Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in
Speaker 1: Business with Barry Ritholts on Bloomberg Radio.
Speaker 2: This week on the podcast, I have an extra special guest,
Speaker 2: really fascinating conversation with Philip Carlson Sleesact. He's got a
Speaker 2: really interesting background. Chief economists at Sanford, Bernstein, worked at
Speaker 2: the OECD, began at McKenzie, ended up as global chief
Speaker 2: economists for the Boston Consultant Group, and really approaches economic
Speaker 2: analysis from a very different perspective, critical of the industry's
Speaker 2: over reliance on models, which have proven themselves to be
Speaker 2: not great predictors of what happens next, especially when the
Speaker 2: future in any way differs from the past, and so
Speaker 2: when we have things like the dot com implosion, or
Speaker 2: especially internal to the market, the financial crisis of OEEDO
Speaker 2: nine and even COVID models just don't give you a
Speaker 2: good assessment. And he describes how he reached this conclusion
Speaker 2: in his book Shocks, Crises and False Alarms, How to
Speaker 2: Assess True Macroeconomic Risk. He calls out a lot of
Speaker 2: people who get things wrong, especially the doomsayers who not
Speaker 2: only have been forecasting recessions incorrectly for I don't know
Speaker 2: the better part of fifteen years, most especially since COVID,
Speaker 2: but their models just simply don't allow them to understanding
Speaker 2: a dynamic, changing, global, interconnected economy. I thought the book
Speaker 2: was fascinating, and I thought our conversation was fascinating, and
Speaker 2: I know you will also with no further ado, my
Speaker 2: discussion with the Boston Consulting Groups. Philip Colson Slezak.
Speaker 3: Thank you for having me.
Speaker 2: So let's start with a little bit. I want to
Speaker 2: talk about the book, but before we get to that,
Speaker 2: let's talk a little bit about your background, which is
Speaker 2: kind of fascinating for an American. You get a bachelor's
Speaker 2: at Oxford, a PhD at the London School of Economics.
Speaker 2: Was becoming an economist always the career plan.
Speaker 3: Well, let me correct you right there.
Speaker 2: I'm not American, You're not Where are you originally from?
Speaker 4: I was born in Switzerland. I grew up there, but
Speaker 4: in a number of other countries as well.
Speaker 2: So you have sort of an American accent. How long
Speaker 2: have you been here?
Speaker 4: Early on as well in my youth and so, growing
Speaker 4: up in different places, I always compared and contrasted what
Speaker 4: I saw, So I developed an interest in economics. So
Speaker 4: when it came to going to college, studying economics was
Speaker 4: a very natural choice.
Speaker 2: Where did you grow up in Switzerland?
Speaker 3: Was born there? Okay?
Speaker 2: I recently visited both Geneva and Lake Geneva up and
Speaker 2: it's just spectacular. What a beautiful part of the world
Speaker 2: it is. It really really impressive. So first job out
Speaker 2: of school McKinsey, Is that right?
Speaker 3: That's right?
Speaker 2: And what was that experience like?
Speaker 4: Well, so I studied economics at LSC actually not at Oxford,
Speaker 4: at my PhD at Oxford, so the other way around,
Speaker 4: and that was at the turn of the century.
Speaker 3: Let me take a step back.
Speaker 4: It was a turn of the century, and I emphasized
Speaker 4: that because that was peak economics. So, you know, the
Speaker 4: the ubras and the arrogance of the economics profession.
Speaker 3: Was at its peak.
Speaker 4: And you know, you're still seven eight years out from
Speaker 4: the global financial crisis, which was a big humbling moment
Speaker 4: for the profession. So everything was very model driven theory,
Speaker 4: econometrics and all that.
Speaker 2: Uh huh.
Speaker 3: So you know, I didn't.
Speaker 4: Feel comfortable even then as an undergraduate. Then as a
Speaker 4: graduate student, I branched out. I started reading a lot more,
Speaker 4: you know, going to political theory, finance history, much broader,
Speaker 4: building a mosaic of knowledge and also methods and approaches,
Speaker 4: frameworks and so at the end of my graduate studies
Speaker 4: with a PhD, that's when I landed and consulting at McKinsey,
Speaker 4: and the work was very different, so very nitty gritty, right,
Speaker 4: you go deep into corporations other organizations.
Speaker 3: You do very very granular work.
Speaker 4: So coming with this big picture view of the world
Speaker 4: and analyzing and going into this supernano micro part of
Speaker 4: business was a big change.
Speaker 2: Let's stay with the concept of peak economist. I think
Speaker 2: it was Paul Krugman who did the saltwater versus freshwater comparison,
Speaker 2: which was essentially the economists along the coasts seem to
Speaker 2: have a very different model and very different approach to
Speaker 2: doing macro versus people more inland, at least in the US.
Speaker 2: Does that sort of di economy resonate with you, How
Speaker 2: do you think about that?
Speaker 4: Well, I generally view all of mainstream economics as as
Speaker 4: too model based, master model mentality.
Speaker 3: In the book sort of this belief.
Speaker 4: That economics is a bit like a natural science and
Speaker 4: we can pass it off as a natural science. That
Speaker 4: belief is still still very much alive, and so physics envy,
Speaker 4: which has long been identified as a problem of the discipline,
Speaker 4: still reigns supreme in my view, and the book is
Speaker 4: really partly a repudiation of that. So my co author
Speaker 4: and I we take master model mentality to task in
Speaker 4: the book, and we think economics deserves a much more
Speaker 4: collectic approach, drawing many more disciplines than just sort of standards.
Speaker 2: What are your thoughts on the impact of behavioral economics
Speaker 2: that really took apart the homo economists that was front
Speaker 2: and center of classical economics and showed, hey, people aren't
Speaker 2: rational profit maximizing, they're emotional and flawed and human.
Speaker 3: Right.
Speaker 4: I think that is very very interesting. It's very valuable
Speaker 4: that we have that strand of research and economics, but
Speaker 4: it's more in the microside. It's not really macro predominantly,
Speaker 4: and so I firmly live in the global macro space
Speaker 4: where I think we still have very commoditized economics. You know,
Speaker 4: it's all about a set of forecasts. People are still
Speaker 4: wet it to their models. It's very much point forecast driven,
Speaker 4: and I think what we need is much more narrative
Speaker 4: based judgment based, more eclectic approaches to reading the landscape.
Speaker 4: And that's what the book is really really about.
Speaker 2: So we're going to talk more about how poorly economists
Speaker 2: have done as forecasters over the past few decades. And
Speaker 2: you have numerous, numerous examples, but let's stay with your
Speaker 2: early career. You're going deep at McKenzie into the granularity
Speaker 2: of corporate behavior. Then you very much a seat Angeliance
Speaker 2: Bernstein or Sanford Bernstein, you become chief economist. How different
Speaker 2: is it applying those wares on Wall Street in an
Speaker 2: investment environment versus the corporate world in a more execution basis.
Speaker 4: You know, the switch to the cell side was really
Speaker 4: good for me. There was something I'd been missing in
Speaker 4: my skill set. I'd done a lot of deep thinking, writing, researching,
Speaker 4: I'd done the more microeconomics. I learned more about the
Speaker 4: corporate world, but I hadn't been exposed to the finance
Speaker 4: angle of it as much. I hadn't talked to the
Speaker 4: byside at all really before. And being a Sanford Bernstein,
Speaker 4: a firm with a storied history and in equity of
Speaker 4: research really and swimming in this pool of really great
Speaker 4: equity analysts just taught me a lot of things, not
Speaker 4: least how to frame research angles, how to be quick
Speaker 4: with research notes, how to get the thoughts out, and
Speaker 4: then the concept exposure to investors on the buy side
Speaker 4: really really helped me sharpen my research skills. So that
Speaker 4: was almost like a missing piece in my recipe. I
Speaker 4: really unlocked something for me and I learned a lot
Speaker 4: there and I had a really good time doing that work,
Speaker 4: publishing you many many research reports over those years and
Speaker 4: often going very very deep, often going very historical in
Speaker 4: the approach. So Bernstein is a firm that that very
Speaker 4: much appreciates lateral thinking, differentiated approaches out there kind of ideas,
Speaker 4: And so I ran wild for a while, just doing
Speaker 4: work that I don't think I would have done anywhere else.
Speaker 2: So you started a consultant, You briefly had a NGO
Speaker 2: at the Organization of Economic Cooperation and OECD Development. As
Speaker 2: I guess the last day, you're on the cell side.
Speaker 2: So you see the universe of career options as an economist.
Speaker 2: What brought you back to the Boston consulting room.
Speaker 4: So I had a history with BCG already, and I
Speaker 4: was well connected there, and at some point I was
Speaker 4: approached if I'd like to come back and do the
Speaker 4: same kind of work I was doing on the cell side.
