It was?

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Speaker 5: It was a little strange.

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Speaker 4: So we had registered side unseen my parents and I

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Speaker 4: we drove up the fifteen hour drive from Austin, Texas

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Speaker 4: to Denver. The first twelve hours are in the state

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Speaker 4: of Texas and then you finally get out of the state.

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Speaker 5: That's that's starting in the middle.

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Speaker 2: That wait, so New York to cut Texas to Colorado,

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Speaker 2: Austin to Denver, Boston to Denver, fifteen hours, eighty percent

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Speaker 2: of which are still in the stot are still in.

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Speaker 5: The state of Texas.

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Speaker 4: So you go through one tiny corner called Ratone Pass.

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Speaker 4: That's where my Texas comes out. Rattone Pass right there

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Speaker 4: where Colorado and New Mexico and Texas come together, and

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Speaker 4: you just slip right through into Colorado. And so we

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Speaker 4: registered side unseen. My mother woke me up. I was

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Speaker 4: sleeping in the backseat of the car, and she said, Ellen,

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Speaker 4: look and I woke up and I looked out of

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Speaker 4: the window and I saw the mountains, and I was like, Mama,

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Speaker 4: I'm home.

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Speaker 5: I had never seen mountains before.

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Speaker 2: Had you seen snow before?

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Speaker 4: I had seen snow in Austin once every six years

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Speaker 4: on average, it snowed. And so we made a snowman

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Speaker 4: with a lot of rocks and sticks in it and leaves,

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Speaker 4: but it was a snowman. But my mother had spent

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Speaker 4: summers in Boulder. So my grandfather taught. Both my grandparents

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Speaker 4: taught at University of Texas. My grandmother got her PhD

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Speaker 4: from Cornell in the early thirties. My grandfather got his

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Speaker 4: PhD from Columbia here in New York. They were both

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Speaker 4: teaching at the University of Texas. He founded the physical

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Speaker 4: education department at the University of Texas, and so here

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Speaker 4: was a legacy. My mother grew up spending summers living

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Speaker 4: in the dorm in Boulder because he would teach summers

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Speaker 4: at University of Colorado and Boulder, and so she always

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Speaker 4: talked about the mountains. And just when I decided to

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Speaker 4: leave Texas for school, I said, that's where I want

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Speaker 4: to go, is the mountains, even though I had no

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Speaker 4: idea exactly what I was saying.

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Speaker 2: But you ended up not leaving Texas permanently. After you

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Speaker 2: get your MBA Revenu Estimating Division at the Texas State

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Speaker 2: Controller's Office, working with some guy named George W.

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Speaker 4: Bush, tell ye, tell us a little bit about this

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Speaker 4: guy that used to be the governor of the state

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Speaker 4: of Texas, you know.

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Speaker 5: But no, that was great.

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Speaker 4: So I got my master's degree in economic and said, well,

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Speaker 4: what do I do now? And so made sense to

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Speaker 4: go back home to Austin. Now, at that time, for economists,

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Speaker 4: your option was to work for the state, or you

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Speaker 4: could work for a u TEMCO, which is University of

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Speaker 4: Texas Investment arm Like there's not a lot of areas

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Speaker 4: for economists. Then now there's a thriving investment community, hedge funds,

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Speaker 4: you name it. But then you worked for the state,

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Speaker 4: and so it was a great way to start. Texas

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Speaker 4: legislature is a binding a legislature. It's only in session

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Speaker 4: in odd years. So I think I worked really really

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Speaker 4: hard for five months every other year, and it was

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Speaker 4: a wonderful, wonderful way.

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Speaker 5: The rest of the time. Let's see, in the.

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Speaker 4: Late nineties, there was this thing called day trading with

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Speaker 4: no restrictions in a firm. You just sort of like

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Speaker 4: have fun and be like, oh, I made a few

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Speaker 4: thousand dollars today day trading. No, it was it was

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Speaker 4: sort of a let's let's put it this way. It

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Speaker 4: was a wonderful way to start where I could really

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Speaker 4: dive deep into topics such as studying the fairness of

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Speaker 4: the tax system in the state of Texas, doing economic

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Speaker 4: development studies. We were a part of the study that

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Speaker 4: helped attract the first Toyota tundra plant to the state

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Speaker 4: of Texas in San Antonio and working for Tamra Ploute,

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Speaker 4: who was just so important in steering my career.

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Speaker 5: She was the chief.

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Speaker 4: Economist for the State of Texas at the time PhD

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Speaker 4: from University of Pennsylvania. You mentioned the Lawrence Arkline Award.

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Speaker 4: It was such an honor to receive that twice because

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Speaker 4: Tamar had studied under Lawrence Klin at University of Pennsylvania,

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Speaker 4: and so it was just being thrown into a macro

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Speaker 4: role was such a huge determinant of my entire career

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Speaker 4: and studying things like household behavior in the state of Texas,

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Speaker 4: which gave me my love for the consumer and household behavior,

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Speaker 4: which has lasted my whole career. So I lasted there

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Speaker 4: for about five years and then started looking for something

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Speaker 4: in New.

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Speaker 2: York, and consumer and household behavior lasted your whole career

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Speaker 2: to good effect and good result, because as we've seen

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Speaker 2: over the past fifty years, the US consumers what drives

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Speaker 2: the entire economy. So being an expert in that space,

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Speaker 2: I can't imagine that hurt your either your career hasn't hurt,

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Speaker 2: or your economic forecast.

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Speaker 4: And I've propelled many an economist off of the back

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Speaker 4: of bringing them onto my team and saying, here you go,

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Speaker 4: here's a huge consumer platform, learn it and run it.

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Speaker 4: And they have gone on to do amazing things. One

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Speaker 4: of them still with me at Morgan Stanley. Paula Campbell Roberts.

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Speaker 4: One of my shining, shining achievements in my career is

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Speaker 4: seeing her career at KKR flourish.

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Speaker 2: That's really interesting. So how do you go from the

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Speaker 2: Revenue Estimating Division in the Texas government to Bank of

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Speaker 2: Tokyo Mitsubishi on Wall Street. That seems like a big jump.

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Speaker 5: It is a big jump.

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Speaker 4: So part of it was that I felt State government

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Speaker 4: was not where I wanted to be for the long run.

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Speaker 4: There's something about uh something in my DNA, as it

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Speaker 4: is with many people in finance, that attracts me to

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Speaker 4: just a fast moving environment. I needed something that was

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Speaker 4: much more.

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Speaker 2: Dynamic and not closed every other year.

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Speaker 4: Yeah, not closed every other year, although I do sometimes

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Speaker 4: long for the boring days of working at the State.

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Speaker 5: Uh So, I knew that I needed.

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Speaker 4: To go to either a d C or Chicago or

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Speaker 4: in New York. I wasn't quite sure where. And so

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Speaker 4: while I was job searching, which back then involved looking

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Speaker 4: in the newspapers or which is going to sound I mean,

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Speaker 4: people are just being like.

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Speaker 5: Mailing them, so many of them.

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Speaker 4: But also, you know, I have a long, rich history

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Speaker 4: now with the National Association for Business Economics and their

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Speaker 4: jobs Board, which was extremely antiquated then. Well didn't seem

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Speaker 4: antiquated back then. People would be appalled at that job's

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Speaker 4: board now. But I actually found my job at.

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Speaker 5: Bank of Tokyo Mitsubishi.

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Speaker 4: Through the NABE Jobs Board, which is still econjobs dot org.

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Speaker 4: And so I think of Nabe as being my partner

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Speaker 4: in my career since I joined Nabe in the late nineties.

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Speaker 4: Long story short, I get this great job at Bank

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Speaker 4: of Tokyo Mitsubishi. The as the senior economists there, I

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Speaker 4: basically was a one man band, which was great because

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Speaker 4: I had to wear every hat as economists for smaller

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Speaker 4: institutions or with smaller research arms have to do. And

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Speaker 4: what's so interesting about my time there, and I was

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Speaker 4: there for eight years, is that during that time the

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Speaker 4: financial crisis hit and I felt so lucky to be

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Speaker 4: at a Japanese firm at that time because we had

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Speaker 4: not taken part in mortgage backed security investing. We had

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Speaker 4: already gone through a financial crisis of our own that

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Speaker 4: had lasted a long time. Japanese firms were sitting on

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Speaker 4: a pile of cash, and it was at that time

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Speaker 4: that the ceremonial check was walked across Broadway to purchase

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Speaker 4: twenty percent of Morgan Stanley to keep Morgan Stanley.

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Speaker 2: Afloat from banks from from MUFG, which the check is

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Speaker 2: written from Bank of Tokyo Mitsubishi.

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Speaker 5: So that happened.

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Speaker 4: And what was interesting was when I eventually ended up

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Speaker 4: at Morgan Stanley to hear what it was like from

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Speaker 4: my colleagues from the other side on a Friday, being told,

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Speaker 4: you know, go home and we'll let you know on

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Speaker 4: Sunday if you still have a job, if the doors

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Speaker 4: are going to be open, and then being told on

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Speaker 4: Sunday that you can go back to work, and the

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Speaker 4: fear that they felt versus I didn't feel total job

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Speaker 4: security because I for the first time I was seeing

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Speaker 4: economics teams just on the whole, just being cut and

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Speaker 4: you had never seen that poor. The economists are sort of,

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Speaker 4: you know, we're kind of we've got decent job security

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Speaker 4: compared to the rest in finance.

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Speaker 5: But sorry.

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Speaker 4: This is when I could make a joke about certain

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Speaker 4: news that came out after.

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Speaker 5: Free but no I didn't. But anyhow, what I.

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Speaker 2: Really so vividly remember, similar to you, I was in

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Speaker 2: an institution that, through a combination of dumb luck and

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Speaker 2: what have you, was on the right side of that.

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Speaker 2: So while the street was freaking out, I didn't feel

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Speaker 2: personally the same job in security of pressure that everybody

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Speaker 2: else did. But I had maintained an email list of

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Speaker 2: ten or fifteen thousand readers, and most of the addresses

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Speaker 2: were you know, MS dot com, mL dot com, whatever,

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Speaker 2: the various institutional and you know, you would occasionally have

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Speaker 2: somebody leave a position and you would have a bounce

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Speaker 2: back rate each week of two three emails, but eight

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Speaker 2: or nine I was seeing like three hundred, four hundred,

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Speaker 2: five hundred emails a week come back. This is no

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Speaker 2: longer a valid email address at GS dot com or

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Speaker 2: whatever it happened it was.

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Speaker 5: It was really alarming.

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Speaker 2: It very like that was nothing I've ever experienced. Even

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Speaker 2: two thousand, which seemed like it was a disaster, didn't

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Speaker 2: compare to this.

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Speaker 4: Yeah, yeah, so never experienced anything like it, And so,

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Speaker 4: and you know, I really think that that's when LinkedIn

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Speaker 4: took off because I had signed up for LinkedIn at

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Speaker 4: the time but didn't use it. I'm still not a

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Speaker 4: huge fan of social media. I know that's terrible to say.

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Speaker 4: How can anybody be successful today with that?

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Speaker 5: Using social media?

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Speaker 2: I'm going to tell you I think that was a

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Speaker 2: formally minority position, like an outlier position, And now I

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Speaker 2: think the consensus has built that the algorithm is awful.

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Speaker 2: It manipulates us towards outrage. You look at the rising

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Speaker 2: levels of depression amongst teenagers. It's really tracks the rise

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Speaker 2: of smartphones and social media. So I don't think it's

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Speaker 2: as bad a thing to say in twenty twenty five

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Speaker 2: more Yeah, But in twenty fifteen people would have looked

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Speaker 2: at you like, what do you mean?

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Speaker 3: You don't like, what do you mean?

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Speaker 1: Yeah?

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Speaker 2: And now I think the verdict is in or yeah.

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Speaker 4: Well, I think for two thousand and eight. You know,

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Speaker 4: in finance, oftentimes the jobs we have, when your time

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Speaker 4: is up, you're ripped out of your seat and.

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Speaker 2: With the box and a security guard escorting you to

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Speaker 2: the time.

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Speaker 4: Because you have access to sensitive information like that's how

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Speaker 4: for most of us in finance, that's how you're is

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Speaker 4: going to look one day, and so if you had

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Speaker 4: joined LinkedIn, it was the way that you didn't lose

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Speaker 4: all those contacts. And so I really think that's where

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Speaker 4: and certainly that's where I was like, Okay, maybe I

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Speaker 4: should keep up with people through LinkedIn, but I'll tell

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Speaker 4: you that I have learned how to train those algorithms.

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Speaker 4: So with Instagram, which I have since dropped all together.

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Speaker 4: But when I was on Instagram, I got so tired

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Speaker 4: of being marketed to as a fifty plus year old woman.

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Speaker 4: It was every single ad was the best mascarra for

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Speaker 4: insert you know, or it was the best insert you

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Speaker 4: know blank for women over fifty. So it's the best

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Speaker 4: mass scare for women over fifty, the best shampoo for

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Speaker 4: women over fifty, the best whatever. And it would always

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Speaker 4: somehow show this beautiful woman that happened to be over fifty.

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Speaker 2: Wait till you're over sixty and just go through your

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Speaker 2: spam folder and see you the sort of stuff that

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Speaker 2: they marketing.

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Speaker 5: Yeah, it's a little insulting.

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Speaker 4: But what I did was I saw an ad one

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Speaker 4: time for dog food. Now I don't have any pets,

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Speaker 4: so I clicked on that ad and it started showing

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Speaker 4: me dog food adds. So I stopped purchasing things because

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Speaker 4: this was the problem. I'm an impulse buyer, so I

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Speaker 4: would purchase things on Instagram and so, but then Instagram started,

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Speaker 4: It got my number, It knew what I was doing,

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Speaker 4: and so that I thought, okay, I need to click

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Speaker 4: on the dog food ad and now poke around in

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Speaker 4: that site a little bit, and then okay, I need

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Speaker 4: to poke around the side of it and then add

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Speaker 4: something to my cart, and then just abandoned it. And

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Speaker 4: so for a while I was able to train. If

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Speaker 4: I just did that a couple times, then for thirty days,

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Speaker 4: I would get dog ads and I easily could continue

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Speaker 4: to enjoy Instagram without buying a thing.

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Speaker 2: One of the things that has made Facebook so valuable

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Speaker 2: is its ability to create not just targeted ads to

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Speaker 2: you and your demographics. All right, you're a woman over fifty,

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Speaker 2: that's two blunts. They can also track your browsing history.

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Speaker 2: They can link it to your zip code. They know

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Speaker 2: how your town and county voted in the last election.

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Speaker 2: They know your credit score and your purchase history, so

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Speaker 2: you could really find you know, the old joke in

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Speaker 2: advertising is half of advertising dollars are wasted. We just

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Speaker 2: don't know which half. As you bring in more and

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Speaker 2: more technology to this, we're starting to figure out exactly

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Speaker 2: how to not waste any dollars, which is why some

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Speaker 2: of the ads you get are kind of spooky and creepy, like, hey,

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Speaker 2: is my phone listening to me? No, Well, whether it

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Speaker 2: is or not, your browsing just is so revealing of THEIA.

