Economic Risks Versus Media Alarmism with Philipp Carlsson-Szlezak

Barry speaks with Philipp Carlsson-Szlezak, Boston Consulting Group's Global Chief Economist. Prior to this role at BCG, Philipp advised financial institutions and governments at the Organization for Economic Co-operation and Development (OECD) as well as McKinsey & Company. He was also Chief Economist at Stanford C. Bernstein. He is a frequent contributor to Harvard Business Review, World Economic Forum, and various other business publications. Philipp also leads the Center for Macroeconomics at the BCG Henderson Institute. On this episode, Barry and Philipp discuss structural changes to the global economy, doom-saying, and his book Shocks, Crises, and False Alarms: How to Assess True Macroeconomic Risk.

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Episode Transcript

Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, radio News. This is Masters in
Business with Barry Ritholts on Bloomberg Radio.

Speaker 2 (00:17):
This week on the podcast, I have an extra special guest,
really fascinating conversation with Philip Carlson Sleesact. He's got a
really interesting background. Chief economists at Sanford, Bernstein, worked at
the OECD, began at McKenzie, ended up as global chief
economists for the Boston Consultant Group, and really approaches economic

(00:41):
analysis from a very different perspective, critical of the industry's
over reliance on models, which have proven themselves to be
not great predictors of what happens next, especially when the
future in any way differs from the past, and so

(01:03):
when we have things like the dot com implosion, or
especially internal to the market, the financial crisis of OEEDO
nine and even COVID models just don't give you a
good assessment. And he describes how he reached this conclusion
in his book Shocks, Crises and False Alarms, How to

(01:25):
Assess True Macroeconomic Risk. He calls out a lot of
people who get things wrong, especially the doomsayers who not
only have been forecasting recessions incorrectly for I don't know
the better part of fifteen years, most especially since COVID,
but their models just simply don't allow them to understanding

(01:48):
a dynamic, changing, global, interconnected economy. I thought the book
was fascinating, and I thought our conversation was fascinating, and
I know you will also with no further ado, my
discussion with the Boston Consulting Groups. Philip Colson Slezak.

Speaker 3 (02:06):
Thank you for having me.

Speaker 2 (02:07):
So let's start with a little bit. I want to
talk about the book, but before we get to that,
let's talk a little bit about your background, which is
kind of fascinating for an American. You get a bachelor's
at Oxford, a PhD at the London School of Economics.
Was becoming an economist always the career plan.

Speaker 3 (02:26):
Well, let me correct you right there.

Speaker 2 (02:28):
I'm not American, You're not Where are you originally from?

Speaker 4 (02:30):
I was born in Switzerland. I grew up there, but
in a number of other countries as well.

Speaker 2 (02:35):
So you have sort of an American accent. How long
have you been here?

Speaker 4 (02:39):
Early on as well in my youth and so, growing
up in different places, I always compared and contrasted what
I saw, So I developed an interest in economics. So
when it came to going to college, studying economics was
a very natural choice.

Speaker 2 (02:56):
Where did you grow up in Switzerland?

Speaker 3 (02:58):
Was born there? Okay?

Speaker 2 (02:59):
I recently visited both Geneva and Lake Geneva up and
it's just spectacular. What a beautiful part of the world
it is. It really really impressive. So first job out
of school McKinsey, Is that right?

Speaker 3 (03:14):
That's right?

Speaker 2 (03:15):
And what was that experience like?

Speaker 4 (03:17):
Well, so I studied economics at LSC actually not at Oxford,
at my PhD at Oxford, so the other way around,
and that was at the turn of the century.

Speaker 3 (03:26):
Let me take a step back.

Speaker 4 (03:27):
It was a turn of the century, and I emphasized
that because that was peak economics. So, you know, the
the ubras and the arrogance of the economics profession.

Speaker 3 (03:35):
Was at its peak.

Speaker 4 (03:37):
And you know, you're still seven eight years out from
the global financial crisis, which was a big humbling moment
for the profession. So everything was very model driven theory,
econometrics and all that.

Speaker 2 (03:48):
Uh huh.

Speaker 3 (03:48):
So you know, I didn't.

Speaker 4 (03:49):
Feel comfortable even then as an undergraduate. Then as a
graduate student, I branched out. I started reading a lot more,
you know, going to political theory, finance history, much broader,
building a mosaic of knowledge and also methods and approaches,
frameworks and so at the end of my graduate studies
with a PhD, that's when I landed and consulting at McKinsey,

(04:13):
and the work was very different, so very nitty gritty, right,
you go deep into corporations other organizations.

Speaker 3 (04:19):
You do very very granular work.

Speaker 4 (04:21):
So coming with this big picture view of the world
and analyzing and going into this supernano micro part of
business was a big change.

Speaker 2 (04:31):
Let's stay with the concept of peak economist. I think
it was Paul Krugman who did the saltwater versus freshwater comparison,
which was essentially the economists along the coasts seem to
have a very different model and very different approach to
doing macro versus people more inland, at least in the US.

(04:54):
Does that sort of di economy resonate with you, How
do you think about that?

Speaker 4 (04:58):
Well, I generally view all of mainstream economics as as
too model based, master model mentality.

Speaker 3 (05:07):
In the book sort of this belief.

Speaker 4 (05:10):
That economics is a bit like a natural science and
we can pass it off as a natural science. That
belief is still still very much alive, and so physics envy,
which has long been identified as a problem of the discipline,
still reigns supreme in my view, and the book is
really partly a repudiation of that. So my co author

(05:31):
and I we take master model mentality to task in
the book, and we think economics deserves a much more
collectic approach, drawing many more disciplines than just sort of standards.

Speaker 2 (05:45):
What are your thoughts on the impact of behavioral economics
that really took apart the homo economists that was front
and center of classical economics and showed, hey, people aren't
rational profit maximizing, they're emotional and flawed and human.

Speaker 3 (06:04):
Right.

Speaker 4 (06:04):
I think that is very very interesting. It's very valuable
that we have that strand of research and economics, but
it's more in the microside. It's not really macro predominantly,
and so I firmly live in the global macro space
where I think we still have very commoditized economics. You know,
it's all about a set of forecasts. People are still

(06:25):
wet it to their models. It's very much point forecast driven,
and I think what we need is much more narrative
based judgment based, more eclectic approaches to reading the landscape.
And that's what the book is really really about.

Speaker 2 (06:40):
So we're going to talk more about how poorly economists
have done as forecasters over the past few decades. And
you have numerous, numerous examples, but let's stay with your
early career. You're going deep at McKenzie into the granularity
of corporate behavior. Then you very much a seat Angeliance

(07:00):
Bernstein or Sanford Bernstein, you become chief economist. How different
is it applying those wares on Wall Street in an
investment environment versus the corporate world in a more execution basis.

Speaker 4 (07:17):
You know, the switch to the cell side was really
good for me. There was something I'd been missing in
my skill set. I'd done a lot of deep thinking, writing, researching,
I'd done the more microeconomics. I learned more about the
corporate world, but I hadn't been exposed to the finance

(07:39):
angle of it as much. I hadn't talked to the
byside at all really before. And being a Sanford Bernstein,
a firm with a storied history and in equity of
research really and swimming in this pool of really great
equity analysts just taught me a lot of things, not
least how to frame research angles, how to be quick
with research notes, how to get the thoughts out, and

(07:59):
then the concept exposure to investors on the buy side
really really helped me sharpen my research skills. So that
was almost like a missing piece in my recipe. I
really unlocked something for me and I learned a lot
there and I had a really good time doing that work,
publishing you many many research reports over those years and

(08:22):
often going very very deep, often going very historical in
the approach. So Bernstein is a firm that that very
much appreciates lateral thinking, differentiated approaches out there kind of ideas,
And so I ran wild for a while, just doing
work that I don't think I would have done anywhere else.