Speaker 4: But at BCG. BCG is a really great platform because
Speaker 4: not only is it deeply ingrained in the corporate world,
Speaker 4: so you know, the access to boardrooms is very wide.
Speaker 4: You get to meet a lot of interesting executives and
Speaker 4: the problems they're grappling with, but you also still have
Speaker 4: access into the institutional investor world who are also clients,
Speaker 4: so you really get both sides of the landscape, and
Speaker 4: they were really different. Right on the by side, it's
Speaker 4: mostly a look at firms outside in they're outside of
Speaker 4: what's happening in the boardrooms. They're trying to decode it
Speaker 4: from the outside. Being a consultants working and talking with them,
Speaker 4: you're much closer to what's actually happening in their deliberations,
Speaker 4: the problems they're facing, the questions.
Speaker 3: They're trying to answer.
Speaker 4: So to me, that platform is very attractive because it's
Speaker 4: it's very versatile, it's it's it never gets boring, and
Speaker 4: I've I've had a good run the last five years
Speaker 4: doing my work on that BCG platform.
Speaker 2: So I have no expertise in the consulting world, but
Speaker 2: I kind of hear people lump all the consultants together, Mackenzy, BCG,
Speaker 2: all these different firms. I get the sense from speaking
Speaker 2: to various people that that's kind of inaccurate, that BCG
Speaker 2: is not McKenzie. The very different organizations.
Speaker 4: What's your experience been, Yeah, I mean they have different
Speaker 4: cultures for sure. They certainly vie for the same business,
Speaker 4: the three that you mentioned, so so you you constantly
Speaker 4: bump into those other two competitors if you're at any
Speaker 4: one of those three firms, a third being Bane, I think, yeah, mckenzy, BCG, Baane,
Speaker 4: those three are there others, but those are the core
Speaker 4: strategy consultants, if you will, and you know, I would
Speaker 4: think the type of work that has done is obviously
Speaker 4: very similar. They're vying for the same business, but culturally
Speaker 4: it is different and uh uh, you know they're they're
Speaker 4: slightly different sizes, these three firms. BCG today is about
Speaker 4: twelve billion and revenues annually. We have about I think
Speaker 4: sixty seventy offices and no, sorry, well well over one
Speaker 4: hundred offices in sixty countries. I think it's the right
Speaker 4: metric here, right, and you know it's it's it's a
Speaker 4: space that is is very, very competitive, but that that
Speaker 4: keeps everyone on their toes.
Speaker 2: I would imagine. So let's let's talk about advising companies
Speaker 2: and advising executives. You talk about explaining economic uncertainty and
Speaker 2: as we'll get into in the book, why there is
Speaker 2: this risk aversion and these fears of crises that never
Speaker 2: seem to come around. How do you approaching executives on
Speaker 2: navigating all this. It seems like there's always this fear
Speaker 2: of a disaster, and lately it hasn't really showed up.
Speaker 4: Yeah, So a lot of what I do in conversations
Speaker 4: with executives is to unskew, if you will, some of
Speaker 4: the perceptions they pick up in the press, in public discourse,
Speaker 4: which is reliably dialed down to the sort of domongering
Speaker 4: side of things.
Speaker 3: Right, that's really true.
Speaker 4: It's not just lately, since you mentioned it, sort of
Speaker 4: the inevitable recession that never that never came. We're really
Speaker 4: at the end of a string of such false alarms.
Speaker 4: You know, when COVID hit, it was very common to
Speaker 4: predict a depression, not just the recession, but a depression
Speaker 4: was very conventional wisdom in twenty twenty that this would
Speaker 4: take many years to recover. Then when interest rates rose,
Speaker 4: it was it was fashionable to predict an emerging market
Speaker 4: a cascade of defaults. Then, of course, when inflation spiked,
Speaker 4: it was cast as a hyperinflation, hyper inflation, structural inflation,
Speaker 4: regime break, the nineteen seventies, all that stuff that clearly,
Speaker 4: even then I think was very clearly not not what
Speaker 4: was playing out. And then the inevitable recession is really
Speaker 4: just the most recent in a string of false alarm.
Speaker 4: So often what I do is to meet people where
Speaker 4: they are. They pick up doomsday narratives because they're very
Speaker 4: prevalent in public discourse, and we often go back to
Speaker 4: basics and ask, well, how does the system work and importantly,
Speaker 4: what would it take for these big bad outcomes to happen.
Speaker 4: It's not that they can't happen. They're part of a
Speaker 4: risk distribution, but very often we take these risks in
Speaker 4: public discourse that are the edges of the risk distribution
Speaker 4: tail risks, tail risks, and we pretend that they're in
Speaker 4: the middle of the distribution. Right, if you go through
Speaker 4: financial news, if you go to financial TV kind of conversations,
Speaker 4: you often get the impression that these rips which are
Speaker 4: genin rest, they're real, they're part of the distribution, but
Speaker 4: you get the impression that they're really the center of
Speaker 4: everything we should be watching.
Speaker 2: And so this leads to an obvious question. Whenever I
Speaker 2: have an author in I often asked what inspired them
Speaker 2: to write their book? It's pretty clear what inspired you?
Speaker 2: It seems like it got to the point where, hey,
Speaker 2: everybody is freaking out about things that are either not
Speaker 2: happening or just so low probability events that they're not
Speaker 2: contextualizing it. Well, what actually was the aha moment that
Speaker 2: said I got to put all this down in a
Speaker 2: book and instead of repeating myself over and over here,
Speaker 2: read this and it'll it'll explain why you're fearing all
Speaker 2: the wrong things.
Speaker 4: Yeah, it was the It was the accumulation of situations
Speaker 4: where my co author Paul Schwartz, and I felt we
Speaker 4: had a pretty good access to this topic. We kind
Speaker 4: of got that one right, not because we were using
Speaker 4: models and sophisticated analysis, but we looked at it from
Speaker 4: a narrative driven perspective. We asked the right questions about
Speaker 4: what does it take to get to that really bad
Speaker 4: structural situation, and so we wanted to wrap that into
Speaker 4: a coherent story of how we think about economics, not
Speaker 4: because we can get it right every single time. Even
Speaker 4: if you use a more eclectic approach to economics, you
Speaker 4: will get things wrong, but I think your hit rate
Speaker 4: can improve. And that was the motivation to write that
Speaker 4: all down in the book. And yeah, that's how this
Speaker 4: came about.
Speaker 2: So first, let's just start out generally. You described the
Speaker 2: book as calling out pervasive doom saying in public discourse
Speaker 2: about the economy, and demonstrating how to navigate real financial
Speaker 2: and global risks more productively. Explain.
Speaker 4: So, over the last few years, call it since the
Speaker 4: COVID pandemic, we've had a string of false alarms, as
Speaker 4: I would call them, right out the gate. In twenty twenty,
Speaker 4: we were told this will be a greater depression, maybe
Speaker 4: as bad as the nineteen thirty is, worse than two
Speaker 4: thousand and eight. That wasn't the case at all. Then
Speaker 4: we had an inflation spike that was spun into an
Speaker 4: inflation regime break forever inflation, hyperinflation that didn't pan out.
Speaker 4: Then we had rising interest rates, and that was spun
Speaker 4: into a doomsday story of emerging markets, cascade of defaults,
Speaker 4: and then we had the story of an inevitable recession
Speaker 4: that we're still waiting for.
Speaker 3: Right, So we have across the.
Speaker 4: Board a lot of negativity. Across the board, we have
Speaker 4: a lot of doom saying public discourse is pervasive in
Speaker 4: that regard, the story is always skewed to the downside.
Speaker 4: And what the book does it provides a framework to
Speaker 4: think about this differently and more productively. And it does
Speaker 4: so across real economy risks, I think recession, but also
Speaker 4: sort of longer term growth. It does so in the
Speaker 4: financial economy, think about stimulus and the effectiveness of stimulus,
Speaker 4: interest rates, inflation bubbles, that type of stuff. And it
Speaker 4: does so across the global space, the institutions that governed trade, etc.
Speaker 2: So you combine data analysis with both narrative storytelling and
Speaker 2: judgment over traditional macroeconomic models. Explain what led you to
Speaker 2: this way to contextualize what's going on in the real
Speaker 2: world economy.
Speaker 4: So my path through economics was fairly eclectic. I started
Speaker 4: out studying economics in a traditional theoretical macroeconomic econometric sense,
Speaker 4: and then I went into studying much broader adjacent fields
Speaker 4: that are relevant to economics, finance, history, political theory, political economy, etc.
Speaker 4: Then I had different experiences in my career, just just
Speaker 4: putting together different views of how to approach these problems.
Speaker 4: And over time and working on the cell side, we
Speaker 4: discussed they put all these together. And so it is
Speaker 4: just the insight that the models will not deliver. You
Speaker 4: cannot accurately forecast the economy. Economists shouldn't feel so ashamed
Speaker 4: about that. It's not like natural scientists are always doing better.