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Speaker 5: And it's true.

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Speaker 4: But if you think about it, if we tie that

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Speaker 4: back to the old days of just having to send

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Speaker 4: out surveys for data and such, you know, as an economist,

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Speaker 4: I want as much data as possible. I want it

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Speaker 4: to measure everything you could possibly you know, look at sideways,

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Speaker 4: and I appreciate having that detailed data. My husband used

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Speaker 4: to get irritated because, again, back in the old days,

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Speaker 4: when someone might actually call to do a survey, I

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Speaker 4: would be the one that would give them the time

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Speaker 4: of day and answer the survey because I knew that

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Speaker 4: as a practicing economist, I would really appreciate having that detail.

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Speaker 5: Instead.

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Speaker 4: Now, because it's being done by algorithms and machines and

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Speaker 4: there's not a personal call behind it, we're sort of

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Speaker 4: alarmed that someone is getting that much information, But it's

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Speaker 4: also because a good deal of it's not used to

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Speaker 4: make the government more data more accurate, right, It's used

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Speaker 4: to make a company more profitable by selling to you.

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Speaker 5: So it is a bit different.

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Speaker 4: But you know, if the government could employ those techniques

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Speaker 4: and give me that kind of detailed data on our population,

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Speaker 4: I would use it all day long.

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Speaker 2: Coming up, we continue our conversation with Allen Zenner, chief

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Speaker 2: economic strategist and global head of Madick and Macro Investing

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Speaker 2: at Morgan Stanley, discussing the Mattic Investing and her macro

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Speaker 2: work at Morgan Stanley. I'm Barry Dults. You're listening to

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Speaker 2: Masters in Business on Bloomberg Radio. Allan Zantner is my

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Speaker 2: extra special guest. She's chief economic strategist and global head

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Speaker 2: of Thematic and macro Investing from Morgan Stanley Wealth Management. Overall,

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Speaker 2: the firm manages over seven trillion dollars. Let's talk a

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Speaker 2: little bit about your role at Morgan Stanley. What brought

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Speaker 2: you there from? Previously you were at Nomura and Banka

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Speaker 2: Tokyo Mitsubishi. What brought you to Morgan Stanley?

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Speaker 5: Vincent Reinhardt, Oh really, yeah.

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Speaker 2: Of.

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Speaker 4: Rein Hart and Rogoff fame well, rhin Hart rein Harten Roguoff.

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Speaker 4: So the rhin Harten rogue offf mostly is Carmen Reinhardt.

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Speaker 4: And but yeah, Vincent called me up one day and said,

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Speaker 4: would you like to come work for me? And I

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Speaker 4: us of course I knew him previously. I was an economist,

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Speaker 4: you know.

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Speaker 2: I mean I knew of him. But did you know

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Speaker 2: I knew of him?

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Speaker 4: I did not know him on a personal basis, right,

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Speaker 4: And it was an absolute surprise to get that call.

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Speaker 4: And I couldn't go there fast enough.

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Speaker 3: Huh.

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Speaker 4: So it wasn't just the Morgan Stanley name, which is

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Speaker 4: wonderful to go to a place where just the name

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Speaker 4: alone gives you a certain amount of gravitas. I was

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Speaker 4: the same economist I was previously doing the same work

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Speaker 4: and the same methodologies, employing the same tools. But suddenly

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Speaker 4: it was like, oh, she's at Morgan Stanley. So just

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Speaker 4: changing the name to such a well respected firm meant

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Speaker 4: all the difference in my career.

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Speaker 5: But to specifically be able to.

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Speaker 4: Go and learn from an economist who sat at the

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Speaker 4: at the right hand of Alan Greenspan for so many years,

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Speaker 4: you know, being a fed watcher and being able to

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Speaker 4: then work for the quintessential fed watcher and sort of

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Speaker 4: plug the holes. In my knowledge, it was just an

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Speaker 4: opportunity I couldn't pass up.

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Speaker 2: What was the role? You obviously didn't start as chief economy.

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Speaker 4: I started as his senior economist.

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Speaker 2: Oh really? And then how much longer was it before

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Speaker 2: you were elevated as a chief economist?

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Speaker 4: Oh gosh, about a year and a half. So Vincent

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Speaker 4: and I were able to overlap for about a year

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Speaker 4: and a half before I took the chief economist role.

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Speaker 4: You may or may not know that that he and

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Speaker 4: Carmen reside in Boston, and so being able to work

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Speaker 4: full time from Boston continue to support Carmen in her

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Speaker 4: role at Harvard, and also a role that fits him

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Speaker 4: so perfectly well as the chief economist, the financial chief

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Speaker 4: economist at Bnymellon is just the perfect place to be.

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Speaker 4: So I am very thankful for the time that we

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Speaker 4: were able to spend together overlapping there at Morgan Stanley,

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Speaker 4: And so in twenty fifteen I then became the chief

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Speaker 4: US economist.

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Speaker 2: So on the Morgan Stanley website is a little bio

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Speaker 2: of you, and in it you described twenty sixteen as

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Speaker 2: a very significant and for you personally career defining year.

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Speaker 2: Why is that.

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Speaker 4: I like to think back of periods in my career

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Speaker 4: when my limits were tested, and it might be the

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Speaker 4: financial crisis, it might be some other recession, it might

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Speaker 4: have been COVID, But certainly twenty sixteen we had a

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Speaker 4: presidential election year and my limits were absolutely tested, both

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Speaker 4: physically and mentally. So I had gone to DC the

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Speaker 4: morning of the election. I had already voted in early voting.

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Speaker 4: I had left on a sixth flight, which means I

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Speaker 4: had to get up at four in the morning, and

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Speaker 4: went to DC for meetings. Then I flew on to

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Speaker 4: New Orleans to prep for a conference and decided that

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Speaker 4: I would go to the gym, as I love to

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Speaker 4: do when I'm at the hotel, and then you know,

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Speaker 4: buckle down and get ready to watch the fun electional

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Speaker 4: results come in, and watching the election results come in,

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Speaker 4: and then answering client questions at the same time, and

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Speaker 4: then seeing all of that unfold in a way that

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Speaker 4: was surprising to many people.

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Speaker 5: Where this cycle kicked off. Where okay, wait, I thought

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Speaker 5: I was going to go to the gym.

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Speaker 4: Okay, not going to the gym, Wait, I need to

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Speaker 4: order some sort of dinner to the room.

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Speaker 5: Okay, I can't beat.

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Speaker 4: Then it was then it was oh gosh, Asia is awake.

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Speaker 4: Got to get on calls with Asia. Then it was

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Speaker 4: oh boy, Europe's waking up. Got to get on calls

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Speaker 4: with Europe. Calls with my coll calls with these clients, calls, calls, calls, calls, calls.

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Speaker 4: At eleven am in the morning, which was now more

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Speaker 4: than twenty four hours later, after I had gotten up,

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Speaker 4: I decided that maybe I should at least try to

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Speaker 4: close my eyes for a little bit. I closed my eyes,

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Speaker 4: couldn't fall asleep. I had to go down stairs at

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Speaker 4: the hotel to deliver an economic outlook to what had

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Speaker 4: then become a standing room only event, because look what's

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Speaker 4: just happened. Let's hear from the economist. And we had

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Speaker 4: just putten out put out. We had just put out

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Speaker 4: our year ahead outlooks because those come.

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Speaker 5: Out in November.

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Speaker 4: And so I was there standing at the front of

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Speaker 4: the room and I just left my PowerPoint presentation on

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Speaker 4: the front page the holding screen as a holding screen,

443
00:24:49,520 --> 00:24:53,520
Speaker 4: and said, let's go ask me whatever questions you have.

444
00:24:54,240 --> 00:24:56,800
Speaker 4: I'm not going to have all the answers, but let's talk,

445
00:24:57,960 --> 00:25:00,760
Speaker 4: and I don't even remember what I said. The time

446
00:25:00,800 --> 00:25:06,400
Speaker 4: flew by. I then went back to the airport, tried

447
00:25:06,400 --> 00:25:08,960
Speaker 4: to get on an earlier flight to go back, was

448
00:25:09,000 --> 00:25:12,720
Speaker 4: still delayed. Finally got back at eleven pm at night

449
00:25:13,280 --> 00:25:16,760
Speaker 4: to New York. I could not fall asleep still either

450
00:25:16,800 --> 00:25:20,080
Speaker 4: on the flight or when I got home, and ultimately,

451
00:25:20,119 --> 00:25:23,040
Speaker 4: finally I just gave up sleeping, went into the office,

452
00:25:23,200 --> 00:25:27,120
Speaker 4: and forty two hours I went without sleeping.

453
00:25:27,520 --> 00:25:30,400
Speaker 2: At a certain point, your cognitive functioning just starts to

454
00:25:30,480 --> 00:25:34,160
Speaker 2: fall off a cliff. But that was real. I similarly

455
00:25:34,200 --> 00:25:38,920
Speaker 2: have a vivid recollection of just shock from so many

456
00:25:38,960 --> 00:25:41,800
Speaker 2: people questions that had to be really exciting.

457
00:25:42,040 --> 00:25:43,880
Speaker 5: Yeah, so was it? And see you say exciting.

458
00:25:43,960 --> 00:25:48,720
Speaker 4: Now I live off of that stuff because oh you're adrenaline. Jumpee, adrenaline.

459
00:25:48,960 --> 00:25:52,159
Speaker 4: You're tested, your limits are tested. And what a great

460
00:25:52,200 --> 00:25:55,200
Speaker 4: story to tell. I was also on the trading floor

461
00:25:55,200 --> 00:25:59,280
Speaker 4: at one am when Brexit happened. I had gone to

462
00:25:59,320 --> 00:26:02,800
Speaker 4: sleep at eleven and set the alarm for midnight. The

463
00:26:02,840 --> 00:26:05,960
Speaker 4: alarm went off, I know that my husband immediately checked

464
00:26:05,960 --> 00:26:09,080
Speaker 4: the phone. I heard him say, oh, sh and I

465
00:26:09,359 --> 00:26:12,399
Speaker 4: was like, what what? And I was like, oh my god,

466
00:26:12,520 --> 00:26:13,840
Speaker 4: I had to get in the shower and get to

467
00:26:13,880 --> 00:26:15,040
Speaker 4: the trading floor by one am.

468
00:26:15,280 --> 00:26:19,480
Speaker 2: I just read this morning. Nobody talks about Brexit anymore.

469
00:26:19,640 --> 00:26:23,000
Speaker 2: I just read a data point that shocked me, which

470
00:26:23,200 --> 00:26:27,720
Speaker 2: was the GDP of Italy just pasted the GDP of

471
00:26:27,840 --> 00:26:33,520
Speaker 2: the UK. Mind blown. And there are a lot of reasons,

472
00:26:33,520 --> 00:26:37,080
Speaker 2: but clearly Brexit has to be a significant part of that. Yeah, yeah,

473
00:26:37,160 --> 00:26:37,879
Speaker 2: giant part of that.

474
00:26:37,920 --> 00:26:40,760
Speaker 4: It's like, thank you UK for bringing some business back

475
00:26:40,800 --> 00:26:43,720
Speaker 4: to us, because here's a country that is dying. Their

476
00:26:43,760 --> 00:26:48,120
Speaker 4: birth rates are non existent, their population has been shrinking,

477
00:26:48,480 --> 00:26:52,679
Speaker 4: So how can GDP be growing. There's no fundamental basis

478
00:26:52,720 --> 00:26:55,040
Speaker 4: for it, so it must be some sort of tectonic

479
00:26:55,080 --> 00:26:56,480
Speaker 4: shift like Brexit.

480
00:26:56,880 --> 00:27:00,359
Speaker 2: Pretty pretty fascinating. There's so much stuff. I don't want

481
00:27:00,400 --> 00:27:05,240
Speaker 2: to just get stuck in twenty sixteen. Let's go forward.

482
00:27:05,320 --> 00:27:07,920
Speaker 2: Let's look forward. One of the things you wrote about

483
00:27:08,320 --> 00:27:12,080
Speaker 2: was the coming youth boom economy. And when we look

484
00:27:12,119 --> 00:27:15,280
Speaker 2: at gen z born between ninety seven and twenty twelve,

485
00:27:16,560 --> 00:27:20,960
Speaker 2: they and gen y are going to dominate the US

486
00:27:21,000 --> 00:27:26,000
Speaker 2: economy really in the next ten years or so. They'll

487
00:27:26,040 --> 00:27:30,240
Speaker 2: yield higher consumption. You wrote wages and housing demand, stimulating

488
00:27:30,280 --> 00:27:33,679
Speaker 2: GDP growth. This was a few years ago, do you.

489
00:27:33,600 --> 00:27:36,160
Speaker 5: Still hold to that was in twenty nineteen.

490
00:27:35,880 --> 00:27:38,119
Speaker 2: Yeah, So the youth boom is this still coming?

491
00:27:38,280 --> 00:27:41,399
Speaker 4: Yeah? So we're here, we're in it, and we were

492
00:27:41,400 --> 00:27:43,960
Speaker 4: at the cusp of it. Then Millennials were already starting

493
00:27:43,960 --> 00:27:46,840
Speaker 4: to outnumber baby boomers, and then you've got Gen Z

494
00:27:47,040 --> 00:27:49,679
Speaker 4: coming up behind them at that time that were just

495
00:27:49,720 --> 00:27:52,080
Speaker 4: as large. So when you combine the two, and that's

496
00:27:52,080 --> 00:27:54,720
Speaker 4: what we mean by the youth boom, you've got a

497
00:27:54,800 --> 00:27:58,680
Speaker 4: demographic that is larger than any in our country's past

498
00:27:58,800 --> 00:28:01,640
Speaker 4: and sets us apart on the globe stage. Because our

499
00:28:01,680 --> 00:28:06,000
Speaker 4: major trading partners are across G ten, nobody has those demographics. Now,

500
00:28:06,000 --> 00:28:09,040
Speaker 4: our birth rates have been falling, and that is a problem,

501
00:28:09,359 --> 00:28:11,600
Speaker 4: and that's a problem that by the way, lights of

502
00:28:11,640 --> 00:28:16,280
Speaker 4: Fire under the need for AI as well. But our

503
00:28:16,320 --> 00:28:19,280
Speaker 4: birth rates are higher than our major trading partners, and

504
00:28:19,320 --> 00:28:24,879
Speaker 4: so comparatively speaking, that is something that's very important that

505
00:28:24,960 --> 00:28:28,800
Speaker 4: drives the back drop. Now, economists love demographics. Demographics make

506
00:28:28,840 --> 00:28:33,520
Speaker 4: the world go round, and demographics, you know, it's when

507
00:28:33,560 --> 00:28:36,240
Speaker 4: you look at any point in time, how well did

508
00:28:36,280 --> 00:28:40,720
Speaker 4: the Census Bureau get demographic projections pretty well because it

509
00:28:40,760 --> 00:28:43,320
Speaker 4: turns out we sort of all age kind of along

510
00:28:43,360 --> 00:28:48,040
Speaker 4: the same track. And what we know from detailed government

511
00:28:48,120 --> 00:28:51,200
Speaker 4: data is we know how we tend to move through

512
00:28:51,200 --> 00:28:54,520
Speaker 4: the world and spend and behave at certain age ranges.

