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

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

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Speaker 1: Her name I almost said Mandina Parwan. Her name is

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Speaker 1: Ellen Zentner, and she is the chief US economist at

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Speaker 1: Morgan Stanley. She has a fascinating career and is one

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Speaker 1: of the highest ranked women in the world of finance today.

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Speaker 1: She is very, very insightful, bringing a unique perspective to

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Speaker 1: really what has been We talked about this during the podcast.

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Speaker 1: A boys club filled with middle aged white dudes who

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Speaker 1: are uh the average economist of days gone by, and

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Speaker 1: Allen and I couldn't agree more. Argues that the more

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Speaker 1: diversity of opinion and thought we have in various organizations,

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Speaker 1: the less likely we are to have group think, the

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Speaker 1: more likely we are to consider different perspectives, and that's

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Speaker 1: enormously helpful when you have literally hundreds of billions of

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Speaker 1: dollars at risk in the marketplace, and and being able

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Speaker 1: to look at everything from that perspective is enormously helpful.

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Speaker 1: This was a fascinating conversation if you are at all

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Speaker 1: interested in economics, Wall Street, how big firms operate, what

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Speaker 1: it's like to travel around the world speaking to clients

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Speaker 1: and have them ask you all sorts of really interesting questions.

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Speaker 1: Then you're going to really enjoy this conversation. So, with

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Speaker 1: no further ado, my conversation with Morgan Stanley's Ellen Sentner.

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Speaker 1: My special guest today is Ellen Zentner. She is currently

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Speaker 1: the chief US economist for Morgan Stanley. UH. Previously, she

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Speaker 1: had held senior economist positions with such August firms as

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Speaker 1: Nomura and Bank of Tokyo Mitsubishi. Previous to joining Morgan Stanley,

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Speaker 1: she was a senior economist with the Texas State Controller's Office.

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Speaker 1: Ellen Setner, Welcome to Bloomberg. Thanks, Thanks Barry. So that's

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Speaker 1: a kind of interesting progression. How do you go from

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Speaker 1: Texas State Controller and the office overseeing Texas is I

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Speaker 1: guess government spending and we held the purse strings right

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Speaker 1: to I guess it would be somewhat similar to a

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Speaker 1: big brokerage firm like Morgan Stanley. Yeah, I don't know.

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Speaker 1: Working private versus public UM is very different. It's a

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Speaker 1: different pace of life. Uh, it's a different focus of study. UM.

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Speaker 1: I think starting out in government is a great way

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Speaker 1: to cultivate the career of an economist because you can

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Speaker 1: start out in a slower paced environment where you can

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Speaker 1: really learn deeply and think those deep thoughts were supposed

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Speaker 1: to have time to think, but often don't have time

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Speaker 1: when you move into investment banking. Um. And for me

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Speaker 1: it was a great first job out of graduate school. Uh.

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Speaker 1: The no brainer was to go back to Austin, Texas,

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Speaker 1: where I'm from and work for the state government. We

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Speaker 1: were just in Austin. It's such a fabulous city. It

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Speaker 1: was a ten pound trip because the food there is

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Speaker 1: so fanta. I imagine you got in a lot of barbecue.

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Speaker 1: So so what years were you working in the controller's office?

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Speaker 1: So I was there from ninety eight to two thousand three.

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Speaker 1: So you missed the Great Financial Crisis, right. I had

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Speaker 1: the uh lovely experience of being right in the thick

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Speaker 1: of it in New York by then. But Texas did

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Speaker 1: miss uh miss it in large part because of having

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Speaker 1: a rainy day fund, which I think after the financial

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Speaker 1: crisis was a great example to other state governments that

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Speaker 1: you know, in in in good times, when revenues were

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Speaker 1: good in the State of Texas or are good, especially

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Speaker 1: in energy, you siphon those off past a certain point

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Speaker 1: and put them in a so called rainy day fund

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Speaker 1: to tap should you ever need it. Um and I

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Speaker 1: think after the financial crisis it was the first time

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Speaker 1: that Texas ever had to tap it's rainy day fund.

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Speaker 1: But it's one reason why it's been able to keep

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Speaker 1: its triple A rating. Financial planning one on one counter

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Speaker 1: cyclical that precautionary savings that unfortunately America's households didn't have

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Speaker 1: after the financial crisis, but but Texas had. And there's

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Speaker 1: a little known thing about Texas that I find fascinating

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Speaker 1: that given the boom and bus cycle with energy long

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Speaker 1: before the Great Financial Crisis, they used to I believe

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Speaker 1: they have something I want to say. It's it's in

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Speaker 1: the state constitution that you cannot use your home mortgage,

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Speaker 1: your home equity for cash out financing. So Texas had

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Speaker 1: a much lower rate of default and subsequent issues foreclosures

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Speaker 1: than the rest of the country. Yeah, I think that,

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Speaker 1: And I could also draw a line between that to Japan.

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Speaker 1: Of course, I was working for a Japanese firm at

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Speaker 1: the time of the financial crisis, and one of the

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Speaker 1: thing I things I witnessed at Bank of Tokyo Mitsubishi

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Speaker 1: was I was there at the time that we UH.

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Speaker 1: Not literally I did not walk a check over to

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Speaker 1: Morgan Stanley, but a check was walked over to Morgan

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Speaker 1: Stanley in theory and and a chunk of Morgan Stanley

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Speaker 1: was purchased UM. And now there's this amazing partnership between

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Speaker 1: m uf G, It'subishi United Financial Group UH AND and

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Speaker 1: Morgan Stanley. Because the Japanese were the ones that were

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Speaker 1: cash rich at the time of the financial crisis, because

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Speaker 1: they had already gone through their big financial crisis, they

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Speaker 1: did not participate in the mortgage crisis. UH, they were

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Speaker 1: not over leveraged in that area, and so they had

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Speaker 1: a lot of cash to deploy UH and it was

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Speaker 1: much needed by by many of the firms that were

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Speaker 1: bought up at that time. So so I'm gonna sort

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Speaker 1: of jump ahead. But given this relationship that came out

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Speaker 1: of the financial crisis, how has that impacted Morgan Stanley

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Speaker 1: as an international company. Has that broadened their footprints around

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Speaker 1: the globe? It absolutely has. UH and Morgan Stanley already

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Speaker 1: had a strong presence in Asia, but by partnering with

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Speaker 1: m uf G, opening access to to markets more fully

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Speaker 1: and mind share across more of the global economy. I

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Speaker 1: think it's been a fantastic partnership. Was it a coincidence

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Speaker 1: that this deal took place and then you end up

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Speaker 1: at Morgan Stanley or did you just happen out of

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Speaker 1: this relationship to meet people and one thing led to another.

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Speaker 1: It was a coincidence. Um. I'm when I began at

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Speaker 1: Morgan Stanley, back in my colleagues at Bank of Tokyo Mitsubishi,

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Speaker 1: had felt like I had come full circle. I was

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Speaker 1: back in the family, so to speak. Um. And but

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Speaker 1: I'll tell you, Uh, probably the most important reason of

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Speaker 1: how I became connected at Morgan Stanley, UM, is that

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Speaker 1: I'm a nice person. Berry, And I always say this,

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Speaker 1: You've got to be a nice person. And when people

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Speaker 1: think about who do you want to work with, who

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Speaker 1: do you want to have on your team? Uh, the

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Speaker 1: ones yes, write it down. Be a nice person. It's

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Speaker 1: very simple. But it's not something everyone can do. Um.

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Speaker 1: And especially in finance, we've heard of yellers and screamers

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Speaker 1: and exactly, but you will be rewarded. And so when

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Speaker 1: people when we all reach out to each other when

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Speaker 1: we're networking, saying hey, I've got an open spot on

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Speaker 1: my team? Who have you worked within the past, Who

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Speaker 1: do you really like? Who do you think I should

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Speaker 1: reach out to? Uh, if you were a jerk and

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Speaker 1: nobody liked working with you, you're not going to be

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Speaker 1: one of the names on that list. And then that's

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Speaker 1: exactly how I came about to be recommended for position

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Speaker 1: at Morgan Stanley was because I was good people and

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Speaker 1: you started as a senior economist And how long have

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Speaker 1: you been chief economist in Worrow? I became chief a

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Speaker 1: conist Morgan Stanley in February. Oh, so you've been here

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Speaker 1: for in the role for over two years. For over

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Speaker 1: two years? Yeah to me, and judging from your reaction,

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Speaker 1: it sounds like it's been a lengthy time. But let

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Speaker 1: me tell you. One of the things I love about

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Speaker 1: Morgan Stanley is that people are shocked when they hear

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Speaker 1: that I've only been there a total of four years,

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Speaker 1: because you talked to anyone their lifers. The longevity is

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Speaker 1: amazing and it and it uh says something about Morgan

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Speaker 1: Stanley is a place to work. It really is a

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Speaker 1: family um and it just makes me feel good that

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Speaker 1: there's there's just not a lot of turnover. We were

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Speaker 1: talking earlier about the transition from private sector to public sector.

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Speaker 1: You had an interesting experience this past election. How did

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Speaker 1: the politics that have gotten kind of crazy in America

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Speaker 1: affect clients, investing and just generally interacting with Morgan Stanley customers.

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Speaker 1: You know, I think this is this is definitely Uh,

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Speaker 1: this has definitely been a unique election cycle. Uh. And

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Speaker 1: I know that we've beat that word unique to death,

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Speaker 1: it feels, but I can tell you that I don't

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Speaker 1: believe I've ever experienced emotions being this high and uh

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Speaker 1: it affecting sort of Let's say, the emotional data this much,

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Speaker 1: the survey based data of how do you feel? And

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Speaker 1: it has been swinging wildly. There are massive divergences between say, uh,

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Speaker 1: your your Trump voter, that's middle income America where uh

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Speaker 1: you can see that by voter preference. Consumer confidence is

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Speaker 1: a record high for Republicans but at a record home

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Speaker 1: low for Democrats. And we saw a similar similar split

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Speaker 1: among clients and among my internal colleagues as well. Just

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Speaker 1: trying to dissect which economists, generally, I believe are very

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Speaker 1: good at staying objective and trying not to let emotion

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Speaker 1: drive your work. That was my next question is how

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Speaker 1: do you keep clients from allowing their own emotions, political biases,

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Speaker 1: just reactions to the crazingens on TV, from impacting their

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Speaker 1: investing in trading. What you have to do is be

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Speaker 1: the is remained, the calm voice in the room, and

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Speaker 1: just keep coming back to the fundamentals, the fundamentals, the fundamentals,

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Speaker 1: and try to create a story with a very strong

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Speaker 1: argument based in fundamentals so that you you just keep

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Speaker 1: coming back to that and saying, let's keep emotions out

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Speaker 1: of it, let's keep feelings out of it, and just

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Speaker 1: stick to the basics. And that has been extraordinarily difficult,

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Speaker 1: UH post election, where emotions have run high. What I

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Speaker 1: like now, what makes me more confident in the US

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Speaker 1: outlook going forward, is that I can see that investors

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Speaker 1: have adjusted their expectations for fiscal policy over time, and

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Speaker 1: you don't see companies UH providing forward guidance on what

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Speaker 1: Congress might deliver, and you you hear households talking more

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Speaker 1: UH realistically about what Congress might deliver. And so that

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Speaker 1: makes me more confident that what we're seeing in the

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Speaker 1: economic activity is being driven by a stronger global economy,

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Speaker 1: stronger fundamental US economy, legitimately and not being driven anymore

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Speaker 1: by expectations of what Congress made de lyrics. As we

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Speaker 1: all know, campaigning is easy. Policy making is difficult, for sure,

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Speaker 1: And so I think I think that makes me less

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Speaker 1: worried about what happens in the event of complete fiscal failure.

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Speaker 1: You mentioned campaigning. I think we were all kind of

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Speaker 1: hoping that once the election came and went, everything would

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Speaker 1: settle down. And here we are in the middle of

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Speaker 1: the summer, where more than six months through the first

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Speaker 1: year of the first term of President Trump, and you

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Speaker 1: travel extensively, you meet with clients in the United States abroad.

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Speaker 1: I know when I travel around, it's all anybody wants

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Speaker 1: to talk about. Is President Trump. Are you finding similar

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Speaker 1: things that it dominates at least in the beginning of

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Speaker 1: the conversation, Republican, Democrat, conservative, liberal, It does matter. Everybody

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Speaker 1: is transfixed by the world's greatest reality show. Yeah, you know, Actually,

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Speaker 1: I'm glad that you brought up regional differences because, uh,

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Speaker 1: here in the U s. It has petered out. Right,

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Speaker 1: My conversations with clients have gone back to simply talking

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Speaker 1: about the economy, talking about global central banks and liquidity

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Speaker 1: and everything other than fiscal policy, almost like we've gone

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Speaker 1: back to a I'll believe it when I see it,

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Speaker 1: but when I do go abroad, it is a very

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Speaker 1: different story, And the conversations do still start out with

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Speaker 1: what's going on with politics with Congress UH United At

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Speaker 1: first it was our investors outside of the U S

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Speaker 1: trying to understand what is the political process? How do

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Speaker 1: things move through Congress? How much power does the president

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Speaker 1: have to do X, y and z unilaterally or how

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Speaker 1: much does a president need Congress for? And working Sometimes

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Speaker 1: it would take an entire client meeting just working through

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Speaker 1: the process, which could at times open their eyes to oh, okay,

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Speaker 1: policy making is difficult, is it? Are they perplexed by

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Speaker 1: the show? Are they um curious? Because I have recently

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Speaker 1: been in Germany, I've been in in various places in Europe.

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Speaker 1: The response in different areas are it's almost detached amusement

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Speaker 1: versus the UK is like, oh, we have the same

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Speaker 1: thing here, we are we're on the same page, not

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Speaker 1: exactly brexiting, and the most recent UM change candidates seemed

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Speaker 1: to be very different. What what are you finding overseas?

