This week on the podcast Matt sits down with Michael Brown, Principal US Economist for Visa and member of the Walton College’s Dean’s Executive Advisory Board. They begin the conversation with Michael sharing how he got interested in economics during his undergraduate studies at the University of Arkansas, what he does in his role at Visa in the macroeconomic space and how he synthesizes so much data down to communicate it to their clients. They go on to discuss the methods of forecasting the US economy and also the challenges and trade offs associated with short term versus medium term versus long term predictions. They also touch on the most challenging pieces to forecast. They end the discussion with a focus on supply chain and the macro picture of the economy as well as some medium to long run trends on the consumer side of the economy that Michael sees coming up.

Whether it is the invasion of Ukraine, whether it

is COVID restrictions in China, every single one of those policy

actions, physical actions has global ramifications, not just

for the global economy, but for on the ground, individual

business owners who are trying to manage through that lack of

clarity.

Excellence, professionalism, innovation, and

collegiality. These are the values the Sam M. Walton College

of Business explores in education, business and the

lives of people we meet every day, I'm Matt Waller, Dean of

the Walton College, and welcome to the be epic podcast. I have

with me today, Michael Brown, who's principal US Economist at

Visa. And Michael received his undergraduate degree from the

Walton College and his Master's of Arts degree in economics from

the Walton College. And he is currently on my Executive

Advisory Board. Michael, thank you so much for joining me

today.

Great to be here, Matt.

So, Michael, you've really been in economics your

whole career and you studied economics as a student. Would

you mind just sharing a little bit about how you got interested

in economics?

Sure. Yeah, I guess it all started as an

undergraduate at the Walton College, you know, I was taking,

I believe the intermediate level courses. And it was really micro

that was fascinating to me. So the application of mathematics

to business problems, was intriguing to me. And I kind of

stayed down that path for a while, partnered with a

professor at the time by the name of Cary Deck, who was a

very inspirational person in my career track. And Cary was

adamant that the research really helped us understand business

problems in an applied sense. And Cary was an experimental and

behavioral economist. And that was also quite fascinating. I

have a minor in Psychology also, from the University of Arkansas

there and blending those two passions, I thought was a

natural intersection to do micro.

Michael, of course, I've heard your presentations,

and I followed, I followed your career. And it's really

impressive, the knowledge you've gained, of course, you've been

with Visa for almost five years. And prior to that you were vice

president and economist at Wells Fargo for over eight years, and

you've had other experiences as well. But would you mind telling

us a little bit about what you do as principal US economist for

Visa?

Sure. Well, it's a far cry from that micro

undergrad student that I described to you just a moment

ago. You know, the reality is I spend a lot of my time focused

on developments in the macro economy, there is some

behavioral aspects of what we do in terms of understanding shifts

in consumer confidence and psychology, and how that may

impact consumption patterns. But more broadly, if I had to divide

up kind of my time into a pie chart, if you will, I'm an

economist, I love charts, right? So, you know, you divide the

time up, I'd say about 70% of my time, is focused on interfacing

with our clients and communicating the work that's

done here in the office by our incredibly talented team of

analysts, economists and data scientists distilling that down

into an easily digestible format for CEOs CFOs, and senior

leaders across our clients, which include banks, large

merchants, you know, there's fintechs are kind of in the

fold, and big tech companies today, as they're starting to

wade into the payments ecosystem. So a lot of what I do

is information, distilling, and presentation. Still have the

hard skills, though, you know, just this morning, we were

designing a new round of forecast models. So still

heavily involved in econometric design as well, as you know, we

do some academic work here as well to keep us fresh on some of

the new and innovative techniques out there in both

economic forecasting, but also sort of pushing the envelope

with broader economic research topics.

Well, I could imagine that it's probably hard

to find people that fit into your profile, because a lot of

times people that are real strong in econometrics tend to

be you know, focused on math and maybe more introverted. Clearly

one of yours strengths is communication, but also that

synthesis skill. Anytime you've got a lot of different analyses,

especially when they may be pointing in different

directions, trying to synthesize it in a way that people can

understand and relate to and then make decisions upon isn't

easy. How did you learn to do the synthesis and communication

pieces?