513
00:28:55,000 --> 00:28:57,960
Speaker 4: So you, as an economist, you can just let your

514
00:28:58,120 --> 00:29:04,800
Speaker 4: demographic cohorts age through those buckets and know kind of

515
00:29:04,840 --> 00:29:07,800
Speaker 4: how the spending shifts are going to take place. When

516
00:29:07,840 --> 00:29:10,720
Speaker 4: are participation rates in the labor force going to peak,

517
00:29:11,120 --> 00:29:14,240
Speaker 4: When do we hit peak earning years, in peak working years,

518
00:29:14,240 --> 00:29:18,120
Speaker 4: and therefore first time home buying years, et cetera, et cetera.

519
00:29:18,560 --> 00:29:20,560
Speaker 4: So you mentioned housing as being one of our key

520
00:29:20,560 --> 00:29:25,160
Speaker 4: calls in twenty nineteen, Well, that was only accelerated during COVID,

521
00:29:25,360 --> 00:29:30,600
Speaker 4: for sure, it wasn't. There were many themes that were

522
00:29:30,640 --> 00:29:34,600
Speaker 4: accelerated during COVID, and housing is one of those. In

523
00:29:34,680 --> 00:29:39,040
Speaker 4: terms of of the incredible demand. I mean, we are

524
00:29:39,040 --> 00:29:41,760
Speaker 4: going to be underbuilding housing for a decade.

525
00:29:42,560 --> 00:29:45,000
Speaker 2: We have been under building housing really since the we

526
00:29:45,600 --> 00:29:46,400
Speaker 2: estimate we.

527
00:29:46,360 --> 00:29:51,240
Speaker 4: Will have an eighteen million unit shortfall that.

528
00:29:51,240 --> 00:29:53,880
Speaker 2: We need to make up. That's a giant number. Chrise,

529
00:29:53,920 --> 00:29:55,960
Speaker 2: It's a giant of course, we've been talking about four

530
00:29:55,960 --> 00:29:59,240
Speaker 2: to five million currently and that comes from the National

531
00:29:59,280 --> 00:30:03,080
Speaker 2: Association Realtors and the Association home Builders. So there's a

532
00:30:03,120 --> 00:30:05,080
Speaker 2: little asterisk. Is this same and think.

533
00:30:04,960 --> 00:30:08,640
Speaker 4: About that that's currently and then you grow that over time.

534
00:30:09,080 --> 00:30:12,080
Speaker 4: You pair it with affordability, You pair it with the

535
00:30:12,560 --> 00:30:16,040
Speaker 4: fact that our surveys do show that millennials and Gen

536
00:30:16,160 --> 00:30:20,320
Speaker 4: Z by far still want to live in single family homes.

537
00:30:20,400 --> 00:30:22,720
Speaker 4: They may not all be able to afford single family

538
00:30:23,240 --> 00:30:26,800
Speaker 4: and so single family renting will be in high demand.

539
00:30:26,960 --> 00:30:30,320
Speaker 4: We're going to need to build those units. Home builders

540
00:30:30,360 --> 00:30:33,200
Speaker 4: are going to have to respond by building smaller, less

541
00:30:33,200 --> 00:30:37,360
Speaker 4: expensive homes. We think modular housing will have a big

542
00:30:37,480 --> 00:30:40,280
Speaker 4: role to play as well. And then you start to

543
00:30:40,320 --> 00:30:42,600
Speaker 4: think about all the different ways we need to build

544
00:30:42,600 --> 00:30:45,920
Speaker 4: homes as well that shortfall. In order to ensure all

545
00:30:45,920 --> 00:30:48,800
Speaker 4: those homes, we're going to have to think about climate

546
00:30:48,840 --> 00:30:53,960
Speaker 4: friendly building materials, more resist climate resistance building materials, all

547
00:30:54,000 --> 00:30:57,120
Speaker 4: the different ways that we can appease the insurance companies

548
00:30:57,160 --> 00:30:59,040
Speaker 4: so that we can actually build in the in the

549
00:30:59,120 --> 00:31:01,920
Speaker 4: areas and make up for those shortfalls. So I think

550
00:31:02,280 --> 00:31:05,880
Speaker 4: housing is certainly from a thematic perspective, something that can

551
00:31:06,480 --> 00:31:08,520
Speaker 4: It's a great example to me because it's something where

552
00:31:08,880 --> 00:31:12,760
Speaker 4: this is a longer run structural theme, but it can

553
00:31:12,800 --> 00:31:15,400
Speaker 4: fall out of favorite time cyclically because it is very

554
00:31:15,440 --> 00:31:18,960
Speaker 4: interstrate sensitive. Right now, housing is not in a great

555
00:31:19,000 --> 00:31:23,239
Speaker 4: place in the US. Affordability is terrible, and it's not

556
00:31:23,280 --> 00:31:26,320
Speaker 4: just an interest rate problem. More of the home price

557
00:31:26,520 --> 00:31:31,400
Speaker 4: is made up from regulatory impacts than anything else.

558
00:31:32,040 --> 00:31:35,040
Speaker 2: How much of this is a lack of supply I know,

559
00:31:34,720 --> 00:31:37,960
Speaker 2: I've Jonathan Miller and folks like that have been writing

560
00:31:38,000 --> 00:31:41,080
Speaker 2: supply is running twenty to thirty percent of what it

561
00:31:41,160 --> 00:31:44,280
Speaker 2: normally is. And how much of it is a little

562
00:31:44,320 --> 00:31:47,320
Speaker 2: bit of nimby. Once people buy a home, they don't

563
00:31:47,320 --> 00:31:50,680
Speaker 2: want to see all the pretty scenery get knocked over

564
00:31:50,720 --> 00:31:53,840
Speaker 2: and new houses put up over there. What's the solution

565
00:31:53,960 --> 00:31:54,200
Speaker 2: to this?

566
00:31:54,400 --> 00:31:59,520
Speaker 5: Well, I think the nimby really is a symptom of, or.

567
00:31:59,520 --> 00:32:05,600
Speaker 4: A side of effect of the regulation or sorry that

568
00:32:06,320 --> 00:32:09,240
Speaker 4: the nimbi not in my backyard leads to is part

569
00:32:09,240 --> 00:32:12,200
Speaker 4: of what leads to the heavy handed regulation right and

570
00:32:12,280 --> 00:32:19,280
Speaker 4: heavy handed regulation by far is a key contributor to

571
00:32:19,800 --> 00:32:22,840
Speaker 4: the cost of overall housing. Then you add the cost

572
00:32:22,880 --> 00:32:26,840
Speaker 4: of labor in a sector which has had a shortage

573
00:32:26,880 --> 00:32:30,120
Speaker 4: of labor since two thousand and eight, and we only

574
00:32:30,160 --> 00:32:34,280
Speaker 4: started to make up for that shortfall during the what

575
00:32:34,320 --> 00:32:36,920
Speaker 4: I call the immigration period where we were bringing in

576
00:32:37,000 --> 00:32:39,440
Speaker 4: millions of immigrants a year in twenty twenty two to

577
00:32:39,520 --> 00:32:42,920
Speaker 4: twenty twenty three and part of twenty twenty four, only

578
00:32:42,960 --> 00:32:45,800
Speaker 4: to see that reversal now put labor pressures on that

579
00:32:45,880 --> 00:32:49,360
Speaker 4: sector again and then tear us on materials that go

580
00:32:49,480 --> 00:32:53,560
Speaker 4: into construction. So it's just it's cost upon cost upon

581
00:32:53,720 --> 00:32:56,520
Speaker 4: cost that home builders are having to deal with that

582
00:32:56,760 --> 00:33:01,920
Speaker 4: help drive the affordability issues for the home buyers as well.

583
00:33:02,200 --> 00:33:06,600
Speaker 2: Huh. Really intriguing. So obviously thematic investing is a big

584
00:33:06,640 --> 00:33:12,120
Speaker 2: part of your job. Is there any other theme bigger

585
00:33:12,160 --> 00:33:14,280
Speaker 2: than artificial intelligence today?

586
00:33:15,040 --> 00:33:18,600
Speaker 4: I'm going to say a probably not, But artificial intelligence

587
00:33:18,600 --> 00:33:23,240
Speaker 4: it's a very broad it's very broad, and so I

588
00:33:23,280 --> 00:33:28,440
Speaker 4: would gear it more towards AI tech and diffusion, which

589
00:33:28,480 --> 00:33:32,240
Speaker 4: has been a key pillar, thematic pillar for Morgan Stanley.

590
00:33:33,200 --> 00:33:36,480
Speaker 4: But here's why. It seems like my answer is just

591
00:33:36,520 --> 00:33:40,520
Speaker 4: so easy and almost like not well thought out, almost

592
00:33:40,520 --> 00:33:44,480
Speaker 4: flippant in a way. AI is a generalized technology, so

593
00:33:44,560 --> 00:33:47,880
Speaker 4: it flows through everything. So whether you're thinking about a

594
00:33:47,960 --> 00:33:53,840
Speaker 4: multipolar world, theme, which importantly includes defense. We had gone

595
00:33:53,880 --> 00:33:57,960
Speaker 4: long global defense back in January, and it was based

596
00:33:58,000 --> 00:34:02,120
Speaker 4: on the fact that you've got your pallenteers of the

597
00:34:02,160 --> 00:34:05,840
Speaker 4: world and open aiyes of the world of you know,

598
00:34:06,000 --> 00:34:13,200
Speaker 4: working with the US government to modernize defense for tech

599
00:34:13,280 --> 00:34:16,799
Speaker 4: and AI. And so if you think about you know,

600
00:34:17,000 --> 00:34:22,000
Speaker 4: four themes, say longevity, AI, tech and diffusion, multipolar world,

601
00:34:22,200 --> 00:34:26,799
Speaker 4: and the energy of everything. AI threads through all of that.

602
00:34:27,280 --> 00:34:28,240
Speaker 4: It threads through.

603
00:34:28,080 --> 00:34:28,560
Speaker 5: All of it.

604
00:34:29,400 --> 00:34:34,080
Speaker 4: So when I think about, say conviction weighting those themes,

605
00:34:34,920 --> 00:34:36,919
Speaker 4: your highest conviction weight is going to be on the AI,

606
00:34:37,000 --> 00:34:38,280
Speaker 4: tech and diffusion.

607
00:34:38,400 --> 00:34:39,840
Speaker 5: Because it does thread through everything.

608
00:34:39,880 --> 00:34:43,839
Speaker 2: So what's more important the Magnificent seven or the magnificent

609
00:34:44,000 --> 00:34:47,240
Speaker 2: four ninety three that are going to benefit from AI.

610
00:34:47,640 --> 00:34:50,520
Speaker 4: Well, I think there it's very difficult to not have

611
00:34:50,920 --> 00:34:54,600
Speaker 4: those big, big tech names, let's say, in a multi

612
00:34:54,600 --> 00:34:57,719
Speaker 4: thematic portfolio, or if you're trying to take advantage of

613
00:34:58,120 --> 00:35:02,440
Speaker 4: an AI theme, because they are big players in the space.

614
00:35:02,840 --> 00:35:05,240
Speaker 4: I mean, as soon as someone in this country moves

615
00:35:05,239 --> 00:35:08,759
Speaker 4: into contracts with the US government, you've got an incredible

616
00:35:08,760 --> 00:35:11,600
Speaker 4: amount of funding. Look at someone like an Elon Musk,

617
00:35:11,640 --> 00:35:13,680
Speaker 4: who is a creature of the government. Sure I mean,

618
00:35:13,680 --> 00:35:18,440
Speaker 4: how much of his wealth comes from government contracts exactly.

619
00:35:18,760 --> 00:35:23,319
Speaker 4: And so when these other players are wrapped up in

620
00:35:23,360 --> 00:35:27,839
Speaker 4: government contracts and the government has put its priority in

621
00:35:27,960 --> 00:35:33,040
Speaker 4: winning this seeming two horse race on AI against China,

622
00:35:33,680 --> 00:35:35,879
Speaker 4: you would probably be ill advised to bet against that.

623
00:35:36,280 --> 00:35:39,279
Speaker 4: It doesn't mean that AI tech infusion is just the

624
00:35:39,360 --> 00:35:42,719
Speaker 4: mag seven. So of course, in my role, I can't

625
00:35:42,760 --> 00:35:45,200
Speaker 4: talk about specific companies, and you don't want to ever

626
00:35:45,200 --> 00:35:47,240
Speaker 4: take specific company advice from an economist.

627
00:35:47,320 --> 00:35:48,360
Speaker 5: I'll just say, but.

628
00:35:49,840 --> 00:35:53,680
Speaker 4: You've got very interesting players all the way down to

629
00:35:53,760 --> 00:35:55,759
Speaker 4: mid cap and small cap all the way down to

630
00:35:55,800 --> 00:36:00,000
Speaker 4: rustle three thousand that are important in an AI tech

631
00:36:00,120 --> 00:36:01,279
Speaker 4: and diffusion space.

632
00:36:01,160 --> 00:36:05,839
Speaker 2: Meaning they become more efficient, productive, profitable by deployment, sort

633
00:36:05,880 --> 00:36:08,960
Speaker 2: of like what we saw post Internet end.

634
00:36:08,800 --> 00:36:11,960
Speaker 4: Boss they and they become part of the fabric of

635
00:36:11,960 --> 00:36:16,320
Speaker 4: that generalized technology that all companies end up using as

636
00:36:16,400 --> 00:36:18,600
Speaker 4: AI diffuses across the economy.

637
00:36:19,120 --> 00:36:21,600
Speaker 2: It makes plenty of sense to me, what other big

638
00:36:21,640 --> 00:36:24,080
Speaker 2: themes are you paying close attention to.

639
00:36:25,320 --> 00:36:26,000
Speaker 5: Some big themes?

640
00:36:26,000 --> 00:36:27,840
Speaker 4: And again it's hard for me to get away of

641
00:36:27,920 --> 00:36:31,800
Speaker 4: some sort of flavor of AI. So as an economist,

642
00:36:31,840 --> 00:36:35,360
Speaker 4: I'm going to go back to demographics every time. What

643
00:36:35,440 --> 00:36:40,280
Speaker 4: are the incentives for adopting AI? Right incentives or adopting

644
00:36:40,320 --> 00:36:45,360
Speaker 4: are You've got to replace labor shortfalls. That's a huge incentive.