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Speaker 1: Is it a uniform situation or is it full on like, Wow,

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Speaker 1: what's going on there? Well, I would say the amusement

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Speaker 1: comes up in meetings, but it's very fleeting, sort of

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Speaker 1: at the beginning, kind of chuckling over whatever has happened

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Speaker 1: in the media, most recently in the US, and then

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Speaker 1: we dive right into more serious issues because at the

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Speaker 1: end of the day, I get around quite often around

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Speaker 1: the global economy, but not so often that clients do

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Speaker 1: want to UH. I'll use the word waste, waste an

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Speaker 1: entire hour of of client time, you know, but it

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Speaker 1: comes up being being bemused over US politics, but it

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Speaker 1: will come up as just a sort of a quick

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Speaker 1: quip or two at the beginning of the meeting, and

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Speaker 1: then we move on to more serious things. So let

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Speaker 1: me ask you a more serious question because you have

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Speaker 1: said previously you love going out and talking with clients.

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Speaker 1: Who are they and other than politics, what sort of

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Speaker 1: stuff do they lean on you for? So again, going

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Speaker 1: to UH folks outside of the US, UM. You know,

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Speaker 1: it's different when I sit down in front of equities

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Speaker 1: investors versus fixed income investors. Fixed income investors UM. They

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Speaker 1: love the nuances of how every economist will interpret UH

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Speaker 1: pars fed speak UH and the data differently UH. And

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Speaker 1: the fact that you can have two economists that see

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Speaker 1: the economy the same way but have two completely different

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Speaker 1: calls on what the FED will do, and so really

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Speaker 1: working through the nuances of how is it that I

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Speaker 1: listened to the FED. How do I come to conclusions

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Speaker 1: of what I think I've discerned from from FED speak

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Speaker 1: and meetings with FED policymakers working through those details I

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Speaker 1: think is most important for them. It's a very presentation

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Speaker 1: light and conversation heavy meeting. When I sit down with

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Speaker 1: equities investors, it's more talking talk me through the fundamental

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Speaker 1: see me how companies, show me, how companies are positioned,

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Speaker 1: where are they investing, How to consumers spend if they're

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Speaker 1: given more tax dollars? UH? Will interest rate simply be

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Speaker 1: higher or lower at this time next year? You know?

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Speaker 1: And it's a much more presentation heavy, show me the

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Speaker 1: client deck meeting. UH. And so that's why I think

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Speaker 1: my trips around the globe can be very dynamic because

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Speaker 1: as part of the economics team, we have a foot

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Speaker 1: in fixed income, we have a foot in equities, and

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Speaker 1: we basically of us all sides of the firm UH,

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Speaker 1: And so it can be very dynamic meetings in one trip,

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Speaker 1: and I think that keeps it very very interesting for me.

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Speaker 1: Let's talk a little bit about, um, your time at

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Speaker 1: Morgan Stanley. You've been there about four years. You have

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Speaker 1: a very high profile job in a field that's dominated

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Speaker 1: by men. How is that changing? Because I've noticed more

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Speaker 1: and more women are starting to assume senior piece positions

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Speaker 1: in big farms. Uh they are. Uh, it's still lagging tremendously.

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Speaker 1: Of course, it's still lagging tremendously. And I can tell

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Speaker 1: you that we spend an unbelievable amount of time at

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Speaker 1: Morgan Stanley, uh, beating to death all the ways and

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Speaker 1: have we uncovered every way possible to uh lower attrition

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Speaker 1: rates uh for women in the firm, uh, and keep

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Speaker 1: them moving forward, and be being sure that there's nothing

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Speaker 1: on our end that we haven't done in order to

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Speaker 1: remove barriers to the moving higher. Specifically in economics, we

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Speaker 1: saw cher Yelling spend time on this giving speeches about

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Speaker 1: women in economics. I can tell you that when I

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Speaker 1: was in graduate school, Uh, there were four of us

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Speaker 1: ladies in graduate school and economomics, uh about two hundred. Yeah,

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Speaker 1: it was very tiny and and so but today right.

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Speaker 1: I am not the only woman on the economics team

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Speaker 1: in the U S. I'm not the only U chief

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Speaker 1: economist at Morgan Stanley that is a woman. Elgabarsh, our

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Speaker 1: chief European economist, has been there many many many years. Um,

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Speaker 1: and that is unique that Morgan Stanley has more than

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Speaker 1: one female chief economist in the firm. Um. I think

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Speaker 1: overall finances finances doing a horrible job having women in

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Speaker 1: high positions. But we are doing we are make leaps

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Speaker 1: and bounds trying to overcome that. It seems that the industry,

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Speaker 1: however slowly, the changes are taking place. It's really starting

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Speaker 1: to move in the right direction, with miles still to go.

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Speaker 1: Oh yes, well, And that's exactly how we would characterize

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Speaker 1: it when I say finance overall is a sector is

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Speaker 1: doing a horrible job. That is a feeling shared to

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Speaker 1: the highest rank in Morgan Stanley. It's not some uh,

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Speaker 1: you know, outlandish statement that I'm making. Uh, It's just

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Speaker 1: understood that the industry still has a long way to go.

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Speaker 1: It's a long process to groom people to take over exactly.

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Speaker 1: And what we find at Morgan Stanley, and this is

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Speaker 1: probably not unique to Morgan Stanley is that when you

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Speaker 1: look at our analysts class of new analysts coming in. Uh,

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Speaker 1: it's an extremely good balance between men and women. But

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Speaker 1: the attrition rate is higher for women as they get

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Speaker 1: higher up the chain. Why is that? Is it because

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Speaker 1: they don't come back after leaving to have children. Uh,

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Speaker 1: we don't know. We're scrubbing the data and working with

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Speaker 1: every individual segment within the firm to be sure that

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Speaker 1: the data aren't telling us that we're not creating the

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Speaker 1: right environment for them to come back. We've got a

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Speaker 1: very strong return to work program that's been very successful.

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Speaker 1: And I can tell you what we do recognize, and

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Speaker 1: what I recognize firsthand, is that you get much more

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Speaker 1: diversity of thought on an economics team when when it's

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Speaker 1: diverse between male and female and all walks of life.

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Speaker 1: I'm not just talking about a gender um difference, but

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Speaker 1: I can tell you I developed a love for studying

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Speaker 1: US household behavior from very early on in my career.

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Speaker 1: It was one of the first things I did at

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Speaker 1: the State of Texas out of graduate school. UH. And

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Speaker 1: I also feel, UH, and maybe this is a biased view,

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Speaker 1: that that I bring a unique perspective to studying the

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Speaker 1: US household because as a woman, I'm extremely connected to

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Speaker 1: running the household UH, and so I feel also from

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Speaker 1: being from Texas, I didn't grow up on this island

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Speaker 1: of Manhattan, and so I am not so far removed

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Speaker 1: that I don't remember what the average American experiences like

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Speaker 1: in the US UH. And if I were not on

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Speaker 1: the US economics team and it was all UH men

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Speaker 1: run by your typical mid middle aged white male economist, right,

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Speaker 1: they might miss that perspective. There are a number of

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Speaker 1: FED governors and chiefs of the federal Reserve banks who

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Speaker 1: are either currently held by a woman as president or

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Speaker 1: have previously been held by a woman. What does that

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Speaker 1: shift really over the past decade say to young women

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Speaker 1: who may be considering a career in finance or economics,

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Speaker 1: How important are those roles to driving the industry Greek

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Speaker 1: towards a little more gender parity. I think it's hugely important.

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Speaker 1: Right If if I'm a young woman coming out of

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Speaker 1: UH school and I'm studying economics UM, and I'm thinking

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Speaker 1: about where do I see myself going? Where do I

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Speaker 1: see myself five years from now, ten years from out,

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Speaker 1: fifteen years from now, I might not think, Oh, staff

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Speaker 1: economists somewhere on some team, either on Wall Street or

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Speaker 1: at a think tank or a nonprofit. I might I

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Speaker 1: might actually think, which was the unthinkable just two decades ago.

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Speaker 1: I might actually think that I could run the FED

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Speaker 1: one day, or I could head one of the regional

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Speaker 1: federal reserve banks. Let's talk a little bit about the

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Speaker 1: intersection between economics and markets. So does the stock market

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Speaker 1: drive the economy or does the economy drive the stock market?

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Speaker 1: Or is it a little bit of each? Oh, Barry,

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Speaker 1: I'm an economist. I'm gonna say a little bit of each.

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Speaker 1: Because you opened that door. Um, it's the chicken and

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Speaker 1: the egg. Uh. And you'll have a strategist sit in

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Speaker 1: this chair and tell you that it's the markets. Uh.

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Speaker 1: And then you have an economist that sits in its

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Speaker 1: chair and says it's the economy. Isn't the markets reflecting

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Speaker 1: what the economy is doing or at least discounting what

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Speaker 1: the economy is about to do the discounting. So I

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Speaker 1: will say that markets are forward looking, but there forward

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Speaker 1: looking at their trying to anticipate when things have gotten

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Speaker 1: as bad as they could possibly get or as good

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Speaker 1: as they could possibly get. And typically liquidity and global

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Speaker 1: flows drive the markets first before anyone can see what's

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Speaker 1: going on. Uh, and so that that tends to sort

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Speaker 1: of be the forward looking piece that confirms and then

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Speaker 1: the economic data confirms that. So if you look at

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Speaker 1: every business cycle, uh. And let's go back to the

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Speaker 1: most recent downturn, the financial crisis. The stock market reached

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Speaker 1: its bottom first in March, started turning up and then

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Speaker 1: the ultimately the date that the NBRUH said the recession

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Speaker 1: was over was June of two thousand nine, so it

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Speaker 1: led the economy by a couple of months um and

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Speaker 1: on the flip side in seven, I think the market

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Speaker 1: peaked in October seven NBR December of oh seven. So yeah,

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Speaker 1: and so a lot of that is that the wealth

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Speaker 1: effect is huge earlier late in a cycle. So March,

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Speaker 1: what happened that March, this stock market bottomed, it started

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Speaker 1: uh racing higher. Uh. And in in a cycle, when

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Speaker 1: consumers start spending again, guess who are the ones that

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Speaker 1: spend first? The wealthy because financial assets are rising the wealthy.

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Speaker 1: The top twenty of households in income in the US.

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Speaker 1: Income holders in the US make up of all spending,

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Speaker 1: so that again the top so the top income quintile.

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Speaker 1: So the top of income group in the US makes

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Speaker 1: up of all makes sure that makes a lot. They're

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Speaker 1: buying big ticket it good. So when wealth starts to recover,

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Speaker 1: as the cycle is taking off, the expansion is taking off,

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Speaker 1: they're getting out there buying motor vehicles and recreational vehicles

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Speaker 1: and motorcycles and purchasing trips abroad. And let me push

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Speaker 1: back a little bit on this because a well, we'll

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Speaker 1: have a fuller debate about the wealth effect during the

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Speaker 1: podcast portion. But if you remember back in two thousand

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Speaker 1: and nine, there was kind of a rising um since

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Speaker 1: that people were a little intimidated about either conspicuous consumption

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Speaker 1: or ostentatious spending, and even the wealthy, or at least

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Speaker 1: this was in the papers at the time, we're a

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Speaker 1: little circumspect at really big ticket items. And we saw

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Speaker 1: people come out of their caves and start to spend.

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Speaker 1: But it wasn't real mayhem until a year or two. La. Yeah,

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Speaker 1: so I'll give you the exact so and so you're

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Speaker 1: absolutely right right that that uh, it was a little

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Speaker 1: too You didn't want to get out there and do

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Speaker 1: a bunch of chess chest thumping when your neighbor was

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Speaker 1: still out of a job. Uh. And because this was

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Speaker 1: a very severe downturn. And in fact, if you look

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Speaker 1: at consumer confidence overall, let's just look at it in

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Speaker 1: the aggregate the first five years of the recovery. I

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Speaker 1: simply call it the phase of reparation, because it took

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Speaker 1: five years for consumer confidence to finally reach what was

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Speaker 1: a normal level in expansion. So and that's about the

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Speaker 1: time that we finished, uh, de leveraging the household balance

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Speaker 1: sheet as well, that we actually finished licking our wounds

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Speaker 1: and and paying down dead and defaulting on debt, etcetera.

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Speaker 1: So I'll tell you when it did finally kick in

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Speaker 1: for the wealthy. Uh and uh, because you can't hold

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Speaker 1: the wealthy down for too long, verry. So in SMP

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Speaker 1: five hundred was up about and uh. We we scrub

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Speaker 1: three hundred different categories of consumer spending, and that's how

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Speaker 1: we know who's spending, who's doing the spending. Um. During

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Speaker 1: that year, you saw consumer confidence among the highest income

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Speaker 1: groups track the SMP five one for one, and personal

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Speaker 1: aircraft was the single strongest category of space, followed by

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Speaker 1: pleasure boats. Wow, that's fascinating if you think about Also,

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Speaker 1: was it March breaks out to a new all time high,

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Speaker 1: got above the pre crisis levels, arguably kicking off a

431
00:26:39,480 --> 00:26:42,879
Speaker 1: new secular bowl market. It would make sense they're the

432
00:26:42,920 --> 00:26:45,720
Speaker 1: wealth effect. The wealthiest people who own most of the

433
00:26:45,720 --> 00:26:47,840
Speaker 1: stock are going to go out and spend that money.

434
00:26:47,840 --> 00:26:51,640
Speaker 1: But I never saw that data on pleasure boats and aircraft.

435
00:26:51,720 --> 00:26:57,439
Speaker 1: That's fascinating. It's it's interesting. So it Uh. I love UM.

436
00:26:57,480 --> 00:27:00,200
Speaker 1: I love showing charts that will really make clients think

437
00:27:00,200 --> 00:27:02,080
Speaker 1: that I think are charts that they haven't seen from

438
00:27:02,080 --> 00:27:05,480
Speaker 1: anyone else. And so one of those charts is the

439
00:27:05,880 --> 00:27:08,480
Speaker 1: consumer confidence that I mentioned of the highest income group

440
00:27:08,560 --> 00:27:11,879
Speaker 1: versus the SMP five hundred, where it just tracks it higher.