Speaker 2 (08:41):
So you started a consultant, You briefly had a NGO
at the Organization of Economic Cooperation and OECD Development. As
I guess the last day, you're on the cell side.
So you see the universe of career options as an economist.
What brought you back to the Boston consulting room.

Speaker 4 (09:04):
So I had a history with BCG already, and I
was well connected there, and at some point I was
approached if I'd like to come back and do the
same kind of work I was doing on the cell side.
But at BCG. BCG is a really great platform because
not only is it deeply ingrained in the corporate world,

(09:24):
so you know, the access to boardrooms is very wide.
You get to meet a lot of interesting executives and
the problems they're grappling with, but you also still have
access into the institutional investor world who are also clients,
so you really get both sides of the landscape, and
they were really different. Right on the by side, it's
mostly a look at firms outside in they're outside of

(09:48):
what's happening in the boardrooms. They're trying to decode it
from the outside. Being a consultants working and talking with them,
you're much closer to what's actually happening in their deliberations,
the problems they're facing, the questions.

Speaker 3 (10:00):
They're trying to answer.

Speaker 4 (10:01):
So to me, that platform is very attractive because it's
it's very versatile, it's it's it never gets boring, and
I've I've had a good run the last five years
doing my work on that BCG platform.

Speaker 2 (10:14):
So I have no expertise in the consulting world, but
I kind of hear people lump all the consultants together, Mackenzy, BCG,
all these different firms. I get the sense from speaking
to various people that that's kind of inaccurate, that BCG
is not McKenzie. The very different organizations.

Speaker 4 (10:32):
What's your experience been, Yeah, I mean they have different
cultures for sure. They certainly vie for the same business,
the three that you mentioned, so so you you constantly
bump into those other two competitors if you're at any
one of those three firms, a third being Bane, I think, yeah, mckenzy, BCG, Baane,

(10:54):
those three are there others, but those are the core
strategy consultants, if you will, and you know, I would
think the type of work that has done is obviously
very similar. They're vying for the same business, but culturally
it is different and uh uh, you know they're they're
slightly different sizes, these three firms. BCG today is about

(11:14):
twelve billion and revenues annually. We have about I think
sixty seventy offices and no, sorry, well well over one
hundred offices in sixty countries. I think it's the right
metric here, right, and you know it's it's it's a
space that is is very, very competitive, but that that
keeps everyone on their toes.

Speaker 2 (11:34):
I would imagine. So let's let's talk about advising companies
and advising executives. You talk about explaining economic uncertainty and
as we'll get into in the book, why there is
this risk aversion and these fears of crises that never

(11:56):
seem to come around. How do you approaching executives on
navigating all this. It seems like there's always this fear
of a disaster, and lately it hasn't really showed up.

Speaker 4 (12:11):
Yeah, So a lot of what I do in conversations
with executives is to unskew, if you will, some of
the perceptions they pick up in the press, in public discourse,
which is reliably dialed down to the sort of domongering
side of things.

Speaker 3 (12:31):
Right, that's really true.

Speaker 4 (12:33):
It's not just lately, since you mentioned it, sort of
the inevitable recession that never that never came. We're really
at the end of a string of such false alarms.
You know, when COVID hit, it was very common to
predict a depression, not just the recession, but a depression
was very conventional wisdom in twenty twenty that this would
take many years to recover. Then when interest rates rose,

(12:54):
it was it was fashionable to predict an emerging market
a cascade of defaults. Then, of course, when inflation spiked,
it was cast as a hyperinflation, hyper inflation, structural inflation,
regime break, the nineteen seventies, all that stuff that clearly,
even then I think was very clearly not not what
was playing out. And then the inevitable recession is really

(13:16):
just the most recent in a string of false alarm.
So often what I do is to meet people where
they are. They pick up doomsday narratives because they're very
prevalent in public discourse, and we often go back to
basics and ask, well, how does the system work and importantly,
what would it take for these big bad outcomes to happen.

(13:37):
It's not that they can't happen. They're part of a
risk distribution, but very often we take these risks in
public discourse that are the edges of the risk distribution
tail risks, tail risks, and we pretend that they're in
the middle of the distribution. Right, if you go through
financial news, if you go to financial TV kind of conversations,
you often get the impression that these rips which are

(14:00):
genin rest, they're real, they're part of the distribution, but
you get the impression that they're really the center of
everything we should be watching.

Speaker 2 (14:07):
And so this leads to an obvious question. Whenever I
have an author in I often asked what inspired them
to write their book? It's pretty clear what inspired you?
It seems like it got to the point where, hey,
everybody is freaking out about things that are either not
happening or just so low probability events that they're not

(14:29):
contextualizing it. Well, what actually was the aha moment that
said I got to put all this down in a
book and instead of repeating myself over and over here,
read this and it'll it'll explain why you're fearing all
the wrong things.

Speaker 4 (14:46):
Yeah, it was the It was the accumulation of situations
where my co author Paul Schwartz, and I felt we
had a pretty good access to this topic. We kind
of got that one right, not because we were using
models and sophisticated analysis, but we looked at it from
a narrative driven perspective. We asked the right questions about

(15:08):
what does it take to get to that really bad
structural situation, and so we wanted to wrap that into
a coherent story of how we think about economics, not
because we can get it right every single time. Even
if you use a more eclectic approach to economics, you
will get things wrong, but I think your hit rate
can improve. And that was the motivation to write that

(15:31):
all down in the book. And yeah, that's how this
came about.

Speaker 2 (15:35):
So first, let's just start out generally. You described the
book as calling out pervasive doom saying in public discourse
about the economy, and demonstrating how to navigate real financial
and global risks more productively. Explain.

Speaker 4 (15:54):
So, over the last few years, call it since the
COVID pandemic, we've had a string of false alarms, as
I would call them, right out the gate. In twenty twenty,
we were told this will be a greater depression, maybe
as bad as the nineteen thirty is, worse than two
thousand and eight. That wasn't the case at all. Then

(16:14):
we had an inflation spike that was spun into an
inflation regime break forever inflation, hyperinflation that didn't pan out.
Then we had rising interest rates, and that was spun
into a doomsday story of emerging markets, cascade of defaults,
and then we had the story of an inevitable recession

(16:35):
that we're still waiting for.

Speaker 3 (16:36):
Right, So we have across the.

Speaker 4 (16:38):
Board a lot of negativity. Across the board, we have
a lot of doom saying public discourse is pervasive in
that regard, the story is always skewed to the downside.
And what the book does it provides a framework to
think about this differently and more productively. And it does
so across real economy risks, I think recession, but also
sort of longer term growth. It does so in the

(16:59):
financial economy, think about stimulus and the effectiveness of stimulus,
interest rates, inflation bubbles, that type of stuff. And it
does so across the global space, the institutions that governed trade, etc.