645
00:36:45,920 --> 00:36:48,879
Speaker 4: And so if you are a country with falling birth

646
00:36:48,960 --> 00:36:53,160
Speaker 4: rates and you can make up for that in several

647
00:36:53,200 --> 00:36:57,799
Speaker 4: different ways. One is your existing population. You can put

648
00:36:57,800 --> 00:37:01,239
Speaker 4: in policies to boost labor force participation so have a

649
00:37:01,280 --> 00:37:05,000
Speaker 4: more full participation from your current population. You can be

650
00:37:05,120 --> 00:37:08,960
Speaker 4: sure that you are not just have an open immigration system,

651
00:37:09,920 --> 00:37:13,440
Speaker 4: and I don't mean just opening your borders to indiscriminate flows,

652
00:37:13,480 --> 00:37:17,240
Speaker 4: but an open immigration system, a traditional open immigration system

653
00:37:17,239 --> 00:37:22,400
Speaker 4: where you have a sound process for integrating immigrants into

654
00:37:22,440 --> 00:37:24,920
Speaker 4: the labor market, something in the US has been very

655
00:37:24,960 --> 00:37:29,160
Speaker 4: good at, something Europe is not very good at. Or

656
00:37:29,920 --> 00:37:35,560
Speaker 4: you can replace that labor with AI and robotics. There's

657
00:37:35,600 --> 00:37:40,280
Speaker 4: your incentive. There's your incentive for countries like China, like Japan.

658
00:37:41,040 --> 00:37:44,040
Speaker 4: Maybe not like India right now, but India's demographics are

659
00:37:44,239 --> 00:37:47,240
Speaker 4: not good. Really when you look further out a decade

660
00:37:47,280 --> 00:37:49,600
Speaker 4: from now, fifteen twenty years from now.

661
00:37:49,520 --> 00:37:53,160
Speaker 2: You know, it's funny you keep talking about demographics. Isn't

662
00:37:53,200 --> 00:37:57,680
Speaker 2: the trend throughout history that as a country becomes first

663
00:37:58,120 --> 00:38:02,000
Speaker 2: less poor and then wealthier, the birth rates just drop.

664
00:38:02,040 --> 00:38:06,399
Speaker 4: People absolutely more affluent countries. It is a natural way

665
00:38:06,440 --> 00:38:10,000
Speaker 4: of things. Countries that are able to, let me just say,

666
00:38:10,120 --> 00:38:14,880
Speaker 4: roll with that right and boost productivity by making fuller

667
00:38:15,000 --> 00:38:19,320
Speaker 4: use of your existing labor pool are those that still

668
00:38:19,360 --> 00:38:24,399
Speaker 4: continue along that path of affluency. The US has not

669
00:38:24,520 --> 00:38:28,279
Speaker 4: just higher birth rates than our major trading partners, we've

670
00:38:28,320 --> 00:38:31,040
Speaker 4: got higher rates of productivity. It's part of what US

671
00:38:31,120 --> 00:38:34,560
Speaker 4: exceptionalism is built upon, is that not only have we

672
00:38:34,640 --> 00:38:38,439
Speaker 4: kept birth rates higher, which population growth and specifically growth

673
00:38:38,480 --> 00:38:42,600
Speaker 4: in your labor force goes into the potential growth in

674
00:38:42,640 --> 00:38:47,680
Speaker 4: your economy those calculations, but we're also making those more productive,

675
00:38:48,160 --> 00:38:51,480
Speaker 4: and it's part of our secret sauce of success. You know,

676
00:38:51,560 --> 00:38:54,400
Speaker 4: when I talk about US exceptionalism, I'm not even referring

677
00:38:54,400 --> 00:38:59,040
Speaker 4: to markets financial markets. I'm talking about the US having

678
00:39:00,080 --> 00:39:03,799
Speaker 4: more flexible labor market where we have higher rates of productivity.

679
00:39:04,480 --> 00:39:07,120
Speaker 4: Very important that we continue to hang on to independent

680
00:39:07,160 --> 00:39:11,440
Speaker 4: monetary policy, that we have stable currency, but that comparative

681
00:39:11,480 --> 00:39:15,759
Speaker 4: advantage lies in your labor force and how far you

682
00:39:15,800 --> 00:39:17,839
Speaker 4: can push it, and the US is just really good

683
00:39:17,840 --> 00:39:18,080
Speaker 4: at that.

684
00:39:18,480 --> 00:39:21,560
Speaker 2: So let me ask you a thematic question, only it's

685
00:39:21,600 --> 00:39:25,640
Speaker 2: going to be a negative. What's the one economic myth

686
00:39:25,719 --> 00:39:29,480
Speaker 2: you hear more than others? What question bubbles up from clients,

687
00:39:29,520 --> 00:39:34,200
Speaker 2: from brokers and advisors, from people within that you wish

688
00:39:34,239 --> 00:39:35,160
Speaker 2: would just go away?

689
00:39:36,080 --> 00:39:40,040
Speaker 4: Maybe this gets too nuanced, because economists love nothing more

690
00:39:40,080 --> 00:39:45,040
Speaker 4: than getting nuanced. But it's like you got the chicken

691
00:39:45,080 --> 00:39:49,160
Speaker 4: and the egg backwards, right, right. So it's that the

692
00:39:49,160 --> 00:39:52,200
Speaker 4: markets are pricing in that the FED is going to

693
00:39:52,239 --> 00:39:54,879
Speaker 4: do something at its next meeting, and therefore the Fed

694
00:39:55,000 --> 00:39:56,160
Speaker 4: has to do that.

695
00:39:56,360 --> 00:39:59,320
Speaker 2: The markets have been so wrong about that for so long.

696
00:40:00,080 --> 00:40:01,879
Speaker 4: I think the market's over time it had a very

697
00:40:01,920 --> 00:40:04,760
Speaker 4: difficult So there's another one. Don't fight the Fed? Right,

698
00:40:04,920 --> 00:40:06,719
Speaker 4: how many times do we say don't fight the Fed?

699
00:40:06,719 --> 00:40:10,120
Speaker 4: And markets fight the Fed and they lose. But that

700
00:40:10,160 --> 00:40:14,239
Speaker 4: the markets lead the Fed. Now, the FED makes low

701
00:40:14,320 --> 00:40:16,920
Speaker 4: frequency decisions in a high frequency world. The market is

702
00:40:17,000 --> 00:40:17,880
Speaker 4: very high frequency.

703
00:40:18,200 --> 00:40:19,840
Speaker 2: That's a great way to describe that.

704
00:40:20,000 --> 00:40:22,319
Speaker 4: Yeah, And so the fact of the matter is the

705
00:40:22,360 --> 00:40:25,600
Speaker 4: market can respond on a dime when the data comes

706
00:40:25,600 --> 00:40:29,479
Speaker 4: out when financial conditions change. The Fed can't. The Fed

707
00:40:29,560 --> 00:40:31,879
Speaker 4: has to look at it, it has to deliberate it.

708
00:40:31,880 --> 00:40:34,200
Speaker 4: It has to gain a consensus and then it moves.

709
00:40:34,920 --> 00:40:37,280
Speaker 4: Much of the time, the market doesn't have it wrong.

710
00:40:37,800 --> 00:40:40,399
Speaker 4: The market read the labor report of the most recent

711
00:40:40,440 --> 00:40:42,640
Speaker 4: labor report and said that's not good. And guess what,

712
00:40:42,880 --> 00:40:45,520
Speaker 4: the FED also thinks that's not good. Great, you're on

713
00:40:45,560 --> 00:40:47,439
Speaker 4: the same page. But the market was able to price

714
00:40:47,440 --> 00:40:51,000
Speaker 4: it in well ahead of the FED actually delivering in September.

715
00:40:51,320 --> 00:40:53,400
Speaker 4: So I do believe that the FED is going to

716
00:40:53,400 --> 00:40:56,640
Speaker 4: cut twenty five bases points in September. Now this is

717
00:40:56,680 --> 00:41:00,319
Speaker 4: with my hat on as the chief economic strategist Morgan

718
00:41:00,400 --> 00:41:03,040
Speaker 4: Stanley Wealth Management. There are others in the firm that

719
00:41:03,160 --> 00:41:06,000
Speaker 4: also have FEWS views on the FED, but you've asked me,

720
00:41:06,520 --> 00:41:08,920
Speaker 4: and the beauty of this podcast that I get to

721
00:41:08,920 --> 00:41:11,560
Speaker 4: give my views and you're only talking to me here.

722
00:41:12,040 --> 00:41:15,680
Speaker 4: So I do think though that our focus on September

723
00:41:16,320 --> 00:41:20,279
Speaker 4: it can probably be best spent elsewhere in that the

724
00:41:20,280 --> 00:41:23,440
Speaker 4: first cut is going to be the easiest, because, as

725
00:41:23,520 --> 00:41:27,160
Speaker 4: Chairpell said, modestly restrictive. Do you need to be monstly

726
00:41:27,200 --> 00:41:31,320
Speaker 4: restrictive when job growth has slowed? This sharply If you

727
00:41:31,320 --> 00:41:34,000
Speaker 4: don't need to be mondstly restrictive, just make an adjustment

728
00:41:34,160 --> 00:41:36,480
Speaker 4: they're not making any decisions about what happens after that.

729
00:41:36,840 --> 00:41:39,399
Speaker 4: So the fact that you know, do they or don't

730
00:41:39,440 --> 00:41:42,160
Speaker 4: they cut in September and by the way, fifty basis points.

731
00:41:42,239 --> 00:41:44,840
Speaker 4: That's a hard no from me because I knew I

732
00:41:44,880 --> 00:41:47,040
Speaker 4: could tell, I could tell the question was on your

733
00:41:47,040 --> 00:41:47,920
Speaker 4: lips it was about.

734
00:41:47,719 --> 00:41:50,719
Speaker 2: One hundred points. Someone now that's definitely.

735
00:41:50,239 --> 00:41:50,960
Speaker 5: Even harder no.

736
00:41:52,520 --> 00:41:55,040
Speaker 4: But I do believe that once you have made that cut,

737
00:41:55,120 --> 00:41:57,640
Speaker 4: it's a little harder to justify if the data don't

738
00:41:57,719 --> 00:42:00,680
Speaker 4: keep coming in in the same fashion to say why

739
00:42:00,760 --> 00:42:04,120
Speaker 4: that one adjustment was perfect but not another. So I

740
00:42:04,520 --> 00:42:07,000
Speaker 4: think where I would rather debate is how far do

741
00:42:07,080 --> 00:42:09,319
Speaker 4: they need to go? And this is where I do

742
00:42:09,440 --> 00:42:14,480
Speaker 4: disagree with some powers that be that the FED is

743
00:42:14,520 --> 00:42:17,560
Speaker 4: going to need to cut a lot. I think we're

744
00:42:17,600 --> 00:42:19,640
Speaker 4: going to have a good economy next year. I think

745
00:42:19,680 --> 00:42:22,080
Speaker 4: productivity is going to be picking up even more. I

746
00:42:22,080 --> 00:42:24,080
Speaker 4: think there are parts of the One Big Beautiful Bill

747
00:42:24,400 --> 00:42:27,440
Speaker 4: with the investment incentives that are in it, which are

748
00:42:27,440 --> 00:42:29,480
Speaker 4: going to help put a floor into the economy. And

749
00:42:30,880 --> 00:42:32,640
Speaker 4: we're not going to have an environment where the Fed's

750
00:42:32,680 --> 00:42:34,360
Speaker 4: going to need to cut one hundred and fifty tw

751
00:42:34,400 --> 00:42:35,120
Speaker 4: hundred paces.

752
00:42:35,160 --> 00:42:38,359
Speaker 2: To be fair. Stocks are at all time highs, real

753
00:42:38,440 --> 00:42:41,680
Speaker 2: estates at all time highs, revenue and profits are at

754
00:42:41,800 --> 00:42:44,560
Speaker 2: or near all time highs. It doesn't seem to be

755
00:42:44,640 --> 00:42:48,720
Speaker 2: an economy begging for rate cuts, even as we're starting

756
00:42:48,760 --> 00:42:52,200
Speaker 2: to see a slow down in some consumer spending and

757
00:42:52,280 --> 00:42:55,400
Speaker 2: some hiring. But how much of that.

758
00:42:55,160 --> 00:42:56,640
Speaker 5: That justifies lower rates?

759
00:42:57,120 --> 00:42:59,640
Speaker 4: Doesn't tell you need to cut drastically, right, So do

760
00:42:59,680 --> 00:43:01,200
Speaker 4: you want to good economy or do you want the

761
00:43:01,239 --> 00:43:02,280
Speaker 4: Fed to cut drastically?

762
00:43:02,760 --> 00:43:05,840
Speaker 2: Well, we know what the president wants, Yeah, what the

763
00:43:05,880 --> 00:43:08,880
Speaker 2: economy needs and what the market wants. They may be

764
00:43:09,080 --> 00:43:10,640
Speaker 2: something slightly different.

765
00:43:10,800 --> 00:43:14,279
Speaker 4: Yeah, And if the Fed is watching it and objectively

766
00:43:14,320 --> 00:43:16,520
Speaker 4: doing its job, then we will end up in the

767
00:43:16,560 --> 00:43:17,080
Speaker 4: right place.

768
00:43:17,360 --> 00:43:20,880
Speaker 2: Coming up, we continue our conversation with Allen Zenner, chief

769
00:43:20,920 --> 00:43:25,440
Speaker 2: economic strategist for Morgan Stanley, discussing the state of today's

770
00:43:25,480 --> 00:43:30,120
Speaker 2: economy in light of tariffs and trade policy. I'm Barry Ridults.

771
00:43:30,160 --> 00:43:42,040
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

772
00:43:42,040 --> 00:43:45,600
Speaker 2: Barry Redults. You're listening to Masters in Business on Bloomberg Radio.

773
00:43:46,080 --> 00:43:49,440
Speaker 2: My extra special guest is Alan Zenner. She is chief

774
00:43:49,520 --> 00:43:53,600
Speaker 2: economic strategist and global head of thematic and macroinvesting for

775
00:43:53,760 --> 00:43:58,080
Speaker 2: Morgan Stanley. The firm runs over seven trillion dollars. So

776
00:43:58,200 --> 00:44:02,239
Speaker 2: you've written about tariff and trade policy. My question for

777
00:44:02,280 --> 00:44:07,279
Speaker 2: you is how disruptive or destabilizing is this to either

778
00:44:07,320 --> 00:44:09,040
Speaker 2: the US or global economy.

779
00:44:09,360 --> 00:44:15,480
Speaker 4: So we've certainly seen disruption in confidence. Markets don't like opaqueness,

780
00:44:15,560 --> 00:44:18,799
Speaker 4: they like certainty, and we could see that early on

781
00:44:18,920 --> 00:44:24,160
Speaker 4: in the volatility of Wow. January hit and it was

782
00:44:24,400 --> 00:44:27,400
Speaker 4: tariff's tarifs, tariffs, and the market clearly was caught off sides.

783
00:44:27,719 --> 00:44:31,480
Speaker 4: Policymakers were caught off sides, economists were caught off sides.

784
00:44:31,880 --> 00:44:34,520
Speaker 4: And so then you kick off the flory of activity.