441
00:27:11,920 --> 00:27:14,160
Speaker 1: And it always makes me think of that that's saying,

442
00:27:14,160 --> 00:27:16,120
Speaker 1: and I'm sure I won't get exactly right, but it's

443
00:27:16,119 --> 00:27:20,240
Speaker 1: something about, uh, money can't buy you happiness, but it

444
00:27:20,280 --> 00:27:23,960
Speaker 1: sure makes the suffering easier. The version I remember is

445
00:27:24,480 --> 00:27:27,720
Speaker 1: um David lee Roth said of Van Halen one said

446
00:27:28,240 --> 00:27:30,600
Speaker 1: money can't buy you happiness, but it could pull you

447
00:27:30,720 --> 00:27:33,840
Speaker 1: up in a yacht right next to it. Exactly gets

448
00:27:33,880 --> 00:27:36,320
Speaker 1: you as close as you can. And so yeah, it

449
00:27:36,359 --> 00:27:39,120
Speaker 1: took a while, but you can only hold the wealthy

450
00:27:39,200 --> 00:27:42,840
Speaker 1: back for so long. And uh, and so they were

451
00:27:42,840 --> 00:27:45,600
Speaker 1: really getting their feathers ruffled by the gains in financial

452
00:27:45,600 --> 00:27:48,639
Speaker 1: market wealth that that we're just I mean, just incredible gains.

453
00:27:49,920 --> 00:27:53,600
Speaker 1: And by we had already blown past the previous peak

454
00:27:53,640 --> 00:27:58,119
Speaker 1: toward the end of for financial assets wealth. And and

455
00:27:58,119 --> 00:28:02,560
Speaker 1: I tell you what is another total total total wealth

456
00:28:02,720 --> 00:28:07,120
Speaker 1: in financial assets. Of course, real estate wealth was another matter.

457
00:28:07,160 --> 00:28:11,240
Speaker 1: We've only just popped into positive territory there where we've

458
00:28:11,280 --> 00:28:14,600
Speaker 1: got positive real estate wealth in the first quarter of

459
00:28:14,600 --> 00:28:21,360
Speaker 1: this year, finally, finally relative to the financial christ crisis. Yes, yes, uh.

460
00:28:21,400 --> 00:28:23,240
Speaker 1: And so I think it's interesting that that if I

461
00:28:23,280 --> 00:28:26,480
Speaker 1: were to point to another chart, Uh, that surprises seems

462
00:28:26,520 --> 00:28:29,040
Speaker 1: to surprise everyone is who do you think saves in

463
00:28:29,080 --> 00:28:32,880
Speaker 1: the US? It's the wealthy. Other income groups don't say

464
00:28:33,040 --> 00:28:36,520
Speaker 1: most of most of them live paycheck to paycheck. The

465
00:28:36,560 --> 00:28:39,720
Speaker 1: savings rate is largely determined by the wealthiest income group

466
00:28:39,760 --> 00:28:42,239
Speaker 1: in the US. So if here's another great chart, if

467
00:28:42,280 --> 00:28:46,160
Speaker 1: you take financial assets and you map it against the

468
00:28:46,160 --> 00:28:50,560
Speaker 1: personal savings rate in the US, it shows nearly a

469
00:28:50,600 --> 00:28:55,200
Speaker 1: perfect inverse relationship. As financial assets rise, the savings rate

470
00:28:55,400 --> 00:28:58,280
Speaker 1: falls because the wealthy get out there and spend more. So,

471
00:28:58,320 --> 00:29:02,760
Speaker 1: what does it mean that general, uh, only the highest

472
00:29:02,760 --> 00:29:07,320
Speaker 1: income UM group in the United States is saving and

473
00:29:07,320 --> 00:29:09,760
Speaker 1: and the rest of the country isn't. What What does

474
00:29:09,800 --> 00:29:12,960
Speaker 1: that say to us say about us as a society

475
00:29:13,040 --> 00:29:17,720
Speaker 1: in terms of our propensity to either save or invest well.

476
00:29:18,040 --> 00:29:21,800
Speaker 1: The savings rate overall UM has come down from where

477
00:29:21,800 --> 00:29:25,000
Speaker 1: it peaked after the financial crisis, but it hasn't come

478
00:29:25,000 --> 00:29:26,880
Speaker 1: down all the way. So it's still indicates there's a

479
00:29:26,880 --> 00:29:29,640
Speaker 1: little more precautionary savings out there than there was before.

480
00:29:30,400 --> 00:29:33,080
Speaker 1: But I don't think there's been any fundamental change and

481
00:29:33,160 --> 00:29:36,520
Speaker 1: behavior here. I think the wealthier still mostly the ones

482
00:29:36,560 --> 00:29:39,400
Speaker 1: who are the savers in the US. The difference for

483
00:29:39,440 --> 00:29:42,120
Speaker 1: the middle and lower income households is that the debt

484
00:29:42,160 --> 00:29:46,200
Speaker 1: burden is not as high. Uh, So the crimp that

485
00:29:46,240 --> 00:29:50,160
Speaker 1: they feel, say, when interest rates are rising, the crimp

486
00:29:50,200 --> 00:29:52,720
Speaker 1: they would feel on the interest expense on their out

487
00:29:52,760 --> 00:29:56,640
Speaker 1: their outstanding debt UH is not gonna be as as

488
00:29:56,800 --> 00:30:00,640
Speaker 1: as acutely felt as before. There is some cushion there,

489
00:30:00,680 --> 00:30:03,200
Speaker 1: but I wouldn't say that there's been some fundamental change

490
00:30:03,200 --> 00:30:08,280
Speaker 1: and how they approach precautionary savings. UH. What I think

491
00:30:08,360 --> 00:30:11,320
Speaker 1: is interesting now So going back to to to wealth

492
00:30:11,960 --> 00:30:14,680
Speaker 1: uh and going back to the chart of the highest

493
00:30:14,720 --> 00:30:19,240
Speaker 1: income group confidence matching the climate SMP five hundred. If

494
00:30:19,280 --> 00:30:22,040
Speaker 1: you look at the consumer confidence of the lower income groups,

495
00:30:22,200 --> 00:30:25,880
Speaker 1: it's basically looked like a slow bleed upward upward tracking

496
00:30:25,920 --> 00:30:28,800
Speaker 1: wage gains. So that very kind of slow bleed upward

497
00:30:28,840 --> 00:30:33,240
Speaker 1: and wage gains has really translated into to whom the

498
00:30:33,320 --> 00:30:36,920
Speaker 1: households that rely a lot on labor income. So who

499
00:30:36,960 --> 00:30:40,600
Speaker 1: have I left out? I've noticeably not talked about wealth

500
00:30:40,680 --> 00:30:43,880
Speaker 1: for the middle um, and that's where the housing equity

501
00:30:43,960 --> 00:30:49,520
Speaker 1: comes into play, because housing equity fell sharply housing prices

502
00:30:49,920 --> 00:30:52,640
Speaker 1: once they reached their nature, they flubbed along the bottom

503
00:30:52,640 --> 00:30:55,880
Speaker 1: for a time, finally started turning up staying two thousand

504
00:30:55,960 --> 00:30:58,600
Speaker 1: twelve and have had some nice year over your increases

505
00:30:58,640 --> 00:31:02,520
Speaker 1: in home price appreciation. But we only just moved into

506
00:31:02,560 --> 00:31:06,440
Speaker 1: again at the national level level, into positive territory for

507
00:31:06,480 --> 00:31:09,480
Speaker 1: housing wealth overall in the first quarter of this year.

508
00:31:09,680 --> 00:31:12,760
Speaker 1: So I think my focus as a as a someone

509
00:31:12,800 --> 00:31:15,440
Speaker 1: who loves studying the US consumer, my focus is going

510
00:31:15,520 --> 00:31:17,680
Speaker 1: to be the middle over the next couple of years,

511
00:31:17,680 --> 00:31:20,280
Speaker 1: because I think we're finally seeing wealth effects come through

512
00:31:20,320 --> 00:31:23,200
Speaker 1: for them. So let's let's talk about two things you reference,

513
00:31:23,240 --> 00:31:26,760
Speaker 1: because they're both really interesting. One is you talked about

514
00:31:27,000 --> 00:31:31,080
Speaker 1: general um de leveraging of the household in debt. But

515
00:31:31,280 --> 00:31:35,920
Speaker 1: the story that I've been watching, and I'm not quite

516
00:31:36,120 --> 00:31:39,560
Speaker 1: a believer that this is what's gonna be our undoing,

517
00:31:40,200 --> 00:31:44,680
Speaker 1: is the student debt rise that that's clicked up through

518
00:31:44,920 --> 00:31:50,040
Speaker 1: a few trillions. How significant is that to future household formation,

519
00:31:50,400 --> 00:31:53,719
Speaker 1: home purchases, durable goods, etcetera. And then we can talk

520
00:31:53,760 --> 00:31:56,920
Speaker 1: a little bit about where we are on the labor cycle. Great,

521
00:31:56,920 --> 00:31:59,560
Speaker 1: So I'm glad that you brought up um student debt

522
00:31:59,600 --> 00:32:01,800
Speaker 1: because leieve it or not, it actually does tie into

523
00:32:01,840 --> 00:32:05,600
Speaker 1: this theme of housing equity as well. So, UH, student

524
00:32:05,640 --> 00:32:10,520
Speaker 1: loan debt is a problem. Uh. The the enrollment rates

525
00:32:10,520 --> 00:32:13,200
Speaker 1: have been on a very pronounced upward trend for quite

526
00:32:13,280 --> 00:32:16,520
Speaker 1: some time. But each time there's a downturn, you'll see

527
00:32:16,560 --> 00:32:19,960
Speaker 1: that that enrollment rate escalate, uh, and then we revert

528
00:32:20,000 --> 00:32:22,320
Speaker 1: back to previous trend. Now, of course, with the the

529
00:32:22,800 --> 00:32:25,280
Speaker 1: the depth of the downturn after the financial crisis and

530
00:32:25,360 --> 00:32:29,080
Speaker 1: the length of it sent many many more UH back

531
00:32:29,080 --> 00:32:33,360
Speaker 1: to school seeking higher education. UH. And and it's really

532
00:32:33,480 --> 00:32:36,240
Speaker 1: default rates among those that saw higher education that have

533
00:32:36,320 --> 00:32:40,240
Speaker 1: been the highest because they spent even more on school

534
00:32:40,280 --> 00:32:42,960
Speaker 1: came out and still we're facing just as horrible labor

535
00:32:43,000 --> 00:32:45,400
Speaker 1: market as when they went in. UH. And so we

536
00:32:45,480 --> 00:32:50,960
Speaker 1: saw student debt UH ratchet higher and delinquencies on student

537
00:32:51,000 --> 00:32:53,760
Speaker 1: debt wratchet higher, such that it became a hot button

538
00:32:53,840 --> 00:32:57,880
Speaker 1: issue at least for Democrats during the presidential election. So

539
00:32:58,160 --> 00:33:00,960
Speaker 1: that that the idea that we would do some mass

540
00:33:00,960 --> 00:33:04,240
Speaker 1: forgiveness on on student loans. I can tell you that

541
00:33:04,320 --> 00:33:08,680
Speaker 1: delinquent WHENCE rates have peaked as the labor market has improved.

542
00:33:09,320 --> 00:33:13,920
Speaker 1: Enrollment rates have come off of of UH the UM,

543
00:33:15,520 --> 00:33:19,760
Speaker 1: the previous UH the trend that was the upward trend

544
00:33:19,760 --> 00:33:22,480
Speaker 1: that was already established right for decades going into the

545
00:33:22,480 --> 00:33:26,440
Speaker 1: financial crisis that was escalated, and now we're coming back

546
00:33:26,440 --> 00:33:29,080
Speaker 1: down to that previous trend. So it look like enrollment

547
00:33:29,160 --> 00:33:31,400
Speaker 1: rates are softening, but they're not. They're really just coming

548
00:33:31,440 --> 00:33:35,320
Speaker 1: back to that that pre crisis trend. Here's what I

549
00:33:35,320 --> 00:33:38,280
Speaker 1: think is so important. At the same time that we

550
00:33:38,280 --> 00:33:41,680
Speaker 1: were pushing an unprecedent amount of students to enroll in

551
00:33:41,800 --> 00:33:45,600
Speaker 1: school and take on incredible amounts of student debt. Uh,

552
00:33:45,680 --> 00:33:49,840
Speaker 1: we lost housing equity, which was a primary the primary

553
00:33:49,920 --> 00:33:55,800
Speaker 1: way families were paying for that student's tuition. So most

554
00:33:55,840 --> 00:33:57,880
Speaker 1: of them, because who in their right mind was going

555
00:33:57,920 --> 00:34:00,520
Speaker 1: to give a student in this labor market and vironment

556
00:34:01,600 --> 00:34:06,520
Speaker 1: a private loan for school, they were forced to government loans. Now,

557
00:34:06,560 --> 00:34:10,000
Speaker 1: you can never default on a student loan that you

558
00:34:10,120 --> 00:34:13,520
Speaker 1: got from the government, Barry, if you retire, there's no

559
00:34:13,600 --> 00:34:15,680
Speaker 1: escaping that. If you retire at age sixty five and

560
00:34:15,680 --> 00:34:18,120
Speaker 1: you still haven't paid off your student loans, they will

561
00:34:18,160 --> 00:34:20,560
Speaker 1: garnish your social security How much? How much of an

562
00:34:20,560 --> 00:34:23,560
Speaker 1: insult is that everybody else has to go through a

563
00:34:23,640 --> 00:34:27,879
Speaker 1: legal process except Uncle Sam. You default on that, they

564
00:34:27,920 --> 00:34:30,319
Speaker 1: find you wherever you are right, there's no getting out

565
00:34:30,360 --> 00:34:32,640
Speaker 1: of it. Now. What do you think mom and dad

566
00:34:32,719 --> 00:34:36,799
Speaker 1: do if you default on your soft promise to pay

567
00:34:36,800 --> 00:34:39,640
Speaker 1: them back after funding your education by pulling equity out

568
00:34:39,640 --> 00:34:43,839
Speaker 1: of their home. Nothing? Nothing exactly. And believe me, Mom

569
00:34:43,840 --> 00:34:46,120
Speaker 1: and Dad, no, you're not paying them back when they

570
00:34:46,239 --> 00:34:49,439
Speaker 1: give you that for your student loan. Uh. And So

571
00:34:50,400 --> 00:34:53,640
Speaker 1: families weren't able to fund student loans in that way

572
00:34:53,800 --> 00:34:56,960
Speaker 1: anymore because they weren't able to pull equity out of

573
00:34:56,960 --> 00:35:00,520
Speaker 1: the home now we've as I mentioned, we've us popped

574
00:35:00,560 --> 00:35:04,400
Speaker 1: back into positive equity in housing in the first quarter

575
00:35:04,480 --> 00:35:07,600
Speaker 1: of this year with home price appreciation. That should continue,

576
00:35:08,080 --> 00:35:11,640
Speaker 1: and we saw for the first time this year mortgage

577
00:35:11,719 --> 00:35:16,040
Speaker 1: equity withdrawal pick up. So that means a future generation

578
00:35:16,080 --> 00:35:18,960
Speaker 1: of students going into school now can go back to

579
00:35:19,000 --> 00:35:21,799
Speaker 1: having college funded in the traditional way we used to

580
00:35:21,800 --> 00:35:25,239
Speaker 1: fund college, which is going to alleviate the burden on

581
00:35:25,440 --> 00:35:28,440
Speaker 1: them when they get out of college and start looking

582
00:35:28,480 --> 00:35:31,279
Speaker 1: to buy a home. So while we've got sort of

583
00:35:31,320 --> 00:35:35,440
Speaker 1: a lost generation right that went into college during the

584
00:35:35,440 --> 00:35:37,840
Speaker 1: financial crisis, came out and tried to get a job

585
00:35:38,239 --> 00:35:41,680
Speaker 1: during the after the financial crisis, and we'll have their

586
00:35:41,719 --> 00:35:45,719
Speaker 1: home buying plans delayed for a long time, and just

587
00:35:46,360 --> 00:35:49,839
Speaker 1: big durable goods purchase decisions are delayed for a long time.