That just came with, with experience, you know,

my first job out of graduate school, is actually at the

Arkansas Federal Assembly. And I served as kind of a policy

analyst and, you know, very research focused from the

beginning. But then, you know, you complete a research project,

and then you're often called upon to testify in a committee,

or, you know, in front of, you know, other members of the

legislature. And that was when I learned fairly quickly that, you

know, distilling things down to their constituent components,

and then trying to communicate, just the essentials, was sort of

core to it. So that was really an enlightening experience,

having to take a often 40, 50 page research document, distill

it down into 10, 15 minutes of testimony, just going through

that process was very challenging at first, I'll

admit. When I moved to Wells Fargo, the story changed, right,

it was all about, we have a literally like telling a story

of the economy, and its facets and its developments and its

nuances and turning points, and then trying to convey that in

not just an informative way. But frankly, in a way that's going

to connect with the audience that you're in front of. So for

example, you know, if you're talking to a hedge fund, you can

be a little more technical and a little more rigorous. If you're

talking to CFOs of middle market clients or midsize companies,

that takes a different approach. So that's when the skill set of

adapting communication styles came into it. And now it's more

a function of okay, now we're speaking mostly to CEOs and

CFOs, you kind of have a boilerplate that you can

navigate.

Well, I know you've done a lot of forecasting of the

US economy. And that's so hard to do. Would you talk a little

bit about methods of forecasting the US economy and also the

challenges and trade offs associated with short term

versus medium term versus long term?

All phenomenal questions. So the first comment

I would say is you need to be as humble as you possibly can be to

be an economic forecast, a particularly in today's

environment of if you think you're going to be right, the

majority of the time you are you were mistaken yourself. And I

learned this long ago, right. I mean, when I first started

forecasting, at my time, at Wells Fargo, the most important

aspect of doing it is coming up with a consistent storyline

around what the data supports. And you know, the numbers that

we come out of our models were often kind of ad factoring to

make sure that that consistency is there in the message. And in

the storyline, a macro forecast ranges anywhere between five to

32 line items, we're forecasting about 32, here at Visa

acurately. And those line items should all move consistent with

that fabric of the story that you're telling. And sometimes

the models aren't great at doing that as hard as you try as a as

an a good econometrician. They're not always consistent,

and particularly in a post COVID environment as we're in and

trying to navigate now. So one be humble, two grounded in the

macro theory, if you're not driven by the known principles

and relationships, that, frankly, are our guiding

principles. It's the reason we're economists and not data

scientist, is we're using that framework of macro theory to

guide our thinking around that storyline development. And then

finally communicating it in a clear and concise manner that,

you know, has its footnotes attached, as I like to say, but

you're still, you know, accentuating it with with just

the,

Well, you know, when you talk about all these

different items you're forecasting, what are what are

some of them, you're you're forecasting and which ones are

the most challenging?

Well, so the top line, I mean, the biggest thing

everyone looks at is US GDP growth, right? So we, we don't

even refer to it as GDP when we're talking to clients. We

just say economic growth, you know, again, we're trying to get

out of the realm of, you know, teaching undergrad econ as part

of our updates to our executives, certainly inflation.

Here at Visa a big part of our business is obviously consumer

spending. So for example, we look at both real spending,

which tells us how much foot traffic, they go through our

merchants. But we also look at nominal spending, which is going

to correspond with revenues. So again, taking, you know, the the

inflation piece, adding it to the real or inflation adjusted

variables. All of these are kind of key inputs into not just our

business, but our clients business as well. The great

thing about our business model here at Visa is when our clients

do well, we do well, it's also so you know, it's it's kind of

interesting to come in and provide these additional

perspectives. Now, the hard part, I would say, you know,

interest rates are up there with some of the more difficult line

items to kind of forecast oil prices. Oftentimes, I will

consult a ouija board rather than a forecast model. I mean,

it is really difficult to kind of forecast it. And in the third

bucket, I would say is anything that would involve political

calculations versus economic fundamentals. So for example,

you know, the the hot topic these days is the federal budget

and of course, debt ceiling debates, that is inherently a

political decision. And I learned a long time ago, let's

stick to the economics, we'll let the political forecasting to

the political scientists because it gets really tricky, very

quickly to try to figure out the direction of those kinds of

variables.