785
00:44:34,520 --> 00:44:38,520
Speaker 4: What does this mean when the world order is being reset?

786
00:44:38,800 --> 00:44:40,879
Speaker 4: And it can mean a whole host of things. It's

787
00:44:40,880 --> 00:44:45,160
Speaker 4: one reason why all economists, all forecasters have to take

788
00:44:46,120 --> 00:44:50,319
Speaker 4: a very big slice of humble pie and take a

789
00:44:50,320 --> 00:44:53,759
Speaker 4: big bite out of that because the uncertainty bands of

790
00:44:53,800 --> 00:44:55,560
Speaker 4: any kind of forecast you put out are going to

791
00:44:55,600 --> 00:44:59,840
Speaker 4: be highly uncertain. There's no way to know the impacts

792
00:44:59,840 --> 00:45:03,399
Speaker 4: of tariffs truly until well after the fact. And that's

793
00:45:03,440 --> 00:45:07,440
Speaker 4: because tariffs fall here, there, and everywhere. You're going to

794
00:45:07,520 --> 00:45:10,600
Speaker 4: have some degree of manufacturers and the countries that we

795
00:45:10,680 --> 00:45:14,280
Speaker 4: import from eating the cost. You're going to have importers

796
00:45:14,320 --> 00:45:17,080
Speaker 4: along the way eating the cost, wholesalers eating the cost,

797
00:45:17,640 --> 00:45:21,160
Speaker 4: businesses that sell final goods eating the cost, and consumers

798
00:45:21,480 --> 00:45:24,759
Speaker 4: having to eat some of that as well. The forecasting

799
00:45:24,800 --> 00:45:29,440
Speaker 4: comes in where okay, how much of each? What percentage

800
00:45:29,480 --> 00:45:32,200
Speaker 4: of each? I think one thing that I've observed is

801
00:45:32,840 --> 00:45:36,280
Speaker 4: businesses have been sitting on a good deal more cushion

802
00:45:37,000 --> 00:45:39,680
Speaker 4: in terms of cash and free cash flow than I

803
00:45:39,680 --> 00:45:42,320
Speaker 4: think anybody had suspected that they would.

804
00:45:42,120 --> 00:45:44,240
Speaker 2: Be, Meaning they have the ability to eat.

805
00:45:44,040 --> 00:45:45,759
Speaker 5: Some of the ability to eat some of it.

806
00:45:45,920 --> 00:45:49,640
Speaker 4: I do think that even after Chinese manufacturers surprised us

807
00:45:49,680 --> 00:45:52,560
Speaker 4: in twenty nineteen to the degree that they were willing

808
00:45:52,600 --> 00:45:56,080
Speaker 4: to eat the costs, I think they've been able to

809
00:45:56,160 --> 00:46:03,840
Speaker 4: continue to absorb it. I think ultimately for economists, because

810
00:46:04,080 --> 00:46:06,919
Speaker 4: economists by and large are wearing a lot of egg

811
00:46:06,960 --> 00:46:09,920
Speaker 4: on our face for getting it wrong, for sounding the alarm.

812
00:46:10,360 --> 00:46:12,480
Speaker 4: The companies were sounding the alarm too. We're taking our

813
00:46:12,520 --> 00:46:15,120
Speaker 4: cues from what the surveys are saying, what we're hearing

814
00:46:15,120 --> 00:46:17,799
Speaker 4: directly from companies that I'm going to pass on these

815
00:46:17,840 --> 00:46:20,400
Speaker 4: prices to consumers. I am not going to eat this,

816
00:46:20,760 --> 00:46:22,440
Speaker 4: But then how much of that are companies talking their

817
00:46:22,440 --> 00:46:23,080
Speaker 4: own book as well?

818
00:46:23,480 --> 00:46:26,680
Speaker 2: To be fair, it's the middle of August. Liberation Day

819
00:46:26,800 --> 00:46:30,279
Speaker 2: was early April, we had a ninety day pause. We

820
00:46:30,440 --> 00:46:34,239
Speaker 2: really haven't felt the full impact on tariffs, and we

821
00:46:34,520 --> 00:46:38,680
Speaker 2: probably won't until the fourth quarter or first quarter next year.

822
00:46:38,800 --> 00:46:42,560
Speaker 2: So is it a little early to say, hey, no harm,

823
00:46:42,600 --> 00:46:43,239
Speaker 2: no foul. No.

824
00:46:43,360 --> 00:46:45,680
Speaker 4: I think it's definitely to early say no harm, no foul.

825
00:46:46,719 --> 00:46:49,439
Speaker 4: And I don't think anyone, even the administration, is saying

826
00:46:49,440 --> 00:46:52,279
Speaker 4: there won't be some bit of bearing the brunt of

827
00:46:52,320 --> 00:46:55,480
Speaker 4: that among consumers, among businesses.

828
00:46:55,200 --> 00:46:56,000
Speaker 5: In the US.

829
00:46:56,239 --> 00:46:59,560
Speaker 4: I think it's just that you've got one faction saying

830
00:46:59,840 --> 00:47:01,319
Speaker 4: that it's going to be a lot less of an

831
00:47:01,320 --> 00:47:05,479
Speaker 4: impact than some other factions. And no one really knows,

832
00:47:05,680 --> 00:47:06,600
Speaker 4: so let's all.

833
00:47:06,400 --> 00:47:07,279
Speaker 3: Be humble about it.

834
00:47:07,360 --> 00:47:10,359
Speaker 2: No one knows. But there seems to be a bit

835
00:47:10,480 --> 00:47:14,919
Speaker 2: of a consensus that tariffs are a consumption tax. It's

836
00:47:14,960 --> 00:47:19,600
Speaker 2: like a vat tax on US households and businesses. Is

837
00:47:19,640 --> 00:47:22,440
Speaker 2: that overstating the threat or is that is that accurty?

838
00:47:22,480 --> 00:47:25,279
Speaker 4: No, that's exactly how it works, to the extent that

839
00:47:25,360 --> 00:47:28,839
Speaker 4: they that companies eat it on the margin or pass

840
00:47:28,880 --> 00:47:32,200
Speaker 4: it onto households, and households eat it and paying higher prices.

841
00:47:32,400 --> 00:47:34,920
Speaker 4: That is exactly how it works. I mean, that is

842
00:47:34,960 --> 00:47:37,960
Speaker 4: the economic theory of it. That is sound. It's the

843
00:47:38,160 --> 00:47:41,239
Speaker 4: degree to which the costs are absorbed and by what

844
00:47:41,320 --> 00:47:46,279
Speaker 4: players along the import channel. That is the That is

845
00:47:46,320 --> 00:47:49,560
Speaker 4: the unknown factor. And I can tell you that you know,

846
00:47:49,840 --> 00:47:53,040
Speaker 4: what the President is doing or has been doing, is

847
00:47:53,840 --> 00:47:57,160
Speaker 4: changing global trade in a way that typically would play

848
00:47:57,200 --> 00:48:01,160
Speaker 4: out over a decade or so in a very short

849
00:48:01,200 --> 00:48:04,600
Speaker 4: period of time, and so that's led to a tremendous

850
00:48:04,640 --> 00:48:08,880
Speaker 4: amount of uncertainty. And like you said, this may be

851
00:48:08,920 --> 00:48:12,200
Speaker 4: something where the full tariff impacts aren't felt until the

852
00:48:12,239 --> 00:48:16,080
Speaker 4: fourth quarter or first quarter of next year. And if

853
00:48:16,080 --> 00:48:18,560
Speaker 4: that is the case, we'll deal with it when it

854
00:48:18,600 --> 00:48:21,480
Speaker 4: comes and Chair Pal and the Fed will be there

855
00:48:21,520 --> 00:48:24,440
Speaker 4: to act very nimbly around that. I am confident of.

856
00:48:24,960 --> 00:48:30,239
Speaker 4: But has there been unfair trade practices? Absolutely? Do we

857
00:48:30,320 --> 00:48:34,880
Speaker 4: need to renegotiate trade contracts? Absolutely. I was at the

858
00:48:34,880 --> 00:48:39,960
Speaker 4: State of Texas during NAFTA. NAFTA was not renegotiated until

859
00:48:39,960 --> 00:48:44,400
Speaker 4: it became the USMCA under Trump's first term. Why the

860
00:48:44,400 --> 00:48:47,760
Speaker 4: global economy is so dynamic. How could a trade agreement

861
00:48:48,040 --> 00:48:52,880
Speaker 4: put together in the nineties still be relevant in twenty seventeen,

862
00:48:53,360 --> 00:48:54,600
Speaker 4: twenty eighteen, twenty nineteen.

863
00:48:54,640 --> 00:48:55,520
Speaker 5: It makes no sense.

864
00:48:56,400 --> 00:49:02,160
Speaker 4: So absolutely we need to be revisiting, like alongside a

865
00:49:02,239 --> 00:49:03,319
Speaker 4: dynamic global.

866
00:49:03,000 --> 00:49:05,080
Speaker 2: Economy on a more regular basis, on.

867
00:49:05,000 --> 00:49:06,040
Speaker 5: A more regular basis.

868
00:49:06,040 --> 00:49:08,200
Speaker 4: We're just doing this over a short period of time,

869
00:49:08,239 --> 00:49:12,160
Speaker 4: and that's created a good deal of disruption and uncertainty

870
00:49:12,200 --> 00:49:17,000
Speaker 4: and volatility and guesswork, if you will, among the economics community.

871
00:49:17,239 --> 00:49:19,440
Speaker 2: So let's talk about that guess work. There's going to

872
00:49:19,520 --> 00:49:24,200
Speaker 2: be some of these tariffs showing up as on the

873
00:49:24,239 --> 00:49:28,080
Speaker 2: household level. Is that a head wind for consumption? Same

874
00:49:28,160 --> 00:49:31,040
Speaker 2: question about businesses If they have to eat some of

875
00:49:31,080 --> 00:49:35,520
Speaker 2: the tariffs, that's going to affect profitability. There's no free lunch.

876
00:49:35,320 --> 00:49:35,720
Speaker 1: Is there.

877
00:49:35,880 --> 00:49:38,600
Speaker 4: No, There's never a free lunch. So we are seeing

878
00:49:38,640 --> 00:49:43,160
Speaker 4: consumer spending slow now. It's slowing for several reasons. One,

879
00:49:43,840 --> 00:49:49,440
Speaker 4: we've had a reversal of immigration in the US. That is,

880
00:49:49,920 --> 00:49:53,759
Speaker 4: no small number of people bodies consume and so if

881
00:49:53,800 --> 00:49:55,920
Speaker 4: you've got fewer bodies, they're consuming less.

882
00:49:56,400 --> 00:49:59,239
Speaker 2: And I want to say we have had a negative

883
00:50:00,280 --> 00:50:03,960
Speaker 2: net new population this year for the first time I

884
00:50:04,000 --> 00:50:06,480
Speaker 2: think in US history. Is that is that accurate?

885
00:50:06,600 --> 00:50:08,840
Speaker 4: Yeah, it's I mean we've slowed to a trickle in

886
00:50:08,920 --> 00:50:15,360
Speaker 4: population growth at times, but it is highly unusual, highly unusual.

887
00:50:15,920 --> 00:50:20,680
Speaker 4: You've got less bodies in the US, so you're consuming less.

888
00:50:21,280 --> 00:50:26,480
Speaker 4: Now those bodies contributed to low income consumption. You've also

889
00:50:26,600 --> 00:50:30,120
Speaker 4: got low income consumers in general in the US that

890
00:50:30,239 --> 00:50:33,600
Speaker 4: when prices for goods go up from tariffs or for

891
00:50:33,640 --> 00:50:38,920
Speaker 4: whatever reason, they're going to consume less. So consumer spending

892
00:50:38,960 --> 00:50:43,080
Speaker 4: has been slowing. Now why hasn't it slowed even more

893
00:50:43,160 --> 00:50:46,600
Speaker 4: so than it has when population growth has been negative

894
00:50:46,600 --> 00:50:50,400
Speaker 4: from a reversal in immigration, Because the top end consumers

895
00:50:50,400 --> 00:50:54,920
Speaker 4: are still spending. So the top income quintile in the

896
00:50:55,080 --> 00:50:59,000
Speaker 4: US represents forty five percent of all consumer spending. If

897
00:50:59,040 --> 00:51:01,160
Speaker 4: you take just the top two income quintels, that's more

898
00:51:01,200 --> 00:51:04,279
Speaker 4: than sixty percent of all consumer spending. And so we

899
00:51:04,320 --> 00:51:07,000
Speaker 4: want what we want. And whether you say maybe that's

900
00:51:07,000 --> 00:51:09,359
Speaker 4: still an artifact of COVID, we were all taught we're

901
00:51:09,360 --> 00:51:12,960
Speaker 4: going to die tomorrow, So spending it's God or it's

902
00:51:13,120 --> 00:51:18,400
Speaker 4: just this tremendous, tremendous increase in real estate wealth and

903
00:51:18,480 --> 00:51:22,360
Speaker 4: tremendous increase in financial wealth. And even though our marginal

904
00:51:22,360 --> 00:51:25,200
Speaker 4: propensity to consume out of that wealth is smaller for

905
00:51:25,280 --> 00:51:28,960
Speaker 4: upper income households, the growth in wealth is just enormous,

906
00:51:29,520 --> 00:51:32,600
Speaker 4: and so when they're spending, it tends to mask weakness

907
00:51:33,040 --> 00:51:35,440
Speaker 4: at the low end. But there are some risks along

908
00:51:35,520 --> 00:51:37,719
Speaker 4: the horizon. Student borrows have to.

909
00:51:37,640 --> 00:51:38,800
Speaker 5: Start paying that back.

910
00:51:39,160 --> 00:51:42,160
Speaker 4: I don't think that we're out of the woods and

911
00:51:42,200 --> 00:51:45,319
Speaker 4: that because the economy is growing at half the pace

912
00:51:45,360 --> 00:51:47,400
Speaker 4: it was last year, we're just fine. I think we

913
00:51:47,440 --> 00:51:49,719
Speaker 4: can grow even more slowly before it gets better.

914
00:51:49,960 --> 00:51:53,520
Speaker 2: So let's talk about two issues that are policy concerns

915
00:51:53,560 --> 00:52:00,520
Speaker 2: that you've raised. One is economic data integrity. Durding this

916
00:52:00,719 --> 00:52:03,640
Speaker 2: a few days after Trump fired the head of the BLS.

917
00:52:04,360 --> 00:52:07,200
Speaker 2: What sort of concerns does this raise in terms of

918
00:52:08,360 --> 00:52:10,239
Speaker 2: protection of data integrity?