Speaker 4: Think about epidemiologists. They also struggle to accurately forecast COVID deaths,
Speaker 4: for example. So you know, the whole physics envy and
Speaker 4: the whole inferiority complex that often besids the economics profession
Speaker 4: is misplaced. In my view, we should embrace the uncertainty
Speaker 4: that prevents us from making precise point forecasts, and we
Speaker 4: should deliver that uncertainty. Embrace the eclectic nature of what
Speaker 4: we're trying to solve. It isn't just about economics and policy,
Speaker 4: it's about myriad other things that play into this. And
Speaker 4: when we do that and do it rationally, I think
Speaker 4: often we're going to land and better predictions.
Speaker 2: You know, it's funny about the physics envy. Richard Fiman
Speaker 2: once said, imagine how much harder physics would be if
Speaker 2: electrons had feelings? Right, So it's it's not a pure
Speaker 2: natural world. You have human behavior getting in the way.
Speaker 2: And you know, one of the quotes from the book
Speaker 2: Doom Cells, hasn't that always been the case that it
Speaker 2: appeals not only to our fear of existential threats from
Speaker 2: an evolution perspective, but just generally speaking, good news is
Speaker 2: sort of sneaks by and bad news gets our attention.
Speaker 4: Yeah, it's the clicks and the eyeballs that that we're
Speaker 4: trying to attract in the in the news business model,
Speaker 4: and that that gives you the slant to the downside.
Speaker 4: I think it's it's particularly pronounced these.
Speaker 2: Days social media and the rest.
Speaker 3: That's part of it.
Speaker 4: But it's also the case that when you think about
Speaker 4: the last forty years or so, there was a window
Speaker 4: that we call good macro in the book. So a
Speaker 4: lot of ma economic variables, a lot of macroeconomic context
Speaker 4: was benign and was a tailwind, you know for executives,
Speaker 4: but certainly for investors. So in the real economy, cycles
Speaker 4: grew longer, volatility came down like recessions were less frequent.
Speaker 4: The financial economy, inflation structurally declined, pulling down interest rates
Speaker 4: with it. And in the global realm you had you know,
Speaker 4: institutional growth and where we're aligning value chains and all
Speaker 4: that really was a tailwind to executives and investors, and
Speaker 4: more recently, not just COVID you you can go back
Speaker 4: to two thousand and eight. It's sort of a growing
Speaker 4: crescendo of new noise and new disturbances. I think that
Speaker 4: good macro window is challenged. Right, we had a lot
Speaker 4: of generations, We had a lot of shocks, all of
Speaker 4: whiplash there, and so for executives, when it used to
Speaker 4: be possible to ignore the macro world, to take it
Speaker 4: for granted, it's now moved into the boardroom. Now you
Speaker 4: need to have a view on what these things mean
Speaker 4: for your bisness and you kind of need to do
Speaker 4: that almost ongoingly. So that has changed, I mean, because
Speaker 4: there's more gyrations, there's more whiplash. I think that has
Speaker 4: dialed up all the angst, and it has dialed up
Speaker 4: the doom saying and the string of false alarms that
Speaker 4: I went through earlier, in my mind is pretty dense
Speaker 4: it's you know, every year we had a new doomsday narrative,
Speaker 4: and every single year it just didn't pen out that way.
Speaker 2: You know, there was a I'm trying to remember which
Speaker 2: economist wrote this up. At one point in history, your
Speaker 2: whole world was your local region, and what happened globally
Speaker 2: or what happens across the ocean was not relevant. Now
Speaker 2: it doesn't matter what corner of the earth you're hiding in,
Speaker 2: the global macro world is knocking in your door regardless.
Speaker 2: How significant is that to both to both coming up
Speaker 2: with a better macroeconomic framework, and all of these false
Speaker 2: crises and fears seemed to be never ending.
Speaker 4: Yeah, I think the greater interconnectedness and the real time
Speaker 4: aspect of economics and the pass through of influences and
Speaker 4: in often just hours transmitted often through financial markets, that
Speaker 4: just adds to that. It's it never it never stops,
Speaker 4: It never takes a break. You know, you go to
Speaker 4: sleep with with sort of the latest data, you wake
Speaker 4: up with the latest data, right. I mean, it's seat
Speaker 4: of constant in that regard, and I think that certainly
Speaker 4: feeds into that sense of heightened risk and crisis.
Speaker 2: So let's talk about some shocks Over the past quarter century.
Speaker 2: We had and this is really just less global than
Speaker 2: US focused, but obviously international ramifications. We had the dot
Speaker 2: Com implosion in two thousand, We had the September eleventh
Speaker 2: attacks in O one. Not long after that, we had
Speaker 2: the Great Financial Crisis. We had COVID. In between, we
Speaker 2: had a couple couple of market events, the flash crash
Speaker 2: of them again, and I don't know if you really
Speaker 2: consider those true economic shocks, but certainly dot Com, nine
Speaker 2: to eleven, GFC and COVID were huge. Is this have
Speaker 2: we been through more than the usual number of shocks
Speaker 2: or does it just seem that way recently.
Speaker 4: Well, we've always had shocks. I think two thousand and
Speaker 4: eight stands out among the ones you mentioned, because that's
Speaker 4: where the US economy actually came close to the precipice
Speaker 4: of this could be a structural depression. Without the intervention,
Speaker 4: without the stimulus that was deployed at the time, this
Speaker 4: could have gone a lot worse. COVID, in some sense,
Speaker 4: was a replay of that risk, but action was more
Speaker 4: swift and more decisive. So it seems like we'll learned
Speaker 4: something there, and.
Speaker 2: Much more fiscal as opposed to the financial crisis, which
Speaker 2: was primarily a monetary response, and we ended up with
Speaker 2: two very different years that followed addressed that.
Speaker 4: If you would, Yeah, So I think in two thousand
Speaker 4: and eight, you'll remember tarp Tart was what now looks
Speaker 4: like a paltry sum of seven hundred billion, and it
Speaker 4: got voted down in Congress, right right, So.
Speaker 2: I remember that week in October the market ceased so
Speaker 2: aggressively in the stock market sold off that was voted
Speaker 2: down on a Monday. By Friday it passed overwhelmingly, exactly.
Speaker 4: And I think this is one of the big themes
Speaker 4: that we emphasize on the book. Stimulus comes down to
Speaker 4: the willingness of politicians to act and the ability to act.
Speaker 4: Ability is more about financial markets. Will bond markets finance
Speaker 4: this kind of action, which they do in times of crisis,
Speaker 4: but the willingness has to be there to act, and
Speaker 4: in times of crisis, the willingness to act usually rises.
Speaker 4: Partisanship is put aside. Politicians come together, they act to
Speaker 4: you know, when the house is on fire, you will
Speaker 4: step up and do something about it. And I think
Speaker 4: in twenty twenty that was in display and there was
Speaker 4: a learning curve from the more timid approach in two
Speaker 4: thousand and eight and then and then perhaps it was
Speaker 4: overdone twenty twenty and the following years, but certainly the
Speaker 4: risk was perceived. Perhaps we're doing too little, so let's
Speaker 4: rather go large and backstop the system.
Speaker 2: My favorite story from the twenty twenty Cares Act was
Speaker 2: a week before the country was shut down, Congress couldn't
Speaker 2: agree on renaming a library in DC because it was
Speaker 2: just along partisan lines. Everything got tabled. Then the world
Speaker 2: shut down, and the largest fiscal stimulus since World War Two,
Speaker 2: at least as a percentage of GDP, flew through the
Speaker 2: House and Senate and was signed by Kars. Acting none
Speaker 2: was President Trump Cares Act True two was President Trump Cares.
Speaker 2: Act Three was President Biden. Did we learn something from
Speaker 2: the financial crisis about the lack of fiscal stimulus and
Speaker 2: maybe the pendulum swung too far the other way? What's
Speaker 2: your takeaway from that?
Speaker 3: No, for sure.
Speaker 4: Look, I think two crises were very different. You had
Speaker 4: in two thousand and eight damage balance sheets, not just
Speaker 4: in the banking system, but households. Their balance sheets had
Speaker 4: to be repaired. Households had to dig themselves out of
Speaker 4: that hole, had to rebuild their wealth, and that that
Speaker 4: would have called for more intervention than what we got
Speaker 4: in two thousand and eight. In twenty twenty, I think policymakers, politicians,
Speaker 4: they had internalized that learning, so they went extra large
Speaker 4: on the fiscal side. And that hole that COVID created
Speaker 4: was basically filled with fiscal stimulus. As you know, it's
Speaker 4: widely believed and accepted that this was extremely big, too
Speaker 4: much perhaps, and so we had an overshoot in certain
Speaker 4: consumption areas, particularly in the good space. There was an
Speaker 4: overshoot in consumption. It pushed up demand. It together with
Speaker 4: supply crunches, it pushed up inflation in an idiosyncratic and
Speaker 4: more tactical cyclical way, not structural, but but tactical. And
Speaker 4: so I think, yes, policymakers did learn something and they
Speaker 4: were risk averse, so they went extra large.