588
00:35:49,880 --> 00:35:51,879
Speaker 1: The most the best work on this has been done

589
00:35:51,880 --> 00:35:54,160
Speaker 1: by the New York Fed that's looked into this very

590
00:35:54,200 --> 00:35:58,920
Speaker 1: closely and how it delays household for household formation buying plans.

591
00:35:58,960 --> 00:36:02,160
Speaker 1: Everything has a big anomic impact. But the generation of

592
00:36:02,239 --> 00:36:05,600
Speaker 1: kids now going into school, by the time they graduate,

593
00:36:05,640 --> 00:36:08,000
Speaker 1: they're not going to be saddled anywhere near with the

594
00:36:08,080 --> 00:36:12,080
Speaker 1: student debt burden at least not the government issued student

595
00:36:12,120 --> 00:36:16,399
Speaker 1: dead burton the previous generation had, and that is not

596
00:36:16,560 --> 00:36:19,760
Speaker 1: going to weigh on their decisions, their home purchasing decisions

597
00:36:19,760 --> 00:36:22,880
Speaker 1: in the same way as the unfortunate group, which is,

598
00:36:22,920 --> 00:36:26,160
Speaker 1: you know, sometimes it's just the unfortunate timing of your birth,

599
00:36:26,239 --> 00:36:31,400
Speaker 1: dumb right when you graduate college. Um, it's not going

600
00:36:31,440 --> 00:36:33,640
Speaker 1: to be the same for them. We have been speaking

601
00:36:33,640 --> 00:36:36,960
Speaker 1: with Ellen Zanner. She is the chief US economist for

602
00:36:37,080 --> 00:36:42,120
Speaker 1: Morgan Stanley. We love your comments, feedback and suggestions right

603
00:36:42,200 --> 00:36:45,240
Speaker 1: to us at m IB podcast at Bloomberg dot net.

604
00:36:45,840 --> 00:36:49,680
Speaker 1: Check out my daily column on Bloomberg View dot com.

605
00:36:49,880 --> 00:36:54,279
Speaker 1: Follow me on Twitter at Dholts. I'm Barry Reholts. You're

606
00:36:54,320 --> 00:37:14,759
Speaker 1: listening to Masters in Business on Bloomberg Radio. Welcome to

607
00:37:14,800 --> 00:37:16,920
Speaker 1: the podcast. Thank you Allen so much for doing this.

608
00:37:17,000 --> 00:37:20,080
Speaker 1: I've been looking forward to having this conversation for a

609
00:37:20,080 --> 00:37:23,799
Speaker 1: long time. Glad to be here. So we um. I

610
00:37:23,840 --> 00:37:27,160
Speaker 1: got easily distracted by many of your answers and didn't

611
00:37:27,200 --> 00:37:31,600
Speaker 1: get to a number of questions that I definitely want

612
00:37:31,640 --> 00:37:35,000
Speaker 1: to go back to. But we'll we'll have to start

613
00:37:35,480 --> 00:37:38,280
Speaker 1: before before we have a debate on the wealth effect

614
00:37:38,640 --> 00:37:42,200
Speaker 1: let's at least talk about wages um because you mentioned

615
00:37:42,200 --> 00:37:45,560
Speaker 1: a few things that I think are fascinating. Where are

616
00:37:45,640 --> 00:37:48,200
Speaker 1: we in the cycle, in the wage cycle, are we

617
00:37:48,360 --> 00:37:54,200
Speaker 1: finally starting to see an uptick in compensation or is

618
00:37:54,239 --> 00:37:57,359
Speaker 1: it gonna be you know, flat wages for as far

619
00:37:57,400 --> 00:38:01,480
Speaker 1: as the eye can see. So as the unemployment rate

620
00:38:01,520 --> 00:38:07,400
Speaker 1: has come down, uh, we have seen wages growth in

621
00:38:07,480 --> 00:38:11,560
Speaker 1: wages increase, but it's been pretty amnemic. Right, It's not

622
00:38:11,840 --> 00:38:14,160
Speaker 1: been nearly to the extent you would think you would

623
00:38:14,160 --> 00:38:18,319
Speaker 1: get given how quickly the unemployment rate has fallen. But

624
00:38:18,360 --> 00:38:21,399
Speaker 1: the truth of the matter is unemployment rate in this

625
00:38:21,560 --> 00:38:25,799
Speaker 1: environment with such a huge swath of the population, UH,

626
00:38:25,960 --> 00:38:28,560
Speaker 1: continuing to move further and further out into the age

627
00:38:28,560 --> 00:38:33,400
Speaker 1: groups associated with very low participation rates in the labor force.

628
00:38:33,600 --> 00:38:37,839
Speaker 1: There's gonna be, right, more older folks working and retiring,

629
00:38:38,000 --> 00:38:43,000
Speaker 1: leaving the labor force. There's this this gravitational pull just

630
00:38:43,080 --> 00:38:47,520
Speaker 1: from the demographic trend that that pulls that unemployment rate lower.

631
00:38:48,200 --> 00:38:52,080
Speaker 1: That's not indicative of labor market that's getting tighter and tighter.

632
00:38:52,120 --> 00:38:54,920
Speaker 1: So the point at which we reach a tight labor market,

633
00:38:54,960 --> 00:38:58,279
Speaker 1: which would really start to push the wage growth up

634
00:38:58,320 --> 00:39:01,520
Speaker 1: more quickly. It's just simply a lot lower where where

635
00:39:01,560 --> 00:39:04,600
Speaker 1: where than where it has been in the past. UM.

636
00:39:04,680 --> 00:39:07,839
Speaker 1: That said, the the growth in wages has been sort

637
00:39:07,880 --> 00:39:11,200
Speaker 1: of on a pretty steady, slow bleed upward. What we

638
00:39:11,280 --> 00:39:13,040
Speaker 1: do Bury and I find that a lot of our

639
00:39:13,080 --> 00:39:16,320
Speaker 1: work on the U S Economics team at Morgan Stanley

640
00:39:16,440 --> 00:39:19,080
Speaker 1: is now we slice and dice it to a degree

641
00:39:19,080 --> 00:39:24,640
Speaker 1: that we never had to before. UH because after financial crisis,

642
00:39:25,040 --> 00:39:27,440
Speaker 1: there are lots of things that are going under the on,

643
00:39:27,600 --> 00:39:29,600
Speaker 1: under the hood where you just can't look at anything

644
00:39:29,600 --> 00:39:32,640
Speaker 1: in the aggregate anymore, and wages are a great example

645
00:39:32,719 --> 00:39:36,120
Speaker 1: of that. Wage pressures have been rising much more quickly

646
00:39:36,560 --> 00:39:39,279
Speaker 1: in areas of the labor market where we have been

647
00:39:39,320 --> 00:39:42,200
Speaker 1: creating a lot of jobs. So more than six percent

648
00:39:42,239 --> 00:39:44,960
Speaker 1: of all new jobs that we've created since the recovery

649
00:39:45,000 --> 00:39:49,200
Speaker 1: began have been in the very UH typically low wage

650
00:39:49,239 --> 00:39:53,600
Speaker 1: paying service sector, low productivity enhancing areas of the economy

651
00:39:53,680 --> 00:39:57,520
Speaker 1: like retail and leisure and hospitality UH and home healthcare

652
00:39:57,640 --> 00:40:00,760
Speaker 1: workers and temporary workers and THO. Those are the areas

653
00:40:00,920 --> 00:40:05,520
Speaker 1: where as the unemployment rate has fallen, wages have accelerated

654
00:40:05,560 --> 00:40:08,160
Speaker 1: there because we're hiring a lot more workers there and

655
00:40:08,280 --> 00:40:11,799
Speaker 1: labor markets are actually tighter. They're how significant are the

656
00:40:12,160 --> 00:40:18,359
Speaker 1: raises in minimum wage to that cohortive of the workforce. Well,

657
00:40:18,400 --> 00:40:21,360
Speaker 1: let me put it two different ways. Uh. From a

658
00:40:21,440 --> 00:40:26,280
Speaker 1: socio economics perspective, it matters greatly for that worker making

659
00:40:26,280 --> 00:40:32,360
Speaker 1: a minimum wage. From a market perspective, not much, because

660
00:40:32,360 --> 00:40:34,680
Speaker 1: if you look at wage growth in the aggregate, what

661
00:40:34,719 --> 00:40:39,080
Speaker 1: you're doing is raising wages for the most marginally paid worker,

662
00:40:39,200 --> 00:40:41,799
Speaker 1: and their total wage bill just does not move the

663
00:40:41,840 --> 00:40:45,800
Speaker 1: needle much in the aggregum. Very often there is in healthcare,

664
00:40:45,920 --> 00:40:49,240
Speaker 1: it's just a modest increase. It's just a modest increase

665
00:40:49,239 --> 00:40:53,680
Speaker 1: in salary from what is already very low salary. And

666
00:40:53,760 --> 00:40:59,360
Speaker 1: remember when states raise their minimum wages, uh, you know,

667
00:40:59,480 --> 00:41:02,959
Speaker 1: in or when let me back up, when we talk

668
00:41:03,040 --> 00:41:06,759
Speaker 1: about if the federal government raises the minimum wage, uh,

669
00:41:06,800 --> 00:41:09,760
Speaker 1: and all of the discussion that ensues on both sides

670
00:41:09,840 --> 00:41:11,960
Speaker 1: of the argument of is it good, is it bad?

671
00:41:12,120 --> 00:41:15,600
Speaker 1: Is it gonna lead to high unemployment? Or is it

672
00:41:15,680 --> 00:41:18,920
Speaker 1: necessary for those workers to have a living wage and survive.

673
00:41:19,560 --> 00:41:23,360
Speaker 1: Oftentimes what's lost in that argument is that most states

674
00:41:23,360 --> 00:41:26,239
Speaker 1: already pay above the federal minimum, So you really have

675
00:41:26,280 --> 00:41:29,359
Speaker 1: to look to see what states are doing. UH. And

676
00:41:29,680 --> 00:41:34,440
Speaker 1: individually states have been many many states have been raising

677
00:41:34,480 --> 00:41:37,880
Speaker 1: the minimum wage UM. But those are really some of

678
00:41:37,920 --> 00:41:42,080
Speaker 1: the higher success You know, when we see California or

679
00:41:42,480 --> 00:41:46,200
Speaker 1: Seattle or even I was gonna say Washington State or

680
00:41:46,200 --> 00:41:49,799
Speaker 1: even the city of Seattle REGI minium wage, raise the

681
00:41:49,800 --> 00:41:53,960
Speaker 1: minimum wage, that's really very specific to a part of

682
00:41:53,960 --> 00:41:57,200
Speaker 1: the country that's just booming. But what does that mean

683
00:41:57,360 --> 00:42:02,080
Speaker 1: for Oklahoma or Arkansas or Kentucky where you may not

684
00:42:02,200 --> 00:42:07,840
Speaker 1: have the same in this case, the technology driven boom towns.

685
00:42:08,680 --> 00:42:14,600
Speaker 1: What do their minimum wage increases mean relative to UH

686
00:42:14,840 --> 00:42:18,440
Speaker 1: states that aren't doing as well? So typically, so you

687
00:42:18,480 --> 00:42:22,440
Speaker 1: do have to differentiate state by state. I think it

688
00:42:22,520 --> 00:42:24,840
Speaker 1: is very important, right because the cost of living in

689
00:42:24,920 --> 00:42:26,840
Speaker 1: one state, which I think is what you're getting at,

690
00:42:26,920 --> 00:42:30,759
Speaker 1: is very different than another state. So raising you can

691
00:42:30,880 --> 00:42:33,600
Speaker 1: raise them inimum wage to fifteen dollars in Seattle, but

692
00:42:33,680 --> 00:42:38,080
Speaker 1: you can't do it in Birmingham, Alabama. UH. And so

693
00:42:38,160 --> 00:42:40,759
Speaker 1: this is from the company by company perspective. This is

694
00:42:40,800 --> 00:42:43,520
Speaker 1: the argument that they'll make. And there actually has been

695
00:42:43,560 --> 00:42:46,279
Speaker 1: a good body of academic work that has suggested that

696
00:42:46,400 --> 00:42:51,200
Speaker 1: raising the minimum wage, particularly in states like Alabama. UH

697
00:42:51,600 --> 00:42:56,200
Speaker 1: doesn't necessarily lead to immediate layoffs UH in certain service

698
00:42:56,200 --> 00:43:02,000
Speaker 1: sector industries, but it certainly depresses hiring UH from there forward. UH.