Speaker 2 (17:11):
So you combine data analysis with both narrative storytelling and
judgment over traditional macroeconomic models. Explain what led you to
this way to contextualize what's going on in the real
world economy.

Speaker 4 (17:29):
So my path through economics was fairly eclectic. I started
out studying economics in a traditional theoretical macroeconomic econometric sense,
and then I went into studying much broader adjacent fields
that are relevant to economics, finance, history, political theory, political economy, etc.

(17:51):
Then I had different experiences in my career, just just
putting together different views of how to approach these problems.
And over time and working on the cell side, we
discussed they put all these together. And so it is
just the insight that the models will not deliver. You
cannot accurately forecast the economy. Economists shouldn't feel so ashamed

(18:13):
about that. It's not like natural scientists are always doing better.
Think about epidemiologists. They also struggle to accurately forecast COVID deaths,
for example. So you know, the whole physics envy and
the whole inferiority complex that often besids the economics profession
is misplaced. In my view, we should embrace the uncertainty

(18:35):
that prevents us from making precise point forecasts, and we
should deliver that uncertainty. Embrace the eclectic nature of what
we're trying to solve. It isn't just about economics and policy,
it's about myriad other things that play into this. And
when we do that and do it rationally, I think
often we're going to land and better predictions.

Speaker 2 (18:55):
You know, it's funny about the physics envy. Richard Fiman
once said, imagine how much harder physics would be if
electrons had feelings? Right, So it's it's not a pure
natural world. You have human behavior getting in the way.
And you know, one of the quotes from the book

(19:16):
Doom Cells, hasn't that always been the case that it
appeals not only to our fear of existential threats from
an evolution perspective, but just generally speaking, good news is
sort of sneaks by and bad news gets our attention.

Speaker 4 (19:35):
Yeah, it's the clicks and the eyeballs that that we're
trying to attract in the in the news business model,
and that that gives you the slant to the downside.
I think it's it's particularly pronounced these.

Speaker 2 (19:49):
Days social media and the rest.

Speaker 3 (19:51):
That's part of it.

Speaker 4 (19:52):
But it's also the case that when you think about
the last forty years or so, there was a window
that we call good macro in the book. So a
lot of ma economic variables, a lot of macroeconomic context
was benign and was a tailwind, you know for executives,
but certainly for investors. So in the real economy, cycles
grew longer, volatility came down like recessions were less frequent.

(20:15):
The financial economy, inflation structurally declined, pulling down interest rates
with it. And in the global realm you had you know,
institutional growth and where we're aligning value chains and all
that really was a tailwind to executives and investors, and
more recently, not just COVID you you can go back
to two thousand and eight. It's sort of a growing

(20:36):
crescendo of new noise and new disturbances. I think that
good macro window is challenged. Right, we had a lot
of generations, We had a lot of shocks, all of
whiplash there, and so for executives, when it used to
be possible to ignore the macro world, to take it
for granted, it's now moved into the boardroom. Now you

(20:57):
need to have a view on what these things mean
for your bisness and you kind of need to do
that almost ongoingly. So that has changed, I mean, because
there's more gyrations, there's more whiplash. I think that has
dialed up all the angst, and it has dialed up
the doom saying and the string of false alarms that
I went through earlier, in my mind is pretty dense
it's you know, every year we had a new doomsday narrative,

(21:20):
and every single year it just didn't pen out that way.

Speaker 2 (21:24):
You know, there was a I'm trying to remember which
economist wrote this up. At one point in history, your
whole world was your local region, and what happened globally
or what happens across the ocean was not relevant. Now
it doesn't matter what corner of the earth you're hiding in,
the global macro world is knocking in your door regardless.

(21:46):
How significant is that to both to both coming up
with a better macroeconomic framework, and all of these false
crises and fears seemed to be never ending.

Speaker 4 (22:01):
Yeah, I think the greater interconnectedness and the real time
aspect of economics and the pass through of influences and
in often just hours transmitted often through financial markets, that
just adds to that. It's it never it never stops,
It never takes a break. You know, you go to
sleep with with sort of the latest data, you wake

(22:22):
up with the latest data, right. I mean, it's seat
of constant in that regard, and I think that certainly
feeds into that sense of heightened risk and crisis.

Speaker 2 (22:33):
So let's talk about some shocks Over the past quarter century.
We had and this is really just less global than
US focused, but obviously international ramifications. We had the dot
Com implosion in two thousand, We had the September eleventh
attacks in O one. Not long after that, we had

(22:54):
the Great Financial Crisis. We had COVID. In between, we
had a couple couple of market events, the flash crash
of them again, and I don't know if you really
consider those true economic shocks, but certainly dot Com, nine
to eleven, GFC and COVID were huge. Is this have
we been through more than the usual number of shocks

(23:15):
or does it just seem that way recently.

Speaker 4 (23:19):
Well, we've always had shocks. I think two thousand and
eight stands out among the ones you mentioned, because that's
where the US economy actually came close to the precipice
of this could be a structural depression. Without the intervention,
without the stimulus that was deployed at the time, this
could have gone a lot worse. COVID, in some sense,
was a replay of that risk, but action was more

(23:41):
swift and more decisive. So it seems like we'll learned
something there, and.

Speaker 2 (23:44):
Much more fiscal as opposed to the financial crisis, which
was primarily a monetary response, and we ended up with
two very different years that followed addressed that.

Speaker 4 (23:56):
If you would, Yeah, So I think in two thousand
and eight, you'll remember tarp Tart was what now looks
like a paltry sum of seven hundred billion, and it
got voted down in Congress, right right, So.

Speaker 2 (24:08):
I remember that week in October the market ceased so
aggressively in the stock market sold off that was voted
down on a Monday. By Friday it passed overwhelmingly, exactly.

Speaker 4 (24:20):
And I think this is one of the big themes
that we emphasize on the book. Stimulus comes down to
the willingness of politicians to act and the ability to act.
Ability is more about financial markets. Will bond markets finance
this kind of action, which they do in times of crisis,
but the willingness has to be there to act, and
in times of crisis, the willingness to act usually rises.

(24:42):
Partisanship is put aside. Politicians come together, they act to
you know, when the house is on fire, you will
step up and do something about it. And I think
in twenty twenty that was in display and there was
a learning curve from the more timid approach in two
thousand and eight and then and then perhaps it was
overdone twenty twenty and the following years, but certainly the

(25:05):
risk was perceived. Perhaps we're doing too little, so let's
rather go large and backstop the system.

Speaker 2 (25:11):
My favorite story from the twenty twenty Cares Act was
a week before the country was shut down, Congress couldn't
agree on renaming a library in DC because it was
just along partisan lines. Everything got tabled. Then the world
shut down, and the largest fiscal stimulus since World War Two,

(25:35):
at least as a percentage of GDP, flew through the
House and Senate and was signed by Kars. Acting none
was President Trump Cares Act True two was President Trump Cares.
Act Three was President Biden. Did we learn something from
the financial crisis about the lack of fiscal stimulus and
maybe the pendulum swung too far the other way? What's

(25:57):
your takeaway from that?

Speaker 3 (25:58):
No, for sure.