Speaker 2: So you said, the financial crisis clearly a shock. The
Speaker 2: other things not as much as a shock. And we've
Speaker 2: had plenty of false alarms. How do you define what
Speaker 2: a true shock or crisis is and what do you
Speaker 2: put in the category of false alarms or things that
Speaker 2: are genuine but just don't rise to the level as described.
Speaker 4: Yeah, there are two things to consider. One, instead of
Speaker 4: the new cycle level, we have a constant doom saying
Speaker 4: about supposed things that could lead to recession or otherwise
Speaker 4: downgrade the economy.
Speaker 3: You know.
Speaker 4: Just the last few years, we went numerous you know,
Speaker 4: so for example, consumers were supposed to run out of
Speaker 4: cash and consumers were not going to keep up their spending.
Speaker 4: We had lots of false alarms about the labor market,
Speaker 4: even last summer, right we had last summer in August,
Speaker 4: there was a somewhat of a panic because supposedly the
Speaker 4: labor market was going to be very soft and very weak.
Speaker 4: So we have these new cycle false alarms stories that
Speaker 4: often are rooted in a data point that is noteworthy,
Speaker 4: that is interesting, that does signify risk, but we extrapolating
Speaker 4: from the data point to conclusions that don't hold up.
Speaker 4: That is one category of false alarms. The other categories
Speaker 4: where you have real crises. But the question is are
Speaker 4: they going to have structural impact? Are they going to
Speaker 4: have a long term impact on the economy? Are they
Speaker 4: going to downgrade the economy's capacity? So two thousand and
Speaker 4: eight does qualify. Two thousand and eight left an indelible
Speaker 4: mark on the US economy, but twenty twenty didn't in
Speaker 4: terms of performance and output. We've regained the output to
Speaker 4: trend output that we were wrong the path that we're
Speaker 4: traveling on pre COVID. We've come back to that trend
Speaker 4: output path. It has not left the kind of permanent
Speaker 4: mark on economic performance that you saw after two thousand
Speaker 4: and eight. So in that sense, we need to differentiate
Speaker 4: between what is a likely shock that will pass and
Speaker 4: that we can fix, versus what is something that changes
Speaker 4: the structural composition structural setup of the economy durably. Those
Speaker 4: are two very different types of situations.
Speaker 2: That sounds like a usable framework for distinguishing between real crises.
Speaker 2: And do I call it media alarmism or you know,
Speaker 2: everybody is blaming the media these days, especially with this administration,
Speaker 2: but there has been a fairly relentless negativity, especially in
Speaker 2: social media. What's the best framework for you know, separating
Speaker 2: the wheed from the chaff.
Speaker 4: Well, typically when we see knee jerk reactions and doom
Speaker 4: say stories, they're taking a data point and then they're extrapolating,
Speaker 4: usually on the basis of a model. So I mean,
Speaker 4: think about the inevitable recession. Even Larry summers people like
Speaker 4: that that came out and said, look to bring down
Speaker 4: wage growth, to bring down inflation, you need I don't know,
Speaker 4: five years of unemployment at this and that level.
Speaker 2: Why because he threw out ten percent, well, ten.
Speaker 3: Percent for one year or five percent for five years.
Speaker 4: So he had different configurations, but they're all based on
Speaker 4: basically the Phillips curve. This was all a Phillips curve
Speaker 4: take on the economy, which is.
Speaker 2: Which was a great model fifty years ago, wasn't it.
Speaker 4: Yeah, it described the UK and certain other countries empirically
Speaker 4: quite well. It wasn't ever really a model and a theory.
Speaker 4: It was more of a description of empirical facts. But
Speaker 4: certainly it was useful for a window. It's still useful
Speaker 4: as as an instrument to think about dynamics, right, But
Speaker 4: it was basically used as as the truth. You know,
Speaker 4: there's an input and there's an output, and my model
Speaker 4: gives me the truth if I give it certain inputs,
Speaker 4: and then well what happens. We're extrapolating data points, often
Speaker 4: outside the range of empirical facts. The models are only
Speaker 4: trained on historical facts. You know, you can't make up
Speaker 4: data points to train your model. So when a crisis hits,
Speaker 4: likely you get data points that were not empirically known
Speaker 4: in the past. So what does the model do. It
Speaker 4: extrapolates outside its historical empirical range, and then you get
Speaker 4: these kind of point forecasts. It just don't work. I mean,
Speaker 4: case in point, in two thousand and eight, unemployment goes
Speaker 4: up to around ten percent, right, and it takes almost
Speaker 4: the whole twenty tens a full decade almost to bring
Speaker 4: down this very high unemployment rate. So in COVID, when
Speaker 4: unemployment shoots up to fourteen percent, what does the model do.
Speaker 4: It says, Well, if it takes a decade to bring
Speaker 4: down ten percent unemployment, it will take even longer to
Speaker 4: bring that fourteen percent of unemployment, right, And that is
Speaker 4: exactly this kind of limitation of the model based approach. Empirically,
Speaker 4: you never had fourteen percent unemployment, So if the model
Speaker 4: extrapolates from past data points, it's going to go off
Speaker 4: the tracks. And that's exactly what happened in that instance.
Speaker 2: So the underlying flaw built into most models is that
Speaker 2: the future will look like the past, and as we've learned,
Speaker 2: that often is not the case.
Speaker 4: It's always idiosyncratic look the US economy since the Second
Speaker 4: World War has only seen a dozen recessions. Now, each
Speaker 4: of those recessions is totally idiosyncratic, and even if they
Speaker 4: had a lot of commonalities, twelve is not a sample
Speaker 4: size that a natural scientist would consider large enough to
Speaker 4: build sort of an empirical model around. Right, each of
Speaker 4: these crises, or each of these recessions was idiosyncratic, and
Speaker 4: the idiosyncrasy demands much more than a simple model or
Speaker 4: even a sophisticated model. It demands the eclectic view across many,
Speaker 4: many drivers. And that comes down to judgment. There isn't
Speaker 4: There isn't an output in an Excel sheet or a
Speaker 4: Python model or anything. In the end, it comes down
Speaker 4: to human judgment, and I think that that is something
Speaker 4: we lose sight of way too often.
Speaker 2: You very much strike me as a fan of Professor
Speaker 2: George Box. All models are wrong, but some are useful.
Speaker 2: Tell us a little bit of about how models can
Speaker 2: be useful.
Speaker 3: Well.
Speaker 4: They are always a good starting point. Even the Phillips
Speaker 4: curve has a lot of validity to think about what
Speaker 4: might be happening. There are always this sketch of reality,
Speaker 4: but the moment we're translating that from you know, a
Speaker 4: sketch and a map into something that is hardwired in
Speaker 4: a quantity quantified model and the moment we then expect
Speaker 4: that the output will resemble anything like the truth we're
Speaker 4: we're sort of denying the reality of this. It just
Speaker 4: doesn't work that way. Look, I'm not the first person
Speaker 4: to make that point. In fact, you know, Hayek canes
Speaker 4: fund mesas they've long basically trashed economics for saying like,
Speaker 4: you're too gullible and you're too naive about the constant
Speaker 4: nature of these variables. They they've long pointed out that
Speaker 4: you don't have this this uh, what the natural sciences provide,
Speaker 4: which is stability in all these relations of variables.
Speaker 3: You don't have that in economics.
Speaker 4: And there's a there's an anecdote that we pick up
Speaker 4: in the book when Hayek receives the Nobel Prize in
Speaker 4: nineteen seventy four, he actually uses his acceptance speech, or
Speaker 4: I think it was a dinner speech he gave right
Speaker 4: after being awarded the prize. He uses that speech to say, look,
Speaker 4: you shouldn't do this prize in economics. You should you
Speaker 4: should have you should have never done the Nobel Prize.
Speaker 4: In economics. But if you must have this prize, at
Speaker 4: least ask the recipients to swear an oath of humility,
Speaker 4: because unlike physicists and in chemistry and other natural sciences,
Speaker 4: economists have a big microphone, right. Policymakers listen to them,
Speaker 4: politicians listen, public listens to them. But they don't have
Speaker 4: that certainty of analysis. They don't have that stability in
Speaker 4: their model. So they're going to go off the tracks
Speaker 4: all the time. So at least ask them to be
Speaker 4: humble about what they're doing. And I think that that
Speaker 4: is a good reminder of the long history of recognizing
Speaker 4: the limits of model based approaches through the eyes of
Speaker 4: some of the leading leading thinkers in the space.
Speaker 2: So let's talk a little bit about a lot of
Speaker 2: the false alarms and faux crises. So many economists got
Speaker 2: twenty twenty two, Room, twenty twenty three, twenty twenty four.