919
00:52:10,560 --> 00:52:14,640
Speaker 4: So data integrity cuts both ways. So prior to that

920
00:52:14,880 --> 00:52:19,640
Speaker 4: very high profile firing of the BLS commissioner, the concern

921
00:52:19,880 --> 00:52:23,279
Speaker 4: among the economics community for quite some time has been

922
00:52:23,360 --> 00:52:28,399
Speaker 4: that data integrity has been slipping. And the way we

923
00:52:28,440 --> 00:52:31,560
Speaker 4: look measure that is we look at survey response rates,

924
00:52:32,080 --> 00:52:36,520
Speaker 4: and especially because the Labor Market report is the end

925
00:52:36,600 --> 00:52:39,080
Speaker 4: all be all number one data.

926
00:52:39,200 --> 00:52:41,080
Speaker 5: Point in the US that we follow.

927
00:52:41,520 --> 00:52:46,319
Speaker 4: The response rates had been slipping and now why is that, Well,

928
00:52:46,400 --> 00:52:52,960
Speaker 4: they're myriad reasons. One is that we have frequent government shutdowns,

929
00:52:53,320 --> 00:52:55,640
Speaker 4: and so when the lights aren't on and no one's

930
00:52:55,680 --> 00:52:58,080
Speaker 4: there to police the survey and call you the business

931
00:52:58,080 --> 00:53:00,680
Speaker 4: and say hey, it's really important that you respond, and

932
00:53:00,760 --> 00:53:03,640
Speaker 4: you don't get that call as of business, it starts

933
00:53:03,719 --> 00:53:06,480
Speaker 4: to instill in you the sense of maybe this survey

934
00:53:06,680 --> 00:53:10,440
Speaker 4: isn't so important, maybe I don't need to answer that.

935
00:53:10,719 --> 00:53:13,040
Speaker 4: And so what we've seen is after those episodes, you

936
00:53:13,120 --> 00:53:15,840
Speaker 4: tend to have a slippage and response rates that you

937
00:53:15,920 --> 00:53:19,959
Speaker 4: never quite get back. Another issue is we talked about

938
00:53:20,040 --> 00:53:22,600
Speaker 4: the youth boom. I don't see a lot of youthful

939
00:53:22,640 --> 00:53:25,600
Speaker 4: people jumping up and down to work for the government.

940
00:53:26,560 --> 00:53:31,720
Speaker 4: Maybe that's because the systems are antiquated. I wonder, because

941
00:53:31,760 --> 00:53:34,680
Speaker 4: you've got older generations at the government that are having

942
00:53:34,719 --> 00:53:39,240
Speaker 4: to teach an antiquated programming language to younger generations coming

943
00:53:39,280 --> 00:53:44,279
Speaker 4: in programming languages that don't exist anywhere else, And so

944
00:53:44,880 --> 00:53:49,520
Speaker 4: how does that instill excitement among young people to come

945
00:53:49,560 --> 00:53:53,200
Speaker 4: in and work for the government. We have also had

946
00:53:53,280 --> 00:53:57,799
Speaker 4: a systematic underfunding of data agencies for quite some time

947
00:53:57,880 --> 00:54:02,840
Speaker 4: as well. How can you you overhaul your systems without

948
00:54:02,920 --> 00:54:08,359
Speaker 4: the proper funding, and so it's something that the NAY

949
00:54:08,360 --> 00:54:10,920
Speaker 4: of the National Associate for Business Economics has really followed

950
00:54:10,920 --> 00:54:14,680
Speaker 4: this closely. We have a Statistics Committee that meets with

951
00:54:14,719 --> 00:54:17,640
Speaker 4: all the heads of the statistical agencies, and the statistical

952
00:54:17,719 --> 00:54:23,279
Speaker 4: agencies have a very strong outreach program to economists in academia,

953
00:54:23,719 --> 00:54:27,799
Speaker 4: in government, and in the private sector to say, here

954
00:54:27,800 --> 00:54:29,240
Speaker 4: are methodologies, how.

955
00:54:29,080 --> 00:54:30,120
Speaker 5: Can we do it better?

956
00:54:30,440 --> 00:54:33,880
Speaker 4: And so we're constantly searching for ways to improve and honestly,

957
00:54:33,920 --> 00:54:36,360
Speaker 4: to their credit, half the time, the private sector economists

958
00:54:36,400 --> 00:54:39,719
Speaker 4: are like crickets, how can we do it better? Oh,

959
00:54:39,719 --> 00:54:41,759
Speaker 4: you don't like the way we measure housing, tell us

960
00:54:41,800 --> 00:54:44,880
Speaker 4: how we can do it better. Cricket, cricket. No, I

961
00:54:45,000 --> 00:54:46,319
Speaker 4: just like to say I don't like the way you

962
00:54:46,360 --> 00:54:49,200
Speaker 4: do it. I mean, but we're not really offering a

963
00:54:49,200 --> 00:54:53,040
Speaker 4: lot of sound solutions. We're a massive economy. It's not

964
00:54:53,120 --> 00:54:55,480
Speaker 4: easy to measure the data. But one thing that we

965
00:54:55,560 --> 00:55:00,160
Speaker 4: do well historically is we measure data well, and we

966
00:55:00,239 --> 00:55:03,600
Speaker 4: have the best most robust data sets out of any

967
00:55:03,640 --> 00:55:06,560
Speaker 4: other country. We compare ourselves to, but it has been

968
00:55:06,920 --> 00:55:10,279
Speaker 4: slipping so very fun. What I will advocate for is

969
00:55:10,440 --> 00:55:13,920
Speaker 4: funding the data agencies and encouraging them to overhaul their systems.

970
00:55:14,400 --> 00:55:17,800
Speaker 2: So let's talk a little bit about the Federal Reserve independence.

971
00:55:18,520 --> 00:55:22,800
Speaker 2: How much risk is there that the FED could get politicized.

972
00:55:22,520 --> 00:55:24,440
Speaker 5: So we have to take the risk seriously.

973
00:55:24,840 --> 00:55:29,480
Speaker 4: And I understand why folks might be concerned that we

974
00:55:29,520 --> 00:55:33,600
Speaker 4: could be headed for a time when there's collusion between

975
00:55:34,160 --> 00:55:36,520
Speaker 4: the White House and the FED, because we've been there before,

976
00:55:36,560 --> 00:55:40,560
Speaker 4: so you could understand the concern. And that was a

977
00:55:40,680 --> 00:55:45,759
Speaker 4: very different time between Arthur Burns and the Nixon White House.

978
00:55:45,760 --> 00:55:47,080
Speaker 5: But it was a very real time.

979
00:55:47,120 --> 00:55:50,120
Speaker 4: And then it led to the hyperinflation, and those of

980
00:55:50,200 --> 00:55:52,600
Speaker 4: us of a certain age, we don't want to live through.

981
00:55:52,640 --> 00:55:55,800
Speaker 2: Nineteen seventies inflation. That was an ugly decade.

982
00:55:55,960 --> 00:55:57,600
Speaker 3: Economics, that was an ugly decade.

983
00:55:57,600 --> 00:56:01,680
Speaker 4: And I tell those harrowing tales to my team of

984
00:56:01,760 --> 00:56:05,880
Speaker 4: waiting in line for gasoline with my mother, you know,

985
00:56:05,920 --> 00:56:08,560
Speaker 4: because it was rationed or we couldn't get gasoline on

986
00:56:09,000 --> 00:56:09,680
Speaker 4: a Sunday.

987
00:56:10,080 --> 00:56:13,000
Speaker 2: I remember I had a lawn mowing business and I

988
00:56:13,040 --> 00:56:16,239
Speaker 2: would show up with my little red gas can and

989
00:56:16,280 --> 00:56:18,440
Speaker 2: they would say do you have an odd number license

990
00:56:18,520 --> 00:56:21,440
Speaker 2: plate or an even number license plate? And my answer

991
00:56:21,480 --> 00:56:24,800
Speaker 2: was always, I'm twelve. I don't have a license plate.

992
00:56:25,000 --> 00:56:26,640
Speaker 2: I just need a gallon of gas so I can

993
00:56:27,160 --> 00:56:29,680
Speaker 2: mow missus McCarthy's lawn down the street.

994
00:56:29,840 --> 00:56:30,040
Speaker 4: Yeah.

995
00:56:30,280 --> 00:56:30,600
Speaker 3: Always.

996
00:56:30,600 --> 00:56:31,960
Speaker 4: I can't believe they had the nerve to ask a

997
00:56:31,960 --> 00:56:33,960
Speaker 4: twelve year old, Oh, no, you show up, But it

998
00:56:34,040 --> 00:56:36,359
Speaker 4: shows you why should you a twelve year old get

999
00:56:36,480 --> 00:56:38,160
Speaker 4: priority or someone that needs.

1000
00:56:37,920 --> 00:56:38,840
Speaker 5: To commute to work.

1001
00:56:38,960 --> 00:56:39,920
Speaker 3: But apparently, but.

1002
00:56:39,920 --> 00:56:43,080
Speaker 4: My parents bought a house at eighteen percent mortgage interest

1003
00:56:43,120 --> 00:56:43,879
Speaker 4: in nineteen.

1004
00:56:43,600 --> 00:56:44,600
Speaker 2: Eighty eighteen percent.

1005
00:56:44,920 --> 00:56:47,600
Speaker 4: That was normal because if you didn't buy it that day,

1006
00:56:47,760 --> 00:56:51,040
Speaker 4: it was more expensive the next day. That's what strikes

1007
00:56:51,080 --> 00:56:54,239
Speaker 4: fear in the hearts of monetary policy makers because that

1008
00:56:54,400 --> 00:56:57,120
Speaker 4: is inflation expectations. The price was going to be more

1009
00:56:57,160 --> 00:56:59,680
Speaker 4: expensive tomorrow, so you better buy it today.

1010
00:57:00,840 --> 00:57:05,800
Speaker 2: Inflation expectations lead to consumer and behavior that helps the drive.

1011
00:57:05,600 --> 00:57:08,800
Speaker 4: Prices, yes, and it starts off that sort of vicious cycle.

1012
00:57:09,040 --> 00:57:12,080
Speaker 4: And so this is at the heart of why you

1013
00:57:12,120 --> 00:57:15,799
Speaker 4: need independent monetary policy making, because if the market believes

1014
00:57:16,520 --> 00:57:21,160
Speaker 4: that the FED might keep rates easier than the economy

1015
00:57:21,200 --> 00:57:26,240
Speaker 4: would otherwise dictate, then is that going to again lead

1016
00:57:26,320 --> 00:57:30,040
Speaker 4: to something like runaway inflation is going to lead to stagnation.

1017
00:57:30,600 --> 00:57:34,920
Speaker 4: And that's why every time there's some headline where the

1018
00:57:35,880 --> 00:57:40,800
Speaker 4: FEDS and dependence may be threatened, you see term premium

1019
00:57:40,840 --> 00:57:43,360
Speaker 4: increase at the long end of the yield curve. You

1020
00:57:43,480 --> 00:57:48,520
Speaker 4: see the stagnation playbook go go into effect among investors,

1021
00:57:49,080 --> 00:57:53,600
Speaker 4: and you know, going back to US exceptionalism. Independent monetary

1022
00:57:53,600 --> 00:57:58,120
Speaker 4: policy making is a pillar of US exceptionalism.

1023
00:57:58,160 --> 00:58:02,160
Speaker 2: Really interesting in a bunch of names floated for FED

1024
00:58:02,280 --> 00:58:06,480
Speaker 2: chair other than Scott Besson, who has said he's not

1025
00:58:06,640 --> 00:58:11,120
Speaker 2: interested and I think is probably the most thoughtful person

1026
00:58:11,160 --> 00:58:14,320
Speaker 2: that I've heard names I've heard thrown out. Any of

1027
00:58:14,360 --> 00:58:19,160
Speaker 2: those names make you remotely comfortable? Or what do you

1028
00:58:19,280 --> 00:58:22,320
Speaker 2: think about some of these trial balloons that keep getting

1029
00:58:22,440 --> 00:58:23,120
Speaker 2: tossed around?

1030
00:58:23,320 --> 00:58:26,000
Speaker 5: Yeah, so I think I agree with you.

1031
00:58:26,720 --> 00:58:31,800
Speaker 4: I like the steady hand and careful thinking that comes

1032
00:58:31,840 --> 00:58:35,960
Speaker 4: from Treasury Secretary Besson. It would actually, in policy circles,

1033
00:58:36,000 --> 00:58:40,800
Speaker 4: be a demotion to send the Treasury secretary to become

1034
00:58:41,880 --> 00:58:42,480
Speaker 4: a chair.

1035
00:58:42,320 --> 00:58:45,240
Speaker 3: Of the FMC. That's the emotion we think of it.

1036
00:58:45,320 --> 00:58:49,560
Speaker 4: So in markets, I often hear this from investors is wait,

1037
00:58:49,720 --> 00:58:51,720
Speaker 4: but the chair of the FED is the most powerful

1038
00:58:51,760 --> 00:58:56,200
Speaker 4: person in the world, but from in policy circles, it

1039
00:58:56,280 --> 00:58:58,960
Speaker 4: is a lesser position than Treasury secretary.

1040
00:58:59,280 --> 00:59:04,280
Speaker 2: Very interesting, it's a longer tenure, especially if we look

1041
00:59:04,320 --> 00:59:09,600
Speaker 2: at recent administrations. It's not like someone becomes treasure secretary

1042
00:59:09,640 --> 00:59:11,480
Speaker 2: and they're there for all four years. They seem to

1043
00:59:11,520 --> 00:59:12,880
Speaker 2: turn over pretty rapidly.

1044
00:59:13,040 --> 00:59:16,120
Speaker 5: It can be the case, right, but we've had not always.

1045
00:59:16,200 --> 00:59:19,880
Speaker 2: We've had back to back six year terms for pal.

1046
00:59:20,080 --> 00:59:23,040
Speaker 2: That's a pretty yeah, yeah for.

1047
00:59:23,160 --> 00:59:26,600
Speaker 4: Your terms, but yeah, and there tends to be a

1048
00:59:26,600 --> 00:59:30,400
Speaker 4: lot of longevity with FED chairs because they also don't

1049
00:59:30,520 --> 00:59:36,000
Speaker 4: change typically with administrations. Uh, and so in political parties,

1050
00:59:36,280 --> 00:59:38,000
Speaker 4: they tend to span political parties.

1051
00:59:38,400 --> 00:59:40,080
Speaker 5: So, look, there are a lot.

1052
00:59:39,920 --> 00:59:43,040
Speaker 4: Of you know, I obviously am going to have some

1053
00:59:43,120 --> 00:59:45,640
Speaker 4: personal favorites of mine that have been thrown out there.

1054
00:59:45,640 --> 00:59:49,080
Speaker 4: But unfortunately I'm not going to give you those names.

1055
00:59:49,000 --> 00:59:51,280
Speaker 3: But they will just tell me who you really don't like.

1056
00:59:51,400 --> 00:59:54,520
Speaker 4: There is yes, yes, I'll do the opposite. No, but

1057
00:59:54,560 --> 00:59:57,120
Speaker 4: there there there are plenty of names in there that

1058
00:59:57,160 --> 01:00:01,560
Speaker 4: have been tossed around as possibilities that would make fine

1059
01:00:02,240 --> 01:00:05,160
Speaker 4: FMC chairs. I think what you're going to see is

1060
01:00:05,200 --> 01:00:07,720
Speaker 4: with each of those names of a float to the top.