Your point about, you know, trying to come up with

the story that weaves everything together, where the forecasts of

each of the lines makes sense together. Because you would

expect certain things to react in certain ways, under certain

circumstances, based on theory, but that gets into something

really interesting, which is the short run versus the long run.

Because even if your story's right, because of the stochastic

nature of the variables, the short term forecasts could just

have some noise in them that make the make it inconsistent

with the story.

You're absolutely right, I mean, you know, we

actually divide our time up. Usually, the mornings of our

day, are really focused on the short term, macro fluctuations.

And for better or worse, the bulk of our client base is

really keyed in and dialed in to that short run view. That said,

your eye isn't on the long run ball, some, you're gonna miss a

lot. And so the afternoons here, are really us jumping into our

huddle rooms, or our little side chat rooms. And kicking around

these longer run themes. We spend a lot of time talking

about the implications of Gen Z, on productivity, growth, and

technological adoption, you know, the trends that are going

to shift across demographic groups, but also what a large

and growing retirement base means for the potential GDP

growth of the US. And all of these things, if you're doing

any kind of medium to long run planning are essential to kind

of inform clients of as well. So it's not just demographics. I

mean, we go back again, to macro principles, right? We're talking

about productivity growth technology, or, you know, labor

augmenting technology in that production function. And then

capital flows globally. And all of that feeds into kind of our

medium to long run perspective. But it's all grounded in those

simple production functions that we all learned, you know, as

undergrad and grad students.

Michael, there's economists in a lot of different

industries, I would think you have a little bit of an

advantage, because any economist has access to archival data, you

also have access to payment, and data.

We do I mean, you know, in terms of our payment

volumes, we roughly see close to 25 cents of every dollar spent

here in the United States. And that's about, you know,

somewhere around 21 to 22 cents globally. So it's, it's a fairly

large penetration rate. And it does allow us to get a pretty

high degree of visibility into the consumer side. But you know,

we also have small business cards and commercial cards as

well. So we are able to gather intelligence on multiple facets

of the US economy through the lens of of our card payment

data. But I would say that there's also challenges with

that, you know, I mean, I'm sure your students are learning about

big data there at the Walton College in excruciating detail.

It has a tremendous amount of noise associated with it. And as

an economist, you know, it's a little funny to say working for

a business. But there's a lot of what I call business noise in

our big data. So I'll give you an example. Let's say we have a

bank that was issuing a competitor's product and they

convert to issuing Visa. Well, that was economic activity that

was going on before we could see that in our big data set. And

we've got to figure out ways, creative ways, that control for

that. And we've got folks migrating in and out of our

ecosystem daily, right. So it would be impossible to trace

down every single, inbound and outbound kind of transaction,

when we're talking, you know, millions and millions a day. So

we've come up with clever ways, thanks to our talented data

scientists to kind of scrub the big data and extract what I call

the economic signal from from the big data and make it a

useful economic measure. So yes, there are a lot of advantages,

but there's also a tremendous amount of sort of data cleaning,

and then it, then there's, then there's the sort of balance

issue that we're always struggling with as particularly

in our profession, as Applied Business economists, because

you're right, we have a lot of insider information. And as a

publicly traded company, we have to protect a lot of that

information. I would love nothing more than to start

publishing forecasts based off of what I saw two days ago in

our transactions data. But that would end up front running our

forward guidance, our executive team, our financials. So there's

a business ethics angle to this as well, that we have to be

very, very cognizant of. With that big data, we also have a

big level of responsibility to protect the confidentiality of

some of that data as well.

I know that, under your leadership, Bloomberg News

ranked Visa's US economics team as among the top forecasters of

the US economy. How how did you lead your team to that

achievement? What what did you have to do?

First, you know, I'll just kind of walk you

through how we onboard our our economist here. And phase one is

really the indoctrination of our approach to econometrics. And

basically, systems thinking, for me to think about the economy as

a system? And what are what are the things that could break the

system? What are the things that can make the system run faster?