Speaker 2: They were expecting a recession. It never showed up. Why
Speaker 2: is that?
Speaker 4: It starts with the master model mentality that we call
Speaker 4: out in the book, where we placed too much trust
Speaker 4: in models. So the Phillips curve was essentially used by
Speaker 4: many forecasters.
Speaker 2: Call it define the Phillips curve for the lay reader
Speaker 2: who may not be free.
Speaker 4: The Phillips curve is as an old theory going back
Speaker 4: middle of the last century, describing the relationship between wage
Speaker 4: growth and unemployment. So the idea is that you trade
Speaker 4: off the two variables, and that led commentators like Larry
Speaker 4: Summers to say to bring inflation out of control, you
Speaker 4: wouldn't need either many years of high unemployment or a
Speaker 4: sharp recession ten percent uneployment for a year to reset
Speaker 4: the picture. In other words, in layperson's terms, a soft
Speaker 4: landing is impossible, right, and this is what fed into
Speaker 4: the inevitable recession that was the dominant received wisdom in
Speaker 4: the last few years.
Speaker 3: Now, you know, these.
Speaker 4: Things are good starting points, they have validity historically and
Speaker 4: a lot of empirical data. But in the end it's idiosyncratic.
Speaker 4: It's very idiosyncratic constellation of drivers and risks, and so
Speaker 4: it was in the last few years. So let's look
Speaker 4: at that for a moment. One of these master models
Speaker 4: was also interst rate sensitivity.
Speaker 3: Right.
Speaker 4: We think interest rates go up and that eats into
Speaker 4: disposable incomes for households, right, But in reality, mortgages in
Speaker 4: the US unlike in Canada, mortgages or long term didn't
Speaker 4: actually take a big bite at a disposable exactly, very
Speaker 4: long term, fixed, very low and most of them were
Speaker 4: done at low rates because we had low rates for
Speaker 4: a long time. Contrasts that with the flexible contracts and
Speaker 4: mortgages in Canada where they lost a lot of disposable income.
Speaker 4: That wasn't the case here. Same thing about interest rate
Speaker 4: sensitivity in the corporate sector. So the textbook tells you
Speaker 4: interest rates go up and investment will fall, but does
Speaker 4: it You know, when you do the empirical analysis for
Speaker 4: whatever window, you'll see a very flimsy correlation between interest
Speaker 4: rates and capex. Firms invest when they have a narrative
Speaker 4: to do so, when they see a return on the investment,
Speaker 4: and if they believe the investment is beneficial to them,
Speaker 4: they'll do it whether the interest rate is two, three
Speaker 4: or four percent. And just look at what happened in
Speaker 4: the last few years. You had a lot of narrative
Speaker 4: and belief in worthwhile investments data centers software. So with
Speaker 4: or without higher interest rates, firms are going to do that.
Speaker 4: Particularly also because a lot of our investment has shifted
Speaker 4: away from you know, fixed structures physical investment to intellectual
Speaker 4: property software type of investment which has a much higher
Speaker 4: rate of depreciation. So a bridge or road will be
Speaker 4: good for thirty forty years, but software is maybe three
Speaker 4: or four years. So you constantly have to invest just
Speaker 4: to stay and still just to keep the stock of
Speaker 4: investment in the space to keep it steady. You constantly
Speaker 4: have to run faster just to maintain that. And so
Speaker 4: there is a lot of idiosyncratic drivers that that led
Speaker 4: two very different outcomes from what was predicted from a
Speaker 4: model based Philip's Carve type approach to reading that context.
Speaker 2: So a lot of highly regarded economists like Larry Summers
Speaker 2: kind of reminded me of the Paul Graham quote. All
Speaker 2: experts are experts in the way the world used to be,
Speaker 2: and we're seeing a lot of that in that. So,
Speaker 2: not only did people get the recession calls wrong for
Speaker 2: the past couple of years, what have we had two
Speaker 2: months of recessions in the past fifteen years? Are we
Speaker 2: in a post recession economy? Now?
Speaker 4: You can still get recessions, but I think we've become
Speaker 4: better at fighting them. So this is the topic of stimulus.
Speaker 4: There are three different types of There are two different
Speaker 4: types of stimulus that we describe in the book across
Speaker 4: three chapters, and we differentiate between what we call tactical stimulus,
Speaker 4: which is just too smooth the cycle, accelerate growth in
Speaker 4: between recessions, maybe dearist the cycle when necessary, versus existential stimulus,
Speaker 4: which is when policymakers politicians step in when the economy
Speaker 4: is truly at risk of a structural break. Those two
Speaker 4: types of stimulus, they're evolving differently. I think the tactical
Speaker 4: kind is more challenge going forward. It was very easy
Speaker 4: when inflation was below target. It was very easy when
Speaker 4: interest rates were very very low. There was little cost
Speaker 4: to the Fed put you could do that. There wasn't
Speaker 4: sort of an inflation risk associated with it. That's different now,
Speaker 4: and I think they will remain different now that we're
Speaker 4: skewed to the upside in terms of inflation, where interest
Speaker 4: rates are likely to be higher for much longer. But
Speaker 4: the existential type of stimulus, the ability to step up
Speaker 4: when it's needed, I think that is still very strong.
Speaker 4: And if you have another shock or a crisis or
Speaker 4: a recession, I think will be able to deploy stimulus effectively.
Speaker 2: Still so we said earlier, all recessions are not homogeneous.
Speaker 2: They're all idiosyncratic and unique. But one of the things
Speaker 2: you mentioned in the book that kind of intrigued me,
Speaker 2: we shouldn't conflate recession intensity and recovery. Explain what that means.
Speaker 4: Yeah, when COVID hit, we had extreme data prints. Unemployment
Speaker 4: is sort of exhibit A of this story. Unemployment went
Speaker 4: to ten percent in two thousand and eight, but it
Speaker 4: went to fourteen percent in twenty twenty. Right, So the
Speaker 4: intensity that the sudden collapse of activity was much more
Speaker 4: pronounced in COVID than it was in two thousand and eight.
Speaker 2: GDP also much worse during the first few months of
Speaker 2: COVID than all variables.
Speaker 4: So we have a chart early in the book that
Speaker 4: shows the fifth to ninetieth percentile of historical experience of
Speaker 4: these variables, and COVID is like far outside that historical range.
Speaker 4: So you get data prints that you're not used to,
Speaker 4: that the models don't know. The models were trained on
Speaker 4: data points that were simply not experienced until they happened
Speaker 4: in COVID. Now all of that fed into extreme intensity
Speaker 4: was equated with This will be a very long and
Speaker 4: difficult recovery. Why the ten percent unemployment rate led to
Speaker 4: many years of recovering the twenty tens. Right, So now
Speaker 4: if the unemployment rate is even higher, it's going to
Speaker 4: take even longer to work it down to a level
Speaker 4: that is, you know, a good economy again. But that
Speaker 4: wasn't that wasn't the case. Twenty twenty wasn't about a
Speaker 4: balance sheet recession. It wasn't about banks repairing their balance sheets.
Speaker 4: It wasn't about households repairing the balance sheet. We took
Speaker 4: care of that with stimulus, and therefore the ability to
Speaker 4: recover was much faster and much stronger. There were other
Speaker 4: idiosyncratic factors. Essentially, what was underestimated was the ability to
Speaker 4: adapt of society. You know, societies found ways to work
Speaker 4: around the virus. The pathway to a vaccine was faster.
Speaker 4: So there were a lot of things that were underestimated.
Speaker 3: You know.
Speaker 2: Kind of reminds me of the why two K fear
Speaker 2: that when there's a little bit of a fear of panic,
Speaker 2: the expected crisis may not show up because we're taking
Speaker 2: steps to avoid it. We don't know what was Y
Speaker 2: two K a false alarm or did the fear lead
Speaker 2: us to make sufficient changes to avoid problems. I honestly
Speaker 2: can't answer that question. I'm wondering how you look at
Speaker 2: crises in terms of do some of the fear mongering
Speaker 2: and some of the you know, media absolute extremism lead
Speaker 2: to government action that prevents the worst case scenario from happening.
Speaker 4: It's possible that has shapes the perception of policy makers
Speaker 4: and politicians, but I think the reality is on the ground.
Speaker 4: You know, the variables that are visible and measurable, unemployment rates, GDP, growth,
Speaker 4: you know, imports, exports, all of that was under pressure.
Speaker 4: I think that is more telling for those who take
Speaker 4: decisions than what public discourse does. Is public discorse, particularly fearful,
Speaker 4: and a lot of angst pervades how we think about
Speaker 4: the economy.
Speaker 3: Does that spur action? Maybe that's part of it.