1061
01:00:08,120 --> 01:00:11,840
Speaker 4: The markets will have their say on whether that is

1062
01:00:12,080 --> 01:00:15,040
Speaker 4: a candidate that would be believed to be a mouthpiece

1063
01:00:15,200 --> 01:00:17,200
Speaker 4: of President Trump or not.

1064
01:00:17,760 --> 01:00:25,120
Speaker 2: When I look at various cabinet members Defense, Intelligence, Health

1065
01:00:25,560 --> 01:00:32,080
Speaker 2: and Welfare and most recently now BLS, can't say these

1066
01:00:32,120 --> 01:00:35,640
Speaker 2: are the best and the brightest. It's not camelot under Kennedy,

1067
01:00:36,200 --> 01:00:40,280
Speaker 2: and you could kind of under John F. Kennedy in

1068
01:00:40,400 --> 01:00:43,280
Speaker 2: nineteen sixty you could kind of get away with that

1069
01:00:44,280 --> 01:00:50,960
Speaker 2: in certain cabinet positions. Am I wrong in saying markets

1070
01:00:51,040 --> 01:00:56,560
Speaker 2: won't tolerate someone like an RFK junior and all of

1071
01:00:56,600 --> 01:01:01,280
Speaker 2: his anti vaccination attitudes at a place like NIH or

1072
01:01:01,400 --> 01:01:06,520
Speaker 2: CDC with a FED chair. Is the bar higher for

1073
01:01:06,640 --> 01:01:12,440
Speaker 2: the chairman of the Federal Reserve than other specific cabinet positions.

1074
01:01:12,000 --> 01:01:14,439
Speaker 4: Well, I think piggybacking on, you know, sort of your

1075
01:01:14,520 --> 01:01:21,080
Speaker 4: exact examples there, Who directly has a hand in influencing

1076
01:01:21,200 --> 01:01:26,000
Speaker 4: financial markets. That is the FED chair, That is the

1077
01:01:26,120 --> 01:01:29,680
Speaker 4: FOMC collectively, not just the FED chair, but the FOMC

1078
01:01:29,800 --> 01:01:32,560
Speaker 4: is a collective body, and that's why the markets will

1079
01:01:32,560 --> 01:01:35,320
Speaker 4: always be most sensitive to who is the chair of

1080
01:01:35,320 --> 01:01:35,720
Speaker 4: the FED.

1081
01:01:36,240 --> 01:01:40,040
Speaker 2: So I want to ask a question about policy, not politics.

1082
01:01:40,440 --> 01:01:43,640
Speaker 2: But very often when we talk about you know, anytime

1083
01:01:43,680 --> 01:01:47,760
Speaker 2: something comes up, like taco whatever, it seems to get

1084
01:01:47,800 --> 01:01:52,680
Speaker 2: overly politicized. But the one descriptor I heard that's kind

1085
01:01:52,680 --> 01:01:56,520
Speaker 2: of fascinating is that there isn't a Trump put, there's

1086
01:01:56,600 --> 01:02:00,360
Speaker 2: a Trump collar. And what that means is when markets

1087
01:02:00,360 --> 01:02:03,360
Speaker 2: are near all time highs, he's someone in bolden and

1088
01:02:03,440 --> 01:02:06,840
Speaker 2: can be very aggressive in doing things like firing the

1089
01:02:06,920 --> 01:02:11,880
Speaker 2: BLS commissioner when the market sells off and suddenly we're ten, fifteen,

1090
01:02:12,040 --> 01:02:15,000
Speaker 2: almost twenty percent off the highs. Hey, we're going to

1091
01:02:15,040 --> 01:02:18,320
Speaker 2: put a pause on taris for ninety days. There's a

1092
01:02:18,360 --> 01:02:21,240
Speaker 2: little bit of a floor there, and hence the phrase

1093
01:02:21,320 --> 01:02:25,080
Speaker 2: Trump collar. I know, we only have six or eight

1094
01:02:25,120 --> 01:02:30,480
Speaker 2: months worth of recent data. How important do you believe

1095
01:02:31,480 --> 01:02:35,360
Speaker 2: market prices are to this president and this administration?

1096
01:02:35,800 --> 01:02:39,400
Speaker 4: So in the first administration, you know, we we were like, okay,

1097
01:02:39,440 --> 01:02:42,400
Speaker 4: we've got his number. We've got his number. He takes

1098
01:02:42,440 --> 01:02:45,280
Speaker 4: the stock market as the single best indicator of his

1099
01:02:45,320 --> 01:02:48,960
Speaker 4: approval rating, right, and so if the stock market pukes,

1100
01:02:49,000 --> 01:02:52,400
Speaker 4: if it's a huge sell off, he's going to listen.

1101
01:02:52,880 --> 01:02:57,640
Speaker 4: And so we went into this second Trump term with

1102
01:02:57,720 --> 01:03:01,480
Speaker 4: the markets assuming Aha, Yes, all we have to do

1103
01:03:01,800 --> 01:03:05,200
Speaker 4: is speak and will speak volumes with a sell off

1104
01:03:05,320 --> 01:03:08,000
Speaker 4: and he will change his tune. Well, that is not

1105
01:03:08,200 --> 01:03:11,240
Speaker 4: what happened. That's not what happened, because the markets did

1106
01:03:11,320 --> 01:03:14,040
Speaker 4: puke when it became apparent that he was going to

1107
01:03:14,080 --> 01:03:18,240
Speaker 4: be very aggressive on a trade policy in his second term.

1108
01:03:18,560 --> 01:03:21,520
Speaker 4: The market puked and the President stayed the course.

1109
01:03:21,960 --> 01:03:25,720
Speaker 2: So someone asked me my opinion as to what I

1110
01:03:25,760 --> 01:03:28,480
Speaker 2: think trade policy is going to look like going forward,

1111
01:03:29,280 --> 01:03:32,440
Speaker 2: given how frequently we've seen flip flops and back and

1112
01:03:32,480 --> 01:03:37,400
Speaker 2: forth and extensions, and what I answered, And I'm curious

1113
01:03:37,440 --> 01:03:40,600
Speaker 2: as to your perspective on this. Tell me the last

1114
01:03:40,600 --> 01:03:44,960
Speaker 2: person who whispers and President Trump's year before a decision

1115
01:03:45,240 --> 01:03:49,120
Speaker 2: is made, and that'll tell me where the market will go.

1116
01:03:49,680 --> 01:03:53,000
Speaker 2: If it's Treasury Secretary Scott Bessen is the last person

1117
01:03:53,040 --> 01:03:55,480
Speaker 2: to speak to him. I think the markets would be

1118
01:03:55,560 --> 01:03:59,640
Speaker 2: pretty steady and on a gradual move higher if it

1119
01:03:59,800 --> 01:04:03,000
Speaker 2: had happens to be someone like Pena Navara will buckle

1120
01:04:03,120 --> 01:04:07,160
Speaker 2: up wearing for a bumpy ride. Fair way to describe

1121
01:04:07,200 --> 01:04:10,520
Speaker 2: the policy making in DC, I think.

1122
01:04:10,440 --> 01:04:12,800
Speaker 4: So, I mean basically what you're getting at in a

1123
01:04:12,880 --> 01:04:16,960
Speaker 4: roundabout way is just who do the markets trust? Who

1124
01:04:16,960 --> 01:04:20,320
Speaker 4: do the markets trust? And I think you've had Treasure

1125
01:04:20,360 --> 01:04:23,640
Speaker 4: Secretary Bessant that had an active role in that hair

1126
01:04:23,720 --> 01:04:28,040
Speaker 4: raising time between April second and April ninth, meeting with

1127
01:04:28,280 --> 01:04:33,800
Speaker 4: Chair Pal, helping to persuade the President to sort of

1128
01:04:33,800 --> 01:04:37,600
Speaker 4: back off at that time, adding to that hair raising

1129
01:04:37,600 --> 01:04:40,640
Speaker 4: moment by threatening to fire Pal like the markets have

1130
01:04:40,720 --> 01:04:43,600
Speaker 4: come to know Bessn't as a calm and steady voice.

1131
01:04:43,720 --> 01:04:46,600
Speaker 2: And steady is the word that always seems to pop

1132
01:04:46,640 --> 01:04:47,280
Speaker 2: into my head.

1133
01:04:47,360 --> 01:04:51,520
Speaker 4: Any equals certainty, equals your tea equals the opposite of volatility,

1134
01:04:51,760 --> 01:04:55,560
Speaker 4: and so you know the markets will speak volumes as

1135
01:04:55,560 --> 01:04:58,280
Speaker 4: to who they believe they can trust.

1136
01:04:58,560 --> 01:05:02,040
Speaker 2: Coming up, we continue our comversation with Ellen Zenner, chief

1137
01:05:02,080 --> 01:05:06,520
Speaker 2: economic strategist from Morgan Stanley. I'm Barry Riddults. You're listening

1138
01:05:06,520 --> 01:05:17,400
Speaker 2: to Masters in Business on Bloomberg Radio. All right, so

1139
01:05:17,520 --> 01:05:19,480
Speaker 2: I only have you for a limited amount of time.

1140
01:05:19,560 --> 01:05:23,880
Speaker 2: Let's jump to our favorite questions, starting with who are

1141
01:05:23,920 --> 01:05:27,440
Speaker 2: your mentors who helped shape your career? Well?

1142
01:05:27,480 --> 01:05:30,720
Speaker 4: Tamer Plout, so I have mentioned I worked for her

1143
01:05:30,760 --> 01:05:33,840
Speaker 4: at the State of Texas. She was a very influential

1144
01:05:34,000 --> 01:05:36,960
Speaker 4: chief economist at the State of Texas, and that was

1145
01:05:37,040 --> 01:05:38,560
Speaker 4: my She was my first.

1146
01:05:38,600 --> 01:05:38,920
Speaker 5: Barry.

1147
01:05:38,920 --> 01:05:41,320
Speaker 4: You always remember your first, So she was the first

1148
01:05:41,360 --> 01:05:45,320
Speaker 4: chief economist that I worked for, and he has followed

1149
01:05:45,360 --> 01:05:49,400
Speaker 4: my career for the next twenty five years. She's followed

1150
01:05:49,440 --> 01:05:54,720
Speaker 4: my career. I think my first foura fora into investment banking,

1151
01:05:55,600 --> 01:06:00,080
Speaker 4: my chief economist was David Wrestler at Numerous Security. He

1152
01:06:00,360 --> 01:06:04,520
Speaker 4: was a twenty six year veteran chief economist at twenty

1153
01:06:04,560 --> 01:06:10,560
Speaker 4: six year veteran of Numerous Securities and he's now playing

1154
01:06:10,600 --> 01:06:15,320
Speaker 4: golf twenty four to seven in the South. But he

1155
01:06:15,480 --> 01:06:20,240
Speaker 4: because it was my first foray into investment banking, into

1156
01:06:20,280 --> 01:06:24,680
Speaker 4: the high frequency world trading as a trading desk economist.

1157
01:06:24,960 --> 01:06:29,000
Speaker 4: He was very influential there and I still hear from

1158
01:06:29,080 --> 01:06:32,280
Speaker 4: him all the time when he sees me in the

1159
01:06:32,360 --> 01:06:36,880
Speaker 4: media or he hears of some forecasting award or something

1160
01:06:37,000 --> 01:06:40,640
Speaker 4: like that, like he's still the proud Papa today. And

1161
01:06:40,680 --> 01:06:43,480
Speaker 4: so those were two big early mentors of mine that

1162
01:06:43,560 --> 01:06:44,400
Speaker 4: helped shape my career.

1163
01:06:44,520 --> 01:06:47,120
Speaker 2: That's great. Before we get to books, and you actually

1164
01:06:47,240 --> 01:06:51,520
Speaker 2: brought a few books, I want to ask you about streaming.

1165
01:06:51,560 --> 01:06:54,680
Speaker 2: What are you listening to or watching What's What's keeping

1166
01:06:54,760 --> 01:06:55,480
Speaker 2: you entertained?

1167
01:06:55,960 --> 01:06:58,600
Speaker 5: I really developed a love for streaming.

1168
01:06:58,680 --> 01:07:03,080
Speaker 3: I've watched TV before very similar cod the TV was.

1169
01:07:03,080 --> 01:07:06,760
Speaker 4: Never on in our apartment, and so with COVID, I

1170
01:07:06,920 --> 01:07:14,120
Speaker 4: really my eyes were open. And so I really love documentaries.

1171
01:07:14,120 --> 01:07:16,640
Speaker 4: The one that I'm watching right now is on Billy Joel.

1172
01:07:16,960 --> 01:07:21,280
Speaker 2: I'm literally just wrapping up the first we stopped just

1173
01:07:21,520 --> 01:07:22,880
Speaker 2: before the Stranger.

1174
01:07:23,160 --> 01:07:23,400
Speaker 5: Yeah.

1175
01:07:23,440 --> 01:07:25,800
Speaker 4: So they must have made it for fifty somethings in

1176
01:07:25,840 --> 01:07:28,360
Speaker 4: this world, right, So well, if.

1177
01:07:28,280 --> 01:07:32,560
Speaker 2: You grew up in the sixties, seventies, eighties Bill, especially

1178
01:07:32,600 --> 01:07:35,920
Speaker 2: in New York or Long Island, Billy Joel was everywhere.

1179
01:07:35,960 --> 01:07:39,360
Speaker 4: Yeah, which I'm of an age that I know him

1180
01:07:39,400 --> 01:07:42,280
Speaker 4: in real time. But I'm from the South, so I

1181
01:07:42,440 --> 01:07:47,160
Speaker 4: didn't know all of these things. So my streaming habits

1182
01:07:47,600 --> 01:07:52,840
Speaker 4: are extremely polarized and polarizing probably so it's anywhere from

1183
01:07:52,840 --> 01:07:57,000
Speaker 4: documentary so I can expand my knowledge and expand my

1184
01:07:57,120 --> 01:08:04,960
Speaker 4: mind to the most streaming reality shows like Love Island

1185
01:08:05,720 --> 01:08:08,880
Speaker 4: and I Am not kidding you if anyone wants to say, wow,

1186
01:08:08,960 --> 01:08:10,920
Speaker 4: she really is a real person.

1187
01:08:11,280 --> 01:08:12,560
Speaker 5: It's the fact that I can.

1188
01:08:12,480 --> 01:08:15,960
Speaker 4: Enjoy Love Island and then in the next hour, I

1189
01:08:15,960 --> 01:08:18,519
Speaker 4: can enjoy a documentary on Billy Joel.

1190
01:08:18,880 --> 01:08:20,880
Speaker 2: So you have a couple of books here, let's talk

1191
01:08:20,880 --> 01:08:23,360
Speaker 2: about books are what are you reading now? What are

1192
01:08:23,400 --> 01:08:24,240
Speaker 2: some of your favorites?