What are the levers we could pull to, to perhaps make the

system run more efficiently, so you start thinking of it as a

system. And we basically, to divert it a little bit, as I

like to say, we basically apply that thought process to our

econometric structures and models. So we have, we use

machine learning techniques, such as Bayesian vector auto

regressions, and other types of applied techniques as well, that

will take both public and proprietary economic data that

we subscribe to feed that through our systems of

equations. And that helps us understand the co-movements

between things. But again, we also have a lot of experience

here. I mean, get off get to a team with close to 90 years of

combined experience, you know, forecasting and understanding

the economy. And that's invaluable, having folks that

have lived through different types of recessions have seen

data develop in different ways. And probably the death kill of

an economist is have seen the magnitudes of the revisions that

can come with some of the data that we rely on heavily to find

turning points in the economy. And all of that just kind of

gels. I think probably the most rewarding moment of my months,

is sitting in a forecast, which we have the first Friday of

every month after the Non Farm Payroll report comes out. And we

sit there and everybody goes around the room. And they they

talk about what what they think is right what they think is

wrong, and there is no hierarchy in that room. Everyone's idea

matters. Everyone's thought matters. Everyone's research

matters when it comes to that. And creating that flat

structure. In that one meeting that sets the tone for what

we're going to say, for the next 30 to 60 days is is incredibly

important as a leader to make sure that you're creating that

inclusive environment, a free flow of ideas, particularly in a

research function like we serve. So just setting up the

environment and setting the team up for success in their

endeavors as an applied econometrician as well.

So you must have had to build a lot of trust to

get that to happen. Because it's easy to say. But when you get

that many people into a room, it's there's a lot of

complicated sociological and political issues that go into

it. So how did you build the trust?

Well, so one of the sort of foundations of my

management style is I really am important, I really place a lot

of emphasis on one on one conversations. So every single

week, whether I'm traveling or not, it may be 15 minutes that

week, or sometimes it's an hour, and I'm sitting down with each

member of the team, and understanding what's working,

what's not working, helping them set priorities, and knowing that

I have their back when they can't get to priority number

five on that list, is also an important aspect of kind of

managing workflow. And I think probably another dimension to it

is really listening more than you're talking, you know, those

one on one conversations, if I leave the room, feeling like I

spoke more than 30% of the time, I think I walk away saying I

probably did something wrong. And I need to kind of reassess

and reevaluate my approach the next time.

Well, congratulations on your terrific

achievement there. One shifting gears just a little bit. You

know, throughout history, there are these shocks that occur to

economies I, the biggest one, of course, COVID, that's the most

memorable, had an incredible impact in so many different

ways. It's hard to even conceive of all these changes. And you

know, you could think and we would be, we have to bring up

generative AI. But I wonder, is generative AI, a shock? That, I

mean, maybe we're not seeing it yet, but I know, I know, so many

alumni in various industries that are telling me really

innovative ways that they're using it. But even in the

university, we're finding ways of using it that are quite

remarkable, that truly make us more efficient. But what are

your thoughts? Do you think? Do you think this is going to be a

shock?

Shock might be a little strong of a word. I, as

an economist, I go back to first principles. And I think the

power of what you described is really the augmentation of

labor. And if you think about the long run trends here in the

US, we're in a country with an aging population, slow net in

migration, slow year on year population growth. The key story

in macro economics today is the high rates of inflation. So how

do you bring that down? Well, in light of not being able to grow

your labor force, you bring that down through enhancing your

existing labor force. And so I'm kind of a glass half full kind

of guy on this, because I think the power to enhance the

abilities of the human capital that we have in this country and

others around the world, I think it's going to be an unlock of

potentially higher GDP growth than we otherwise would have had

in the absence of this kind of innovation and technology. Now,

that said, I don't think we're ready to just run full steam

ahead. I'll just give you the example here at Visa, it is

prohibited on our systems to use chat GPT or other generative AI

tools. And the reason is that the fear of putting in something

that is company proprietary into that ecosystem and having it

uploaded into a public database for other future consumption,

maybe to answer other questions out there. That risk of

intellectual property CPG or loss is really high.

Yeah, that's a really good point. The World

Wide Web was similar. When it was created, many companies

banned it because it was so easy for information to flow outside

through the World Wide Web. So Michael, it's funny prior to the

pandemic, no one very few people knew what Supply Chain

Management was and Logistics was a term they didn't really use

much. And during the pandemic, it was on the front page every

day and now people know about it. It's amazing. And of course,

as you know, our undergraduate program in supply chain is now

ranked number one in North America by Gartner. And the

Masters program is ranked number two in North America by Gartner

which we're very grateful for. But would you mind speaking

little bit to supply chain the macro picture of the economy?