Speaker 4: So we don't know, as you rightly say, what would
Speaker 4: have been in a counterfactual world. But essentially, when the
Speaker 4: economy is genuinely in trouble, I think the willingness to
Speaker 4: act on the stimulus side is very strong.
Speaker 2: So let's talk about some of those metrics. You have
Speaker 2: an image in the book Scanning the Recession Barcode, So
Speaker 2: tell us about that and the history of US recessions,
Speaker 2: which seem to have been more frequent and more intense.
Speaker 2: You go back a century, they were depressions, not even recessions.
Speaker 2: Tell us about how this has changed over the past,
Speaker 2: I don't know, a couple of hundred years.
Speaker 4: Yeah, So if you do a very long run chart
Speaker 4: for sessions in the US economy and you shade each
Speaker 4: recession as a bar what you get as a barcode
Speaker 4: of image that looks a bit like a barcode, but
Speaker 4: it thins out.
Speaker 3: As you move to the right.
Speaker 4: So you had recessions very frequently one hundred years ago
Speaker 4: and further back, the economy was constantly in recession.
Speaker 3: Essentially half the time it was in.
Speaker 2: Recession, banking panics all the time.
Speaker 4: Yeah, but also the real economy, you know, the economy
Speaker 4: was very agrarian. A bad harvest could drag down performance
Speaker 4: of the economy, so there were a lot of shocks.
Speaker 4: But yes, they're also banking crises and things like that.
Speaker 4: And what we identify in the book is a recession
Speaker 4: risk framework. We say, look, all recessions come in one
Speaker 4: of three flavors. They are either real economy or recessions,
Speaker 4: which is when investment in consumption drop abruptly and pull
Speaker 4: GDP growth down. So that's the real economy time of recession.
Speaker 4: The second is a policy era when policy makers get
Speaker 4: it wrong they raise interest rates too fast or too high,
Speaker 4: which only you ever know expost whether it was the
Speaker 4: right thing to do. It's a very tricky thing to do.
Speaker 4: And the third type of recession is the most pernicious kind.
Speaker 4: It's a financial recession, when something blows up in the
Speaker 4: financial system like two thousand and eight, and what we're
Speaker 4: showing in this chapter of the book, over the long run,
Speaker 4: the composition of these two drivers has changed over the
Speaker 4: last forty years. The real economy recessions, they really took
Speaker 4: a back seat because the economy calmed down, the volatility
Speaker 4: came down. Services play a bigger role in the economy today,
Speaker 4: so the less volatile than physical production. But also policy
Speaker 4: makers just got better at managing the cycle, so you know,
Speaker 4: policy errors kind of also lost a lot of share,
Speaker 4: if you will, in the overall prevalence of recessions. But
Speaker 4: when you think about what has given us the biggest headaches,
Speaker 4: it was two thousand and eight a financial recession, and
Speaker 4: dot com in away is also a financial type of recession.
Speaker 4: So the share and the risk from financial blow ups
Speaker 4: is significant if you look at it in recent history.
Speaker 4: And that doesn't mean that the next recession will be
Speaker 4: that type, but its share of the risk spectrum is
Speaker 4: relatively high.
Speaker 2: So what should we be listening to when we hear
Speaker 2: economists discussing various risks? What are the red flags that hey,
Speaker 2: maybe this is a little too doom and gloomy for
Speaker 2: our own portfolio's best interests.
Speaker 4: Yeah, I think the the litmus test for me is
Speaker 4: often what would it take for a certain outcome of
Speaker 4: a certain doomsday outcome.
Speaker 3: To actually come to pass?
Speaker 4: Not just will it happen and what would be the damage,
Speaker 4: but walk me through the conditions that actually lead us
Speaker 4: to the precipice and then make us fall off that
Speaker 4: microeconomic cliff. Right, we need to talk about drivers, causes,
Speaker 4: We need to talk about their probabilities and their constellations. So,
Speaker 4: you know, it's it's not good enough to say, you know,
Speaker 4: the model says the recession will happen. Walk us through
Speaker 4: exactly what is the confluence of headwinds that together to
Speaker 4: make that credible? Right, It's more than the point forecast.
Speaker 2: Really kind of intriguing. I also noticed that I'm not
Speaker 2: an economist, but when I listened to economists talk about
Speaker 2: the possibility of a black swan or the possibility of
Speaker 2: this event, it's almost as if there won't be any
Speaker 2: intervening actions, either by the market or the policymakers. Tell
Speaker 2: us a little bit about that. What was George Soros's phrase, reflexivity,
Speaker 2: that when certain events happen, there are going to be
Speaker 2: natural reactions that just prevent this extrapolation to infinity or
Speaker 2: to zero, as the case may be.
Speaker 4: Yeah, I mean this is back to the topic of stimulus.
Speaker 4: For first and foremost, two thousand and eight came as
Speaker 4: a big surprise because the models in the early part
Speaker 4: of the two thousands, they didn't even really look at
Speaker 4: the financial sector as a risk driver. They kind of
Speaker 4: assume the financial system away. And then when the problem
Speaker 4: brewed and the financial system itself, the models were kind
Speaker 4: of blind to that, and the reaction couldn't couldn't be
Speaker 4: gauged if you didn't have view of that, And the
Speaker 4: reaction really depended on stimulus, and stimulus is about politics.
Speaker 4: It is about policy, it's not about economics. First and foremost,
Speaker 4: it's about political economy, it's about people coming together and
Speaker 4: fighting crises.
Speaker 3: And so.
Speaker 4: I think that remains the case that the idiosyncrasy happens
Speaker 4: before the crisis. The drivers are idiosyncratic, but the moment
Speaker 4: a crisis starts, as shock hits, what happens as a
Speaker 4: reaction is also idiosyncratic. It is political, it is about society,
Speaker 4: it's about choices. It's not stuff that you can model
Speaker 4: in a rigid natural science way.
Speaker 2: So let's talk about something that clearly wasn't in the models.
Speaker 2: Forget twenty years ago, they weren't in the models five
Speaker 2: years ago or even three years ago. And that's the
Speaker 2: impact of artificial intelligence on our economy, on the labor pool,
Speaker 2: and on productivity. How do you look at a giant
Speaker 2: structural change like AI. How do you put this into
Speaker 2: context as to what it might mean across all these
Speaker 2: different areas within both traditional economic modeling and the real world.
Speaker 4: You know, we've had productivity growth the last few decades,
Speaker 4: even though often the narrative as productivity growth is really
Speaker 4: really low. We've had productivity growth, just not as services,
Speaker 4: but in the physical economy, there's been pretty decent productivity
Speaker 4: growth even the last twenty years where we didn't have
Speaker 4: prouctivity growth with services because it didn't have the technology
Speaker 4: to move that part of the economy along. Now, why
Speaker 4: is that Essentially productivity growth goes up when technology displaces labor.
Speaker 4: That is really the definition of productivity growth. You need
Speaker 4: to produce the same with less labor inputs or more
Speaker 4: with the same labor inputs. But either way, technology, whether
Speaker 4: we like it or not, is about the displacement of labor,
Speaker 4: and we weren't able to do that in the service economy.
Speaker 4: Now with AI, I think you have a better chance
Speaker 4: of doing this. At least the promise is very strong
Speaker 4: that this will work. But I think we're getting ahead
Speaker 4: of ourselves. And I'm not saying that now. We've published
Speaker 4: on this over the last few years, even as COVID
Speaker 4: head and even before AI, when the zoom economy it
Speaker 4: was sort of this dominant narrative. It's a hard slog
Speaker 4: to do this. It happens over years, and it's little
Speaker 4: by little. It's not a flip of the switch. It
Speaker 4: happens very incrementally, and I don't think AI will turbocharge
Speaker 4: GDP growth. It is a lift to growth over the
Speaker 4: medium term. But there are many little obstacles. There are
Speaker 4: many little things that need to fall into place for
Speaker 4: people to really adopt the technology and for this to,
Speaker 4: little by little give us a tailwind. So it's not
Speaker 4: an abrupt step change. It's something that is credible, something
Speaker 4: we need to work through and then it will it
Speaker 4: will show impact over a ten year frame, fifteen year frame.
Speaker 2: So let me push back a little bit on one
Speaker 2: thing you said. And I seem to have this ongoing
Speaker 2: debate with economists who work in a larger corporate framework.
Speaker 2: We're here in Bloomberg giant company Big Operation. My day
Speaker 2: job is a much smaller company, under one hundred employees.
Speaker 2: And I have noticed just over the course of the
Speaker 2: past decade how our productivity has skyrocketed. And it's a
Speaker 2: services busy, finance as a services business, and it just
Speaker 2: feels like the things that used to take so long
Speaker 2: to do fifteen and twenty years ago are now automated.