Speaker 4 (25:59):
Look, I think two crises were very different. You had
in two thousand and eight damage balance sheets, not just
in the banking system, but households. Their balance sheets had
to be repaired. Households had to dig themselves out of
that hole, had to rebuild their wealth, and that that
would have called for more intervention than what we got
in two thousand and eight. In twenty twenty, I think policymakers, politicians,

(26:21):
they had internalized that learning, so they went extra large
on the fiscal side. And that hole that COVID created
was basically filled with fiscal stimulus. As you know, it's
widely believed and accepted that this was extremely big, too
much perhaps, and so we had an overshoot in certain

(26:45):
consumption areas, particularly in the good space. There was an
overshoot in consumption. It pushed up demand. It together with
supply crunches, it pushed up inflation in an idiosyncratic and
more tactical cyclical way, not structural, but but tactical. And
so I think, yes, policymakers did learn something and they
were risk averse, so they went extra large.

Speaker 2 (27:08):
So you said, the financial crisis clearly a shock. The
other things not as much as a shock. And we've
had plenty of false alarms. How do you define what
a true shock or crisis is and what do you
put in the category of false alarms or things that
are genuine but just don't rise to the level as described.

Speaker 4 (27:31):
Yeah, there are two things to consider. One, instead of
the new cycle level, we have a constant doom saying
about supposed things that could lead to recession or otherwise
downgrade the economy.

Speaker 3 (27:45):
You know.

Speaker 4 (27:45):
Just the last few years, we went numerous you know,
so for example, consumers were supposed to run out of
cash and consumers were not going to keep up their spending.
We had lots of false alarms about the labor market,
even last summer, right we had last summer in August,
there was a somewhat of a panic because supposedly the
labor market was going to be very soft and very weak.

(28:05):
So we have these new cycle false alarms stories that
often are rooted in a data point that is noteworthy,
that is interesting, that does signify risk, but we extrapolating
from the data point to conclusions that don't hold up.
That is one category of false alarms. The other categories
where you have real crises. But the question is are

(28:26):
they going to have structural impact? Are they going to
have a long term impact on the economy? Are they
going to downgrade the economy's capacity? So two thousand and
eight does qualify. Two thousand and eight left an indelible
mark on the US economy, but twenty twenty didn't in
terms of performance and output. We've regained the output to
trend output that we were wrong the path that we're

(28:47):
traveling on pre COVID. We've come back to that trend
output path. It has not left the kind of permanent
mark on economic performance that you saw after two thousand
and eight. So in that sense, we need to differentiate
between what is a likely shock that will pass and
that we can fix, versus what is something that changes

(29:07):
the structural composition structural setup of the economy durably. Those
are two very different types of situations.

Speaker 2 (29:14):
That sounds like a usable framework for distinguishing between real crises.
And do I call it media alarmism or you know,
everybody is blaming the media these days, especially with this administration,
but there has been a fairly relentless negativity, especially in

(29:34):
social media. What's the best framework for you know, separating
the wheed from the chaff.

Speaker 4 (29:41):
Well, typically when we see knee jerk reactions and doom
say stories, they're taking a data point and then they're extrapolating,
usually on the basis of a model. So I mean,
think about the inevitable recession. Even Larry summers people like
that that came out and said, look to bring down
wage growth, to bring down inflation, you need I don't know,

(30:03):
five years of unemployment at this and that level.

Speaker 2 (30:05):
Why because he threw out ten percent, well, ten.

Speaker 3 (30:08):
Percent for one year or five percent for five years.

Speaker 4 (30:11):
So he had different configurations, but they're all based on
basically the Phillips curve. This was all a Phillips curve
take on the economy, which is.

Speaker 2 (30:18):
Which was a great model fifty years ago, wasn't it.

Speaker 4 (30:21):
Yeah, it described the UK and certain other countries empirically
quite well. It wasn't ever really a model and a theory.
It was more of a description of empirical facts. But
certainly it was useful for a window. It's still useful
as as an instrument to think about dynamics, right, But
it was basically used as as the truth. You know,

(30:43):
there's an input and there's an output, and my model
gives me the truth if I give it certain inputs,
and then well what happens. We're extrapolating data points, often
outside the range of empirical facts. The models are only
trained on historical facts. You know, you can't make up
data points to train your model. So when a crisis hits,

(31:04):
likely you get data points that were not empirically known
in the past. So what does the model do. It
extrapolates outside its historical empirical range, and then you get
these kind of point forecasts. It just don't work. I mean,
case in point, in two thousand and eight, unemployment goes
up to around ten percent, right, and it takes almost
the whole twenty tens a full decade almost to bring

(31:25):
down this very high unemployment rate. So in COVID, when
unemployment shoots up to fourteen percent, what does the model do.
It says, Well, if it takes a decade to bring
down ten percent unemployment, it will take even longer to
bring that fourteen percent of unemployment, right, And that is
exactly this kind of limitation of the model based approach. Empirically,

(31:46):
you never had fourteen percent unemployment, So if the model
extrapolates from past data points, it's going to go off
the tracks. And that's exactly what happened in that instance.

Speaker 2 (31:55):
So the underlying flaw built into most models is that
the future will look like the past, and as we've learned,
that often is not the case.

Speaker 4 (32:05):
It's always idiosyncratic look the US economy since the Second
World War has only seen a dozen recessions. Now, each
of those recessions is totally idiosyncratic, and even if they
had a lot of commonalities, twelve is not a sample
size that a natural scientist would consider large enough to
build sort of an empirical model around. Right, each of

(32:26):
these crises, or each of these recessions was idiosyncratic, and
the idiosyncrasy demands much more than a simple model or
even a sophisticated model. It demands the eclectic view across many,
many drivers. And that comes down to judgment. There isn't
There isn't an output in an Excel sheet or a
Python model or anything. In the end, it comes down

(32:47):
to human judgment, and I think that that is something
we lose sight of way too often.

Speaker 2 (32:51):
You very much strike me as a fan of Professor
George Box. All models are wrong, but some are useful.
Tell us a little bit of about how models can
be useful.

Speaker 3 (33:02):
Well.

Speaker 4 (33:02):
They are always a good starting point. Even the Phillips
curve has a lot of validity to think about what
might be happening. There are always this sketch of reality,
but the moment we're translating that from you know, a
sketch and a map into something that is hardwired in
a quantity quantified model and the moment we then expect

(33:24):
that the output will resemble anything like the truth we're
we're sort of denying the reality of this. It just
doesn't work that way. Look, I'm not the first person
to make that point. In fact, you know, Hayek canes
fund mesas they've long basically trashed economics for saying like,
you're too gullible and you're too naive about the constant

(33:48):
nature of these variables. They they've long pointed out that
you don't have this this uh, what the natural sciences provide,
which is stability in all these relations of variables.

Speaker 3 (33:58):
You don't have that in economics.

Speaker 4 (33:59):
And there's a there's an anecdote that we pick up
in the book when Hayek receives the Nobel Prize in
nineteen seventy four, he actually uses his acceptance speech, or
I think it was a dinner speech he gave right
after being awarded the prize. He uses that speech to say, look,
you shouldn't do this prize in economics. You should you
should have you should have never done the Nobel Prize.
In economics. But if you must have this prize, at

(34:23):
least ask the recipients to swear an oath of humility,
because unlike physicists and in chemistry and other natural sciences,
economists have a big microphone, right. Policymakers listen to them,
politicians listen, public listens to them. But they don't have
that certainty of analysis. They don't have that stability in
their model. So they're going to go off the tracks

(34:44):
all the time. So at least ask them to be
humble about what they're doing. And I think that that
is a good reminder of the long history of recognizing
the limits of model based approaches through the eyes of
some of the leading leading thinkers in the space.