699
00:43:02,040 --> 00:43:04,040
Speaker 1: And so I think it is something that has to

700
00:43:04,040 --> 00:43:05,839
Speaker 1: be looked at on the state by state basis. If

701
00:43:05,840 --> 00:43:08,600
Speaker 1: you're going to do it at the federal level, I

702
00:43:08,680 --> 00:43:11,759
Speaker 1: think something like the earned income tax credit, which we

703
00:43:11,800 --> 00:43:15,040
Speaker 1: already have in place, which you can expand, is a

704
00:43:15,160 --> 00:43:18,480
Speaker 1: much better way to do it than a federal minimum wage.

705
00:43:18,680 --> 00:43:22,360
Speaker 1: So rather than putting the burden on businesses and telling

706
00:43:22,600 --> 00:43:26,040
Speaker 1: where businesses have no choice but to adjust their practices

707
00:43:26,120 --> 00:43:29,879
Speaker 1: because they're being forced to raise wages whether it's good

708
00:43:29,920 --> 00:43:33,040
Speaker 1: for that particular business in that particular state, in that

709
00:43:33,080 --> 00:43:37,120
Speaker 1: particular town, doing it through expanding the earned income tax

710
00:43:37,120 --> 00:43:39,520
Speaker 1: credit is a way you hit the lowest paid workers

711
00:43:39,520 --> 00:43:42,960
Speaker 1: in the US, and the government, meaning the taxpayer more broadly,

712
00:43:43,239 --> 00:43:45,640
Speaker 1: is bearing the burden of that cost. See my beef

713
00:43:45,680 --> 00:43:50,120
Speaker 1: about minimum wage outside of the high earning cities the

714
00:43:50,120 --> 00:43:52,719
Speaker 1: East Coast, the West Coast has always been Do you

715
00:43:52,719 --> 00:43:56,200
Speaker 1: remember the helpline that McDonald's had set up. They were

716
00:43:56,280 --> 00:43:59,840
Speaker 1: hiring people, they were capping them at thirty hours, and

717
00:44:00,040 --> 00:44:03,040
Speaker 1: then they were sending them to aid to dependent children

718
00:44:03,200 --> 00:44:09,239
Speaker 1: and welfare and medicaid, effectively having the taxpayer subsidize the

719
00:44:09,320 --> 00:44:13,080
Speaker 1: labor force of a profitable company as a taxpayer. I

720
00:44:13,120 --> 00:44:16,200
Speaker 1: always was offended by that, and that's what first sent

721
00:44:16,280 --> 00:44:20,680
Speaker 1: me looking at minimum wage. It's wait, why why are

722
00:44:20,800 --> 00:44:25,439
Speaker 1: you subsidizing your workforce with tax David Dollar? If if,

723
00:44:26,120 --> 00:44:29,240
Speaker 1: if I need to give you a minimum wage raise

724
00:44:29,719 --> 00:44:33,120
Speaker 1: in order to have these people no longer qualify, you

725
00:44:33,120 --> 00:44:35,040
Speaker 1: should pay for that, not me. And if it means

726
00:44:35,080 --> 00:44:37,640
Speaker 1: the burger is going to cose fifty cents more, I

727
00:44:37,680 --> 00:44:40,920
Speaker 1: don't care if burger shouldn't be taxpayer subsidized. And I

728
00:44:40,960 --> 00:44:43,400
Speaker 1: think a lot of that sort of gets lost in

729
00:44:43,440 --> 00:44:46,520
Speaker 1: the minimum wage debate. That's very specific to a handful

730
00:44:46,560 --> 00:44:50,239
Speaker 1: of company market and it's a very sensitive topic. It's

731
00:44:50,400 --> 00:44:54,319
Speaker 1: um with a with a lot of heated debate on

732
00:44:54,440 --> 00:44:56,400
Speaker 1: both sides of the coin. I think one of the

733
00:44:57,040 --> 00:45:01,439
Speaker 1: one of the most interesting um uh things I think

734
00:45:01,480 --> 00:45:04,800
Speaker 1: I've studied in the past in terms of company behavior

735
00:45:04,840 --> 00:45:09,200
Speaker 1: on the back of minimum wages. When uh we were

736
00:45:09,239 --> 00:45:12,960
Speaker 1: facing UM I think starting in in fourteen or so,

737
00:45:13,000 --> 00:45:15,120
Speaker 1: we were going to be facing a lot of states

738
00:45:15,160 --> 00:45:17,319
Speaker 1: that were raising minimum wages, and so to get out

739
00:45:17,320 --> 00:45:22,240
Speaker 1: ahead of that, Walmart raised the wages of its workers

740
00:45:22,320 --> 00:45:25,319
Speaker 1: across the board UM and I thought that it was

741
00:45:25,520 --> 00:45:29,839
Speaker 1: a brilliant marketing UM tool for them, because you're gonna

742
00:45:29,880 --> 00:45:32,640
Speaker 1: be forced, um via a lot of your states to

743
00:45:32,680 --> 00:45:34,840
Speaker 1: be raised anyway, why don't do it ahead of time

744
00:45:35,320 --> 00:45:37,359
Speaker 1: and get the pad on the back for doing this

745
00:45:37,480 --> 00:45:42,520
Speaker 1: public service first. And on top of that, uh, there

746
00:45:42,560 --> 00:45:47,799
Speaker 1: has been a real, a credible study out there that

747
00:45:48,040 --> 00:45:53,480
Speaker 1: many of Walmart's workers spend their paychecks in Walmart. So

748
00:45:53,719 --> 00:45:56,440
Speaker 1: pay them more. It comes right back to you and

749
00:45:56,520 --> 00:46:00,960
Speaker 1: increased purchasing in your stores. It's brilliant plause. Walmart had

750
00:46:01,000 --> 00:46:04,040
Speaker 1: a big issue with once we came out of the

751
00:46:04,120 --> 00:46:08,160
Speaker 1: financial crisis, they had a big employee turnover issue and

752
00:46:08,200 --> 00:46:12,160
Speaker 1: a big not only retention but recruitment issue. And it's

753
00:46:12,360 --> 00:46:15,560
Speaker 1: very expensive to find and hire people and then train

754
00:46:15,680 --> 00:46:18,600
Speaker 1: them in a Walmart and then they leave after three months.

755
00:46:19,000 --> 00:46:21,719
Speaker 1: And so the most recent I want to say it

756
00:46:21,800 --> 00:46:24,799
Speaker 1: was last February, maybe it was a quarter before that,

757
00:46:25,080 --> 00:46:28,720
Speaker 1: CEO Walmart came out and said our retention numbers are better?

758
00:46:28,800 --> 00:46:32,960
Speaker 1: Are are? Employee turnover numbers have reduced and the pay

759
00:46:33,000 --> 00:46:36,279
Speaker 1: increase effectively is paid for itself, which is really a

760
00:46:36,320 --> 00:46:40,719
Speaker 1: shocking thing because the previous management was really pushing back

761
00:46:40,760 --> 00:46:43,680
Speaker 1: against Uh. This, This was very much a seat change.

762
00:46:43,680 --> 00:46:45,879
Speaker 1: And yeah, I think it's a great example of how

763
00:46:46,480 --> 00:46:49,560
Speaker 1: a company can very creatively get around something that's a

764
00:46:49,560 --> 00:46:52,760
Speaker 1: sticky issue, like like minimum wage increase, and it doesn't

765
00:46:52,800 --> 00:46:54,480
Speaker 1: have to be the end of the world. We were

766
00:46:54,520 --> 00:46:58,160
Speaker 1: talking a little earlier, or I was referencing the technology

767
00:46:58,239 --> 00:47:01,240
Speaker 1: boom on the West Coast, But since we're talking about labor,

768
00:47:01,920 --> 00:47:08,680
Speaker 1: how significant has been technology and automation and software to

769
00:47:09,520 --> 00:47:14,600
Speaker 1: either the wage malaise or the quality of jobs that

770
00:47:14,719 --> 00:47:20,640
Speaker 1: we are creative? So it's a huge topic productivity, automation,

771
00:47:21,760 --> 00:47:25,279
Speaker 1: replacing jobs with robots. Um. And it's something that's very

772
00:47:25,360 --> 00:47:29,360
Speaker 1: difficult to see in real time. UM. I think this

773
00:47:29,440 --> 00:47:31,359
Speaker 1: is gonna be something very where we wake up twenty

774
00:47:31,400 --> 00:47:33,840
Speaker 1: years from now and realize that we're living through Wally

775
00:47:33,960 --> 00:47:37,040
Speaker 1: the movie right right now. Um. But I can tell

776
00:47:37,080 --> 00:47:42,120
Speaker 1: you that, um uh. From an economic theory perspective, UH,

777
00:47:42,200 --> 00:47:45,640
Speaker 1: it's easy for me to argue why putting in chaos

778
00:47:45,719 --> 00:47:48,560
Speaker 1: at McDonald's is a good thing because it will raise

779
00:47:48,600 --> 00:47:55,920
Speaker 1: the productivity of its workers. And ultimately, UH, wages follow productivity. UH,

780
00:47:55,960 --> 00:47:59,880
Speaker 1: you know, raising productivity if a business is UH makes

781
00:48:00,320 --> 00:48:04,560
Speaker 1: that capex, that capital expenditure to raise its productivity. Productivity

782
00:48:04,640 --> 00:48:10,200
Speaker 1: drives profits. And so even as as as profits rise,

783
00:48:10,600 --> 00:48:14,680
Speaker 1: you can pay your workers more, but your labor share

784
00:48:14,680 --> 00:48:19,640
Speaker 1: of income, UH, your labor share of costs does not rise.

785
00:48:20,360 --> 00:48:24,640
Speaker 1: Fewer people making more money theoretically right, but it lifts

786
00:48:24,719 --> 00:48:27,640
Speaker 1: wages of everyone because those workers that have been displaced,

787
00:48:27,840 --> 00:48:31,959
Speaker 1: right are business overall expands and you have to hire

788
00:48:32,000 --> 00:48:34,600
Speaker 1: them in other ways. So for McDonald's, go back to

789
00:48:34,680 --> 00:48:37,759
Speaker 1: that example, they put in more chios where you go

790
00:48:37,800 --> 00:48:40,440
Speaker 1: in and order, so you no longer need the people

791
00:48:40,480 --> 00:48:43,040
Speaker 1: at the front desk taking your money. You're ordering from

792
00:48:43,080 --> 00:48:47,480
Speaker 1: the kiosk. But maybe they're able to service more uh

793
00:48:47,640 --> 00:48:50,640
Speaker 1: folks faster at lunch time and produce a lot more

794
00:48:50,719 --> 00:48:53,480
Speaker 1: lunches in one hour, and so they need more people

795
00:48:53,480 --> 00:48:57,200
Speaker 1: working behind the counter. So they extend their existing workforce,

796
00:48:57,280 --> 00:49:00,520
Speaker 1: and they're able to pay their existing workforce. Are plus,

797
00:49:00,560 --> 00:49:03,560
Speaker 1: somebody's building those chiosks, someone's doing them, making the screens,

798
00:49:03,640 --> 00:49:06,640
Speaker 1: writing the software there exactly, so we can also the

799
00:49:06,640 --> 00:49:09,720
Speaker 1: theory is kind of easy to explain right, why higher

800
00:49:09,760 --> 00:49:13,879
Speaker 1: productivity wages tend to track productivity higher, and so why

801
00:49:14,080 --> 00:49:18,600
Speaker 1: you as a policy maker, Uh, whether your fiscal policy maker,

802
00:49:18,680 --> 00:49:21,719
Speaker 1: a monetary policy maker, or economists, you all want productivity

803
00:49:21,760 --> 00:49:24,680
Speaker 1: to be higher because it still is the best indicator

804
00:49:24,719 --> 00:49:28,640
Speaker 1: of overall health and well being of your economy, of

805
00:49:28,640 --> 00:49:33,160
Speaker 1: your labor force. Uh. The the difficult thing to deal

806
00:49:33,239 --> 00:49:36,800
Speaker 1: with is that there's always a temporal effect. There's always

807
00:49:36,880 --> 00:49:39,600
Speaker 1: a segment of the population where labor is displaced for

808
00:49:39,640 --> 00:49:43,480
Speaker 1: a time. Uh. And either that labor is never absorbed

809
00:49:43,800 --> 00:49:46,839
Speaker 1: back um or it takes time to absorb it into

810
00:49:46,880 --> 00:49:51,160
Speaker 1: other industries that are expanding. Uh. And so UM, I

811
00:49:51,200 --> 00:49:53,600
Speaker 1: think that's the difficult part to deal with, is the

812
00:49:53,640 --> 00:49:56,040
Speaker 1: temporal effect. So let's let's get a little wonky. Since

813
00:49:56,080 --> 00:50:01,600
Speaker 1: you mentioned productivity. We've seen really me yoker productivity gains

814
00:50:02,000 --> 00:50:04,799
Speaker 1: not just for quarters or years, but this seems to

815
00:50:04,840 --> 00:50:07,759
Speaker 1: be going on for decades. What's the old joke? The

816
00:50:09,480 --> 00:50:13,480
Speaker 1: productivity effect is seen everywhere except in the statistics. Do

817
00:50:13,520 --> 00:50:16,000
Speaker 1: we have a productivity issue or do we have a

818
00:50:16,040 --> 00:50:21,320
Speaker 1: productivity measuring issue? Okay, so the measuring issues, since you

819
00:50:21,360 --> 00:50:23,120
Speaker 1: brought it up, because that is a that is a

820
00:50:25,160 --> 00:50:29,560
Speaker 1: it's a hot button issue. Measurement mismeasurement has always been there,

821
00:50:29,920 --> 00:50:32,440
Speaker 1: and so as an economist, I would want to show

822
00:50:32,480 --> 00:50:35,239
Speaker 1: that miss measurement is worse today than it has been

823
00:50:35,239 --> 00:50:36,759
Speaker 1: in the past. And I'm not sure that I can

824
00:50:36,760 --> 00:50:39,680
Speaker 1: show that because I certainly don't want to hang my

825
00:50:39,760 --> 00:50:43,239
Speaker 1: hat on trying to say that, oh, well, productivity is

826
00:50:43,239 --> 00:50:46,480
Speaker 1: not low like everyone thinks it is, it's just simply mismeasurement.