Speaker 2: And it's not that we're hiring fewer people, and it's
Speaker 2: not that we're working shorter hours, but the same sized
Speaker 2: team can just accomplish so much more than they were
Speaker 2: capable of. Per Like I RecA all the days of
Speaker 2: quarterly reporting and having to literally run a model, create
Speaker 2: a print out for every client, print it out, stick
Speaker 2: it into the right and like it was like a
Speaker 2: week long process that all hands on deck every quarter
Speaker 2: and now it's updated twenty four to seven, tick by tickets, automated.
Speaker 2: No one cares about quarterly reports because you could get it.
Speaker 2: And the joke is, you have twenty four to seven
Speaker 2: access to your daily, weekly, monthly, year to date, five year,
Speaker 2: ten year performance reports. Just try not to check it
Speaker 2: second by second, but the way, and that's just one example.
Speaker 2: Being able to communicate with clients to record and embed
Speaker 2: an interactive video with charts and everything else. That was
Speaker 2: like a massive undertaking and now it's like child's play.
Speaker 2: Even though you're doing the same thing, you're just doing
Speaker 2: it faster, better, cheaper, easier. Are we somehow underestimating the
Speaker 2: productivity gains or are these just specific to you know that?
Speaker 3: Yeah?
Speaker 2: One area?
Speaker 4: Yeah, so I have some pushback on that. I think
Speaker 4: the bar for productivity growth is a little higher and
Speaker 4: it's very specific. It's less inputs per output. So do
Speaker 4: things get more comfortable? Are they moving faster? Are they
Speaker 4: qualitatively perhaps better?
Speaker 3: Yes?
Speaker 4: But are we using less inputs to generate the same value,
Speaker 4: or are we using the same level of inputs to
Speaker 4: generate more value. That is what we need to achieve
Speaker 4: to speak of productivity growth. And let me give you an
Speaker 4: example that we use in the book. You know, I
Speaker 4: took an Uber from my apartment to come here into
Speaker 4: the studio today. And Uber is often upheld as the
Speaker 4: epitome of progress and tech and it is fascinating. It's
Speaker 4: a great app. I love to use it. It's nice.
Speaker 4: But look, if you want to improve the productivity growth
Speaker 4: in taxi transportation, we have to talk about inputs and outputs, right,
Speaker 4: And the inputs are on the capitol side of car
Speaker 4: and you're not getting rid of that car. And on
Speaker 4: the labor side it's the driver and the Uber car
Speaker 4: still has that driver.
Speaker 2: Not weaim in parts of the West Coast.
Speaker 4: Yes, and this is why I said it takes time. Incrementally,
Speaker 4: that will happen and that will unfold. But do you
Speaker 4: think you're gonna have driverless taxis in New York in
Speaker 4: twenty twenty eight or twenty thirty.
Speaker 3: I don't.
Speaker 2: Well, we'll have it in twenty fifty, probably in twenty forty.
Speaker 2: I can't tell you what exact year it'll happen, but
Speaker 2: it's coming.
Speaker 3: I agree with you.
Speaker 2: And the sooner we embed those RFID devices in vehicles
Speaker 2: and on street corners, like doing it visually in light
Speaker 2: r is very twentieth century.
Speaker 4: Right, Yeah, And that's why I said it takes time.
Speaker 4: Over time, this will be substantial lyft to economic output.
Speaker 4: But it doesn't have happen overnight. It's actually it takes time, right,
Speaker 4: And there's an additional important point about productivity growth that
Speaker 4: can also be shown in this taxi example. When technology
Speaker 4: is truly productivity enhancing, you see that in falling prices,
Speaker 4: technology is deflationary.
Speaker 3: Right.
Speaker 4: As technology does a way with input cost, firms will
Speaker 4: compete with lower prices to gain market share. So across history,
Speaker 4: wherever you look, as technology is becoming a credible force
Speaker 4: in production, prices will fall. Now look at Uber. Uber
Speaker 4: prices in New York tend to be higher than a
Speaker 4: yellow cab. Why because despite this expensive technology, you're not
Speaker 4: able to produce this ride more cheaply.
Speaker 3: You're not.
Speaker 4: In fact, you kind of have to monetize the technological expense.
Speaker 4: The app is expensive, all is expensive, so generally you're
Speaker 4: paying a premium for the smoothness of the app and
Speaker 4: all that. Over time that may change, but watch prices.
Speaker 4: You want to see productivity growth, whether it's happening or not,
Speaker 4: you got to look at prices. And that's one of
Speaker 4: the arguments we're making the book.
Speaker 2: So let's hordonically adjust. We'll stay with Uber. Let's hordonically
Speaker 2: adjust that. In New York City, if you want a
Speaker 2: taxi during rush hour, hey, sorry, you're out of luck
Speaker 2: because the monopoly that was imbued by the Taxing Limousine
Speaker 2: Commission and a handful of big medallion chain owners decided
Speaker 2: in their infinite wisdom that we don't need to move
Speaker 2: people in them rush hour. We're going to change shifts then, which,
Speaker 2: by the way, is my pet theory for how Uber penetrated.
Speaker 2: And so a, you could get an Uber during rush
Speaker 2: hour that you can't during cab rides. You could get
Speaker 2: an Uber when it's raining. Good luck hailing a cab
Speaker 2: in New York City rain, and you have the ability
Speaker 2: to schedule an Uber. You have the ability to get
Speaker 2: a higher quality car. You could get an electric car
Speaker 2: if you choose a larger car. Like I'm not a
Speaker 2: huge fan of traditional hit donic adjustment because it was
Speaker 2: a way of kind of tamping down on the cost
Speaker 2: of living. Adjustments always felt sort of disingenuous. But I
Speaker 2: don't think you could get anybody to say that Uber
Speaker 2: is not only better. And I'm not a big Uber fan,
Speaker 2: but as a user, Uber is certainly better than a
Speaker 2: cab and in many ways orders of magnitude better, more choices,
Speaker 2: more options, and just a higher quality experience. Plus you know,
Speaker 2: just the idea of having hey, is this a work
Speaker 2: thing or I'm going to use that card on the app? Well, no,
Speaker 2: this is personal, I'll use that card. So maybe taxis
Speaker 2: aren't the best example. But when let's talk about economists,
Speaker 2: I want again, I want to stay with this because
Speaker 2: I love the topic. Think about the quantity of research
Speaker 2: you push, you push out, the ability to integrate charts
Speaker 2: and data, and like, I have been in this business
Speaker 2: long enough that I can remember, first of all, when
Speaker 2: I started, the guys in the technical group, they were
Speaker 2: doing charts with pencil and graph paper. I'm not exaggerating.
Speaker 2: Maybe that's just a function of my age, but think
Speaker 2: about how and the cheat was you get a different
Speaker 2: feel when you're doing it point by point than when
Speaker 2: you're just generating it. Whether that's true or not, at
Speaker 2: least that was the When computers came along, people continued
Speaker 2: to do that. But think about the access you have
Speaker 2: to the just endless array of data, the ability to
Speaker 2: do that. I haven't even mentioned your Fortune column. Think
Speaker 2: about how much time and effort goes into putting out
Speaker 2: a column and you go back twenty five years and
Speaker 2: it was just a horrific grind. Like at this point,
Speaker 2: everybody seems to use some version of Grammarly or some
Speaker 2: other editing saw where the ability to put out and
Speaker 2: I'm not talking about asking chat GPT to generate a
Speaker 2: garbage article for you. You writing something, cleaning it up and
Speaker 2: betting a lot of data and images. It just feels like,
Speaker 2: you know, to quote Hemingway, you know, gradually and then
Speaker 2: all at once. It just feels like it's so much
Speaker 2: easier to put out a much higher quality product with
Speaker 2: either the same or less effort than twenty five years ago.
Speaker 2: Maybe I'm just hyper focused on the junk I do,
Speaker 2: but what's your experience?
Speaker 4: Incrementally there's progress, But again, the bar we need to
Speaker 4: meet is value. Are we generating more value with the
Speaker 4: same inputs, or are we generating the same value with
Speaker 4: less inputs. That's the definition of productivity growth. So if
Speaker 4: you can make all these charts faster and you save
Speaker 4: one economist on the team, well that's productivity growth. Or
Speaker 4: you keep the economist and you double your number of
Speaker 4: reports and you also manage to monetize them and earn
Speaker 4: revenue for it, well that's productivity growth. If the charts
Speaker 4: get prettier, faster, fancier, with the same number of economists
Speaker 4: and the same number of revenues, well from an economic
Speaker 4: sense perspective, that's not productivity growth. So it's got to
Speaker 4: be a change in the relationship of inputs to outputs
Speaker 4: if we're comfortably talking about productivity growth. And back to
Speaker 4: the Uber example. You write you can get different cars
Speaker 4: to write in. You can get the car, the Uber
Speaker 4: car when it's raining, but you're paying for that, so
Speaker 4: it's not produced more productively. Right, You're paying a search arge,
Speaker 4: you're paying the search pricing. I think they call it
Speaker 4: an uber so you know, yeah, you can get it
Speaker 4: when it rains, but you'll pay twice as much.