Speaker 2 (35:00):
So let's talk a little bit about a lot of
the false alarms and faux crises. So many economists got
twenty twenty two, Room, twenty twenty three, twenty twenty four.
They were expecting a recession. It never showed up. Why
is that?

Speaker 4 (35:19):
It starts with the master model mentality that we call
out in the book, where we placed too much trust
in models. So the Phillips curve was essentially used by
many forecasters.

Speaker 2 (35:31):
Call it define the Phillips curve for the lay reader
who may not be free.

Speaker 4 (35:34):
The Phillips curve is as an old theory going back
middle of the last century, describing the relationship between wage
growth and unemployment. So the idea is that you trade
off the two variables, and that led commentators like Larry
Summers to say to bring inflation out of control, you
wouldn't need either many years of high unemployment or a

(35:56):
sharp recession ten percent uneployment for a year to reset
the picture. In other words, in layperson's terms, a soft
landing is impossible, right, and this is what fed into
the inevitable recession that was the dominant received wisdom in
the last few years.

Speaker 3 (36:10):
Now, you know, these.

Speaker 4 (36:11):
Things are good starting points, they have validity historically and
a lot of empirical data. But in the end it's idiosyncratic.
It's very idiosyncratic constellation of drivers and risks, and so
it was in the last few years. So let's look
at that for a moment. One of these master models
was also interst rate sensitivity.

Speaker 3 (36:28):
Right.

Speaker 4 (36:29):
We think interest rates go up and that eats into
disposable incomes for households, right, But in reality, mortgages in
the US unlike in Canada, mortgages or long term didn't
actually take a big bite at a disposable exactly, very
long term, fixed, very low and most of them were
done at low rates because we had low rates for
a long time. Contrasts that with the flexible contracts and

(36:51):
mortgages in Canada where they lost a lot of disposable income.
That wasn't the case here. Same thing about interest rate
sensitivity in the corporate sector. So the textbook tells you
interest rates go up and investment will fall, but does
it You know, when you do the empirical analysis for
whatever window, you'll see a very flimsy correlation between interest
rates and capex. Firms invest when they have a narrative

(37:15):
to do so, when they see a return on the investment,
and if they believe the investment is beneficial to them,
they'll do it whether the interest rate is two, three
or four percent. And just look at what happened in
the last few years. You had a lot of narrative
and belief in worthwhile investments data centers software. So with
or without higher interest rates, firms are going to do that.

(37:37):
Particularly also because a lot of our investment has shifted
away from you know, fixed structures physical investment to intellectual
property software type of investment which has a much higher
rate of depreciation. So a bridge or road will be
good for thirty forty years, but software is maybe three
or four years. So you constantly have to invest just

(37:59):
to stay and still just to keep the stock of
investment in the space to keep it steady. You constantly
have to run faster just to maintain that. And so
there is a lot of idiosyncratic drivers that that led
two very different outcomes from what was predicted from a
model based Philip's Carve type approach to reading that context.

Speaker 2 (38:20):
So a lot of highly regarded economists like Larry Summers
kind of reminded me of the Paul Graham quote. All
experts are experts in the way the world used to be,
and we're seeing a lot of that in that. So,
not only did people get the recession calls wrong for
the past couple of years, what have we had two

(38:42):
months of recessions in the past fifteen years? Are we
in a post recession economy? Now?

Speaker 4 (38:49):
You can still get recessions, but I think we've become
better at fighting them. So this is the topic of stimulus.
There are three different types of There are two different
types of stimulus that we describe in the book across
three chapters, and we differentiate between what we call tactical stimulus,
which is just too smooth the cycle, accelerate growth in

(39:11):
between recessions, maybe dearist the cycle when necessary, versus existential stimulus,
which is when policymakers politicians step in when the economy
is truly at risk of a structural break. Those two
types of stimulus, they're evolving differently. I think the tactical
kind is more challenge going forward. It was very easy
when inflation was below target. It was very easy when

(39:33):
interest rates were very very low. There was little cost
to the Fed put you could do that. There wasn't
sort of an inflation risk associated with it. That's different now,
and I think they will remain different now that we're
skewed to the upside in terms of inflation, where interest
rates are likely to be higher for much longer. But
the existential type of stimulus, the ability to step up

(39:53):
when it's needed, I think that is still very strong.
And if you have another shock or a crisis or
a recession, I think will be able to deploy stimulus effectively.

Speaker 2 (40:02):
Still so we said earlier, all recessions are not homogeneous.
They're all idiosyncratic and unique. But one of the things
you mentioned in the book that kind of intrigued me,
we shouldn't conflate recession intensity and recovery. Explain what that means.

Speaker 4 (40:19):
Yeah, when COVID hit, we had extreme data prints. Unemployment
is sort of exhibit A of this story. Unemployment went
to ten percent in two thousand and eight, but it
went to fourteen percent in twenty twenty. Right, So the
intensity that the sudden collapse of activity was much more
pronounced in COVID than it was in two thousand and eight.

Speaker 2 (40:41):
GDP also much worse during the first few months of
COVID than all variables.

Speaker 4 (40:46):
So we have a chart early in the book that
shows the fifth to ninetieth percentile of historical experience of
these variables, and COVID is like far outside that historical range.
So you get data prints that you're not used to,
that the models don't know. The models were trained on
data points that were simply not experienced until they happened

(41:07):
in COVID. Now all of that fed into extreme intensity
was equated with This will be a very long and
difficult recovery. Why the ten percent unemployment rate led to
many years of recovering the twenty tens. Right, So now
if the unemployment rate is even higher, it's going to
take even longer to work it down to a level
that is, you know, a good economy again. But that

(41:27):
wasn't that wasn't the case. Twenty twenty wasn't about a
balance sheet recession. It wasn't about banks repairing their balance sheets.
It wasn't about households repairing the balance sheet. We took
care of that with stimulus, and therefore the ability to
recover was much faster and much stronger. There were other
idiosyncratic factors. Essentially, what was underestimated was the ability to

(41:49):
adapt of society. You know, societies found ways to work
around the virus. The pathway to a vaccine was faster.
So there were a lot of things that were underestimated.

Speaker 3 (41:59):
You know.

Speaker 2 (42:00):
Kind of reminds me of the why two K fear
that when there's a little bit of a fear of panic,
the expected crisis may not show up because we're taking
steps to avoid it. We don't know what was Y
two K a false alarm or did the fear lead

(42:20):
us to make sufficient changes to avoid problems. I honestly
can't answer that question. I'm wondering how you look at
crises in terms of do some of the fear mongering
and some of the you know, media absolute extremism lead
to government action that prevents the worst case scenario from happening.

Speaker 4 (42:43):
It's possible that has shapes the perception of policy makers
and politicians, but I think the reality is on the ground.
You know, the variables that are visible and measurable, unemployment rates, GDP, growth,
you know, imports, exports, all of that was under pressure.
I think that is more telling for those who take

(43:05):
decisions than what public discourse does. Is public discorse, particularly fearful,
and a lot of angst pervades how we think about
the economy.

Speaker 3 (43:13):
Does that spur action? Maybe that's part of it.