827
00:50:46,520 --> 00:50:48,759
Speaker 1: I think that's too cute, too easy of an explanation,

828
00:50:48,760 --> 00:50:51,080
Speaker 1: because I think miss measurement has always been been there.

829
00:50:51,400 --> 00:50:54,520
Speaker 1: Doesn't exist today absolutely, um, but was it there in

830
00:50:54,560 --> 00:50:56,799
Speaker 1: the past as well? Absolutely. I can remember the dot

831
00:50:56,800 --> 00:51:00,080
Speaker 1: com boom or or the Y two K come and

832
00:51:00,120 --> 00:51:02,319
Speaker 1: he's going out and buying all of this software that

833
00:51:02,400 --> 00:51:04,160
Speaker 1: was going to help them kind of get over that

834
00:51:04,280 --> 00:51:08,920
Speaker 1: hump of Y two k. Um it uh took the

835
00:51:08,960 --> 00:51:12,040
Speaker 1: government about five years to fully reflect how all of

836
00:51:12,080 --> 00:51:16,760
Speaker 1: that that purchasing of software and deploying across business platforms

837
00:51:16,800 --> 00:51:21,800
Speaker 1: affected productivity. Um. So I do believe there's ever present

838
00:51:22,080 --> 00:51:26,160
Speaker 1: and always mismeasurement. But if you look at trend productivity

839
00:51:26,200 --> 00:51:29,719
Speaker 1: over time, it's been falling for decades over time, we've

840
00:51:29,719 --> 00:51:36,279
Speaker 1: been moving from a production lead economy, or let's say,

841
00:51:36,280 --> 00:51:40,840
Speaker 1: manufacturing led economy, to a service sector led economy. Manufacturing

842
00:51:41,000 --> 00:51:45,319
Speaker 1: has much higher associated rates of productivity than service industries,

843
00:51:45,400 --> 00:51:50,359
Speaker 1: and so over time trend productivity has slowed. Now, do

844
00:51:50,440 --> 00:51:54,320
Speaker 1: I think that it's flat to up half a percent,

845
00:51:54,360 --> 00:51:56,160
Speaker 1: which is where it's been over the past five or

846
00:51:56,200 --> 00:51:59,239
Speaker 1: six years. No, I don't think that's trend rate of productivity.

847
00:51:59,239 --> 00:52:01,879
Speaker 1: I think we can see after the financial crisis there

848
00:52:01,960 --> 00:52:07,160
Speaker 1: was an extreme shortfall in capital expenditures UM where only

849
00:52:07,239 --> 00:52:10,800
Speaker 1: just now I'm seeing the kind of data that shows

850
00:52:10,840 --> 00:52:14,359
Speaker 1: me companies are convinced now that it's time to go

851
00:52:14,400 --> 00:52:17,879
Speaker 1: ahead and start adding UH two capex and doing some

852
00:52:17,920 --> 00:52:22,000
Speaker 1: capital deepening. And part of that is the the cyclical

853
00:52:22,080 --> 00:52:25,960
Speaker 1: rebound we're finally seeing in this cycle from global stronger

854
00:52:25,960 --> 00:52:29,680
Speaker 1: global growth, and just companies in the U S having underbuilt,

855
00:52:29,840 --> 00:52:33,640
Speaker 1: underinvested for so long and labor costs rising to a

856
00:52:33,680 --> 00:52:39,280
Speaker 1: point where now UH capital is being incentivized over labor. Okay,

857
00:52:39,400 --> 00:52:42,080
Speaker 1: So I don't think productivity is going to be stuck

858
00:52:42,360 --> 00:52:44,279
Speaker 1: flat on its back where it has been for five

859
00:52:44,360 --> 00:52:47,120
Speaker 1: or six years. But I think around one percent productivity

860
00:52:47,200 --> 00:52:50,359
Speaker 1: is probably the best we get to, because I think

861
00:52:50,360 --> 00:52:53,600
Speaker 1: the run rate of UH investment in the US it's

862
00:52:53,640 --> 00:52:56,720
Speaker 1: probably around three to five not seven to nine percent,

863
00:52:56,760 --> 00:52:59,040
Speaker 1: which is where it's been in the past. And a

864
00:52:59,040 --> 00:53:01,680
Speaker 1: lot of that is just the elongating that trend, continued

865
00:53:01,719 --> 00:53:04,239
Speaker 1: trend that where is the economy growing. It's growing in

866
00:53:04,239 --> 00:53:06,560
Speaker 1: the service side. So let me push back on that

867
00:53:06,640 --> 00:53:09,680
Speaker 1: a little bit, or share with you the standard pushback.

868
00:53:10,200 --> 00:53:13,759
Speaker 1: When we were manufacturing, you could count the number of

869
00:53:13,800 --> 00:53:15,640
Speaker 1: widgets and how much they were sold for and what

870
00:53:15,719 --> 00:53:21,879
Speaker 1: it cost to actually create these individual widgets, and our

871
00:53:21,920 --> 00:53:26,200
Speaker 1: productivity gains were easy to see and measure. Today, given

872
00:53:26,200 --> 00:53:30,440
Speaker 1: the rise of technology, it's so much more nuanced. Let's

873
00:53:30,480 --> 00:53:34,520
Speaker 1: take this conversation just as an example thirty years ago.

874
00:53:34,680 --> 00:53:37,720
Speaker 1: This is a radio broadcast that reaches whoever it reaches,

875
00:53:38,280 --> 00:53:40,799
Speaker 1: and so the amount of time and energy we put

876
00:53:40,800 --> 00:53:46,440
Speaker 1: into this is heard by X number of listeners. Today,

877
00:53:46,600 --> 00:53:48,839
Speaker 1: this will get done, it'll get edited, it will get

878
00:53:48,960 --> 00:53:51,880
Speaker 1: nicely polished up, and in two or three weeks it

879
00:53:51,920 --> 00:53:56,480
Speaker 1: will go up on Apple, iTunes and SoundCloud and overcast

880
00:53:56,560 --> 00:53:59,120
Speaker 1: and Bloomberg dot com. And not only will it be

881
00:53:59,160 --> 00:54:01,600
Speaker 1: listened to by a whole plus, it will go on

882
00:54:01,640 --> 00:54:05,160
Speaker 1: the radio, so you get the original audience times four,

883
00:54:05,400 --> 00:54:09,719
Speaker 1: time six. But it persists forever in somebody two years

884
00:54:09,760 --> 00:54:12,920
Speaker 1: from now, doesn't Ellen Zentner google search and said, oh,

885
00:54:12,960 --> 00:54:15,960
Speaker 1: what's this podcast that is? That is terrifying. It will

886
00:54:16,000 --> 00:54:18,160
Speaker 1: haunt me for the rest of my life. Essentially, we'll

887
00:54:18,239 --> 00:54:19,960
Speaker 1: let it out all the cuss words. None will hear

888
00:54:20,000 --> 00:54:23,480
Speaker 1: the terrible things you've said. So here's another wrinkle on productivity,

889
00:54:23,560 --> 00:54:30,640
Speaker 1: which which um, I think presents an interesting, um, not dilemma,

890
00:54:30,840 --> 00:54:37,320
Speaker 1: but but let's say food for thought for policymakers going forward. UM.

891
00:54:37,440 --> 00:54:41,479
Speaker 1: We can see in the data that we track on

892
00:54:42,160 --> 00:54:44,920
Speaker 1: R and D spending research and development, we can see

893
00:54:44,960 --> 00:54:49,080
Speaker 1: that it has been soaring yet and and typically productivity

894
00:54:49,120 --> 00:54:52,880
Speaker 1: would follow that. Yet productivity hasn't. So where where is

895
00:54:52,880 --> 00:54:54,759
Speaker 1: all that R and D going? What is it going into?

896
00:54:54,840 --> 00:54:58,040
Speaker 1: There must be some change going on. And what we

897
00:54:58,160 --> 00:55:00,640
Speaker 1: found when we look at where is that R and

898
00:55:00,719 --> 00:55:06,200
Speaker 1: D going? Um, rather than old world technology, that that

899
00:55:06,360 --> 00:55:08,799
Speaker 1: R and D is going into, say developing a robot

900
00:55:08,920 --> 00:55:12,359
Speaker 1: arm to work in an auto manufacturing facility, a lot

901
00:55:12,400 --> 00:55:16,000
Speaker 1: of that is going into the biotech space. So we're

902
00:55:16,040 --> 00:55:18,880
Speaker 1: elongating the age of an eighty year old to ninety

903
00:55:18,960 --> 00:55:23,279
Speaker 1: years And while that's extremely socially desirable, it's not a

904
00:55:23,320 --> 00:55:27,240
Speaker 1: productivity enhancer. It's not going to help that that eighty

905
00:55:27,320 --> 00:55:29,960
Speaker 1: year old work for ten more years at at Walmart

906
00:55:30,040 --> 00:55:32,400
Speaker 1: is a greeter all right. I'm just saying it's not

907
00:55:32,480 --> 00:55:36,480
Speaker 1: a traditional productory enhancer. Now here's another thing. Uh, And

908
00:55:36,560 --> 00:55:38,600
Speaker 1: so does that mean we don't do it? We shouldn't

909
00:55:38,600 --> 00:55:40,440
Speaker 1: do it because it doesn't raise it productivity in a

910
00:55:40,560 --> 00:55:43,560
Speaker 1: in a GDP marketable way, right, And I would think

911
00:55:43,560 --> 00:55:45,640
Speaker 1: the answer is no, we should absolutely do it, but

912
00:55:45,680 --> 00:55:47,680
Speaker 1: it's not going to translate into productivity in the way

913
00:55:47,719 --> 00:55:51,239
Speaker 1: we are used to. Here's another example. There's been a

914
00:55:51,280 --> 00:55:54,520
Speaker 1: massive shift in R and D spending, uh, in the

915
00:55:54,600 --> 00:55:58,160
Speaker 1: area of consumer discretionary. Now, what the heck does that mean?

916
00:55:58,560 --> 00:56:01,640
Speaker 1: We had to dig into it. It's Amazon. It's Amazon

917
00:56:01,760 --> 00:56:05,959
Speaker 1: creating the type of platform that allows me to order

918
00:56:06,719 --> 00:56:09,479
Speaker 1: a makeup product that I've run out of when I'm

919
00:56:09,680 --> 00:56:14,160
Speaker 1: walking down the hallway to the bathroom. Right within fifteen seconds,

920
00:56:14,200 --> 00:56:16,680
Speaker 1: I've placed a one click order and ordered that makeup

921
00:56:16,719 --> 00:56:20,799
Speaker 1: before I've even hit the bathroom stall. And now my

922
00:56:20,880 --> 00:56:25,520
Speaker 1: productivity rises, or what's happening instead is all of these

923
00:56:25,560 --> 00:56:30,120
Speaker 1: technological advancements are helping you enjoy your life better. They're

924
00:56:30,160 --> 00:56:33,440
Speaker 1: giving me more hours of my day back to do

925
00:56:33,560 --> 00:56:35,279
Speaker 1: other things. To it, you don't have to go to

926
00:56:35,320 --> 00:56:37,319
Speaker 1: the mall to get whatever it is that you just

927
00:56:37,480 --> 00:56:41,400
Speaker 1: ordered exactly. So is that worth nothing because it doesn't

928
00:56:41,480 --> 00:56:44,560
Speaker 1: raise my productivity? It makes my life more enjoyable. Is

929
00:56:44,560 --> 00:56:47,480
Speaker 1: there not an amenity value in that? Right? But it

930
00:56:47,600 --> 00:56:51,040
Speaker 1: just doesn't raise my quality of life? Except that productivity

931
00:56:51,080 --> 00:56:53,319
Speaker 1: is how we measure quality, and so so what's the

932
00:56:53,360 --> 00:56:57,719
Speaker 1: old Drucker quote. Not everything that's measure that can be

933
00:56:57,760 --> 00:57:00,920
Speaker 1: measured matters, and not everything that matters can be measured

934
00:57:01,160 --> 00:57:05,000
Speaker 1: exactly exactly. So I think that is the conundrum for

935
00:57:05,080 --> 00:57:12,400
Speaker 1: policymakers because they're constantly disappointed with low productivity. I and

936
00:57:12,680 --> 00:57:15,760
Speaker 1: because that is economic theory tells you that's the single

937
00:57:15,800 --> 00:57:19,120
Speaker 1: best indicator for standard of living in your economy of

938
00:57:19,240 --> 00:57:21,720
Speaker 1: living has not gone up in five or some nothing

939
00:57:21,800 --> 00:57:24,760
Speaker 1: but the fact that I don't have to go to

940
00:57:24,840 --> 00:57:27,320
Speaker 1: the mall with my wife to get lipstick anymore or

941
00:57:27,360 --> 00:57:32,360
Speaker 1: whatever lead in quality of life. I actually don't mind shopping.

942
00:57:33,160 --> 00:57:34,960
Speaker 1: We go through the malls, we make I make fun

943
00:57:35,000 --> 00:57:38,840
Speaker 1: of stuff it's entertainment. That's not shopping. Barry Berry is

944
00:57:38,920 --> 00:57:41,880
Speaker 1: walking in, actually buying things and helping the economy, well,

945
00:57:42,000 --> 00:57:44,480
Speaker 1: walking on and making fun. We call it economic research

946
00:57:44,560 --> 00:57:48,200
Speaker 1: or economic stimulus. Absolutely, But I would given the choice

947
00:57:48,240 --> 00:57:50,600
Speaker 1: between the half hour takes to drive and park and

948
00:57:50,640 --> 00:57:53,920
Speaker 1: actually get into a store and then do our thing

949
00:57:54,000 --> 00:57:57,760
Speaker 1: and then head out. Wait, I could just scroll through

950
00:57:57,880 --> 00:58:01,520
Speaker 1: Amazon find what I want in fives and save myself

951
00:58:01,640 --> 00:58:05,479
Speaker 1: ninety minutes of my weekend. Hence the death of the mall.

952
00:58:05,800 --> 00:58:07,960
Speaker 1: But no doubt about it. Well, we all you know,

953
00:58:08,000 --> 00:58:11,520
Speaker 1: the United States has this huge retail footprint. We're overbuilt

954
00:58:12,000 --> 00:58:14,760
Speaker 1: on a per capita basis, at least compared to Europe,

955
00:58:15,200 --> 00:58:19,960
Speaker 1: and that is certainly going through its secular changes. Um.