Well, it's interesting, I'll take you back

to one of my trips to Southern California, I was flying from

San Diego back here to the San Francisco Bay Area. And this was

early in 2021. So February, March kind of timeframe. And I

remember flying over the Port of Long Beach, looking at all the

vessels lined up. And that was the clearest visual

representation of the public economic data. And what I would

say is, it was incredible the amount of tug and pull on US GDP

growth that we saw from the collapse of inventory, the

rebuilding of inventory. You know, I rarely talk about an

esoteric concept we call real final sales to private domestic

purchasers, essentially, its core GDP growth, right? How are

consumers, businesses and government spending and

investing? But I had to talk about that measure, because the

inventory cycle was so inconsistent with core demand in

the economy, they were so out of sync for so long, that you, it

was sending false signals about the strength or weakness of the

economy, depending on the quarter. So that was an

incredible time, where one we learned just how globally

interconnected the world is, and how, when we're doing better,

and everybody else is doing better, it uplifts us all.

Because, whether it is the invasion of Ukraine, whether it

is COVID restrictions in China, every single one of those policy

actions, physical actions has global ramifications, not just

for the global economy, but for on the ground individual

business owners who are trying to manage through that lack of

clarity. So I think the scarring of what we went through with

supply chain is going to be with us for a while, and it's gonna

have some very important implications for business

management going forward.

Michael, I know you've done extensive research

on the consumer side of the economy. Would you mind talking

a little bit about some of the medium to long run trends that

you're seeing on the consumer side?

Well, I think there's probably two broad

buckets. One is kind of the demographic shifts. And right

now we're in this phase where a boomers are kind of moving into

their retirement years, they're leaving the labor force,

frankly, post pandemic a little bit earlier than we had

anticipated. And that starts having some profound impacts on

aggregate consumption. And I'll give you one example. In

economics and macro, we talked a little bit about this concept of

the wealth effect. So as one's value of its of their home, or

their stock portfolio rises, there is some knock on effect on

aggregate consumption. And historically, you know, if you

look at the economic literature, it's been range bound. I mean,

depending on the studies you look at, it's between five cents

and 10 cents, meaning that for every dollar of wealth, that

increases, you tend to spend about five cents or 10 cents.

Now some of this is psychological, right, I feel

better about my financial situation. And therefore, I can

go out and spend a little bit more. But what we're seeing is

this wealth effect, as we measure it, has now grown to

roughly around 34 cents. So it's a profound shift in the

deviation from what we've seen over the last decade and a half.

And the reason that's so important is because you start

putting the pieces together, we accumulated a tremendous amount

of housing and financial wealth over the last five years as an

economy. The second key aspect is we are much more sensitive to

changes in stock markets today than we were, say, 20 years ago.

The third piece, as I mentioned, is you look at the share of the

population over the age of 55, who has retired early, it's

incredible post pandemic. And so it makes some sense that this

wealth effect and wealth accumulation is having some

impact as well. So that's a big trend that we're looking at for

the next five years as the retirements continue to

accelerate in the coming years. And so I think what we're about

to see over the next 10 years is the evolution of working at just

a tech company to that permeating multiple other

sectors of the economy. And you're already seeing this in

financial services with automated trading and trying to

enhance productivity, and that's that sector. And I think, you

know, we're just now seeing the tip of the iceberg as this tech

stack moves from Silicon Valley, as I like to say out to the rest

of the country, to kind of permeate some of these

industries and that enables purchasing power of consumers in

a whole new way. Any new markets that I think we're just now

starting to get a handle?

Well, Michael, this has been really interesting.

Congratulations on your tremendous success in your

career and for taking time to visit with me about this. I

really appreciate it.

It's been fun Matt, always great chatting with

you and very, very pleased to always support the Walton

College here as well.

On behalf of the Sam M Walton College of

Business, I want to thank everyone for spending time with

us for another engaging conversation. You can subscribe

by going to your favorite podcast service and searching.