Speaker 4 (43:16):
So we don't know, as you rightly say, what would
have been in a counterfactual world. But essentially, when the
economy is genuinely in trouble, I think the willingness to
act on the stimulus side is very strong.

Speaker 2 (43:29):
So let's talk about some of those metrics. You have
an image in the book Scanning the Recession Barcode, So
tell us about that and the history of US recessions,
which seem to have been more frequent and more intense.
You go back a century, they were depressions, not even recessions.

(43:50):
Tell us about how this has changed over the past,
I don't know, a couple of hundred years.

Speaker 4 (43:55):
Yeah, So if you do a very long run chart
for sessions in the US economy and you shade each
recession as a bar what you get as a barcode
of image that looks a bit like a barcode, but
it thins out.

Speaker 3 (44:09):
As you move to the right.

Speaker 4 (44:11):
So you had recessions very frequently one hundred years ago
and further back, the economy was constantly in recession.

Speaker 3 (44:19):
Essentially half the time it was in.

Speaker 2 (44:20):
Recession, banking panics all the time.

Speaker 4 (44:22):
Yeah, but also the real economy, you know, the economy
was very agrarian. A bad harvest could drag down performance
of the economy, so there were a lot of shocks.
But yes, they're also banking crises and things like that.
And what we identify in the book is a recession
risk framework. We say, look, all recessions come in one
of three flavors. They are either real economy or recessions,
which is when investment in consumption drop abruptly and pull

(44:46):
GDP growth down. So that's the real economy time of recession.
The second is a policy era when policy makers get
it wrong they raise interest rates too fast or too high,
which only you ever know expost whether it was the
right thing to do. It's a very tricky thing to do.
And the third type of recession is the most pernicious kind.
It's a financial recession, when something blows up in the

(45:07):
financial system like two thousand and eight, and what we're
showing in this chapter of the book, over the long run,
the composition of these two drivers has changed over the
last forty years. The real economy recessions, they really took
a back seat because the economy calmed down, the volatility
came down. Services play a bigger role in the economy today,

(45:29):
so the less volatile than physical production. But also policy
makers just got better at managing the cycle, so you know,
policy errors kind of also lost a lot of share,
if you will, in the overall prevalence of recessions. But
when you think about what has given us the biggest headaches,
it was two thousand and eight a financial recession, and

(45:51):
dot com in away is also a financial type of recession.
So the share and the risk from financial blow ups
is significant if you look at it in recent history.
And that doesn't mean that the next recession will be
that type, but its share of the risk spectrum is
relatively high.

Speaker 2 (46:07):
So what should we be listening to when we hear
economists discussing various risks? What are the red flags that hey,
maybe this is a little too doom and gloomy for
our own portfolio's best interests.

Speaker 4 (46:23):
Yeah, I think the the litmus test for me is
often what would it take for a certain outcome of
a certain doomsday outcome.

Speaker 3 (46:32):
To actually come to pass?

Speaker 4 (46:33):
Not just will it happen and what would be the damage,
but walk me through the conditions that actually lead us
to the precipice and then make us fall off that
microeconomic cliff. Right, we need to talk about drivers, causes,
We need to talk about their probabilities and their constellations. So,
you know, it's it's not good enough to say, you know,

(46:54):
the model says the recession will happen. Walk us through
exactly what is the confluence of headwinds that together to
make that credible? Right, It's more than the point forecast.

Speaker 2 (47:06):
Really kind of intriguing. I also noticed that I'm not
an economist, but when I listened to economists talk about
the possibility of a black swan or the possibility of
this event, it's almost as if there won't be any
intervening actions, either by the market or the policymakers. Tell

(47:28):
us a little bit about that. What was George Soros's phrase, reflexivity,
that when certain events happen, there are going to be
natural reactions that just prevent this extrapolation to infinity or
to zero, as the case may be.

Speaker 4 (47:46):
Yeah, I mean this is back to the topic of stimulus.
For first and foremost, two thousand and eight came as
a big surprise because the models in the early part
of the two thousands, they didn't even really look at
the financial sector as a risk driver. They kind of
assume the financial system away. And then when the problem
brewed and the financial system itself, the models were kind

(48:08):
of blind to that, and the reaction couldn't couldn't be
gauged if you didn't have view of that, And the
reaction really depended on stimulus, and stimulus is about politics.
It is about policy, it's not about economics. First and foremost,
it's about political economy, it's about people coming together and
fighting crises.

Speaker 3 (48:27):
And so.

Speaker 4 (48:29):
I think that remains the case that the idiosyncrasy happens
before the crisis. The drivers are idiosyncratic, but the moment
a crisis starts, as shock hits, what happens as a
reaction is also idiosyncratic. It is political, it is about society,
it's about choices. It's not stuff that you can model
in a rigid natural science way.

Speaker 2 (48:50):
So let's talk about something that clearly wasn't in the models.
Forget twenty years ago, they weren't in the models five
years ago or even three years ago. And that's the
impact of artificial intelligence on our economy, on the labor pool,
and on productivity. How do you look at a giant
structural change like AI. How do you put this into

(49:14):
context as to what it might mean across all these
different areas within both traditional economic modeling and the real world.

Speaker 4 (49:25):
You know, we've had productivity growth the last few decades,
even though often the narrative as productivity growth is really
really low. We've had productivity growth, just not as services,
but in the physical economy, there's been pretty decent productivity
growth even the last twenty years where we didn't have
prouctivity growth with services because it didn't have the technology

(49:46):
to move that part of the economy along. Now, why
is that Essentially productivity growth goes up when technology displaces labor.
That is really the definition of productivity growth. You need
to produce the same with less labor inputs or more
with the same labor inputs. But either way, technology, whether
we like it or not, is about the displacement of labor,

(50:07):
and we weren't able to do that in the service economy.
Now with AI, I think you have a better chance
of doing this. At least the promise is very strong
that this will work. But I think we're getting ahead
of ourselves. And I'm not saying that now. We've published
on this over the last few years, even as COVID
head and even before AI, when the zoom economy it

(50:28):
was sort of this dominant narrative. It's a hard slog
to do this. It happens over years, and it's little
by little. It's not a flip of the switch. It
happens very incrementally, and I don't think AI will turbocharge
GDP growth. It is a lift to growth over the
medium term. But there are many little obstacles. There are

(50:48):
many little things that need to fall into place for
people to really adopt the technology and for this to,
little by little give us a tailwind. So it's not
an abrupt step change. It's something that is credible, something
we need to work through and then it will it
will show impact over a ten year frame, fifteen year frame.

Speaker 2 (51:05):
So let me push back a little bit on one
thing you said. And I seem to have this ongoing
debate with economists who work in a larger corporate framework.
We're here in Bloomberg giant company Big Operation. My day
job is a much smaller company, under one hundred employees.

(51:25):
And I have noticed just over the course of the
past decade how our productivity has skyrocketed. And it's a
services busy, finance as a services business, and it just
feels like the things that used to take so long
to do fifteen and twenty years ago are now automated.

(51:47):
And it's not that we're hiring fewer people, and it's
not that we're working shorter hours, but the same sized
team can just accomplish so much more than they were
capable of. Per Like I RecA all the days of
quarterly reporting and having to literally run a model, create

(52:08):
a print out for every client, print it out, stick
it into the right and like it was like a
week long process that all hands on deck every quarter
and now it's updated twenty four to seven, tick by tickets, automated.
No one cares about quarterly reports because you could get it.