956
00:58:20,120 --> 00:58:23,360
Speaker 1: I could talk about this stuff forever. It's fascinating, But

957
00:58:23,440 --> 00:58:25,200
Speaker 1: what I want to do is get to specially since

958
00:58:25,240 --> 00:58:27,240
Speaker 1: you brought up shopping. Yeah, oh, for sure, we can

959
00:58:27,280 --> 00:58:30,360
Speaker 1: talk a ton about But let's instead jump into my

960
00:58:30,480 --> 00:58:33,760
Speaker 1: favorite questions. Um. These are what I ask all my

961
00:58:33,880 --> 00:58:37,920
Speaker 1: guests and and they're always kind of generate interesting responses.

962
00:58:38,040 --> 00:58:42,600
Speaker 1: Let's start with what's the most important thing people don't

963
00:58:42,640 --> 00:58:45,920
Speaker 1: know about your background? Oh? My gosh, I think if

964
00:58:45,960 --> 00:58:49,120
Speaker 1: I if we keep it from a business perspective, any perspective,

965
00:58:49,440 --> 00:58:53,720
Speaker 1: any perspective at all. Uh Like, I'm always surprised when

966
00:58:53,760 --> 00:58:57,280
Speaker 1: someone says I climbed Mount Kilimanjaro. I'm like, what, But

967
00:58:57,400 --> 00:58:59,600
Speaker 1: people have dropped that sort of Yeah, I'm sure I've

968
00:58:59,640 --> 00:59:04,160
Speaker 1: done a lot of amazing things. Just there's so many

969
00:59:04,200 --> 00:59:06,800
Speaker 1: of them. Nothing leaves the mind exactly, There's so many

970
00:59:06,880 --> 00:59:09,680
Speaker 1: of them. Uh so keep it on the professional side.

971
00:59:09,680 --> 00:59:14,480
Speaker 1: What what do people not know about you? I think

972
00:59:14,600 --> 00:59:19,520
Speaker 1: if you were to start the conversation by saying, this

973
00:59:19,600 --> 00:59:22,920
Speaker 1: is Ellen Sentner, she's US chief economist of Morgan Stanley,

974
00:59:23,000 --> 00:59:27,720
Speaker 1: and then work backwards to how I started my career,

975
00:59:28,000 --> 00:59:32,760
Speaker 1: or work backwards even further to how I even approached

976
00:59:32,840 --> 00:59:36,360
Speaker 1: university and went through school, that whole process. You would

977
00:59:36,520 --> 00:59:39,800
Speaker 1: you would never backtrack to where I started and draw

978
00:59:39,840 --> 00:59:43,080
Speaker 1: a line to chief economy. What did you do at

979
00:59:43,120 --> 00:59:46,040
Speaker 1: the beginning that didn't say economics in the future When

980
00:59:46,080 --> 00:59:49,439
Speaker 1: I was in high school? Um, you go way back. Yeah,

981
00:59:49,440 --> 00:59:51,280
Speaker 1: when I was in high school, there was no just

982
00:59:51,320 --> 00:59:54,320
Speaker 1: because I compare it to what my niece and nephew,

983
00:59:54,320 --> 00:59:57,240
Speaker 1: who are teenagers today are going through and how they

984
00:59:57,280 --> 00:59:59,680
Speaker 1: prepare for college, and I think, oh my god, I

985
00:59:59,720 --> 01:00:02,320
Speaker 1: wasn't even thinking about college at your age. I was

986
01:00:02,400 --> 01:00:06,200
Speaker 1: thinking about having fun in high school every day. We

987
01:00:06,280 --> 01:00:10,480
Speaker 1: did not have a high school counselor that helped me

988
01:00:10,480 --> 01:00:13,600
Speaker 1: think about what schools I wanted to apply to. Uh.

989
01:00:13,640 --> 01:00:15,600
Speaker 1: It was always understood in my family that I went

990
01:00:15,640 --> 01:00:18,320
Speaker 1: to college. My both of my grandparents were professors at

991
01:00:18,320 --> 01:00:22,120
Speaker 1: the University of Texas. Everybody went and got a degree. Um.

992
01:00:22,400 --> 01:00:24,760
Speaker 1: But I graduated high school and I just wanted to

993
01:00:24,800 --> 01:00:27,760
Speaker 1: work and have fun. So I worked and had fun.

994
01:00:27,960 --> 01:00:33,040
Speaker 1: You're of the era Dazed and confused that movie with Yes.

995
01:00:33,240 --> 01:00:35,880
Speaker 1: Top Notch, which is the burger joint in that movie

996
01:00:36,120 --> 01:00:38,480
Speaker 1: is right in my neighborhood. And I still eat a

997
01:00:38,520 --> 01:00:41,800
Speaker 1: top Notch every time I go back to Austin. So kids,

998
01:00:41,920 --> 01:00:44,480
Speaker 1: I don't want to say kids today, these kids today,

999
01:00:44,520 --> 01:00:48,240
Speaker 1: but my niece and nephews yours. So they've been thinking

1000
01:00:48,240 --> 01:00:51,720
Speaker 1: about college for ten years exactly. I didn't think I had.

1001
01:00:51,800 --> 01:00:56,280
Speaker 1: I Basically, everything came more organically for me once I

1002
01:00:56,320 --> 01:01:00,160
Speaker 1: got tired of working and having fun and thought to myself, well,

1003
01:01:00,200 --> 01:01:02,240
Speaker 1: I'm gonna am I gonna be a manager of a

1004
01:01:02,280 --> 01:01:05,600
Speaker 1: swimwear shop for the rest of my life. No, maybe

1005
01:01:05,680 --> 01:01:08,080
Speaker 1: I should go ahead and go to college. UH. And

1006
01:01:08,160 --> 01:01:11,160
Speaker 1: so I went off to college, and then I have

1007
01:01:11,240 --> 01:01:13,400
Speaker 1: the the you know, the kids that I mentor today

1008
01:01:13,440 --> 01:01:15,880
Speaker 1: asked me, well, how did you choose the school? You

1009
01:01:15,920 --> 01:01:19,920
Speaker 1: know how I chose school. My mother loved spending summers

1010
01:01:20,200 --> 01:01:23,040
Speaker 1: UH in Boulder, in the mountains, because my grandfather would

1011
01:01:23,040 --> 01:01:25,880
Speaker 1: teach summers at See You Boulder. He taught during the

1012
01:01:25,880 --> 01:01:29,000
Speaker 1: school year at U T Austin, UH. And because she

1013
01:01:29,160 --> 01:01:31,960
Speaker 1: said she loved the mountains, I decided I wanted to

1014
01:01:32,000 --> 01:01:35,800
Speaker 1: go to See You also, and I applied and went

1015
01:01:35,960 --> 01:01:40,480
Speaker 1: side Unseen and then I stayed there and to graduate

1016
01:01:40,520 --> 01:01:42,479
Speaker 1: school there. And then when I got to graduate school,

1017
01:01:42,520 --> 01:01:43,880
Speaker 1: I was like, well, what the heck do you do

1018
01:01:43,960 --> 01:01:47,040
Speaker 1: with a graduate degree in economics? I had specialized in

1019
01:01:47,080 --> 01:01:53,080
Speaker 1: econometric econometrics, I love statistics UM, and so I thought, well, heck,

1020
01:01:53,080 --> 01:01:55,760
Speaker 1: I'll just go back home to Austin. And at that time,

1021
01:01:55,760 --> 01:01:57,920
Speaker 1: if you're an economist, you worked for the state. I mean,

1022
01:01:57,960 --> 01:01:59,680
Speaker 1: so you can see where I'm getting at if you

1023
01:01:59,720 --> 01:02:03,720
Speaker 1: were to look at that. Ellen Ellen Beeson, I was

1024
01:02:03,760 --> 01:02:07,480
Speaker 1: at the time high school student in Austin Texas, and

1025
01:02:07,480 --> 01:02:10,360
Speaker 1: and think would this girl be chief US economist of

1026
01:02:10,400 --> 01:02:13,960
Speaker 1: Morgan's Ley decades from now? Uh No one would have

1027
01:02:14,040 --> 01:02:18,120
Speaker 1: drawn that line. And and Boulder and Denver and Colorado

1028
01:02:18,120 --> 01:02:21,800
Speaker 1: in general is now booming. Yeah. My sister lives in

1029
01:02:21,840 --> 01:02:24,760
Speaker 1: Denver now and it's unbelievable. The housing boom there is

1030
01:02:24,840 --> 01:02:27,640
Speaker 1: just just well, I don't know how much of it

1031
01:02:27,680 --> 01:02:31,480
Speaker 1: is attributable to decriminalization of marijuana and how much of

1032
01:02:31,480 --> 01:02:34,680
Speaker 1: it is organic. They have a burgeon no pun intended.

1033
01:02:34,960 --> 01:02:37,640
Speaker 1: They have a burgeoning tech community there as well, and

1034
01:02:37,680 --> 01:02:39,600
Speaker 1: they have when I lived there in the nineties, that

1035
01:02:39,720 --> 01:02:43,840
Speaker 1: tech community was really up and coming, the whole complex

1036
01:02:43,880 --> 01:02:47,800
Speaker 1: being built out for it in south South Denver um.

1037
01:02:47,960 --> 01:02:50,760
Speaker 1: And it's an incredible because I like areas of North

1038
01:02:50,800 --> 01:02:54,240
Speaker 1: Carolina and Texas, there are areas of Colorado that never

1039
01:02:54,360 --> 01:02:56,520
Speaker 1: had a housing boom, so they never never had a

1040
01:02:56,600 --> 01:03:00,400
Speaker 1: bust and now they're well above previous peak for home releasing.

1041
01:03:00,480 --> 01:03:02,960
Speaker 1: My sister is tickled, tickled pink that she's already a

1042
01:03:03,000 --> 01:03:07,400
Speaker 1: homeowner there at least, so she's gaining equity uh day

1043
01:03:07,440 --> 01:03:12,360
Speaker 1: by day. Uh yeah, So it's it's uh, it's on fire. Yeah,

1044
01:03:12,360 --> 01:03:15,200
Speaker 1: it's on fire. And Austin. I think I must be.

1045
01:03:15,400 --> 01:03:17,760
Speaker 1: I must be good luck for places that I've lived in.

1046
01:03:18,800 --> 01:03:21,120
Speaker 1: All right, let's talk about mentors. Who were some of

1047
01:03:21,120 --> 01:03:25,919
Speaker 1: your early mentors? Uh? So in my career the very

1048
01:03:25,960 --> 01:03:29,120
Speaker 1: first I think I was lucky right off the bat

1049
01:03:29,160 --> 01:03:31,680
Speaker 1: when I went back to the State of Texas, UH,

1050
01:03:31,720 --> 01:03:35,160
Speaker 1: to be assigned to be the right hand woman to

1051
01:03:35,240 --> 01:03:37,919
Speaker 1: Tamra Plout, who was the chief economist at the State

1052
01:03:37,920 --> 01:03:40,520
Speaker 1: of Texas at that time. UM. She and I had

1053
01:03:40,560 --> 01:03:44,120
Speaker 1: an amazing partnership. She took me under her wing. UM

1054
01:03:44,200 --> 01:03:49,720
Speaker 1: and having that kind of slow paced environment that government is. UH.

1055
01:03:49,760 --> 01:03:52,560
Speaker 1: Going back to the beginning of the podcast where I

1056
01:03:52,600 --> 01:03:56,560
Speaker 1: said it was just a a great time to cultivate

1057
01:03:57,040 --> 01:03:59,920
Speaker 1: deep thinking. I learned a lot from her. We remain

1058
01:04:00,120 --> 01:04:05,040
Speaker 1: extremely close today. UM. Also UH when I moved on,

1059
01:04:05,920 --> 01:04:08,320
Speaker 1: I had gone there the Bank of Tokyo Mitsubishi, and

1060
01:04:08,320 --> 01:04:10,840
Speaker 1: then when I went on from there to Nomeura Securities

1061
01:04:11,240 --> 01:04:14,480
Speaker 1: David Wrestler, who when he by the time he retired,

1062
01:04:14,520 --> 01:04:16,600
Speaker 1: he had been at Nomura for twenty six years, the

1063
01:04:16,640 --> 01:04:21,000
Speaker 1: longest practicing chief economist on Wall Street. UM. Dave was

1064
01:04:21,040 --> 01:04:23,840
Speaker 1: an amazing mentor to me from a market perspective. So

1065
01:04:23,920 --> 01:04:28,000
Speaker 1: taking me from sort of that academic thinking economist UM

1066
01:04:28,040 --> 01:04:30,640
Speaker 1: to how a market economist thinks and how to move

1067
01:04:30,640 --> 01:04:33,439
Speaker 1: into that fast paced world of working for an investment bank.

1068
01:04:33,640 --> 01:04:36,360
Speaker 1: He and I are both very good friends today. If

1069
01:04:36,400 --> 01:04:38,440
Speaker 1: I can ever catch him when he's off the golf course,

1070
01:04:38,680 --> 01:04:42,040
Speaker 1: he's living a very nice retirement right now. Uh. And

1071
01:04:42,080 --> 01:04:46,320
Speaker 1: so that those those were very important UM mentors UM

1072
01:04:46,360 --> 01:04:50,360
Speaker 1: in in the business world in Texas. I started off

1073
01:04:50,400 --> 01:04:54,919
Speaker 1: at a pretty young age UM in gymnastics competitive gymnastics UM.

1074
01:04:54,960 --> 01:04:59,200
Speaker 1: And the coaches there, uh, Jim and Cheryl and at

1075
01:04:59,240 --> 01:05:03,800
Speaker 1: Capital gymnast Sticks in Austin were were incredible. Let's talk

1076
01:05:03,840 --> 01:05:06,480
Speaker 1: about books. This is the question everybody always asks. Tell

1077
01:05:06,560 --> 01:05:11,040
Speaker 1: us about some of your favorite books, fiction, nonfiction, economics

1078
01:05:11,080 --> 01:05:15,200
Speaker 1: or markets related or not. I'm gonna talk about one book.