1193
01:08:24,320 --> 01:08:25,400
Speaker 5: Yeah, I have a couple of books.

1194
01:08:25,400 --> 01:08:27,920
Speaker 4: So when I first as you mentioned, I was on

1195
01:08:27,960 --> 01:08:30,559
Speaker 4: almost exactly eight years ago, and I talked about Jonah

1196
01:08:30,600 --> 01:08:33,040
Speaker 4: Sarah's book A Piece of the Action, How the Middle

1197
01:08:33,080 --> 01:08:36,160
Speaker 4: Class Became the Money Class, still one of my favorite

1198
01:08:36,240 --> 01:08:39,360
Speaker 4: books on the rise of consumer credit in the US

1199
01:08:39,360 --> 01:08:41,360
Speaker 4: and our love hate relationship with it.

1200
01:08:41,439 --> 01:08:45,719
Speaker 2: But it's been that analysis of how the middle class

1201
01:08:46,080 --> 01:08:51,599
Speaker 2: suddenly gained entry to homes, mortgages, cars, and lots of

1202
01:08:51,920 --> 01:08:57,559
Speaker 2: consumer discretionary goods. Huge boom for middle class America, right.

1203
01:08:57,800 --> 01:08:58,400
Speaker 5: Incredible.

1204
01:08:59,479 --> 01:09:02,480
Speaker 4: It really is still an incredible book. And every economist

1205
01:09:02,560 --> 01:09:04,840
Speaker 4: of mine that I have covered the consumer and study

1206
01:09:04,880 --> 01:09:09,000
Speaker 4: household behavior, they have to read it. So I brought

1207
01:09:09,040 --> 01:09:11,320
Speaker 4: in today Kurt Vonnegutz player piano.

1208
01:09:11,600 --> 01:09:13,719
Speaker 3: Can't go wrong with Vonni and so I.

1209
01:09:13,680 --> 01:09:16,200
Speaker 4: Have not read this book, but I'll tell you that

1210
01:09:16,439 --> 01:09:19,120
Speaker 4: what I'm showing you, if the listeners could see, is

1211
01:09:19,280 --> 01:09:23,639
Speaker 4: a handwritten note from a colleague after watching a webcast

1212
01:09:23,680 --> 01:09:25,920
Speaker 4: of my how many people get handwritten notes.

1213
01:09:25,960 --> 01:09:28,920
Speaker 2: Still not many, right, but they catch your attention.

1214
01:09:29,200 --> 01:09:34,719
Speaker 4: And the webcast was me and Adam Jonas and Adam

1215
01:09:34,800 --> 01:09:39,320
Speaker 4: Jonas is he was always referred to as the Tesla guy.

1216
01:09:39,439 --> 01:09:45,280
Speaker 4: He's probably the quintessential thought leader at Morgan Stanley. He's

1217
01:09:45,280 --> 01:09:49,120
Speaker 4: just got a celebrity following, and he is leading the

1218
01:09:49,200 --> 01:09:54,560
Speaker 4: charge on robotics and humanoids. And so after that webcast,

1219
01:09:55,000 --> 01:09:59,120
Speaker 4: I was sent this because this book, written in the

1220
01:09:59,200 --> 01:10:03,040
Speaker 4: nineteen fifty covered rise of the corporation and replacement of

1221
01:10:03,040 --> 01:10:07,360
Speaker 4: the state, the ruthless efficiency of capitalism in dealing with labor,

1222
01:10:07,960 --> 01:10:10,520
Speaker 4: the overpowering of the worker.

1223
01:10:10,280 --> 01:10:11,800
Speaker 5: By AI and automation.

1224
01:10:12,320 --> 01:10:14,400
Speaker 3: That's all in this book from the nineteen seventy five

1225
01:10:14,439 --> 01:10:15,040
Speaker 3: years ago.

1226
01:10:15,000 --> 01:10:16,000
Speaker 5: Maybe five years ago.

1227
01:10:16,160 --> 01:10:18,160
Speaker 4: The other book I brought in so again, just like

1228
01:10:18,240 --> 01:10:23,760
Speaker 4: my streaming habits a cleft, is called The Bluegrass Conspiracy,

1229
01:10:24,560 --> 01:10:27,920
Speaker 4: An inside story of power, greed, drugs and murder. This

1230
01:10:28,000 --> 01:10:31,000
Speaker 4: is the backstory to Cocaine Bear, the movie Oh, which

1231
01:10:31,040 --> 01:10:32,519
Speaker 4: is one of my favorite movies.

1232
01:10:32,840 --> 01:10:37,040
Speaker 3: I haven't seen it because it sounds so bare crazy.

1233
01:10:37,400 --> 01:10:40,000
Speaker 2: Come on, yeah, I mean, it just sounds like a

1234
01:10:40,120 --> 01:10:44,839
Speaker 2: wildly fictionalized account of a highly unlikely event.

1235
01:10:45,240 --> 01:10:46,360
Speaker 3: Yeah, how's the book?

1236
01:10:46,760 --> 01:10:49,880
Speaker 4: The book, I am just starting and I cannot wait

1237
01:10:49,920 --> 01:10:53,200
Speaker 4: to get through it because the movie, the only thing

1238
01:10:53,680 --> 01:10:56,200
Speaker 4: that the movie that really happened that was in the

1239
01:10:56,200 --> 01:10:59,000
Speaker 4: movie was that there was a dead bear found in

1240
01:10:59,080 --> 01:11:01,519
Speaker 4: a national park with a belly full of cocaine.

1241
01:11:01,520 --> 01:11:02,200
Speaker 5: That is the.

1242
01:11:02,040 --> 01:11:06,000
Speaker 4: Only thing in the movie that was accurate. It was accurate.

1243
01:11:07,360 --> 01:11:10,479
Speaker 4: That actually is in the book. But there's a whole

1244
01:11:10,560 --> 01:11:13,280
Speaker 4: backstory here and I cannot wait to read it. It comes

1245
01:11:13,360 --> 01:11:15,720
Speaker 4: highly recommended, So you can see that my taste in

1246
01:11:15,800 --> 01:11:19,640
Speaker 4: books runs the gambit as well, just like my streaming.

1247
01:11:19,880 --> 01:11:21,760
Speaker 3: So if you haven't.

1248
01:11:21,479 --> 01:11:25,080
Speaker 2: Read Player Piano yet, have you read other Vonnugut? Have

1249
01:11:25,160 --> 01:11:29,800
Speaker 2: you read Kat's Cradle or Swaterhouse Vonnagut? All right, so

1250
01:11:30,680 --> 01:11:34,519
Speaker 2: everybody should read Slaughterhouse five, And if you're at all

1251
01:11:34,560 --> 01:11:40,839
Speaker 2: remotely interested in science and technology, run a mock Cat's

1252
01:11:40,880 --> 01:11:44,479
Speaker 2: Cradle is his version of that. What makes him so

1253
01:11:44,680 --> 01:11:49,760
Speaker 2: fascinating is he finds these incredible concepts and just so

1254
01:11:50,000 --> 01:11:55,040
Speaker 2: simply explains them in such an compelling and entertaining fashion.

1255
01:11:55,880 --> 01:11:58,280
Speaker 4: But isn't it also scary how books can be written

1256
01:11:58,280 --> 01:12:01,200
Speaker 4: that long ago? And then here we are talking about

1257
01:12:01,280 --> 01:12:05,560
Speaker 4: humanoids and robotics because another I have to say piggybacking

1258
01:12:05,600 --> 01:12:09,320
Speaker 4: off of this idea of robotics and humanoids. Twenty thirteen.

1259
01:12:10,280 --> 01:12:14,120
Speaker 4: Have you seen the movie Robot and Frank. No, Robot

1260
01:12:14,160 --> 01:12:17,920
Speaker 4: and Frank, Frank Langella was in it. Susan Sarandon, Peter

1261
01:12:18,000 --> 01:12:22,839
Speaker 4: sars Guard, James Marsden, Live Tyler Grow.

1262
01:12:23,000 --> 01:12:23,839
Speaker 3: That's some cavy.

1263
01:12:24,280 --> 01:12:25,599
Speaker 5: It is so.

1264
01:12:25,560 --> 01:12:29,040
Speaker 4: Talk about when we think about thematics, longevity is a

1265
01:12:29,080 --> 01:12:33,200
Speaker 4: thematic AI tech and diffusion is a thematic in terms

1266
01:12:33,280 --> 01:12:40,040
Speaker 4: of thematic investing. Robot and Frank is about a senior

1267
01:12:40,240 --> 01:12:43,479
Speaker 4: gentleman that he wants to age in place, and to

1268
01:12:43,520 --> 01:12:46,559
Speaker 4: help him do that, his family buys him a home

1269
01:12:46,640 --> 01:12:48,200
Speaker 4: companion robot.

1270
01:12:48,040 --> 01:12:51,840
Speaker 2: To which him, which is really not decades away.

1271
01:12:51,920 --> 01:12:53,960
Speaker 4: No, we're not that far off from that. In Japan,

1272
01:12:54,000 --> 01:12:57,280
Speaker 4: they're already testing it. So this was in twenty thirteen.

1273
01:12:57,880 --> 01:13:01,479
Speaker 4: The kicker, though, is that it just so happens that

1274
01:13:01,960 --> 01:13:05,639
Speaker 4: Frank was a petty thief in his prior life. He's

1275
01:13:05,680 --> 01:13:08,800
Speaker 4: now going through early dementia. He was a petty thief

1276
01:13:08,800 --> 01:13:11,000
Speaker 4: and he co opts the robot to help him. That's

1277
01:13:11,000 --> 01:13:14,519
Speaker 4: the fun part of the movie. But Robot and Frank

1278
01:13:14,560 --> 01:13:14,960
Speaker 4: twenty three.

1279
01:13:15,000 --> 01:13:18,680
Speaker 2: I'm check absolutely check that out. Our last two questions,

1280
01:13:18,720 --> 01:13:21,240
Speaker 2: what sort of advice would you give a recent college

1281
01:13:21,240 --> 01:13:26,280
Speaker 2: grad interest in the career in economics, finance, investing. What

1282
01:13:26,320 --> 01:13:27,519
Speaker 2: would your advice you to that.

1283
01:13:27,960 --> 01:13:30,439
Speaker 4: I would say for them to find any and everyone

1284
01:13:30,479 --> 01:13:33,800
Speaker 4: they can think of that works in that field already.

1285
01:13:34,040 --> 01:13:35,680
Speaker 5: The best is to if you.

1286
01:13:35,720 --> 01:13:38,360
Speaker 4: Can, not to cold call, but to try to find

1287
01:13:38,400 --> 01:13:41,599
Speaker 4: some sort of connection, whether it's your wealth advisor, and

1288
01:13:41,680 --> 01:13:44,040
Speaker 4: see who your wealth advice. I get contacted by our

1289
01:13:44,080 --> 01:13:47,880
Speaker 4: wealth advisors that say, hey, my client has a son

1290
01:13:48,160 --> 01:13:50,400
Speaker 4: who this Do you mind if I put you in

1291
01:13:50,439 --> 01:13:53,360
Speaker 4: touch with him? Find some way, And when you start

1292
01:13:53,400 --> 01:13:56,599
Speaker 4: to have conversations with people that are already working in

1293
01:13:56,720 --> 01:13:59,519
Speaker 4: areas where you think you want to work, never leave

1294
01:13:59,560 --> 01:14:02,680
Speaker 4: that converse without getting two more names from them of

1295
01:14:02,760 --> 01:14:06,840
Speaker 4: people they think you should contact, and can they make

1296
01:14:06,880 --> 01:14:09,439
Speaker 4: that opening for you so that you always have another

1297
01:14:09,479 --> 01:14:10,639
Speaker 4: conversation to be had.

1298
01:14:10,960 --> 01:14:13,880
Speaker 2: Each call always asks for two more names. That's great

1299
01:14:13,880 --> 01:14:16,960
Speaker 2: advice for someone right out of college. And our final question,

1300
01:14:17,200 --> 01:14:21,040
Speaker 2: what do you know about the world of economics, investing,

1301
01:14:21,200 --> 01:14:25,839
Speaker 2: thematic investing, macro economy today that might have been helpful

1302
01:14:25,960 --> 01:14:28,680
Speaker 2: twenty five or so years ago, really, when you were

1303
01:14:28,680 --> 01:14:29,599
Speaker 2: first starting out.

1304
01:14:30,920 --> 01:14:33,160
Speaker 5: I think if I were to know that.

1305
01:14:35,280 --> 01:14:39,599
Speaker 4: Models are not the end all be all, I would

1306
01:14:39,600 --> 01:14:44,439
Speaker 4: have started using anecdotal evidence a lot earlier. I am

1307
01:14:44,479 --> 01:14:47,519
Speaker 4: a very big believer in anecdotal evidence, and I've been

1308
01:14:47,520 --> 01:14:51,799
Speaker 4: criticized for that in my career. It's not statistically sound.

1309
01:14:52,080 --> 01:14:55,719
Speaker 4: I like to use my one man data sample, which

1310
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Speaker 4: is my husband when I studied behavior, and and I

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Speaker 4: just it's a great way to connect to people, connect

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Speaker 4: to your audience, get a message across. And I'm a

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Speaker 4: big believer in using anecdotal evidence when thinking about how

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Speaker 4: to adjust your forecast subjectively, and so I wish I

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Speaker 4: had started using that my career even earlier.

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Speaker 2: Ellen, this has been absolutely a pleasure. It's been way

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Speaker 2: too long since we had you in here. We have

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Speaker 2: been speaking with Alan Zenner. She's chief economic strategist and

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Speaker 2: global head of thematic and macro investing for Morgan Stanley

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Speaker 2: Wealth Management. They manage over seven trillion dollars in total assets.

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Speaker 2: If you enjoy this conversation, well, be sure and check

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Speaker 2: out any of the five hundred and forty seven we've

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Speaker 2: done over the past twelve years. You can find those

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01:15:46,560 --> 01:15:52,640
Speaker 2: at iTunes, Spotify, Bloomberg YouTube, wherever you find your favorite podcast,

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01:15:53,120 --> 01:15:55,639
Speaker 2: and be sure and check out my new book, How

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01:15:55,720 --> 01:15:59,960
Speaker 2: Not to Invest The ideas, numbers and behaviors that they're

1327
01:16:00,160 --> 01:16:03,160
Speaker 2: joy wealth and how to avoid them, How Not to

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01:16:03,280 --> 01:16:05,160
Speaker 2: Invest at your favorite bookstore.

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Speaker 3: I would be remiss.

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Speaker 2: If I didn't thank the Crack team that helps with

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Speaker 2: these conversations together each week. Peter Nicolino is my audio engineer.

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Speaker 2: Anna Luke is my producer. Sean Russo is my researcher.

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Speaker 2: Sage Bauman is the Heavy podcast at Bloomberg. I'm Barry Rutolts.

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Speaker 2: You've been listening to Masters in Business on Bloomberg Radio