(52:30):
And the joke is, you have twenty four to seven
access to your daily, weekly, monthly, year to date, five year,
ten year performance reports. Just try not to check it
second by second, but the way, and that's just one example.
Being able to communicate with clients to record and embed

(52:52):
an interactive video with charts and everything else. That was
like a massive undertaking and now it's like child's play.
Even though you're doing the same thing, you're just doing
it faster, better, cheaper, easier. Are we somehow underestimating the

(53:12):
productivity gains or are these just specific to you know that?

Speaker 3 (53:18):
Yeah?

Speaker 2 (53:18):
One area?

Speaker 4 (53:19):
Yeah, so I have some pushback on that. I think
the bar for productivity growth is a little higher and
it's very specific. It's less inputs per output. So do
things get more comfortable? Are they moving faster? Are they
qualitatively perhaps better?

Speaker 3 (53:34):
Yes?

Speaker 4 (53:35):
But are we using less inputs to generate the same value,
or are we using the same level of inputs to
generate more value. That is what we need to achieve
to speak of productivity growth. And let me give you an
example that we use in the book. You know, I
took an Uber from my apartment to come here into
the studio today. And Uber is often upheld as the
epitome of progress and tech and it is fascinating. It's

(53:57):
a great app. I love to use it. It's nice.
But look, if you want to improve the productivity growth
in taxi transportation, we have to talk about inputs and outputs, right,
And the inputs are on the capitol side of car
and you're not getting rid of that car. And on
the labor side it's the driver and the Uber car
still has that driver.

Speaker 2 (54:17):
Not weaim in parts of the West Coast.

Speaker 4 (54:21):
Yes, and this is why I said it takes time. Incrementally,
that will happen and that will unfold. But do you
think you're gonna have driverless taxis in New York in
twenty twenty eight or twenty thirty.

Speaker 3 (54:30):
I don't.

Speaker 2 (54:30):
Well, we'll have it in twenty fifty, probably in twenty forty.
I can't tell you what exact year it'll happen, but
it's coming.

Speaker 3 (54:39):
I agree with you.

Speaker 2 (54:39):
And the sooner we embed those RFID devices in vehicles
and on street corners, like doing it visually in light
r is very twentieth century.

Speaker 4 (54:51):
Right, Yeah, And that's why I said it takes time.
Over time, this will be substantial lyft to economic output.
But it doesn't have happen overnight. It's actually it takes time, right,
And there's an additional important point about productivity growth that
can also be shown in this taxi example. When technology

(55:12):
is truly productivity enhancing, you see that in falling prices,
technology is deflationary.

Speaker 3 (55:17):
Right.

Speaker 4 (55:17):
As technology does a way with input cost, firms will
compete with lower prices to gain market share. So across history,
wherever you look, as technology is becoming a credible force
in production, prices will fall. Now look at Uber. Uber
prices in New York tend to be higher than a
yellow cab. Why because despite this expensive technology, you're not

(55:40):
able to produce this ride more cheaply.

Speaker 3 (55:42):
You're not.

Speaker 4 (55:43):
In fact, you kind of have to monetize the technological expense.
The app is expensive, all is expensive, so generally you're
paying a premium for the smoothness of the app and
all that. Over time that may change, but watch prices.
You want to see productivity growth, whether it's happening or not,
you got to look at prices. And that's one of
the arguments we're making the book.

Speaker 2 (56:03):
So let's hordonically adjust. We'll stay with Uber. Let's hordonically
adjust that. In New York City, if you want a
taxi during rush hour, hey, sorry, you're out of luck
because the monopoly that was imbued by the Taxing Limousine
Commission and a handful of big medallion chain owners decided

(56:25):
in their infinite wisdom that we don't need to move
people in them rush hour. We're going to change shifts then, which,
by the way, is my pet theory for how Uber penetrated.
And so a, you could get an Uber during rush
hour that you can't during cab rides. You could get
an Uber when it's raining. Good luck hailing a cab

(56:46):
in New York City rain, and you have the ability
to schedule an Uber. You have the ability to get
a higher quality car. You could get an electric car
if you choose a larger car. Like I'm not a
huge fan of traditional hit donic adjustment because it was
a way of kind of tamping down on the cost
of living. Adjustments always felt sort of disingenuous. But I

(57:08):
don't think you could get anybody to say that Uber
is not only better. And I'm not a big Uber fan,
but as a user, Uber is certainly better than a
cab and in many ways orders of magnitude better, more choices,
more options, and just a higher quality experience. Plus you know,

(57:32):
just the idea of having hey, is this a work
thing or I'm going to use that card on the app? Well, no,
this is personal, I'll use that card. So maybe taxis
aren't the best example. But when let's talk about economists,
I want again, I want to stay with this because
I love the topic. Think about the quantity of research

(57:55):
you push, you push out, the ability to integrate charts
and data, and like, I have been in this business
long enough that I can remember, first of all, when
I started, the guys in the technical group, they were
doing charts with pencil and graph paper. I'm not exaggerating.
Maybe that's just a function of my age, but think

(58:15):
about how and the cheat was you get a different
feel when you're doing it point by point than when
you're just generating it. Whether that's true or not, at
least that was the When computers came along, people continued
to do that. But think about the access you have
to the just endless array of data, the ability to

(58:38):
do that. I haven't even mentioned your Fortune column. Think
about how much time and effort goes into putting out
a column and you go back twenty five years and
it was just a horrific grind. Like at this point,
everybody seems to use some version of Grammarly or some
other editing saw where the ability to put out and

(59:02):
I'm not talking about asking chat GPT to generate a
garbage article for you. You writing something, cleaning it up and
betting a lot of data and images. It just feels like,
you know, to quote Hemingway, you know, gradually and then
all at once. It just feels like it's so much

(59:22):
easier to put out a much higher quality product with
either the same or less effort than twenty five years ago.
Maybe I'm just hyper focused on the junk I do,
but what's your experience?

Speaker 4 (59:37):
Incrementally there's progress, But again, the bar we need to
meet is value. Are we generating more value with the
same inputs, or are we generating the same value with
less inputs. That's the definition of productivity growth. So if
you can make all these charts faster and you save
one economist on the team, well that's productivity growth. Or

(59:58):
you keep the economist and you double your number of
reports and you also manage to monetize them and earn
revenue for it, well that's productivity growth. If the charts
get prettier, faster, fancier, with the same number of economists
and the same number of revenues, well from an economic
sense perspective, that's not productivity growth. So it's got to

(01:00:18):
be a change in the relationship of inputs to outputs
if we're comfortably talking about productivity growth. And back to
the Uber example. You write you can get different cars
to write in. You can get the car, the Uber
car when it's raining, but you're paying for that, so
it's not produced more productively. Right, You're paying a search arge,
you're paying the search pricing. I think they call it

(01:00:40):
an uber so you know, yeah, you can get it
when it rains, but you'll pay twice as much.

Speaker 3 (01:00:45):
So it wasn't it wasn't done more productively, right, Huh?

Speaker 2 (01:00:47):
Really? Interesting, the gap between the increased quantity and quality
of output. If we're not monetizing it or as a consumer,
if you're not seeing price declines, then it doesn't really
count as productivity.

Speaker 4 (01:01:02):
No, It's got to be a change in the ratio
of inputs to outputs on either side. Either we keep
all the staff and we earn more revenue with.