1079
01:05:15,360 --> 01:05:18,880
Speaker 1: I'm gonna talk about one book because it's the first

1080
01:05:18,880 --> 01:05:23,840
Speaker 1: one that always comes to mind anytime anyone asked me. Okay, So,

1081
01:05:23,880 --> 01:05:28,120
Speaker 1: because it's a lot of pressure for one book. Joe

1082
01:05:28,160 --> 01:05:33,040
Speaker 1: No Sarah wrote a book called A Piece of the Action,

1083
01:05:33,480 --> 01:05:37,080
Speaker 1: How the Middle Class Became the Money Class. UM. It

1084
01:05:37,120 --> 01:05:40,160
Speaker 1: goes all the way up through the late nineties. It

1085
01:05:40,640 --> 01:05:45,480
Speaker 1: covers the love of Americans, Americans love affair with credit cards?

1086
01:05:45,640 --> 01:05:48,960
Speaker 1: How did that all come about? How did this credit

1087
01:05:49,000 --> 01:05:52,080
Speaker 1: explosion and debt explosion the US affect the middle class?

1088
01:05:52,960 --> 01:05:55,120
Speaker 1: It was now I'm gonna say this from a very

1089
01:05:55,240 --> 01:05:59,240
Speaker 1: nerdy economist perspective, the book read like a novel. I

1090
01:05:59,240 --> 01:06:02,840
Speaker 1: could not put down. It was the best book. It

1091
01:06:02,920 --> 01:06:05,920
Speaker 1: goes right up there with best books fiction or nonfiction

1092
01:06:06,000 --> 01:06:09,800
Speaker 1: that I've read. I'm going to have to unbelievable. And

1093
01:06:09,840 --> 01:06:12,440
Speaker 1: I just tell myself if Joe ever reached out and

1094
01:06:12,520 --> 01:06:14,400
Speaker 1: wanted to do an update to the book, because I've

1095
01:06:14,440 --> 01:06:17,680
Speaker 1: covered the consumer and so much depth the US household

1096
01:06:17,680 --> 01:06:22,080
Speaker 1: and so much depth, and that book singularly has influenced

1097
01:06:22,200 --> 01:06:26,680
Speaker 1: so much of my study around the US household and

1098
01:06:26,720 --> 01:06:29,600
Speaker 1: slicing and dicing it by income group and studying the

1099
01:06:29,680 --> 01:06:34,040
Speaker 1: household experience. It is. It's huge. And I always told myself,

1100
01:06:34,160 --> 01:06:37,000
Speaker 1: if Joe calls me and says, help me update this book,

1101
01:06:37,120 --> 01:06:39,760
Speaker 1: I'll take you upstairs to me. I'm assuming you've met

1102
01:06:39,840 --> 01:06:41,320
Speaker 1: Joe over the years. I would love to, and I

1103
01:06:41,320 --> 01:06:43,200
Speaker 1: have never met him. He's on the he now writes

1104
01:06:43,200 --> 01:06:46,520
Speaker 1: for Bloomberg Views. I know that one floor I suppose

1105
01:06:46,600 --> 01:06:48,320
Speaker 1: that was my way of trying to get an invite.

1106
01:06:48,440 --> 01:06:51,480
Speaker 1: He's literally like if I could drill a hole through

1107
01:06:51,520 --> 01:06:55,240
Speaker 1: the ceiling. We're practically but I'm not kidding you. Every

1108
01:06:55,280 --> 01:06:58,120
Speaker 1: economist that comes on my team, if I want them

1109
01:06:58,160 --> 01:07:01,600
Speaker 1: to know the consume us, consume Mr inside and out,

1110
01:07:01,680 --> 01:07:03,400
Speaker 1: I just give them that book and I say you

1111
01:07:03,520 --> 01:07:07,040
Speaker 1: need to read this. So let's talk about a time

1112
01:07:07,200 --> 01:07:10,400
Speaker 1: you failed. Tell us about something that didn't go as

1113
01:07:10,400 --> 01:07:13,160
Speaker 1: you expected and what you learned from it. It's not

1114
01:07:13,200 --> 01:07:15,640
Speaker 1: that I never failed. I came up with an example,

1115
01:07:16,240 --> 01:07:19,440
Speaker 1: but it's it's not that I never failed. It's that

1116
01:07:19,800 --> 01:07:25,680
Speaker 1: I was raised in the South, in Texas, and there's

1117
01:07:25,720 --> 01:07:29,840
Speaker 1: this sense of perpetual optimism for some reason. I think

1118
01:07:29,840 --> 01:07:34,200
Speaker 1: when you grow up in his sunny climate and every

1119
01:07:34,240 --> 01:07:37,920
Speaker 1: failure is embraced and turned into something positive, so that

1120
01:07:37,960 --> 01:07:40,000
Speaker 1: when you look back on it's hard to see it

1121
01:07:40,080 --> 01:07:43,120
Speaker 1: as a failure because all I can think of is

1122
01:07:43,160 --> 01:07:46,320
Speaker 1: the positive thing that came out of it. Isn't that

1123
01:07:46,760 --> 01:07:51,960
Speaker 1: a characteristic of America in general? Coast, in Europe or elsewhere,

1124
01:07:52,440 --> 01:07:56,600
Speaker 1: when a business person fails, it's a black mark. And

1125
01:07:56,600 --> 01:07:59,280
Speaker 1: in the United States, I mean, how many companies did

1126
01:07:59,320 --> 01:08:03,360
Speaker 1: Ford have? How many times it did Edison fail before

1127
01:08:03,400 --> 01:08:05,880
Speaker 1: they hit their market? It seems, you know, there is

1128
01:08:05,920 --> 01:08:09,680
Speaker 1: a second act in American life. But I always found

1129
01:08:09,760 --> 01:08:13,760
Speaker 1: that entrepreneurship, that ability to just get up, put dust

1130
01:08:13,800 --> 01:08:15,920
Speaker 1: yourself off, and move on to the next is a

1131
01:08:16,040 --> 01:08:20,120
Speaker 1: uniquely American phenomenon the rest of the world, it is.

1132
01:08:20,160 --> 01:08:23,280
Speaker 1: And I think and I think embracing the failure um.

1133
01:08:23,439 --> 01:08:25,240
Speaker 1: And this is something that I that I think is

1134
01:08:25,280 --> 01:08:27,800
Speaker 1: great um and that the world of finance. I think

1135
01:08:27,800 --> 01:08:29,960
Speaker 1: we do well at Morgan Stanley, or at least promoting

1136
01:08:29,960 --> 01:08:31,839
Speaker 1: it Morgan Stanley, but I think the world of finance

1137
01:08:31,880 --> 01:08:36,639
Speaker 1: could do better about is uh embracing when you fall

1138
01:08:36,720 --> 01:08:43,080
Speaker 1: on your face publicly with a bad call and say

1139
01:08:43,240 --> 01:08:46,439
Speaker 1: that was a bad call. I thought I had sound

1140
01:08:46,600 --> 01:08:49,679
Speaker 1: research behind it. Turns out out was I was wrong.

1141
01:08:50,160 --> 01:08:52,760
Speaker 1: And then let's move on. People will trust you so

1142
01:08:52,840 --> 01:08:56,120
Speaker 1: much more when you make the next call. Then if

1143
01:08:56,120 --> 01:08:58,960
Speaker 1: you're one of these and I've met plenty that have

1144
01:08:59,040 --> 01:09:04,200
Speaker 1: tried to cover it over time, revisionist history. I always said, X, Y,

1145
01:09:04,280 --> 01:09:08,120
Speaker 1: and Z and and believe me, with things like these podcasts,

1146
01:09:08,600 --> 01:09:11,559
Speaker 1: you can't go back and have revision. It's a generational thing.

1147
01:09:11,680 --> 01:09:15,479
Speaker 1: People forget that the internet is forever and it's amazing.

1148
01:09:15,520 --> 01:09:17,559
Speaker 1: All right, we're I only have you for a few

1149
01:09:17,560 --> 01:09:20,599
Speaker 1: more minutes. I see your your handler jumping up and down.

1150
01:09:21,000 --> 01:09:23,640
Speaker 1: I have to ask my sound like a performing monkey. No,

1151
01:09:23,760 --> 01:09:25,519
Speaker 1: not at all, not at all. This is great stuff.

1152
01:09:25,800 --> 01:09:28,240
Speaker 1: So what sort of advice would you give to a

1153
01:09:28,280 --> 01:09:32,400
Speaker 1: millennial or recent graduate who's interested in going into economics

1154
01:09:32,880 --> 01:09:37,960
Speaker 1: as a profession. Uh. I would start more generally by saying,

1155
01:09:38,000 --> 01:09:40,200
Speaker 1: stop worrying about where you're going to be ten to

1156
01:09:40,240 --> 01:09:42,240
Speaker 1: twenty years from now. Worry about where you're going to

1157
01:09:42,320 --> 01:09:44,920
Speaker 1: be a year from now, and then as you get older,

1158
01:09:45,280 --> 01:09:47,160
Speaker 1: widen it out to where I'm I going to be

1159
01:09:47,280 --> 01:09:50,439
Speaker 1: three years from now, five years from now. Communicate and

1160
01:09:50,479 --> 01:09:53,560
Speaker 1: communicate face to face, no matter how much your generation

1161
01:09:53,640 --> 01:09:55,920
Speaker 1: hates it because it's not the way you were raised.

1162
01:09:56,280 --> 01:09:58,960
Speaker 1: Communicate face to face, because those of you who do

1163
01:09:59,360 --> 01:10:01,800
Speaker 1: will make it further than those of you who just text.

1164
01:10:01,920 --> 01:10:06,280
Speaker 1: You cannot get body language from a text. You just

1165
01:10:06,400 --> 01:10:11,280
Speaker 1: can't uh, And people will appreciate that. UH. For the

1166
01:10:11,280 --> 01:10:17,840
Speaker 1: economists specifically, it is half about showmanship and delivery uh,

1167
01:10:17,880 --> 01:10:21,080
Speaker 1: and half about the analytical work that you put into

1168
01:10:21,160 --> 01:10:24,120
Speaker 1: a steak and the sizzle. You have to have the

1169
01:10:24,280 --> 01:10:28,200
Speaker 1: sizzle and not a lot of economist sizzle because we

1170
01:10:28,280 --> 01:10:32,759
Speaker 1: tend to be nerds and attract nerds to our industry. UM.

1171
01:10:32,800 --> 01:10:38,439
Speaker 1: Embrace public speaking, Embrace being comfortable in front of people,

1172
01:10:39,240 --> 01:10:42,839
Speaker 1: and you will go much further than your counterpart. That's terrific.

1173
01:10:42,880 --> 01:10:45,880
Speaker 1: And then my final and favorite question, what is it

1174
01:10:46,000 --> 01:10:50,320
Speaker 1: that you know about econometrics investing markets today that you

1175
01:10:50,360 --> 01:10:57,960
Speaker 1: wish you knew twenty years ago? I wish that I

1176
01:10:58,000 --> 01:11:00,599
Speaker 1: was told, which you never would be told when you're

1177
01:11:00,680 --> 01:11:04,920
Speaker 1: learning economic theory that it doesn't always make sense because

1178
01:11:04,920 --> 01:11:07,080
Speaker 1: I think the peril that many of us ran into

1179
01:11:07,200 --> 01:11:09,519
Speaker 1: right after the financial crisis, there was all of these

1180
01:11:09,560 --> 01:11:14,799
Speaker 1: economic theories we learned in school made no sense anymore. UM.

1181
01:11:14,920 --> 01:11:19,760
Speaker 1: And uh only be just beginning with my generation did

1182
01:11:19,800 --> 01:11:23,400
Speaker 1: we start to really fully employ econometrics and statistical modeling

1183
01:11:23,439 --> 01:11:26,639
Speaker 1: and economics. UM. And it's those of us that were

1184
01:11:26,640 --> 01:11:29,040
Speaker 1: able to rely on that more so than trying to

1185
01:11:29,080 --> 01:11:32,400
Speaker 1: make everything fit into a theory that we're able to

1186
01:11:32,439 --> 01:11:36,880
Speaker 1: adapt better after the financial crisis. Fantastic stuff. Thank you

1187
01:11:36,920 --> 01:11:39,479
Speaker 1: Ellen for being so generous with your time. You didn't

1188
01:11:39,520 --> 01:11:42,439
Speaker 1: think we'd go the full ninety minutes, but we did. UH.

1189
01:11:42,479 --> 01:11:44,960
Speaker 1: We have been speaking to Ellen Zentner. She is the

1190
01:11:45,080 --> 01:11:50,320
Speaker 1: chief US economist for Morgan Stanley. UH. If you enjoy

1191
01:11:50,439 --> 01:11:52,639
Speaker 1: this conversation, be sure and look up an intro Down

1192
01:11:52,640 --> 01:11:57,599
Speaker 1: an Inch on Apple iTunes or SoundCloud, overcast Bloomberg dot

1193
01:11:57,640 --> 01:11:59,839
Speaker 1: com and you can see any of the other hundred

1194
01:11:59,840 --> 01:12:03,519
Speaker 1: and fifty three or so such conversations that we've had

1195
01:12:03,560 --> 01:12:06,799
Speaker 1: over the past three years. We love your comments, feedback

1196
01:12:06,800 --> 01:12:10,679
Speaker 1: and suggestions right to us at m IB podcast at

1197
01:12:10,680 --> 01:12:14,160
Speaker 1: Bloomberg dot net. I would be remiss if I did

1198
01:12:14,160 --> 01:12:17,640
Speaker 1: not thank the wonderful team I have who helps us

1199
01:12:17,720 --> 01:12:21,160
Speaker 1: put this podcast together and then send it out into

1200
01:12:21,240 --> 01:12:25,519
Speaker 1: the world via the technology we were talking about. Medina

1201
01:12:25,560 --> 01:12:29,599
Speaker 1: Parwana is my technical producer. Taylor Riggs is our booker

1202
01:12:29,640 --> 01:12:33,360
Speaker 1: slash producer. Michael Batnick is our head of research. I'm

1203
01:12:33,400 --> 01:12:36,640
Speaker 1: Barry Ridholts. You've been listening to Masters in Business on

1204
01:12:36,760 --> 01:12:37,719
Speaker 1: Bloomberg Radio.