Speaker 3: So it wasn't it wasn't done more productively, right, Huh?
Speaker 2: Really? Interesting, the gap between the increased quantity and quality
Speaker 2: of output. If we're not monetizing it or as a consumer,
Speaker 2: if you're not seeing price declines, then it doesn't really
Speaker 2: count as productivity.
Speaker 4: No, It's got to be a change in the ratio
Speaker 4: of inputs to outputs on either side. Either we keep
Speaker 4: all the staff and we earn more revenue with.
Speaker 3: It, that's productivity growth.
Speaker 4: Or we keep the revenue constant and we do it
Speaker 4: with less inputs. That's more productivity growth. But you know, again,
Speaker 4: I'm not saying there isn't productivity. There is, and there
Speaker 4: will be more and AI will have impact. It just
Speaker 4: needs to show up in value in that relationship between
Speaker 4: inputs and outputs.
Speaker 2: I see it qualitatively, but I completely get what you're
Speaker 2: saying quantitatively. Are you still doing the Fortune column on.
Speaker 4: A regular Yeah. We publish in Fortune relatively regularly. Whenever
Speaker 4: we see a cyclical or thematic topic that we feel
Speaker 4: is pressing, we publish with Fortune.
Speaker 2: Yeah. Really really interesting. All right, I only have you
Speaker 2: for a limited amount of time. I know you're catching
Speaker 2: a flight today. Let me jump to our favorite questions
Speaker 2: that we ask all of our guests, starting with what
Speaker 2: are you streaming these days. What's keeping you entertained? Either
Speaker 2: Netflix or podcasts or whatever.
Speaker 4: Yeah, I'm not very big on on shows or Hollywood.
Speaker 4: I mean to give an idea. I think I'm on
Speaker 4: the second season of Slow Horses. I think I think
Speaker 4: there are four seasons of it, and I'm kind of
Speaker 4: slowly making my way through the second one.
Speaker 3: It's very entertaining. I love Gary Old.
Speaker 4: Yeah, it was sort of the taking down the genre
Speaker 4: of spy movies in a very entertaining way.
Speaker 3: So I'm doing that.
Speaker 4: But also I tend to watch late in the day
Speaker 4: when I'm tired, so it's it's entirely possible I fall
Speaker 4: asleep and I take like two three evenings to get.
Speaker 3: Through one episode.
Speaker 4: Yeah, so I'm not I'm not all that big on that.
Speaker 2: On that front, tell us about your mentors who helped
Speaker 2: to shape your career.
Speaker 4: So many people, right, because a lot of it is teamwork,
Speaker 4: and you don't you don't progress with that mentors and
Speaker 4: role models. I would say in that in my current role,
Speaker 4: I would probably call out two people. Rich Lesser are
Speaker 4: a long time CEO and our chairman. He had the
Speaker 4: vision for a Macro product, as did Martin Reeves, who
Speaker 4: runs our research institute at Henderson Institute, And they are
Speaker 4: really the two people who brought me into this role
Speaker 4: and coached me, so they stand out outside of BCG.
Speaker 4: Kathleen Stephanson, she had many, many different roles on Wall
Speaker 4: Street and economist roles. She's been a great help navigating
Speaker 4: my career the last many years, and further back and academia,
Speaker 4: thesis advisors and many others. There's always teamwork in a way,
Speaker 4: so you have many, many role models and mentors.
Speaker 2: Let's talk about books. What are some of your favorites?
Speaker 2: What are you reading right now?
Speaker 4: All right now, I'm almost done with Making Sense of
Speaker 4: Chaos by Doin Farmer, came out last year.
Speaker 3: Doun Farmer is a very interesting character.
Speaker 4: He's a complexity scientist at the Santa Fe Institute and
Speaker 4: I think at Oxford University as well. And his book
Speaker 4: is interesting to me. I bumped into him at one
Speaker 4: or two conferences. But it's interesting to me, particularly because
Speaker 4: he kind of argues the opposite of what we are
Speaker 4: in our book. So he thinks he agrees that economics
Speaker 4: is poor if you just take standard models and theory,
Speaker 4: but he believes he can crack the complexity of it.
Speaker 4: So he thinks, with complexity signs and better data and
Speaker 4: better models, you'll essentially be able to make those forecasts.
Speaker 4: I read it because it's always important to see what
Speaker 4: others are arguing. I don't read stuff that reconfirms what
Speaker 4: I think. I want to see what other people are
Speaker 4: saying about the same topic from different angles of That
Speaker 4: book's been very useful and also well written. That's what
Speaker 4: I'm currently reading. I think of other books that have
Speaker 4: read over the years, I mean, there's so many, many
Speaker 4: great ones. Of course, I think one that early on
Speaker 4: made an impression on me was Seeing Like a State
Speaker 4: by James Scott, a't at least twenty five years old.
Speaker 3: I read it as a grand student.
Speaker 4: And what he does he looks at the ability of
Speaker 4: governments to do top down policy to improve the lie
Speaker 4: of large amounts of people, and he shows all the
Speaker 4: pitfalls in a sort of Hyaekian way. It's tough to
Speaker 4: have the local knowledge, it's tough to do the top
Speaker 4: down improvements.
Speaker 3: Things have to.
Speaker 4: Grow bottom up. And that book kind of stood out
Speaker 4: for being very, very eclectic, very multidisciplinary, and still I
Speaker 4: think an excellent book to how to think laterally and
Speaker 4: not in a sort of strict model based way.
Speaker 2: Huh. Really interesting. Our final two questions what sort of
Speaker 2: advice would you give a recent college grad interested in
Speaker 2: the career and economics, investment, finance, anything along those lines.
Speaker 4: Yeah, you know, I think a career as an economist
Speaker 4: is challenging in some ways. There's so many economists out there.
Speaker 4: Often when I hire, you see the flood of cvs,
Speaker 4: and often very good cvs, and there's I think there's
Speaker 4: been an overproduction of economists. So I think doing something
Speaker 4: adjacent to economics, you know, working finance, work on the
Speaker 4: buy side, work on the cell side. Unless unless your
Speaker 4: heart truly beats for economics, I think, you know, you
Speaker 4: can use economic skills and many adjacent disciplines and careers
Speaker 4: I think are plentiful in those adjacent disciplines. If economics
Speaker 4: graduates really feel strongly about economics, it's fascinating, but your
Speaker 4: heart has to be in it, and there aren't all
Speaker 4: that many seats as economists, so one has to build
Speaker 4: that over the long term.
Speaker 2: And our final question, what do you know about the
Speaker 2: world of economics today? You wish you knew twenty five
Speaker 2: thirty years ago when you were first getting started.
Speaker 4: Yeah, well, I mean that's really what I wrote down
Speaker 4: on the book.
Speaker 3: You know.
Speaker 4: The book is the twenty twenty five year journey through
Speaker 4: the maze of the economics profession and discipline. The themes
Speaker 4: we touched on the master model mentality, the pitfalls of
Speaker 4: treating economics like a like a physical science, the dow
Speaker 4: mongering which we have to simply ignore most of the time.
Speaker 4: And then the eclectic approach to economics. I call it
Speaker 4: economic eclecticism, drawing on a broader range of disciplines. Those
Speaker 4: are the things that I that I learned through that
Speaker 4: path the last twenty years.
Speaker 3: I wrote them up on the book.
Speaker 4: You know, it would have been would have been interesting
Speaker 4: for me to read that twenty years ago, but I
Speaker 4: wrote it now, and so I'm happy with that.
Speaker 2: Really really intriguing. Philip, Thank you for being so generous
Speaker 2: with your time. We have been speaking with Philip Carlson Slezak.
Speaker 2: He's global chief economist for the Boston Consulting Group. His
Speaker 2: new book, Shocks, Crises and False Alarms, How to Assess
Speaker 2: True Macroeconomic Risk, co authored with Paul Schwartz, is an
Speaker 2: absolutely fascinating read. If you enjoyed this conversation, well, check
Speaker 2: out any of the past five hundred we've done over
Speaker 2: the previous ten years. You can find those that iTunes, Spotify, YouTube,
Speaker 2: wherever you find your favorite podcasts, and be sure to
Speaker 2: check out my new book, How Not to Invest The
Speaker 2: Bad Ideas, Numbers and Behavior that Destroys Wealth, coming out
Speaker 2: March eighteenth, twenty twenty five. I would be remiss if
Speaker 2: I did not thank the Crack team that helps us
Speaker 2: put these conversations together each week. My audio engineer is
Speaker 2: Andrew Gavin, My producer is Anna Luke. Sage Bauman is
Speaker 2: ahead of podcasts at Bloomberg. Sean Russo is my researcher.
Speaker 2: I'm Barry Ridults. You've been listening to Master's in Business
Speaker 2: on Bloomberg Radio.