Speaker 3 (01:01:09):
It, that's productivity growth.

Speaker 4 (01:01:10):
Or we keep the revenue constant and we do it
with less inputs. That's more productivity growth. But you know, again,
I'm not saying there isn't productivity. There is, and there
will be more and AI will have impact. It just
needs to show up in value in that relationship between
inputs and outputs.

Speaker 2 (01:01:28):
I see it qualitatively, but I completely get what you're
saying quantitatively. Are you still doing the Fortune column on.

Speaker 4 (01:01:36):
A regular Yeah. We publish in Fortune relatively regularly. Whenever
we see a cyclical or thematic topic that we feel
is pressing, we publish with Fortune.

Speaker 2 (01:01:47):
Yeah. Really really interesting. All right, I only have you
for a limited amount of time. I know you're catching
a flight today. Let me jump to our favorite questions
that we ask all of our guests, starting with what
are you streaming these days. What's keeping you entertained? Either
Netflix or podcasts or whatever.

Speaker 4 (01:02:07):
Yeah, I'm not very big on on shows or Hollywood.
I mean to give an idea. I think I'm on
the second season of Slow Horses. I think I think
there are four seasons of it, and I'm kind of
slowly making my way through the second one.

Speaker 3 (01:02:19):
It's very entertaining. I love Gary Old.

Speaker 4 (01:02:23):
Yeah, it was sort of the taking down the genre
of spy movies in a very entertaining way.

Speaker 3 (01:02:27):
So I'm doing that.

Speaker 4 (01:02:28):
But also I tend to watch late in the day
when I'm tired, so it's it's entirely possible I fall
asleep and I take like two three evenings to get.

Speaker 3 (01:02:34):
Through one episode.

Speaker 4 (01:02:36):
Yeah, so I'm not I'm not all that big on that.

Speaker 2 (01:02:39):
On that front, tell us about your mentors who helped
to shape your career.

Speaker 4 (01:02:44):
So many people, right, because a lot of it is teamwork,
and you don't you don't progress with that mentors and
role models. I would say in that in my current role,
I would probably call out two people. Rich Lesser are
a long time CEO and our chairman. He had the
vision for a Macro product, as did Martin Reeves, who
runs our research institute at Henderson Institute, And they are

(01:03:06):
really the two people who brought me into this role
and coached me, so they stand out outside of BCG.
Kathleen Stephanson, she had many, many different roles on Wall
Street and economist roles. She's been a great help navigating
my career the last many years, and further back and academia,

(01:03:28):
thesis advisors and many others. There's always teamwork in a way,
so you have many, many role models and mentors.

Speaker 2 (01:03:34):
Let's talk about books. What are some of your favorites?
What are you reading right now?

Speaker 4 (01:03:39):
All right now, I'm almost done with Making Sense of
Chaos by Doin Farmer, came out last year.

Speaker 3 (01:03:46):
Doun Farmer is a very interesting character.

Speaker 4 (01:03:48):
He's a complexity scientist at the Santa Fe Institute and
I think at Oxford University as well. And his book
is interesting to me. I bumped into him at one
or two conferences. But it's interesting to me, particularly because
he kind of argues the opposite of what we are
in our book. So he thinks he agrees that economics
is poor if you just take standard models and theory,

(01:04:10):
but he believes he can crack the complexity of it.
So he thinks, with complexity signs and better data and
better models, you'll essentially be able to make those forecasts.
I read it because it's always important to see what
others are arguing. I don't read stuff that reconfirms what
I think. I want to see what other people are
saying about the same topic from different angles of That

(01:04:30):
book's been very useful and also well written. That's what
I'm currently reading. I think of other books that have
read over the years, I mean, there's so many, many
great ones. Of course, I think one that early on
made an impression on me was Seeing Like a State
by James Scott, a't at least twenty five years old.

Speaker 3 (01:04:50):
I read it as a grand student.

Speaker 4 (01:04:52):
And what he does he looks at the ability of
governments to do top down policy to improve the lie
of large amounts of people, and he shows all the
pitfalls in a sort of Hyaekian way. It's tough to
have the local knowledge, it's tough to do the top
down improvements.

Speaker 3 (01:05:10):
Things have to.

Speaker 4 (01:05:10):
Grow bottom up. And that book kind of stood out
for being very, very eclectic, very multidisciplinary, and still I
think an excellent book to how to think laterally and
not in a sort of strict model based way.

Speaker 2 (01:05:24):
Huh. Really interesting. Our final two questions what sort of
advice would you give a recent college grad interested in
the career and economics, investment, finance, anything along those lines.

Speaker 4 (01:05:36):
Yeah, you know, I think a career as an economist
is challenging in some ways. There's so many economists out there.
Often when I hire, you see the flood of cvs,
and often very good cvs, and there's I think there's
been an overproduction of economists. So I think doing something

(01:05:56):
adjacent to economics, you know, working finance, work on the
buy side, work on the cell side. Unless unless your
heart truly beats for economics, I think, you know, you
can use economic skills and many adjacent disciplines and careers
I think are plentiful in those adjacent disciplines. If economics

(01:06:18):
graduates really feel strongly about economics, it's fascinating, but your
heart has to be in it, and there aren't all
that many seats as economists, so one has to build
that over the long term.

Speaker 2 (01:06:30):
And our final question, what do you know about the
world of economics today? You wish you knew twenty five
thirty years ago when you were first getting started.

Speaker 4 (01:06:39):
Yeah, well, I mean that's really what I wrote down
on the book.

Speaker 3 (01:06:42):
You know.

Speaker 4 (01:06:42):
The book is the twenty twenty five year journey through
the maze of the economics profession and discipline. The themes
we touched on the master model mentality, the pitfalls of
treating economics like a like a physical science, the dow
mongering which we have to simply ignore most of the time.

(01:07:02):
And then the eclectic approach to economics. I call it
economic eclecticism, drawing on a broader range of disciplines. Those
are the things that I that I learned through that
path the last twenty years.

Speaker 3 (01:07:16):
I wrote them up on the book.

Speaker 4 (01:07:17):
You know, it would have been would have been interesting
for me to read that twenty years ago, but I
wrote it now, and so I'm happy with that.

Speaker 2 (01:07:24):
Really really intriguing. Philip, Thank you for being so generous
with your time. We have been speaking with Philip Carlson Slezak.
He's global chief economist for the Boston Consulting Group. His
new book, Shocks, Crises and False Alarms, How to Assess
True Macroeconomic Risk, co authored with Paul Schwartz, is an
absolutely fascinating read. If you enjoyed this conversation, well, check

(01:07:48):
out any of the past five hundred we've done over
the previous ten years. You can find those that iTunes, Spotify, YouTube,
wherever you find your favorite podcasts, and be sure to
check out my new book, How Not to Invest The
Bad Ideas, Numbers and Behavior that Destroys Wealth, coming out

(01:08:09):
March eighteenth, twenty twenty five. I would be remiss if
I did not thank the Crack team that helps us
put these conversations together each week. My audio engineer is
Andrew Gavin, My producer is Anna Luke. Sage Bauman is
ahead of podcasts at Bloomberg. Sean Russo is my researcher.
I'm Barry Ridults. You've been listening to Master's in Business

(01:08:33):
on Bloomberg Radio.